[
{
"path": ".coveragerc",
"content": "[run]\nomit =\n src/zvt/recorders/*\n src/zvt/autocode/*\n src/zvt/samples/*\n *__init__*\n"
},
{
"path": ".github/workflows/build.yaml",
"content": "name: build\n\non: [push]\n\njobs:\n build:\n\n runs-on: ubuntu-latest\n strategy:\n matrix:\n python-version: [3.12]\n\n steps:\n - uses: actions/checkout@v2\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v2\n with:\n python-version: ${{ matrix.python-version }}\n - name: Cache pip\n uses: actions/cache@v4\n with:\n # This path is specific to Ubuntu\n path: ~/.cache/pip\n # Look to see if there is a cache hit for the corresponding requirements file\n key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}\n restore-keys: |\n ${{ runner.os }}-pip-\n ${{ runner.os }}-\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n - name: Test with pytest\n run: |\n pip install pytest\n pip install pytest-cov\n pytest ./tests --cov-config=.coveragerc --cov-report=xml --cov=./src/zvt --ignore=tests/recorders/ --ignore=tests/domain/\n - name: Codecov\n uses: codecov/codecov-action@v2\n with:\n verbose: true"
},
{
"path": ".github/workflows/package.yaml",
"content": "name: package\n\non:\n release:\n types: [published]\n\njobs:\n deploy:\n\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v2\n - name: Set up Python\n uses: actions/setup-python@v2\n with:\n python-version: '3.12'\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install build\n - name: Build package\n run: python -m build\n - name: Publish package\n uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29\n with:\n user: __token__\n password: ${{ secrets.PYPI }}"
},
{
"path": ".gitignore",
"content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n#idea\n.idea\n.vscode\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\ndocs/source/api/_autosummary/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n/ve/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n/zvt-home\n/zvt-test-home\n\n# Local/VIP extensions (not part of open source)\nzvt_vip/\n*.tar.gz\n\n.pytest_cache/\n\n# LibreOffice locks\n.~lock.*#\n\nnode_modules/\n\n*running.ipynb\n\n.DS_Store\n\na.json"
},
{
"path": ".pre-commit-config.yaml",
"content": "ci:\n autoupdate_schedule: monthly\nrepos:\n - repo: https://github.com/psf/black\n rev: 24.10.0\n hooks:\n - id: black\n# - repo: https://github.com/PyCQA/flake8\n# rev: 4.0.1\n# hooks:\n# - id: flake8\n# additional_dependencies:\n# - flake8-bugbear\n# - flake8-implicit-str-concat\n# - repo: https://github.com/pre-commit/pre-commit-hooks\n# rev: v4.0.1\n# hooks:\n# - id: fix-byte-order-marker\n# - id: trailing-whitespace\n# - id: end-of-file-fixer\n"
},
{
"path": ".readthedocs.yaml",
"content": "version: 2\nbuild:\n os: \"ubuntu-20.04\"\n tools:\n python: \"3.12\"\npython:\n install:\n - requirements: requirements/docs.txt\n - method: pip\n path: .\nsphinx:\n builder: dirhtml\n fail_on_warning: false\n configuration: docs/source/conf.py\n#formats:\n# - pdf\n"
},
{
"path": "LICENSE",
"content": "MIT License\n\nCopyright (c) 2023-2026 zvtvz\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "MANIFEST.in",
"content": "include *.md\ninclude *.txt\ninclude LICENSE\nrecursive-include src/zvt/* *"
},
{
"path": "README-cn.md",
"content": "[](https://github.com/zvtvz/zvt)\n[](https://pypi.org/project/zvt/)\n[](https://pypi.org/project/zvt/)\n[](https://pypi.org/project/zvt/)\n[](https://github.com/zvtvz/zvt/actions/workflows/build.yml)\n[](https://github.com/zvtvz/zvt/actions/workflows/package.yaml)\n[](https://zvt.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/github/zvtvz/zvt)\n[](https://pepy.tech/project/zvt)\n\n**缘起**\n\n[炒股的三大原理](https://mp.weixin.qq.com/s/FoFR63wFSQIE_AyFubkZ6Q)\n\n**声明**\n\n本项目目前不保证任何向后兼容性，请谨慎升级。 \n随着作者思想的变化，一些以前觉得重要的东西可能也变得不重要，从而可能不会进行维护。 \n而一些新的东西的加入对你是否有用，需要自己去评估。\n\n\n**Read this in other languages: [English](README-cn.md).** \n\n**详细文档:[https://zvt.readthedocs.io/en/latest/](https://zvt.readthedocs.io/en/latest/)**\n\n## 市场模型\nZVT 将市场抽象为如下的模型:\n\n
\n\n* TradableEntity (交易标的)\n* ActorEntity (市场参与者)\n* EntityEvent (交易标的 和 市场参与者 发生的事件)\n\n## 快速开始\n\n### 安装\n```\npython3 -m pip install -U zvt\n```\n\n### 使用展示\n\n#### 主界面\n\n#### Dash & Plotly UI\n> 适用于回测和研究，不太适用于实时行情和用户交互\n\n安装完成后，在命令行下输入 zvt\n```shell\nzvt\n```\n打开 [http://127.0.0.1:8050/](http://127.0.0.1:8050/)\n\n> 这里展示的例子依赖后面的下载历史数据，数据更新请参考后面文档\n\n
\n
\n\n> 系统的核心概念是可视化的，界面的名称与其一一对应，因此也是统一可扩展的。\n\n> 你可以在你喜欢的ide里编写和运行策略，然后运行界面查看其相关的标的，因子，信号和净值展示。\n\n#### 前后端分离的UI\n> 更灵活和可扩展，更适合于处理实时行情和用户交互，结合ZVT的动态tag系统，提供了一种量化结合主观的交易方式\n\n- 初始化tag系统\n\n运行以下脚本: \n\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/init_tag_system.py\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/stock_pool_runner.py\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_data_runner.py\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_tick_runner.py\n\n- 安装 uvicorn\n```shell\npip install uvicorn\n```\n- 运行 zvt server\n\n安装完成后，在命令行下输入 zvt_server\n```shell\nzvt_server\n```\n或者从代码运行:\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/zvt_server.py\n\n- api 文档 \n\nopen [http://127.0.0.1:8090/docs](http://127.0.0.1:8090/docs)\n\n- 部署前端\n\n前端代码: https://github.com/zvtvz/zvt_ui\n\n修改前端环境文件:\nhttps://github.com/zvtvz/zvt_ui/blob/main/.env\n\n设置 {your server IP}, 即zvt_server服务的地址\n\n```text\nNEXT_PUBLIC_SERVER = {your server IP}\n```\n\n然后参考前端的readme启动前端服务\n\n打开 [http://127.0.0.1:3000/trade](http://127.0.0.1:3000/trade)\n\n
\n\n#### 见证奇迹的时刻\n```\n>>> from zvt.domain import Stock, Stock1dHfqKdata\n>>> from zvt.ml import MaStockMLMachine\n>>> Stock.record_data(provider=\"em\")\n>>> entity_ids = [\"stock_sz_000001\", \"stock_sz_000338\", \"stock_sh_601318\"]\n>>> Stock1dHfqKdata.record_data(provider=\"em\", entity_ids=entity_ids, sleeping_time=1)\n>>> machine = MaStockMLMachine(entity_ids=[\"stock_sz_000001\"], data_provider=\"em\")\n>>> machine.train()\n>>> machine.predict()\n>>> machine.draw_result(entity_id=\"stock_sz_000001\")\n```\n
\n\n> 以上几行代码实现了：数据的抓取，持久化，增量更新，机器学习，预测，展示结果。\n> 熟悉系统的核心概念后，可以应用到市场中的任何标的。\n\n### 核心概念\n```\n>>> from zvt.domain import *\n```\n\n### TradableEntity (交易标的)\n\n#### A股交易标的\n```\n>>> Stock.record_data()\n>>> df = Stock.query_data(index='code')\n>>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\ncode\n000001 stock_sz_000001 stock_sz_000001 1991-04-03 stock sz 000001 平安银行 1991-04-03 None\n000002 stock_sz_000002 stock_sz_000002 1991-01-29 stock sz 000002 万 科Ａ 1991-01-29 None\n000004 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None\n000005 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None\n000006 stock_sz_000006 stock_sz_000006 1992-04-27 stock sz 000006 深振业Ａ 1992-04-27 None\n... ... ... ... ... ... ... ... ... ...\n605507 stock_sh_605507 stock_sh_605507 2021-08-02 stock sh 605507 国邦医药 2021-08-02 None\n605577 stock_sh_605577 stock_sh_605577 2021-08-24 stock sh 605577 龙版传媒 2021-08-24 None\n605580 stock_sh_605580 stock_sh_605580 2021-08-19 stock sh 605580 恒盛能源 2021-08-19 None\n605588 stock_sh_605588 stock_sh_605588 2021-08-12 stock sh 605588 冠石科技 2021-08-12 None\n605589 stock_sh_605589 stock_sh_605589 2021-08-10 stock sh 605589 圣泉集团 2021-08-10 None\n\n[4136 rows x 9 columns]\n```\n\n#### 美股交易标的\n```\n>>> Stockus.record_data()\n>>> df = Stockus.query_data(index='code')\n>>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\ncode\nA stockus_nyse_A stockus_nyse_A NaT stockus nyse A 安捷伦 None None\nAA stockus_nyse_AA stockus_nyse_AA NaT stockus nyse AA 美国铝业 None None\nAAC stockus_nyse_AAC stockus_nyse_AAC NaT stockus nyse AAC Ares Acquisition Corp-A None None\nAACG stockus_nasdaq_AACG stockus_nasdaq_AACG NaT stockus nasdaq AACG ATA Creativity Global ADR None None\nAACG stockus_nyse_AACG stockus_nyse_AACG NaT stockus nyse AACG ATA Creativity Global ADR None None\n... ... ... ... ... ... ... ... ... ...\nZWRK stockus_nasdaq_ZWRK stockus_nasdaq_ZWRK NaT stockus nasdaq ZWRK Z-Work Acquisition Corp-A None None\nZY stockus_nasdaq_ZY stockus_nasdaq_ZY NaT stockus nasdaq ZY Zymergen Inc None None\nZYME stockus_nyse_ZYME stockus_nyse_ZYME NaT stockus nyse ZYME Zymeworks Inc None None\nZYNE stockus_nasdaq_ZYNE stockus_nasdaq_ZYNE NaT stockus nasdaq ZYNE Zynerba Pharmaceuticals Inc None None\nZYXI stockus_nasdaq_ZYXI stockus_nasdaq_ZYXI NaT stockus nasdaq ZYXI Zynex Inc None None\n\n[5826 rows x 9 columns]\n\n>>> Stockus.query_data(code='AAPL')\n id entity_id timestamp entity_type exchange code name list_date end_date\n0 stockus_nasdaq_AAPL stockus_nasdaq_AAPL None stockus nasdaq AAPL 苹果 None None\n```\n\n#### 港股交易标的\n```\n>>> Stockhk.record_data()\n>>> df = Stockhk.query_data(index='code')\n>>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\ncode\n00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None\n00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None\n00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None\n00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None\n00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None\n... ... ... ... ... ... ... ... ... ...\n09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None\n09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None\n09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None\n09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None\n80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None\n\n[2597 rows x 9 columns]\n\n>>> df[df.code=='00700']\n\n id entity_id timestamp entity_type exchange code name list_date end_date\n2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None\n\n```\n\n#### 还有更多\n```\n>>> from zvt.contract import *\n>>> zvt_context.tradable_schema_map\n\n{'stockus': zvt.domain.meta.stockus_meta.Stockus,\n 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk,\n 'index': zvt.domain.meta.index_meta.Index,\n 'etf': zvt.domain.meta.etf_meta.Etf,\n 'stock': zvt.domain.meta.stock_meta.Stock,\n 'block': zvt.domain.meta.block_meta.Block,\n 'fund': zvt.domain.meta.fund_meta.Fund}\n```\n\n其中key为交易标的的类型，value为其schema，系统为schema提供了统一的 **记录(record_data)** 和 **查询(query_data)** 方法。\n\n```\n>>> Index.record_data()\n>>> df=Index.query_data(filters=[Index.category=='scope',Index.exchange='sh'])\n>>> print(df)\n id entity_id timestamp entity_type exchange code name list_date end_date publisher category base_point\n0 index_sh_000001 index_sh_000001 1990-12-19 index sh 000001 上证指数 1991-07-15 None csindex scope 100.00\n1 index_sh_000002 index_sh_000002 1990-12-19 index sh 000002 Ａ股指数 1992-02-21 None csindex scope 100.00\n2 index_sh_000003 index_sh_000003 1992-02-21 index sh 000003 B股指数 1992-08-17 None csindex scope 100.00\n3 index_sh_000010 index_sh_000010 2002-06-28 index sh 000010 上证180 2002-07-01 None csindex scope 3299.06\n4 index_sh_000016 index_sh_000016 2003-12-31 index sh 000016 上证50 2004-01-02 None csindex scope 1000.00\n.. ... ... ... ... ... ... ... ... ... ... ... ...\n25 index_sh_000020 index_sh_000020 2007-12-28 index sh 000020 中型综指 2008-05-12 None csindex scope 1000.00\n26 index_sh_000090 index_sh_000090 2009-12-31 index sh 000090 上证流通 2010-12-02 None csindex scope 1000.00\n27 index_sh_930903 index_sh_930903 2012-12-31 index sh 930903 中证Ａ股 2016-10-18 None csindex scope 1000.00\n28 index_sh_000688 index_sh_000688 2019-12-31 index sh 000688 科创50 2020-07-23 None csindex scope 1000.00\n29 index_sh_931643 index_sh_931643 2019-12-31 index sh 931643 科创创业50 2021-06-01 None csindex scope 1000.00\n\n[30 rows x 12 columns]\n\n```\n\n### EntityEvent (交易标的 发生的事件)\n有了交易标的，才有交易标的 发生的事。\n\n#### 行情数据\n交易标的 **行情schema** 遵从如下的规则:\n```\n{entity_shema}{level}{adjust_type}Kdata\n```\n* entity_schema\n\n就是前面说的TradableEntity，比如Stock,Stockus等。\n\n* level\n```\n>>> for level in IntervalLevel:\n print(level.value)\n```\n\n* adjust type\n```\n>>> for adjust_type in AdjustType:\n print(adjust_type.value)\n```\n> 注意: 为了兼容历史数据，前复权是个例外，{adjust_type}不填\n\n前复权\n```\n>>> Stock1dKdata.record_data(code='000338', provider='em')\n>>> df = Stock1dKdata.query_data(code='000338', provider='em')\n>>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 2.33 2.00 2.40 1.87 207375.0 1.365189e+09 3.2472 0.1182\n1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 2.11 1.94 2.20 1.87 86299.0 5.563198e+08 -0.0300 0.0492\n2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 1.90 1.81 1.94 1.66 93823.0 5.782065e+08 -0.0670 0.0535\n3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 1.78 1.85 1.98 1.75 47720.0 2.999226e+08 0.0221 0.0272\n4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 1.81 1.73 1.81 1.66 39273.0 2.373126e+08 -0.0649 0.0224\n... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 19.39 20.30 20.30 19.25 1688497.0 3.370241e+09 0.0601 0.0398\n3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 20.30 20.09 20.31 19.78 1187601.0 2.377957e+09 -0.0103 0.0280\n3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 20.20 20.07 20.63 19.70 1143985.0 2.295195e+09 -0.0010 0.0270\n3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 19.98 19.68 19.98 19.15 1218697.0 2.383841e+09 -0.0194 0.0287\n3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 19.71 19.85 19.97 19.24 1023545.0 2.012006e+09 0.0086 0.0241\n\n[3431 rows x 15 columns]\n\n>>> Stockus1dKdata.record_data(code='AAPL', provider='em')\n>>> df = Stockus1dKdata.query_data(code='AAPL', provider='em')\n>>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n0 stockus_nasdaq_AAPL_1984-09-07 stockus_nasdaq_AAPL 1984-09-07 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2981600.0 0.000000e+00 0.0000 0.0002\n1 stockus_nasdaq_AAPL_1984-09-10 stockus_nasdaq_AAPL 1984-09-10 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2346400.0 0.000000e+00 0.0000 0.0001\n2 stockus_nasdaq_AAPL_1984-09-11 stockus_nasdaq_AAPL 1984-09-11 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 5444000.0 0.000000e+00 0.0018 0.0003\n3 stockus_nasdaq_AAPL_1984-09-12 stockus_nasdaq_AAPL 1984-09-12 None AAPL 苹果 1d -5.58 -5.59 -5.58 -5.59 4773600.0 0.000000e+00 -0.0018 0.0003\n4 stockus_nasdaq_AAPL_1984-09-13 stockus_nasdaq_AAPL 1984-09-13 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 7429600.0 0.000000e+00 0.0018 0.0004\n... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n8765 stockus_nasdaq_AAPL_2021-08-27 stockus_nasdaq_AAPL 2021-08-27 None AAPL 苹果 1d 147.48 148.60 148.75 146.83 55802388.0 8.265452e+09 0.0072 0.0034\n8766 stockus_nasdaq_AAPL_2021-08-30 stockus_nasdaq_AAPL 2021-08-30 None AAPL 苹果 1d 149.00 153.12 153.49 148.61 90956723.0 1.383762e+10 0.0304 0.0055\n8767 stockus_nasdaq_AAPL_2021-08-31 stockus_nasdaq_AAPL 2021-08-31 None AAPL 苹果 1d 152.66 151.83 152.80 151.29 86453117.0 1.314255e+10 -0.0084 0.0052\n8768 stockus_nasdaq_AAPL_2021-09-01 stockus_nasdaq_AAPL 2021-09-01 None AAPL 苹果 1d 152.83 152.51 154.98 152.34 80313711.0 1.235321e+10 0.0045 0.0049\n8769 stockus_nasdaq_AAPL_2021-09-02 stockus_nasdaq_AAPL 2021-09-02 None AAPL 苹果 1d 153.87 153.65 154.72 152.40 71171317.0 1.093251e+10 0.0075 0.0043\n\n[8770 rows x 15 columns]\n```\n后复权\n```\n>>> Stock1dHfqKdata.record_data(code='000338', provider='em')\n>>> df = Stock1dHfqKdata.query_data(code='000338', provider='em')\n>>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 70.00 64.93 71.00 62.88 207375.0 1.365189e+09 2.1720 0.1182\n1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 66.60 64.00 68.00 62.88 86299.0 5.563198e+08 -0.0143 0.0492\n2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 63.32 62.00 63.88 59.60 93823.0 5.782065e+08 -0.0313 0.0535\n3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 61.50 62.49 64.48 61.01 47720.0 2.999226e+08 0.0079 0.0272\n4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 61.90 60.65 61.90 59.70 39273.0 2.373126e+08 -0.0294 0.0224\n... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 331.97 345.95 345.95 329.82 1688497.0 3.370241e+09 0.0540 0.0398\n3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 345.95 342.72 346.10 337.96 1187601.0 2.377957e+09 -0.0093 0.0280\n3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 344.41 342.41 351.02 336.73 1143985.0 2.295195e+09 -0.0009 0.0270\n3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 341.03 336.42 341.03 328.28 1218697.0 2.383841e+09 -0.0175 0.0287\n3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 336.88 339.03 340.88 329.67 1023545.0 2.012006e+09 0.0078 0.0241\n\n[3431 rows x 15 columns]\n```\n\n#### 财务因子\n```\n>>> FinanceFactor.record_data(code='000338')\n>>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp')\n\n basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp\ntimestamp\n2002-12-31 NaN 1.962000e+07 2.471000e+06 NaN NaN NaN NaN 0.2068 0.1259 2002-12-31\n2003-12-31 1.27 3.574000e+09 2.739000e+08 181.2022 109.8778 0.7729 0.1783 0.2551 0.0766 2003-12-31\n2004-12-31 1.75 6.188000e+09 5.369000e+08 0.7313 0.9598 0.3245 0.1474 0.2489 0.0868 2004-12-31\n2005-12-31 0.93 5.283000e+09 3.065000e+08 -0.1463 -0.4291 0.1327 0.0603 0.2252 0.0583 2005-12-31\n2006-03-31 0.33 1.859000e+09 1.079000e+08 NaN NaN NaN NaN NaN 0.0598 2006-03-31\n... ... ... ... ... ... ... ... ... ... ...\n2020-08-28 0.59 9.449000e+10 4.680000e+09 0.0400 -0.1148 0.0983 0.0229 0.1958 0.0603 2020-08-28\n2020-10-31 0.90 1.474000e+11 7.106000e+09 0.1632 0.0067 0.1502 0.0347 0.1949 0.0590 2020-10-31\n2021-03-31 1.16 1.975000e+11 9.207000e+09 0.1327 0.0112 0.1919 0.0444 0.1931 0.0571 2021-03-31\n2021-04-30 0.42 6.547000e+10 3.344000e+09 0.6788 0.6197 0.0622 0.0158 0.1916 0.0667 2021-04-30\n2021-08-31 0.80 1.264000e+11 6.432000e+09 0.3375 0.3742 0.1125 0.0287 0.1884 0.0653 2021-08-31\n\n[66 rows x 10 columns]\n```\n\n#### 财务三张表\n```\n#资产负债表\n>>> BalanceSheet.record_data(code='000338')\n#利润表\n>>> IncomeStatement.record_data(code='000338')\n#现金流量表\n>>> CashFlowStatement.record_data(code='000338')\n```\n\n#### 还有更多\n```\n>>> zvt_context.schemas\n[zvt.domain.dividend_financing.DividendFinancing,\n zvt.domain.dividend_financing.DividendDetail,\n zvt.domain.dividend_financing.SpoDetail...]\n```\n\nzvt_context.schemas为系统支持的schema,schema即表结构，即数据，其字段含义的查看方式如下：\n\n* help\n\n输入schema.按tab提示其包含的字段，或者.help()\n```\n>>> FinanceFactor.help()\n```\n\n* 源码\n\n[domain](https://github.com/zvtvz/zvt/tree/master/src/zvt/domain)里的文件为schema的定义，查看相应字段的注释即可。\n\n通过以上的例子，你应该掌握了统一的记录数据的方法：\n\n> Schema.record_data(provider='your provider',codes='the codes')\n\n注意可选参数provider，其代表数据提供商，一个schema可以有多个provider，这是系统稳定的基石。\n\n查看**已实现**的provider\n```\n>>> Stock.provider_map_recorder\n{'joinquant': zvt.recorders.joinquant.meta.jq_stock_meta_recorder.JqChinaStockRecorder,\n 'exchange': zvt.recorders.exchange.exchange_stock_meta_recorder.ExchangeStockMetaRecorder,\n 'em': zvt.recorders.em.meta.em_stock_meta_recorder.EMStockRecorder,\n 'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder.EastmoneyChinaStockListRecorder}\n\n```\n你可以使用任意一个provider来获取数据，默认使用第一个。\n\n再举个例子，股票板块数据获取：\n```\n>>> Block.provider_map_recorder\n{'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_block_meta_recorder.EastmoneyChinaBlockRecorder,\n 'sina': zvt.recorders.sina.meta.sina_block_recorder.SinaBlockRecorder}\n\n>>> Block.record_data(provider='sina')\nBlock registered recorders:{'eastmoney': , 'sina': }\n2020-03-04 23:56:48,931 INFO MainThread finish record sina blocks:industry\n2020-03-04 23:56:49,450 INFO MainThread finish record sina blocks:concept\n```\n\n再多了解一点record_data：\n* 参数code[单个]，codes[多个]代表需要抓取的股票代码\n* 不传入code,codes则是全市场抓取\n* 该方法会把数据存储到本地并只做增量更新\n\n定时任务的方式更新可参考[定时更新](https://github.com/zvtvz/zvt/blob/master/examples/data_runner)\n\n#### 全市场选股\n查询数据使用的是query_data方法，把全市场的数据记录下来后，就可以在本地快速查询需要的数据了。\n\n一个例子：2018年年报 roe>8% 营收增长>8% 的前20个股\n```\n>>> df=FinanceFactor.query_data(filters=[FinanceFactor.roe>0.08,FinanceFactor.report_period=='year',FinanceFactor.op_income_growth_yoy>0.08],start_timestamp='2019-01-01',order=FinanceFactor.roe.desc(),limit=20,columns=[\"code\"]+FinanceFactor.important_cols(),index='code')\n\n code basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp\ncode\n000048 000048 2.7350 4.919000e+09 1.101000e+09 0.4311 1.5168 0.7035 0.1988 0.5243 0.2355 2020-04-30\n000912 000912 0.3500 4.405000e+09 3.516000e+08 0.1796 1.2363 4.7847 0.0539 0.2175 0.0795 2019-03-20\n002207 002207 0.2200 3.021000e+08 5.189000e+07 0.1600 1.1526 1.1175 0.1182 0.1565 0.1718 2020-04-27\n002234 002234 5.3300 3.276000e+09 1.610000e+09 0.8023 3.2295 0.8361 0.5469 0.5968 0.4913 2020-04-21\n002458 002458 3.7900 3.584000e+09 2.176000e+09 1.4326 4.9973 0.8318 0.6754 0.6537 0.6080 2020-02-20\n... ... ... ... ... ... ... ... ... ... ... ...\n600701 600701 -3.6858 7.830000e+08 -3.814000e+09 1.3579 -0.0325 1.9498 -0.7012 0.4173 -4.9293 2020-04-29\n600747 600747 -1.5600 3.467000e+08 -2.290000e+09 2.1489 -0.4633 3.1922 -1.5886 0.0378 -6.6093 2020-06-30\n600793 600793 1.6568 1.293000e+09 1.745000e+08 0.1164 0.8868 0.7490 0.0486 0.1622 0.1350 2019-04-30\n600870 600870 0.0087 3.096000e+07 4.554000e+06 0.7773 1.3702 0.7458 0.0724 0.2688 0.1675 2019-03-30\n688169 688169 15.6600 4.205000e+09 7.829000e+08 0.3781 1.5452 0.7172 0.4832 0.3612 0.1862 2020-04-28\n\n[20 rows x 11 columns]\n```\n\n以上，你应该会回答如下的三个问题了：\n* 有什么数据?\n* 如何记录数据?\n* 如何查询数据?\n\n更高级的用法以及扩展数据，可以参考详细文档里的数据部分。\n\n### 写个策略\n有了 **交易标的** 和 **交易标的发生的事**，就可以写策略了。\n\n所谓策略回测，无非就是，重复以下过程：\n#### 在某时间点，找到符合条件的标的，对其进行买卖，看其表现。\n\n系统支持两种模式:\n* solo (随意的)\n\n在 某个时间 根据发生的事件 计算条件 并买卖\n\n* formal (正式的)\n\n系统设计的二维索引多标的计算模型\n\n#### 一个很随便的人(solo)\n嗯，这个策略真的很随便，就像我们大部分时间做的那样。\n> 报表出来的时，我看一下报表，机构加仓超过5%我就买入，机构减仓超过50%我就卖出。\n\n代码如下:\n```\n# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.api import get_recent_report_date\nfrom zvt.contract import ActorType, AdjustType\nfrom zvt.domain import StockActorSummary, Stock1dKdata\nfrom zvt.trader import StockTrader\nfrom zvt.utils import pd_is_not_null, is_same_date, to_pd_timestamp\n\n\nclass FollowIITrader(StockTrader):\n finish_date = None\n\n def on_time(self, timestamp: pd.Timestamp):\n recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp))\n if self.finish_date and is_same_date(recent_report_date, self.finish_date):\n return\n filters = [StockActorSummary.actor_type == ActorType.raised_fund.value,\n StockActorSummary.report_date == recent_report_date]\n\n if self.entity_ids:\n filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)]\n\n df = StockActorSummary.query_data(filters=filters)\n\n if pd_is_not_null(df):\n self.logger.info(f'{df}')\n self.finish_date = recent_report_date\n\n long_df = df[df['change_ratio'] > 0.05]\n short_df = df[df['change_ratio'] < -0.5]\n try:\n self.trade_the_targets(due_timestamp=timestamp, happen_timestamp=timestamp,\n long_selected=set(long_df['entity_id'].to_list()),\n short_selected=set(short_df['entity_id'].to_list()))\n except Exception as e:\n self.logger.error(e)\n\n\nif __name__ == '__main__':\n entity_id = 'stock_sh_600519'\n Stock1dKdata.record_data(entity_id=entity_id, provider='em')\n StockActorSummary.record_data(entity_id=entity_id, provider='em')\n FollowIITrader(start_timestamp='2002-01-01', end_timestamp='2021-01-01', entity_ids=[entity_id],\n provider='em', adjust_type=AdjustType.qfq, profit_threshold=None).run()\n```\n\n所以，写一个策略其实还是很简单的嘛。\n你可以发挥想象力，社保重仓买买买，外资重仓买买买，董事长跟小姨子跑了卖卖卖......\n\n然后，刷新一下[http://127.0.0.1:8050/](http://127.0.0.1:8050/)，看你运行策略的performance\n\n更多可参考[策略例子](https://github.com/zvtvz/zvt/tree/master/examples/trader)\n\n#### 严肃一点(formal)\n简单的计算可以通过query_data来完成，这里说的是系统设计的二维索引多标的计算模型。\n\n下面以技术因子为例对**计算流程**进行说明:\n```\nIn [7]: from zvt.factors import *\nIn [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer(count_live_dead=True))\n```\n### data_df\ndata_df为factor的原始数据，即通过query_data从数据库读取到的数据,为一个**二维索引**DataFrame\n```\nIn [11]: factor.data_df\nOut[11]:\n level high id entity_id open low timestamp close\nentity_id timestamp\nstock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94\n 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42\n 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31\n 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03\n 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54\n... ... ... ... ... ... ... ... ...\nstock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81\n 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73\n 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59\n 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65\n 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02\n\n[208 rows x 8 columns]\n```\n\n### factor_df\nfactor_df为transformer对data_df进行计算后得到的数据，设计因子即对[transformer](https://github.com/zvtvz/zvt/blob/master/src/zvt/contract/factor.py#L34)进行扩展，例子中用的是MacdTransformer()。\n\n```\nIn [12]: factor.factor_df\nOut[12]:\n level high id entity_id open low timestamp close diff dea macd\nentity_id timestamp\nstock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94 NaN NaN NaN\n 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42 NaN NaN NaN\n 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31 NaN NaN NaN\n 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03 NaN NaN NaN\n 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54 NaN NaN NaN\n... ... ... ... ... ... ... ... ... ... ... ...\nstock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81 -0.121336 -0.145444 0.048215\n 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73 -0.133829 -0.143121 0.018583\n 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59 -0.153260 -0.145149 -0.016223\n 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65 -0.161951 -0.148509 -0.026884\n 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02 -0.137399 -0.146287 0.017776\n\n[208 rows x 11 columns]\n```\n\n### result_df\nresult_df为可用于选股器的**二维索引**DataFrame，通过对data_df或factor_df计算来实现。\n该例子在计算macd之后，利用factor_df,黄白线在0轴上为True,否则为False，[具体代码](https://github.com/zvtvz/zvt/blob/master/src/zvt/factors/technical_factor.py#L56)\n\n```\nIn [14]: factor.result_df\nOut[14]:\n score\nentity_id timestamp\nstock_sh_601318 2019-01-02 False\n 2019-01-03 False\n 2019-01-04 False\n 2019-01-07 False\n 2019-01-08 False\n... ...\nstock_sz_000338 2019-06-03 False\n 2019-06-04 False\n 2019-06-05 False\n 2019-06-06 False\n 2019-06-10 False\n\n[208 rows x 1 columns]\n```\n\nresult_df的格式如下：\n\n\n
\n\nfilter_result 为 True 或 False, score_result 取值为 0 到 1。\n\n\n结合选股器和回测，整个流程如下：\n
\n\n## 环境设置（可选）\n```\n>>> from zvt import *\n>>> zvt_env\n{'zvt_home': '/Users/foolcage/zvt-home',\n 'data_path': '/Users/foolcage/zvt-home/data',\n 'tmp_path': '/Users/foolcage/zvt-home/tmp',\n 'ui_path': '/Users/foolcage/zvt-home/ui',\n 'log_path': '/Users/foolcage/zvt-home/logs'}\n\n>>> zvt_config \n```\n\n* jq_username 聚宽数据用户名\n* jq_password 聚宽数据密码\n* smtp_host 邮件服务器host\n* smtp_port 邮件服务器端口\n* email_username smtp邮箱账户\n* email_password smtp邮箱密码\n* wechat_app_id\n* wechat_app_secrect\n\n```\n>>> init_config(current_config=zvt_config, jq_username='xxx', jq_password='yyy')\n```\n> 通用的配置方式为: init_config(current_config=zvt_config, **kv)\n\n### 历史数据\n\nZVT支持数据增量更新，用户之间可以共享历史数据，这样可以节省很多时间。\n\n#### 数据源\n> 新UI实时行情的计算基于QMT数据源，需要开通的同学可联系作者。\n\n项目数据支持多provider，在数据schema一致性的基础上，可根据需要进行选择和扩展，目前支持新浪，东财，交易所等免费数据。\n\n#### 数据的设计上是让provider来适配schema,而不是反过来，这样即使某provider不可用了，换一个即可，不会影响整个系统的使用。\n\n但免费数据的缺点是显而易见的:不稳定，爬取清洗数据耗时耗力，维护代价巨大，且随时可能不可用。 \n个人建议：如果只是学习研究，可以使用免费数据；如果是真正有意投身量化，还是选一家可靠的数据提供商。\n\n\n> 项目中大部分的免费数据目前都是比较稳定的，且做过严格测试，特别是东财的数据，可放心使用\n\n> 添加其他数据提供商， 请参考[数据扩展教程](https://zvtvz.github.io/zvt/#/data_extending)\n\n## 开发\n\n### clone代码\n\n```\ngit clone https://github.com/zvtvz/zvt.git\n```\n\n设置项目的virtual env(python>=3.6),安装依赖\n```\npip3 install -r requirements.txt\npip3 install pytest\n```\n\n### 测试案例\npycharm导入工程(推荐,你也可以使用其他ide)，然后pytest跑测试案例\n\n
\n\n大部分功能使用都可以从tests里面参考\n\n## 贡献\n期待能有更多的开发者参与到 zvt 的开发中来，我会保证尽快 Reivew PR 并且及时回复。但提交 PR 请确保\n\n先看一下[1分钟代码规范](https://github.com/zvtvz/zvt/blob/master/code_of_conduct.md)\n\n1. 通过所有单元测试，如若是新功能，请为其新增单元测试\n2. 遵守开发规范\n3. 如若需要，请更新相对应的文档\n\n也非常欢迎开发者能为 zvt 提供更多的示例，共同来完善文档。\n\n## 请作者喝杯咖啡\n\n如果你觉得项目对你有帮助,可以请作者喝杯咖啡 \n![\"Alipay\"](\"https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/alipay-cn.png\")
\n![\"Wechat\"](\"https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/wechat-cn.png\")
\n\n## 联系方式 \n\n目前只接受开户朋友的进群申请，微信号 foolcage \n![\"Wechat\"](\"https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/wechat.jpeg\")
\n\n------\n微信公众号: \n![\"Wechat\"](\"https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/gongzhonghao.jpg\")
\n\n知乎专栏: \nhttps://zhuanlan.zhihu.com/automoney\n\n## Thanks\n![\"jetbrains\"](\"https://raw.githubusercontent.com/zvtvz/zvt/master/docs/imgs/jetbrains.png\")
\n"
},
{
"path": "README.md",
"content": "[](https://github.com/zvtvz/zvt)\n[](https://pypi.org/project/zvt/)\n[](https://pypi.org/project/zvt/)\n[](https://pypi.org/project/zvt/)\n[](https://github.com/zvtvz/zvt/actions/workflows/build.yml)\n[](https://github.com/zvtvz/zvt/actions/workflows/package.yaml)\n[](https://zvt.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/github/zvtvz/zvt)\n[](https://pepy.tech/project/zvt)\n\n**The origin of ZVT**\n\n[The Three Major Principles of Stock Trading](https://mp.weixin.qq.com/s/FoFR63wFSQIE_AyFubkZ6Q)\n\n**Declaration**\n\nThis project does not currently guarantee any backward compatibility, so please upgrade with caution. \nAs the author's thoughts evolve, some things that were once considered important may become less so, and thus may not be maintained. \nWhether the addition of some new elements will be useful to you needs to be assessed by yourself.\n\n**Read this in other languages: [中文](README-cn.md).** \n\n**Read the docs:[https://zvt.readthedocs.io/en/latest/](https://zvt.readthedocs.io/en/latest/)**\n\n### Install\n```\npython3 -m pip install -U zvt\n```\n\n### Main ui\n\n#### Dash & Plotly UI\n\n> It's good for backtest and research, but it is not applicable for real-time market data and user interaction.\n\nAfter the installation is complete, enter zvt on the command line\n```shell\nzvt\n```\nopen [http://127.0.0.1:8050/](http://127.0.0.1:8050/)\n\n> The example shown here relies on data, factor, trader, please read [docs](https://zvt.readthedocs.io/en/latest/)\n\n
\n
\n\n> The core concept of the system is visual, and the name of the interface corresponds to it one-to-one, so it is also uniform and extensible.\n\n> You can write and run the strategy in your favorite ide, and then view its related targets, factor, signal and performance on the UI.\n\n#### Rest api and standalone UI\n> It is more flexible and more scalable, more suitable for handling real-time market data and user interaction. \n> Combined with the dynamic tag system provided by ZVT, it offers a trading approach that combines AI with human intervention.\n\n- Init tag system\n\nrun following scripts: \n\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/init_tag_system.py\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/stock_pool_runner.py\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_data_runner.py\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_tick_runner.py\n\n- Install uvicorn\n```shell\npip install uvicorn\n```\n- Run zvt server\n\nAfter the installation is complete, enter zvt_server on the command line\n```shell\nzvt_server\n```\nOr run it from source code:\nhttps://github.com/zvtvz/zvt/blob/master/src/zvt/zvt_server.py\n\n- Check the api docs \n\nopen [http://127.0.0.1:8090/docs](http://127.0.0.1:8090/docs)\n\n- Deploy the front end service\n\nFront end source code: https://github.com/zvtvz/zvt_ui\n\nChange the env file:\nhttps://github.com/zvtvz/zvt_ui/blob/main/.env\n\nSet {your server IP} to zvt_server IP\n\n```text\nNEXT_PUBLIC_SERVER = {your server IP}\n```\n\nThen refer to the frontend's README to start the frontend service.\n\nopen [http://127.0.0.1:3000/trade](http://127.0.0.1:3000/trade)\n\n
\n\n### Behold, the power of zvt:\n```\n>>> from zvt.domain import Stock, Stock1dHfqKdata\n>>> from zvt.ml import MaStockMLMachine\n>>> Stock.record_data(provider=\"em\")\n>>> entity_ids = [\"stock_sz_000001\", \"stock_sz_000338\", \"stock_sh_601318\"]\n>>> Stock1dHfqKdata.record_data(provider=\"em\", entity_ids=entity_ids, sleeping_time=1)\n>>> machine = MaStockMLMachine(entity_ids=[\"stock_sz_000001\"], data_provider=\"em\")\n>>> machine.train()\n>>> machine.predict()\n>>> machine.draw_result(entity_id=\"stock_sz_000001\")\n```\n
\n\n> The few lines of code above has done: data capture, persistence, incremental update, machine learning, prediction, and display results.\n> Once you are familiar with the core concepts of the system, you can apply it to any target in the market.\n\n### Data\n\n#### China stock\n```\n>>> from zvt.domain import *\n>>> Stock.record_data(provider=\"em\")\n>>> df = Stock.query_data(provider=\"em\", index='code')\n>>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\ncode\n000001 stock_sz_000001 stock_sz_000001 1991-04-03 stock sz 000001 平安银行 1991-04-03 None\n000002 stock_sz_000002 stock_sz_000002 1991-01-29 stock sz 000002 万 科Ａ 1991-01-29 None\n000004 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None\n000005 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None\n000006 stock_sz_000006 stock_sz_000006 1992-04-27 stock sz 000006 深振业Ａ 1992-04-27 None\n... ... ... ... ... ... ... ... ... ...\n605507 stock_sh_605507 stock_sh_605507 2021-08-02 stock sh 605507 国邦医药 2021-08-02 None\n605577 stock_sh_605577 stock_sh_605577 2021-08-24 stock sh 605577 龙版传媒 2021-08-24 None\n605580 stock_sh_605580 stock_sh_605580 2021-08-19 stock sh 605580 恒盛能源 2021-08-19 None\n605588 stock_sh_605588 stock_sh_605588 2021-08-12 stock sh 605588 冠石科技 2021-08-12 None\n605589 stock_sh_605589 stock_sh_605589 2021-08-10 stock sh 605589 圣泉集团 2021-08-10 None\n\n[4136 rows x 9 columns]\n```\n\n#### USA stock\n```\n>>> Stockus.record_data()\n>>> df = Stockus.query_data(index='code')\n>>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\ncode\nA stockus_nyse_A stockus_nyse_A NaT stockus nyse A 安捷伦 None None\nAA stockus_nyse_AA stockus_nyse_AA NaT stockus nyse AA 美国铝业 None None\nAAC stockus_nyse_AAC stockus_nyse_AAC NaT stockus nyse AAC Ares Acquisition Corp-A None None\nAACG stockus_nasdaq_AACG stockus_nasdaq_AACG NaT stockus nasdaq AACG ATA Creativity Global ADR None None\nAACG stockus_nyse_AACG stockus_nyse_AACG NaT stockus nyse AACG ATA Creativity Global ADR None None\n... ... ... ... ... ... ... ... ... ...\nZWRK stockus_nasdaq_ZWRK stockus_nasdaq_ZWRK NaT stockus nasdaq ZWRK Z-Work Acquisition Corp-A None None\nZY stockus_nasdaq_ZY stockus_nasdaq_ZY NaT stockus nasdaq ZY Zymergen Inc None None\nZYME stockus_nyse_ZYME stockus_nyse_ZYME NaT stockus nyse ZYME Zymeworks Inc None None\nZYNE stockus_nasdaq_ZYNE stockus_nasdaq_ZYNE NaT stockus nasdaq ZYNE Zynerba Pharmaceuticals Inc None None\nZYXI stockus_nasdaq_ZYXI stockus_nasdaq_ZYXI NaT stockus nasdaq ZYXI Zynex Inc None None\n\n[5826 rows x 9 columns]\n\n>>> Stockus.query_data(code='AAPL')\n id entity_id timestamp entity_type exchange code name list_date end_date\n0 stockus_nasdaq_AAPL stockus_nasdaq_AAPL None stockus nasdaq AAPL 苹果 None None\n```\n\n#### Hong Kong stock\n```\n>>> Stockhk.record_data()\n>>> df = Stockhk.query_data(index='code')\n>>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\ncode\n00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None\n00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None\n00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None\n00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None\n00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None\n... ... ... ... ... ... ... ... ... ...\n09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None\n09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None\n09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None\n09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None\n80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None\n\n[2597 rows x 9 columns]\n\n>>> df[df.code=='00700']\n\n id entity_id timestamp entity_type exchange code name list_date end_date\n2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None\n\n```\n\n#### And more\n```\n>>> from zvt.contract import *\n>>> zvt_context.tradable_schema_map\n\n{'stockus': zvt.domain.meta.stockus_meta.Stockus,\n 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk,\n 'index': zvt.domain.meta.index_meta.Index,\n 'etf': zvt.domain.meta.etf_meta.Etf,\n 'stock': zvt.domain.meta.stock_meta.Stock,\n 'block': zvt.domain.meta.block_meta.Block,\n 'fund': zvt.domain.meta.fund_meta.Fund}\n```\n\nThe key is tradable entity type, and the value is the schema. The system provides unified **record (record_data)** and **query (query_data)** methods for the schema.\n\n```\n>>> Index.record_data()\n>>> df=Index.query_data(filters=[Index.category=='scope',Index.exchange='sh'])\n>>> print(df)\n id entity_id timestamp entity_type exchange code name list_date end_date publisher category base_point\n0 index_sh_000001 index_sh_000001 1990-12-19 index sh 000001 上证指数 1991-07-15 None csindex scope 100.00\n1 index_sh_000002 index_sh_000002 1990-12-19 index sh 000002 Ａ股指数 1992-02-21 None csindex scope 100.00\n2 index_sh_000003 index_sh_000003 1992-02-21 index sh 000003 B股指数 1992-08-17 None csindex scope 100.00\n3 index_sh_000010 index_sh_000010 2002-06-28 index sh 000010 上证180 2002-07-01 None csindex scope 3299.06\n4 index_sh_000016 index_sh_000016 2003-12-31 index sh 000016 上证50 2004-01-02 None csindex scope 1000.00\n.. ... ... ... ... ... ... ... ... ... ... ... ...\n25 index_sh_000020 index_sh_000020 2007-12-28 index sh 000020 中型综指 2008-05-12 None csindex scope 1000.00\n26 index_sh_000090 index_sh_000090 2009-12-31 index sh 000090 上证流通 2010-12-02 None csindex scope 1000.00\n27 index_sh_930903 index_sh_930903 2012-12-31 index sh 930903 中证Ａ股 2016-10-18 None csindex scope 1000.00\n28 index_sh_000688 index_sh_000688 2019-12-31 index sh 000688 科创50 2020-07-23 None csindex scope 1000.00\n29 index_sh_931643 index_sh_931643 2019-12-31 index sh 931643 科创创业50 2021-06-01 None csindex scope 1000.00\n\n[30 rows x 12 columns]\n\n```\n\n### EntityEvent\nWe have tradable entity and then events about them.\n\n#### Market quotes\nthe TradableEntity quote schema follows the following rules:\n```\n{entity_shema}{level}{adjust_type}Kdata\n```\n* entity_schema\n\nTradableEntity class，e.g., Stock,Stockus.\n\n* level\n```\n>>> for level in IntervalLevel:\n print(level.value)\n```\n\n* adjust type\n```\n>>> for adjust_type in AdjustType:\n print(adjust_type.value)\n```\n\n> Note: In order to be compatible with historical data, the pre-reset is an exception, {adjust_type} is left empty\n\nqfq\n```\n>>> Stock1dKdata.record_data(code='000338', provider='em')\n>>> df = Stock1dKdata.query_data(code='000338', provider='em')\n>>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 2.33 2.00 2.40 1.87 207375.0 1.365189e+09 3.2472 0.1182\n1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 2.11 1.94 2.20 1.87 86299.0 5.563198e+08 -0.0300 0.0492\n2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 1.90 1.81 1.94 1.66 93823.0 5.782065e+08 -0.0670 0.0535\n3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 1.78 1.85 1.98 1.75 47720.0 2.999226e+08 0.0221 0.0272\n4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 1.81 1.73 1.81 1.66 39273.0 2.373126e+08 -0.0649 0.0224\n... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 19.39 20.30 20.30 19.25 1688497.0 3.370241e+09 0.0601 0.0398\n3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 20.30 20.09 20.31 19.78 1187601.0 2.377957e+09 -0.0103 0.0280\n3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 20.20 20.07 20.63 19.70 1143985.0 2.295195e+09 -0.0010 0.0270\n3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 19.98 19.68 19.98 19.15 1218697.0 2.383841e+09 -0.0194 0.0287\n3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 19.71 19.85 19.97 19.24 1023545.0 2.012006e+09 0.0086 0.0241\n\n[3431 rows x 15 columns]\n\n>>> Stockus1dKdata.record_data(code='AAPL', provider='em')\n>>> df = Stockus1dKdata.query_data(code='AAPL', provider='em')\n>>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n0 stockus_nasdaq_AAPL_1984-09-07 stockus_nasdaq_AAPL 1984-09-07 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2981600.0 0.000000e+00 0.0000 0.0002\n1 stockus_nasdaq_AAPL_1984-09-10 stockus_nasdaq_AAPL 1984-09-10 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2346400.0 0.000000e+00 0.0000 0.0001\n2 stockus_nasdaq_AAPL_1984-09-11 stockus_nasdaq_AAPL 1984-09-11 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 5444000.0 0.000000e+00 0.0018 0.0003\n3 stockus_nasdaq_AAPL_1984-09-12 stockus_nasdaq_AAPL 1984-09-12 None AAPL 苹果 1d -5.58 -5.59 -5.58 -5.59 4773600.0 0.000000e+00 -0.0018 0.0003\n4 stockus_nasdaq_AAPL_1984-09-13 stockus_nasdaq_AAPL 1984-09-13 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 7429600.0 0.000000e+00 0.0018 0.0004\n... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n8765 stockus_nasdaq_AAPL_2021-08-27 stockus_nasdaq_AAPL 2021-08-27 None AAPL 苹果 1d 147.48 148.60 148.75 146.83 55802388.0 8.265452e+09 0.0072 0.0034\n8766 stockus_nasdaq_AAPL_2021-08-30 stockus_nasdaq_AAPL 2021-08-30 None AAPL 苹果 1d 149.00 153.12 153.49 148.61 90956723.0 1.383762e+10 0.0304 0.0055\n8767 stockus_nasdaq_AAPL_2021-08-31 stockus_nasdaq_AAPL 2021-08-31 None AAPL 苹果 1d 152.66 151.83 152.80 151.29 86453117.0 1.314255e+10 -0.0084 0.0052\n8768 stockus_nasdaq_AAPL_2021-09-01 stockus_nasdaq_AAPL 2021-09-01 None AAPL 苹果 1d 152.83 152.51 154.98 152.34 80313711.0 1.235321e+10 0.0045 0.0049\n8769 stockus_nasdaq_AAPL_2021-09-02 stockus_nasdaq_AAPL 2021-09-02 None AAPL 苹果 1d 153.87 153.65 154.72 152.40 71171317.0 1.093251e+10 0.0075 0.0043\n\n[8770 rows x 15 columns]\n```\n\nhfq\n```\n>>> Stock1dHfqKdata.record_data(code='000338', provider='em')\n>>> df = Stock1dHfqKdata.query_data(code='000338', provider='em')\n>>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 70.00 64.93 71.00 62.88 207375.0 1.365189e+09 2.1720 0.1182\n1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 66.60 64.00 68.00 62.88 86299.0 5.563198e+08 -0.0143 0.0492\n2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 63.32 62.00 63.88 59.60 93823.0 5.782065e+08 -0.0313 0.0535\n3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 61.50 62.49 64.48 61.01 47720.0 2.999226e+08 0.0079 0.0272\n4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 61.90 60.65 61.90 59.70 39273.0 2.373126e+08 -0.0294 0.0224\n... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 331.97 345.95 345.95 329.82 1688497.0 3.370241e+09 0.0540 0.0398\n3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 345.95 342.72 346.10 337.96 1187601.0 2.377957e+09 -0.0093 0.0280\n3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 344.41 342.41 351.02 336.73 1143985.0 2.295195e+09 -0.0009 0.0270\n3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 341.03 336.42 341.03 328.28 1218697.0 2.383841e+09 -0.0175 0.0287\n3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 336.88 339.03 340.88 329.67 1023545.0 2.012006e+09 0.0078 0.0241\n\n[3431 rows x 15 columns]\n```\n\n#### Finance factor\n```\n>>> FinanceFactor.record_data(code='000338')\n>>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp')\n\n basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp\ntimestamp\n2002-12-31 NaN 1.962000e+07 2.471000e+06 NaN NaN NaN NaN 0.2068 0.1259 2002-12-31\n2003-12-31 1.27 3.574000e+09 2.739000e+08 181.2022 109.8778 0.7729 0.1783 0.2551 0.0766 2003-12-31\n2004-12-31 1.75 6.188000e+09 5.369000e+08 0.7313 0.9598 0.3245 0.1474 0.2489 0.0868 2004-12-31\n2005-12-31 0.93 5.283000e+09 3.065000e+08 -0.1463 -0.4291 0.1327 0.0603 0.2252 0.0583 2005-12-31\n2006-03-31 0.33 1.859000e+09 1.079000e+08 NaN NaN NaN NaN NaN 0.0598 2006-03-31\n... ... ... ... ... ... ... ... ... ... ...\n2020-08-28 0.59 9.449000e+10 4.680000e+09 0.0400 -0.1148 0.0983 0.0229 0.1958 0.0603 2020-08-28\n2020-10-31 0.90 1.474000e+11 7.106000e+09 0.1632 0.0067 0.1502 0.0347 0.1949 0.0590 2020-10-31\n2021-03-31 1.16 1.975000e+11 9.207000e+09 0.1327 0.0112 0.1919 0.0444 0.1931 0.0571 2021-03-31\n2021-04-30 0.42 6.547000e+10 3.344000e+09 0.6788 0.6197 0.0622 0.0158 0.1916 0.0667 2021-04-30\n2021-08-31 0.80 1.264000e+11 6.432000e+09 0.3375 0.3742 0.1125 0.0287 0.1884 0.0653 2021-08-31\n\n[66 rows x 10 columns]\n```\n\n#### Three financial tables\n```\n>>> BalanceSheet.record_data(code='000338')\n>>> IncomeStatement.record_data(code='000338')\n>>> CashFlowStatement.record_data(code='000338')\n```\n\n#### And more\n```\n>>> zvt_context.schemas\n[zvt.domain.dividend_financing.DividendFinancing,\n zvt.domain.dividend_financing.DividendDetail,\n zvt.domain.dividend_financing.SpoDetail...]\n```\n\nAll schemas is registered in zvt_context.schemas, **schema** is table, data structure.\nThe fields and meaning could be checked in following ways:\n\n* help\n\ntype the schema. and press tab to show its fields or .help()\n```\n>>> FinanceFactor.help()\n```\n\n* source code\n\nSchemas defined in [domain](https://github.com/zvtvz/zvt/tree/master/src/zvt/domain)\n\nFrom above examples, you should know the unified way of recording data:\n\n> Schema.record_data(provider='your provider',codes='the codes')\n\nNote the optional parameter provider, which represents the data provider. \nA schema can have multiple providers, which is the cornerstone of system stability.\n\nCheck the provider has been implemented:\n```\n>>> Stock.provider_map_recorder\n{'joinquant': zvt.recorders.joinquant.meta.jq_stock_meta_recorder.JqChinaStockRecorder,\n 'exchange': zvt.recorders.exchange.exchange_stock_meta_recorder.ExchangeStockMetaRecorder,\n 'em': zvt.recorders.em.meta.em_stock_meta_recorder.EMStockRecorder,\n 'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder.EastmoneyChinaStockListRecorder}\n\n```\nYou can use any provider to get the data, the first one is used by default.\n\nOne more example, the stock sector data recording:\n```\n>>> Block.provider_map_recorder\n{'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_block_meta_recorder.EastmoneyChinaBlockRecorder,\n 'sina': zvt.recorders.sina.meta.sina_block_recorder.SinaBlockRecorder}\n\n>>> Block.record_data(provider='sina')\nBlock registered recorders:{'eastmoney': , 'sina': }\n2020-03-04 23:56:48,931 INFO MainThread finish record sina blocks:industry\n2020-03-04 23:56:49,450 INFO MainThread finish record sina blocks:concept\n```\n\nLearn more about record_data\n\n* The parameter code[single], codes[multiple] represent the stock codes to be recorded\n* Recording the whole market if not set code, codes\n* This method will store the data locally and only do incremental updates\n\nRefer to the scheduling recoding way[data runner](https://github.com/zvtvz/zvt/blob/master/examples/data_runner)\n\n#### Market-wide stock selection\n\nAfter recording the data of the whole market, you can quickly query the required data locally.\n\nAn example: the top 20 stocks with roe>8% and revenue growth>8% in the 2018 annual report\n```\n>>> df=FinanceFactor.query_data(filters=[FinanceFactor.roe>0.08,FinanceFactor.report_period=='year',FinanceFactor.op_income_growth_yoy>0.08],start_timestamp='2019-01-01',order=FinanceFactor.roe.desc(),limit=20,columns=[\"code\"]+FinanceFactor.important_cols(),index='code')\n\n code basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp\ncode\n000048 000048 2.7350 4.919000e+09 1.101000e+09 0.4311 1.5168 0.7035 0.1988 0.5243 0.2355 2020-04-30\n000912 000912 0.3500 4.405000e+09 3.516000e+08 0.1796 1.2363 4.7847 0.0539 0.2175 0.0795 2019-03-20\n002207 002207 0.2200 3.021000e+08 5.189000e+07 0.1600 1.1526 1.1175 0.1182 0.1565 0.1718 2020-04-27\n002234 002234 5.3300 3.276000e+09 1.610000e+09 0.8023 3.2295 0.8361 0.5469 0.5968 0.4913 2020-04-21\n002458 002458 3.7900 3.584000e+09 2.176000e+09 1.4326 4.9973 0.8318 0.6754 0.6537 0.6080 2020-02-20\n... ... ... ... ... ... ... ... ... ... ... ...\n600701 600701 -3.6858 7.830000e+08 -3.814000e+09 1.3579 -0.0325 1.9498 -0.7012 0.4173 -4.9293 2020-04-29\n600747 600747 -1.5600 3.467000e+08 -2.290000e+09 2.1489 -0.4633 3.1922 -1.5886 0.0378 -6.6093 2020-06-30\n600793 600793 1.6568 1.293000e+09 1.745000e+08 0.1164 0.8868 0.7490 0.0486 0.1622 0.1350 2019-04-30\n600870 600870 0.0087 3.096000e+07 4.554000e+06 0.7773 1.3702 0.7458 0.0724 0.2688 0.1675 2019-03-30\n688169 688169 15.6600 4.205000e+09 7.829000e+08 0.3781 1.5452 0.7172 0.4832 0.3612 0.1862 2020-04-28\n\n[20 rows x 11 columns]\n```\n\nSo, you should be able to answer the following three questions now:\n* What data is there?\n* How to record data?\n* How to query data?\n\nFor more advanced usage and extended data, please refer to the data section in the detailed document.\n\n### Write strategy\nNow we could write strategy basing on TradableEntity and EntityEvent.\nThe so-called strategy backtesting is nothing but repeating the following process：\n\n#### At a certain time, find the targets which matching conditions, buy and sell them, and see the performance.\n\nTwo modes to write strategy:\n* solo (free style)\n\nAt a certain time, calculate conditions according to the events, buy and sell\n\n* formal\n\nThe calculation model of the two-dimensional index and multi-entity\n\n#### a too simple,sometimes naive person (solo)\nWell, this strategy is really too simple,sometimes naive, as we do most of the time.\n> When the report comes out, I look at the report. \n> If the institution increases its position by more than 5%, I will buy it, and if the institution reduces its position by more than 50%, I will sell it.\n\nShow you the code:\n```\n# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.api import get_recent_report_date\nfrom zvt.contract import ActorType, AdjustType\nfrom zvt.domain import StockActorSummary, Stock1dKdata\nfrom zvt.trader import StockTrader\nfrom zvt.utils import pd_is_not_null, is_same_date, to_pd_timestamp\n\n\nclass FollowIITrader(StockTrader):\n finish_date = None\n\n def on_time(self, timestamp: pd.Timestamp):\n recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp))\n if self.finish_date and is_same_date(recent_report_date, self.finish_date):\n return\n filters = [StockActorSummary.actor_type == ActorType.raised_fund.value,\n StockActorSummary.report_date == recent_report_date]\n\n if self.entity_ids:\n filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)]\n\n df = StockActorSummary.query_data(filters=filters)\n\n if pd_is_not_null(df):\n self.logger.info(f'{df}')\n self.finish_date = recent_report_date\n\n long_df = df[df['change_ratio'] > 0.05]\n short_df = df[df['change_ratio'] < -0.5]\n try:\n self.trade_the_targets(due_timestamp=timestamp, happen_timestamp=timestamp,\n long_selected=set(long_df['entity_id'].to_list()),\n short_selected=set(short_df['entity_id'].to_list()))\n except Exception as e:\n self.logger.error(e)\n\n\nif __name__ == '__main__':\n entity_id = 'stock_sh_600519'\n Stock1dKdata.record_data(entity_id=entity_id, provider='em')\n StockActorSummary.record_data(entity_id=entity_id, provider='em')\n FollowIITrader(start_timestamp='2002-01-01', end_timestamp='2021-01-01', entity_ids=[entity_id],\n provider='em', adjust_type=AdjustType.qfq, profit_threshold=None).run()\n```\n\nSo, writing a strategy is not that complicated.\nJust use your imagination, find the relation of the price and the events.\n\nThen refresh [http://127.0.0.1:8050/](http://127.0.0.1:8050/)，check the performance of your strategy.\n\nMore examples is in [Strategy example](https://github.com/zvtvz/zvt/tree/master/examples/trader)\n\n#### Be serious (formal)\nSimple calculation can be done through query_data.\nNow it's time to introduce the two-dimensional index multi-entity calculation model.\n\nTakes technical factors as an example to illustrate the **calculation process**:\n```\nIn [7]: from zvt.factors import *\nIn [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer(count_live_dead=True))\n```\n### data_df\n\n**two-dimensional index** DataFrame read from the schema by query_data.\n```\nIn [11]: factor.data_df\nOut[11]:\n level high id entity_id open low timestamp close\nentity_id timestamp\nstock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94\n 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42\n 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31\n 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03\n 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54\n... ... ... ... ... ... ... ... ...\nstock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81\n 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73\n 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59\n 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65\n 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02\n\n[208 rows x 8 columns]\n```\n\n### factor_df\n**two-dimensional index** DataFrame which calculating using data_df by [transformer](https://github.com/zvtvz/zvt/blob/master/src/zvt/contract/factor.py#L34)\ne.g., MacdTransformer.\n```\nIn [12]: factor.factor_df\nOut[12]:\n level high id entity_id open low timestamp close diff dea macd\nentity_id timestamp\nstock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94 NaN NaN NaN\n 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42 NaN NaN NaN\n 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31 NaN NaN NaN\n 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03 NaN NaN NaN\n 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54 NaN NaN NaN\n... ... ... ... ... ... ... ... ... ... ... ...\nstock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81 -0.121336 -0.145444 0.048215\n 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73 -0.133829 -0.143121 0.018583\n 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59 -0.153260 -0.145149 -0.016223\n 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65 -0.161951 -0.148509 -0.026884\n 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02 -0.137399 -0.146287 0.017776\n\n[208 rows x 11 columns]\n```\n\n### result_df\n**two-dimensional index** DataFrame which calculating using factor_df or(and) data_df.\nIt's used by TargetSelector.\n\ne.g.,[macd](https://github.com/zvtvz/zvt/blob/master/src/zvt/factors/technical_factor.py#L56)\n\n```\nIn [14]: factor.result_df\nOut[14]:\n filter_result\nentity_id timestamp\nstock_sh_601318 2019-01-02 False\n 2019-01-03 False\n 2019-01-04 False\n 2019-01-07 False\n 2019-01-08 False\n... ...\nstock_sz_000338 2019-06-03 False\n 2019-06-04 False\n 2019-06-05 False\n 2019-06-06 False\n 2019-06-10 False\n\n[208 rows x 1 columns]\n```\n\nThe format of result_df is as follows:\n\n
\n\nfilter_result is True or False, score_result is from 0 to 1\n\nCombining the stock picker and backtesting, the whole process is as follows:\n
\n\n## Env settings（optional）\n```\n>>> from zvt import *\n>>> zvt_env\n{'zvt_home': '/Users/foolcage/zvt-home',\n 'data_path': '/Users/foolcage/zvt-home/data',\n 'tmp_path': '/Users/foolcage/zvt-home/tmp',\n 'ui_path': '/Users/foolcage/zvt-home/ui',\n 'log_path': '/Users/foolcage/zvt-home/logs'}\n\n>>> zvt_config \n```\n\n* jq_username 聚宽数据用户名\n* jq_password 聚宽数据密码\n* smtp_host 邮件服务器host\n* smtp_port 邮件服务器端口\n* email_username smtp邮箱账户\n* email_password smtp邮箱密码\n* wechat_app_id\n* wechat_app_secrect\n\n```\n>>> init_config(current_config=zvt_config, jq_username='xxx', jq_password='yyy')\n```\n> config others this way: init_config(current_config=zvt_config, **kv)\n\n### History data\n\nZVT supports incremental data updates, and sharing historical data among users is encouraged for time-saving efficiency\n\n#### Data providers\n> The new UI's real-time quotes are based on the QMT data source. To obtain access, please contact the author.\n\nthe data could be updated from different provider, this make the system stable.\n\n> add other providers， [Data extension tutorial](https://zvtvz.github.io/zvt/#/data_extending)\n\n## Development\n\n### Clone\n\n```\ngit clone https://github.com/zvtvz/zvt.git\n```\n\nset up virtual env(python>=3.8),install requirements\n```\npip3 install -r requirements.txt\npip3 install pytest\n```\n\n### Tests\n```shell\npytest ./tests --ignore=tests/recorders/ \n```\n\n
\n\nMost of the features can be referenced from the tests\n\n## Contribution\n\n[code of conduct](https://github.com/zvtvz/zvt/blob/master/code_of_conduct.md)\n\n1. Pass all unit tests, if it is a new feature, please add a new unit test for it\n2. Compliance with development specifications\n3. If necessary, please update the corresponding document\n\nDevelopers are also very welcome to provide more examples for zvt, and work together to improve the documentation.\n\n## Buy me a coffee\n\n \n\n\n## Contact\n\nwechat:foolcage \n\n\n------\nwechat subscription: \n\n\nzhihu: \nhttps://zhuanlan.zhihu.com/automoney\n\n## Thanks\n
\n"
},
{
"path": "api-tests/create_stock_pool_info.http",
"content": "POST http://127.0.0.1:8090/api/work/create_stock_pool_info\naccept: application/json\nContent-Type: application/json\n\n{\n \"stock_pool_name\": \"核心资产\",\n \"stock_pool_type\": \"custom\"\n}"
},
{
"path": "api-tests/create_stock_pools.http",
"content": "POST http://127.0.0.1:8090/api/work/create_stock_pools\naccept: application/json\nContent-Type: application/json\n\n{\n \"stock_pool_name\": \"核心资产\",\n \"entity_ids\": [\n \"stock_sh_600519\"\n ]\n}"
},
{
"path": "api-tests/del_stock_pool.http",
"content": "DELETE http://127.0.0.1:8090/api/work/delete_stock_pool?stock_pool_name=今日异动\naccept: application/json\nContent-Type: application/json\n"
},
{
"path": "api-tests/event/create_stock_topic.http",
"content": "POST http://127.0.0.1:8090/api/event/create_stock_topic\naccept: application/json\nContent-Type: application/json\n\n{\n \"name\": \"特斯拉FSD入华\",\n \"desc\": \"Tesla Al在社交媒体平台“X”上发帖称，特斯拉计划明年第一季度在中国和欧洲推出被其称为“全自动驾驶”(Full Self Driving)的高级驾驶辅助系统，目前正在等待监管部门的批准。\",\n \"created_timestamp\": \"2024-09-05 09:00:00\",\n \"trigger_date\": \"2024-09-05\",\n \"due_date\": \"2025-01-01\",\n \"main_tag\": \"车路云\",\n \"sub_tag_list\": [\n \"自动驾驶\",\n \"车路云\"\n ]\n}\n\n\n\n"
},
{
"path": "api-tests/event/get_stock_event.http",
"content": "GET http://127.0.0.1:8090/api/event/get_stock_event?entity_id=stock_sz_000034\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/event/get_stock_news_analysis.http",
"content": "GET http://127.0.0.1:8090/api/event/get_stock_news_analysis\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/event/get_tag_suggestions_stats.http",
"content": "GET http://127.0.0.1:8090/api/event/get_tag_suggestions_stats\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/event/ignore_stock_news.http",
"content": "POST http://127.0.0.1:8090/api/event/ignore_stock_news\naccept: application/json\nContent-Type: application/json\n\n{\n \"news_id\": \"stock_sz_000034_2024-07-17 16:08:17\"\n}\n\n\n\n"
},
{
"path": "api-tests/event/query_stock_topic.http",
"content": "POST http://127.0.0.1:8090/api/event/query_stock_topic\naccept: application/json\nContent-Type: application/json\n\n{\n \"limit\": 20\n}\n\n\n\n"
},
{
"path": "api-tests/event/update_stock_topic.http",
"content": "POST http://127.0.0.1:8090/api/event/update_stock_topic\naccept: application/json\nContent-Type: application/json\n\n{\n \"id\": \"admin_特斯拉FSD入华\",\n \"desc\": \"Tesla Al在社交媒体平台“X”上发帖称，特斯拉计划明年第一季度在中国和欧洲推出被其称为“全自动驾驶”(Full Self Driving)的高级驾驶辅助系统，目前正在等待监管部门的批准。\",\n \"created_timestamp\": \"2024-09-05 09:00:00\",\n \"trigger_date\": \"2024-09-05\",\n \"due_date\": \"2025-01-01\",\n \"main_tag\": \"车路云\",\n \"sub_tag_list\": [\n \"自动驾驶\",\n \"车路云\"\n ]\n}\n\n\n"
},
{
"path": "api-tests/factor/get_factors.http",
"content": "GET http://127.0.0.1:8090/api/factor/get_factors\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/factor/query_factor_result.http",
"content": "POST http://127.0.0.1:8090/api/factor/query_factor_result\naccept: application/json\nContent-Type: application/json\n\n\n{\n \"entity_ids\": [\n \"stock_sz_300133\"\n ],\n \"factor_name\": \"LiveOrDeadFactor\"\n}\n"
},
{
"path": "api-tests/get_main_tags_in_stock_pool.http",
"content": "GET http://127.0.0.1:8090/api/work/get_main_tags_in_stock_pool?stock_pool_name=大局\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/get_stock_pool_info.http",
"content": "GET http://127.0.0.1:8090/api/work/get_stock_pool_info\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/get_stock_pools.http",
"content": "GET http://127.0.0.1:8090/api/work/get_stock_pools?stock_pool_name=main_line\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/tag/batch_set_stock_tags.http",
"content": "POST http://127.0.0.1:8090/api/work/batch_set_stock_tags\naccept: application/json\nContent-Type: application/json\n\n\n{\n \"tag\": \"脑机接口\",\n \"tag_type\": \"sub_tag\",\n \"entity_ids\": [\n \"stock_sh_600775\",\n \"stock_sz_002173\",\n \"stock_sz_301293\",\n \"stock_sz_300753\",\n \"stock_sz_300430\",\n \"stock_sz_002243\"\n ],\n \"tag_reason\": \"脑机接口消息刺激\"\n}\n"
},
{
"path": "api-tests/tag/build_main_tag_industry_relation.http",
"content": "POST http://127.0.0.1:8090/api/work/build_main_tag_industry_relation\naccept: application/json\nContent-Type: application/json\n\n{\n \"main_tag\": \"商业航天\",\n \"industry_list\": [\n \"航天航空\"\n ]\n}"
},
{
"path": "api-tests/tag/build_main_tag_sub_tag_relation.http",
"content": "POST http://127.0.0.1:8090/api/work/build_main_tag_sub_tag_relation\naccept: application/json\nContent-Type: application/json\n\n{\n \"main_tag\": \"商业航天\",\n \"sub_tag_list\": [\n \"航天概念\",\n \"天基互联\",\n \"北斗导航\",\n \"通用航空\"\n ],\n \"activate\": true\n}"
},
{
"path": "api-tests/tag/build_stock_tags.http",
"content": "POST http://127.0.0.1:8090/api/work/build_stock_tags\naccept: application/json\nContent-Type: application/json\n\n[\n {\n \"entity_id\": \"stock_sz_002085\",\n \"name\": \"万丰奥威\",\n \"main_tag\": \"低空经济\",\n \"main_tag_reason\": \"2023年12月27日回复称,公司钻石eDA40纯电动飞机已成功首飞;eVTOL项目已联动海外钻石技术开发团队,在绿色、智能、垂直起降等方面的设计体现未来领域应用场景。\",\n \"sub_tag\": \"低空经济\",\n \"sub_tag_reason\": \"2023年12月27日回复称,公司钻石eDA40纯电动飞机已成功首飞;eVTOL项目已联动海外钻石技术开发团队,在绿色、智能、垂直起降等方面的设计体现未来领域应用场景。\",\n \"active_hidden_tags\": null\n }\n]"
},
{
"path": "api-tests/tag/change_main_tag.http",
"content": "POST http://127.0.0.1:8090/api/work/change_main_tag\naccept: application/json\nContent-Type: application/json\n\n{\n \"current_main_tag\": \"医疗器械\",\n \"new_main_tag\": \"医药\"\n}"
},
{
"path": "api-tests/tag/create_hidden_tag_info.http",
"content": "POST http://127.0.0.1:8090/api/work/create_hidden_tag_info\naccept: application/json\nContent-Type: application/json\n\n{\n \"tag\": \"中字头\",\n \"tag_reason\": \"央企，国资委控股\"\n}"
},
{
"path": "api-tests/tag/create_main_tag_info.http",
"content": "POST http://127.0.0.1:8090/api/work/create_main_tag_info\naccept: application/json\nContent-Type: application/json\n\n{\n \"tag\": \"稳定币\",\n \"tag_reason\": \"数字货币的一种，通常与法定货币挂钩，旨在减少价格波动。\"\n}"
},
{
"path": "api-tests/tag/create_sub_tag_info.http",
"content": "POST http://127.0.0.1:8090/api/work/create_sub_tag_info\naccept: application/json\nContent-Type: application/json\n\n{\n \"tag\": \"低空经济\",\n \"tag_reason\": \"低空经济是飞行器和各种产业形态的融合，例如\\\"无人机+配送\\\"、\\\"直升机或evto载人飞行\\\"、\\\"无人机+应急救援\\\"、\\\"无人机+工业场景巡检\\\"等\"\n}"
},
{
"path": "api-tests/tag/del_hidden_tag.http",
"content": "DELETE http://127.0.0.1:8090/api/work/delete_hidden_tag?tag=妖\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/del_main_tag.http",
"content": "DELETE http://127.0.0.1:8090/api/work/delete_main_tag?tag=国企\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/del_sub_tag.http",
"content": "DELETE http://127.0.0.1:8090/api/work/delete_sub_tag?tag=天然气\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_hidden_tag_info.http",
"content": "GET http://127.0.0.1:8090/api/work/get_hidden_tag_info\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_industry_info.http",
"content": "GET http://127.0.0.1:8090/api/work/get_industry_info\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_main_tag_industry_relation.http",
"content": "GET http://127.0.0.1:8090/api/work/get_main_tag_industry_relation?main_tag=AI\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_main_tag_info.http",
"content": "GET http://127.0.0.1:8090/api/work/get_main_tag_info\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_main_tag_sub_tag_relation.http",
"content": "GET http://127.0.0.1:8090/api/work/get_main_tag_sub_tag_relation?main_tag=军工\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_stock_tag_options.http",
"content": "GET http://127.0.0.1:8090/api/work/get_stock_tag_options?entity_id=stock_sh_600733\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/get_sub_tag_info.http",
"content": "GET http://127.0.0.1:8090/api/work/get_sub_tag_info\naccept: application/json\n\n\n"
},
{
"path": "api-tests/tag/query_simple_stock_tags.http",
"content": "POST http://127.0.0.1:8090/api/work/query_simple_stock_tags\naccept: application/json\nContent-Type: application/json\n\n\n{\n \"entity_ids\": [\n \"stock_sz_002085\",\n \"stock_sz_000099\",\n \"stock_sz_002130\"\n ]\n}"
},
{
"path": "api-tests/tag/query_stock_tag_stats.http",
"content": "POST http://127.0.0.1:8090/api/work/query_stock_tag_stats\naccept: application/json\nContent-Type: application/json\n\n{\n \"stock_pool_name\": \"main_line\"\n}"
},
{
"path": "api-tests/tag/query_stock_tags.http",
"content": "POST http://127.0.0.1:8090/api/work/query_stock_tags\naccept: application/json\nContent-Type: application/json\n\n\n{\n \"entity_ids\": [\n \"stock_sz_000099\",\n \"stock_sz_002085\",\n \"stock_sz_001696\"\n ]\n}\n"
},
{
"path": "api-tests/tag/set_stock_tags.http",
"content": "POST http://127.0.0.1:8090/api/work/set_stock_tags\naccept: application/json\nContent-Type: application/json\n\n{\n \"entity_id\": \"stock_sz_001366\",\n \"name\": \"播恩集团\",\n \"main_tag\": \"医药\",\n \"main_tag_reason\": \"合成生物概念\",\n \"main_tags\": {\n \"农业\": \"来自行业:农牧饲渔\"\n },\n \"sub_tag\": \"生物医药\",\n \"sub_tag_reason\": \"合成生物概念\",\n \"sub_tags\": null,\n \"active_hidden_tags\": null\n }"
},
{
"path": "api-tests/test.http",
"content": "\n"
},
{
"path": "api-tests/trading/build_query_stock_quote_setting.http",
"content": "POST http://127.0.0.1:8090/api/trading/build_query_stock_quote_setting\naccept: application/json\nContent-Type: application/json\n\n{\n \"stock_pool_name\": \"main_line\",\n \"main_tags\": [\"低空经济\",\"新能源\"]\n}"
},
{
"path": "api-tests/trading/build_trading_plan.http",
"content": "POST http://127.0.0.1:8090/api/trading/build_trading_plan\naccept: application/json\nContent-Type: application/json\n\n{\n \"stock_id\": \"stock_sz_300133\",\n \"trading_date\": \"2024-04-23\",\n \"expected_open_pct\": 0.02,\n \"buy_price\": 6.9,\n \"sell_price\": null,\n \"trading_reason\": \"主线\",\n \"trading_signal_type\": \"open_long\"\n}\n"
},
{
"path": "api-tests/trading/get_current_trading_plan.http",
"content": "GET http://127.0.0.1:8090/api/trading/get_current_trading_plan\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/trading/get_future_trading_plan.http",
"content": "GET http://127.0.0.1:8090/api/trading/get_future_trading_plan\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/trading/get_query_stock_quote_setting.http",
"content": "GET http://127.0.0.1:8090/api/trading/get_query_stock_quote_setting\naccept: application/json\n\n\n\n"
},
{
"path": "api-tests/trading/get_quote_stats.http",
"content": "GET http://127.0.0.1:8090/api/trading/get_quote_stats\naccept: application/json\n"
},
{
"path": "api-tests/trading/query_kdata.http",
"content": "POST http://127.0.0.1:8090/api/trading/query_kdata\naccept: application/json\nContent-Type: application/json\n\n\n{\n \"data_provider\": \"em\",\n \"entity_ids\": [\n \"stock_sz_002085\",\n \"stock_sz_300133\"\n ],\n \"adjust_type\": \"hfq\"\n}\n"
},
{
"path": "api-tests/trading/query_stock_quotes.http",
"content": "POST http://127.0.0.1:8090/api/trading/query_stock_quotes\naccept: application/json\nContent-Type: application/json\n\n{\n \"main_tag\": \"低空经济\",\n \"stock_pool_name\": \"vol_up\",\n \"limit\": 500,\n \"order_by_field\": \"change_pct\"\n}\n"
},
{
"path": "api-tests/trading/query_tag_quotes.http",
"content": "POST http://127.0.0.1:8090/api/trading/query_tag_quotes\naccept: application/json\nContent-Type: application/json\n\n{\n \"main_tags\": [\n \"低空经济\",\n \"半导体\",\n \"化工\",\n \"消费电子\"\n ],\n \"stock_pool_name\": \"main_line\"\n}\n"
},
{
"path": "api-tests/trading/query_trading_plan.http",
"content": "POST http://127.0.0.1:8090/api/trading/query_trading_plan\naccept: application/json\nContent-Type: application/json\n\n{\n \"time_range\": {\n \"relative_time_range\": {\n \"interval\": -30,\n \"time_unit\": \"day\"\n }\n }\n}\n"
},
{
"path": "api-tests/trading/query_ts.http",
"content": "POST http://127.0.0.1:8090/api/trading/query_ts\naccept: application/json\nContent-Type: application/json\n\n\n{\n \"entity_ids\": [\n \"stock_sz_002085\",\n \"stock_sz_300133\"\n ]\n}\n"
},
{
"path": "build.sh",
"content": "python3 setup.py sdist bdist_wheel"
},
{
"path": "code_of_conduct.md",
"content": "## 1. 命名\n* snake_case适用于\n * variable(变量)\n * package(包)\n * module(模块)\n * function(函数)\n * method(方法)\n\n* CamelCase适用于\n * class(类)\n\n## 2. 对外接口\n各module对外暴露的接口应显式声明于__all__,例子:\n\n[contract模块](https://github.com/zvtvz/zvt/blob/master/zvt/contract/__init__.py)以此结尾:\n```\n__all__ = ['IntervalLevel', 'Mixin', 'NormalMixin', 'EntityMixin', 'NormalEntityMixin', 'zvt_context']\n```\n\n## 3. 引入接口\n不要使用 from zvt.module import *,你要什么就import什么，不要污染。\n\n## 4. 没了\n\nNo api is the best api.\n\nCode as comment."
},
{
"path": "docs/Makefile",
"content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n"
},
{
"path": "docs/make.bat",
"content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=source\r\nset BUILDDIR=build\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.https://www.sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\n\r\n:end\r\npopd\r\n"
},
{
"path": "docs/source/_templates/custom-class-template.rst",
"content": "{{ objname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n :members:\n :show-inheritance:\n :inherited-members:\n\n {% block methods %}\n .. automethod:: __init__\n\n\n {% endblock %}\n"
},
{
"path": "docs/source/_templates/custom-module-template.rst",
"content": "{{ fullname | escape | underline}}\n\n.. automodule:: {{ fullname }}\n\n {% block attributes %}\n {% if attributes %}\n .. rubric:: {{ _('Module Attributes') }}\n\n .. autosummary::\n :toctree:\n {% for item in attributes %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block functions %}\n {% if functions %}\n .. rubric:: {{ _('Functions') }}\n\n .. autosummary::\n :toctree:\n {% for item in functions %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block classes %}\n {% if classes %}\n .. rubric:: {{ _('Classes') }}\n\n .. autosummary::\n :toctree:\n :template: custom-class-template.rst\n {% for item in classes %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block exceptions %}\n {% if exceptions %}\n .. rubric:: {{ _('Exceptions') }}\n\n .. autosummary::\n :toctree:\n {% for item in exceptions %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n{% block modules %}\n{% if modules %}\n.. rubric:: Modules\n\n.. autosummary::\n :toctree:\n :template: custom-module-template.rst\n :recursive:\n{% for item in modules %}\n {{ item }}\n{%- endfor %}\n{% endif %}\n{% endblock %}\n"
},
{
"path": "docs/source/_templates/sidebarlogo.html",
"content": "\n \n
\n"
},
{
"path": "docs/source/api/index.rst",
"content": "API\n===\n\n.. autosummary::\n :toctree: _autosummary\n :template: custom-module-template.rst\n :recursive:\n\n zvt\n zvt.api\n zvt.autocode\n zvt.consts\n zvt.common\n zvt.contract\n zvt.domain\n zvt.factors\n zvt.fill_project\n zvt.informer\n zvt.misc\n zvt.ml\n zvt.plugin\n zvt.recorders\n zvt.rest\n zvt.samples\n zvt.tag\n zvt.trader\n zvt.trading\n zvt.ui\n zvt.utils"
},
{
"path": "docs/source/conf.py",
"content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\n\nsys.path.insert(0, os.path.abspath(\"../../src\"))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = \"zvt\"\ncopyright = \"2026, foolcage\"\nauthor = \"foolcage\"\n\n# The full version, including alpha/beta/rc tags\nrelease = \"0.13.5\"\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"IPython.sphinxext.ipython_directive\",\n \"IPython.sphinxext.ipython_console_highlighting\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"alabaster\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\nhtml_sidebars = {\n \"**\": [\"sidebarlogo.html\", \"globaltoc.html\", \"sourcelink.html\", \"searchbox.html\", \"localtoc.html\", \"relations.html\"]\n}\n\nautosummary_generate = True\n\nautodoc_default_options = {\n \"member-order\": \"bysource\",\n}\n"
},
{
"path": "docs/source/contributor.rst",
"content": "====================\nContributor\n====================\n\nThanks, my friends.\n\n* 隋鹏飞\n* `Tiger `_\n* 蓝小球\n* 青玄\n* \\*崇\n* \\*阳\n* 张凯\n* Rubicon_陈小虾\n* `看门大爷 `_\n* 芝华\n* 雨多田光\n* 叶金荣\n* 冷冷\n* Landy\n* Sunny\n* Ccc国毅\n* Jim Tong\n* tom\n* 小七\n* 小强\n* Cascara Latte- 刘可烁\n"
},
{
"path": "docs/source/core_concepts.rst",
"content": "====================\nCore concepts\n====================\n\nEntity\n the existence described by itself, classification of existential concepts.\n\nEntityEvent\n the event happened on the entity.\n\nTradableEntity\n the Entity could be traded, e.g, Stock, Future.\n\nActorEntity\n the Entity acting in market, e.g, Fund, Individual, Government.\n\nIntervalLevel\n repeated fixed time interval, e.g, 5m, 1d.\n\nSchema\n the data structure with fields, one schema could have multiple storage with different Providers.\n\nKdata(Quote)\n the candlestick data with OHLC.\n\nProvider\n the data provider.\n\nStorage\n the sql database, e.g, sqlite, mysql.\n\nRecorder\n class for recording data for Schema.\n\nFactor\n data describing market.It's computed from from Schema, and save as new Schema if need.\n\nTargetSelector\n the class select targets according to Factor.\n\nTrader\n the backtest engine using TargetSelector, MLMachine or free style.\n\nTagger\n classify TradableEntity by different dimensions, could be used as ml category feature.\n\nMLMachine\n the ml engine.\n\nTradingSignal\n the signal contains information about how to trade.\n\nDrawer\n the class for draw charts.\n\nIntent\n what do you want to do, e.g. compare, distribute, composite.\n\nNormal data\n the pandas dataframe with multiple index which level 0 is entity_id and level 1 is timestamp:\n\n=============== ========== ===== ===== ===== =====\nentity_id timestamp col1 col2 col3 col4\n=============== ========== ===== ===== ===== =====\nstock_sz_000338 2020-05-05 1.2 0.5 0.3 a\n... 2020-05-06 1.0 0.7 0.2 b\nstock_sz_000778 2020-05-05 1.2 0.5 0.3 a\n... 2020-05-06 1.0 0.7 0.2 b\n=============== ========== ===== ===== ===== =====\n"
},
{
"path": "docs/source/data/adding_new_entity.rst",
"content": ".. _adding_new_entity:\n\n=================\nAdding new entity\n=================\n\nIt's human nature to like the new and hate the old. Adding new TradableEntity is easy in zvt.\n\nAdding new entity is nothing than a specific case of :ref:`Adding data `.\nLet's show the key steps below which add :class:`~.zvt.domain.meta.future_meta.Future`.\n\nDefine entity Schema\n--------------------------\n\n::\n\n # -*- coding: utf-8 -*-\n from sqlalchemy.ext.declarative import declarative_base\n\n from zvt.contract.register import register_schema, register_entity\n from zvt.contract.schema import TradableEntity\n\n FutureMetaBase = declarative_base()\n\n\n @register_entity(entity_type=\"future\")\n class Future(FutureMetaBase, TradableEntity):\n __tablename__ = \"future\"\n\n\n register_schema(providers=[\"em\"], db_name=\"future_meta\", schema_base=FutureMetaBase)\n\nImplement recorder for the entity\n---------------------------------\n\n::\n\n from zvt.contract.api import df_to_db\n from zvt.contract.recorder import Recorder\n from zvt.domain import Future\n from zvt.recorders.em import em_api\n\n\n class EMFutureRecorder(Recorder):\n provider = \"em\"\n data_schema = Future\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"future\")\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nDefine OHLC schema(kdata) for the entity\n----------------------------------------\n\nzvt provide a standard way to generate OHLC schema for the tradable entity.\nAll `OHLC schemas `_ is generated by\n`fill project script `_.\n\ne.g generate Future OHLC schema.\n\n::\n\n gen_kdata_schema(\n pkg=\"zvt\",\n providers=[\"em\"],\n entity_type=\"future\",\n levels=[IntervalLevel.LEVEL_1DAY],\n entity_in_submodule=True,\n )\n\nThe OHLC schema definition principle is: **one level one file**\n\nSo we would define a common OHLC schema for each entity type in `quotes module `_.\n\ne.g. Future common OHLC schema\n\n::\n\n class FutureKdataCommon(KdataCommon):\n #: 持仓量\n interest = Column(Float)\n #: 结算价\n settlement = Column(Float)\n #: 涨跌幅(按收盘价)\n # change_pct = Column(Float)\n #: 涨跌幅(按结算价)\n change_pct1 = Column(Float)\n\nAnd we could relate the common kdata schema with the recorder and route level to specific schema automatically.\n\nImplement recorder for OHLC schema(kdata)\n-----------------------------------------\n\nCheck `em quotes recorder `_ for\nthe details.\n\n::\n\n class EMFutureKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Future\n\n data_schema = FutureKdataCommon\n\n\nUse them in zvt way\n-------------------\n\nFetch the entity list:\n\n::\n\n >>> from zvt.domain import Future\n >>> Future.record_data()\n >>> df = Future.query_data()\n >>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\n 0 future_cffex_IC future_cffex_IC NaT future cffex IC 中证当月连续 NaT None\n 1 future_cffex_IF future_cffex_IF NaT future cffex IF 沪深当月连续 NaT None\n 2 future_cffex_IH future_cffex_IH NaT future cffex IH 上证当月连续 NaT None\n 3 future_cffex_T future_cffex_T NaT future cffex T 十债当季连续 NaT None\n 4 future_cffex_TF future_cffex_TF NaT future cffex TF 五债当季连续 NaT None\n .. ... ... ... ... ... ... ... ... ...\n 65 future_ine_LU future_ine_LU 2020-06-22 future ine LU 低硫燃油主力 2020-06-22 None\n 66 future_czce_PF future_czce_PF 2020-10-12 future czce PF 短纤主力 2020-10-12 None\n 67 future_ine_BC future_ine_BC 2020-11-19 future ine BC 国际铜主力 2020-11-19 None\n 68 future_dce_LH future_dce_LH 2021-01-08 future dce LH 生猪主力 2021-01-08 None\n 69 future_czce_PK future_czce_PK 2021-02-01 future czce PK 花生主力 2021-02-01 None\n\n [70 rows x 9 columns]\n\nFetch the quotes:\n\n::\n\n >>> from zvt.domain import Future1dKdata\n >>> Future1dKdata.record_data(code=\"CU\")\n >>> df = Future1dKdata.query_data(code=\"CU\")\n >>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate interest settlement change_pct1\n 0 future_shfe_CU_1996-04-03 future_shfe_CU 1996-04-03 em CU 沪铜主力 1d 22930.0 22840.0 23000.0 22840.0 353.0 0.000000e+00 0.0000 0.0 None None None\n 1 future_shfe_CU_1996-04-04 future_shfe_CU 1996-04-04 em CU 沪铜主力 1d 22700.0 22750.0 22820.0 22650.0 251.0 0.000000e+00 -0.0039 0.0 None None None\n 2 future_shfe_CU_1996-04-05 future_shfe_CU 1996-04-05 em CU 沪铜主力 1d 22520.0 22780.0 22820.0 22500.0 298.0 0.000000e+00 0.0013 0.0 None None None\n 3 future_shfe_CU_1996-04-08 future_shfe_CU 1996-04-08 em CU 沪铜主力 1d 22660.0 22650.0 22680.0 22600.0 98.0 0.000000e+00 -0.0057 0.0 None None None\n 4 future_shfe_CU_1996-04-09 future_shfe_CU 1996-04-09 em CU 沪铜主力 1d 22830.0 22810.0 22860.0 22810.0 56.0 0.000000e+00 0.0071 0.0 None None None\n ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 6343 future_shfe_CU_2022-04-21 future_shfe_CU 2022-04-21 em CU 沪铜主力 1d 74140.0 74480.0 74750.0 74140.0 48008.0 1.787678e+10 -0.0004 0.0 None None None\n 6344 future_shfe_CU_2022-04-22 future_shfe_CU 2022-04-22 em CU 沪铜主力 1d 74800.0 75010.0 75200.0 74690.0 58874.0 2.205633e+10 0.0073 0.0 None None None\n 6345 future_shfe_CU_2022-04-25 future_shfe_CU 2022-04-25 em CU 沪铜主力 1d 74900.0 73660.0 75190.0 73660.0 107455.0 3.989090e+10 -0.0168 0.0 None None None\n 6346 future_shfe_CU_2022-04-26 future_shfe_CU 2022-04-26 em CU 沪铜主力 1d 73170.0 73260.0 73750.0 72500.0 113019.0 4.130931e+10 -0.0132 0.0 None None None\n 6347 future_shfe_CU_2022-04-27 future_shfe_CU 2022-04-27 em CU 沪铜主力 1d 72990.0 73100.0 73560.0 72910.0 61563.0 2.254089e+10 0.0000 0.0 None None None\n\n [6348 rows x 18 columns]\n"
},
{
"path": "docs/source/data/data_concepts.rst",
"content": "=============\nData concepts\n=============\n\n\n.. _data.tradable_entity:\n\nTradableEntity\n------------------------------\n:class:`~.zvt.contract.schema.TradableEntity` is anything could be traded, it could be :class:`~.zvt.domain.meta.stock_meta.Stock`,\n:class:`~.zvt.domain.meta.etf_meta.Etf`, :class:`~.zvt.domain.meta.index_meta.Index`, future, cryptocurrency, or even a **sports match**.\n\nLet's start with the real world tradable entities: China stock and USA stock ——— the world's most involved trading targets.\n\nrecord and query stock:\n::\n\n >>> from zvt.domain import Stock\n >>> Stock.record_data()\n >>> df = Stock.query_data()\n >>> print(df)\n id entity_id timestamp entity_type exchange code name list_date end_date\n 0 stock_sh_600651 stock_sh_600651 1986-09-26 stock sh 600651 *ST飞乐 1986-09-26 None\n 1 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None\n 2 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None\n 3 stock_sh_600601 stock_sh_600601 1990-12-19 stock sh 600601 方正科技 1990-12-19 None\n 4 stock_sh_600602 stock_sh_600602 1990-12-19 stock sh 600602 云赛智联 1990-12-19 None\n ... ... ... ... ... ... ... ... ... ...\n 4615 stock_sh_603176 stock_sh_603176 2021-12-31 stock sh 603176 汇通集团 2021-12-31 None\n 4616 stock_sh_600941 stock_sh_600941 2022-01-05 stock sh 600941 中国移动 2022-01-05 None\n 4617 stock_sh_688262 stock_sh_688262 2022-01-06 stock sh 688262 国芯科技 2022-01-06 None\n 4618 stock_sh_688176 stock_sh_688176 2022-01-07 stock sh 688176 亚虹医药 2022-01-07 None\n 4619 stock_sz_301159 stock_sz_301159 2022-01-07 stock sz 301159 N三维 2022-01-07 None\n\n [4620 rows x 9 columns]\n\nrecord and query stockus:\n::\n\n >>> from zvt.domain import Stockus\n >>> Stockus.record_data()\n >>> df = Stockus.query_data()\n >>> print(df)\n id entity_id timestamp entity_type exchange code name list_date end_date\n 0 stockus_nasdaq_CMII stockus_nasdaq_CMII None stockus nasdaq CMII CM Life Sciences II Inc-A None None\n 1 stockus_nasdaq_CBLI stockus_nasdaq_CBLI None stockus nasdaq CBLI Cytocom Inc None None\n 2 stockus_nyse_XPOw stockus_nyse_XPOw None stockus nyse XPOw XPO Logistics Inc WI None None\n 3 stockus_nyse_WRI stockus_nyse_WRI None stockus nyse WRI 魏因加滕房地产投资 None None\n 4 stockus_nyse_PROS stockus_nyse_PROS None stockus nyse PROS ProSight Global Inc None None\n ... ... ... ... ... ... ... ... ... ...\n 6345 stockus_nyse_FATH stockus_nyse_FATH None stockus nyse FATH Fathom Digital Manufacturing Co None None\n 6346 stockus_nyse_LOCL stockus_nyse_LOCL None stockus nyse LOCL Local Bounti Corp None None\n 6347 stockus_nyse_VLD stockus_nyse_VLD None stockus nyse VLD Velo3D Inc None None\n 6348 stockus_nyse_AHI stockus_nyse_AHI None stockus nyse AHI Advanced Human Imaging Ltd ADR None None\n 6349 stockus_nyse_HLGN stockus_nyse_HLGN None stockus nyse HLGN Heliogen Inc None None\n\n| what about other tradable entities?\n| Show current registered tradable entity type and its schema:\n\n::\n\n >>> from zvt.contract import zvt_context\n >>> zvt_context.tradable_schema_map\n {'stockus': zvt.domain.meta.stockus_meta.Stockus,\n 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk,\n 'index': zvt.domain.meta.index_meta.Index,\n 'etf': zvt.domain.meta.etf_meta.Etf,\n 'stock': zvt.domain.meta.stock_meta.Stock,\n 'block': zvt.domain.meta.block_meta.Block,\n 'fund': zvt.domain.meta.fund_meta.Fund}\n\nThe key is **entity_type** and the value is its :ref:`Schema`.\n\nFrom intuition, stockhk should be stock of hongkong:\n::\n\n >>> from zvt.domain import Stockhk\n >>> Stockhk.record_data()\n >>> df = Stockhk.query_data(index='code')\n >>> print(df)\n\n id entity_id timestamp entity_type exchange code name list_date end_date\n code\n 00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None\n 00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None\n 00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None\n 00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None\n 00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None\n ... ... ... ... ... ... ... ... ... ...\n 09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None\n 09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None\n 09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None\n 09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None\n 80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None\n\n [2597 rows x 9 columns]\n\n >>> df[df.code=='00700']\n\n id entity_id timestamp entity_type exchange code name list_date end_date\n 2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None\n\nFrom intuition, other tradable entities could be added to the system and used in the same way.\nJust follow :ref:`Add tradable entity `\n\n.. _data.actor_entity:\n\nActorEntity\n------------------------------\n:class:`~.zvt.contract.schema.ActorEntity` is the beings acting in the market, it could be government,\ncompany, fund or individual.\n\n::\n\n >>> from zvt.domain import StockInstitutionalInvestorHolder\n >>> entity_ids = [\"stock_sz_000338\", \"stock_sz_000001\"]\n >>> StockInstitutionalInvestorHolder.record_data(entity_ids=entity_ids)\n >>> df = StockInstitutionalInvestorHolder.query_data(entity_ids=entity_ids)\n >>> print(df)\n id entity_id timestamp code name actor_id actor_type actor_code actor_name report_period report_date holding_numbers holding_ratio holding_values\n 0 stock_sz_000001_1998-06-30_raised_fund_cn_184688 stock_sz_000001 1998-06-30 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 half_year 1998-06-30 1.896697e+06 0.001771 3.269906e+07\n 1 stock_sz_000001_1998-09-30_raised_fund_cn_184688 stock_sz_000001 1998-09-30 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 season3 1998-09-30 2.634093e+06 0.002460 4.151331e+07\n 2 stock_sz_000001_1998-12-31_raised_fund_cn_184688 stock_sz_000001 1998-12-31 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 year 1998-12-31 2.673900e+06 0.002497 3.992133e+07\n 3 stock_sz_000001_1999-03-31_raised_fund_cn_184688 stock_sz_000001 1999-03-31 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 season1 1999-03-31 2.378977e+06 0.002221 3.256820e+07\n 4 stock_sz_000001_1999-06-30_raised_fund_cn_500005 stock_sz_000001 1999-06-30 000001 平安银行 raised_fund_cn_500005 raised_fund 500005 基金汉盛 half_year 1999-06-30 4.989611e+06 0.004659 1.386613e+08\n ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 22463 stock_sz_000338_2021-09-30_broker_cn_71067063 stock_sz_000338 2021-09-30 000338 潍柴动力 broker_cn_71067063 broker 71067063 东方红信和添安4号 season3 2021-09-30 5.000000e+04 0.000012 8.580000e+05\n 22464 stock_sz_000338_2021-09-30_corporation_cn_1003... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10030838 corporation 10030838 潍柴控股集团有限公司 season3 2021-09-30 1.422551e+09 0.018071 2.441097e+10\n 22465 stock_sz_000338_2021-09-30_corporation_cn_1067... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10671586 corporation 10671586 香港中央结算有限公司 season3 2021-09-30 4.992710e+08 0.117713 8.567490e+09\n 22466 stock_sz_000338_2021-09-30_corporation_cn_1019... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10196008 corporation 10196008 中国证券金融股份有限公司 season3 2021-09-30 1.636089e+08 0.038574 2.807529e+09\n 22467 stock_sz_000338_2021-09-30_corporation_cn_1008... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10086358 corporation 10086358 奥地利IVM技术咨询维也纳有限公司 season3 2021-09-30 1.139387e+08 0.026863 1.955188e+09\n\n [22468 rows x 14 columns]\n\n.. note::\n\n A good actor should know the good or bad actors in history, more importantly,\n the mind behind them.\n\n.. _data.schema:\n\nSchema\n------------------------------\nData structure describing :class:`~.zvt.contract.schema.TradableEntity`, :class:`~.zvt.contract.schema.ActorEntity` or events happen on them.\nPhysically it's table with columns in sql database. One schema could have multiple storage\nwith different providers.\n\n.. _data.schema_usage:\n\nFrom specific to general, all zvt schema usage is in the same way.\n\n* from zvt.domain import {Schema}\n* {Schema}.record_data\n* {Schema}.query_data\n\nExplore :py:mod:`~.zvt.domain` for pre defined schemas. And check :ref:`record_data & query_data details `\n\n::\n\n >>> from zvt.domain import *\n >>> entity_ids = [\"stock_sz_000338\", \"stock_sz_000001\"]\n >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=\"em\")\n >>> df = Stock1dHfqKdata.query_data(entity_ids=entity_ids, provider=\"em\")\n >>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n 0 stock_sz_000001_1991-04-03 stock_sz_000001 1991-04-03 em 000001 平安银行 1d 49.00 49.00 49.00 49.00 1.0 5.000000e+03 0.2250 0.0000\n 1 stock_sz_000001_1991-04-04 stock_sz_000001 1991-04-04 em 000001 平安银行 1d 48.76 48.76 48.76 48.76 3.0 1.500000e+04 -0.0049 0.0000\n 2 stock_sz_000001_1991-04-05 stock_sz_000001 1991-04-05 em 000001 平安银行 1d 48.52 48.52 48.52 48.52 2.0 1.000000e+04 -0.0049 0.0000\n 3 stock_sz_000001_1991-04-06 stock_sz_000001 1991-04-06 em 000001 平安银行 1d 48.28 48.28 48.28 48.28 7.0 3.400000e+04 -0.0049 0.0000\n 4 stock_sz_000001_1991-04-08 stock_sz_000001 1991-04-08 em 000001 平安银行 1d 48.04 48.04 48.04 48.04 2.0 1.000000e+04 -0.0050 0.0000\n ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 10859 stock_sz_000338_2022-01-10 stock_sz_000338 2022-01-10 em 000338 潍柴动力 1d 314.38 314.38 320.37 312.69 956271.0 1.735153e+09 0.0149 0.0190\n 10860 stock_sz_000001_2022-01-11 stock_sz_000001 2022-01-11 em 000001 平安银行 1d 2974.07 2998.45 3019.58 2954.57 1581999.0 2.752485e+09 0.0121 0.0082\n 10861 stock_sz_000338_2022-01-11 stock_sz_000338 2022-01-11 em 000338 潍柴动力 1d 312.69 307.01 314.23 306.70 812187.0 1.444640e+09 -0.0234 0.0161\n 10862 stock_sz_000001_2022-01-12 stock_sz_000001 2022-01-12 em 000001 平安银行 1d 2998.45 2931.82 3004.95 2915.56 1502164.0 2.561266e+09 -0.0222 0.0077\n 10863 stock_sz_000338_2022-01-12 stock_sz_000338 2022-01-12 em 000338 潍柴动力 1d 307.01 305.78 309.62 302.86 882165.0 1.542044e+09 -0.0040 0.0175\n\n [10864 rows x 15 columns]\n\nThe data of the schema is recorded in local database by default and could be updated incrementally.\n\nFind them in this way:\n\n::\n\n {Schema}.get_storages()\n\ne.g.\n\n::\n\n >>> Stock1dHfqKdata.get_storages()\n [Engine(sqlite:////Users/foolcage/zvt-home/data/joinquant_stock_1d_hfq_kdata.db?check_same_thread=False),\n Engine(sqlite:////Users/foolcage/zvt-home/data/em_stock_1d_hfq_kdata.db?check_same_thread=False)]\n\nIntervalLevel\n------------------------------\n:class:`~.zvt.contract.IntervalLevel` is repeated fixed time interval, e.g, 5m, 1d.\nIt's used in OHLC data for describing time window.\n\n::\n\n >>> from zvt.contract import *\n >>> for level in IntervalLevel:\n >>> print(level.value)\n tick\n 1m\n 5m\n 15m\n 30m\n 1h\n 4h\n 1d\n 1wk\n 1mon\n\nKdata(Quote, OHLC)\n------------------------------\nthe candlestick data with OHLC.\n\nthe :class:`~.zvt.contract.schema.TradableEntity` quote schema name follows below rules:\n\n::\n\n {entity_shema}{level}{adjust_type}Kdata\n\n* entity_schema\n\nTradableEntity class，e.g. Stock,Stockus.\n\n* level\n\nIntervalLevel value, e.g. 1d,1wk.\n\n* adjust type\n\npre adjusted(qfq), post adjusted(hfq), or not adjusted(bfq).\n\n::\n\n >>> for adjust_type in AdjustType:\n >>> print(adjust_type.value)\n\n.. note::\n\n In order to be compatible with historical data, the qfq is an exception, {adjust_type} is left empty\n\nThe pre defined kdata schema could be found in :py:mod:`~.zvt.domain.quotes`, it's separated by\nentity_schema, level, and adjust type.\n\ne.g. Stock1dHfqKdata means China Stock daily hfq quotes.\n\n::\n\n >>> from zvt.domain import Stock1dHfqKdata\n >>> Stock1dHfqKdata.record_data(code='000338', provider='em')\n >>> df = Stock1dHfqKdata.query_data(code='000338', provider='em')\n >>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n 0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 70.00 64.93 71.00 62.88 207375.0 1.365189e+09 2.1720 0.1182\n 1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 66.60 64.00 68.00 62.88 86299.0 5.563198e+08 -0.0143 0.0492\n 2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 63.32 62.00 63.88 59.60 93823.0 5.782065e+08 -0.0313 0.0535\n 3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 61.50 62.49 64.48 61.01 47720.0 2.999226e+08 0.0079 0.0272\n 4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 61.90 60.65 61.90 59.70 39273.0 2.373126e+08 -0.0294 0.0224\n ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 331.97 345.95 345.95 329.82 1688497.0 3.370241e+09 0.0540 0.0398\n 3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 345.95 342.72 346.10 337.96 1187601.0 2.377957e+09 -0.0093 0.0280\n 3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 344.41 342.41 351.02 336.73 1143985.0 2.295195e+09 -0.0009 0.0270\n 3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 341.03 336.42 341.03 328.28 1218697.0 2.383841e+09 -0.0175 0.0287\n 3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 336.88 339.03 340.88 329.67 1023545.0 2.012006e+09 0.0078 0.0241\n\n [3431 rows x 15 columns]\n\n\ne.g. Stock30mHfqKdata means China Stock 30 minutes hfq quotes.\n\n::\n\n >>> from zvt.domain import Stock30mHfqKdata\n >>> Stock30mHfqKdata.record_data(code='000338', provider='em')\n >>> df = Stock30mHfqKdata.query_data(code='000338', provider='em')\n >>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n 0 stock_sz_000338_2022-01-07T10:00:00.000 stock_sz_000338 2022-01-07 10:00:00 em 000338 潍柴动力 30m 312.23 313.77 317.30 310.70 288036.0 521397671.0 0.0049 0.0057\n 1 stock_sz_000338_2022-01-07T10:30:00.000 stock_sz_000338 2022-01-07 10:30:00 em 000338 潍柴动力 30m 313.92 313.77 315.15 312.54 111887.0 201667653.0 0.0000 0.0022\n 2 stock_sz_000338_2022-01-07T11:00:00.000 stock_sz_000338 2022-01-07 11:00:00 em 000338 潍柴动力 30m 313.77 314.07 314.69 313.00 80072.0 144303962.0 0.0010 0.0016\n 3 stock_sz_000338_2022-01-07T11:30:00.000 stock_sz_000338 2022-01-07 11:30:00 em 000338 潍柴动力 30m 314.23 316.23 316.84 313.61 160797.0 291742498.0 0.0069 0.0032\n 4 stock_sz_000338_2022-01-07T13:30:00.000 stock_sz_000338 2022-01-07 13:30:00 em 000338 潍柴动力 30m 316.23 314.07 316.99 314.07 115775.0 210236422.0 -0.0068 0.0023\n .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 251 stock_sz_000338_2022-02-28T11:30:00.000 stock_sz_000338 2022-02-28 11:30:00 em 000338 潍柴动力 30m 268.15 268.30 268.61 267.99 34581.0 52053276.0 0.0006 0.0007\n 252 stock_sz_000338_2022-02-28T13:30:00.000 stock_sz_000338 2022-02-28 13:30:00 em 000338 潍柴动力 30m 268.46 268.46 268.61 268.15 38019.0 57268380.0 0.0006 0.0008\n 253 stock_sz_000338_2022-02-28T14:00:00.000 stock_sz_000338 2022-02-28 14:00:00 em 000338 潍柴动力 30m 268.46 269.22 269.53 268.30 41713.0 62994140.0 0.0028 0.0008\n 254 stock_sz_000338_2022-02-28T14:30:00.000 stock_sz_000338 2022-02-28 14:30:00 em 000338 潍柴动力 30m 269.22 269.22 269.53 268.61 40815.0 61676966.0 0.0000 0.0008\n 255 stock_sz_000338_2022-02-28T15:00:00.000 stock_sz_000338 2022-02-28 15:00:00 em 000338 潍柴动力 30m 269.07 269.84 269.84 268.76 60190.0 91032952.0 0.0023 0.0012\n\n [256 rows x 15 columns]\n\nFinanceFactor\n------------------------------\nThe usage is same as other entity events.\n\n::\n\n >>> from zvt.domain import FinanceFactor\n >>> FinanceFactor.record_data(code='000338')\n >>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp')\n\n basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp\n timestamp\n 2002-12-31 NaN 1.962000e+07 2.471000e+06 NaN NaN NaN NaN 0.2068 0.1259 2002-12-31\n 2003-12-31 1.27 3.574000e+09 2.739000e+08 181.2022 109.8778 0.7729 0.1783 0.2551 0.0766 2003-12-31\n 2004-12-31 1.75 6.188000e+09 5.369000e+08 0.7313 0.9598 0.3245 0.1474 0.2489 0.0868 2004-12-31\n 2005-12-31 0.93 5.283000e+09 3.065000e+08 -0.1463 -0.4291 0.1327 0.0603 0.2252 0.0583 2005-12-31\n 2006-03-31 0.33 1.859000e+09 1.079000e+08 NaN NaN NaN NaN NaN 0.0598 2006-03-31\n ... ... ... ... ... ... ... ... ... ... ...\n 2020-08-28 0.59 9.449000e+10 4.680000e+09 0.0400 -0.1148 0.0983 0.0229 0.1958 0.0603 2020-08-28\n 2020-10-31 0.90 1.474000e+11 7.106000e+09 0.1632 0.0067 0.1502 0.0347 0.1949 0.0590 2020-10-31\n 2021-03-31 1.16 1.975000e+11 9.207000e+09 0.1327 0.0112 0.1919 0.0444 0.1931 0.0571 2021-03-31\n 2021-04-30 0.42 6.547000e+10 3.344000e+09 0.6788 0.6197 0.0622 0.0158 0.1916 0.0667 2021-04-30\n 2021-08-31 0.80 1.264000e+11 6.432000e+09 0.3375 0.3742 0.1125 0.0287 0.1884 0.0653 2021-08-31\n\n [66 rows x 10 columns]\n\nThree financial tables\n\n::\n\n >>> BalanceSheet.record_data(code='000338')\n >>> IncomeStatement.record_data(code='000338')\n >>> CashFlowStatement.record_data(code='000338')\n\n.. note::\n Just remember, all :ref:`schema usage ` is in the same way.\n"
},
{
"path": "docs/source/data/extending_data.rst",
"content": ".. _extending_data:\n\n==============\nExtend data\n==============\n\n\nData structure\n--------------------------\nzvt use sql database(default sqlite, and easy extend to others) to store data\nand provide uniform api to record and query data.(Thanks to the great `Sqlalchemy `_)\n\nAs we know, :ref:`TradableEntity` and :ref:`ActorEntity` is everything about the market, data is the events happen on them.\nThe Schema of the data would always in following structure:\n\n.. image:: ../_static/data_structure.png\n\nIt shows how we define the Entity in the market:\n\n* entity_type\n\ntradable entity type, e.g. stock, future, bond and so on.\n\n* exchange\n\nthe exchange of entity\n\n* code\n\nthe code of the entity\n\nNaturally the id of the entity is defined:\n\n::\n\n {entity_type}_{exchange}_{code}\n\nAnd the common fields:\n\n* entity_id\n\nentity id\n\n* timestamp\n\nhappen time\n\n* id\n\nthe id of the record, most of time, it's:\n\n::\n\n{entity_id}_{timestamp}\n\nData definition\n--------------------------\n\nzvt data is defined by three concepts:\n\n* Database\n\nThe rule is one module define one db.\n\n* Schema\n\nThe Schema defined in the module belongs to the Database defined inside.\n\n* Provider\n\nThe Database could have different providers\n\nMany pre supported data is in `zvt.domain modules `_,\ne.g. `Finance data `_, you could\nrefer them when you want to extend data.\n\n\n.. _extending_data.add_data:\n\nKey steps to add data\n--------------------------\n\nLet's show the key steps to add data.\n\n1. Intent\n~~~~~~~~~~~~~~~~~~~~\nI want to add some data about the news on Stock.\n\n2. Find data source\n~~~~~~~~~~~~~~~~~~~~\nThe data source could be whatever you can see in the web ——— just write some\nweb crawlers to get them, e.g, most of `pre-implemented recorders `_.\nAnd you could use data from commercial data provider in the same way, e.g. `joinquant recorders `_.\n\nLet's show the crawler way here:\n\nOpen `eastmoney wap `_ and Find `news api `_\n\nThe news item result:\n\n::\n\n {\n \"Art_ShowTime\": \"2022-02-09 14:12:30\",\n \"Art_Image\": \"\",\n \"Art_MediaName\": \"每日经济新闻\",\n \"Art_Code\": \"202202092271136373\",\n \"Art_Title\": \"首创证券维持索菲亚买入评级 公司简评报告：多重因素影响短期压制业绩 看好2022年利润修复\",\n \"Art_SortStart\": \"1644387150036373\",\n \"Art_VideoCount\": 0,\n \"Art_OriginUrl\": \"http://finance.eastmoney.com/news/1354,202202092271136373.html\",\n \"Art_Url\": \"http://finance.eastmoney.com/a/202202092271136373.html\"\n }\n\n3. Define Schema\n~~~~~~~~~~~~~~~~~~~~\n\nNo matter what the format of the data outside zvt is, we use zvt simple and\nuniform contract to define them.\n\n::\n\n >>> from sqlalchemy import Column, String\n >>> from sqlalchemy.orm import declarative_base\n >>> from zvt.contract import Mixin\n >>> from zvt.contract.register import register_schema\n >>> NewsBase = declarative_base()\n >>> class StockNews(NewsBase, Mixin):\n >>> __tablename__ = \"stock_news\"\n >>> #: news title\n >>> news_title = Column(String)\n >>>\n >>> register_schema(providers=[\"em\"], db_name=\"stock_news\", schema_base=NewsBase, entity_type=\"stock\")\n\nCheck the defined db\n\n::\n\n >>> StockNews.get_storages()\n\n [Engine(sqlite:////Users/foolcage/zvt-home/data/em_stock_news.db?check_same_thread=False)]\n\nAs you see, the db file name format is:\n\n::\n\n {provider}_{db_name}\n\nIf you have another provider, e.g. sina, just register it too:\n\n::\n\n >>> register_schema(providers=[\"em\", \"sina\"], db_name=\"stock_news\", schema_base=NewsBase, entity_type=\"stock\")\n\nAnd you could find another db file:\n\n::\n\n >>> StockNews.get_storages()\n\n [Engine(sqlite:////Users/foolcage/zvt-home/data/sina_stock_news.db?check_same_thread=False)]\n\nThe advantage of this mechanism is:\n\n* schema is the way you want\n* provider could be switched seamlessly\n\n4. Implement recorder\n~~~~~~~~~~~~~~~~~~~~~\nLet's implement a recorder for StockNews.\n\n::\n\n import pandas as pd\n from zvt.contract.api import df_to_db\n from zvt.contract.recorder import FixedCycleDataRecorder\n from zvt.domain import Stock\n from zvt.domain.misc.stock_news import StockNews\n from zvt.recorders.em import em_api\n\n\n class EMStockNewsRecorder(FixedCycleDataRecorder):\n original_page_url = \"https://wap.eastmoney.com/quote/stock/0.002572.html\"\n url = \"https://np-listapi.eastmoney.com/comm/wap/getListInfo?cb=callback&client=wap&type=1&mTypeAndCode=0.002572&pageSize=200&pageIndex={}&callback=jQuery1830017478247906740352_1644568731256&_=1644568879493\"\n\n # the entity class you record for\n entity_schema = Stock\n # connect schema with recorder\n data_schema = StockNews\n # entity data provider\n entity_provider = \"em\"\n # data schema provider\n provider = \"em\"\n\n def record(self, entity, start, end, size, timestamps):\n news = em_api.get_news(entity_id=entity.id)\n df = pd.DataFrame.from_records(news)\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nAfter that, you could use the data in zvt way:\n\n::\n\n >>> StockNews.record_data(provider=\"em\", entity_id=\"stock_sz_002572\")\n >>> df = StockNews.query_data(entity_id=\"stock_sz_002572\")\n >>> print(df)\n\n id entity_id timestamp news_title\n 0 stock_sz_002572_2019-05-20 19:50:42 stock_sz_002572 2019-05-20 19:50:42 索菲亚（002572.SZ）：股价回撤超65% 是低吸机会吗?\n 1 stock_sz_002572_2019-05-24 22:39:47 stock_sz_002572 2019-05-24 22:39:47 索菲亚拟发行不超过5亿元超短期融资券\n 2 stock_sz_002572_2019-05-24 22:48:54 stock_sz_002572 2019-05-24 22:48:54 索菲亚(002572.SZ)拟终止发行不超10亿元的可转换公司债券\n 3 stock_sz_002572_2019-06-04 14:06:42 stock_sz_002572 2019-06-04 14:06:42 索菲亚(002572.SZ)截至5月底已累计回购2.02亿元的股份\n 4 stock_sz_002572_2019-06-13 06:24:44 stock_sz_002572 2019-06-13 06:24:44 索菲亚功臣王飚能否扶起汉森中国\n .. ... ... ... ...\n 367 stock_sz_002572_2022-02-07 14:04:40 stock_sz_002572 2022-02-07 14:04:40 开源证券维持索菲亚买入评级 近期获6份券商研报关注 目标均价涨幅59.82%\n 368 stock_sz_002572_2022-02-07 15:32:46 stock_sz_002572 2022-02-07 15:32:46 【调研快报】索菲亚接待机构投资者调研\n 369 stock_sz_002572_2022-02-08 16:50:37 stock_sz_002572 2022-02-08 16:50:37 索菲亚：公司承接了容东片区安置房项目\n 370 stock_sz_002572_2022-02-08 21:59:00 stock_sz_002572 2022-02-08 21:59:00 9亿坏账拖累净利下降九成 家具巨头索菲亚“甩包袱”起跑 腰斩的股价能否趁势抬头？\n 371 stock_sz_002572_2022-02-09 14:12:30 stock_sz_002572 2022-02-09 14:12:30 首创证券维持索菲亚买入评级 公司简评报告：多重因素影响短期压制业绩 看好2022年利润修复\n\n [372 rows x 4 columns]\n\n\n.. _extending_data.tradable_entity:\n\nAdd new TradableEntity\n--------------------------\nIt's human nature to like the new and hate the old. Adding new TradableEntity is easy in zvt.\n\nAnd from a higher perspective, trading is everywhere. you make trading everytime when you make the\ndecision.\n\nSo you could treat Country as TradableEntity and make trading when making decision where to live or invest.\n\nLet's show the key steps to add new TradableEntity.\n\n1. Define entity Schema\n~~~~~~~~~~~~~~~~~~~~~~~\n\n::\n\n # -*- coding: utf-8 -*-\n\n from sqlalchemy import Column, String, Float\n from sqlalchemy.orm import declarative_base\n\n from zvt.contract.schema import TradableEntity\n from zvt.contract.register import register_schema, register_entity\n\n CountryMetaBase = declarative_base()\n\n\n @register_entity(entity_type=\"country\")\n class Country(CountryMetaBase, TradableEntity):\n __tablename__ = \"country\"\n\n #: 区域\n #: region\n region = Column(String(length=128))\n #: 首都\n #: capital city\n capital_city = Column(String(length=128))\n #: 收入水平\n #: income level\n income_level = Column(String(length=64))\n #: 贷款类型\n #: lending type\n lending_type = Column(String(length=64))\n #: 经度\n #: longitude\n longitude = Column(Float)\n #: 纬度\n #: latitude\n latitude = Column(Float)\n\n\n register_schema(providers=[\"wb\"], db_name=\"country_meta\", schema_base=CountryMetaBase)\n\nentity_type, exchange and code define the entity, for country, it's in following way:\n\n::\n\n entity_type = \"country\"\n exchange = \"galaxy\"\n code = \"iso code\"\n\ne.g. country_galaxy_CN = China, country_galaxy_US = United States of America\n\n\n2. Implement recorder for the entity\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n::\n\n from zvt.contract.api import df_to_db\n from zvt.contract.recorder import Recorder\n from zvt.domain.meta.country_meta import Country\n from zvt.recorders.wb import wb_api\n\n\n class WBCountryRecorder(Recorder):\n provider = \"wb\"\n data_schema = Country\n\n def run(self):\n df = wb_api.get_countries()\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n3. Define schema for the entity\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\ne.g treasury yield of the country\n::\n\n # -*- coding: utf-8 -*-\n from sqlalchemy import Column, String, Float\n from sqlalchemy.orm import declarative_base\n\n from zvt.contract import Mixin\n from zvt.contract.register import register_schema\n\n CurrencyBase = declarative_base()\n\n\n class TreasuryYield(CurrencyBase, Mixin):\n __tablename__ = \"treasury_yield\"\n\n code = Column(String(length=32))\n\n # 2年期\n yield_2 = Column(Float)\n # 5年期\n yield_5 = Column(Float)\n # 10年期\n yield_10 = Column(Float)\n # 30年期\n yield_30 = Column(Float)\n\n\n register_schema(providers=[\"em\"], db_name=\"currency\", schema_base=CurrencyBase)\n\nAnd the `recorder `_ for the schema\n\n4. Use them in zvt way\n~~~~~~~~~~~~~~~~~~~~~~\n\nFind the rich country:\n\n::\n\n >>> from zvt.domain import Country\n >>> Country.record_data()\n >>> df = Country.query_data()\n >>> df[df['income_level']=='High income']\n\n id entity_id timestamp entity_type exchange code name list_date end_date region capital_city income_level lending_type longitude latitude\n 0 country_galaxy_AW country_galaxy_AW None country galaxy AW Aruba None None Latin America & Caribbean Oranjestad High income Not classified -70.016700 12.516700\n 7 country_galaxy_AD country_galaxy_AD None country galaxy AD Andorra None None Europe & Central Asia Andorra la Vella High income Not classified 1.521800 42.507500\n 9 country_galaxy_AE country_galaxy_AE None country galaxy AE United Arab Emirates None None Middle East & North Africa Abu Dhabi High income Not classified 54.370500 24.476400\n 13 country_galaxy_AG country_galaxy_AG None country galaxy AG Antigua and Barbuda None None Latin America & Caribbean Saint John's High income IBRD -61.845600 17.117500\n 14 country_galaxy_AU country_galaxy_AU None country galaxy AU Australia None None East Asia & Pacific Canberra High income Not classified 149.129000 -35.282000\n .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 277 country_galaxy_TW country_galaxy_TW None country galaxy TW Taiwan, China None None East Asia & Pacific High income Not classified NaN NaN\n 282 country_galaxy_UY country_galaxy_UY None country galaxy UY Uruguay None None Latin America & Caribbean Montevideo High income IBRD -56.067500 -34.894100\n 283 country_galaxy_US country_galaxy_US None country galaxy US United States None None North America Washington D.C. High income Not classified -77.032000 38.889500\n 287 country_galaxy_VG country_galaxy_VG None country galaxy VG British Virgin Islands None None Latin America & Caribbean Road Town High income Not classified -64.623056 18.431389\n 288 country_galaxy_VI country_galaxy_VI None country galaxy VI Virgin Islands (U.S.) None None Latin America & Caribbean Charlotte Amalie High income Not classified -64.896300 18.335800\n\n [80 rows x 15 columns]\n\n\nCompare treasury yields of different maturities:\n\n::\n\n >>> from zvt.domain import TreasuryYield\n >>> from zvt.api.intent import compare\n >>> TreasuryYield.record_data()\n >>> compare(codes=[\"US\"], schema=TreasuryYield, columns=[\"yield_2\", \"yield_10\", \"yield_30\"])\n\n.. image:: ../_static/compare_yields.png"
},
{
"path": "docs/source/data/index.rst",
"content": "==========\nData\n==========\n\n| Without data, quant becomes a castle in the air.\n| So, what exactly is data?\n| zvt makes a concise and unified abstraction of data:\n\n In the world of zvt, there are two kinds of entities, one is :ref:`tradable entity `,\n the other is :ref:`actor entity `. Data is the events happened on them.\n\n.. image:: ../_static/view.png\n\n.. note::\n\n Philosophically, entity is the existence described by itself, classification of existential concepts.\n\n.. toctree::\n :maxdepth: 2\n\n data_concepts\n record_and_query\n extending_data\n adding_new_entity\n trading_anything\n"
},
{
"path": "docs/source/data/record_and_query.rst",
"content": ".. _record_and_query:\n\n==============\nRecord & query\n==============\n\n\nDualism\n--------------------------\nIn and out, write and read, and so record and query.\n\n\nRecord data\n--------------------------\n\nQuery data\n--------------------------\n"
},
{
"path": "docs/source/data/trading_anything.rst",
"content": ".. trading_anything:\n\n=======================\nCapital without country\n=======================\n\nFrom a higher perspective, trading is everywhere. you make trading everytime when you make the\ndecision.\n\nSo you could treat Country as TradableEntity and make trading when making decision where to live or invest.\n\nIt's nothing than a specific case of :ref:`Adding new entity `.\n\nLet's show the key steps below.\n\nDefine entity Schema\n--------------------------\n\n::\n\n # -*- coding: utf-8 -*-\n\n from sqlalchemy import Column, String, Float\n from sqlalchemy.orm import declarative_base\n\n from zvt.contract.schema import TradableEntity\n from zvt.contract.register import register_schema, register_entity\n\n CountryMetaBase = declarative_base()\n\n\n @register_entity(entity_type=\"country\")\n class Country(CountryMetaBase, TradableEntity):\n __tablename__ = \"country\"\n\n #: 区域\n #: region\n region = Column(String(length=128))\n #: 首都\n #: capital city\n capital_city = Column(String(length=128))\n #: 收入水平\n #: income level\n income_level = Column(String(length=64))\n #: 贷款类型\n #: lending type\n lending_type = Column(String(length=64))\n #: 经度\n #: longitude\n longitude = Column(Float)\n #: 纬度\n #: latitude\n latitude = Column(Float)\n\n\n register_schema(providers=[\"wb\"], db_name=\"country_meta\", schema_base=CountryMetaBase)\n\nentity_type, exchange and code define the entity, for country, it's in following way:\n\n::\n\n entity_type = \"country\"\n exchange = \"galaxy\"\n code = \"iso code\"\n\ne.g. country_galaxy_CN = China, country_galaxy_US = United States of America\n\n\nImplement recorder for the entity\n---------------------------------\n\n::\n\n from zvt.contract.api import df_to_db\n from zvt.contract.recorder import Recorder\n from zvt.domain.meta.country_meta import Country\n from zvt.recorders.wb import wb_api\n\n\n class WBCountryRecorder(Recorder):\n provider = \"wb\"\n data_schema = Country\n\n def run(self):\n df = wb_api.get_countries()\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\nDefine schema for the entity\n----------------------------\n\ne.g treasury yield of the country\n::\n\n # -*- coding: utf-8 -*-\n from sqlalchemy import Column, String, Float\n from sqlalchemy.orm import declarative_base\n\n from zvt.contract import Mixin\n from zvt.contract.register import register_schema\n\n MonetaryBase = declarative_base()\n\n\n class TreasuryYield(MonetaryBase, Mixin):\n __tablename__ = \"treasury_yield\"\n\n code = Column(String(length=32))\n\n # 2年期\n yield_2 = Column(Float)\n # 5年期\n yield_5 = Column(Float)\n # 10年期\n yield_10 = Column(Float)\n # 30年期\n yield_30 = Column(Float)\n\n\n register_schema(providers=[\"em\"], db_name=\"monetary\", schema_base=MonetaryBase)\n # the __all__ is generated\n __all__ = [\"TreasuryYield\"]\n\nAnd the `recorder `_ for the schema\n\nUse them in zvt way\n-------------------\n\nFind the rich country:\n\n::\n\n >>> from zvt.domain import Country\n >>> Country.record_data()\n >>> df = Country.query_data()\n >>> df[df['income_level']=='High income']\n\n id entity_id timestamp entity_type exchange code name list_date end_date region capital_city income_level lending_type longitude latitude\n 0 country_galaxy_AW country_galaxy_AW None country galaxy AW Aruba None None Latin America & Caribbean Oranjestad High income Not classified -70.016700 12.516700\n 7 country_galaxy_AD country_galaxy_AD None country galaxy AD Andorra None None Europe & Central Asia Andorra la Vella High income Not classified 1.521800 42.507500\n 9 country_galaxy_AE country_galaxy_AE None country galaxy AE United Arab Emirates None None Middle East & North Africa Abu Dhabi High income Not classified 54.370500 24.476400\n 13 country_galaxy_AG country_galaxy_AG None country galaxy AG Antigua and Barbuda None None Latin America & Caribbean Saint John's High income IBRD -61.845600 17.117500\n 14 country_galaxy_AU country_galaxy_AU None country galaxy AU Australia None None East Asia & Pacific Canberra High income Not classified 149.129000 -35.282000\n .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 277 country_galaxy_TW country_galaxy_TW None country galaxy TW Taiwan, China None None East Asia & Pacific High income Not classified NaN NaN\n 282 country_galaxy_UY country_galaxy_UY None country galaxy UY Uruguay None None Latin America & Caribbean Montevideo High income IBRD -56.067500 -34.894100\n 283 country_galaxy_US country_galaxy_US None country galaxy US United States None None North America Washington D.C. High income Not classified -77.032000 38.889500\n 287 country_galaxy_VG country_galaxy_VG None country galaxy VG British Virgin Islands None None Latin America & Caribbean Road Town High income Not classified -64.623056 18.431389\n 288 country_galaxy_VI country_galaxy_VI None country galaxy VI Virgin Islands (U.S.) None None Latin America & Caribbean Charlotte Amalie High income Not classified -64.896300 18.335800\n\n [80 rows x 15 columns]\n\n\nCompare treasury yields of different maturities:\n\n::\n\n >>> from zvt.domain import TreasuryYield\n >>> from zvt.api.intent import compare\n >>> TreasuryYield.record_data()\n >>> compare(codes=[\"US\"], schema=TreasuryYield, columns=[\"yield_2\", \"yield_10\", \"yield_30\"])\n\n.. image:: ../_static/compare_yields.png"
},
{
"path": "docs/source/drawer/drawer_concepts.rst",
"content": "===============\nDrawer concepts\n===============\n\n\nIntent\n------------------------------\nThere are many kinds of charts, but the intent is much less. zvt classify\nintent as compare, distribute and composite.\n\nWe could analyze the entity itself or look at it in the whole market by\ncomparing with others. :ref:`NormalData` uses\ndifferent structure to express different intents.\n\nCompare\n------------------------------\nBased on zvt's world view and data model, you could compare anything you care about.\n\nThink about that you want to compare S&P 500 index with Shanghai index. It's easy\nand comfortable with no pain.\n\n::\n\n >>> from zvt.api.intent import compare\n >>> from zvt.domain import Indexus1dKdata, Index, Indexus, Index1dKdata\n >>> Index.record_data(provider=\"em\")\n >>> Indexus.record_data(provider=\"em\")\n >>> Index1dKdata.record_data(entity_id=\"index_sh_000001\", provider=\"em\")\n >>> Indexus1dKdata.record_data(entity_id=\"indexus_us_SPX\", provider=\"em\")\n >>> compare(entity_ids=[\"index_sh_000001\", \"indexus_us_SPX\"], start_timestamp=\"2000-01-01\", scale_value=100)\n\n\n.. image:: ../_static/compare_cn_us.png\n\nTry some other style:\n\n::\n\n >>> compare(entity_ids=[\"index_sh_000001\", \"indexus_us_SPX\"], start_timestamp=\"2000-01-01\", schema_map_columns={Index1dKdata:[\"close\"],Indexus1dKdata:[\"close\"]})\n"
},
{
"path": "docs/source/drawer/index.rst",
"content": "==========\nDrawer\n==========\n\n| A picture is worth a thousand words.\n| So, what exactly is behind the picture?\n| it's drawer in zvt:\n\n Drawer is who can draw the picture with NormalData by Intent.\n\n.. toctree::\n :maxdepth: 2\n\n drawer_concepts\n\n\n"
},
{
"path": "docs/source/factor/extending_factor.rst",
"content": ".. _factor.extending_factor:\n\n================\nExtending Factor\n================\n\nRethink NormalData\n--------------------------\nNormal data format is as below:\n\n.. image:: ../_static/normal_data.png\n\n| Why use this format?\n| The reason is that human is comfortable for two-dimensional space and\n high-dimensional space could be reduced to it.\n\n\nObviously, It's complete and consistent. You could **calculate oneself in time intervals**\nor **calculate with others in specific time or intervals**. And it's easy to analyze the\ndata with charts by Intent.\n\nFactor data structure\n--------------------------\nThe factor computing flow is as below:\n\n.. image:: ../_static/factor_flow.png\n\n* data_df\n\nNormalData df read from the schema.\n\n* factor_df\n\nNormalData df computed by data_df using use :class:`~.zvt.contract.factor.Transformer`, :class:`~.zvt.contract.factor.Accumulator`\nor your custom logic.\n\n* result_df\n\nNormalData df containing columns **filter_result** and(or) **score_result**\nwhich calculated using factor_df or(and) data_df.\nFilter_result is True or False, score_result is from 0 to 1.\nYou could use TargetSelector to select targets at specific time when\nfilter_result is True and(or) score_result >=0.8 by default or do more\nprecise control by setting other arguments.\n\nLet's take BullFactor to illustrate the calculation process:\n::\n\n >>> from zvt.factors.macd import BullFactor\n >>> from zvt.domain import Stock1dHfqKdata\n >>> entity_ids = [\"stock_sh_601318\", \"stock_sz_000338\"]\n >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=\"em\")\n >>> factor = BullFactor(entity_ids=entity_ids, provider=\"em\", start_timestamp='2019-01-01', end_timestamp='2019-06-10')\n\ncheck the dfs:\n::\n\n >>> factor.data_df\n >>> factor.factor_df\n >>> factor.result_df\n\n.. _factor.write_transformer:\n\nWrite transformer\n--------------------------\nTransformer works as bellow:\n\n.. image:: ../_static/transformer.png\n\nWhat's in your mind is the NormalData format, and then practice the skills to\nmanipulate it.\n\nYou could use other ta lib with it easily, e.g Bollinger Bands using `TA lib `_\n\ninstall ta at first:\n::\n\n pip install --upgrade ta\n\nwrite boll transformer and factor:\n::\n\n from typing import Optional, List\n\n import pandas as pd\n from ta.volatility import BollingerBands\n\n from zvt.contract.factor import *\n from zvt.factors import TechnicalFactor\n\n\n class BollTransformer(Transformer):\n def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame:\n indicator_bb = BollingerBands(close=df[\"close\"], window=20, window_dev=2)\n\n # Add Bollinger Bands features\n df[\"bb_bbm\"] = indicator_bb.bollinger_mavg()\n df[\"bb_bbh\"] = indicator_bb.bollinger_hband()\n df[\"bb_bbl\"] = indicator_bb.bollinger_lband()\n\n # Add Bollinger Band high indicator\n df[\"bb_bbhi\"] = indicator_bb.bollinger_hband_indicator()\n\n # Add Bollinger Band low indicator\n df[\"bb_bbli\"] = indicator_bb.bollinger_lband_indicator()\n\n # Add Width Size Bollinger Bands\n df[\"bb_bbw\"] = indicator_bb.bollinger_wband()\n\n # Add Percentage Bollinger Bands\n df[\"bb_bbp\"] = indicator_bb.bollinger_pband()\n return df\n\n\n class BollFactor(TechnicalFactor):\n transformer = BollTransformer()\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n # set the factor to show\n return [self.factor_df[[\"bb_bbm\", \"bb_bbh\", \"bb_bbl\"]]]\n\n def compute_result(self):\n # set filter_result, which bb_bbli=1.0 buy and bb_bbhi=1.0 sell\n super().compute_result()\n self.result_df = (self.factor_df[\"bb_bbli\"] - self.factor_df[\"bb_bbhi\"]).to_frame(name=\"filter_result\")\n self.result_df[self.result_df == 0] = None\n self.result_df[self.result_df == 1] = True\n self.result_df[self.result_df == -1] = False\n\nLet's show it:\n::\n\n >>> from zvt.domain import Stock1dHfqKdata\n\n >>> provider = \"em\"\n >>> entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=provider,)\n >>> factor = BollFactor(\n >>> entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp=\"2019-01-01\"\n >>> )\n >>> factor.draw(show=True)\n\n.. image:: ../_static/boll_factor.png\n\nMost of ta lib support calculate single target by default, so we implement\ntransform_one of the Transformer. If you want to calculate many targets at\nthe same time you could implement transform directly and it would be faster.\n\nAnd Transformer is stateless, so it's easy to reuse in different factor if need.\n\nFactor with IntervalLevel\n--------------------------\nAfter you write the transformer and construct the factor, it's easy to use in\ndifferent levels.\n\nLet's use BollFactor in IntervalLevel 30m:\n::\n\n >>> from zvt.domain import Stock30mHfqKdata\n\n >>> provider = \"em\"\n >>> entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n\n >>> Stock30mHfqKdata.record_data(entity_ids=entity_ids, provider=provider)\n >>> factor = BollFactor(\n entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp=\"2021-01-01\"\n )\n >>> factor.draw(show=True)\n\nStream computing\n--------------------------\nThe data is coming continuously and the factor using the data need computing\ncontinuously too.\n\nIt's simple and straightforward:\n\n* {Schema}.record_data in one process\n* {Factor}.move_on which call {Schema}.query_data in another process\n\n.. image:: ../_static/stream.png\n\nWe keep the simple enough philosophy: single process and thread. Enjoy\nprogramming and make everything clear.\n\nFactor persistence\n--------------------------\nGetting data and computing factor continuously is cool.\nBut...If It took a long time to calculate the factor and crashed.How would you feel?\n\n.. image:: ../_static/bear.gif\n :align: center\n\nSelect the targets\n--------------------------\nYou could select the targets according result_df of the factor by yourself.\nOr use TargetSelector do it for you.\n\n.. image:: ../_static/factor_result.png\n\n\nWrite accumulator\n--------------------------\n\n.. image:: ../_static/accumulator.png\n"
},
{
"path": "docs/source/factor/factor_concepts.rst",
"content": ".. _factor.factor_concepts:\n\n===============\nFactor concepts\n===============\n\n.. _factor.normal_data:\n\nNormal data\n------------------------------\n:class:`~.zvt.contract.normal_data.NormalData` is the data containing pandas dataframe\nwith multiple index which level 0 named entity_id and level 1 named timestamp:\n\n=============== ========== ===== ===== ===== =====\nentity_id timestamp col1 col2 col3 col4\n=============== ========== ===== ===== ===== =====\nstock_sz_000338 2020-05-05 1.2 0.5 0.3 a\n... 2020-05-06 1.0 0.7 0.2 b\nstock_sz_000778 2020-05-05 1.2 0.5 0.3 a\n... 2020-05-06 1.0 0.7 0.2 b\n=============== ========== ===== ===== ===== =====\n\nThis data structure is used heavily in zvt computing, you should be familiar with it.\n`Pandas multiple index guide `_ is\na good start.\n\nQuery data returning normal data in this way:\n\n::\n\n {Schema}.query_data(index=[\"entity_id, timestamp\"])\n\ne.g.\n\n::\n\n >>> from zvt.domain import *\n >>> entity_ids = [\"stock_sz_000338\", \"stock_sz_000001\"]\n >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=\"em\")\n >>> df = Stock1dHfqKdata.query_data(entity_ids=entity_ids, provider=\"em\", index=[\"entity_id\", \"timestamp\"])\n >>> print(df)\n\n id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate\n entity_id timestamp\n stock_sz_000001 1991-04-03 stock_sz_000001_1991-04-03 stock_sz_000001 1991-04-03 em 000001 平安银行 1d 49.00 49.00 49.00 49.00 1.0 5.000000e+03 0.2250 0.0000\n 1991-04-04 stock_sz_000001_1991-04-04 stock_sz_000001 1991-04-04 em 000001 平安银行 1d 48.76 48.76 48.76 48.76 3.0 1.500000e+04 -0.0049 0.0000\n 1991-04-05 stock_sz_000001_1991-04-05 stock_sz_000001 1991-04-05 em 000001 平安银行 1d 48.52 48.52 48.52 48.52 2.0 1.000000e+04 -0.0049 0.0000\n 1991-04-06 stock_sz_000001_1991-04-06 stock_sz_000001 1991-04-06 em 000001 平安银行 1d 48.28 48.28 48.28 48.28 7.0 3.400000e+04 -0.0049 0.0000\n 1991-04-08 stock_sz_000001_1991-04-08 stock_sz_000001 1991-04-08 em 000001 平安银行 1d 48.04 48.04 48.04 48.04 2.0 1.000000e+04 -0.0050 0.0000\n ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n stock_sz_000338 2022-01-17 stock_sz_000338_2022-01-17 stock_sz_000338 2022-01-17 em 000338 潍柴动力 1d 296.26 297.64 298.71 293.49 504866.0 8.546921e+08 0.0026 0.0100\n 2022-01-18 stock_sz_000338_2022-01-18 stock_sz_000338 2022-01-18 em 000338 潍柴动力 1d 298.10 300.87 302.71 296.10 622455.0 1.064735e+09 0.0109 0.0124\n 2022-01-19 stock_sz_000338_2022-01-19 stock_sz_000338 2022-01-19 em 000338 潍柴动力 1d 299.64 299.48 304.24 298.56 610096.0 1.049195e+09 -0.0046 0.0121\n 2022-01-20 stock_sz_000338_2022-01-20 stock_sz_000338 2022-01-20 em 000338 潍柴动力 1d 298.10 294.87 299.18 290.11 812949.0 1.361764e+09 -0.0154 0.0161\n 2022-01-21 stock_sz_000338_2022-01-21 stock_sz_000338 2022-01-21 em 000338 潍柴动力 1d 292.72 287.04 293.34 284.58 754156.0 1.234360e+09 -0.0266 0.0150\n\n [10878 rows x 15 columns]\n\n\n.. _factor.factor:\n\nFactor\n------------------------------\n:class:`~.zvt.contract.factor.Factor` is a computing facility to build *factor* according your mind ——— algorithm.\nIt reads data from schema, use :class:`~.zvt.contract.factor.Transformer`, :class:`~.zvt.contract.factor.Accumulator`\nor your custom logic to compute, and save the result to new schema if need.\nIt also provides a standard way to evaluate the targets which could be used by :class:`~.zvt.factors.target_selector.TargetSelector`\nand :class:`~.zvt.trader.trader.Trader` for backtesting or real trading.\n\nTransformer\n------------------------------\nComputing factor which depends on input data only.\nHere is an example: :class:`~.zvt.factors.algorithm.MaTransformer`\n\nAccumulator\n------------------------------\nComputing factor which depends on input data and previous result of the factor.\nHere is an example: :class:`~.zvt.factors.ma.ma_stats_factor.MaStatsAccumulator.`\n\nLet's have a look by example:\n\n::\n\n >>> from zvt.factors import GoldCrossFactor\n >>> from zvt.domain import Stock1dHfqKdata\n >>> entity_ids = [\"stock_sz_000338\"]\n >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=\"em\")\n >>> factor = GoldCrossFactor(entity_ids=entity_ids, provider=\"em\", start_timestamp=\"2018-01-01\")\n >>> print(factor.factor_df)\n >>> print(factor.result_df)\n >>> factor.draw(show=True)\n level turnover high id open low entity_id timestamp close turnover_rate volume diff dea macd live bull live_count\n entity_id timestamp\n stock_sz_000338 2018-01-02 1d 8.325588e+08 145.97 stock_sz_000338_2018-01-02 141.21 141.06 stock_sz_000338 2018-01-02 145.67 0.0225 972471.0 NaN NaN NaN -1 False -1\n 2018-01-03 1d 7.530370e+08 147.66 stock_sz_000338_2018-01-03 146.13 144.29 stock_sz_000338 2018-01-03 144.44 0.0202 870225.0 NaN NaN NaN -1 False -2\n 2018-01-04 1d 4.917067e+08 145.51 stock_sz_000338_2018-01-04 144.75 143.67 stock_sz_000338 2018-01-04 145.21 0.0133 574335.0 NaN NaN NaN -1 False -3\n 2018-01-05 1d 5.282211e+08 146.59 stock_sz_000338_2018-01-05 146.44 143.21 stock_sz_000338 2018-01-05 143.21 0.0143 616244.0 NaN NaN NaN -1 False -4\n 2018-01-08 1d 1.255871e+09 150.43 stock_sz_000338_2018-01-08 143.82 143.82 stock_sz_000338 2018-01-08 150.12 0.0331 1426567.0 NaN NaN NaN -1 False -5\n ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n 2022-01-17 1d 8.546921e+08 298.71 stock_sz_000338_2022-01-17 296.26 293.49 stock_sz_000338 2022-01-17 297.64 0.0100 504866.0 -1.386687 1.781615 -6.336603 -1 False -12\n 2022-01-18 1d 1.064735e+09 302.71 stock_sz_000338_2022-01-18 298.10 296.10 stock_sz_000338 2022-01-18 300.87 0.0124 622455.0 -1.694421 1.086407 -5.561657 -1 False -13\n 2022-01-19 1d 1.049195e+09 304.24 stock_sz_000338_2022-01-19 299.64 298.56 stock_sz_000338 2022-01-19 299.48 0.0121 610096.0 -2.027097 0.463707 -4.981607 -1 False -14\n 2022-01-20 1d 1.361764e+09 299.18 stock_sz_000338_2022-01-20 298.10 290.11 stock_sz_000338 2022-01-20 294.87 0.0161 812949.0 -2.632389 -0.155513 -4.953753 -1 False -15\n 2022-01-21 1d 1.234360e+09 293.34 stock_sz_000338_2022-01-21 292.72 284.58 stock_sz_000338 2022-01-21 287.04 0.0150 754156.0 -3.701237 -0.864657 -5.673159 -1 False -16\n\n [987 rows x 17 columns]\n filter_result\n entity_id timestamp\n stock_sz_000338 2018-01-02 False\n 2018-01-03 False\n 2018-01-04 False\n 2018-01-05 False\n 2018-01-08 False\n ... ...\n 2022-01-17 False\n 2022-01-18 False\n 2022-01-19 False\n 2022-01-20 False\n 2022-01-21 False\n\n [987 rows x 1 columns]\n\n.. image:: ../_static/factor_draw.png\n\nFollow :ref:`Extending factor ` to do the funny part.\n\n\nTargetSelector\n------------------------------\nThe class select targets according to Factors.\nYou could calculate factors in the whole market and use selector to choose the targets.\n\n::\n\n from zvt.contract import IntervalLevel\n from zvt.factors.target_selector import TargetSelector\n from zvt.factors.ma.ma_factor import CrossMaFactor\n\n entity_ids = [\"stock_sz_000338\"]\n entity_type = \"stock\"\n start_timestamp = \"2018-01-01\"\n end_timestamp = \"2019-06-30\"\n my_selector = TargetSelector(\n entity_ids=entity_ids, entity_schema=entity_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp\n )\n # add the factors\n my_selector.add_factor(\n CrossMaFactor(\n entity_provider=\"em\",\n provider=\"em\",\n entity_ids=entity_ids,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n computing_window=10,\n windows=[5, 10],\n need_persist=False,\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=\"hfq\",\n )\n )\n my_selector.run()\n print(my_selector.open_long_df)\n print(my_selector.open_short_df)\n my_selector.get_open_long_targets(\"2019-06-27\")\n\nIf not set entity_ids arguments, the selected targets would be in whole market.\nAnd it provides get_open_long_targets function to select targets on the timestamp.\nFor multiple targets backtesting, this pre computed factor would be very fast."
},
{
"path": "docs/source/factor/index.rst",
"content": "==========\nFactor\n==========\n\n| Without factor, the heart of quant is empty.\n| So, what exactly is factor?\n| zvt makes a concise and unified abstraction of factor:\n\n In the world of zvt, factor is data describing market.\n It's computed from from Schema, and save as new Schema if need.\n\n.. toctree::\n :maxdepth: 2\n\n factor_concepts\n extending_factor\n\n"
},
{
"path": "docs/source/index.rst",
"content": "Welcome to zvt\n==============\n\nContents\n--------\n\n.. toctree::\n :maxdepth: 2\n\n install\n intro\n data/index\n factor/index\n trader/index\n ml/index\n drawer/index\n contributor\n api/index\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n"
},
{
"path": "docs/source/install.rst",
"content": ".. _install:\n\nInstallation of zvt\n========================\n\nThis part of the documentation covers the installation of zvt.\nThe first step to using any software package is getting it properly installed.\n\n\nPython version support\n----------------------\n\nOfficially Python 3.7, and 3.8.\n\n\n$ python -m pip install -U zvt\n--------------------------------\n\nTo install zvt, simply run this simple command in your terminal of choice::\n\n $ python -m pip install -U zvt\n\n\nGet the Source Code\n-------------------\n\nzvt is actively developed on GitHub, where the code is\n`always available `_.\n\nYou can clone the public repository::\n\n $ git clone git://github.com/zvtvz/zvt.git\n\nOnce you have a copy of the source, you can embed it in your own Python\npackage, or install it into your site-packages easily::\n\n $ cd zvt\n $ python -m pip install .\n"
},
{
"path": "docs/source/intro.rst",
"content": "==========\nIntro\n==========\n\nThis is a short introduction to zvt, you would learn the basic usage and\nglance a global view here.\n\nRethink market and programming\n------------------------------\nFor practical trading, complex algorithm is fragile, complex algorithm building\non complex facility is more fragile, complex algorithm building on complex\nfacility by complex team is more and more fragile.\n\nzvt want to provide a simple facility for building straightforward algorithm, it\nshould be:\n\n* **use the most basic programming concepts**\n\n* **concise abstraction of the market**\n\n* **correctness is obvious**\n\nCore concepts building zvt\n------------------------------\n| Technologies come and technologies go, but market insight is forever.\n| Your world is built by core concepts inside you, so it's you.\n| zvt world is built by core concepts inside market, so it's zvt.\n| We would show how core concepts building zvt in four aspects：\n\n.. toctree::\n :maxdepth: 2\n\n data/index\n factor/index\n trader/index\n ml/index\n"
},
{
"path": "docs/source/ml/index.rst",
"content": "====================\nMachine learning\n====================\n\n| In today's world, you're not fashion without machine learning.\n| So, what could zvt do with ML?\n| Being a fashion-feeling person, zvt love ML in this way:\n\n zvt provide a simple facility to define X and y.\n you could adapt any sophisticated ML library to zvt.\n\nTagger\n------------------------------\n:class:`~.zvt.tag.tag.Tagger` is a computing facility for classifying\nTradableEntity by different dimensions as tag which could be used as\nml category feature.\n\nPeople are divided into groups, things are grouped together.\n\n.. image:: ../_static/tag.png\n\nThe most important part for trading is which category is suitable to trade\nnow. ML could help you find which category perform better in different cases\n——— deterministic factor.\n\nThe tag could be **priori** or calculated from characteristics of the entity ——— some factor.\n\nMLMachine\n------------------------------\nthe ml engine.\n"
},
{
"path": "docs/source/trader/index.rst",
"content": "==========\nTrader\n==========\n\n| What we want in the end is to trade and make money.\n| So, what exactly is Trader?\n\n Trader is a simple facility helping you backtesting and generating trading signals\n using Factor, TargetSelector, MLMachine or you custom free style algorithm.\n\n.. toctree::\n :maxdepth: 2\n\n trader_concepts\n extending_trader\n\n"
},
{
"path": "docs/source/trader/trader_concepts.rst",
"content": "====================\nTrader\n====================\n\nTrader\n------------------------------\nthe backtest engine using TargetSelector, MLMachine or free style.\n\nTradingSignal\n------------------------------\nthe signal contains information about how to trade."
},
{
"path": "docs/source/usage.rst",
"content": "====================\nUsage\n====================\n\nHere show some use cases of zvt.\n\nData\n------------------------------\n\nFactor\n------------------------------\n\nBacktesting\n------------------------------\n\nMachine learning\n------------------------------\n\nDraw by intent\n------------------------------\n\nInformer\n------------------------------"
},
{
"path": "docs/storage_config.md",
"content": "# Storage 配置说明 (Phase 2)\n\n在 `config.json`（或 `zvt_home/config.json`）中可添加 `storage` 配置块，用于自定义存储路径和路由。\n\n## 配置结构\n\n```json\n{\n \"storage\": {\n \"base_path\": null,\n \"path_template\": null,\n \"storage_routes\": {},\n \"schema_providers\": {}\n }\n}\n```\n\n### 字段说明\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| `base_path` | string \\| null | 数据根路径。`null` 时使用 `zvt_env[\"data_path\"]`（即 `zvt_home/data`） |\n| `path_template` | string \\| null | 路径模板。占位符：`{base_path}`, `{provider}`, `{db_name}`, `{storage_id}`。`null` 时使用默认规则 |\n| `storage_routes` | object | 路由覆盖。键格式 `\"provider\\|db_name\"`，值为自定义 `storage_id` |\n| `schema_providers` | object | 无 Recorder 的 schema 的 provider 映射。键为 `db_name`，值为 `[\"provider\"]` 数组。用于 fallback 或 internal schema |\n\n### 默认行为（不配置时）\n\n- `storage_id = \"{provider}_{db_name}\"`（如 `em_stock_meta`）\n- 路径：`{data_path}/{provider}/{provider}_{db_name}.db`\n\n### 示例\n\n**自定义 base_path：**\n```json\n{\n \"storage\": {\n \"base_path\": \"/custom/data/path\"\n }\n}\n```\n\n**自定义 path_template（扁平目录）：**\n```json\n{\n \"storage\": {\n \"path_template\": \"{base_path}/{storage_id}.db\"\n }\n}\n```\n\n**自定义 storage_routes（某 provider 使用不同 storage_id）：**\n```json\n{\n \"storage\": {\n \"storage_routes\": {\n \"qmt|stock_1d_kdata\": \"qmt_realtime_stock_1d\"\n }\n }\n}\n```\n\n## Internal Schema（内部 schema）\n\n业务逻辑类数据（如 `zvt_info`、`trader_info`、`stock_tags`）与外部数据源无关，通过 `register_schema(..., internal=True)` 标记为 internal。Internal schema 只做存储路由，不参与 provider 切换。其 provider（如 `zvt`）在 `schema_providers` 中配置，用于确定存储路径。\n"
},
{
"path": "examples/README.MD",
"content": "The examples are updating with master source code.\n"
},
{
"path": "examples/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/data_runner/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/data_runner/actor_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.domain import (\n StockInstitutionalInvestorHolder,\n StockTopTenFreeHolder,\n StockActorSummary,\n)\nfrom zvt.utils.recorder_utils import run_data_recorder\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n@sched.scheduled_job(\"cron\", hour=1, minute=00, day_of_week=2)\ndef record_actor_data(data_provider=\"em\", entity_provider=\"em\"):\n run_data_recorder(\n domain=StockInstitutionalInvestorHolder,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n )\n run_data_recorder(\n domain=StockTopTenFreeHolder, data_provider=data_provider, entity_provider=entity_provider, day_data=True\n )\n run_data_recorder(\n domain=StockActorSummary, data_provider=data_provider, entity_provider=entity_provider, day_data=True\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"actor_runner.log\")\n\n record_actor_data()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/finance_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.domain import (\n Stock,\n StockDetail,\n FinanceFactor,\n BalanceSheet,\n IncomeStatement,\n CashFlowStatement,\n)\nfrom zvt.utils.recorder_utils import run_data_recorder\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n@sched.scheduled_job(\"cron\", hour=1, minute=00, day_of_week=5)\ndef record_actor_data(data_provider=\"eastmoney\", entity_provider=\"eastmoney\"):\n run_data_recorder(domain=Stock, data_provider=data_provider)\n run_data_recorder(domain=StockDetail, data_provider=data_provider)\n run_data_recorder(domain=FinanceFactor, data_provider=data_provider, entity_provider=entity_provider, day_data=True)\n run_data_recorder(domain=BalanceSheet, data_provider=data_provider, entity_provider=entity_provider, day_data=True)\n run_data_recorder(\n domain=IncomeStatement, data_provider=data_provider, entity_provider=entity_provider, day_data=True\n )\n run_data_recorder(\n domain=CashFlowStatement, data_provider=data_provider, entity_provider=entity_provider, day_data=True\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"finance_runner.log\")\n\n record_actor_data()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/index_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.consts import IMPORTANT_INDEX\nfrom zvt.domain import Index, Index1dKdata, IndexStock\nfrom zvt.utils.recorder_utils import run_data_recorder\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n# 自行更改定定时运行时间\n@sched.scheduled_job(\"cron\", hour=1, minute=00, day_of_week=5)\ndef record_index():\n run_data_recorder(domain=Index, data_provider=\"exchange\")\n # 默认只抓取 国证1000 国证2000 国证成长 国证价值 的组成个股\n index_ids = [\"index_sz_399311\", \"index_sz_399303\", \"index_sz_399370\", \"index_sz_399371\"]\n run_data_recorder(domain=IndexStock, data_provider=\"exchange\", entity_provider=\"exchange\", entity_ids=index_ids)\n\n\n@sched.scheduled_job(\"cron\", hour=16, minute=20)\ndef record_index_kdata():\n run_data_recorder(domain=Index, data_provider=\"em\")\n run_data_recorder(\n domain=Index1dKdata, data_provider=\"em\", entity_provider=\"em\", codes=IMPORTANT_INDEX, day_data=True\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"index_runner.log\")\n\n record_index()\n record_index_kdata()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/joinquant_fund_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.domain import Fund, FundStock, StockValuation\nfrom zvt.utils.recorder_utils import run_data_recorder\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n# 周6抓取\n@sched.scheduled_job(\"cron\", hour=10, minute=00, day_of_week=5)\ndef record_fund_data(data_provider=\"joinquant\", entity_provider=\"joinquant\"):\n # 基金\n run_data_recorder(domain=Fund, data_provider=data_provider, sleeping_time=0)\n # 基金持仓\n run_data_recorder(domain=FundStock, data_provider=data_provider, entity_provider=entity_provider, sleeping_time=0)\n # 个股估值\n run_data_recorder(\n domain=StockValuation,\n data_provider=data_provider,\n entity_provider=entity_provider,\n sleeping_time=0,\n day_data=True,\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"joinquant_fund_runner.log\")\n\n record_fund_data()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/joinquant_kdata_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt.domain import StockTradeDay\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt.utils.recorder_utils import run_data_recorder\nfrom zvt import init_log\nfrom zvt.domain import Stock, Stock1dHfqKdata\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n@sched.scheduled_job(\"cron\", hour=15, minute=30)\ndef record_stock_data(data_provider=\"joinquant\", entity_provider=\"joinquant\"):\n # A股标的\n run_data_recorder(domain=Stock, data_provider=data_provider, force_update=False)\n # 交易日\n run_data_recorder(domain=StockTradeDay, data_provider=data_provider)\n # A股后复权行情\n run_data_recorder(\n domain=Stock1dHfqKdata,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=0,\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"joinquant_kdata_runner.log\")\n\n record_stock_data()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/kdata_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom examples.report_utils import inform\nfrom examples.utils import get_hot_topics\nfrom zvt import init_log, zvt_config\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.domain import (\n Stock,\n Stock1dHfqKdata,\n Stockhk,\n Stockhk1dHfqKdata,\n Block,\n Block1dKdata,\n BlockCategory,\n Index,\n Index1dKdata,\n StockNews,\n LimitUpInfo,\n)\nfrom zvt.informer import EmailInformer\nfrom zvt.utils.time_utils import current_date\nfrom zvt.utils.recorder_utils import run_data_recorder\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n@sched.scheduled_job(\"cron\", hour=16, minute=30, day_of_week=\"mon-fri\")\ndef record_stock_news(data_provider=\"em\"):\n normal_stock_ids = get_entity_ids_by_filter(\n provider=\"em\", ignore_delist=True, ignore_st=False, ignore_new_stock=False\n )\n\n run_data_recorder(\n entity_ids=normal_stock_ids,\n day_data=True,\n domain=StockNews,\n data_provider=data_provider,\n force_update=False,\n sleeping_time=2,\n )\n\n\ndef report_limit_up():\n latest_data = LimitUpInfo.query_data(order=LimitUpInfo.timestamp.desc(), limit=1, return_type=\"domain\")\n timestamp = latest_data[0].timestamp\n df = LimitUpInfo.query_data(start_timestamp=timestamp, end_timestamp=timestamp, columns=[\"code\", \"name\", \"reason\"])\n df[\"reason\"] = df[\"reason\"].str.split(\"+\")\n print(df)\n EmailInformer().send_message(zvt_config[\"email_username\"], f\"{timestamp} 热门报告\", f\"{df}\")\n\n\ndef report_hot_topics():\n topics_long = get_hot_topics(days_ago=20)\n topics_short = get_hot_topics(days_ago=5)\n\n set1 = set(topics_long.keys())\n set2 = set(topics_short.keys())\n\n same = set1 & set2\n print(same)\n\n old_topics = set1 - set2\n print(old_topics)\n new_topics = set2 - set1\n print(new_topics)\n\n msg = f\"\"\"\n 一直热门:{same}\n ---:{old_topics}\n +++:{new_topics}\n\n 长期统计:{topics_long}\n 短期统计:{topics_short}\n \"\"\"\n\n print(msg)\n EmailInformer().send_message(zvt_config[\"email_username\"], f\"{current_date()} 热门报告\", msg)\n\n\n@sched.scheduled_job(\"cron\", hour=15, minute=30, day_of_week=\"mon-fri\")\ndef record_stock_data(data_provider=\"em\", entity_provider=\"em\", sleeping_time=0):\n email_action = EmailInformer()\n # 涨停数据\n run_data_recorder(domain=LimitUpInfo, data_provider=None, force_update=False)\n report_limit_up()\n\n # A股指数\n run_data_recorder(domain=Index, data_provider=data_provider, force_update=False)\n # A股指数行情\n run_data_recorder(\n domain=Index1dKdata,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=sleeping_time,\n )\n\n # 板块(概念，行业)\n run_data_recorder(domain=Block, entity_provider=entity_provider, data_provider=entity_provider, force_update=False)\n # 板块行情(概念，行业)\n run_data_recorder(\n domain=Block1dKdata,\n entity_provider=entity_provider,\n data_provider=entity_provider,\n day_data=True,\n sleeping_time=sleeping_time,\n )\n # run_data_recorder(\n # domain=BlockStock,\n # entity_provider=entity_provider,\n # data_provider=entity_provider,\n # sleeping_time=sleeping_time,\n # )\n\n # 报告新概念和行业\n df = Block.query_data(\n filters=[Block.category == BlockCategory.concept.value],\n order=Block.list_date.desc(),\n index=\"entity_id\",\n limit=7,\n )\n\n inform(\n action=email_action,\n entity_ids=df.index.tolist(),\n target_date=current_date(),\n title=\"report 新概念\",\n entity_provider=entity_provider,\n entity_type=\"block\",\n em_group=None,\n em_group_over_write=False,\n )\n\n # A股标的\n run_data_recorder(domain=Stock, data_provider=data_provider, force_update=False)\n # A股后复权行情\n normal_stock_ids = get_entity_ids_by_filter(\n provider=\"em\", ignore_delist=True, ignore_st=False, ignore_new_stock=False\n )\n\n run_data_recorder(\n entity_ids=normal_stock_ids,\n domain=Stock1dHfqKdata,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=sleeping_time,\n return_unfinished=True,\n )\n\n\n@sched.scheduled_job(\"cron\", hour=16, minute=30, day_of_week=\"mon-fri\")\ndef record_stockhk_data(data_provider=\"em\", entity_provider=\"em\", sleeping_time=2):\n # 港股标的\n run_data_recorder(domain=Stockhk, data_provider=data_provider, force_update=False)\n # 港股后复权行情\n df = Stockhk.query_data(filters=[Stockhk.south == True], index=\"entity_id\")\n run_data_recorder(\n domain=Stockhk1dHfqKdata,\n entity_ids=df.index.tolist(),\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=sleeping_time,\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"kdata_runner.log\")\n\n record_stock_data()\n record_stockhk_data()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/sina_data_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.domain import *\nfrom zvt.utils.recorder_utils import run_data_recorder\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n@sched.scheduled_job(\"cron\", hour=15, minute=30, day_of_week=3)\ndef record_block():\n run_data_recorder(domain=Block, data_provider=\"sina\")\n run_data_recorder(domain=Block, data_provider=\"sina\", entity_provider=\"sina\")\n\n\n@sched.scheduled_job(\"cron\", hour=15, minute=30)\ndef record_money_flow():\n run_data_recorder(domain=BlockMoneyFlow, data_provider=\"sina\", entity_provider=\"sina\", day_data=True)\n\n\nif __name__ == \"__main__\":\n init_log(\"sina_data_runner.log\")\n\n record_block()\n record_money_flow()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/data_runner/trading_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom sqlalchemy import or_, and_\n\nfrom examples.report_utils import inform\nfrom zvt import init_log\nfrom zvt.api.kdata import get_latest_kdata_date\nfrom zvt.api.selector import get_big_players\nfrom zvt.domain import (\n DragonAndTiger,\n Stock1dHfqKdata,\n)\nfrom zvt.informer import EmailInformer\nfrom zvt.utils.recorder_utils import run_data_recorder\nfrom zvt.utils.time_utils import date_time_by_interval, current_date, to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n@sched.scheduled_job(\"cron\", hour=18, minute=00, day_of_week=\"mon-fri\")\ndef record_dragon_tiger(data_provider=\"em\", entity_provider=\"em\", sleeping_time=2):\n # 龙虎榜数据\n run_data_recorder(\n domain=DragonAndTiger,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=sleeping_time,\n )\n\n email_action = EmailInformer()\n # recent year\n start_timestamp = date_time_by_interval(current_date(), -400)\n # 最近一年牛x的营业部\n players = get_big_players(start_timestamp=start_timestamp)\n\n # 最近30天有牛x的营业部上榜的个股\n recent_date = date_time_by_interval(current_date(), -30)\n selected = []\n for player in players:\n filters = [\n or_(\n and_(DragonAndTiger.dep1 == player, DragonAndTiger.dep1_rate >= 5),\n and_(DragonAndTiger.dep2 == player, DragonAndTiger.dep2_rate >= 5),\n and_(DragonAndTiger.dep3 == player, DragonAndTiger.dep3_rate >= 5),\n and_(DragonAndTiger.dep4 == player, DragonAndTiger.dep4_rate >= 5),\n and_(DragonAndTiger.dep5 == player, DragonAndTiger.dep5_rate >= 5),\n )\n ]\n df = DragonAndTiger.query_data(\n start_timestamp=recent_date,\n filters=filters,\n columns=[DragonAndTiger.timestamp, DragonAndTiger.entity_id, DragonAndTiger.code, DragonAndTiger.name],\n index=\"entity_id\",\n )\n selected = selected + df.index.tolist()\n\n if selected:\n selected = list(set(selected))\n\n target_date = get_latest_kdata_date(provider=data_provider, entity_type=\"stock\", adjust_type=\"hfq\")\n df = Stock1dHfqKdata.query_data(\n provider=data_provider,\n entity_ids=selected,\n filters=[\n Stock1dHfqKdata.turnover_rate > 0.02,\n Stock1dHfqKdata.timestamp == to_pd_timestamp(target_date),\n Stock1dHfqKdata.turnover > 300000000,\n ],\n index=[\"entity_id\"],\n )\n inform(\n action=email_action,\n entity_ids=df.index.tolist(),\n target_date=current_date(),\n title=\"report 龙虎榜\",\n entity_provider=entity_provider,\n entity_type=\"stock\",\n em_group=\"重要指数\",\n em_group_over_write=False,\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"trading_runner.log\")\n\n record_dragon_tiger()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/factors/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/factors/boll_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import Optional, List\n\nimport pandas as pd\nfrom ta.volatility import BollingerBands\n\nfrom zvt.contract.factor import Transformer\nfrom zvt.factors.technical_factor import TechnicalFactor\n\n\nclass BollTransformer(Transformer):\n def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame:\n indicator_bb = BollingerBands(close=df[\"close\"], window=20, window_dev=2)\n\n # Add Bollinger Bands features\n df[\"bb_bbm\"] = indicator_bb.bollinger_mavg()\n df[\"bb_bbh\"] = indicator_bb.bollinger_hband()\n df[\"bb_bbl\"] = indicator_bb.bollinger_lband()\n\n # Add Bollinger Band high indicator\n df[\"bb_bbhi\"] = indicator_bb.bollinger_hband_indicator()\n\n # Add Bollinger Band low indicator\n df[\"bb_bbli\"] = indicator_bb.bollinger_lband_indicator()\n\n # Add Width Size Bollinger Bands\n df[\"bb_bbw\"] = indicator_bb.bollinger_wband()\n\n # Add Percentage Bollinger Bands\n df[\"bb_bbp\"] = indicator_bb.bollinger_pband()\n return df\n\n\nclass BollFactor(TechnicalFactor):\n transformer = BollTransformer()\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n return [self.factor_df[[\"bb_bbm\", \"bb_bbh\", \"bb_bbl\"]]]\n\n def compute_result(self):\n super().compute_result()\n self.result_df = (self.factor_df[\"bb_bbli\"] - self.factor_df[\"bb_bbhi\"]).to_frame(name=\"filter_result\")\n self.result_df[self.result_df == 0] = None\n self.result_df[self.result_df == 1] = True\n self.result_df[self.result_df == -1] = False\n\n\nif __name__ == \"__main__\":\n from zvt.domain import Stock1dHfqKdata\n\n provider = \"em\"\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=provider)\n factor = BollFactor(\n entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp=\"2019-01-01\"\n )\n factor.draw(show=True)\n\n from zvt.domain import Stock30mHfqKdata\n\n provider = \"em\"\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n\n Stock30mHfqKdata.record_data(entity_ids=entity_ids, provider=provider)\n factor = BollFactor(\n entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp=\"2021-01-01\"\n )\n factor.draw(show=True)\n"
},
{
"path": "examples/factors/fundamental_selector.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import BalanceSheet\nfrom zvt.factors.fundamental.finance_factor import GoodCompanyFactor\nfrom zvt.factors.target_selector import TargetSelector\n\n\nclass FundamentalSelector(TargetSelector):\n def init_factors(self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, level):\n # 核心资产=(高ROE 高现金流 高股息 低应收 低资本开支 低财务杠杆 有增长)\n # 高roe 高现金流 低财务杠杆 有增长\n factor1 = GoodCompanyFactor(\n entity_ids=entity_ids,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"eastmoney\",\n )\n\n self.factors.append(factor1)\n\n # 高股息 低应收\n factor2 = GoodCompanyFactor(\n data_schema=BalanceSheet,\n entity_ids=entity_ids,\n codes=codes,\n columns=[BalanceSheet.accounts_receivable],\n filters=[BalanceSheet.accounts_receivable <= 0.3 * BalanceSheet.total_current_assets],\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"eastmoney\",\n col_period_threshold=None,\n )\n self.factors.append(factor2)\n\n\nif __name__ == \"__main__\":\n selector: TargetSelector = FundamentalSelector(start_timestamp=\"2015-01-01\", end_timestamp=\"2019-06-30\")\n selector.run()\n print(selector.get_targets(\"2019-06-30\"))\n"
},
{
"path": "examples/factors/tech_factor.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom typing import Type, List, Union\n\nimport pandas as pd\n\nfrom zvt.contract import AdjustType, TradableEntity, IntervalLevel\nfrom zvt.contract.factor import Transformer, Accumulator\nfrom zvt.domain import Stock\nfrom zvt.factors.macd.macd_factor import MacdFactor\nfrom zvt.factors.transformers import CrossMaTransformer\n\n\nclass BullAndUpFactor(MacdFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n turnover_threshold=400000000,\n turnover_rate_threshold=0.02,\n ) -> None:\n self.turnover_threshold = turnover_threshold\n self.turnover_rate_threshold = turnover_rate_threshold\n\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n def compute_result(self):\n super().compute_result()\n t = CrossMaTransformer(windows=[5, 120, 250])\n self.factor_df = t.transform(self.factor_df)\n s = (self.factor_df[\"turnover\"] > self.turnover_threshold) & (\n self.factor_df[\"turnover_rate\"] > self.turnover_rate_threshold\n )\n self.result_df = (self.factor_df[\"filter_result\"] & self.factor_df[\"bull\"] & s).to_frame(name=\"filter_result\")\n"
},
{
"path": "examples/hot.json",
"content": "{\n \"减肥药\": [\n \"减肥药\"\n ],\n \"房地产\": [\n \"房地产\",\n \"新型城镇化\",\n \"棚改\",\n \"建材\"\n ],\n \"新型工业化\": [\n \"新型工业化\",\n \"工业母机\"\n ],\n \"华为\": [\n \"华为\",\n \"mate60 pro,mate\",\n \"星闪\",\n \"问界\",\n \"麒麟\",\n \"昇腾\",\n \"鸿蒙\"\n ],\n \"新能源\": [\n \"新能源\",\n \"锂电,锂电池\",\n \"钠离子电池\",\n \"光伏\",\n \"太阳能\",\n \"储能\",\n \"TOPCON电池\",\n \"风电\",\n \"核电\"\n ],\n \"新能车\": [\n \"新能车,新能源汽车\",\n \"整车,汽车整车\",\n \"汽车零部件,汽车零件\",\n \"无人驾驶\",\n \"压铸一体化,一体化压铸\"\n ],\n \"人工智能\": [\n \"人工智能,AI\",\n \"GPT,CHATGPT\",\n \"算力\"\n ],\n \"机器人\": [\n \"机器人\",\n \"减速器\",\n \"伺服,伺服系统\",\n \"控制系统\",\n \"电机\"\n ],\n \"核心资产\": [\n \"核心资产\",\n \"消费,白酒,食品,饮料\",\n \"白马\",\n \"沪深300\",\n \"基金重仓\",\n \"上证50\"\n ],\n \"一带一路\": [\n \"一带一路\",\n \"人民币国际化\",\n \"跨境支付\"\n ]\n}\n"
},
{
"path": "examples/intent/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/intent/intent.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.intent import compare\nfrom zvt.domain import Indexus1dKdata, Index, Indexus, Index1dKdata, Currency1dKdata\nfrom zvt.domain import TreasuryYield\n\n\ndef china_vs_us_stock():\n # 上证，道琼斯指数\n Index.record_data()\n Indexus.record_data()\n Index1dKdata.record_data(entity_id=\"index_sh_000001\")\n Indexus1dKdata.record_data(entity_id=\"indexus_us_SPX\")\n compare(entity_ids=[\"index_sh_000001\", \"indexus_us_SPX\"], start_timestamp=\"2000-01-01\", scale_value=100)\n\n\ndef us_yield_and_stock():\n # 美债收益率，道琼斯指数\n entity_ids = [\"country_galaxy_US\", \"indexus_us_SPX\"]\n compare(\n entity_ids=entity_ids,\n start_timestamp=\"1990-01-01\",\n scale_value=None,\n schema_map_columns={TreasuryYield: [\"yield_2\", \"yield_5\"], Indexus1dKdata: [\"close\"]},\n )\n\n\ndef commodity_and_stock():\n # 江西铜业，沪铜\n entity_ids = [\"stock_sh_600362\", \"future_shfe_CU\"]\n compare(\n entity_ids=entity_ids,\n start_timestamp=\"2005-01-01\",\n scale_value=100,\n )\n\n\ndef compare_metal():\n # 沪铜,沪铝,螺纹钢\n entity_ids = [\"future_shfe_CU\", \"future_shfe_AL\", \"future_shfe_RB\"]\n compare(\n entity_ids=entity_ids,\n start_timestamp=\"2009-04-01\",\n scale_value=100,\n )\n\n\ndef compare_udi_and_stock():\n # 美股指数\n # Indexus.record_data()\n entity_ids = [\"indexus_us_NDX\", \"indexus_us_SPX\", \"indexus_us_UDI\"]\n # Indexus1dKdata.record_data(entity_ids=entity_ids, sleeping_time=0)\n compare(\n entity_ids=entity_ids,\n start_timestamp=\"2015-01-01\",\n scale_value=100,\n schema_map_columns={Indexus1dKdata: [\"close\"]},\n )\n\n\ndef compare_cny_and_stock():\n Currency1dKdata.record_data(entity_id=\"currency_forex_USDCNYC\")\n entity_ids = [\"index_sh_000001\", \"currency_forex_USDCNYC\"]\n compare(\n entity_ids=entity_ids,\n start_timestamp=\"2005-01-01\",\n scale_value=100,\n schema_map_columns={Currency1dKdata: [\"close\"], Index1dKdata: [\"close\"]},\n )\n\n\nif __name__ == \"__main__\":\n # compare_kline()\n # us_yield_and_stock()\n # commodity_and_stock()\n # compare_metal()\n # compare_udi_and_stock()\n compare_cny_and_stock()\n"
},
{
"path": "examples/main_line_hidden_tags.json",
"content": "[\n \"次新股\"\n]"
},
{
"path": "examples/main_line_tags.json",
"content": "[\n \"智能机器\",\n \"半导体\",\n \"AI\"\n]"
},
{
"path": "examples/migration.py",
"content": "# -*- coding: utf-8 -*-\nfrom datetime import datetime\nfrom typing import Dict\n\nfrom pydantic import BaseModel, ConfigDict\nfrom sqlalchemy import Column, String, JSON\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.api import get_db_session\nfrom zvt.contract.register import register_schema\nfrom zvt.contract.schema import Mixin\n\nZvtInfoBase = declarative_base()\n\n\nclass User(Mixin, ZvtInfoBase):\n __tablename__ = \"user\"\n added_col = Column(String)\n json_col = Column(JSON)\n\n\nclass UserModel(BaseModel):\n model_config = ConfigDict(from_attributes=True)\n\n id: str\n entity_id: str\n timestamp: datetime\n added_col: str\n json_col: Dict\n\n\nregister_schema(providers=[\"zvt\"], db_name=\"test\", schema_base=ZvtInfoBase)\n\nif __name__ == \"__main__\":\n user_model = UserModel(\n id=\"user_cn_jack_2020-01-01\",\n entity_id=\"user_cn_jack\",\n timestamp=\"2020-01-01\",\n added_col=\"test\",\n json_col={\"a\": 1},\n )\n session = get_db_session(provider=\"zvt\", data_schema=User)\n\n user = session.query(User).filter(User.id == \"user_cn_jack_2020-01-01\").first()\n print(UserModel.validate(user))\n"
},
{
"path": "examples/ml/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/ml/sgd.py",
"content": "# -*- coding: utf-8 -*-\nfrom sklearn.linear_model import SGDClassifier, SGDRegressor\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom zvt.ml import MaStockMLMachine\n\n\ndef sgd_classification():\n machine = MaStockMLMachine(data_provider=\"em\", entity_ids=[\"stock_sz_000001\"], label_method=\"behavior_cls\")\n clf = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000, tol=1e-3))\n machine.train(model=clf)\n machine.predict()\n machine.draw_result(entity_id=\"stock_sz_000001\")\n\n\ndef sgd_regressor():\n machine = MaStockMLMachine(data_provider=\"em\", entity_ids=[\"stock_sz_000001\"], label_method=\"raw\")\n reg = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))\n machine.train(model=reg)\n machine.predict()\n machine.draw_result(entity_id=\"stock_sz_000001\")\n\n\nif __name__ == \"__main__\":\n sgd_classification()\n sgd_regressor()\n"
},
{
"path": "examples/query_snippet.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import func\n\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.contract import Exchange\nfrom zvt.domain import Stock, BlockStock\nfrom zvt.recorders.em import em_api\nfrom zvt.tag.tag_schemas import StockTags\n\n\ndef query_json():\n\n df = StockTags.query_data(\n filters=[func.json_extract(StockTags.sub_tags, '$.\"低空经济\"') != None], columns=[StockTags.sub_tags]\n )\n print(df)\n\n\ndef get_stocks_has_tag():\n df = StockTags.query_data(filters=[StockTags.latest.is_(True)], columns=[StockTags.entity_id])\n return df[\"entity_id\"].tolist()\n\n\ndef get_stocks_without_tag():\n entity_ids = get_entity_ids_by_filter(provider=\"em\", ignore_delist=True, ignore_st=True, ignore_new_stock=False)\n stocks_has_tag = get_stocks_has_tag()\n return list(set(entity_ids) - set(stocks_has_tag))\n\n\ndef get_all_delist_stocks():\n stocks = []\n df1 = em_api.get_tradable_list(entity_type=\"stock\", exchange=Exchange.sh)\n stocks = stocks + df1[\"entity_id\"].tolist()\n df2 = em_api.get_tradable_list(entity_type=\"stock\", exchange=Exchange.sz)\n stocks = stocks + df2[\"entity_id\"].tolist()\n df3 = em_api.get_tradable_list(entity_type=\"stock\", exchange=Exchange.bj)\n stocks = stocks + df3[\"entity_id\"].tolist()\n return stocks\n\n\ndef get_block_stocks(name=\"低空经济\"):\n df = BlockStock.query_data(provider=\"em\", filters=[BlockStock.name == name], columns=[BlockStock.stock_id])\n return df[\"stock_id\"].tolist()\n\n\ndef get_sub_tag_stocks(tag=\"低空经济\"):\n df = StockTags.query_data(\n provider=\"zvt\",\n filters=[func.json_extract(StockTags.sub_tags, f'$.\"{tag}\"') != None],\n columns=[StockTags.entity_id],\n )\n return df[\"entity_id\"].tolist()\n\n\nif __name__ == \"__main__\":\n # a = get_block_stocks()\n # b = get_sub_tag_stocks()\n # print(set(a) - set(b))\n print(Stock.query_data(provider=\"em\", return_type=\"dict\"))\n"
},
{
"path": "examples/report_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import Type\n\nfrom examples.tag_utils import group_stocks_by_tag, get_main_line_tags, get_main_line_hidden_tags\nfrom examples.utils import msg_group_stocks_by_topic\nfrom zvt import zvt_config\nfrom zvt.api.kdata import get_latest_kdata_date, get_kdata_schema, default_adjust_type\nfrom zvt.api.selector import get_limit_up_stocks\nfrom zvt.api.stats import get_top_performance_entities_by_periods, get_top_volume_entities, TopType\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import get_entities, get_entity_schema\nfrom zvt.contract.factor import Factor, TargetType\nfrom zvt.domain import StockNews\nfrom zvt.informer import EmailInformer\nfrom zvt.informer.inform_utils import add_to_eastmoney\nfrom zvt.utils.time_utils import date_time_by_interval\n\nlogger = logging.getLogger(\"__name__\")\n\n\ndef inform(\n action: EmailInformer,\n entity_ids,\n target_date,\n title,\n entity_provider,\n entity_type,\n em_group,\n em_group_over_write,\n em_group_over_write_tag=False,\n group_by_topic=False,\n group_by_tag=True,\n special_hidden_tag=\"北交所\",\n):\n msg = \"no targets\"\n if entity_ids:\n entities = get_entities(\n provider=entity_provider, entity_type=entity_type, entity_ids=entity_ids, return_type=\"domain\"\n )\n entities = [entity for entity in entities if entity.entity_id in entity_ids]\n print(len(entities))\n print(len(entity_ids))\n assert len(entities) == len(entity_ids)\n\n if group_by_topic and (entity_type == \"stock\"):\n StockNews.record_data(\n entity_ids=entity_ids,\n provider=\"em\",\n force_update=False,\n sleeping_time=0.05,\n day_data=True,\n )\n\n msg = msg_group_stocks_by_topic(entities=entities, threshold=1, days_ago=60)\n logger.info(msg)\n action.send_message(zvt_config[\"email_username\"], f\"{target_date} {title}\", msg)\n\n if group_by_tag and (entity_type == \"stock\"):\n main_line_hidden_tags = get_main_line_hidden_tags()\n sorted_entities = group_stocks_by_tag(\n entities=entities, hidden_tags=main_line_hidden_tags + [special_hidden_tag]\n )\n msg = \"\"\n main_line = []\n others = []\n special = []\n main_line_tags = get_main_line_tags()\n for index, (tag, stocks) in enumerate(sorted_entities):\n msg = msg + f\"^^^^^^ {tag}[{len(stocks)}/{len(entities)}] ^^^^^^\\n\"\n msg = msg + \"\\n\".join([f\"{stock.name}({stock.code})\" for stock in stocks]) + \"\\n\"\n if tag == special_hidden_tag:\n special = special + stocks\n elif (not main_line_tags) and (tag != \"未知\") and (index < 3):\n main_line = main_line + stocks\n elif main_line_tags and (tag in main_line_tags):\n main_line = main_line + stocks\n elif main_line_hidden_tags and (tag in main_line_hidden_tags):\n main_line = main_line + stocks\n else:\n others = others + stocks\n\n # 主线\n if main_line:\n codes = [entity.code for entity in main_line]\n add_to_eastmoney(codes=codes, entity_type=entity_type, group=\"主线\", over_write=em_group_over_write_tag)\n\n # 其他\n if others:\n codes = [entity.code for entity in others]\n if not em_group:\n em_group = \"其他\"\n add_to_eastmoney(codes=codes, entity_type=entity_type, group=em_group, over_write=em_group_over_write)\n # 特别处理\n if special:\n codes = [entity.code for entity in special]\n add_to_eastmoney(\n codes=codes, entity_type=entity_type, group=special_hidden_tag, over_write=em_group_over_write_tag\n )\n else:\n if em_group:\n try:\n codes = [entity.code for entity in entities]\n add_to_eastmoney(\n codes=codes, entity_type=entity_type, group=em_group, over_write=em_group_over_write\n )\n except Exception as e:\n action.send_message(\n zvt_config[\"email_username\"],\n f\"{target_date} {title} error\",\n f\"{target_date} {title} error: {e}\",\n )\n\n infos = [f\"{entity.name}({entity.code})\" for entity in entities]\n msg = \"\\n\".join(infos) + \"\\n\"\n\n logger.info(msg)\n action.send_message(zvt_config[\"email_username\"], f\"{target_date} {title}\", msg)\n\n\ndef report_targets(\n factor_cls: Type[Factor],\n entity_provider,\n data_provider,\n title,\n entity_type=\"stock\",\n informer: EmailInformer = None,\n em_group=None,\n em_group_over_write=True,\n em_group_over_write_tag=False,\n filter_by_volume=True,\n adjust_type=None,\n start_timestamp=\"2019-01-01\",\n **factor_kv,\n):\n logger.info(\n f\"entity_provider: {entity_provider}, data_provider: {data_provider}, entity_type: {entity_type}, start_timestamp: {start_timestamp}\"\n )\n error_count = 0\n\n while error_count <= 10:\n try:\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n\n target_date = get_latest_kdata_date(\n provider=data_provider, entity_type=entity_type, adjust_type=adjust_type\n )\n logger.info(f\"target_date :{target_date}\")\n\n current_entity_pool = None\n if filter_by_volume:\n # 成交量\n vol_df = get_top_volume_entities(\n entity_type=entity_type,\n start_timestamp=date_time_by_interval(target_date, -30),\n end_timestamp=target_date,\n adjust_type=adjust_type,\n pct=0.4,\n data_provider=data_provider,\n )\n current_entity_pool = vol_df.index.tolist()\n logger.info(f\"current_entity_pool({len(current_entity_pool)}): {current_entity_pool}\")\n\n kdata_schema = get_kdata_schema(entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type)\n filters = []\n if \"turnover_threshold\" in factor_kv:\n filters = filters + [kdata_schema.turnover >= factor_kv.get(\"turnover_threshold\")]\n if \"turnover_rate_threshold\" in factor_kv:\n filters = filters + [kdata_schema.turnover_rate >= factor_kv.get(\"turnover_rate_threshold\")]\n if filters:\n filters = filters + [kdata_schema.timestamp == target_date]\n kdata_df = kdata_schema.query_data(\n provider=data_provider, filters=filters, columns=[\"entity_id\", \"timestamp\"], index=\"entity_id\"\n )\n if current_entity_pool:\n current_entity_pool = set(current_entity_pool) & set(kdata_df.index.tolist())\n else:\n current_entity_pool = kdata_df.index.tolist()\n\n if \"entity_ids\" in factor_kv:\n if current_entity_pool:\n current_entity_pool = set(current_entity_pool) & set(factor_kv.pop(\"entity_ids\"))\n else:\n current_entity_pool = set(factor_kv.pop(\"entity_ids\"))\n\n # add the factor\n entity_schema = get_entity_schema(entity_type=entity_type)\n tech_factor = factor_cls(\n entity_schema=entity_schema,\n entity_provider=entity_provider,\n provider=data_provider,\n entity_ids=current_entity_pool,\n start_timestamp=start_timestamp,\n end_timestamp=target_date,\n adjust_type=adjust_type,\n **factor_kv,\n )\n\n long_stocks = tech_factor.get_targets(timestamp=target_date, target_type=TargetType.positive)\n\n inform(\n informer,\n entity_ids=long_stocks,\n target_date=target_date,\n title=f\"{entity_type} {title}({len(long_stocks)})\",\n entity_provider=entity_provider,\n entity_type=entity_type,\n em_group=em_group,\n em_group_over_write=em_group_over_write,\n em_group_over_write_tag=em_group_over_write_tag,\n )\n\n break\n except Exception as e:\n logger.exception(\"report error:{}\".format(e))\n time.sleep(60 * 3)\n error_count = error_count + 1\n if error_count == 10:\n informer.send_message(\n zvt_config[\"email_username\"],\n f\"report {entity_type}{factor_cls.__name__} error\",\n f\"report {entity_type}{factor_cls.__name__} error: {e}\",\n )\n\n\ndef report_top_entities(\n entity_provider,\n data_provider,\n periods=None,\n ignore_new_stock=True,\n ignore_st=True,\n entity_ids=None,\n entity_type=\"stock\",\n adjust_type=None,\n top_count=30,\n turnover_threshold=100000000,\n turnover_rate_threshold=0.02,\n informer: EmailInformer = None,\n title=\"最强\",\n em_group=None,\n em_group_over_write=True,\n em_group_over_write_tag=False,\n return_type=TopType.positive,\n include_limit_up=False,\n):\n error_count = 0\n\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n\n while error_count <= 10:\n try:\n target_date = get_latest_kdata_date(\n provider=data_provider, entity_type=entity_type, adjust_type=adjust_type\n )\n\n selected, real_period = get_top_performance_entities_by_periods(\n entity_provider=entity_provider,\n data_provider=data_provider,\n periods=periods,\n ignore_new_stock=ignore_new_stock,\n ignore_st=ignore_st,\n entity_ids=entity_ids,\n entity_type=entity_type,\n adjust_type=adjust_type,\n top_count=top_count,\n turnover_threshold=turnover_threshold,\n turnover_rate_threshold=turnover_rate_threshold,\n return_type=return_type,\n )\n\n if include_limit_up and (entity_type == \"stock\"):\n limit_up_stocks = get_limit_up_stocks(timestamp=target_date)\n if limit_up_stocks:\n selected = list(set(selected + limit_up_stocks))\n\n inform(\n informer,\n entity_ids=selected,\n target_date=target_date,\n title=f\"{entity_type} {title}({len(selected)})\",\n entity_provider=entity_provider,\n entity_type=entity_type,\n em_group=em_group,\n em_group_over_write=em_group_over_write,\n em_group_over_write_tag=em_group_over_write_tag,\n )\n return real_period\n except Exception as e:\n logger.exception(\"report error:{}\".format(e))\n time.sleep(30)\n error_count = error_count + 1\n\n\nif __name__ == \"__main__\":\n report_top_entities(\n entity_type=\"block\",\n entity_provider=\"em\",\n data_provider=\"em\",\n top_count=10,\n periods=[365, 750],\n ignore_new_stock=False,\n ignore_st=False,\n adjust_type=None,\n turnover_threshold=50000000,\n turnover_rate_threshold=0.005,\n em_group=None,\n em_group_over_write=False,\n return_type=TopType.negative,\n )\n\n# the __all__ is generated\n__all__ = [\"report_targets\", \"report_top_entities\"]\n"
},
{
"path": "examples/reports/__init__.py",
"content": "# -*- coding: utf-8 -*-\nimport datetime\nimport json\nimport os\nfrom typing import List\n\nfrom sqlalchemy import or_\n\nfrom zvt.api.utils import float_to_pct_str\nfrom zvt.contract import ActorType\nfrom zvt.domain import FinanceFactor, BalanceSheet, IncomeStatement, Stock, StockActorSummary\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp, now_date_time_str\n\n\ndef get_subscriber_emails():\n emails_file = os.path.abspath(os.path.join(os.path.dirname(__file__), \"subscriber_emails.json\"))\n with open(emails_file) as f:\n return json.load(f)\n\n\ndef risky_company(the_date=to_pd_timestamp(now_date_time_str()), income_yoy=-0.1, profit_yoy=-0.1, entity_ids=None):\n codes = []\n start_timestamp = to_pd_timestamp(the_date) - datetime.timedelta(130)\n # 营收降，利润降,流动比率低，速动比率低\n finance_filter = or_(\n FinanceFactor.op_income_growth_yoy < income_yoy,\n FinanceFactor.net_profit_growth_yoy <= profit_yoy,\n FinanceFactor.current_ratio < 0.7,\n FinanceFactor.quick_ratio < 0.5,\n )\n df = FinanceFactor.query_data(\n entity_ids=entity_ids, start_timestamp=start_timestamp, filters=[finance_filter], columns=[\"code\"]\n )\n if pd_is_not_null(df):\n codes = codes + df.code.tolist()\n\n # 高应收，高存货，高商誉\n balance_filter = (\n BalanceSheet.accounts_receivable + BalanceSheet.inventories + BalanceSheet.goodwill\n ) > BalanceSheet.total_equity\n df = BalanceSheet.query_data(\n entity_ids=entity_ids, start_timestamp=start_timestamp, filters=[balance_filter], columns=[\"code\"]\n )\n if pd_is_not_null(df):\n codes = codes + df.code.tolist()\n\n # 应收>利润*1/2\n df1 = BalanceSheet.query_data(\n entity_ids=entity_ids,\n start_timestamp=start_timestamp,\n columns=[BalanceSheet.code, BalanceSheet.accounts_receivable],\n )\n if pd_is_not_null(df1):\n df1.drop_duplicates(subset=\"code\", keep=\"last\", inplace=True)\n df1 = df1.set_index(\"code\", drop=True).sort_index()\n\n df2 = IncomeStatement.query_data(\n entity_ids=entity_ids,\n start_timestamp=start_timestamp,\n columns=[IncomeStatement.code, IncomeStatement.net_profit],\n )\n if pd_is_not_null(df2):\n df2.drop_duplicates(subset=\"code\", keep=\"last\", inplace=True)\n df2 = df2.set_index(\"code\", drop=True).sort_index()\n\n if pd_is_not_null(df1) and pd_is_not_null(df2):\n codes = codes + df1[df1.accounts_receivable > df2.net_profit / 2].index.tolist()\n\n return list(set(codes))\n\n\ndef stocks_with_info(stocks: List[Stock]):\n infos = []\n for stock in stocks:\n info = f\"{stock.name}({stock.code})\"\n summary: List[StockActorSummary] = StockActorSummary.query_data(\n entity_id=stock.entity_id,\n order=StockActorSummary.timestamp.desc(),\n filters=[StockActorSummary.actor_type == ActorType.raised_fund.value],\n limit=1,\n return_type=\"domain\",\n )\n if summary:\n info = (\n info\n + f\"([{summary[0].timestamp}]共{summary[0].actor_count}家基金持股占比:{float_to_pct_str(summary[0].holding_ratio)}, 变化: {float_to_pct_str(summary[0].change_ratio)})\"\n )\n\n summary: List[StockActorSummary] = StockActorSummary.query_data(\n entity_id=stock.entity_id,\n order=StockActorSummary.timestamp.desc(),\n filters=[StockActorSummary.actor_type == ActorType.qfii.value],\n limit=1,\n return_type=\"domain\",\n )\n if summary:\n info = (\n info\n + f\"([{summary[0].timestamp}]共{summary[0].actor_count}家qfii持股占比:{float_to_pct_str(summary[0].holding_ratio)}, 变化: {float_to_pct_str(summary[0].change_ratio)})\"\n )\n\n infos.append(info)\n return infos\n\n\nif __name__ == \"__main__\":\n print(get_subscriber_emails())\n"
},
{
"path": "examples/reports/report_bull.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom examples.factors.tech_factor import BullAndUpFactor\nfrom examples.report_utils import report_targets\nfrom zvt import init_log\nfrom zvt.api.kdata import get_latest_kdata_date\nfrom zvt.api.selector import get_middle_and_big_stock\nfrom zvt.contract import AdjustType\nfrom zvt.informer import EmailInformer\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\nemail_informer = EmailInformer()\n\n\n@sched.scheduled_job(\"cron\", hour=18, minute=0, day_of_week=\"mon-fri\")\ndef report_bull():\n target_date = get_latest_kdata_date(entity_type=\"stock\", adjust_type=AdjustType.hfq, provider=\"em\")\n entity_ids = get_middle_and_big_stock(timestamp=target_date)\n\n report_targets(\n factor_cls=BullAndUpFactor,\n entity_provider=\"em\",\n data_provider=\"em\",\n title=\"bull股票\",\n entity_type=\"stock\",\n informer=email_informer,\n em_group=\"bull股票\",\n em_group_over_write=False,\n filter_by_volume=False,\n adjust_type=AdjustType.hfq,\n start_timestamp=\"2019-01-01\",\n turnover_threshold=300000000,\n turnover_rate_threshold=0.02,\n entity_ids=entity_ids,\n )\n report_targets(\n factor_cls=BullAndUpFactor,\n entity_provider=\"em\",\n data_provider=\"em\",\n title=\"bull板块\",\n entity_type=\"block\",\n informer=email_informer,\n em_group=\"bull股票\",\n em_group_over_write=False,\n filter_by_volume=False,\n adjust_type=AdjustType.qfq,\n start_timestamp=\"2019-01-01\",\n turnover_threshold=10000000000,\n turnover_rate_threshold=0.02,\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"report_bull.log\")\n\n report_bull()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/reports/report_core_compay.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom examples.factors.fundamental_selector import FundamentalSelector\nfrom examples.reports import get_subscriber_emails, stocks_with_info\nfrom zvt import init_log, zvt_config\nfrom zvt.contract.api import get_entities\nfrom zvt.domain import Stock\nfrom zvt.factors.target_selector import TargetSelector\nfrom zvt.informer.inform_utils import add_to_eastmoney\nfrom zvt.informer.informer import EmailInformer\nfrom zvt.utils.time_utils import now_pd_timestamp, to_date_time_str\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\n# 基本面选股 每周一次即可 基本无变化\n@sched.scheduled_job(\"cron\", hour=16, minute=0, day_of_week=\"6\")\ndef report_core_company():\n while True:\n error_count = 0\n email_action = EmailInformer()\n\n try:\n # StockTradeDay.record_data(provider='joinquant')\n # Stock.record_data(provider='joinquant')\n # FinanceFactor.record_data(provider='eastmoney')\n # BalanceSheet.record_data(provider='eastmoney')\n\n target_date = to_date_time_str(now_pd_timestamp())\n\n my_selector: TargetSelector = FundamentalSelector(start_timestamp=\"2016-01-01\", end_timestamp=target_date)\n my_selector.run()\n\n long_targets = my_selector.get_open_long_targets(timestamp=target_date)\n if long_targets:\n stocks = get_entities(\n provider=\"joinquant\", entity_schema=Stock, entity_ids=long_targets, return_type=\"domain\"\n )\n\n # add them to eastmoney\n try:\n codes = [stock.code for stock in stocks]\n add_to_eastmoney(codes=codes, entity_type=\"stock\", group=\"core\")\n except Exception as e:\n email_action.send_message(\n zvt_config[\"email_username\"],\n f\"report_core_company error\",\n \"report_core_company error:{}\".format(e),\n )\n\n infos = stocks_with_info(stocks)\n msg = \"\\n\".join(infos)\n else:\n msg = \"no targets\"\n\n logger.info(msg)\n\n email_action.send_message(get_subscriber_emails(), f\"{to_date_time_str(target_date)} 核心资产选股结果\", msg)\n break\n except Exception as e:\n logger.exception(\"report_core_company error:{}\".format(e))\n time.sleep(60 * 3)\n error_count = error_count + 1\n if error_count == 10:\n email_action.send_message(\n zvt_config[\"email_username\"], f\"report_core_company error\", \"report_core_company error:{}\".format(e)\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"report_core_company.log\")\n\n report_core_company()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/reports/report_tops.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom examples.report_utils import report_top_entities, inform\nfrom zvt import init_log\nfrom zvt.api.stats import TopType, get_latest_kdata_date\nfrom zvt.contract import AdjustType\nfrom zvt.domain import Block, BlockCategory\nfrom zvt.factors.top_stocks import get_top_stocks\nfrom zvt.informer import EmailInformer\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\nemail_informer = EmailInformer()\n\n\n@sched.scheduled_job(\"cron\", hour=17, minute=0, day_of_week=\"mon-fri\")\ndef report_top_stocks():\n # compute_top_stocks()\n provider = \"em\"\n entity_type = \"stock\"\n target_date = get_latest_kdata_date(provider=provider, entity_type=entity_type, adjust_type=AdjustType.hfq)\n selected = get_top_stocks(target_date=target_date, return_type=\"short\")\n\n inform(\n email_informer,\n entity_ids=selected,\n target_date=target_date,\n title=f\"stock 短期最强({len(selected)})\",\n entity_provider=provider,\n entity_type=entity_type,\n em_group=\"短期最强\",\n em_group_over_write=True,\n em_group_over_write_tag=True,\n )\n selected = get_top_stocks(target_date=target_date, return_type=\"long\")\n\n inform(\n email_informer,\n entity_ids=selected,\n target_date=target_date,\n title=f\"stock 中期最强({len(selected)})\",\n entity_provider=provider,\n entity_type=entity_type,\n em_group=\"中期最强\",\n em_group_over_write=True,\n em_group_over_write_tag=False,\n )\n\n # report_top_entities(\n # entity_type=\"stock\",\n # entity_provider=\"em\",\n # data_provider=\"em\",\n # periods=[365, 750],\n # ignore_new_stock=False,\n # ignore_st=True,\n # adjust_type=None,\n # top_count=25,\n # turnover_threshold=100000000,\n # turnover_rate_threshold=0.01,\n # informer=email_informer,\n # em_group=\"谁有我惨\",\n # em_group_over_write=True,\n # return_type=TopType.negative,\n # )\n\n\n@sched.scheduled_job(\"cron\", hour=17, minute=30, day_of_week=\"mon-fri\")\ndef report_top_blocks():\n df = Block.query_data(filters=[Block.category == BlockCategory.industry.value], index=\"entity_id\")\n\n entity_ids = df.index.tolist()\n report_top_entities(\n entity_type=\"block\",\n entity_provider=\"em\",\n data_provider=\"em\",\n periods=[*range(2, 30)],\n ignore_new_stock=False,\n ignore_st=False,\n adjust_type=None,\n top_count=10,\n turnover_threshold=0,\n turnover_rate_threshold=0,\n informer=email_informer,\n em_group=\"最强行业\",\n title=\"最强行业\",\n em_group_over_write=True,\n return_type=TopType.positive,\n entity_ids=entity_ids,\n )\n\n df = Block.query_data(filters=[Block.category == BlockCategory.concept.value], index=\"entity_id\")\n df = df[~df.name.str.contains(\"昨日\")]\n entity_ids = df.index.tolist()\n report_top_entities(\n entity_type=\"block\",\n entity_provider=\"em\",\n data_provider=\"em\",\n periods=[*range(2, 30)],\n ignore_new_stock=False,\n ignore_st=False,\n adjust_type=None,\n top_count=10,\n turnover_threshold=0,\n turnover_rate_threshold=0,\n informer=email_informer,\n em_group=\"最强概念\",\n title=\"最强概念\",\n em_group_over_write=True,\n return_type=TopType.positive,\n entity_ids=entity_ids,\n )\n\n\n@sched.scheduled_job(\"cron\", hour=17, minute=30, day_of_week=\"mon-fri\")\ndef report_top_stockhks():\n report_top_entities(\n entity_type=\"stockhk\",\n entity_provider=\"em\",\n data_provider=\"em\",\n top_count=10,\n periods=[*range(1, 15)],\n ignore_new_stock=False,\n ignore_st=False,\n adjust_type=None,\n turnover_threshold=30000000,\n turnover_rate_threshold=0.01,\n informer=email_informer,\n em_group=\"短期最强\",\n title=\"短期最强\",\n em_group_over_write=False,\n return_type=TopType.positive,\n )\n\n report_top_entities(\n entity_type=\"stockhk\",\n entity_provider=\"em\",\n data_provider=\"em\",\n top_count=10,\n periods=[30, 50],\n ignore_new_stock=True,\n ignore_st=False,\n adjust_type=None,\n turnover_threshold=30000000,\n turnover_rate_threshold=0.01,\n informer=email_informer,\n em_group=\"中期最强\",\n title=\"中期最强\",\n em_group_over_write=False,\n return_type=TopType.positive,\n )\n\n # report_top_entities(\n # entity_type=\"stockhk\",\n # entity_provider=\"em\",\n # data_provider=\"em\",\n # top_count=20,\n # periods=[365, 750],\n # ignore_new_stock=True,\n # ignore_st=False,\n # adjust_type=None,\n # turnover_threshold=50000000,\n # turnover_rate_threshold=0.005,\n # informer=email_informer,\n # em_group=\"谁有我惨\",\n # em_group_over_write=False,\n # return_type=TopType.negative,\n # )\n\n\nif __name__ == \"__main__\":\n init_log(\"report_tops.log\")\n\n report_top_stocks()\n # report_top_blocks()\n report_top_stockhks()\n\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/reports/report_vol_up.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom zvt.factors.ma import VolumeUpMaFactor\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom examples.report_utils import report_targets, inform\nfrom zvt import init_log\nfrom zvt.api.kdata import get_latest_kdata_date\nfrom zvt.contract import AdjustType\nfrom zvt.factors.top_stocks import get_top_stocks\nfrom zvt.informer import EmailInformer\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\nemail_informer = EmailInformer()\n\n\n@sched.scheduled_job(\"cron\", hour=17, minute=0, day_of_week=\"mon-fri\")\ndef report_vol_up_stocks():\n provider = \"em\"\n entity_type = \"stock\"\n target_date = get_latest_kdata_date(provider=provider, entity_type=entity_type, adjust_type=AdjustType.hfq)\n selected = get_top_stocks(target_date=target_date, return_type=\"small_vol_up\")\n\n inform(\n email_informer,\n entity_ids=selected,\n target_date=target_date,\n title=f\"stock 放量突破(半)年线小市值股票({len(selected)})\",\n entity_provider=provider,\n entity_type=entity_type,\n em_group=\"年线股票\",\n em_group_over_write=True,\n em_group_over_write_tag=False,\n )\n selected = get_top_stocks(target_date=target_date, return_type=\"big_vol_up\")\n\n inform(\n email_informer,\n entity_ids=selected,\n target_date=target_date,\n title=f\"stock 放量突破(半)年线大市值股票({len(selected)})\",\n entity_provider=provider,\n entity_type=entity_type,\n em_group=\"年线股票\",\n em_group_over_write=False,\n em_group_over_write_tag=False,\n )\n\n\n@sched.scheduled_job(\"cron\", hour=17, minute=30, day_of_week=\"mon-fri\")\ndef report_vol_up_stockhks():\n report_targets(\n factor_cls=VolumeUpMaFactor,\n entity_provider=\"em\",\n data_provider=\"em\",\n informer=email_informer,\n em_group=\"年线股票\",\n title=\"放量突破(半)年线港股\",\n entity_type=\"stockhk\",\n em_group_over_write=False,\n filter_by_volume=False,\n adjust_type=AdjustType.hfq,\n start_timestamp=\"2021-01-01\",\n # factor args\n windows=[120, 250],\n over_mode=\"or\",\n up_intervals=60,\n turnover_threshold=100000000,\n turnover_rate_threshold=0.01,\n )\n\n\nif __name__ == \"__main__\":\n init_log(\"report_vol_up.log\")\n\n report_vol_up_stocks()\n report_vol_up_stockhks()\n sched.start()\n\n sched._thread.join()\n"
},
{
"path": "examples/reports/subscriber_emails.json",
"content": "[\n \"test@qq.com\",\n \"test1@qq.com\"\n]\n"
},
{
"path": "examples/requirements.txt",
"content": "zvt >= 0.10.1\napscheduler >= 3.4.0\ntabulate>=0.8.8\nta"
},
{
"path": "examples/research/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/research/dragon_and_tiger.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.selector import get_big_players, get_player_success_rate\nfrom zvt.domain import DragonAndTiger\nfrom zvt.utils.time_utils import date_time_by_interval, current_date\n\nif __name__ == \"__main__\":\n provider = \"em\"\n DragonAndTiger.record_data(provider=provider)\n end_timestamp = date_time_by_interval(current_date(), -60)\n # recent year\n start_timestamp = date_time_by_interval(end_timestamp, -400)\n print(f\"{start_timestamp} to {end_timestamp}\")\n players = get_big_players(start_timestamp=start_timestamp, end_timestamp=end_timestamp)\n print(players)\n df = get_player_success_rate(\n start_timestamp=start_timestamp, end_timestamp=end_timestamp, intervals=[3, 5, 10], players=players\n )\n print(df)\n"
},
{
"path": "examples/research/top_dragon_tiger.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import Optional, Type, List, Union\n\nimport pandas as pd\n\nfrom zvt.api.selector import get_players\nfrom zvt.api.stats import get_top_performance_by_month\nfrom zvt.contract import TradableEntity, IntervalLevel, AdjustType\nfrom zvt.contract.factor import Transformer, Accumulator\nfrom zvt.domain import Stock\nfrom zvt.factors.technical_factor import TechnicalFactor\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import pre_month_start_date, date_time_by_interval\n\n\ndef top_dragon_and_tiger(data_provider=\"em\", start_timestamp=\"2021-01-01\", end_timestamp=\"2022-01-01\"):\n dfs = []\n for start_date, end_date, df in get_top_performance_by_month(\n start_timestamp=start_timestamp, end_timestamp=end_timestamp, list_days=250, data_provider=data_provider\n ):\n pre_month_start = pre_month_start_date(start_date)\n for entity_id in df.index[:30]:\n players = get_players(\n entity_id=entity_id,\n start_timestamp=date_time_by_interval(start_date, 15),\n end_timestamp=end_timestamp,\n provider=data_provider,\n direction=\"in\",\n )\n print(players)\n dfs.append(players)\n\n player_df = pd.concat(dfs, sort=True)\n return player_df.sort_index(level=[0, 1])\n\n\nclass DragonTigerFactor(TechnicalFactor):\n def __init__(\n self,\n entity_id: str,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = \"em\",\n entity_provider: str = \"em\",\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n ) -> None:\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n [entity_id],\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n self.player_df = get_players(\n entity_id=entity_id,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"em\",\n direction=\"in\",\n )\n\n def drawer_annotation_df(self) -> Optional[pd.DataFrame]:\n def order_type_flag(df):\n return \"
\".join(df.tolist())\n\n if pd_is_not_null(self.player_df):\n annotation_df = self.player_df.copy()\n annotation_df[\"value\"] = self.factor_df.loc[annotation_df.index][\"close\"]\n annotation_df[\"flag\"] = annotation_df[[\"dep1\", \"dep2\", \"dep3\", \"dep4\", \"dep5\"]].apply(\n lambda x: order_type_flag(x), axis=1\n )\n annotation_df[\"color\"] = \"#ff7f0e\"\n return annotation_df\n\n\nif __name__ == \"__main__\":\n top_dragon_and_tiger()\n # Stock1dHfqKdata.record_data(entity_id=\"stock_sz_002561\", provider=\"em\")\n # f = DragonTigerFactor(entity_id=\"stock_sz_002561\", provider=\"em\")\n # f.draw(show=True)\n"
},
{
"path": "examples/research/top_tags.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.stats import get_top_performance_by_month\nfrom zvt.domain import Stock1dHfqKdata\nfrom zvt.utils.time_utils import date_time_by_interval, month_end_date, is_same_date\n\n\n# 每月涨幅前30，市值90%分布在100亿以下\n# 重复上榜的有1/4左右\n# 连续两个月上榜的1/10左右\ndef top_tags(data_provider=\"em\", start_timestamp=\"2020-01-01\", end_timestamp=\"2021-01-01\"):\n records = []\n for _, timestamp, df in get_top_performance_by_month(\n start_timestamp=start_timestamp, end_timestamp=end_timestamp, list_days=250, data_provider=data_provider\n ):\n for entity_id in df.index[:30]:\n query_timestamp = timestamp\n while True:\n kdata = Stock1dHfqKdata.query_data(\n provider=data_provider,\n entity_id=entity_id,\n start_timestamp=query_timestamp,\n order=Stock1dHfqKdata.timestamp.asc(),\n limit=1,\n return_type=\"domain\",\n )\n if not kdata or kdata[0].turnover_rate == 0:\n if is_same_date(query_timestamp, month_end_date(query_timestamp)):\n break\n query_timestamp = date_time_by_interval(query_timestamp)\n continue\n cap = kdata[0].turnover / kdata[0].turnover_rate\n break\n\n records.append(\n {\"entity_id\": entity_id, \"timestamp\": timestamp, \"cap\": cap, \"score\": df.loc[entity_id, \"score\"]}\n )\n\n return records\n\n\nif __name__ == \"__main__\":\n print(top_tags())\n"
},
{
"path": "examples/result.json",
"content": "{}"
},
{
"path": "examples/stock_tags.json",
"content": "[\n {\n \"code\": \"000972\",\n \"name\": \"中基健康\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"002269\",\n \"name\": \"美邦服饰\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002762\",\n \"name\": \"金发拉比\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603170\",\n \"name\": \"宝立食品\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600257\",\n \"name\": \"大湖股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002803\",\n \"name\": \"吉宏股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"002186\",\n \"name\": \"全 聚 德\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600365\",\n \"name\": \"ST通葡\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600252\",\n \"name\": \"中恒集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"002419\",\n \"name\": \"天虹股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600771\",\n \"name\": \"广誉远\",\n \"tag\": \"医药\",\n \"hidden_tag\": \"央企\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600655\",\n \"name\": \"豫园股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"000501\",\n \"name\": \"武商集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600332\",\n \"name\": \"白云山\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"002183\",\n \"name\": \"怡 亚 通\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002374\",\n \"name\": \"中锐股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002342\",\n \"name\": \"巨力索具\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600382\",\n \"name\": \"广东明珠\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600696\",\n \"name\": \"岩石股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603919\",\n \"name\": \"金徽酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603779\",\n \"name\": \"威龙股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603777\",\n \"name\": \"来伊份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600059\",\n \"name\": \"古越龙山\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000860\",\n \"name\": \"顺鑫农业\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600467\",\n \"name\": \"好当家\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603299\",\n \"name\": \"苏盐井神\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000995\",\n \"name\": \"*ST皇台\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002646\",\n \"name\": \"天佑德酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000596\",\n \"name\": \"古井贡酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600559\",\n \"name\": \"老白干酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600702\",\n \"name\": \"舍得酒业\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"601579\",\n \"name\": \"会稽山\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600153\",\n \"name\": \"建发股份\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603589\",\n \"name\": \"口子窖\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600809\",\n \"name\": \"山西汾酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600197\",\n \"name\": \"伊力特\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600238\",\n \"name\": \"海南椰岛\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000568\",\n \"name\": \"泸州老窖\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603198\",\n \"name\": \"迎驾贡酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600199\",\n \"name\": \"金种子酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000930\",\n \"name\": \"中粮科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"中字头\"\n },\n {\n \"code\": \"600779\",\n \"name\": \"水井坊\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600519\",\n \"name\": \"贵州茅台\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600381\",\n \"name\": \"青海春天\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603369\",\n \"name\": \"今世缘\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000858\",\n \"name\": \"五 粮 液\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000799\",\n \"name\": \"酒鬼酒\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"央企\"\n },\n {\n \"code\": \"002304\",\n \"name\": \"洋河股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600679\",\n \"name\": \"上海凤凰\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"央企\"\n },\n {\n \"code\": \"603983\",\n \"name\": \"丸美股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301187\",\n \"name\": \"欧圣电气\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600493\",\n \"name\": \"凤竹纺织\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"605136\",\n \"name\": \"丽人丽妆\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600448\",\n \"name\": \"华纺股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600439\",\n \"name\": \"瑞贝卡\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000521\",\n \"name\": \"长虹美菱\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603499\",\n \"name\": \"翔港科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301193\",\n \"name\": \"家联科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600935\",\n \"name\": \"华塑股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301376\",\n \"name\": \"致欧科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"603863\",\n \"name\": \"松炀资源\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002615\",\n \"name\": \"哈尔斯\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000850\",\n \"name\": \"华茂股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"001209\",\n \"name\": \"洪兴股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001216\",\n \"name\": \"华瓷股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"001368\",\n \"name\": \"通达创智\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001238\",\n \"name\": \"浙江正特\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"000025\",\n \"name\": \"特力A\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603661\",\n \"name\": \"恒林股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002059\",\n \"name\": \"云南旅游\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"央企\"\n },\n {\n \"code\": \"001387\",\n \"name\": \"雪祺电气\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"603600\",\n \"name\": \"永艺股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600156\",\n \"name\": \"华升股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"601113\",\n \"name\": \"华鼎股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002731\",\n \"name\": \"萃华珠宝\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600400\",\n \"name\": \"红豆股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"605180\",\n \"name\": \"华生科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000592\",\n \"name\": \"平潭发展\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002357\",\n \"name\": \"富临运业\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603958\",\n \"name\": \"哈森股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002969\",\n \"name\": \"嘉美包装\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000428\",\n \"name\": \"华天酒店\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600882\",\n \"name\": \"妙可蓝多\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600697\",\n \"name\": \"欧亚集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"300740\",\n \"name\": \"水羊股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000888\",\n \"name\": \"峨眉山Ａ\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002356\",\n \"name\": \"赫美集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000610\",\n \"name\": \"西安旅游\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"605188\",\n \"name\": \"国光连锁\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"000861\",\n \"name\": \"海印股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603199\",\n \"name\": \"九华旅游\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600706\",\n \"name\": \"曲江文旅\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600824\",\n \"name\": \"益民集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"002820\",\n \"name\": \"桂发祥\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603719\",\n \"name\": \"良品铺子\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"003000\",\n \"name\": \"劲仔食品\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600828\",\n \"name\": \"茂业商业\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002561\",\n \"name\": \"徐家汇\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600280\",\n \"name\": \"中央商场\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"000679\",\n \"name\": \"大连友谊\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"300783\",\n \"name\": \"三只松鼠\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"000017\",\n \"name\": \"深中华A\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"000523\",\n \"name\": \"红棉股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603518\",\n \"name\": \"锦泓集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603877\",\n \"name\": \"太平鸟\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"601086\",\n \"name\": \"国芳集团\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603231\",\n \"name\": \"索宝蛋白\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"002719\",\n \"name\": \"麦趣尔\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"000558\",\n \"name\": \"莱茵体育\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600628\",\n \"name\": \"新世界\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"600630\",\n \"name\": \"龙头股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600250\",\n \"name\": \"南京商旅\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"605080\",\n \"name\": \"浙江自然\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603099\",\n \"name\": \"长白山\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002033\",\n \"name\": \"丽江股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"600593\",\n \"name\": \"大连圣亚\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002780\",\n \"name\": \"三夫户外\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000978\",\n \"name\": \"桂林旅游\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688363\",\n \"name\": \"华熙生物\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002612\",\n \"name\": \"朗姿股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300896\",\n \"name\": \"爱美客\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"000715\",\n \"name\": \"中兴商业\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603536\",\n \"name\": \"惠发食品\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"002495\",\n \"name\": \"佳隆股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"601566\",\n \"name\": \"九牧王\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603711\",\n \"name\": \"香飘飘\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"688577\",\n \"name\": \"浙海德曼\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"003025\",\n \"name\": \"思进智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603289\",\n \"name\": \"泰瑞机器\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002708\",\n \"name\": \"光洋股份\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300276\",\n \"name\": \"三丰智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"603211\",\n \"name\": \"晋拓股份\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603095\",\n \"name\": \"越剑智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603015\",\n \"name\": \"弘讯科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": null\n },\n {\n \"code\": \"301083\",\n \"name\": \"百胜智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002931\",\n \"name\": \"锋龙股份\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": 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\"常宝股份\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"601018\",\n \"name\": \"宁波港\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002482\",\n \"name\": \"*ST广田\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002483\",\n \"name\": \"润邦股份\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002484\",\n \"name\": \"江海股份\",\n \"tag\": \"消费电子\",\n \"reason\": \"\"\n },\n {\n \"code\": \"601177\",\n \"name\": \"杭齿前进\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300127\",\n \"name\": \"银河磁体\",\n \"tag\": \"资源\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002486\",\n \"name\": \"嘉麟杰\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002485\",\n \"name\": \"ST雪发\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300130\",\n \"name\": \"新国都\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300129\",\n \"name\": \"泰胜风能\",\n \"tag\": \"赛道\",\n \"reason\": \"\"\n },\n {\n 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\"name\": \"宝馨科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002519\",\n \"name\": \"银河电子\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002516\",\n \"name\": \"旷达科技\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300143\",\n \"name\": \"盈康生命\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300144\",\n \"name\": \"宋城演艺\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300145\",\n \"name\": \"中金环境\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002522\",\n \"name\": \"浙江众成\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002521\",\n \"name\": \"齐峰新材\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300148\",\n \"name\": \"天舟文化\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"002524\",\n \"name\": \"光正眼科\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"300151\",\n \"name\": \"昌红科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n 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{\n \"code\": \"301101\",\n \"name\": \"明月镜片\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301100\",\n \"name\": \"风光股份\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301096\",\n \"name\": \"百诚医药\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301211\",\n \"name\": \"亨迪药业\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301113\",\n \"name\": \"雅艺科技\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"870357\",\n \"name\": \"雅葆轩\",\n \"tag\": \"消费电子\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"600927\",\n \"name\": \"永安期货\",\n \"tag\": \"金融\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301186\",\n \"name\": \"超达装备\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301190\",\n \"name\": \"善水科技\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001296\",\n \"name\": \"长江材料\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688210\",\n \"name\": \"统联精密\",\n \"tag\": \"消费电子\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688206\",\n \"name\": \"概伦电子\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301189\",\n \"name\": \"奥尼电子\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301166\",\n \"name\": \"优宁维\",\n \"tag\": \"专业服务\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301127\",\n \"name\": \"天源环保\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688227\",\n \"name\": \"品高股份\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603176\",\n \"name\": \"汇通集团\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301159\",\n \"name\": \"三维天地\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"873001\",\n \"name\": \"纬达光电\",\n \"tag\": \"VR\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"001234\",\n \"name\": \"泰慕士\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301196\",\n \"name\": \"唯科科技\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301136\",\n \"name\": \"招标股份\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301117\",\n \"name\": \"佳缘科技\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301158\",\n \"name\": \"德石股份\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301201\",\n \"name\": \"诚达药业\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603150\",\n \"name\": \"万朗磁塑\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688223\",\n \"name\": \"晶科能源\",\n \"tag\": \"赛道\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688171\",\n \"name\": \"纬德信息\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301228\",\n \"name\": \"实朴检测\",\n \"tag\": \"专业服务\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301235\",\n \"name\": \"华康医疗\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301106\",\n \"name\": \"骏成科技\",\n \"tag\": \"VR\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688225\",\n \"name\": \"亚信安全\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688283\",\n \"name\": \"坤恒顺维\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001313\",\n \"name\": \"粤海饲料\",\n \"tag\": \"农业\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688267\",\n \"name\": \"中触媒\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603215\",\n \"name\": \"比依股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"873593\",\n \"name\": \"鼎智科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"301181\",\n \"name\": \"标榜股份\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"873665\",\n \"name\": \"科强股份\",\n \"tag\": \"化工\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"301229\",\n \"name\": \"纽泰格\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301130\",\n \"name\": \"西点药业\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301200\",\n \"name\": \"大族数控\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001266\",\n \"name\": \"宏英智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688281\",\n \"name\": \"华秦科技\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301218\",\n \"name\": \"华是科技\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301222\",\n \"name\": \"浙江恒威\",\n \"tag\": \"赛道\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603070\",\n \"name\": \"万控智造\",\n \"tag\": \"电力\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301110\",\n \"name\": \"青木股份\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688115\",\n \"name\": \"思林杰\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301131\",\n \"name\": \"聚赛龙\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688175\",\n \"name\": \"高凌信息\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603261\",\n \"name\": \"立航科技\",\n \"tag\": \"军工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688207\",\n \"name\": \"格灵深瞳\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688282\",\n \"name\": \"理工导航\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688150\",\n \"name\": \"莱特光电\",\n \"tag\": \"VR\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301256\",\n \"name\": \"华融化学\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301103\",\n \"name\": \"何氏眼科\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688238\",\n \"name\": \"和元生物\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301237\",\n \"name\": \"和顺科技\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603209\",\n \"name\": \"兴通股份\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301226\",\n \"name\": \"祥明智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301258\",\n \"name\": \"富士莱\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301216\",\n \"name\": \"万凯新材\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301263\",\n \"name\": \"泰恩康\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301268\",\n \"name\": \"铭利达\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688337\",\n \"name\": \"普源精电\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301151\",\n \"name\": \"冠龙节能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301279\",\n \"name\": \"金道科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301109\",\n \"name\": \"军信股份\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688125\",\n \"name\": \"安达智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301212\",\n \"name\": \"联盛化学\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301163\",\n \"name\": \"宏德股份\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301120\",\n \"name\": \"新特电气\",\n \"tag\": \"电力\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688326\",\n \"name\": \"经纬恒润-W\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301248\",\n \"name\": \"杰创智能\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301148\",\n \"name\": \"嘉戎技术\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301288\",\n \"name\": \"清研环境\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301259\",\n \"name\": \"艾布鲁\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603191\",\n \"name\": \"望变电气\",\n \"tag\": \"电力\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688320\",\n \"name\": \"禾川科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301162\",\n \"name\": \"国能日新\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688170\",\n \"name\": \"德龙激光\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001228\",\n \"name\": \"永泰运\",\n \"tag\": \"统一大市场\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001319\",\n \"name\": \"铭科精技\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301257\",\n \"name\": \"普蕊斯\",\n \"tag\": \"专业服务\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301153\",\n \"name\": \"中科江南\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603272\",\n \"name\": \"联翔股份\",\n \"tag\": \"房地产\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301183\",\n \"name\": \"东田微\",\n \"tag\": \"VR\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301107\",\n \"name\": \"瑜欣电子\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688287\",\n \"name\": \"观典防务\",\n \"tag\": \"军工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301191\",\n \"name\": \"菲菱科思\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"873690\",\n \"name\": \"捷众科技\",\n \"tag\": \"汽车\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"301160\",\n \"name\": \"翔楼新材\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688251\",\n \"name\": \"井松智能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"838837\",\n \"name\": \"华原股份\",\n \"tag\": \"汽车\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"301125\",\n \"name\": \"腾亚精工\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688348\",\n \"name\": \"昱能科技\",\n \"tag\": \"赛道\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301286\",\n \"name\": \"侨源股份\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301156\",\n \"name\": \"美农生物\",\n \"tag\": \"农业\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001226\",\n \"name\": \"拓山重工\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301220\",\n \"name\": \"亚香股份\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688349\",\n \"name\": \"三一重能\",\n \"tag\": \"赛道\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301289\",\n \"name\": \"国缆检测\",\n \"tag\": \"专业服务\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001323\",\n \"name\": \"慕思股份\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001316\",\n \"name\": \"润贝航科\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"601089\",\n \"name\": \"福元医药\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001268\",\n \"name\": \"联合精密\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"873833\",\n \"name\": \"美心翼申\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"688237\",\n \"name\": \"超卓航科\",\n \"tag\": \"军工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301239\",\n \"name\": \"普瑞眼科\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301233\",\n \"name\": \"盛帮股份\",\n \"tag\": \"化工\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688400\",\n \"name\": \"凌云光\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688322\",\n \"name\": \"奥比中光-UW\",\n \"tag\": \"消费电子\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301208\",\n \"name\": \"中亦科技\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301139\",\n \"name\": \"元道通信\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688053\",\n \"name\": \"思科瑞\",\n \"tag\": \"消费电子\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301312\",\n \"name\": \"智立方\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001230\",\n \"name\": \"劲旅环境\",\n \"tag\": \"公用\",\n \"reason\": \"\"\n },\n {\n \"code\": \"001336\",\n \"name\": \"楚环科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301306\",\n \"name\": \"西测测试\",\n \"tag\": \"半导体\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301269\",\n \"name\": \"华大九天\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"603201\",\n \"name\": \"常润股份\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301195\",\n \"name\": \"北路智控\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688371\",\n \"name\": \"菲沃泰\",\n \"tag\": \"消费电子\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301333\",\n \"name\": \"诺思格\",\n \"tag\": \"医药\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301197\",\n \"name\": \"工大科雅\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688205\",\n \"name\": \"德科立\",\n \"tag\": \"VR\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301132\",\n \"name\": \"满坤科技\",\n \"tag\": \"消费电子\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301192\",\n \"name\": \"泰祥股份\",\n \"tag\": \"汽车\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301336\",\n \"name\": \"趣睡科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\"\n },\n {\n \"code\": \"836547\",\n \"name\": \"无锡晶海\",\n \"tag\": \"医药\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"873570\",\n \"name\": \"坤博精工\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"834950\",\n \"name\": \"迅安科技\",\n \"tag\": \"消费电子\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"836699\",\n \"name\": \"海达尔\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"870726\",\n \"name\": \"鸿智科技\",\n \"tag\": \"大消费\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"872953\",\n \"name\": \"国子软件\",\n \"tag\": \"AI\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"873726\",\n \"name\": \"卓兆点胶\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"873693\",\n \"name\": \"阿为特\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"873679\",\n \"name\": \"前进科技\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"873703\",\n \"name\": \"广厦环能\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"871263\",\n \"name\": \"莱赛激光\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"873806\",\n \"name\": \"云星宇\",\n \"tag\": \"AI\",\n \"reason\": \"\",\n \"hidden_tag\": \"北交所\"\n },\n {\n \"code\": \"603082\",\n \"name\": \"C北自\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"301502\",\n \"name\": \"N华阳智\",\n \"tag\": \"智能机器\",\n \"reason\": \"\",\n \"hidden_tag\": \"次新股\"\n },\n {\n \"code\": \"688709\",\n \"name\": \"C华微\",\n \"tag\": \"半导体\",\n \"reason\": \"\"\n },\n {\n \"code\": \"688584\",\n \"name\": \"C合晶\",\n \"tag\": \"半导体\",\n \"reason\": \"\"\n },\n {\n \"code\": \"301589\",\n \"name\": \"C诺瓦\",\n \"tag\": \"AI\",\n \"reason\": \"\"\n }\n]"
},
{
"path": "examples/tag_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport os\nfrom collections import Counter\n\nfrom zvt.api.utils import china_stock_code_to_id, get_china_exchange\nfrom zvt.domain import BlockStock, Block, Stock, LimitUpInfo\n\n\ndef get_limit_up_reasons(entity_id):\n info = LimitUpInfo.query_data(\n entity_id=entity_id, order=LimitUpInfo.timestamp.desc(), limit=1, return_type=\"domain\"\n )\n\n topics = []\n if info and info[0].reason:\n topics = topics + info[0].reason.split(\"+\")\n return topics\n\n\ndef get_concept(code):\n with open(os.path.join(os.path.dirname(__file__), \"concept.json\")) as f:\n concept_map = json.load(f)\n concepts = [item for sublist in concept_map.values() for item in sublist]\n df = BlockStock.query_data(filters=[BlockStock.stock_code == code, BlockStock.name.in_(concepts)])\n return df[\"name\"].tolist()\n\n\ndef industry_to_tag(industry):\n if industry in [\"风电设备\", \"电池\", \"光伏设备\", \"能源金属\", \"电源设备\"]:\n return \"赛道\"\n if industry in [\"半导体\", \"电子化学品\"]:\n return \"半导体\"\n if industry in [\"医疗服务\", \"中药\", \"化学制药\", \"生物制品\", \"医药商业\"]:\n return \"医药\"\n if industry in [\"医疗器械\"]:\n return \"医疗器械\"\n if industry in [\"教育\"]:\n return \"教育\"\n if industry in [\"贸易行业\", \"家用轻工\", \"造纸印刷\", \"酿酒行业\", \"珠宝首饰\", \"美容护理\", \"食品饮料\", \"旅游酒店\", \"商业百货\", \"纺织服装\", \"家电行业\"]:\n return \"大消费\"\n if industry in [\"小金属\", \"贵金属\", \"有色金属\", \"煤炭行业\"]:\n return \"资源\"\n if industry in [\"消费电子\", \"电子元件\"]:\n return \"消费电子\"\n if industry in [\"汽车零部件\", \"汽车服务\", \"汽车整车\"]:\n return \"汽车\"\n if industry in [\"电机\", \"通用设备\", \"专用设备\", \"仪器仪表\"]:\n return \"智能机器\"\n if industry in [\"电网设备\", \"电力行业\"]:\n return \"电力\"\n if industry in [\"光学光电子\"]:\n return \"VR\"\n if industry in [\"房地产开发\", \"房地产服务\", \"工程建设\", \"水泥建材\", \"装修装饰\", \"装修建材\", \"工程咨询服务\", \"钢铁行业\", \"工程机械\"]:\n return \"房地产\"\n if industry in [\"非金属材料\", \"包装材料\", \"化学制品\", \"化肥行业\", \"化学原料\", \"化纤行业\", \"塑料制品\", \"玻璃玻纤\", \"橡胶制品\"]:\n return \"化工\"\n if industry in [\"交运设备\", \"船舶制造\", \"航运港口\", \"公用事业\", \"燃气\", \"航空机场\", \"环保行业\", \"石油行业\", \"铁路公路\", \"采掘行业\"]:\n return \"公用\"\n if industry in [\"证券\", \"保险\", \"银行\", \"多元金融\"]:\n return \"金融\"\n if industry in [\"互联网服务\", \"软件开发\", \"计算机设备\", \"游戏\", \"通信服务\", \"通信设备\"]:\n return \"AI\"\n if industry in [\"文化传媒\"]:\n return \"传媒\"\n if industry in [\"农牧饲渔\", \"农药兽药\"]:\n return \"农业\"\n if industry in [\"物流行业\"]:\n return \"统一大市场\"\n if industry in [\"航天航空\", \"船舶制造\"]:\n return \"军工\"\n if industry in [\"专业服务\"]:\n return \"专业服务\"\n\n\ndef build_default_tags(codes, provider=\"em\"):\n df_block = Block.query_data(provider=provider, filters=[Block.category == \"industry\"])\n industry_codes = df_block[\"code\"].tolist()\n tags = []\n for code in codes:\n block_stocks = BlockStock.query_data(\n provider=provider,\n filters=[BlockStock.code.in_(industry_codes), BlockStock.stock_code == code],\n return_type=\"domain\",\n )\n if block_stocks:\n block_stock = block_stocks[0]\n tags.append(\n {\n \"code\": block_stock.stock_code,\n \"name\": block_stock.stock_name,\n \"tag\": industry_to_tag(block_stock.name),\n \"reason\": \"\",\n }\n )\n else:\n print(f\"no industry for {code}\")\n\n return tags\n\n\ndef get_main_line_tags():\n with open(os.path.join(os.path.dirname(__file__), \"main_line_tags.json\")) as f:\n return json.load(f)\n\n\ndef get_main_line_hidden_tags():\n with open(os.path.join(os.path.dirname(__file__), \"main_line_hidden_tags.json\")) as f:\n return json.load(f)\n\n\ndef replace_tags(old_tag=\"仪器仪表\"):\n with open(os.path.join(os.path.dirname(__file__), \"stock_tags.json\")) as f:\n stock_tags = json.load(f)\n for stock_tag in stock_tags:\n if stock_tag[\"tag\"] == old_tag:\n df = BlockStock.query_data(filters=[BlockStock.stock_code == stock_tag[\"code\"]])\n blocks = df[\"name\"].tolist()\n for block in blocks:\n tag = industry_to_tag(industry=block)\n if tag:\n stock_tag[\"tag\"] = tag\n break\n\n with open(\"result.json\", \"w\") as json_file:\n json.dump(stock_tags, json_file, indent=2, ensure_ascii=False)\n\n\ndef check_tags():\n with open(os.path.join(os.path.dirname(__file__), \"stock_tags.json\")) as f:\n stock_tags = json.load(f)\n tags = set()\n hidden_tags = set()\n stocks = []\n final_tags = []\n for stock_tag in stock_tags:\n stock_code = stock_tag[\"code\"]\n if not stock_code.isdigit() or (len(stock_code) != 6):\n print(stock_code)\n tags.add(stock_tag[\"tag\"])\n hidden_tags.add(stock_tag.get(\"hidden_tag\"))\n if stock_code in stocks:\n print(stock_tag)\n else:\n final_tags.append(stock_tag)\n stocks.append(stock_code)\n\n # with open(\"result.json\", \"w\") as json_file:\n # json.dump(final_tags, json_file, indent=2, ensure_ascii=False)\n\n print(tags)\n print(hidden_tags)\n print(len(stocks))\n count = Counter(stocks)\n duplicates = [item for item, frequency in count.items() if frequency > 1]\n print(duplicates)\n\n\ndef get_hidden_code(code):\n exchange = get_china_exchange(code=code)\n if exchange == \"bj\":\n return \"北交所\"\n\n\ndef get_core_tag(codes):\n # 从stock_tags.json读取\n other_codes = []\n with open(os.path.join(os.path.dirname(__file__), \"stock_tags.json\")) as f:\n stock_tags = json.load(f)\n code_tag_hidden_tag_list = [\n (\n stock_tag[\"code\"],\n stock_tag[\"tag\"],\n stock_tag.get(\"hidden_tag\") if stock_tag.get(\"hidden_tag\") else get_hidden_code(stock_tag[\"code\"]),\n )\n for stock_tag in stock_tags\n if stock_tag[\"code\"] in codes\n ]\n other_codes = [code for code in codes if code not in [item[0] for item in code_tag_hidden_tag_list]]\n for code in other_codes:\n tags = get_limit_up_reasons(entity_id=china_stock_code_to_id(code=code))\n if tags:\n code_tag_hidden_tag_list.append((code, tags[0], None))\n else:\n code_tag_hidden_tag_list.append((code, \"未知\", get_hidden_code(code)))\n\n return code_tag_hidden_tag_list\n\n\ndef group_stocks_by_tag(entities, hidden_tags=None):\n code_entities_map = {entity.code: entity for entity in entities}\n\n tag_stocks = {}\n code_tag_hidden_tag_list = get_core_tag([entity.code for entity in entities])\n for code, tag, hidden_tag in code_tag_hidden_tag_list:\n if hidden_tags and (hidden_tag in hidden_tags):\n tag_stocks.setdefault(hidden_tag, [])\n tag_stocks.get(hidden_tag).append(code_entities_map.get(code))\n if (tag != hidden_tag) or (not hidden_tags):\n tag_stocks.setdefault(tag, [])\n tag_stocks.get(tag).append(code_entities_map.get(code))\n\n sorted_entities = sorted(tag_stocks.items(), key=lambda x: len(x[1]), reverse=True)\n\n return sorted_entities\n\n\ndef build_stock_tags_by_block(block_name, tag, hidden_tag):\n block_stocks = BlockStock.query_data(filters=[BlockStock.name == block_name], return_type=\"domain\")\n datas = [\n {\n \"code\": block_stock.stock_code,\n \"name\": block_stock.stock_name,\n \"tag\": tag,\n \"hidden_tag\": hidden_tag,\n \"reason\": \"\",\n }\n for block_stock in block_stocks\n ]\n\n # Specify the file path where you want to save the JSON data\n file_path = f\"{tag}.json\"\n\n # Write JSON data to the file\n with open(file_path, \"w\") as json_file:\n json.dump(datas, json_file, indent=2, ensure_ascii=False)\n\n\ndef merge_tags(current_tags, added_tags, force_update=False):\n code_tags_map = {item[\"code\"]: item for item in current_tags}\n\n # Merge\n for added_tag in added_tags:\n code_from_added = added_tag[\"code\"]\n if code_from_added not in code_tags_map:\n current_tags.append(added_tag)\n else:\n # update hidden_tag from added_tag\n if force_update or (not code_tags_map[code_from_added].get(\"hidden_tag\")):\n code_tags_map[code_from_added][\"hidden_tag\"] = added_tag[\"hidden_tag\"]\n return current_tags\n\n\ndef merge_tags_file(current_tags_file, added_tags_file, result_file, force_update=False):\n # current_tags_file读取\n with open(os.path.join(os.path.dirname(__file__), current_tags_file)) as f:\n current_tags = json.load(f)\n # added_tags_file读取\n with open(os.path.join(os.path.dirname(__file__), added_tags_file)) as f:\n added_tags = json.load(f)\n\n current_tags = merge_tags(current_tags, added_tags, force_update)\n with open(result_file, \"w\") as json_file:\n json.dump(current_tags, json_file, indent=2, ensure_ascii=False)\n\n\ndef complete_tags():\n with open(os.path.join(os.path.dirname(__file__), \"stock_tags.json\")) as f:\n stock_tags = json.load(f)\n current_codes = [stock_tag[\"code\"] for stock_tag in stock_tags]\n df = Stock.query_data(\n provider=\"em\",\n filters=[\n Stock.code.not_in(current_codes),\n Stock.name.not_like(\"%退%\"),\n ],\n )\n\n codes = df[\"code\"].tolist()\n print(len(codes))\n added_tags = build_default_tags(codes=codes, provider=\"em\")\n\n with open(\"result.json\", \"w\") as json_file:\n json.dump(stock_tags + added_tags, json_file, indent=2, ensure_ascii=False)\n\n\ndef refresh_hidden_tags():\n with open(os.path.join(os.path.dirname(__file__), \"stock_tags.json\")) as f:\n stock_tags = json.load(f)\n for stock_tag in stock_tags:\n if not stock_tag.get(\"hidden_tag\"):\n exchange = get_china_exchange(code=stock_tag[\"code\"])\n if exchange == \"bj\":\n stock_tag[\"hidden_tag\"] = \"北交所\"\n\n with open(\"result.json\", \"w\") as json_file:\n json.dump(stock_tags, json_file, indent=2, ensure_ascii=False)\n\n\nif __name__ == \"__main__\":\n # build_stock_tags(block_name=\"化工原料\", tag=\"化工\", hidden_tag=None)\n # merge_tags(tags_file=\"stock_tags.json\", hidden_tags_file=\"化工.json\", result_file=\"result.json\", force_update=False)\n # replace_tags(old_tag=\"仪器仪表\")\n # check_tags()\n # complete_tags()\n # refresh_hidden_tags()\n print(get_concept(code=\"688787\"))\n"
},
{
"path": "examples/trader/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "examples/trader/dragon_and_tiger_trader.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Union\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.factor import Factor, Transformer, Accumulator\nfrom zvt.domain import Stock, DragonAndTiger\nfrom zvt.trader import StockTrader\n\n\nclass DragonTigerFactor(Factor):\n def __init__(\n self,\n provider: str = \"em\",\n entity_provider: str = \"em\",\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = [DragonAndTiger.dep1 == \"机构专用\"],\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n ) -> None:\n super().__init__(\n DragonAndTiger,\n Stock,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n )\n\n def compute_result(self):\n self.factor_df[\"filter_result\"] = True\n super().compute_result()\n\n\nclass MyTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n return [\n DragonTigerFactor(\n entity_ids=entity_ids,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n ]\n\n\nif __name__ == \"__main__\":\n trader = MyTrader(start_timestamp=\"2020-01-01\", end_timestamp=\"2022-05-01\")\n trader.run()\n"
},
{
"path": "examples/trader/follow_ii_trader.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.api.utils import get_recent_report_date\nfrom zvt.contract import ActorType, AdjustType\nfrom zvt.domain import StockActorSummary, Stock1dKdata, Stock\nfrom zvt.trader import StockTrader\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import is_same_date, to_pd_timestamp\n\n\nclass FollowIITrader(StockTrader):\n finish_date = None\n\n def on_time(self, timestamp: pd.Timestamp):\n recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp))\n if self.finish_date and is_same_date(recent_report_date, self.finish_date):\n return\n filters = [\n StockActorSummary.actor_type == ActorType.raised_fund.value,\n StockActorSummary.report_date == recent_report_date,\n ]\n\n if self.entity_ids:\n filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)]\n\n df = StockActorSummary.query_data(filters=filters)\n\n if pd_is_not_null(df):\n self.logger.info(f\"{df}\")\n self.finish_date = recent_report_date\n\n long_df = df[df[\"change_ratio\"] > 0.05]\n short_df = df[df[\"change_ratio\"] < -0.5]\n try:\n long_targets = set(long_df[\"entity_id\"].to_list())\n short_targets = set(short_df[\"entity_id\"].to_list())\n if long_targets:\n self.buy(timestamp=timestamp, entity_ids=long_targets)\n if short_targets:\n self.sell(timestamp=timestamp, entity_ids=short_targets)\n except Exception as e:\n self.logger.error(e)\n\n\nif __name__ == \"__main__\":\n code = \"600519\"\n Stock.record_data(provider=\"em\")\n Stock1dKdata.record_data(code=code, provider=\"em\")\n StockActorSummary.record_data(code=code, provider=\"em\")\n FollowIITrader(\n start_timestamp=\"2002-01-01\",\n end_timestamp=\"2021-01-01\",\n codes=[code],\n provider=\"em\",\n adjust_type=AdjustType.qfq,\n profit_threshold=None,\n ).run()\n"
},
{
"path": "examples/trader/keep_run_trader.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom zvt.api.stats import get_top_fund_holding_stocks\nfrom zvt.api.stats import get_top_volume_entities\nfrom zvt.contract import IntervalLevel\nfrom zvt.factors.macd.macd_factor import BullFactor\nfrom zvt.trader import StockTrader\nfrom zvt.trader.trader_info_api import clear_trader\nfrom zvt.utils.time_utils import split_time_interval, date_time_by_interval\n\nlogger = logging.getLogger(__name__)\n\n\nclass MultipleLevelTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n start_timestamp = date_time_by_interval(start_timestamp, -50)\n\n return [\n BullFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=date_time_by_interval(start_timestamp, -200),\n end_timestamp=end_timestamp,\n provider=\"joinquant\",\n level=IntervalLevel.LEVEL_1WEEK,\n ),\n GoldCrossFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"joinquant\",\n level=IntervalLevel.LEVEL_1DAY,\n ),\n ]\n\n\nif __name__ == \"__main__\":\n start = \"2019-01-01\"\n end = \"2021-01-01\"\n trader_name = \"keep_run_trader\"\n clear_trader(trader_name=trader_name)\n for time_interval in split_time_interval(start=start, end=end, interval=40):\n start_timestamp = time_interval[0]\n end_timestamp = time_interval[-1]\n # 成交量\n vol_df = get_top_volume_entities(\n entity_type=\"stock\",\n start_timestamp=date_time_by_interval(start_timestamp, -50),\n end_timestamp=start_timestamp,\n pct=0.3,\n )\n # 机构重仓\n ii_df = get_top_fund_holding_stocks(timestamp=start_timestamp, pct=0.3, by=\"trading\")\n\n current_entity_pool = list(set(vol_df.index.tolist()) & set(ii_df.index.tolist()))\n\n logger.info(f\"current_entity_pool({len(current_entity_pool)}):{current_entity_pool}\")\n\n trader = MultipleLevelTrader(\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n entity_ids=current_entity_pool,\n trader_name=trader_name,\n keep_history=True,\n draw_result=False,\n rich_mode=False,\n )\n trader.run()\n"
},
{
"path": "examples/trader/ma_trader.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel\nfrom zvt.factors.ma.ma_factor import CrossMaFactor\nfrom zvt.factors.macd.macd_factor import BullFactor\n\nfrom zvt.trader.trader import StockTrader\n\n\nclass MyMaTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n return [\n CrossMaFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n windows=[5, 10],\n need_persist=False,\n )\n ]\n\n\nclass MyBullTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n return [\n BullFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n adjust_type=\"hfq\",\n )\n ]\n\n\nif __name__ == \"__main__\":\n # single stock with cross ma factor\n MyBullTrader(\n codes=[\"000338\"],\n level=IntervalLevel.LEVEL_1DAY,\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-06-30\",\n trader_name=\"000338_ma_trader\",\n ).run()\n\n # single stock with bull factor\n # MyBullTrader(codes=['000338'], level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01',\n # end_timestamp='2019-06-30', trader_name='000338_bull_trader').run()\n\n # multiple stocks with cross ma factor\n # MyMaTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01',\n # end_timestamp='2019-06-30', trader_name='sample_stocks_ma_trader').run()\n\n # multiple stocks with bull factor\n # MyBullTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01',\n # end_timestamp='2019-06-30', trader_name='sample_stocks_bull_trader').run()\n"
},
{
"path": "examples/trader/macd_day_trader.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Tuple\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.factor import Factor\nfrom zvt.factors.macd.macd_factor import GoldCrossFactor\nfrom zvt.trader import TradingSignal\nfrom zvt.trader.trader import StockTrader\n\n# 依赖数据\n# data_schema: Stock1dHfqKdata\n# provider: joinquant\nfrom zvt.utils.time_utils import date_time_by_interval\n\n\nclass MacdDayTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n # 日线策略\n start_timestamp = date_time_by_interval(start_timestamp, -50)\n return [\n GoldCrossFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"joinquant\",\n level=IntervalLevel.LEVEL_1DAY,\n )\n ]\n\n def on_profit_control(self):\n # 覆盖该函数做止盈 止损\n return super().on_profit_control()\n\n def on_time(self, timestamp: pd.Timestamp):\n # 对selectors选出的标的做进一步处理，或者不使用selector完全自己根据时间和数据生成交易信号\n super().on_time(timestamp)\n\n def on_trading_signals(self, trading_signals: List[TradingSignal]):\n # 批量处理交易信号，比如连接交易接口，发邮件，微信推送等\n super().on_trading_signals(trading_signals)\n\n def on_trading_open(self, timestamp):\n # 开盘自定义逻辑\n super().on_trading_open(timestamp)\n\n def on_trading_close(self, timestamp):\n # 收盘自定义逻辑\n super().on_trading_close(timestamp)\n\n def on_trading_finish(self, timestamp):\n # 策略退出自定义逻辑\n super().on_trading_finish(timestamp)\n\n def on_trading_error(self, timestamp, error):\n # 出错处理\n super().on_trading_error(timestamp, error)\n\n def long_position_control(self):\n # 多头仓位管理\n return super().long_position_control()\n\n def short_position_control(self):\n # 空头仓位管理\n return super().short_position_control()\n\n def on_factor_targets_filtered(\n self, timestamp, level, factor: Factor, long_targets: List[str], short_targets: List[str]\n ) -> Tuple[List[str], List[str]]:\n # 过滤某级别选出的 标的\n return super().on_factor_targets_filtered(timestamp, level, factor, long_targets, short_targets)\n\n\nif __name__ == \"__main__\":\n trader = MacdDayTrader(start_timestamp=\"2019-01-01\", end_timestamp=\"2020-01-01\")\n trader.run()\n # f = VolFactor(start_timestamp='2020-01-01', end_timestamp='2020-04-01')\n # print(f.result_df)\n"
},
{
"path": "examples/trader/macd_week_and_day_trader.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Tuple\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.factors.macd.macd_factor import GoldCrossFactor\nfrom zvt.trader.trader import StockTrader\n\n\n# 依赖数据\n# dataschema: Stock1dHfqKdata Stock1wkHfqKdata\n# provider: joinquant\nclass MultipleLevelTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n # 同时使用周线和日线策略\n return [\n GoldCrossFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"joinquant\",\n level=IntervalLevel.LEVEL_1WEEK,\n ),\n GoldCrossFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=\"joinquant\",\n level=IntervalLevel.LEVEL_1DAY,\n ),\n ]\n\n def on_targets_selected_from_levels(self, timestamp) -> Tuple[List[str], List[str]]:\n # 过滤多级别做 多/空 的标的\n return super().on_targets_selected_from_levels(timestamp)\n\n\nif __name__ == \"__main__\":\n trader = MultipleLevelTrader(start_timestamp=\"2019-01-01\", end_timestamp=\"2020-01-01\")\n trader.run()\n"
},
{
"path": "examples/utils.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport logging\nimport os\n\nimport pandas as pd\n\nfrom zvt.domain import StockNews, Stock, LimitUpInfo\nfrom zvt.utils.time_utils import date_time_by_interval, now_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_hot_words_config():\n with open(os.path.join(os.path.dirname(__file__), \"hot.json\")) as f:\n return json.load(f)\n\n\ndef count_hot_words(text: str):\n text = text.upper()\n hot_words_config = get_hot_words_config()\n word_stats = {}\n topic_stats = {}\n for topic in hot_words_config:\n topic_count = 0\n for word in hot_words_config[topic]:\n word_stats[word] = text.lower().count(word)\n topic_count = topic_count + word_stats[word]\n topic_stats[topic] = topic_count\n return topic_stats, word_stats\n\n\ndef hot_stats(data: pd.Series):\n pass\n\n\ndef group_stocks_by_topic(\n keyword=None, entities=None, hot_words_config=None, start_timestamp=None, days_ago=60, threshold=3\n):\n \"\"\"\n\n :param keyword:\n :param entities:\n :param hot_words_config: hot words config为二重结构，即 主题:[分支1，分支2,...]的形式\n 比如一个有效的item：{\"华为\":[\"华为\", \"mate pro\", \"星闪\", \"问界\"]}\n :param start_timestamp:\n :param days_ago:\n :param threshold:\n :return:\n \"\"\"\n if not start_timestamp:\n start_timestamp = date_time_by_interval(now_pd_timestamp(), -days_ago)\n stock_map = {}\n\n entity_ids = None\n if entities:\n entity_ids = [entity.entity_id for entity in entities]\n else:\n entities = Stock.query_data(provider=\"em\", return_type=\"domain\")\n\n for entity in entities:\n stock_map[entity.entity_id] = {\"code\": entity.code, \"name\": entity.name}\n\n filters = None\n if keyword:\n filters = [StockNews.news_title.contains(keyword)]\n df = StockNews.query_data(start_timestamp=start_timestamp, entity_ids=entity_ids, filters=filters)\n df = df.groupby(\"entity_id\")[\"news_title\"].apply(\",\".join).reset_index()\n\n if not hot_words_config:\n hot_words_config = get_hot_words_config()\n\n hot_stocks_map = {}\n topic_count = {}\n word_count = {}\n for _, row in df[[\"entity_id\", \"news_title\"]].iterrows():\n entity_id = row[\"entity_id\"]\n text = row[\"news_title\"]\n\n is_hot = False\n for topic in hot_words_config:\n topic_count.setdefault(topic, 0)\n for words in hot_words_config[topic]:\n hot_stocks_map.setdefault(words, [])\n word_count.setdefault(words, 0)\n count = 0\n for word in words.split(\",\"):\n count = text.lower().count(word) + count\n if count >= threshold:\n word_count[words] = word_count[words] + 1\n topic_count[topic] = topic_count[topic] + 1\n hot_stocks_map[words].append(\n (f\"{stock_map[entity_id]['code']}({stock_map[entity_id]['name']})\", count)\n )\n is_hot = True\n if not is_hot:\n hot_stocks_map.setdefault(\"其他\", [])\n hot_stocks_map[\"其他\"].append((f\"{stock_map[entity_id]['code']}({stock_map[entity_id]['name']})\", 0))\n\n sorted_topics = sorted(topic_count.items(), key=lambda item: item[1], reverse=True)\n sorted_words = sorted(word_count.items(), key=lambda item: item[1], reverse=True)\n\n result = []\n for topic, count in sorted_topics:\n topic_words = hot_words_config[topic]\n topic_words_stocks = [\n (f\"{words}({count})\", sorted(hot_stocks_map[words], key=lambda item: item[1], reverse=True))\n for (words, count) in sorted_words\n if words in topic_words\n ]\n result.append((f\"{topic}({count})\", topic_words_stocks))\n\n result.append((\"其他\", [(\"其他\", hot_stocks_map.get(\"其他\", \"\"))]))\n\n return result\n\n\ndef msg_group_stocks_by_topic(\n keyword=None, entities=None, hot_words_config=None, start_timestamp=None, days_ago=60, threshold=3\n):\n group_info = group_stocks_by_topic(\n keyword=keyword,\n entities=entities,\n hot_words_config=hot_words_config,\n start_timestamp=start_timestamp,\n days_ago=days_ago,\n threshold=threshold,\n )\n msg = \"\"\n for group in group_info:\n topic = group[0]\n msg = msg + f\"^^^^^^ {topic} ^^^^^^\\n\"\n for topic_word, stocks_count in group[1]:\n msg = msg + f\"{topic_word}\\n\"\n stocks = [f\"{stock_count[0]} {stock_count[1]}\" for stock_count in stocks_count]\n msg = msg + \"\\n\".join(stocks) + \"\\n\"\n return msg\n\n\ndef get_hot_topics(start_timestamp=None, days_ago=20, limit=15):\n if not start_timestamp:\n start_timestamp = date_time_by_interval(now_pd_timestamp(), -days_ago)\n df = LimitUpInfo.query_data(start_timestamp=start_timestamp, columns=[\"reason\"])\n df[\"reason\"] = df[\"reason\"].str.split(\"+\")\n result = df[\"reason\"].tolist()\n result = [item for sublist in result for item in sublist]\n result = pd.Series(result)\n result = result.value_counts()\n result = result[:limit].to_dict()\n return result\n\n\nif __name__ == \"__main__\":\n # ids = get_top_performance_entities_by_periods(entity_provider=\"em\", data_provider=\"em\")\n #\n # entities = get_entities(provider=\"em\", entity_type=\"stock\", entity_ids=ids, return_type=\"domain\")\n #\n # print(msg_group_stocks_by_topic(entities=entities, threshold=1))\n get_hot_topics(days_ago=10)\n"
},
{
"path": "examples/z.sh",
"content": "nohup python examples/data_runner/kdata_runner.py >/dev/null 2>&1 &\nnohup python examples/reports/report_tops.py >/dev/null 2>&1 &\nnohup python examples/reports/report_vol_up.py >/dev/null 2>&1 &"
},
{
"path": "init_env.sh",
"content": "#!/bin/bash -e\n\nplatform='unknown'\nunamestr=`uname`\nif [[ \"$unamestr\" == 'Linux' ]]; then\n platform='linux'\nelif [[ \"$unamestr\" == 'Darwin' ]]; then\n platform='mac'\nfi\n\ninstall_cmd='sudo apt-get install'\nif [[ \"$platform\" == 'mac' ]]; then\n install_cmd='brew install'\nfi\n\nBASEDIR=`dirname $0`\n\nif ! which python > /dev/null; then\n echo -e \"Python3 not found! Install? (y/n) \\c\"\n read\n if [ \"$REPLY\" = \"y\" ]; then\n $install_cmd install python3\n fi\nfi\n\npip_opt=''\n#pip_opt='-i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn'\n\nif ! which virtualenv > /dev/null; then\n echo -e \"virtualenv not found! Install? (y/n) \\c\"\n read\n if [ \"$REPLY\" = \"y\" ]; then\n pip3 install virtualenv $pip_opt\n fi\nfi\n\nif [ ! -d \"$BASEDIR/ve\" ]; then\n virtualenv -p python3 $BASEDIR/ve --no-download\n echo \"Virtualenv created.\"\nfi\n\nsource $BASEDIR/ve/bin/activate\ncd $BASEDIR\nexport PYTHONPATH=$PYTHONPATH:./src\n\nif [ ! -f \"$BASEDIR/ve/updated\" -o $BASEDIR/requirements.txt -nt $BASEDIR/ve/updated ]; then\n pip install -r $BASEDIR/requirements.txt $pip_opt\n touch $BASEDIR/ve/updated\n echo \"Requirements installed.\"\nfi\n\npip install ipython jupyterlab $pip_opt\n\necho \"env ok\""
},
{
"path": "pyproject.toml",
"content": "[tool.black]\nline-length = 120"
},
{
"path": "requirements/dev.txt",
"content": "# dev tools\npytest == 6.2.0\npre-commit == 2.15.0\nrst2pdf\n"
},
{
"path": "requirements/docs.txt",
"content": "sphinx==8.1.3\nsphinx_autodoc_typehints==3.0.1\nrst2pdf==0.103.1\nipython==8.29.0\nsphinx_rtd_theme==3.0.2\nreadthedocs-sphinx-search==0.3.2"
},
{
"path": "requirements.txt",
"content": "requests==2.32.0\nSQLAlchemy==2.0.36\npandas==2.2.3\npydantic==2.6.4\narrow==1.3.0\nopenpyxl==3.1.1\ndemjson3==3.0.6\nplotly==5.13.0\ndash==2.18.2\njqdatapy==0.1.8\ndash-bootstrap-components==1.3.1\ndash_daq==0.5.0\nscikit-learn==1.5.2\nfastapi==0.110.0\nfastapi-pagination==0.12.23\napscheduler==3.10.4\neastmoneypy==0.2.0\norjson==3.10.3\nnumpy==2.1.3"
},
{
"path": "setup.py",
"content": "#!/usr/bin/env python\n\n# To use a consistent encoding\nfrom codecs import open\nfrom os import path\n\n# Always prefer setuptools over distutils\nfrom setuptools import setup, find_packages\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the README file\nwith open(path.join(here, \"README.md\"), encoding=\"utf-8\") as f:\n long_description = f.read()\n\nsetup(\n name=\"zvt\",\n version=\"0.13.5\",\n description=\"unified,modular quant framework for human beings \",\n long_description=long_description,\n url=\"https://github.com/zvtvz/zvt\",\n author=\"foolcage\",\n author_email=\"5533061@qq.com\",\n classifiers=[ # Optional\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: Customer Service\",\n \"Intended Audience :: Education\",\n \"Intended Audience :: Financial and Insurance Industry\",\n \"Topic :: Software Development :: Build Tools\",\n \"Topic :: Office/Business :: Financial :: Investment\",\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n ],\n keywords=\"quant stock finance fintech big-data zvt technical-analysis trading-platform pandas fundamental-analysis\",\n package_dir={\"\": \"src\"},\n packages=find_packages(where=\"src\"),\n python_requires=\">=3.9, <4\",\n include_package_data=True,\n install_requires=[\n \"requests==2.32.0\",\n \"SQLAlchemy==2.0.36\",\n \"pandas==2.2.3\",\n \"pydantic==2.6.4\",\n \"arrow==1.3.0\",\n \"openpyxl==3.1.1\",\n \"demjson3==3.0.6\",\n \"plotly==5.13.0\",\n \"dash==2.18.2\",\n \"jqdatapy==0.1.8\",\n \"dash-bootstrap-components==1.3.1\",\n \"dash_daq==0.5.0\",\n \"scikit-learn==1.5.2\",\n \"fastapi==0.110.0\",\n \"fastapi-pagination==0.12.23\",\n \"apscheduler==3.10.4\",\n \"eastmoneypy==0.2.0\",\n \"orjson==3.10.3\",\n \"numpy==2.1.3\",\n ],\n project_urls={ # Optional\n \"Bug Reports\": \"https://github.com/zvtvz/zvt/issues\",\n \"Funding\": \"https://www.foolcage.com/zvt\",\n \"Say Thanks!\": \"https://saythanks.io/to/foolcage\",\n \"Source\": \"https://github.com/zvtvz/zvt\",\n },\n long_description_content_type=\"text/markdown\",\n entry_points={\n \"console_scripts\": [\n \"zvt = zvt.main:main\",\n \"zvt_server = zvt.zvt_server:main\",\n \"zvt_export = zvt.plugin:export\",\n ],\n },\n license_file=\"LICENSE\",\n)\n"
},
{
"path": "sql/change_indices.sql",
"content": "-- 由于创建的table有不少使用了enum,导致有类似的约束\n-- \tconstraint provider\n-- \t\tcheck (provider IN ('eastmoney', 'sina', 'netease'))\n-- 当需要在原来的数据文件上做扩展时,可以去掉约束并做数据迁移\ncreate table indices1\n(\n id VARCHAR(128) not null,\n provider VARCHAR(9) not null,\n timestamp DATETIME,\n exchange VARCHAR(32),\n type VARCHAR(5),\n code VARCHAR(32),\n name VARCHAR(32),\n is_delisted BOOLEAN,\n category VARCHAR(8),\n primary key (id, provider),\n check (is_delisted IN (0, 1))\n);\n\nINSERT INTO indices1\nSELECT *\nFROM index;\n\n\nDROP TABLE index;\nALTER TABLE indices1\n RENAME TO indices;\n\nupdate income_statement set report_period='season3' where report_period='seanson3'\nupdate finance_factor set report_period='season3' where report_period='seanson3'\nupdate balance_sheet set report_period='season3' where report_period='seanson3'\nupdate cash_flow_statement set report_period='season3' where report_period='seanson3'\n"
},
{
"path": "sql/change_stock_index.sql",
"content": "alter table index_stock\n add report_period VARCHAR(32);\nalter table index_stock\n\tadd report_date DATETIME;\nalter table index_stock\n\tadd proportion FLOAT;\nalter table index_stock\n\tadd shares FLOAT;\nalter table index_stock\n\tadd market_cap FLOAT;"
},
{
"path": "sql/reduce_size.sql",
"content": "-- k线数据去除索引，方便传输原始数据，重跑zvt会重建\n-- 再压缩一下，大小为原来的1/10\ndrop index stock_1d_hfq_kdata_entity_id_index;\ndrop index stock_1d_hfq_kdata_code_index;\ndrop index stock_1d_hfq_kdata_timestamp_index;\nVACUUM;"
},
{
"path": "sql/samples.sql",
"content": "select * from stock_valuation where CAST(strftime('%s', timestamp) AS integer) ==CAST(strftime('%s', '2009-12-14') AS integer) ;\n"
},
{
"path": "src/zvt/__init__.py",
"content": "# -*- coding: utf-8 -*-\nimport importlib\nimport json\nimport logging\nimport os\nimport pkgutil\nimport pprint\nimport shutil\nfrom logging.handlers import RotatingFileHandler\nfrom typing import List\n\nimport pandas as pd\nimport pkg_resources\nfrom pkg_resources import get_distribution, DistributionNotFound\n\nfrom zvt.consts import DATA_SAMPLE_ZIP_PATH, ZVT_TEST_HOME, ZVT_HOME, ZVT_TEST_DATA_PATH, ZVT_TEST_ZIP_DATA_PATH\n\ntry:\n dist_name = __name__\n __version__ = get_distribution(dist_name).version\nexcept DistributionNotFound:\n __version__ = \"unknown\"\nfinally:\n del get_distribution, DistributionNotFound\n\nlogger = logging.getLogger(__name__)\n\n\ndef init_log(file_name=\"zvt.log\", log_dir=None, simple_formatter=True):\n if not log_dir:\n log_dir = zvt_env[\"log_path\"]\n\n root_logger = logging.getLogger()\n\n # reset the handlers\n root_logger.handlers = []\n\n root_logger.setLevel(logging.INFO)\n\n file_name = os.path.join(log_dir, file_name)\n\n file_log_handler = RotatingFileHandler(file_name, maxBytes=524288000, backupCount=10)\n\n file_log_handler.setLevel(logging.INFO)\n\n console_log_handler = logging.StreamHandler()\n console_log_handler.setLevel(logging.INFO)\n\n # create formatter and add it to the handlers\n if simple_formatter:\n formatter = logging.Formatter(\"%(asctime)s %(levelname)s %(threadName)s %(message)s\")\n else:\n formatter = logging.Formatter(\n \"%(asctime)s %(levelname)s %(threadName)s %(name)s:%(filename)s:%(lineno)s %(funcName)s %(message)s\"\n )\n file_log_handler.setFormatter(formatter)\n console_log_handler.setFormatter(formatter)\n\n # add the handlers to the logger\n root_logger.addHandler(file_log_handler)\n root_logger.addHandler(console_log_handler)\n\n\nos.environ.setdefault(\"SQLALCHEMY_WARN_20\", \"1\")\npd.set_option(\"expand_frame_repr\", False)\npd.set_option(\"mode.chained_assignment\", \"raise\")\npd.set_option(\"display.max_rows\", None)\npd.set_option(\"display.max_columns\", None)\n\nzvt_env = {}\n\n# load default config\nwith open(pkg_resources.resource_filename(\"zvt\", \"config.json\")) as f:\n zvt_config = json.load(f)\n\n_plugins = {}\n\n\ndef init_env(zvt_home: str, **kwargs) -> dict:\n \"\"\"\n init env\n\n :param zvt_home: home path for zvt\n \"\"\"\n data_path = os.path.join(zvt_home, \"data\")\n resource_path = os.path.join(zvt_home, \"resources\")\n tmp_path = os.path.join(zvt_home, \"tmp\")\n if not os.path.exists(data_path):\n os.makedirs(data_path)\n\n if not os.path.exists(resource_path):\n os.makedirs(resource_path)\n\n if not os.path.exists(tmp_path):\n os.makedirs(tmp_path)\n\n zvt_env[\"zvt_home\"] = zvt_home\n zvt_env[\"data_path\"] = data_path\n zvt_env[\"resource_path\"] = resource_path\n zvt_env[\"tmp_path\"] = tmp_path\n\n # path for storing ui results\n zvt_env[\"ui_path\"] = os.path.join(zvt_home, \"ui\")\n if not os.path.exists(zvt_env[\"ui_path\"]):\n os.makedirs(zvt_env[\"ui_path\"])\n\n # path for storing logs\n zvt_env[\"log_path\"] = os.path.join(zvt_home, \"logs\")\n if not os.path.exists(zvt_env[\"log_path\"]):\n os.makedirs(zvt_env[\"log_path\"])\n\n init_log()\n\n pprint.pprint(zvt_env)\n\n init_resources(resource_path=resource_path)\n # init config\n init_config(current_config=zvt_config, **kwargs)\n # init plugin\n # init_plugins()\n\n return zvt_env\n\n\ndef init_resources(resource_path, force_overwrite=True):\n package_name = \"zvt\"\n package_dir = pkg_resources.resource_filename(package_name, \"resources\")\n from zvt.utils.file_utils import list_all_files\n\n files: List[str] = list_all_files(package_dir, ext=None)\n for source_file in files:\n dst_file = os.path.join(resource_path, source_file[len(package_dir) + 1 :])\n if force_overwrite or not os.path.exists(dst_file):\n shutil.copyfile(source_file, dst_file)\n\n\ndef init_config(pkg_name: str = None, current_config: dict = None, **kwargs) -> dict:\n \"\"\"\n init config\n \"\"\"\n\n # create default config.json if not exist\n if pkg_name:\n config_file = f\"{pkg_name}_config.json\"\n else:\n pkg_name = \"zvt\"\n config_file = \"config.json\"\n\n logger.info(f\"init config for {pkg_name}, current_config:{current_config}\")\n\n config_path = os.path.join(zvt_env[\"zvt_home\"], config_file)\n if not os.path.exists(config_path):\n try:\n sample_config = pkg_resources.resource_filename(pkg_name, \"config.json\")\n if os.path.exists(sample_config):\n shutil.copyfile(sample_config, config_path)\n except Exception as e:\n logger.warning(f\"could not load config.json from package {pkg_name}\")\n\n if os.path.exists(config_path):\n with open(config_path) as f:\n config_json = json.load(f)\n for k in config_json:\n current_config[k] = config_json[k]\n\n # set and save the config\n for k in kwargs:\n current_config[k] = kwargs[k]\n with open(config_path, \"w+\") as outfile:\n json.dump(current_config, outfile)\n\n pprint.pprint(current_config)\n logger.info(f\"current_config:{current_config}\")\n\n return current_config\n\n\ndef init_plugins():\n for finder, name, ispkg in pkgutil.iter_modules():\n if name.startswith(\"zvt_\"):\n try:\n _plugins[name] = importlib.import_module(name)\n except Exception as e:\n logger.warning(f\"failed to load plugin {name}\", e)\n logger.info(f\"loaded plugins:{_plugins}\")\n\n\ndef old_db_to_provider_dir(data_path):\n files = os.listdir(data_path)\n for file in files:\n if file.endswith(\".db\"):\n # Split the file name to extract the provider\n provider = file.split(\"_\")[0]\n\n # Define the destination directory\n destination_dir = os.path.join(data_path, provider)\n\n # Create the destination directory if it doesn't exist\n if not os.path.exists(destination_dir):\n os.makedirs(destination_dir)\n\n # Define the source and destination paths\n source_path = os.path.join(data_path, file)\n destination_path = os.path.join(destination_dir, file)\n\n # Move the file to the destination directory\n if not os.path.exists(destination_path):\n shutil.move(source_path, destination_path)\n logger.info(f\"Moved {file} to {destination_dir}\")\n\n\nif os.getenv(\"TESTING_ZVT\"):\n init_env(zvt_home=ZVT_TEST_HOME)\n\n # init the sample data if need\n same = False\n if os.path.exists(ZVT_TEST_ZIP_DATA_PATH):\n import filecmp\n\n same = filecmp.cmp(ZVT_TEST_ZIP_DATA_PATH, DATA_SAMPLE_ZIP_PATH)\n\n if not same:\n from zvt.contract import *\n from zvt.utils.zip_utils import unzip\n\n shutil.copyfile(DATA_SAMPLE_ZIP_PATH, ZVT_TEST_ZIP_DATA_PATH)\n unzip(ZVT_TEST_ZIP_DATA_PATH, ZVT_TEST_DATA_PATH)\nelse:\n init_env(zvt_home=ZVT_HOME)\n\nold_db_to_provider_dir(zvt_env[\"data_path\"])\n\n# register to meta\nimport zvt.contract as zvt_contract\nimport zvt.recorders as zvt_recorders\nimport zvt.factors as zvt_factors\n\nimport platform\n\nif platform.system() == \"Windows\":\n try:\n import zvt.recorders.qmt as qmt_recorder\n except Exception as e:\n logger.error(\"QMT not work\", e)\nelse:\n logger.warning(\"QMT need run in Windows!\")\n\n\n__all__ = [\"zvt_env\", \"zvt_config\", \"init_log\", \"init_env\", \"init_config\", \"__version__\"]\n"
},
{
"path": "src/zvt/api/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/api/intent.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.kdata import get_kdata_schema\nfrom zvt.contract.api import decode_entity_id\nfrom zvt.contract.drawer import Drawer, ChartType\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\ndef compare(\n entity_ids=None,\n codes=None,\n schema=None,\n columns=None,\n schema_map_columns: dict = None,\n chart_type: ChartType = ChartType.line,\n start_timestamp=None,\n scale_value: int = None,\n):\n \"\"\"\n compare indicators(columns) of entities\n\n :param entity_ids:\n :param codes:\n :param schema:\n :param columns:\n :param schema_map_columns: key represents schema, value represents columns\n :param chart_type: \"line\", \"area\", \"scatter\", default \"line\"\n :param start_timestamp: \"\n :param scale_value: compare with same value which scaled to scale_value\n \"\"\"\n\n dfs = []\n # default compare kdata\n if schema_map_columns is None and schema is None:\n entity_type_map_ids = _group_entity_ids(entity_ids=entity_ids)\n for entity_type in entity_type_map_ids:\n schema = get_kdata_schema(entity_type=entity_type)\n df = schema.query_data(entity_ids=entity_type_map_ids.get(entity_type), start_timestamp=start_timestamp)\n dfs.append(df)\n all_df = pd.concat(dfs)\n drawer = Drawer(main_df=all_df, sub_df_list=[all_df[[\"entity_id\", \"timestamp\", \"turnover\"]].copy()])\n drawer.draw_kline(show=True, scale_value=scale_value)\n else:\n if schema_map_columns:\n for schema in schema_map_columns:\n columns = [\"entity_id\", \"timestamp\"] + schema_map_columns.get(schema)\n df = schema.query_data(\n entity_ids=entity_ids, codes=codes, columns=columns, start_timestamp=start_timestamp\n )\n dfs.append(df)\n elif schema:\n columns = [\"entity_id\", \"timestamp\"] + columns\n df = schema.query_data(entity_ids=entity_ids, codes=codes, columns=columns, start_timestamp=start_timestamp)\n dfs.append(df)\n\n all_df = pd.concat(dfs)\n drawer = Drawer(main_df=all_df)\n drawer.draw(main_chart=chart_type, show=True, scale_value=scale_value)\n\n\ndef compare_df(df: pd.DataFrame, chart_type: ChartType = ChartType.line):\n \"\"\"\n compare indicators(columns) of entities in df\n\n :param df: normal df\n :param chart_type:\n \"\"\"\n drawer = Drawer(main_df=df)\n drawer.draw(main_chart=chart_type, show=True)\n\n\ndef distribute(data_schema, columns, entity_ids=None, codes=None, histnorm=\"percent\", nbinsx=20, filters=None):\n \"\"\"\n distribute indicators(columns) of entities\n\n :param data_schema:\n :param columns:\n :param entity_ids:\n :param codes:\n :param histnorm: \"percent\", \"probability\", default \"percent\"\n :param nbinsx:\n :param filters:\n \"\"\"\n columns = [\"entity_id\", \"timestamp\"] + columns\n df = data_schema.query_data(entity_ids=entity_ids, codes=codes, columns=columns, filters=filters)\n if not entity_ids or codes:\n df[\"entity_id\"] = \"entity_x_distribute\"\n distribute_df(df=df, histnorm=histnorm, nbinsx=nbinsx)\n\n\ndef distribute_df(df, histnorm=\"percent\", nbinsx=20):\n \"\"\"\n distribute indicators(columns) of entities in df\n\n :param df: normal df\n :param histnorm: \"percent\", \"probability\", default \"percent\"\n :param nbinsx:\n \"\"\"\n drawer = Drawer(main_df=df)\n drawer.draw_histogram(show=True, histnorm=histnorm, nbinsx=nbinsx)\n\n\ndef composite(entity_id, data_schema, columns, filters=None):\n \"\"\"\n composite indicators(columns) of entity\n\n :param entity_id:\n :param data_schema:\n :param columns:\n :param filters:\n \"\"\"\n columns = [\"entity_id\", \"timestamp\"] + columns\n df = data_schema.query_data(entity_id=entity_id, columns=columns, filters=filters)\n composite_df(df=df)\n\n\ndef composite_df(df):\n \"\"\"\n composite indicators(columns) of entity in df\n\n :param df:\n \"\"\"\n drawer = Drawer(main_df=df)\n drawer.draw_pie(show=True)\n\n\ndef composite_all(data_schema, column, timestamp, provider=None, entity_ids=None, filters=None):\n if type(column) is not str:\n column = column.name\n if filters:\n filters.append([data_schema.timestamp == to_pd_timestamp(timestamp)])\n else:\n filters = [data_schema.timestamp == to_pd_timestamp(timestamp)]\n df = data_schema.query_data(\n provider=provider,\n entity_ids=entity_ids,\n columns=[\"entity_id\", \"timestamp\", column],\n filters=filters,\n index=\"entity_id\",\n )\n entity_type, exchange, _ = decode_entity_id(df[\"entity_id\"].iloc[0])\n pie_df = pd.DataFrame(columns=df.index, data=[df[column].tolist()])\n pie_df[\"entity_id\"] = f\"{entity_type}_{exchange}_{column}\"\n pie_df[\"timestamp\"] = timestamp\n\n drawer = Drawer(main_df=pie_df)\n drawer.draw_pie(show=True)\n\n\ndef _group_entity_ids(entity_ids):\n entity_type_map_ids = {}\n for entity_id in entity_ids:\n entity_type, _, _ = decode_entity_id(entity_id)\n ids: List = entity_type_map_ids.setdefault(entity_type, [])\n ids.append(entity_id)\n return entity_type_map_ids\n\n\nif __name__ == \"__main__\":\n # from zvt.domain import Index1wkKdata\n # from zvt.api.intent import compare\n #\n # Index1wkKdata.record_data(provider=\"em\", codes=[\"399370\", \"399371\"])\n # df1 = Index1wkKdata.query_data(code=\"399371\", index=\"timestamp\")\n # df2 = Index1wkKdata.query_data(code=\"399370\", index=\"timestamp\")\n # se = df1[\"close\"] / (df2[\"close\"])\n #\n # compare(se)\n\n from zvt.domain import CashFlowStatement\n\n #\n # composite(\n # entity_id=\"stock_sz_000338\",\n # data_schema=CashFlowStatement,\n # columns=[\n # CashFlowStatement.net_op_cash_flows,\n # CashFlowStatement.net_investing_cash_flows,\n # CashFlowStatement.net_financing_cash_flows,\n # ],\n # filters=[\n # CashFlowStatement.report_period == \"year\",\n # CashFlowStatement.report_date == to_pd_timestamp(\"2015-12-31\"),\n # ],\n # )\n df = CashFlowStatement.query_data(\n entity_id=\"stock_sz_000338\",\n columns=[\n CashFlowStatement.net_op_cash_flows,\n CashFlowStatement.net_investing_cash_flows,\n CashFlowStatement.net_financing_cash_flows,\n ],\n filters=[\n CashFlowStatement.report_period == \"year\",\n CashFlowStatement.report_date == to_pd_timestamp(\"2015-12-31\"),\n ],\n index=\"timestamp\",\n )\n composite_df(df=df)\n\n\n# the __all__ is generated\n__all__ = [\"compare\", \"compare_df\", \"distribute\", \"distribute_df\", \"composite\", \"composite_df\", \"composite_all\"]\n"
},
{
"path": "src/zvt/api/kdata.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, AdjustType, Mixin\nfrom zvt.contract.api import decode_entity_id, get_schema_by_name\nfrom zvt.domain import Index1dKdata, Indexus1dKdata, Indexhk1dKdata\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import (\n to_date_time_str,\n TIME_FORMAT_DAY,\n TIME_FORMAT_ISO8601,\n to_pd_timestamp,\n date_time_by_interval,\n current_date,\n)\n\n\ndef get_trade_dates(entity_type, start, end=None):\n if entity_type == \"stockus\":\n df = Indexus1dKdata.query_data(\n entity_id=\"indexus_us_SPX\",\n provider=\"em\",\n columns=[\"timestamp\"],\n start_timestamp=start,\n end_timestamp=end,\n order=Indexus1dKdata.timestamp.asc(),\n return_type=\"df\",\n )\n elif entity_type == \"stockhk\":\n df = Indexhk1dKdata.query_data(\n entity_id=\"indexhk_hk_HSI\",\n provider=\"em\",\n columns=[\"timestamp\"],\n start_timestamp=start,\n end_timestamp=end,\n order=Indexhk1dKdata.timestamp.asc(),\n return_type=\"df\",\n )\n\n else:\n df = Index1dKdata.query_data(\n entity_id=\"index_sh_000001\",\n provider=\"em\",\n columns=[\"timestamp\"],\n start_timestamp=start,\n end_timestamp=end,\n order=Index1dKdata.timestamp.asc(),\n return_type=\"df\",\n )\n\n return df[\"timestamp\"].tolist()\n\n\ndef get_recent_trade_dates(entity_type, target_date=current_date(), days_count=5):\n max_start = date_time_by_interval(target_date, -days_count - 15)\n dates = get_trade_dates(entity_type=entity_type, start=max_start)\n if days_count == 0:\n return dates[-1:]\n return dates[-days_count:]\n\n\ndef get_latest_kdata_date(\n entity_type: str,\n provider: str = None,\n level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY,\n adjust_type: Union[AdjustType, str] = None,\n) -> pd.Timestamp:\n data_schema: Mixin = get_kdata_schema(entity_type, level=level, adjust_type=adjust_type)\n\n filters = None\n if entity_type == \"indexus\":\n filters = [data_schema.entity_id == \"indexus_us_SPX\"]\n latest_data = data_schema.query_data(\n provider=provider, filters=filters, order=data_schema.timestamp.desc(), limit=1, return_type=\"domain\"\n )\n return to_pd_timestamp(latest_data[0].timestamp)\n\n\ndef get_kdata_schema(\n entity_type: str,\n level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY,\n adjust_type: Union[AdjustType, str] = None,\n) -> Mixin:\n if type(level) == str:\n level = IntervalLevel(level)\n if type(adjust_type) == str:\n adjust_type = AdjustType(adjust_type)\n\n # kdata schema rule\n # name:{entity_type.capitalize()}{IntervalLevel.value.capitalize()}Kdata\n if adjust_type and (adjust_type != AdjustType.qfq):\n schema_str = \"{}{}{}Kdata\".format(\n entity_type.capitalize(),\n level.value.capitalize(),\n adjust_type.value.capitalize(),\n )\n else:\n schema_str = \"{}{}Kdata\".format(entity_type.capitalize(), level.value.capitalize())\n return get_schema_by_name(schema_str)\n\n\ndef get_kdata(\n entity_id=None,\n entity_ids=None,\n level=IntervalLevel.LEVEL_1DAY.value,\n provider=None,\n columns=None,\n return_type=\"df\",\n start_timestamp=None,\n end_timestamp=None,\n filters=None,\n session=None,\n order=None,\n limit=None,\n index=\"timestamp\",\n drop_index_col=False,\n adjust_type: AdjustType = None,\n):\n assert not entity_id or not entity_ids\n if entity_ids:\n entity_id = entity_ids[0]\n else:\n entity_ids = [entity_id]\n\n entity_type, exchange, code = decode_entity_id(entity_id)\n data_schema: Mixin = get_kdata_schema(entity_type, level=level, adjust_type=adjust_type)\n\n return data_schema.query_data(\n entity_ids=entity_ids,\n level=level,\n provider=provider,\n columns=columns,\n return_type=return_type,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=filters,\n session=session,\n order=order,\n limit=limit,\n index=index,\n drop_index_col=drop_index_col,\n )\n\n\ndef default_adjust_type(entity_type: str) -> AdjustType:\n \"\"\"\n :type entity_type: entity type, e.g stock, stockhk, stockus\n \"\"\"\n if entity_type.lower().startswith(\"stock\"):\n return AdjustType.hfq\n return AdjustType.qfq\n\n\ndef generate_kdata_id(entity_id, timestamp, level):\n if level >= IntervalLevel.LEVEL_1DAY:\n return \"{}_{}\".format(entity_id, to_date_time_str(timestamp, fmt=TIME_FORMAT_DAY))\n else:\n return \"{}_{}\".format(entity_id, to_date_time_str(timestamp, fmt=TIME_FORMAT_ISO8601))\n\n\ndef to_high_level_kdata(kdata_df: pd.DataFrame, to_level: IntervalLevel):\n def to_close(s):\n if pd_is_not_null(s):\n return s[-1]\n\n def to_open(s):\n if pd_is_not_null(s):\n return s[0]\n\n def to_high(s):\n return np.max(s)\n\n def to_low(s):\n return np.min(s)\n\n def to_sum(s):\n return np.sum(s)\n\n original_level = kdata_df[\"level\"][0]\n entity_id = kdata_df[\"entity_id\"][0]\n provider = kdata_df[\"provider\"][0]\n name = kdata_df[\"name\"][0]\n code = kdata_df[\"code\"][0]\n\n entity_type, _, _ = decode_entity_id(entity_id=entity_id)\n\n assert IntervalLevel(original_level) <= IntervalLevel.LEVEL_1DAY\n assert IntervalLevel(original_level) < IntervalLevel(to_level)\n\n df: pd.DataFrame = None\n if to_level == IntervalLevel.LEVEL_1WEEK:\n # loffset='-2' 用周五作为时间标签\n if entity_type == \"stock\":\n df = kdata_df.resample(\"W\", offset=pd.Timedelta(days=-2)).apply(\n {\n \"close\": to_close,\n \"open\": to_open,\n \"high\": to_high,\n \"low\": to_low,\n \"volume\": to_sum,\n \"turnover\": to_sum,\n }\n )\n else:\n df = kdata_df.resample(\"W\", offset=pd.Timedelta(days=-2)).apply(\n {\n \"close\": to_close,\n \"open\": to_open,\n \"high\": to_high,\n \"low\": to_low,\n \"volume\": to_sum,\n \"turnover\": to_sum,\n }\n )\n df = df.dropna()\n # id entity_id timestamp provider code name level\n df[\"entity_id\"] = entity_id\n df[\"provider\"] = provider\n df[\"code\"] = code\n df[\"name\"] = name\n\n return df\n\n\nif __name__ == \"__main__\":\n print(get_trade_dates(entity_type=\"stockhk\", start=\"2025-07-01\"))\n\n\n# the __all__ is generated\n__all__ = [\n \"get_trade_dates\",\n \"get_recent_trade_dates\",\n \"get_latest_kdata_date\",\n \"get_kdata_schema\",\n \"get_kdata\",\n \"default_adjust_type\",\n \"generate_kdata_id\",\n \"to_high_level_kdata\",\n]\n"
},
{
"path": "src/zvt/api/portfolio.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.utils import get_recent_report_date\nfrom zvt.contract import PortfolioStockHistory\nfrom zvt.contract.api import get_schema_by_name\nfrom zvt.domain import ReportPeriod, Fund, Etf\nfrom zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp\n\n\ndef portfolio_relate_stock(df, portfolio):\n df[\"entity_id\"] = portfolio.entity_id\n df[\"entity_type\"] = portfolio.entity_type\n df[\"exchange\"] = portfolio.exchange\n df[\"code\"] = portfolio.code\n df[\"name\"] = portfolio.name\n\n return df\n\n\n# 季报只有前十大持仓，半年报和年报才有全量的持仓信息，故根据离timestamp最近的报表(年报 or 半年报)来确定持仓\ndef get_portfolio_stocks(\n portfolio_entity=Fund,\n code=None,\n codes=None,\n ids=None,\n timestamp=now_pd_timestamp(),\n provider=None,\n):\n portfolio_stock = f\"{portfolio_entity.__name__}Stock\"\n data_schema: PortfolioStockHistory = get_schema_by_name(portfolio_stock)\n latests: List[PortfolioStockHistory] = data_schema.query_data(\n provider=provider,\n code=code,\n end_timestamp=timestamp,\n order=data_schema.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if latests:\n latest_record = latests[0]\n # 获取最新的报表\n df = data_schema.query_data(\n provider=provider,\n code=code,\n codes=codes,\n ids=ids,\n end_timestamp=timestamp,\n filters=[data_schema.report_date == latest_record.report_date],\n )\n # 最新的为年报或者半年报\n if latest_record.report_period == ReportPeriod.year or latest_record.report_period == ReportPeriod.half_year:\n return df\n # 季报，需要结合 年报或半年报 来算持仓\n else:\n step = 0\n while step <= 20:\n report_date = get_recent_report_date(latest_record.report_date, step=step)\n\n pre_df = data_schema.query_data(\n provider=provider,\n code=code,\n codes=codes,\n ids=ids,\n end_timestamp=timestamp,\n filters=[data_schema.report_date == to_pd_timestamp(report_date)],\n )\n # df = df.append(pre_df)\n df = pd.concat([df, pre_df])\n\n # 半年报和年报\n if (ReportPeriod.half_year.value in pre_df[\"report_period\"].tolist()) or (\n ReportPeriod.year.value in pre_df[\"report_period\"].tolist()\n ):\n # 保留最新的持仓\n df = df.drop_duplicates(subset=[\"stock_code\"], keep=\"first\")\n return df\n step = step + 1\n\n\ndef get_etf_stocks(code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None):\n return get_portfolio_stocks(\n portfolio_entity=Etf,\n code=code,\n codes=codes,\n ids=ids,\n timestamp=timestamp,\n provider=provider,\n )\n\n\ndef get_fund_stocks(code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None):\n return get_portfolio_stocks(\n portfolio_entity=Fund,\n code=code,\n codes=codes,\n ids=ids,\n timestamp=timestamp,\n provider=provider,\n )\n\n\n# the __all__ is generated\n__all__ = [\"portfolio_relate_stock\", \"get_portfolio_stocks\", \"get_etf_stocks\", \"get_fund_stocks\"]\n"
},
{
"path": "src/zvt/api/selector.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nimport pandas as pd\nfrom sqlalchemy import or_, and_\n\nfrom zvt.api.kdata import default_adjust_type, get_kdata_schema, get_latest_kdata_date, get_recent_trade_dates\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract.api import get_entity_ids, get_entity_schema\nfrom zvt.domain import DragonAndTiger, Stock1dHfqKdata, Stock, LimitUpInfo, StockQuote, Stock1mQuote\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import (\n to_pd_timestamp,\n date_time_by_interval,\n current_date,\n next_date,\n to_date_time_str,\n TIME_FORMAT_MINUTE2,\n)\n\nlogger = logging.getLogger(__name__)\n\n# 500亿\nBIG_CAP = 50000000000\n# 150亿\nMIDDLE_CAP = 15000000000\n# 40亿\nSMALL_CAP = 4000000000\n\n# 买入榜单\nIN_DEPS = [\"dep1\", \"dep2\", \"dep3\", \"dep4\", \"dep5\"]\n# 卖出入榜单\nOUT_DEPS = [\"dep_1\", \"dep_2\", \"dep_3\", \"dep_4\", \"dep_5\"]\n\n\ndef get_entity_ids_by_filter(\n provider=\"em\",\n ignore_delist=True,\n ignore_st=False,\n ignore_new_stock=False,\n target_date=None,\n entity_type=\"stock\",\n entity_ids=None,\n ignore_bj=False,\n exchange=None,\n):\n entity_schema = get_entity_schema(entity_type=entity_type)\n\n filters = []\n if ignore_new_stock:\n if not target_date:\n target_date = current_date()\n pre_year = date_time_by_interval(target_date, -365)\n filters += [entity_schema.timestamp <= pre_year]\n else:\n if target_date:\n filters += [entity_schema.timestamp <= target_date]\n if ignore_delist:\n filters += [\n entity_schema.name.not_like(\"%退%\"),\n entity_schema.name.not_like(\"%PT%\"),\n ]\n\n if ignore_st:\n filters += [\n entity_schema.name.not_like(\"%ST%\"),\n entity_schema.name.not_like(\"%*ST%\"),\n ]\n if entity_schema == Stock and ignore_bj:\n filters += [entity_schema.exchange != \"bj\"]\n\n if exchange:\n filters += [entity_schema.exchange == exchange]\n\n return get_entity_ids(provider=provider, entity_schema=entity_schema, filters=filters, entity_ids=entity_ids)\n\n\ndef get_limit_up_stocks(timestamp):\n df = LimitUpInfo.query_data(start_timestamp=timestamp, end_timestamp=timestamp, columns=[LimitUpInfo.entity_id])\n if pd_is_not_null(df):\n return df[\"entity_id\"].tolist()\n\n\ndef get_recent_trending_stocks(adjust_type=AdjustType.qfq, provider=\"em\", recent_days=20):\n kdata_schema = get_kdata_schema(\"stock\", level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type)\n recent_dates = get_recent_trade_dates(entity_type=\"stock\", days_count=recent_days)\n start_date = recent_dates[0]\n\n df = kdata_schema.query_data(\n provider=provider,\n filters=[\n kdata_schema.timestamp >= start_date,\n kdata_schema.turnover >= 500000000,\n kdata_schema.change_pct > 0.08,\n ],\n index=\"entity_id\",\n )\n entity_ids = list(set(df[\"entity_id\"].tolist()))\n\n kdata_df = kdata_schema.query_data(\n provider=provider,\n entity_ids=entity_ids,\n filters=[kdata_schema.timestamp >= date_time_by_interval(start_date, -400)],\n index=[\"entity_id\", \"timestamp\"],\n )\n\n from zvt.factors.algorithm import MaTransformer\n\n t = MaTransformer(windows=[10, 20, 30, 60, 120, 250])\n ma_df = t.transform(kdata_df)\n\n end_date = ma_df[\"timestamp\"].max()\n result_df = ma_df[\n (ma_df[\"timestamp\"] == end_date)\n & (ma_df[\"turnover\"] >= 500000000)\n & (ma_df[\"close\"] > ma_df[\"ma20\"])\n & (ma_df[\"ma10\"] > ma_df[\"ma20\"])\n & (ma_df[\"ma20\"] > ma_df[\"ma30\"])\n & (ma_df[\"ma30\"] > ma_df[\"ma60\"])\n & (ma_df[\"ma60\"] > ma_df[\"ma120\"])\n & (ma_df[\"ma120\"] > ma_df[\"ma250\"])\n ]\n return result_df[\"entity_id\"].tolist()\n\n\ndef get_recent_active_stocks(adjust_type=AdjustType.qfq, provider=\"em\", recent_days=10):\n kdata_schema = get_kdata_schema(\"stock\", level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type)\n recent_dates = get_recent_trade_dates(entity_type=\"stock\", days_count=recent_days)\n start_date = recent_dates[0]\n\n df = kdata_schema.query_data(\n provider=provider,\n filters=[kdata_schema.timestamp >= start_date, kdata_schema.is_limit_up == True],\n index=\"entity_id\",\n )\n entity_ids = list(set(df[\"entity_id\"].tolist()))\n\n kdata_df = kdata_schema.query_data(\n provider=provider,\n entity_ids=entity_ids,\n filters=[kdata_schema.timestamp >= start_date],\n index=[\"entity_id\", \"timestamp\"],\n )\n\n from zvt.factors.algorithm import MaTransformer\n\n t = MaTransformer(windows=[10])\n ma_df = t.transform(kdata_df)\n\n end_date = ma_df[\"timestamp\"].max()\n result_df = ma_df[\n (ma_df[\"close\"] > ma_df[\"ma10\"]) & (ma_df[\"timestamp\"] == end_date) & (ma_df[\"turnover\"] >= 500000000)\n ]\n return result_df[\"entity_id\"].tolist()\n\n\ndef get_dragon_and_tigger_player(start_timestamp, end_timestamp=None, direction=\"in\"):\n assert direction in (\"in\", \"out\")\n\n filters = None\n if direction == \"in\":\n filters = [DragonAndTiger.change_pct > 0]\n columns = [\"dep1\", \"dep2\", \"dep3\"]\n elif direction == \"out\":\n filters = [DragonAndTiger.change_pct > 0]\n columns = [\"dep_1\", \"dep_2\", \"dep_3\"]\n\n df = DragonAndTiger.query_data(start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters)\n counts = []\n for col in columns:\n counts.append(df[[col, f\"{col}_rate\"]].groupby(col).count().sort_values(f\"{col}_rate\", ascending=False))\n return counts\n\n\ndef get_big_players(start_timestamp, end_timestamp=None, count=40):\n dep1, dep2, dep3 = get_dragon_and_tigger_player(start_timestamp=start_timestamp, end_timestamp=end_timestamp)\n # 榜1前40\n bang1 = dep1.index.tolist()[:count]\n\n # 榜2前40\n bang2 = dep2.index.tolist()[:count]\n\n # 榜3前40\n bang3 = dep3.index.tolist()[:count]\n\n return list(set(bang1 + bang2 + bang3))\n\n\ndef get_player_performance(start_timestamp, end_timestamp=None, days=5, players=\"机构专用\", provider=\"em\", buy_rate=5):\n filters = []\n if isinstance(players, str):\n players = [players]\n\n if isinstance(players, list):\n for player in players:\n filters.append(\n or_(\n and_(DragonAndTiger.dep1 == player, DragonAndTiger.dep1_rate >= buy_rate),\n and_(DragonAndTiger.dep2 == player, DragonAndTiger.dep2_rate >= buy_rate),\n and_(DragonAndTiger.dep3 == player, DragonAndTiger.dep3_rate >= buy_rate),\n and_(DragonAndTiger.dep4 == player, DragonAndTiger.dep4_rate >= buy_rate),\n and_(DragonAndTiger.dep5 == player, DragonAndTiger.dep5_rate >= buy_rate),\n )\n )\n else:\n raise AssertionError(\"players should be list or str type\")\n\n df = DragonAndTiger.query_data(\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=filters,\n index=[\"entity_id\", \"timestamp\"],\n provider=provider,\n )\n df = df[~df.index.duplicated(keep=\"first\")]\n records = []\n for entity_id, timestamp in df.index:\n end_date = date_time_by_interval(timestamp, days + round(days + days * 2 / 5 + 30))\n kdata = Stock1dHfqKdata.query_data(\n entity_id=entity_id,\n start_timestamp=timestamp,\n end_timestamp=end_date,\n provider=provider,\n index=\"timestamp\",\n )\n if len(kdata) <= days:\n logger.warning(f\"ignore {timestamp} -> end_timestamp: {end_date}\")\n break\n close = kdata[\"close\"]\n change_pct = (close[days] - close[0]) / close[0]\n records.append({\"entity_id\": entity_id, \"timestamp\": timestamp, f\"change_pct\": change_pct})\n return pd.DataFrame.from_records(records)\n\n\ndef get_player_success_rate(\n start_timestamp,\n end_timestamp=None,\n intervals=(3, 5, 10, 60),\n players=(\"机构专用\", \"东方财富证券股份有限公司拉萨团结路第二证券营业部\"),\n provider=\"em\",\n):\n records = []\n for player in players:\n record = {\"player\": player}\n for days in intervals:\n df = get_player_performance(\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n days=days,\n players=player,\n provider=provider,\n )\n rate = len(df[df[\"change_pct\"] > 0]) / len(df)\n record[f\"rate_{days}\"] = rate\n records.append(record)\n return pd.DataFrame.from_records(records, index=\"player\")\n\n\ndef get_players(entity_id, start_timestamp, end_timestamp, provider=\"em\", direction=\"in\", buy_rate=5):\n columns = [\"entity_id\", \"timestamp\"]\n if direction == \"in\":\n for i in range(5):\n columns.append(f\"dep{i + 1}\")\n columns.append(f\"dep{i + 1}_rate\")\n elif direction == \"out\":\n for i in range(5):\n columns.append(f\"dep_{i + 1}\")\n columns.append(f\"dep_{i + 1}_rate\")\n\n df = DragonAndTiger.query_data(\n entity_id=entity_id,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n provider=provider,\n columns=columns,\n index=[\"entity_id\", \"timestamp\"],\n )\n dfs = []\n if direction == \"in\":\n for i in range(5):\n p_df = df[[f\"dep{i + 1}\", f\"dep{i + 1}_rate\"]].copy()\n p_df.columns = [\"player\", \"buy_rate\"]\n dfs.append(p_df)\n elif direction == \"out\":\n for i in range(5):\n p_df = df[[f\"dep_{i + 1}\", f\"dep_{i + 1}_rate\"]].copy()\n p_df.columns = [\"player\", \"buy_rate\"]\n dfs.append(p_df)\n\n player_df = pd.concat(dfs, sort=True)\n return player_df.sort_index(level=[0, 1])\n\n\ndef get_good_players(timestamp=current_date(), recent_days=400, intervals=(3, 5, 10)):\n end_timestamp = date_time_by_interval(timestamp, -intervals[-1] - 30)\n # recent year\n start_timestamp = date_time_by_interval(end_timestamp, -recent_days)\n print(f\"{start_timestamp} to {end_timestamp}\")\n # 最近一年牛x的营业部\n players = get_big_players(start_timestamp=start_timestamp, end_timestamp=end_timestamp)\n logger.info(players)\n df = get_player_success_rate(\n start_timestamp=start_timestamp, end_timestamp=end_timestamp, intervals=intervals, players=players\n )\n good_players = df[(df[\"rate_3\"] > 0.4) & (df[\"rate_5\"] > 0.3) & (df[\"rate_10\"] > 0.3)].index.tolist()\n return good_players\n\n\ndef get_entity_list_by_cap(\n timestamp, cap_start, cap_end, entity_type=\"stock\", provider=None, adjust_type=None, retry_times=20\n):\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n\n kdata_schema = get_kdata_schema(entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type)\n df = kdata_schema.query_data(\n provider=provider,\n filters=[kdata_schema.timestamp == to_pd_timestamp(timestamp)],\n index=\"entity_id\",\n )\n if pd_is_not_null(df):\n df[\"cap\"] = df[\"turnover\"] / df[\"turnover_rate\"]\n df_result = df.copy()\n if cap_start:\n df_result = df_result.loc[(df[\"cap\"] >= cap_start)]\n if cap_end:\n df_result = df_result.loc[(df[\"cap\"] <= cap_end)]\n return df_result.index.tolist()\n else:\n if retry_times == 0:\n return []\n return get_entity_list_by_cap(\n timestamp=next_date(timestamp),\n cap_start=cap_start,\n cap_end=cap_end,\n entity_type=entity_type,\n provider=provider,\n adjust_type=adjust_type,\n retry_times=retry_times - 1,\n )\n\n\ndef get_big_cap_stock(timestamp, provider=\"em\", adjust_type=None):\n return get_entity_list_by_cap(\n timestamp=timestamp,\n cap_start=BIG_CAP,\n cap_end=None,\n entity_type=\"stock\",\n provider=provider,\n adjust_type=adjust_type,\n )\n\n\ndef get_middle_cap_stock(timestamp, provider=\"em\", adjust_type=None):\n return get_entity_list_by_cap(\n timestamp=timestamp,\n cap_start=MIDDLE_CAP,\n cap_end=BIG_CAP,\n entity_type=\"stock\",\n provider=provider,\n adjust_type=adjust_type,\n )\n\n\ndef get_small_cap_stock(timestamp, provider=\"em\", adjust_type=None):\n return get_entity_list_by_cap(\n timestamp=timestamp,\n cap_start=SMALL_CAP,\n cap_end=MIDDLE_CAP,\n entity_type=\"stock\",\n provider=provider,\n adjust_type=adjust_type,\n )\n\n\ndef get_mini_cap_stock(timestamp, provider=\"em\", adjust_type=None):\n return get_entity_list_by_cap(\n timestamp=timestamp,\n cap_start=None,\n cap_end=SMALL_CAP,\n entity_type=\"stock\",\n provider=provider,\n adjust_type=adjust_type,\n )\n\n\ndef get_mini_and_small_stock(timestamp, provider=\"em\", adjust_type=None):\n return get_entity_list_by_cap(\n timestamp=timestamp,\n cap_start=None,\n cap_end=MIDDLE_CAP,\n entity_type=\"stock\",\n provider=provider,\n adjust_type=adjust_type,\n )\n\n\ndef get_middle_and_big_stock(timestamp, provider=\"em\", adjust_type=None):\n return get_entity_list_by_cap(\n timestamp=timestamp,\n cap_start=MIDDLE_CAP,\n cap_end=None,\n entity_type=\"stock\",\n provider=provider,\n adjust_type=adjust_type,\n )\n\n\ndef get_limit_up_today():\n df = StockQuote.query_data(filters=[StockQuote.is_limit_up], columns=[StockQuote.entity_id])\n if pd_is_not_null(df):\n return df[\"entity_id\"].to_list()\n\n\ndef get_top_up_today(n=100):\n df = StockQuote.query_data(columns=[StockQuote.entity_id], order=StockQuote.change_pct.desc(), limit=n)\n if pd_is_not_null(df):\n return df[\"entity_id\"].to_list()\n\n\ndef get_shoot_today(up_change_pct=0.03, down_change_pct=-0.03, interval=1):\n latest = Stock1mQuote.query_data(\n columns=[Stock1mQuote.time], return_type=\"df\", limit=1, order=Stock1mQuote.time.desc()\n )\n latest_time = int(latest[\"time\"][0])\n\n # interval minutes\n start_time = latest_time - ((interval + 1) * 60 * 1000)\n\n filters = [Stock1mQuote.time > start_time, Stock1mQuote.turnover_rate > 0.02]\n df = Stock1mQuote.query_data(\n filters=filters,\n columns=[\n Stock1mQuote.entity_id,\n Stock1mQuote.is_limit_up,\n Stock1mQuote.near_limit_up,\n Stock1mQuote.time,\n Stock1mQuote.price,\n ],\n return_type=\"df\",\n )\n\n if not pd_is_not_null(df):\n logger.warning(\n f\"no data found in Stock1mQuote from {to_date_time_str(start_time,fmt=TIME_FORMAT_MINUTE2)} to {to_date_time_str(latest_time,fmt=TIME_FORMAT_MINUTE2)}\"\n )\n return None, None\n\n up_list = []\n up_df = df[(~df[\"is_limit_up\"]) & df[\"near_limit_up\"]]\n if pd_is_not_null(up_df):\n up_list = up_df[\"entity_id\"]\n up_list = list(set(up_list.to_list()))\n\n df.sort_values(by=[\"entity_id\", \"time\"], inplace=True)\n\n g_df = df.groupby(\"entity_id\").agg(\n first_price=(\"price\", \"first\"),\n last_price=(\"price\", \"last\"),\n last_time=(\"time\", \"last\"),\n change_pct=(\"price\", lambda x: (x.iloc[-1] - x.iloc[0]) / x.iloc[0]),\n )\n up = g_df[g_df[\"change_pct\"] >= up_change_pct]\n down = g_df[g_df[\"change_pct\"] <= down_change_pct]\n\n if up_list:\n up_list = list(set(up_list + up.index.tolist()))\n else:\n up_list = up.index.tolist()\n\n return up_list, down.index.tolist()\n\n\ndef get_top_vol(\n entity_ids,\n target_date=None,\n limit=500,\n provider=\"qmt\",\n):\n if provider == \"qmt\":\n df = StockQuote.query_data(\n entity_ids=entity_ids,\n columns=[StockQuote.entity_id],\n order=StockQuote.turnover.desc(),\n limit=limit,\n )\n return df[\"entity_id\"].to_list()\n else:\n if not target_date:\n target_date = get_latest_kdata_date(provider=\"em\", entity_type=\"stock\", adjust_type=AdjustType.hfq)\n df = Stock1dHfqKdata.query_data(\n provider=\"em\",\n filters=[Stock1dHfqKdata.timestamp == to_pd_timestamp(target_date)],\n entity_ids=entity_ids,\n columns=[Stock1dHfqKdata.entity_id],\n order=Stock1dHfqKdata.turnover.desc(),\n limit=limit,\n )\n return df[\"entity_id\"].to_list()\n\n\ndef get_top_down_today(n=100):\n df = StockQuote.query_data(columns=[StockQuote.entity_id], order=StockQuote.change_pct.asc(), limit=n)\n if pd_is_not_null(df):\n return df[\"entity_id\"].to_list()\n\n\ndef get_limit_down_today():\n df = StockQuote.query_data(filters=[StockQuote.is_limit_down], columns=[StockQuote.entity_id])\n if pd_is_not_null(df):\n return df[\"entity_id\"].to_list()\n\n\ndef get_high_days_count(entity_ids=None, target_date=current_date(), days_count=10, high_days_count=None):\n recent_days = get_recent_trade_dates(entity_type=\"stock\", target_date=target_date, days_count=days_count)\n\n if recent_days:\n filters = [LimitUpInfo.timestamp >= recent_days[0]]\n else:\n filters = [LimitUpInfo.timestamp >= target_date]\n\n if high_days_count:\n filters = filters + [LimitUpInfo.high_days_count >= high_days_count]\n\n df = LimitUpInfo.query_data(\n entity_ids=entity_ids,\n filters=filters,\n columns=[LimitUpInfo.timestamp, LimitUpInfo.entity_id, LimitUpInfo.high_days, LimitUpInfo.high_days_count],\n )\n df_sorted = df.sort_values(by=[\"entity_id\", \"timestamp\"])\n df_latest = df_sorted.drop_duplicates(subset=\"entity_id\", keep=\"last\").reset_index(drop=True)\n\n entity_id_to_high_days_map = df_latest.set_index(\"entity_id\")[\"high_days\"].to_dict()\n return entity_id_to_high_days_map\n\n\nif __name__ == \"__main__\":\n # stocks = get_top_vol(entity_ids=None, provider=\"em\")\n # assert len(stocks) == 500\n # Index1dKdata.record_data(provider=\"em\",sleeping_time=0)\n # print(get_recent_trade_dates(days_count=10))\n # print(get_high_days_count(days_count=3, high_days_count=3))\n print(get_shoot_today())\n\n# the __all__ is generated\n__all__ = [\n \"get_entity_ids_by_filter\",\n \"get_limit_up_stocks\",\n \"get_dragon_and_tigger_player\",\n \"get_big_players\",\n \"get_player_performance\",\n \"get_player_success_rate\",\n \"get_players\",\n \"get_good_players\",\n \"get_entity_list_by_cap\",\n \"get_big_cap_stock\",\n \"get_middle_cap_stock\",\n \"get_small_cap_stock\",\n \"get_mini_cap_stock\",\n \"get_mini_and_small_stock\",\n \"get_middle_and_big_stock\",\n]\n"
},
{
"path": "src/zvt/api/stats.py",
"content": "# -*- coding: utf-8 -*-\nimport enum\nimport itertools\nimport logging\nfrom typing import Union\n\nimport pandas as pd\n\nfrom zvt.api.kdata import get_kdata_schema, default_adjust_type, get_latest_kdata_date, get_trade_dates\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.api.utils import get_recent_report_date\nfrom zvt.contract import Mixin, AdjustType\nfrom zvt.contract.api import decode_entity_id, get_entity_schema, get_entity_ids\nfrom zvt.contract.drawer import Drawer\nfrom zvt.domain import FundStock, StockValuation, BlockStock, Block\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import (\n month_start_end_ranges,\n to_date_time_str,\n is_same_date,\n now_pd_timestamp,\n date_time_by_interval,\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass WindowMethod(enum.Enum):\n change = \"change\"\n avg = \"avg\"\n sum = \"sum\"\n\n\nclass TopType(enum.Enum):\n positive = \"positive\"\n negative = \"negative\"\n\n\ndef get_top_performance_by_month(\n entity_type=\"stock\",\n start_timestamp=\"2015-01-01\",\n end_timestamp=now_pd_timestamp(),\n list_days=None,\n data_provider=None,\n):\n ranges = month_start_end_ranges(start_date=start_timestamp, end_date=end_timestamp)\n\n for month_range in ranges:\n start_timestamp = month_range[0]\n end_timestamp = month_range[1]\n top, _ = get_top_performance_entities(\n entity_type=entity_type,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n list_days=list_days,\n data_provider=data_provider,\n )\n\n yield start_timestamp, end_timestamp, top\n\n\ndef get_top_performance_entities_by_periods(\n entity_provider,\n data_provider,\n target_date=None,\n periods=None,\n entity_ids=None,\n ignore_new_stock=True,\n ignore_st=True,\n entity_type=\"stock\",\n adjust_type=None,\n top_count=50,\n turnover_threshold=100000000,\n turnover_rate_threshold=0.02,\n return_type=TopType.positive,\n):\n if periods is None:\n periods = [*range(1, 21)]\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n kdata_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n entity_schema = get_entity_schema(entity_type=entity_type)\n\n if not target_date:\n target_date = get_latest_kdata_date(provider=data_provider, entity_type=entity_type, adjust_type=adjust_type)\n\n if not entity_ids:\n filter_entity_ids = get_entity_ids_by_filter(\n provider=entity_provider,\n ignore_st=ignore_st,\n ignore_new_stock=ignore_new_stock,\n entity_schema=entity_schema,\n target_date=target_date,\n )\n else:\n filter_entity_ids = entity_ids\n\n if not filter_entity_ids:\n return []\n\n filter_turnover_df = kdata_schema.query_data(\n filters=[\n kdata_schema.turnover >= turnover_threshold,\n kdata_schema.turnover_rate >= turnover_rate_threshold,\n ],\n provider=data_provider,\n start_timestamp=date_time_by_interval(target_date, -7),\n end_timestamp=target_date,\n index=\"entity_id\",\n columns=[\"entity_id\", \"code\"],\n )\n if filter_entity_ids:\n filter_entity_ids = set(filter_entity_ids) & set(filter_turnover_df.index.tolist())\n else:\n filter_entity_ids = filter_turnover_df.index.tolist()\n\n if not filter_entity_ids:\n return []\n\n logger.info(f\"{entity_type} filter_entity_ids size: {len(filter_entity_ids)}\")\n filters = [kdata_schema.entity_id.in_(filter_entity_ids)]\n selected = []\n current_start = None\n real_period = 1\n for i, period in enumerate(periods):\n real_period = max(real_period, period)\n while True:\n start = date_time_by_interval(target_date, -real_period)\n trade_days = get_trade_dates(entity_type=entity_type, start=start, end=target_date)\n if not trade_days:\n logger.info(f\"no trade days in: {start} to {target_date}\")\n real_period = real_period + 1\n continue\n if current_start and is_same_date(current_start, trade_days[0]):\n logger.info(\"ignore same trade days\")\n real_period = real_period + 1\n continue\n break\n current_start = trade_days[0]\n current_end = trade_days[-1]\n\n logger.info(f\"trade days in: {current_start} to {current_end}, real_period: {real_period} \")\n positive_df, negative_df = get_top_performance_entities(\n entity_type=entity_type,\n start_timestamp=current_start,\n end_timestamp=current_end,\n kdata_filters=filters,\n pct=1,\n show_name=True,\n entity_provider=entity_provider,\n data_provider=data_provider,\n adjust_type=adjust_type,\n return_type=return_type,\n )\n\n if return_type == TopType.positive:\n df = positive_df\n else:\n df = negative_df\n if pd_is_not_null(df):\n selected = selected + df.index[:top_count].tolist()\n selected = list(dict.fromkeys(selected))\n return selected, real_period\n\n\ndef get_top_performance_entities(\n entity_type=\"stock\",\n start_timestamp=None,\n end_timestamp=None,\n pct=0.1,\n return_type=None,\n adjust_type: Union[AdjustType, str] = None,\n entity_filters=None,\n kdata_filters=None,\n show_name=False,\n list_days=None,\n entity_provider=None,\n data_provider=None,\n):\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n\n if not entity_filters:\n entity_filters = []\n if list_days:\n entity_schema = get_entity_schema(entity_type=entity_type)\n list_date = date_time_by_interval(start_timestamp, -list_days)\n entity_filters += [entity_schema.list_date <= list_date]\n\n filter_entities = get_entity_ids(\n provider=entity_provider,\n entity_type=entity_type,\n filters=entity_filters,\n )\n if not filter_entities:\n logger.warning(f\"no entities selected\")\n return None, None\n\n if not kdata_filters:\n kdata_filters = []\n kdata_filters = kdata_filters + [data_schema.entity_id.in_(filter_entities)]\n\n return get_top_entities(\n data_schema=data_schema,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n column=\"close\",\n pct=pct,\n method=WindowMethod.change,\n return_type=return_type,\n kdata_filters=kdata_filters,\n show_name=show_name,\n data_provider=data_provider,\n )\n\n\ndef get_top_fund_holding_stocks(timestamp=None, pct=0.3, by=None):\n if not timestamp:\n timestamp = now_pd_timestamp()\n # 季报一般在report_date后1个月内公布，年报2个月内，年报4个月内\n # 所以取时间点的最近的两个公布点，保证取到数据\n # 所以，这是个滞后的数据，只是为了看个大概，毕竟模糊的正确better than 精确的错误\n report_date = get_recent_report_date(timestamp, 1)\n fund_cap_df = FundStock.query_data(\n filters=[\n FundStock.report_date >= report_date,\n FundStock.timestamp <= timestamp,\n ],\n columns=[\"stock_id\", \"market_cap\"],\n )\n fund_cap_df = fund_cap_df.groupby(\"stock_id\")[\"market_cap\"].sum().sort_values(ascending=False)\n\n # 直接根据持有市值返回\n if not by:\n s = fund_cap_df.iloc[: int(len(fund_cap_df) * pct)]\n\n return s.to_frame()\n\n # 按流通盘比例\n if by == \"trading\":\n columns = [\"entity_id\", \"circulating_market_cap\"]\n # 按市值比例\n elif by == \"all\":\n columns = [\"entity_id\", \"market_cap\"]\n\n entity_ids = fund_cap_df.index.tolist()\n start_timestamp = date_time_by_interval(timestamp, -30)\n cap_df = StockValuation.query_data(\n entity_ids=entity_ids,\n filters=[\n StockValuation.timestamp >= start_timestamp,\n StockValuation.timestamp <= timestamp,\n ],\n columns=columns,\n )\n if by == \"trading\":\n cap_df = cap_df.rename(columns={\"circulating_market_cap\": \"cap\"})\n elif by == \"all\":\n cap_df = cap_df.rename(columns={\"market_cap\": \"cap\"})\n\n cap_df = cap_df.groupby(\"entity_id\").mean()\n result_df = pd.concat([cap_df, fund_cap_df], axis=1, join=\"inner\")\n result_df[\"pct\"] = result_df[\"market_cap\"] / result_df[\"cap\"]\n\n pct_df = result_df[\"pct\"].sort_values(ascending=False)\n\n s = pct_df.iloc[: int(len(pct_df) * pct)]\n\n return s.to_frame()\n\n\ndef get_performance(\n entity_ids,\n start_timestamp=None,\n end_timestamp=None,\n adjust_type: Union[AdjustType, str] = None,\n data_provider=None,\n):\n entity_type, _, _ = decode_entity_id(entity_ids[0])\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n\n result, _ = get_top_entities(\n data_schema=data_schema,\n column=\"close\",\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n pct=1,\n method=WindowMethod.change,\n return_type=TopType.positive,\n kdata_filters=[data_schema.entity_id.in_(entity_ids)],\n data_provider=data_provider,\n )\n return result\n\n\ndef get_performance_stats_by_month(\n entity_type=\"stock\",\n start_timestamp=\"2015-01-01\",\n end_timestamp=now_pd_timestamp(),\n adjust_type: Union[AdjustType, str] = None,\n data_provider=None,\n):\n ranges = month_start_end_ranges(start_date=start_timestamp, end_date=end_timestamp)\n\n month_stats = {}\n for month_range in ranges:\n start_timestamp = month_range[0]\n end_timestamp = month_range[1]\n logger.info(f\"calculate range [{start_timestamp}, {end_timestamp}]\")\n stats = get_performance_stats(\n entity_type=entity_type,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n adjust_type=adjust_type,\n data_provider=data_provider,\n )\n if stats:\n month_stats[f\"{to_date_time_str(start_timestamp)}\"] = stats\n\n return pd.DataFrame.from_dict(data=month_stats, orient=\"index\")\n\n\ndef get_performance_stats(\n entity_type=\"stock\",\n start_timestamp=None,\n end_timestamp=None,\n adjust_type: Union[AdjustType, str] = None,\n data_provider=None,\n changes=((-1, -0.5), (-0.5, -0.2), (-0.2, 0), (0, 0.2), (0.2, 0.5), (0.5, 1), (1, 1000)),\n):\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n\n score_df, _ = get_top_entities(\n data_schema=data_schema,\n column=\"close\",\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n pct=1,\n method=WindowMethod.change,\n return_type=TopType.positive,\n data_provider=data_provider,\n )\n\n if pd_is_not_null(score_df):\n result = {}\n for change in changes:\n range_start = change[0]\n range_end = change[1]\n key = f\"pct_{range_start}_{range_end}\"\n df = score_df[(score_df[\"score\"] >= range_start) & (score_df[\"score\"] < range_end)]\n result[key] = len(df)\n return result\n\n\ndef get_top_volume_entities(\n entity_type=\"stock\",\n entity_ids=None,\n start_timestamp=None,\n end_timestamp=None,\n pct=0.1,\n return_type=TopType.positive,\n adjust_type: Union[AdjustType, str] = None,\n method=WindowMethod.avg,\n data_provider=None,\n threshold=None,\n):\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n\n data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n\n filters = []\n if entity_ids:\n filters.append(data_schema.entity_id.in_(entity_ids))\n if threshold:\n filters.append(data_schema.turnover >= threshold)\n\n result, _ = get_top_entities(\n data_schema=data_schema,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n column=\"turnover\",\n pct=pct,\n method=method,\n return_type=return_type,\n kdata_filters=filters,\n data_provider=data_provider,\n )\n return result\n\n\ndef get_top_turnover_rate_entities(\n entity_type=\"stock\",\n entity_ids=None,\n start_timestamp=None,\n end_timestamp=None,\n pct=0.1,\n return_type=TopType.positive,\n adjust_type: Union[AdjustType, str] = None,\n method=WindowMethod.avg,\n data_provider=None,\n threshold=None,\n):\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n\n data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n\n filters = []\n if entity_ids:\n filters.append(data_schema.entity_id.in_(entity_ids))\n if threshold:\n filters.append(data_schema.turnover_rate >= threshold)\n\n result, _ = get_top_entities(\n data_schema=data_schema,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n column=\"turnover_rate\",\n pct=pct,\n method=method,\n return_type=return_type,\n kdata_filters=filters,\n data_provider=data_provider,\n )\n return result\n\n\ndef get_top_entities(\n data_schema: Mixin,\n column: str,\n start_timestamp=None,\n end_timestamp=None,\n pct=0.1,\n method: WindowMethod = WindowMethod.change,\n return_type: TopType = None,\n kdata_filters=None,\n show_name=False,\n data_provider=None,\n):\n \"\"\"\n get top entities in specific domain between time range\n\n :param data_schema: schema in domain\n :param column: schema column\n :param start_timestamp:\n :param end_timestamp:\n :param pct: range (0,1]\n :param method:\n :param return_type:\n :param entity_filters:\n :param kdata_filters:\n :param show_name: show entity name\n :return:\n \"\"\"\n if type(method) == str:\n method = WindowMethod(method)\n\n if type(return_type) == str:\n return_type = TopType(return_type)\n\n if show_name:\n columns = [\"entity_id\", column, \"name\"]\n else:\n columns = [\"entity_id\", column]\n\n all_df = data_schema.query_data(\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n columns=columns,\n filters=kdata_filters,\n provider=data_provider,\n )\n if not pd_is_not_null(all_df):\n return None, None\n g = all_df.groupby(\"entity_id\")\n tops = {}\n names = {}\n for entity_id, df in g:\n if method == WindowMethod.change:\n start = df[column].iloc[0]\n end = df[column].iloc[-1]\n if start != 0:\n change = (end - start) / abs(start)\n else:\n change = 0\n tops[entity_id] = change\n elif method == WindowMethod.avg:\n tops[entity_id] = df[column].mean()\n elif method == WindowMethod.sum:\n tops[entity_id] = df[column].sum()\n\n if show_name:\n names[entity_id] = df[\"name\"].iloc[0]\n\n positive_df = None\n negative_df = None\n top_index = int(len(tops) * pct)\n if return_type is None or return_type == TopType.positive:\n # from big to small\n positive_tops = {k: v for k, v in sorted(tops.items(), key=lambda item: item[1], reverse=True)}\n positive_tops = dict(itertools.islice(positive_tops.items(), top_index))\n positive_df = pd.DataFrame.from_dict(positive_tops, orient=\"index\")\n\n col = \"score\"\n positive_df.columns = [col]\n positive_df.sort_values(by=col, ascending=False)\n if return_type is None or return_type == TopType.negative:\n # from small to big\n negative_tops = {k: v for k, v in sorted(tops.items(), key=lambda item: item[1])}\n negative_tops = dict(itertools.islice(negative_tops.items(), top_index))\n negative_df = pd.DataFrame.from_dict(negative_tops, orient=\"index\")\n\n col = \"score\"\n negative_df.columns = [col]\n negative_df.sort_values(by=col)\n\n if names:\n if pd_is_not_null(positive_df):\n positive_df[\"name\"] = positive_df.index.map(lambda x: names[x])\n if pd_is_not_null(negative_df):\n negative_df[\"name\"] = negative_df.index.map(lambda x: names[x])\n return positive_df, negative_df\n\n\ndef show_month_performance():\n dfs = []\n for timestamp, df in get_top_performance_by_month(start_timestamp=\"2005-01-01\", list_days=250):\n if pd_is_not_null(df):\n df = df.reset_index(drop=True)\n df[\"entity_id\"] = \"stock_cn_performance\"\n df[\"timestamp\"] = timestamp\n dfs.append(df)\n\n all_df = pd.concat(dfs)\n print(all_df)\n\n drawer = Drawer(main_df=all_df)\n drawer.draw_scatter(show=True)\n\n\ndef show_industry_composition(entity_ids, timestamp):\n block_df = Block.query_data(provider=\"eastmoney\", filters=[Block.category == \"industry\"], index=\"entity_id\")\n block_ids = block_df.index.tolist()\n\n block_df = BlockStock.query_data(entity_ids=block_ids, filters=[BlockStock.stock_id.in_(entity_ids)])\n\n s = block_df[\"name\"].value_counts()\n\n cycle_df = pd.DataFrame(columns=s.index, data=[s.tolist()])\n cycle_df[\"entity_id\"] = \"stock_cn_industry\"\n cycle_df[\"timestamp\"] = timestamp\n drawer = Drawer(main_df=cycle_df)\n drawer.draw_pie(show=True)\n\n\ndef get_change_ratio(\n entity_type=\"stock\",\n start_timestamp=None,\n end_timestamp=None,\n adjust_type: Union[AdjustType, str] = None,\n provider=\"em\",\n):\n def cal_ratio(df):\n positive = df[df[\"direction\"]]\n other = df[~df[\"direction\"]]\n return len(positive) / len(other)\n\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_type)\n data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)\n kdata_df = data_schema.query_data(provider=provider, start_timestamp=start_timestamp, end_timestamp=end_timestamp)\n kdata_df[\"direction\"] = kdata_df[\"change_pct\"] > 0\n ratio_df = kdata_df.groupby(\"timestamp\").apply(lambda df: cal_ratio(df))\n return ratio_df\n\n\nif __name__ == \"__main__\":\n normal_stock_ids = get_entity_ids_by_filter(\n entity_type=\"stockus\", provider=\"em\", ignore_delist=True, ignore_st=False, ignore_new_stock=False\n )\n\n print(\n get_top_performance_entities_by_periods(\n entity_provider=\"em\",\n data_provider=\"em\",\n entity_ids=normal_stock_ids,\n entity_type=\"stockus\",\n adjust_type=AdjustType.qfq,\n turnover_threshold=10000000,\n turnover_rate_threshold=0,\n )\n )\n\n\n# the __all__ is generated\n__all__ = [\n \"WindowMethod\",\n \"TopType\",\n \"get_top_performance_by_month\",\n \"get_top_performance_entities_by_periods\",\n \"get_top_performance_entities\",\n \"get_top_fund_holding_stocks\",\n \"get_performance\",\n \"get_performance_stats_by_month\",\n \"get_performance_stats\",\n \"get_top_volume_entities\",\n \"get_top_turnover_rate_entities\",\n \"get_top_entities\",\n \"show_month_performance\",\n \"show_industry_composition\",\n \"get_change_ratio\",\n]\n"
},
{
"path": "src/zvt/api/utils.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import Type\n\nfrom zvt.contract import Mixin\nfrom zvt.domain import ReportPeriod\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp\n\n\ndef to_report_period_type(report_date):\n the_date = to_pd_timestamp(report_date)\n if the_date.month == 3 and the_date.day == 31:\n return ReportPeriod.season1.value\n if the_date.month == 6 and the_date.day == 30:\n return ReportPeriod.half_year.value\n if the_date.month == 9 and the_date.day == 30:\n return ReportPeriod.season3.value\n if the_date.month == 12 and the_date.day == 31:\n return ReportPeriod.year.value\n\n return None\n\n\ndef get_recent_report_date(the_date=now_pd_timestamp(), step=0):\n the_date = to_pd_timestamp(the_date)\n assert step >= 0\n if the_date.month >= 10:\n recent = \"{}{}\".format(the_date.year, \"-09-30\")\n elif the_date.month >= 7:\n recent = \"{}{}\".format(the_date.year, \"-06-30\")\n elif the_date.month >= 4:\n recent = \"{}{}\".format(the_date.year, \"-03-31\")\n else:\n recent = \"{}{}\".format(the_date.year - 1, \"-12-31\")\n\n if step == 0:\n return recent\n else:\n step = step - 1\n return get_recent_report_date(recent, step)\n\n\ndef get_recent_report_period(the_date=now_pd_timestamp(), step=0):\n return to_report_period_type(get_recent_report_date(the_date, step=step))\n\n\ndef get_china_exchange(code):\n code_ = int(code)\n if 800000 >= code_ >= 600000:\n return \"sh\"\n elif code_ >= 400000:\n return \"bj\"\n else:\n return \"sz\"\n\n\ndef china_stock_code_to_id(code):\n return \"{}_{}_{}\".format(\"stock\", get_china_exchange(code), code)\n\n\ndef value_to_pct(value, default=0):\n return value / 100 if value else default\n\n\ndef value_multiply(value, multiplier, default=0):\n return value * multiplier if value else default\n\n\ndef float_to_pct_str(value):\n return f\"{round(value * 100, 2)}%\"\n\n\ndef get_recent_report(data_schema: Type[Mixin], timestamp, entity_id=None, filters=None, max_step=2):\n i = 0\n while i < max_step:\n report_date = get_recent_report_date(the_date=timestamp, step=i)\n if filters:\n filters = filters + [data_schema.report_date == to_pd_timestamp(report_date)]\n else:\n filters = [data_schema.report_date == to_pd_timestamp(report_date)]\n df = data_schema.query_data(entity_id=entity_id, filters=filters)\n if pd_is_not_null(df):\n return df\n i = i + 1\n\n\n# the __all__ is generated\n__all__ = [\n \"to_report_period_type\",\n \"get_recent_report_date\",\n \"get_recent_report_period\",\n \"get_china_exchange\",\n \"china_stock_code_to_id\",\n \"value_to_pct\",\n \"value_multiply\",\n \"float_to_pct_str\",\n \"get_recent_report\",\n]\n"
},
{
"path": "src/zvt/autocode/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule generator\nfrom .generator import *\nfrom .generator import __all__ as _generator_all\n\n__all__ += _generator_all\n\n# import all from submodule templates\nfrom .templates import *\nfrom .templates import __all__ as _templates_all\n\n__all__ += _templates_all\n"
},
{
"path": "src/zvt/autocode/generator.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport os\nfrom typing import List\n\nfrom zvt.autocode.templates import all_tpls\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.utils.file_utils import list_all_files\nfrom zvt.utils.git_utils import get_git_user_name, get_git_user_email\nfrom zvt.utils.time_utils import now_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\ndef all_sub_modules(dir_path: str):\n \"\"\"\n list all module name in specific directory\n\n :param dir_path:\n :return:\n \"\"\"\n modules = []\n for entry in os.scandir(dir_path):\n if entry.is_dir() or (entry.path.endswith(\".py\") and not entry.path.endswith(\"__init__.py\")):\n module_name = os.path.splitext(os.path.basename(entry.path))[0]\n # ignore hidden\n if module_name.startswith(\".\") or not module_name[0].isalpha():\n continue\n modules.append(module_name)\n return modules\n\n\ndef _remove_start_end(line: str, start=\"class \", end=\"(\"):\n if line.startswith(start) and (end in line):\n start_index = len(start)\n end_index = line.index(end)\n return line[start_index:end_index]\n if not start and (end in line):\n end_index = line.index(end)\n return line[:end_index]\n\n\ndef _get_interface_name(line: str):\n \"\"\"\n get interface name of the line\n\n :param line: the line of the source\n :return:\n \"\"\"\n if line.startswith(\"class \"):\n return _remove_start_end(line, \"class \", \"(\")\n elif line.startswith(\"def \"):\n return _remove_start_end(line, \"def \", \"(\")\n\n\ndef _get_var_name(line: str):\n \"\"\"\n get var name of the line\n\n :param line: the line of the source\n :return:\n \"\"\"\n if not _get_interface_name(line):\n words = line.split(\" \")\n if len(words) >= 2 and words[1] == \"=\":\n return words[0]\n\n\ndef all_sub_all(sub_module):\n return \"\"\"\n\n# import all from submodule {0}\nfrom .{0} import *\nfrom .{0} import __all__ as _{0}_all\n\n__all__ += _{0}_all\"\"\".format(\n sub_module\n )\n\n\ndef fill_package_if_not_exist(dir_path: str):\n fill_package(dir_path)\n for entry in os.scandir(dir_path):\n if entry.is_dir():\n fill_package(entry.path)\n fill_package_if_not_exist(entry.path)\n elif entry.is_file():\n pass\n\n\ndef fill_package(dir_path: str):\n base_name = os.path.basename(dir_path)\n if base_name[0].isalpha():\n if os.path.isdir(dir_path):\n pkg_file = os.path.join(dir_path, \"__init__.py\")\n if not os.path.exists(pkg_file):\n package_template = \"# -*- coding: utf-8 -*-\\n\"\n with open(pkg_file, \"w\", encoding=\"utf-8\") as outfile:\n outfile.write(package_template)\n\n\ndef gen_exports(\n dir_path=\"./domain\",\n gen_flag=\"# the __all__ is generated\",\n export_from_package=False,\n exclude_modules=None,\n export_modules=None,\n excludes=None,\n export_var=False,\n):\n if not excludes:\n excludes = [\"logger\"]\n if os.path.isfile(dir_path):\n files = [dir_path]\n else:\n fill_package_if_not_exist(dir_path=dir_path)\n files = list_all_files(dir_path=dir_path)\n for file in files:\n exports = []\n lines = []\n # read and generate __all__\n with open(file, encoding=\"utf-8\") as fp:\n line = fp.readline()\n while line:\n if line.startswith(gen_flag):\n break\n lines.append(line)\n export = _get_interface_name(line)\n if export_var and not export:\n export = _get_var_name(line)\n if export and export[0].isalpha() and export not in excludes:\n exports.append(export)\n line = fp.readline()\n print(f\"{file}:{exports}\")\n end_empty_lines_count = 0\n for i in range(-1, -len(lines) - 1, -1):\n if not lines[i].isspace():\n break\n end_empty_lines_count = end_empty_lines_count + 1\n lines = lines[: len(lines) - end_empty_lines_count]\n\n if not lines:\n lines.append(\"# -*- coding: utf-8 -*-#\")\n\n lines.append(\"\\n\\n\")\n lines.append(gen_flag)\n lines.append(\"\\n\")\n exports_str = f\"__all__ = {exports}\"\n exports_str = exports_str.replace(\"'\", '\"')\n if len(exports_str) > 120:\n exports_wrap = [f'\\n \"{item}\",' for item in exports]\n exports_str = \"__all__ = [\" + \"\".join(exports_wrap) + \"\\n]\"\n exports_str = exports_str.replace(\"'\", '\"')\n lines.append(exports_str)\n lines.append(\"\\n\")\n\n # the package module\n if export_from_package:\n basename = os.path.basename(file)\n if basename == \"__init__.py\":\n dir_path = os.path.dirname(file)\n modules = all_sub_modules(dir_path)\n if modules:\n if exclude_modules:\n modules = set(modules) - set(exclude_modules)\n if export_modules:\n modules = set(modules) & set(export_modules)\n lines.append(\n \"\"\"\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\"\"\"\n )\n for mod in modules:\n lines.append(all_sub_all(mod))\n lines.append(\"\\n\")\n\n # write with __all__\n with open(file, mode=\"w\", encoding=\"utf-8\") as fp:\n fp.writelines(lines)\n\n\n# kdata schema rule\n# 1)name:{entity_type.capitalize()}{IntervalLevel.value.capitalize()}Kdata\n# 2)one db file for one schema\n\n\ndef gen_kdata_schema(\n pkg: str,\n entity_type: str,\n levels: List[IntervalLevel],\n adjust_types=None,\n entity_in_submodule: bool = False,\n kdata_module=\"quotes\",\n):\n if adjust_types is None:\n adjust_types = [None]\n tables = []\n\n base_path = \"./domain\"\n\n if kdata_module:\n base_path = os.path.join(base_path, kdata_module)\n if entity_in_submodule:\n base_path = os.path.join(base_path, entity_type)\n\n if not os.path.exists(base_path):\n logger.info(f\"create dir {base_path}\")\n os.makedirs(base_path)\n\n for level in levels:\n for adjust_type in adjust_types:\n level = IntervalLevel(level)\n\n cap_entity_type = entity_type.capitalize()\n cap_level = level.value.capitalize()\n\n # you should define {EntityType}KdataCommon in kdata_module at first\n kdata_common = f\"{cap_entity_type}KdataCommon\"\n\n if adjust_type and (adjust_type != AdjustType.qfq):\n class_name = f\"{cap_entity_type}{cap_level}{adjust_type.value.capitalize()}Kdata\"\n table_name = f\"{entity_type}_{level.value}_{adjust_type.value.lower()}_kdata\"\n else:\n class_name = f\"{cap_entity_type}{cap_level}Kdata\"\n table_name = f\"{entity_type}_{level.value}_kdata\"\n\n tables.append(table_name)\n\n schema_template = f\"\"\"# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom {pkg}.domain.{kdata_module} import {kdata_common}\n\nKdataBase = declarative_base()\n\n\nclass {class_name}(KdataBase, {kdata_common}):\n __tablename__ = \"{table_name}\"\n\n\nregister_schema(db_name=\"{table_name}\", schema_base=KdataBase, entity_type=\"{entity_type}\")\n\n\"\"\"\n # generate the schema\n with open(os.path.join(base_path, f\"{table_name}.py\"), \"w\", encoding=\"utf-8\") as outfile:\n outfile.write(schema_template)\n\n # generate the package\n pkg_file = os.path.join(base_path, \"__init__.py\")\n if not os.path.exists(pkg_file):\n package_template = \"\"\"# -*- coding: utf-8 -*-\n\"\"\"\n with open(pkg_file, \"w\", encoding=\"utf-8\") as outfile:\n outfile.write(package_template)\n\n # generate exports\n gen_exports(\"./domain\")\n\n\ndef gen_plugin_project(entity_type, prefix: str = \"zvt\", dir_path: str = \".\", providers=[\"joinquant\"]):\n \"\"\"\n generate a standard plugin project\n\n :param entity_type: the entity type of the plugin project\n :param prefix: project prefix\n :param dir_path: the root path for the project\n :param providers: the supported providers\n \"\"\"\n\n # generate project files\n project = f\"{prefix}_{entity_type}\"\n entity_class = entity_type.capitalize()\n project_path = os.path.join(dir_path, project)\n if not os.path.exists(project_path):\n os.makedirs(project_path)\n\n current_time = now_pd_timestamp()\n user_name = get_git_user_name()\n user_email = get_git_user_email()\n\n for tpl in all_tpls(project=project, entity_type=entity_type):\n file_name = tpl[0]\n tpl_content = tpl[1].safe_substitute(\n project=project,\n entity_type=entity_type,\n entity_class=entity_class,\n providers=providers,\n provider=providers[0],\n Provider=providers[0].capitalize(),\n year=current_time.year,\n user=user_name,\n email=user_email,\n )\n file_path = os.path.join(project_path, file_name)\n\n file_dir = os.path.dirname(file_path)\n if not os.path.exists(file_dir):\n os.makedirs(file_dir)\n\n with open(file_path, \"w\", encoding=\"utf-8\") as fh:\n fh.write(tpl_content)\n\n\n# the __all__ is generated\n__all__ = [\n \"all_sub_modules\",\n \"all_sub_all\",\n \"fill_package_if_not_exist\",\n \"fill_package\",\n \"gen_exports\",\n \"gen_kdata_schema\",\n \"gen_plugin_project\",\n]\n"
},
{
"path": "src/zvt/autocode/templates/.coveragerc.template",
"content": "[run]\nomit = ${project}/recorders/*"
},
{
"path": "src/zvt/autocode/templates/.gitignore.template",
"content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n#idea\n.idea\n.vscode\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n/ve/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n/zvt-home\n/zvt-test-home\n*.tar.gz\n\n.pytest_cache/\n\n# LibreOffice locks\n.~lock.*#\n\nnode_modules/\n\n*running.ipynb\n\n.DS_Store"
},
{
"path": "src/zvt/autocode/templates/.travis.yml.template",
"content": "language: python\ncache: pip\npython:\n - '3.6'\ninstall:\n - git fetch --tags --depth=500\n - pip install 'pytest>=3.6' --force-reinstall\n - pip install pytest-cov codecov\n - pip install -r ./requirements.txt\nscript:\n - pytest ./tests --cov-config=.coveragerc --cov-report term --cov=./${project} --ignore=tests/recorders/\nafter_success:\n - codecov\ndeploy:\n provider: pypi\n user: ${user}\n password:\n secure: 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\n on:\n tags: true\nnotifications:\n email:\n recipients:\n - ${email}"
},
{
"path": "src/zvt/autocode/templates/LICENSE.template",
"content": "MIT License\n\nCopyright (c) ${year} zvtvz\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "src/zvt/autocode/templates/MANIFEST.in.template",
"content": "include *.md\ninclude *.txt\ninclude LICENSE\nrecursive-include ${project}/* *"
},
{
"path": "src/zvt/autocode/templates/README-en.md.template",
"content": "[](https://github.com/zvtvz/${project})\n[](https://pypi.org/project/${project}/)\n[](https://pypi.org/project/${project}/)\n[](https://pypi.org/project/${project}/)\n[](https://travis-ci.org/zvtvz/${project})\n\n### introduction\n\n\n### feature\n\n### install\n```\npip install ${project}\n\npip show ${project}\n```\n\nupgrate to latest version\n```\npip install --upgrade ${project}\n```\n\n### usage\n\n### contact\n\nwechat foolcage"
},
{
"path": "src/zvt/autocode/templates/README.md.template",
"content": "[](https://github.com/zvtvz/${project})\n[](https://pypi.org/project/${project}/)\n[](https://pypi.org/project/${project}/)\n[](https://pypi.org/project/${project}/)\n[](https://travis-ci.org/zvtvz/${project})\n\n### 说明\n\n\n### 特性\n\n### 安装\n```\npip install ${project}\n\npip show ${project}\n```\n\n更新到最新版本\n```\npip install --upgrade ${project}\n```\n\n### 使用\n\n### 联系\n\n微信 foolcage"
},
{
"path": "src/zvt/autocode/templates/__init__.py",
"content": "# -*- coding: utf-8 -*-\nimport os\nimport string\n\nfrom pkg_resources import resource_string\n\nfrom zvt.utils.file_utils import list_all_files\n\n\ndef all_tpls(project: str, entity_type: str):\n \"\"\"\n return list of templates(location,Template)\n\n :param project:\n :return:\n \"\"\"\n tpl_dir = os.path.join(os.path.dirname(__file__))\n tpl_files = list_all_files(tpl_dir, ext=\"template\", return_base_name=True)\n tpls = []\n for tpl in tpl_files:\n data = resource_string(__name__, tpl)\n file_location = os.path.splitext(os.path.basename(tpl))[0]\n # we assure that line endings are converted to '\\n' for all OS\n data = data.decode(encoding=\"utf-8\").replace(os.linesep, \"\\n\")\n\n # change path for specific file\n # domain\n if file_location == \"kdata_common.py\":\n file_location = f\"{project}/domain/quotes/__init__.py\"\n elif file_location == \"meta.py\":\n file_location = f\"{project}/domain/{entity_type}_meta.py\"\n # recorder\n elif file_location == \"kdata_recorder.py\":\n file_location = f\"{project}/recorders/{entity_type}_kdata_recorder.py\"\n elif file_location == \"meta_recorder.py\":\n file_location = f\"{project}/recorders/{entity_type}_meta_recorder.py\"\n # fill script\n elif file_location == \"fill_project.py\":\n file_location = f\"{project}/fill_project.py\"\n # tests\n elif file_location == \"test_pass.py\":\n file_location = f\"tests/test_pass.py\"\n elif file_location == \"pkg_init.py\":\n file_location = f\"{project}/__init__.py\"\n\n tpls.append((file_location, string.Template(data)))\n return tpls\n\n\n# the __all__ is generated\n__all__ = [\"all_tpls\"]\n"
},
{
"path": "src/zvt/autocode/templates/fill_project.py.template",
"content": "# script to auto generate some files\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.autocode.generator import gen_exports, gen_kdata_schema\n\nif __name__ == '__main__':\n gen_kdata_schema(pkg='${project}', entity_type='${entity_type}',\n levels=[IntervalLevel.LEVEL_1DAY], adjust_types=[None])\n gen_exports()\n"
},
{
"path": "src/zvt/autocode/templates/kdata_common.py.template",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Float\n\nfrom zvt.domain import KdataCommon\n\n\n# ${entity_type} common kdata\nclass ${entity_class}KdataCommon(KdataCommon):\n pass\n"
},
{
"path": "src/zvt/autocode/templates/kdata_recorder.py.template",
"content": "# -*- coding: utf-8 -*-\n\n# -*- coding: utf-8 -*-\n\nfrom zvt import IntervalLevel\nfrom zvt.api.kdata import get_kdata_schema\nfrom zvt.contract.recorder import FixedCycleDataRecorder\n\nfrom ${project}.domain import ${entity_class}, ${entity_class}KdataCommon\n\n\nclass ${Provider}${entity_class}KdataRecorder(FixedCycleDataRecorder):\n entity_provider = '${provider}'\n entity_schema = ${entity_class}\n\n provider = '${provider}'\n\n # register the recorder to data_schema\n data_schema = ${entity_class}KdataCommon\n\n def __init__(self, entity_type='${entity_type}', exchanges=None, entity_ids=None, codes=None, day_data=False,\n force_update=True, sleeping_time=10, entity_filters=None, real_time=False, fix_duplicate_way='ignore',\n start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False, one_day_trading_minutes=24 * 60) -> None:\n level = IntervalLevel(level)\n self.data_schema = get_kdata_schema(entity_type=entity_type, level=level, adjust_type=None)\n\n super().__init__(entity_type, exchanges, entity_ids, codes, day_data, force_update, sleeping_time,\n entity_filters, real_time, fix_duplicate_way, start_timestamp, end_timestamp, close_hour,\n close_minute, level, kdata_use_begin_time, one_day_trading_minutes)\n\n def record(self, entity, start, end, size, timestamps):\n pass\n"
},
{
"path": "src/zvt/autocode/templates/meta.py.template",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, DateTime, Boolean\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import EntityMixin\nfrom zvt.contract.register import register_schema, register_entity\n\n${entity_class}MetaBase = declarative_base()\n\n@register_entity(entity_type='${entity_type}')\nclass ${entity_class}(EntityMixin, ${entity_class}MetaBase):\n __tablename__ = '${entity_type}'\n # 上市日\n list_date = Column(DateTime)\n # 退市日\n end_date = Column(DateTime)\n\n\nregister_schema(db_name='${entity_type}_meta', schema_base=${entity_class}MetaBase)\n\n"
},
{
"path": "src/zvt/autocode/templates/meta_recorder.py.template",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.recorder import Recorder\nfrom ${project}.domain import ${entity_class}\n\nclass ${Provider}${entity_class}Recorder(Recorder):\n data_schema = ${entity_class}\n provider = '${provider}'\n\n def run(self):\n pass\n"
},
{
"path": "src/zvt/autocode/templates/pkg_init.py.template",
"content": "# -*- coding: utf-8 -*-\nimport functools\n\nfrom zvt import init_config\n\n${project}_config = {}\n\nint_${project}_config = functools.partial(init_config, pkg_name='${project}', current_config=${project}_config)\n\nint_${project}_config()\n\n__all__ = ['int_${project}_config']\n"
},
{
"path": "src/zvt/autocode/templates/requirements.txt.template",
"content": "zvt"
},
{
"path": "src/zvt/autocode/templates/setup.py.template",
"content": "#!/usr/bin/env python\n\n# To use a consistent encoding\nfrom codecs import open\nfrom os import path\n\n# Always prefer setuptools over distutils\nfrom setuptools import setup, find_packages\n\ntry:\n # for pip >= 10\n from pip._internal.req import parse_requirements\nexcept ImportError:\n # for pip <= 9.0.3\n from pip.req import parse_requirements\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the README file\nwith open(path.join(here, \"README.md\"), encoding=\"utf-8\") as f:\n long_description = f.read()\n\n# Arguments marked as \"Required\" below must be included for upload to PyPI.\n# Fields marked as \"Optional\" may be commented out.\n\ninstall_reqs = parse_requirements(\"requirements.txt\", session=False)\n\ntry:\n requirements = [str(ir.req) for ir in install_reqs]\nexcept:\n requirements = [str(ir.requirement) for ir in install_reqs]\n\nsetup(\n name=\"${project}\",\n version=\"0.0.1\",\n description=\"unified,modular quant framework for human beings \",\n long_description=long_description,\n url=\"https://github.com/zvtvz/${project}\",\n author=\"${user}\",\n author_email=\"${email}\",\n classifiers=[ # Optional\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: Customer Service\",\n \"Intended Audience :: Education\",\n \"Intended Audience :: Financial and Insurance Industry\",\n \"Topic :: Software Development :: Build Tools\",\n \"Topic :: Office/Business :: Financial :: Investment\",\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n ],\n keywords=\"quant stock finance fintech big-data zvt ma-analysis trading-platform pandas fundamental-analysis\",\n packages=find_packages(include=[\"${project}.*\", \"${project}\"]),\n python_requires=\">=3.5, <4\",\n include_package_data=True,\n install_requires=requirements,\n project_urls={\n \"Bug Reports\": \"https://github.com/zvtvz/${project}/issues\",\n \"Funding\": \"https://github.com/zvtvz/${project}\",\n \"Say Thanks!\": \"https://saythanks.io/to/foolcage\",\n \"Source\": \"https://github.com/zvtvz/${project}\",\n },\n long_description_content_type=\"text/markdown\",\n)\n"
},
{
"path": "src/zvt/autocode/templates/test_pass.py.template",
"content": "# -*- coding: utf-8 -*-\n\ndef test_pass():\n pass\n# the __all__ is generated\n__all__ = ['test_pass']"
},
{
"path": "src/zvt/broker/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/broker/qmt/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/broker/qmt/context.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import Optional\n\nfrom zvt import zvt_env\nfrom zvt.broker.qmt.qmt_account import QmtStockAccount\n\n\nclass QmtContext(object):\n def __init__(self):\n self.qmt_account: Optional[QmtStockAccount] = None\n\n\nqmt_context = QmtContext()\n\n\ndef init_qmt_account(qmt_mini_data_path=None, qmt_account_id=None):\n if not qmt_mini_data_path:\n qmt_mini_data_path = zvt_env[\"qmt_mini_data_path\"]\n if not qmt_account_id:\n qmt_account_id = zvt_env[\"qmt_account_id\"]\n qmt_context.qmt_account = QmtStockAccount(\n path=qmt_mini_data_path, account_id=qmt_account_id, trader_name=\"zvt\", session_id=None\n )\n\n\ninit_qmt_account()\n\n\n# the __all__ is generated\n__all__ = [\"QmtContext\", \"init_qmt_account\"]\n"
},
{
"path": "src/zvt/broker/qmt/errors.py",
"content": "# -*- coding: utf-8 -*-\nclass TraderError(Exception):\n \"\"\"Base class for exceptions in this module.\"\"\"\n\n pass\n\n\nclass QmtError(TraderError):\n def __init__(self, message=\"qmt error\"):\n self.message = message\n\n\nclass PositionOverflowError(TraderError):\n def __init__(self, message=\"超出仓位限制\"):\n self.message = message\n\n\n# the __all__ is generated\n__all__ = [\"TraderError\", \"QmtError\", \"PositionOverflowError\"]\n"
},
{
"path": "src/zvt/broker/qmt/qmt_account.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import List\n\nfrom xtquant import xtconstant, xtdata\nfrom xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback\nfrom xtquant.xttype import StockAccount, XtPosition\n\nfrom zvt.broker.qmt.errors import QmtError, PositionOverflowError\nfrom zvt.broker.qmt.qmt_quote import _to_qmt_code\nfrom zvt.common.trading_models import BuyParameter, PositionType, SellParameter\nfrom zvt.trader import AccountService, TradingSignal, OrderType, trading_signal_type_to_order_type\nfrom zvt.utils.time_utils import now_pd_timestamp, to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\ndef _to_qmt_order_type(order_type: OrderType):\n if order_type == OrderType.order_long:\n return xtconstant.STOCK_BUY\n elif order_type == OrderType.order_close_long:\n return xtconstant.STOCK_SELL\n\n\nclass MyXtQuantTraderCallback(XtQuantTraderCallback):\n def on_connected(self):\n logger.info(\"qmt on_connected\")\n\n def on_smt_appointment_async_response(self, response):\n logger.info(f\"qmt on_smt_appointment_async_response: {vars(response)}\")\n\n def on_cancel_order_stock_async_response(self, response):\n logger.info(f\"qmt on_cancel_order_stock_async_response: {vars(response)}\")\n\n def on_disconnected(self):\n \"\"\"\n 连接断开\n :return:\n \"\"\"\n logger.info(f\"qmt on_disconnected\")\n\n def on_stock_order(self, order):\n \"\"\"\n 委托回报推送\n :param order: XtOrder对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_stock_order: {vars(order)}\")\n\n def on_stock_asset(self, asset):\n \"\"\"\n 资金变动推送\n :param asset: XtAsset对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_stock_asset: {vars(asset)}\")\n\n def on_stock_trade(self, trade):\n \"\"\"\n 成交变动推送\n :param trade: XtTrade对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_stock_trade: {vars(trade)}\")\n\n def on_stock_position(self, position):\n \"\"\"\n 持仓变动推送\n :param position: XtPosition对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_stock_position: {vars(position)}\")\n\n def on_order_error(self, order_error):\n \"\"\"\n 委托失败推送\n :param order_error:XtOrderError 对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_order_error: {vars(order_error)}\")\n\n def on_cancel_error(self, cancel_error):\n \"\"\"\n 撤单失败推送\n :param cancel_error: XtCancelError 对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_cancel_error: {vars(cancel_error)}\")\n\n def on_order_stock_async_response(self, response):\n \"\"\"\n 异步下单回报推送\n :param response: XtOrderResponse 对象\n :return:\n \"\"\"\n logger.info(f\"qmt on_order_stock_async_response: {vars(response)}\")\n\n def on_account_status(self, status):\n \"\"\"\n :param response: XtAccountStatus 对象\n :return:\n \"\"\"\n logger.info(status.account_id, status.account_type, status.status)\n\n\nclass QmtStockAccount(AccountService):\n def __init__(self, path, account_id, trader_name, session_id=None) -> None:\n if not session_id:\n session_id = int(time.time())\n self.trader_name = trader_name\n logger.info(f\"path: {path}, account: {account_id}, trader_name: {trader_name}, session: {session_id}\")\n\n self.xt_trader = XtQuantTrader(path=path, session=session_id)\n\n # StockAccount可以用第二个参数指定账号类型，如沪港通传'HUGANGTONG'，深港通传'SHENGANGTONG'\n self.account = StockAccount(account_id=account_id, account_type=\"STOCK\")\n\n # 创建交易回调类对象，并声明接收回调\n callback = MyXtQuantTraderCallback()\n self.xt_trader.register_callback(callback)\n\n # 启动交易线程\n self.xt_trader.start()\n\n # 建立交易连接，返回0表示连接成功\n connect_result = self.xt_trader.connect()\n if connect_result != 0:\n logger.error(f\"qmt trader 连接失败: {connect_result}\")\n raise QmtError(f\"qmt trader 连接失败: {connect_result}\")\n logger.info(\"qmt trader 建立交易连接成功！\")\n\n # 对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功\n subscribe_result = self.xt_trader.subscribe(self.account)\n\n if subscribe_result != 0:\n logger.error(f\"账号订阅失败: {subscribe_result}\")\n raise QmtError(f\"账号订阅失败: {subscribe_result}\")\n logger.info(\"账号订阅成功！\")\n\n def get_positions(self):\n positions: List[XtPosition] = self.xt_trader.query_stock_positions(self.account)\n return positions\n\n def get_current_position(self, entity_id, create_if_not_exist=False):\n stock_code = _to_qmt_code(entity_id=entity_id)\n # 根据股票代码查询对应持仓\n return self.xt_trader.query_stock_position(self.account, stock_code)\n\n def get_current_account(self):\n asset = self.xt_trader.query_stock_asset(self.account)\n return asset\n\n def order_by_amount(self, entity_id, order_price, order_timestamp, order_type, order_amount):\n stock_code = _to_qmt_code(entity_id=entity_id)\n fix_result_order_id = self.xt_trader.order_stock(\n account=self.account,\n stock_code=stock_code,\n order_type=_to_qmt_order_type(order_type=order_type),\n order_volume=order_amount,\n price_type=xtconstant.FIX_PRICE,\n price=order_price,\n strategy_name=self.trader_name,\n order_remark=\"order from zvt\",\n )\n logger.info(f\"order result id: {fix_result_order_id}\")\n\n def on_trading_signals(self, trading_signals: List[TradingSignal]):\n for trading_signal in trading_signals:\n try:\n self.handle_trading_signal(trading_signal)\n except Exception as e:\n logger.exception(e)\n self.on_trading_error(timestamp=trading_signal.happen_timestamp, error=e)\n\n def handle_trading_signal(self, trading_signal: TradingSignal):\n entity_id = trading_signal.entity_id\n happen_timestamp = trading_signal.happen_timestamp\n order_type = trading_signal_type_to_order_type(trading_signal.trading_signal_type)\n trading_level = trading_signal.trading_level.value\n # askPrice\t多档委卖价\n # bidPrice\t多档委买价\n # askVol\t多档委卖量\n # bidVol\t多档委买量\n if now_pd_timestamp() > to_pd_timestamp(trading_signal.due_timestamp):\n logger.warning(\n f\"the signal is expired, now {now_pd_timestamp()} is after due time: {trading_signal.due_timestamp}\"\n )\n return\n quote = xtdata.get_l2_quote(stock_code=_to_qmt_code(entity_id=entity_id), start_time=happen_timestamp)\n if order_type == OrderType.order_long:\n price = quote[\"askPrice\"]\n elif order_type == OrderType.order_close_long:\n price = quote[\"bidPrice\"]\n else:\n assert False\n self.order_by_amount(\n entity_id=entity_id,\n order_price=price,\n order_timestamp=happen_timestamp,\n order_type=order_type,\n order_amount=trading_signal.order_amount,\n )\n\n def on_trading_open(self, timestamp):\n pass\n\n def on_trading_close(self, timestamp):\n pass\n\n def on_trading_finish(self, timestamp):\n pass\n\n def on_trading_error(self, timestamp, error):\n pass\n\n def sell(self, position_strategy: SellParameter):\n # account_type\tint\t账号类型，参见数据字典\n # account_id\tstr\t资金账号\n # stock_code\tstr\t证券代码\n # volume\tint\t持仓数量\n # can_use_volume\tint\t可用数量\n # open_price\tfloat\t开仓价\n # market_value\tfloat\t市值\n # frozen_volume\tint\t冻结数量\n # on_road_volume\tint\t在途股份\n # yesterday_volume\tint\t昨夜拥股\n # avg_price\tfloat\t成本价\n # direction\tint\t多空方向，股票不适用；参见数据字典\n stock_codes = [_to_qmt_code(entity_id) for entity_id in position_strategy.entity_ids]\n for i, stock_code in enumerate(stock_codes):\n pct = position_strategy.sell_pcts[i]\n position = self.xt_trader.query_stock_position(self.account, stock_code)\n fix_result_order_id = self.xt_trader.order_stock(\n account=self.account,\n stock_code=stock_code,\n order_type=xtconstant.STOCK_SELL,\n order_volume=int(position.can_use_volume * pct),\n price_type=xtconstant.MARKET_SH_CONVERT_5_CANCEL,\n price=0,\n strategy_name=self.trader_name,\n order_remark=\"order from zvt\",\n )\n logger.info(f\"order result id: {fix_result_order_id}\")\n\n def buy(self, buy_parameter: BuyParameter):\n # account_type\tint\t账号类型，参见数据字典\n # account_id\tstr\t资金账号\n # cash\tfloat\t可用金额\n # frozen_cash\tfloat\t冻结金额\n # market_value\tfloat\t持仓市值\n # total_asset\tfloat\t总资产\n acc = self.get_current_account()\n\n # 优先使用金额下单\n if buy_parameter.money_to_use:\n money_to_use = buy_parameter.money_to_use\n if acc.cash < money_to_use:\n raise QmtError(f\"可用余额不足 {acc.cash} < {money_to_use}\")\n else:\n # 检查仓位\n if buy_parameter.position_type == PositionType.normal:\n current_pct = round(acc.market_value / acc.total_asset, 2)\n if current_pct >= buy_parameter.position_pct:\n raise PositionOverflowError(f\"目前仓位为{current_pct}, 已超过请求的仓位: {buy_parameter.position_pct}\")\n\n money_to_use = acc.total_asset * (buy_parameter.position_pct - current_pct)\n elif buy_parameter.position_type == PositionType.cash:\n money_to_use = acc.cash * buy_parameter.position_pct\n else:\n assert False\n\n stock_codes = [_to_qmt_code(entity_id) for entity_id in buy_parameter.entity_ids]\n ticks = xtdata.get_full_tick(code_list=stock_codes)\n\n if not buy_parameter.weights:\n stocks_count = len(stock_codes)\n money_for_stocks = [round(money_to_use / stocks_count)] * stocks_count\n else:\n weights_sum = sum(buy_parameter.weights)\n money_for_stocks = [round(weight / weights_sum) for weight in buy_parameter.weights]\n\n for i, stock_code in enumerate(stock_codes):\n try_price = ticks[stock_code][\"askPrice\"][3]\n volume = money_for_stocks[i] / try_price\n fix_result_order_id = self.xt_trader.order_stock(\n account=self.account,\n stock_code=stock_code,\n order_type=xtconstant.STOCK_BUY,\n order_volume=volume,\n price_type=xtconstant.MARKET_SH_CONVERT_5_CANCEL,\n price=0,\n strategy_name=self.trader_name,\n order_remark=\"order from zvt\",\n )\n logger.info(f\"order result id: {fix_result_order_id}\")\n\n\nif __name__ == \"__main__\":\n account = QmtStockAccount(path=r\"D:\\qmt\\userdata_mini\", account_id=\"\")\n account.get_positions()\n\n\n# the __all__ is generated\n__all__ = [\"MyXtQuantTraderCallback\", \"QmtStockAccount\"]\n"
},
{
"path": "src/zvt/broker/qmt/qmt_quote.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\n\nimport pandas as pd\nfrom xtquant import xtdata\n\nfrom zvt.api.kdata import get_recent_trade_dates\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract.api import decode_entity_id, df_to_db, get_db_session\nfrom zvt.domain import StockQuote, Stock, Stock1dKdata, StockQuoteLog, Stock1mQuote\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import (\n to_date_time_str,\n current_date,\n to_pd_timestamp,\n now_pd_timestamp,\n date_time_by_interval,\n now_timestamp_ms,\n to_timestamp_ms,\n date_and_time,\n TIME_FORMAT_MINUTE,\n is_same_date,\n)\n\n# https://dict.thinktrader.net/nativeApi/start_now.html?id=e2M5nZ\n\nlogger = logging.getLogger(__name__)\n\n\ndef _to_qmt_code(entity_id):\n _, exchange, code = decode_entity_id(entity_id=entity_id)\n return f\"{code}.{exchange.upper()}\"\n\n\ndef _to_zvt_entity_id(qmt_code):\n code, exchange = qmt_code.split(\".\")\n exchange = exchange.lower()\n return f\"stock_{exchange}_{code}\"\n\n\ndef _to_qmt_dividend_type(adjust_type: AdjustType):\n if adjust_type == AdjustType.qfq:\n return \"front\"\n elif adjust_type == AdjustType.hfq:\n return \"back\"\n else:\n return \"none\"\n\n\ndef _qmt_instrument_detail_to_stock(stock_detail):\n exchange = stock_detail[\"ExchangeID\"].lower()\n code = stock_detail[\"InstrumentID\"]\n name = stock_detail[\"InstrumentName\"]\n list_date = to_pd_timestamp(stock_detail[\"OpenDate\"])\n try:\n end_date = to_pd_timestamp(stock_detail[\"ExpireDate\"])\n except:\n end_date = None\n\n pre_close = stock_detail[\"PreClose\"]\n limit_up_price = stock_detail[\"UpStopPrice\"]\n limit_down_price = stock_detail[\"DownStopPrice\"]\n float_volume = stock_detail[\"FloatVolume\"]\n total_volume = stock_detail[\"TotalVolume\"]\n\n entity_id = f\"stock_{exchange}_{code}\"\n\n return {\n \"id\": entity_id,\n \"entity_id\": entity_id,\n \"timestamp\": list_date,\n \"entity_type\": \"stock\",\n \"exchange\": exchange,\n \"code\": code,\n \"name\": name,\n \"list_date\": list_date,\n \"end_date\": end_date,\n \"pre_close\": pre_close,\n \"limit_up_price\": limit_up_price,\n \"near_limit_up_price\": limit_up_price * 0.98,\n \"limit_down_price\": limit_down_price,\n \"float_volume\": float_volume,\n \"total_volume\": total_volume,\n }\n\n\ndef get_qmt_stocks(include_bj=True):\n stock_list = xtdata.get_stock_list_in_sector(\"沪深A股\")\n\n if include_bj:\n bj_entity_ids = get_entity_ids_by_filter(\n provider=\"em\", ignore_delist=True, ignore_st=False, entity_type=\"stock\", exchange=\"bj\"\n )\n bj_stock_list = map(lambda x: _to_qmt_code(x), bj_entity_ids)\n\n stock_list += bj_stock_list\n return stock_list\n\n\ndef _build_entity_list(qmt_stocks):\n entity_list = []\n for stock in qmt_stocks:\n stock_detail = xtdata.get_instrument_detail(stock, False)\n if stock_detail:\n entity_list.append(_qmt_instrument_detail_to_stock(stock_detail))\n else:\n code, exchange = stock.split(\".\")\n exchange = exchange.lower()\n entity_id = f\"stock_{exchange}_{code}\"\n # get from provider em\n datas = Stock.query_data(provider=\"em\", entity_id=entity_id, return_type=\"dict\")\n if datas:\n entity = datas[0]\n else:\n entity = {\n \"id\": _to_zvt_entity_id(stock),\n \"entity_id\": entity_id,\n \"entity_type\": \"stock\",\n \"exchange\": exchange,\n \"code\": code,\n \"name\": \"未获取\",\n }\n\n # Do this in other task in days timely\n # xtdata.download_financial_data(stock_list=[stock], table_list=[\"Capital\"])\n capital_datas = xtdata.get_financial_data(\n [stock],\n table_list=[\"Capital\"],\n report_type=\"report_time\",\n )\n df = capital_datas[stock][\"Capital\"]\n if pd_is_not_null(df):\n latest_data = df.iloc[-1]\n entity[\"float_volume\"] = latest_data[\"circulating_capital\"]\n entity[\"total_volume\"] = latest_data[\"total_capital\"]\n\n tick = xtdata.get_full_tick(code_list=[stock])\n if tick and tick[stock]:\n if exchange == \"bj\": # 北交所\n limit_up_price = tick[stock][\"lastClose\"] * 1.29\n limit_down_price = tick[stock][\"lastClose\"] * 0.71\n elif code.startswith((\"30\", \"68\")): # 创业板和科创板\n limit_up_price = tick[stock][\"lastClose\"] * 1.19\n limit_down_price = tick[stock][\"lastClose\"] * 0.81\n else:\n limit_up_price = tick[stock][\"lastClose\"] * 1.1\n limit_down_price = tick[stock][\"lastClose\"] * 0.9\n entity[\"limit_up_price\"] = round(limit_up_price, 2)\n entity[\"near_limit_up_price\"] = round(limit_up_price * 0.98, 2)\n entity[\"limit_down_price\"] = round(limit_down_price, 2)\n entity_list.append(entity)\n\n return pd.DataFrame.from_records(data=entity_list)\n\n\ndef get_entity_list(include_bj=True):\n stocks = get_qmt_stocks(include_bj=include_bj)\n return _build_entity_list(qmt_stocks=stocks)\n\n\ndef get_kdata(\n entity_id,\n start_timestamp,\n end_timestamp,\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.qfq,\n download_history=True,\n):\n code = _to_qmt_code(entity_id=entity_id)\n period = level.value\n start_time = to_date_time_str(start_timestamp, fmt=\"YYYYMMDDHHmmss\")\n end_time = to_date_time_str(end_timestamp, fmt=\"YYYYMMDDHHmmss\")\n # download比较耗时，建议单独定时任务来做\n if download_history:\n print(f\"download from {start_time} to {end_time}\")\n xtdata.download_history_data(stock_code=code, period=period, start_time=start_time, end_time=end_time)\n records = xtdata.get_market_data(\n stock_list=[code],\n period=period,\n start_time=to_date_time_str(start_timestamp, fmt=\"YYYYMMDDHHmmss\"),\n end_time=to_date_time_str(end_timestamp, fmt=\"YYYYMMDDHHmmss\"),\n dividend_type=_to_qmt_dividend_type(adjust_type=adjust_type),\n fill_data=False,\n )\n\n dfs = []\n for col in records:\n df = records[col].T\n df.columns = [col]\n dfs.append(df)\n df = pd.concat(dfs, axis=1)\n df[\"volume\"] = df[\"volume\"] * 100\n return df\n\n\ndef tick_to_quote(entity_df):\n # def calculate_limit_up_amount(row):\n # if row[\"is_limit_up\"]:\n # return row[\"price\"] * row[\"bidVol\"][0] * 100\n # else:\n # return None\n #\n # def calculate_limit_down_amount(row):\n # if row[\"is_limit_down\"]:\n # return row[\"price\"] * row[\"askVol\"][0] * 100\n # else:\n # return None\n\n def on_data(datas, stock_df=entity_df):\n stock_finished = False\n if not Stock.in_trading_time():\n stock_finished = True\n start_time = time.time()\n\n time_tag = False\n track_time = None\n\n for code in datas:\n tick_data = datas[code]\n if \"timetag\" in tick_data:\n time_tag = True\n track_time = to_timestamp_ms(tick_data[\"timetag\"])\n else:\n track_time = datas[code][\"time\"]\n\n delay = (now_timestamp_ms() - track_time) / 1000\n logger.info(f\"stock {code} delay {delay} seconds\")\n if delay < 60:\n break\n else:\n logger.warning(f\"delay {delay} seconds, may need to restart this script or qmt client\")\n break\n\n tick_df = pd.DataFrame.from_records(data=[datas[code] for code in datas], index=list(datas.keys()))\n if time_tag:\n tick_df[\"time\"] = tick_df[\"timetag\"].apply(to_timestamp_ms)\n # 过滤无效tick,一般是退市的\n tick_df = tick_df[tick_df[\"lastPrice\"] != 0]\n tick_df.index = tick_df.index.map(_to_zvt_entity_id)\n\n # tick_df = tick_df[tick_df.index.isin(stock_df.index)]\n\n stock_df = stock_df[~stock_df.index.duplicated(keep=\"first\")] # 保留首次出现的行\n tick_df = tick_df[~tick_df.index.duplicated(keep=\"first\")]\n df = pd.concat(\n [\n stock_df,\n tick_df,\n ],\n axis=1,\n join=\"inner\",\n )\n\n df = df.rename(columns={\"lastPrice\": \"price\", \"amount\": \"turnover\"})\n df[\"close\"] = df[\"price\"]\n\n if stock_finished:\n the_time = date_and_time(track_time, \"15:00\")\n df[\"time\"] = to_timestamp_ms(the_time)\n\n df[\"timestamp\"] = df[\"time\"].apply(to_pd_timestamp)\n\n df[\"volume\"] = df[\"pvolume\"]\n df[\"avg_price\"] = df[\"turnover\"] / df[\"volume\"]\n # 换手率\n # df[\"turnover_rate\"] = df[\"pvolume\"] / df[\"float_volume\"]\n # 涨跌幅\n df[\"change_pct\"] = (df[\"price\"] - df[\"lastClose\"]) / df[\"lastClose\"]\n # # 盘口卖单金额\n # df[\"ask_amount\"] = df.apply(\n # lambda row: np.sum(np.array(row[\"askPrice\"]) * (np.array(row[\"askVol\"]) * 100)), axis=1\n # )\n # # 盘口买单金额\n # df[\"bid_amount\"] = df.apply(\n # lambda row: np.sum(np.array(row[\"bidPrice\"]) * (np.array(row[\"bidVol\"]) * 100)), axis=1\n # )\n # 涨停\n df[\"is_limit_up\"] = (df[\"price\"] != 0) & (df[\"price\"] >= df[\"limit_up_price\"])\n df[\"near_limit_up\"] = (df[\"price\"] != 0) & (df[\"price\"] >= df[\"near_limit_up_price\"])\n # df[\"limit_up_amount\"] = df.apply(lambda row: calculate_limit_up_amount(row), axis=1)\n\n # 跌停\n df[\"is_limit_down\"] = (df[\"price\"] != 0) & (df[\"price\"] <= df[\"limit_down_price\"])\n # df[\"limit_down_amount\"] = df.apply(lambda row: calculate_limit_down_amount(row), axis=1)\n\n df[\"float_cap\"] = df[\"float_volume\"] * df[\"price\"]\n df[\"total_cap\"] = df[\"total_volume\"] * df[\"price\"]\n\n # 换手率\n df[\"turnover_rate\"] = df[\"turnover\"] / df[\"float_cap\"]\n\n df[\"provider\"] = \"qmt\"\n\n # 实时行情统计，只保留最新\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(\n lambda se: \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"])), axis=1\n )\n df_to_db(df, data_schema=StockQuote, provider=\"qmt\", force_update=True, drop_duplicates=False)\n\n # 历史记录\n # df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(\n # lambda se: \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], TIME_FORMAT_MINUTE2)), axis=1\n # )\n # df_to_db(df, data_schema=StockQuoteLog, provider=\"qmt\", force_update=False, drop_duplicates=False)\n\n # 1分钟分时\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(\n lambda se: \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_MINUTE)),\n axis=1,\n )\n df_to_db(df, data_schema=Stock1mQuote, provider=\"qmt\", force_update=True, drop_duplicates=False)\n\n # 日线行情\n if stock_finished:\n df[\"timestamp\"] = date_and_time(track_time, \"00:00\")\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(\n lambda se: \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"])), axis=1\n )\n df[\"level\"] = \"1d\"\n df_to_db(df=df, data_schema=Stock1dKdata, provider=\"em\", force_update=True, drop_duplicates=False)\n\n cost_time = time.time() - start_time\n logger.info(f\"Quotes cost_time:{cost_time} for {len(datas.keys())} stocks\")\n\n return track_time\n\n return on_data\n\n\ndef clear_history_quote(target_date=current_date()):\n session = get_db_session(\"qmt\", data_schema=StockQuote)\n session.query(StockQuote).filter(StockQuote.timestamp < target_date).delete()\n session.commit()\n logger.info(f\"clear stock quote data before: {target_date}\")\n\n\ndef clear_history_1m_quote(target_date=current_date()):\n session = get_db_session(\"qmt\", data_schema=StockQuote)\n dates = get_recent_trade_dates(entity_type=\"stock\", target_date=target_date, days_count=5)\n if dates:\n start_date = dates[0]\n else:\n start_date = date_time_by_interval(target_date, -5)\n\n session.query(Stock1mQuote).filter(Stock1mQuote.timestamp < start_date).delete()\n session.commit()\n logger.info(f\"clear stock 1m data before: {start_date}\")\n\n\ndef clear_history_quote_log(target_date):\n dates = get_recent_trade_dates(entity_type=\"stock\", target_date=target_date, days_count=2)\n if dates:\n start_date = dates[0]\n else:\n start_date = date_time_by_interval(target_date, -2)\n\n session = get_db_session(\"qmt\", data_schema=StockQuoteLog)\n session.query(StockQuoteLog).filter(StockQuoteLog.timestamp < start_date).delete()\n session.commit()\n logger.info(f\"clear stock quote log data before: {target_date}\")\n\n\ndef record_stock_quote(subscribe=False):\n clear_history_quote(target_date=current_date())\n qmt_stocks = get_qmt_stocks()\n entity_list = _build_entity_list(qmt_stocks=qmt_stocks)\n entity_df = entity_list[\n [\n \"entity_id\",\n \"code\",\n \"name\",\n \"limit_up_price\",\n \"near_limit_up_price\",\n \"limit_down_price\",\n \"float_volume\",\n \"total_volume\",\n ]\n ]\n entity_df = entity_df.set_index(\"entity_id\", drop=False)\n on_data_func = tick_to_quote(entity_df=entity_df)\n\n if subscribe:\n logger.info(f\"subscribe tick for {len(qmt_stocks)} stocks\")\n sid = xtdata.subscribe_whole_quote(qmt_stocks, callback=on_data_func)\n\n \"\"\"阻塞线程接收行情回调\"\"\"\n import time\n\n client = xtdata.get_client()\n while True:\n time.sleep(3)\n if not client.is_connected():\n raise Exception(\"行情服务连接断开\")\n current_timestamp = now_pd_timestamp()\n if not Stock.in_real_trading_time():\n logger.info(f\"record tick finished at: {current_timestamp}\")\n break\n xtdata.unsubscribe_quote(sid)\n else:\n import time\n\n first_time = True\n last_time = False\n while True:\n if not first_time and Stock.in_trading_time() and not Stock.in_real_trading_time():\n logger.info(\"Sleeping time......\")\n time.sleep(30 * 1)\n continue\n\n if not Stock.in_trading_time():\n logger.info(\"Not in trading time......\")\n time.sleep(60 * 1)\n last_time = True\n\n datas = xtdata.get_full_tick(code_list=qmt_stocks)\n track_time = on_data_func(datas=datas)\n if first_time and not is_same_date(track_time, current_date()):\n last_time = True\n\n first_time = False\n\n if last_time:\n current_timestamp = now_pd_timestamp()\n logger.info(f\"record tick finished at: {current_timestamp}\")\n # clear_history_quote_log(target_date=current_date())\n clear_history_1m_quote(target_date=current_date())\n break\n\n\nif __name__ == \"__main__\":\n from apscheduler.schedulers.background import BackgroundScheduler\n\n sched = BackgroundScheduler()\n record_stock_quote()\n sched.add_job(func=record_stock_quote, trigger=\"cron\", hour=9, minute=22, day_of_week=\"mon-fri\")\n sched.start()\n sched._thread.join()\n\n# the __all__ is generated\n__all__ = [\n \"get_qmt_stocks\",\n \"get_entity_list\",\n \"get_kdata\",\n \"tick_to_quote\",\n \"clear_history_quote\",\n]\n"
},
{
"path": "src/zvt/common/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/common/query_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom datetime import datetime\nfrom enum import Enum\nfrom typing import Optional\n\nfrom pydantic import BaseModel, Field\n\n\nclass OrderByType(Enum):\n asc = \"asc\"\n desc = \"desc\"\n\n\nclass TimeUnit(Enum):\n year = \"year\"\n month = \"month\"\n day = \"day\"\n hour = \"hour\"\n minute = \"minute\"\n second = \"second\"\n\n\nclass AbsoluteTimeRange(BaseModel):\n start_timestamp: datetime\n end_timestamp: datetime\n\n\nclass RelativeTimeRage(BaseModel):\n interval: int\n time_unit: TimeUnit\n\n\nclass TimeRange(BaseModel):\n absolute_time_range: Optional[AbsoluteTimeRange] = Field(default=None)\n relative_time_range: Optional[RelativeTimeRage] = Field(default=None)\n\n\n# the __all__ is generated\n__all__ = [\"OrderByType\", \"TimeUnit\", \"AbsoluteTimeRange\", \"RelativeTimeRage\", \"TimeRange\"]\n"
},
{
"path": "src/zvt/common/trading_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom enum import Enum\nfrom typing import List, Optional\n\nfrom pydantic import BaseModel, Field\n\n\nclass PositionType(Enum):\n # 按整体仓位算\n normal = \"normal\"\n\n # 不管整体仓位多少\n # 按现金算\n cash = \"cash\"\n\n\nclass BuyParameter(BaseModel):\n entity_ids: List[str]\n position_type: PositionType = Field(default=PositionType.normal)\n position_pct: Optional[float] = Field(default=None)\n weights: Optional[List[float]] = Field(default=None)\n money_to_use: Optional[float] = Field(default=None)\n\n\nclass SellParameter(BaseModel):\n entity_ids: List[str]\n sell_pcts: Optional[List[float]] = Field(default=None)\n\n\nclass TradingResult(BaseModel):\n success_entity_ids: Optional[List[str]] = Field(default=None)\n failed_entity_ids: Optional[List[str]] = Field(default=None)\n\n\n# the __all__ is generated\n__all__ = [\"PositionType\", \"BuyParameter\", \"SellParameter\", \"TradingResult\"]\n"
},
{
"path": "src/zvt/config.json",
"content": "{\n \"jq_username\": \"\",\n \"jq_password\": \"\",\n \"http_proxy\": \"127.0.0.1:1087\",\n \"https_proxy\": \"127.0.0.1:1087\",\n \"smtp_host\": \"smtpdm.aliyun.com\",\n \"smtp_port\": \"80\",\n \"email_username\": \"\",\n \"email_password\": \"\",\n \"wechat_app_id\": \"\",\n \"wechat_app_secrect\": \"\",\n \"qiye_wechat_bot_token\": \"\",\n \"qmt_mini_data_path\": \"D:\\\\qmt\\\\userdata_mini\",\n \"qmt_account_id\": \"\",\n \"moonshot_api_key\": \"\",\n \"qwen_api_key\": \"\",\n \"em_header\": \"\",\n \"storage\": {\n \"base_path\": null,\n \"path_template\": null,\n \"storage_routes\": {},\n \"schema_providers\": {\n \"zvt_info\": [\n \"zvt\"\n ],\n \"trader_info\": [\n \"zvt\"\n ],\n \"stock_tags\": [\n \"zvt\"\n ],\n \"stock_trading\": [\n \"zvt\"\n ],\n \"stock_1d_ma_factor\": [\n \"zvt\"\n ],\n \"stock_1d_ma_stats_factor\": [\n \"zvt\"\n ],\n \"stock_1d_zen_factor\": [\n \"zvt\"\n ],\n \"stock_1wk_zen_factor\": [\n \"zvt\"\n ],\n \"index_1d_zen_factor\": [\n \"zvt\"\n ],\n \"top_stocks\": [\n \"zvt\"\n ],\n \"stock_quote\": [\n \"qmt\"\n ],\n \"stock_quote_log\": [\n \"qmt\"\n ]\n }\n }\n}"
},
{
"path": "src/zvt/consts.py",
"content": "# -*- coding: utf-8 -*-\nimport os\nfrom pathlib import Path\n\n# zvt home dir\nZVT_HOME = os.environ.get(\"ZVT_HOME\")\nif not ZVT_HOME:\n ZVT_HOME = os.path.abspath(os.path.join(Path.home(), \"zvt-home\"))\n\n# data for testing\nZVT_TEST_HOME = os.path.abspath(os.path.join(Path.home(), \"zvt-test-home\"))\nZVT_TEST_ZIP_DATA_PATH = os.path.join(ZVT_TEST_HOME, \"data.zip\")\nZVT_TEST_DATA_PATH = os.path.join(ZVT_TEST_HOME, \"data\")\n\nDATA_SAMPLE_ZIP_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), \"samples\", \"data.zip\"))\n\n# ****** setting for stocks ****** #\nSAMPLE_STOCK_CODES = [\"000001\", \"000002\"]\n\n# 沪深300，证券，中证500，上证50，创业板，军工,传媒,资源\nSAMPLE_ETF_CODES = [\"510300\", \"512880\", \"510500\", \"510050\", \"159915\", \"512660\", \"512980\", \"510410\"]\n\n# 上证指数 上证50 沪深300 中证500 中证1000 科创50\n# 深证成指(399001) 创业板指(399006) 国证成长（399370）国证价值（399371）国证基金(399379) 国证ETF(399380)\nIMPORTANT_INDEX = [\n \"000001\",\n \"000016\",\n \"000300\",\n \"000905\",\n \"000852\",\n \"000688\",\n \"399001\",\n \"399006\",\n \"399370\",\n \"399371\",\n \"399379\",\n \"399380\",\n]\n"
},
{
"path": "src/zvt/contract/__init__.py",
"content": "# -*- coding: utf-8 -*-\nfrom enum import Enum\n\n\nclass IntervalLevel(Enum):\n \"\"\"\n Repeated fixed time interval, e.g, 5m, 1d.\n \"\"\"\n\n #: level l2 quote\n LEVEL_L2_QUOTE = \"l2quote\"\n #: level tick\n LEVEL_TICK = \"tick\"\n #: 1 minute\n LEVEL_1MIN = \"1m\"\n #: 5 minutes\n LEVEL_5MIN = \"5m\"\n #: 15 minutes\n LEVEL_15MIN = \"15m\"\n #: 30 minutes\n LEVEL_30MIN = \"30m\"\n #: 1 hour\n LEVEL_1HOUR = \"1h\"\n #: 4 hours\n LEVEL_4HOUR = \"4h\"\n #: 1 day\n LEVEL_1DAY = \"1d\"\n #: 1 week\n LEVEL_1WEEK = \"1wk\"\n #: 1 month\n LEVEL_1MON = \"1mon\"\n\n def to_pd_freq(self):\n if self == IntervalLevel.LEVEL_1MIN:\n return \"1min\"\n if self == IntervalLevel.LEVEL_5MIN:\n return \"5min\"\n if self == IntervalLevel.LEVEL_15MIN:\n return \"15min\"\n if self == IntervalLevel.LEVEL_30MIN:\n return \"30min\"\n if self == IntervalLevel.LEVEL_1HOUR:\n return \"1H\"\n if self == IntervalLevel.LEVEL_4HOUR:\n return \"4H\"\n if self >= IntervalLevel.LEVEL_1DAY:\n return \"1D\"\n\n def floor_timestamp(self, pd_timestamp):\n if self == IntervalLevel.LEVEL_1MIN:\n return pd_timestamp.floor(\"1min\")\n if self == IntervalLevel.LEVEL_5MIN:\n return pd_timestamp.floor(\"5min\")\n if self == IntervalLevel.LEVEL_15MIN:\n return pd_timestamp.floor(\"15min\")\n if self == IntervalLevel.LEVEL_30MIN:\n return pd_timestamp.floor(\"30min\")\n if self == IntervalLevel.LEVEL_1HOUR:\n return pd_timestamp.floor(\"1h\")\n if self == IntervalLevel.LEVEL_4HOUR:\n return pd_timestamp.floor(\"4h\")\n if self == IntervalLevel.LEVEL_1DAY:\n return pd_timestamp.floor(\"1d\")\n\n def to_minute(self):\n return int(self.to_second() / 60)\n\n def to_second(self):\n return int(self.to_ms() / 1000)\n\n def to_ms(self):\n \"\"\"\n To seconds count in the interval\n\n :return: seconds count in the interval\n \"\"\"\n #: we treat tick intervals is 5s, you could change it\n if self == IntervalLevel.LEVEL_TICK:\n return 5 * 1000\n if self == IntervalLevel.LEVEL_1MIN:\n return 60 * 1000\n if self == IntervalLevel.LEVEL_5MIN:\n return 5 * 60 * 1000\n if self == IntervalLevel.LEVEL_15MIN:\n return 15 * 60 * 1000\n if self == IntervalLevel.LEVEL_30MIN:\n return 30 * 60 * 1000\n if self == IntervalLevel.LEVEL_1HOUR:\n return 60 * 60 * 1000\n if self == IntervalLevel.LEVEL_4HOUR:\n return 4 * 60 * 60 * 1000\n if self == IntervalLevel.LEVEL_1DAY:\n return 24 * 60 * 60 * 1000\n if self == IntervalLevel.LEVEL_1WEEK:\n return 7 * 24 * 60 * 60 * 1000\n if self == IntervalLevel.LEVEL_1MON:\n return 31 * 7 * 24 * 60 * 60 * 1000\n\n def __ge__(self, other):\n if self.__class__ is other.__class__:\n return self.to_ms() >= other.to_ms()\n return NotImplemented\n\n def __gt__(self, other):\n\n if self.__class__ is other.__class__:\n return self.to_ms() > other.to_ms()\n return NotImplemented\n\n def __le__(self, other):\n if self.__class__ is other.__class__:\n return self.to_ms() <= other.to_ms()\n return NotImplemented\n\n def __lt__(self, other):\n if self.__class__ is other.__class__:\n return self.to_ms() < other.to_ms()\n return NotImplemented\n\n\nclass AdjustType(Enum):\n \"\"\"\n split-adjusted type for :class:`~.zvt.contract.schema.TradableEntity` quotes\n\n \"\"\"\n\n #: not adjusted\n #: 不复权\n bfq = \"bfq\"\n #: pre adjusted\n #: 前复权\n qfq = \"qfq\"\n #: post adjusted\n #: 后复权\n hfq = \"hfq\"\n\n\nclass ActorType(Enum):\n #: 个人\n individual = \"individual\"\n #: 公募基金\n raised_fund = \"raised_fund\"\n #: 社保\n social_security = \"social_security\"\n #: 保险\n insurance = \"insurance\"\n #: 外资\n qfii = \"qfii\"\n #: 信托\n trust = \"trust\"\n #: 券商\n broker = \"qmt\"\n #: 私募\n private_equity = \"private_equity\"\n #: 公司(可能包括私募)\n corporation = \"corporation\"\n\n\nclass TradableType(Enum):\n #: A股(中国)\n #: China stock\n stock = \"stock\"\n #: 可转债(中国)\n #: China convertible Bond\n cbond = \"cbond\"\n #: A股指数(中国)\n #: China index\n index = \"index\"\n #: A股板块(中国)\n #: China stock block\n block = \"block\"\n #: 美股\n #: USA stock\n stockus = \"stockus\"\n #: 美股指数\n #: USA index\n indexus = \"indexus\"\n #: USA block\n blockus = \"blockus\"\n #: 港股\n #: Hongkong Stock\n stockhk = \"stockhk\"\n #: 港股指数\n #: Hongkong Index\n indexhk = \"indexhk\"\n #: 期货(中国)\n #: China future\n future = \"future\"\n #: 数字货币\n #: Cryptocurrency\n coin = \"coin\"\n #: 期权(中国)\n #: China option\n option = \"option\"\n #: 基金(中国)\n #: China fund\n fund = \"fund\"\n #: 货币汇率\n #: currency exchange rate\n currency = \"currency\"\n\n\nclass Exchange(Enum):\n #: 上证交易所\n sh = \"sh\"\n #: 深证交易所\n sz = \"sz\"\n #: 北交所\n bj = \"bj\"\n\n #: 对于中国的非交易所的 标的\n cn = \"cn\"\n #: 对于美国的非交易所的 标的\n us = \"us\"\n\n #: 纳斯达克\n nasdaq = \"nasdaq\"\n\n #: 纽交所\n nyse = \"nyse\"\n\n #: 港交所\n hk = \"hk\"\n\n #: 数字货币\n binance = \"binance\"\n huobipro = \"huobipro\"\n\n #: 上海期货交易所\n shfe = \"shfe\"\n #: 大连商品交易所\n dce = \"dce\"\n #: 郑州商品交易所\n czce = \"czce\"\n #: 中国金融期货交易所\n cffex = \"cffex\"\n #: 上海国际能源交易中心\n ine = \"ine\"\n\n #: 广州期货所\n gfex = \"gfex\"\n\n #: 外汇交易所(虚拟)\n #: currency exchange(virtual)\n forex = \"forex\"\n #: 人民币中间价\n\n\ntradable_type_map_exchanges = {\n TradableType.block: [Exchange.cn],\n TradableType.index: [Exchange.sh, Exchange.sz],\n TradableType.stock: [Exchange.sh, Exchange.sz, Exchange.bj],\n TradableType.cbond: [Exchange.sh, Exchange.sz],\n TradableType.stockhk: [Exchange.hk],\n TradableType.indexhk: [Exchange.hk],\n TradableType.stockus: [Exchange.nasdaq, Exchange.nyse],\n TradableType.blockus: [Exchange.us],\n TradableType.indexus: [Exchange.us],\n TradableType.future: [Exchange.shfe, Exchange.dce, Exchange.czce, Exchange.cffex, Exchange.ine],\n TradableType.coin: [Exchange.binance, Exchange.huobipro],\n TradableType.currency: [Exchange.forex],\n}\n\n\ndef get_entity_exchanges(entity_type):\n entity_type = TradableType(entity_type)\n return tradable_type_map_exchanges.get(entity_type)\n\n\nfrom .context import zvt_context\n\nzvt_context = zvt_context\n\n\n# the __all__ is generated\n__all__ = [\"IntervalLevel\", \"AdjustType\", \"ActorType\", \"TradableType\", \"Exchange\", \"get_entity_exchanges\"]\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule schema\nfrom .schema import *\nfrom .schema import __all__ as _schema_all\n\n__all__ += _schema_all\n"
},
{
"path": "src/zvt/contract/api.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport platform\nfrom typing import List, Union, Type\n\nimport pandas as pd\nfrom sqlalchemy import func, exists, and_\nfrom sqlalchemy.engine import Engine\nfrom sqlalchemy.ext.declarative import DeclarativeMeta\nfrom sqlalchemy.orm import Query\nfrom sqlalchemy.orm import sessionmaker, Session\nfrom sqlalchemy.sql.expression import text\n\nfrom zvt import zvt_env\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract import zvt_context\nfrom zvt.contract.schema import Mixin, TradableEntity\nfrom zvt.contract.storage import get_storage_backend\nfrom zvt.contract.route_registry import get_route_registry\nfrom zvt.contract.register import ensure_schema_tables_and_indexes\n\n_initialized_storage_ids = set()\n\n\ndef _storage_backend():\n return zvt_context.storage_backend or get_storage_backend()\n\n\ndef _route_registry():\n return zvt_context.route_registry or get_route_registry()\nfrom zvt.utils.pd_utils import pd_is_not_null, index_df\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\ndef _get_db_name(data_schema: DeclarativeMeta) -> str:\n \"\"\"\n get db name of the domain schema\n\n :param data_schema: the data schema\n :return: db name\n \"\"\"\n for db_name, base in zvt_context.dbname_map_base.items():\n if issubclass(data_schema, base):\n return db_name\n\n\ndef get_db_engine(\n provider: str, db_name: str = None, data_schema: object = None, data_path: str = None\n) -> Engine:\n \"\"\"\n get db engine from (provider,db_name) or (provider,data_schema).\n Creates tables and indexes on first use (lazy init).\n \"\"\"\n if data_schema:\n db_name = _get_db_name(data_schema=data_schema)\n\n if data_path is None:\n data_path = zvt_env.get(\"data_path\", \".\")\n\n storage_id = _route_registry().get_storage_id(provider, db_name)\n engine = _storage_backend().get_engine(storage_id, data_path)\n\n if storage_id not in _initialized_storage_ids:\n schema_base = zvt_context.dbname_map_base.get(db_name)\n if schema_base:\n ensure_schema_tables_and_indexes(engine, schema_base, db_name)\n _initialized_storage_ids.add(storage_id)\n\n return engine\n\n\ndef _ensure_schema_providers_loaded():\n \"\"\"Populate zvt_context from config schema_providers for schemas without Recorders.\"\"\"\n if getattr(_ensure_schema_providers_loaded, \"_done\", False):\n return\n try:\n from zvt.contract.schema import _get_schema_providers\n schema_providers = _get_schema_providers()\n for db_name, providers in schema_providers.items():\n for p in providers:\n if p not in zvt_context.providers:\n zvt_context.providers.append(p)\n if p not in zvt_context.provider_map_dbnames:\n zvt_context.provider_map_dbnames[p] = []\n if db_name not in zvt_context.provider_map_dbnames[p]:\n zvt_context.provider_map_dbnames[p].append(db_name)\n except Exception:\n pass\n _ensure_schema_providers_loaded._done = True\n\n\ndef get_providers() -> List[str]:\n _ensure_schema_providers_loaded()\n return zvt_context.providers\n\n\ndef get_schemas(provider: str) -> List[DeclarativeMeta]:\n _ensure_schema_providers_loaded()\n schemas = []\n for provider1, dbs in zvt_context.provider_map_dbnames.items():\n if provider == provider1:\n for dbname in dbs:\n schemas1 = zvt_context.dbname_map_schemas.get(dbname)\n if schemas1:\n schemas += schemas1\n return schemas\n\n\ndef get_db_session(provider: str, db_name: str = None, data_schema: object = None, force_new: bool = False) -> Session:\n \"\"\"\n get db session from (provider,db_name) or (provider,data_schema)\n\n :param provider: data provider\n :param db_name: db name\n :param data_schema: data schema\n :param force_new: True for new session, otherwise use global session\n :return: db session\n \"\"\"\n _ensure_schema_providers_loaded()\n if data_schema:\n db_name = _get_db_name(data_schema=data_schema)\n\n data_path = zvt_env.get(\"data_path\", \".\")\n storage_id = _route_registry().get_storage_id(provider, db_name)\n engine = get_db_engine(provider=provider, db_name=db_name)\n session_fac = _storage_backend().get_session_factory(storage_id, data_path)\n session_fac.configure(bind=engine)\n\n if force_new:\n return session_fac()\n\n session_key = storage_id\n session = zvt_context.sessions.get(session_key)\n # FIXME: should not maintain global session\n if not session:\n session = session_fac()\n zvt_context.sessions[session_key] = session\n return session\n\n\ndef get_db_session_factory(provider: str, db_name: str = None, data_schema: object = None):\n \"\"\"\n get db session factory from (provider,db_name) or (provider,data_schema)\n \"\"\"\n if data_schema:\n db_name = _get_db_name(data_schema=data_schema)\n\n engine = get_db_engine(provider=provider, db_name=db_name)\n data_path = zvt_env.get(\"data_path\", \".\")\n storage_id = _route_registry().get_storage_id(provider, db_name)\n session_fac = _storage_backend().get_session_factory(storage_id, data_path)\n session_fac.configure(bind=engine)\n return session_fac\n\n\nDBSession = get_db_session_factory\n\n\ndef get_entity_schema(entity_type: str) -> Type[TradableEntity]:\n \"\"\"\n get entity schema from name\n\n :param entity_type: entity type, e.g. stock, stockus.\n :return: the Schema of the entity\n \"\"\"\n return zvt_context.tradable_schema_map[entity_type]\n\n\ndef get_schema_by_name(name: str) -> DeclarativeMeta:\n \"\"\"\n get domain schema by the name\n\n :param name: schema name\n :return: schema\n \"\"\"\n for schema in zvt_context.schemas:\n if schema.__name__ == name:\n return schema\n\n\ndef get_schema_columns(schema: DeclarativeMeta) -> List[str]:\n \"\"\"\n get all columns of the domain schema\n\n :param schema: data schema\n :return: columns of the schema\n \"\"\"\n return schema.__table__.columns.keys()\n\n\ndef common_filter(\n query: Query,\n data_schema,\n start_timestamp=None,\n end_timestamp=None,\n filters=None,\n order=None,\n limit=None,\n distinct=None,\n time_field=\"timestamp\",\n):\n \"\"\"\n build filter by the arguments\n\n :param query: sql query\n :param data_schema: data schema\n :param start_timestamp: start timestamp\n :param end_timestamp: end timestamp\n :param filters: sql filters\n :param order: sql order\n :param limit: sql limit size\n :param time_field: time field in columns\n :return: result query\n \"\"\"\n assert data_schema is not None\n time_col = eval(\"data_schema.{}\".format(time_field))\n\n if start_timestamp:\n query = query.filter(time_col >= to_pd_timestamp(start_timestamp))\n if end_timestamp:\n query = query.filter(time_col <= to_pd_timestamp(end_timestamp))\n\n if filters:\n for filter in filters:\n query = query.filter(filter)\n if order is not None:\n query = query.order_by(order)\n else:\n query = query.order_by(time_col.asc())\n if limit:\n query = query.limit(limit)\n if distinct:\n query = query.distinct(distinct)\n\n return query\n\n\ndef del_data(data_schema: Type[Mixin], filters: List = None, provider=None):\n \"\"\"\n delete data by filters\n\n :param data_schema: data schema\n :param filters: filters\n :param provider: data provider\n \"\"\"\n if not provider:\n provider = data_schema.get_providers()[0]\n\n session = get_db_session(provider=provider, data_schema=data_schema)\n query = session.query(data_schema)\n if filters:\n for f in filters:\n query = query.filter(f)\n query.delete()\n session.commit()\n\n\ndef get_by_id(data_schema, id: str, provider: str = None, session: Session = None):\n \"\"\"\n get one record by id from data schema\n\n :param data_schema: data schema\n :param id: the record id\n :param provider: data provider\n :param session: db session\n :return: the record of the id\n \"\"\"\n _ensure_schema_providers_loaded()\n providers = data_schema.get_providers()\n if not providers:\n raise ValueError(f\"no provider registered for: {data_schema}\")\n if not provider:\n provider = providers[0]\n\n if not session:\n session = get_db_session(provider=provider, data_schema=data_schema)\n\n return session.query(data_schema).get(id)\n\n\ndef _row2dict(row):\n d = {}\n for column in row.__table__.columns:\n d[column.name] = getattr(row, column.name)\n return d\n\n\ndef get_data(\n data_schema: Type[Mixin],\n ids: List[str] = None,\n entity_ids: List[str] = None,\n entity_id: str = None,\n codes: List[str] = None,\n code: str = None,\n level: Union[IntervalLevel, str] = None,\n provider: str = None,\n columns: List = None,\n col_label: dict = None,\n return_type: str = \"df\",\n start_timestamp: Union[pd.Timestamp, str] = None,\n end_timestamp: Union[pd.Timestamp, str] = None,\n filters: List = None,\n session: Session = None,\n order=None,\n limit: int = None,\n distinct=None,\n index: Union[str, list] = None,\n drop_index_col=False,\n time_field: str = \"timestamp\",\n):\n \"\"\"\n query data by the arguments\n\n :param data_schema:\n :param ids:\n :param entity_ids:\n :param entity_id:\n :param codes:\n :param code:\n :param level:\n :param provider:\n :param columns:\n :param col_label: dict with key(column), value(label)\n :param return_type: df, domain or dict. default is df\n :param start_timestamp:\n :param end_timestamp:\n :param filters:\n :param session:\n :param order:\n :param limit:\n :param index: index field name, str for single index, str list for multiple index\n :param drop_index_col: whether drop the col if it's in index, default False\n :param time_field:\n :return: results basing on return_type.\n \"\"\"\n _ensure_schema_providers_loaded()\n providers = data_schema.get_providers()\n if not providers:\n raise ValueError(f\"no provider registered for: {data_schema}\")\n if not provider:\n provider = providers[0]\n\n if not session:\n session = get_db_session(provider=provider, data_schema=data_schema)\n\n time_col = eval(\"data_schema.{}\".format(time_field))\n\n if columns:\n # support str\n for i, col in enumerate(columns):\n if isinstance(col, str):\n columns[i] = eval(\"data_schema.{}\".format(col))\n\n # make sure get timestamp\n if time_col not in columns:\n columns.append(time_col)\n\n if col_label:\n columns_ = []\n for col in columns:\n if col.name in col_label:\n columns_.append(col.label(col_label.get(col.name)))\n else:\n columns_.append(col)\n columns = columns_\n\n query = session.query(*columns)\n else:\n query = session.query(data_schema)\n\n if entity_id:\n query = query.filter(data_schema.entity_id == entity_id)\n if entity_ids:\n query = query.filter(data_schema.entity_id.in_(entity_ids))\n if code:\n query = query.filter(data_schema.code == code)\n if codes:\n query = query.filter(data_schema.code.in_(codes))\n if ids:\n query = query.filter(data_schema.id.in_(ids))\n\n # we always store different level in different schema,the level param is not useful now\n if level:\n try:\n #: some schema has no level,just ignore it\n data_schema.level\n if type(level) == IntervalLevel:\n level = level.value\n query = query.filter(data_schema.level == level)\n except Exception as e:\n pass\n\n query = common_filter(\n query,\n data_schema=data_schema,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=filters,\n order=order,\n limit=limit,\n distinct=distinct,\n time_field=time_field,\n )\n\n if return_type == \"df\":\n df = pd.read_sql(query.statement, query.session.bind)\n if pd_is_not_null(df):\n if index:\n df = index_df(df, index=index, drop=drop_index_col, time_field=time_field)\n return df\n elif return_type == \"domain\":\n return query.all()\n elif return_type == \"dict\":\n domains = query.all()\n return [_row2dict(item) for item in domains]\n elif return_type == \"select\":\n return query.selectable\n\n\ndef data_exist(session, schema, id):\n \"\"\"\n whether exist data of the id\n\n :param session:\n :param schema:\n :param id:\n :return:\n \"\"\"\n return session.query(exists().where(and_(schema.id == id))).scalar()\n\n\ndef get_data_count(data_schema, filters=None, provider=None, session=None):\n \"\"\"\n get record count basing on the filters\n\n :param data_schema:\n :param filters:\n :param session:\n :return:\n \"\"\"\n if not session:\n session = get_db_session(provider=provider, data_schema=data_schema)\n\n query = session.query(data_schema)\n if filters:\n for filter in filters:\n query = query.filter(filter)\n\n count_q = query.statement.with_only_columns(func.count(data_schema.id)).order_by(None)\n count = session.execute(count_q).scalar()\n return count\n\n\ndef get_group(provider, data_schema, column, group_func=func.count, session=None):\n if not session:\n session = get_db_session(provider=provider, data_schema=data_schema)\n if group_func:\n query = session.query(column, group_func(column)).group_by(column)\n else:\n query = session.query(column).group_by(column)\n df = pd.read_sql(query.statement, session.bind)\n return df\n\n\ndef decode_entity_id(entity_id: str):\n \"\"\"\n decode entity id to entity_type, exchange, code\n\n :param entity_id:\n :return: tuple with format (entity_type, exchange, code)\n \"\"\"\n result = entity_id.split(\"_\")\n entity_type = result[0]\n exchange = result[1]\n code = \"\".join(result[2:])\n return entity_type, exchange, code\n\n\ndef get_entity_type(entity_id: str):\n \"\"\"\n get entity type by entity id\n\n :param entity_id:\n :return: entity type\n \"\"\"\n entity_type, _, _ = decode_entity_id(entity_id)\n return entity_type\n\n\ndef get_entity_exchange(entity_id: str):\n \"\"\"\n get exchange by entity id\n\n :param entity_id:\n :return: exchange\n \"\"\"\n _, exchange, _ = decode_entity_id(entity_id)\n return exchange\n\n\ndef get_entity_code(entity_id: str):\n \"\"\"\n get code by entity id\n\n :param entity_id:\n :return: code\n \"\"\"\n _, _, code = decode_entity_id(entity_id)\n return code\n\n\ndef df_to_db(\n df: pd.DataFrame,\n data_schema: DeclarativeMeta,\n provider: str,\n force_update: bool = False,\n sub_size: int = 8000,\n drop_duplicates: bool = True,\n dtype=None,\n session=None,\n need_check=True,\n) -> object:\n \"\"\"\n store the df to db\n\n :param df: data with columns of the schema\n :param data_schema: data schema\n :param provider: data provider\n :param force_update: whether update the data with id existed\n :param sub_size: update batch size\n :param drop_duplicates: whether drop duplicates\n :return:\n \"\"\"\n if not pd_is_not_null(df):\n return 0\n\n if drop_duplicates and df.duplicated(subset=\"id\").any():\n logger.warning(f\"remove duplicated:{df[df.duplicated()]}\")\n df = df.drop_duplicates(subset=\"id\", keep=\"last\")\n\n schema_cols = get_schema_columns(data_schema)\n cols = set(df.columns.tolist()) & set(schema_cols)\n\n if not cols:\n print(\"wrong cols\")\n return 0\n\n cols = list(cols)\n df = df[cols]\n\n size = len(df)\n\n if platform.system() == \"Windows\":\n sub_size = 900\n\n if size >= sub_size:\n step_size = int(size / sub_size)\n if size % sub_size:\n step_size = step_size + 1\n else:\n step_size = 1\n\n saved = 0\n\n if not session:\n session = get_db_session(provider=provider, data_schema=data_schema)\n\n for step in range(step_size):\n df_current = df.iloc[sub_size * step : sub_size * (step + 1)]\n\n if need_check:\n if force_update:\n ids = df_current[\"id\"].tolist()\n if len(ids) == 1:\n sql = text(f'delete from `{data_schema.__tablename__}` where id = \"{ids[0]}\"')\n else:\n sql = text(f\"delete from `{data_schema.__tablename__}` where id in {tuple(ids)}\")\n\n session.execute(sql)\n else:\n current = get_data(\n session=session,\n data_schema=data_schema,\n columns=[data_schema.id],\n provider=provider,\n ids=df_current[\"id\"].tolist(),\n )\n if pd_is_not_null(current):\n df_current = df_current[~df_current[\"id\"].isin(current[\"id\"])]\n\n if pd_is_not_null(df_current):\n saved = saved + len(df_current)\n df_current.to_sql(\n data_schema.__tablename__, session.connection(), index=False, if_exists=\"append\", dtype=dtype\n )\n session.commit()\n return saved\n\n\ndef get_entities(\n entity_schema: Type[TradableEntity] = None,\n entity_type: str = None,\n exchanges: List[str] = None,\n ids: List[str] = None,\n entity_ids: List[str] = None,\n entity_id: str = None,\n codes: List[str] = None,\n code: str = None,\n provider: str = None,\n columns: List = None,\n col_label: dict = None,\n return_type: str = \"df\",\n start_timestamp: Union[pd.Timestamp, str] = None,\n end_timestamp: Union[pd.Timestamp, str] = None,\n filters: List = None,\n session: Session = None,\n order=None,\n limit: int = None,\n index: Union[str, list] = \"code\",\n) -> List:\n \"\"\"\n get entities by the arguments\n\n :param entity_schema:\n :param entity_type:\n :param exchanges:\n :param ids:\n :param entity_ids:\n :param entity_id:\n :param codes:\n :param code:\n :param provider:\n :param columns:\n :param col_label:\n :param return_type:\n :param start_timestamp:\n :param end_timestamp:\n :param filters:\n :param session:\n :param order:\n :param limit:\n :param index:\n :return:\n \"\"\"\n if not entity_schema:\n entity_schema = zvt_context.tradable_schema_map[entity_type]\n\n if not provider:\n provider = entity_schema.get_providers()[0]\n\n if not order:\n order = entity_schema.code.asc()\n\n if exchanges:\n if filters:\n filters.append(entity_schema.exchange.in_(exchanges))\n else:\n filters = [entity_schema.exchange.in_(exchanges)]\n\n return get_data(\n data_schema=entity_schema,\n ids=ids,\n entity_ids=entity_ids,\n entity_id=entity_id,\n codes=codes,\n code=code,\n level=None,\n provider=provider,\n columns=columns,\n col_label=col_label,\n return_type=return_type,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=filters,\n session=session,\n order=order,\n limit=limit,\n index=index,\n )\n\n\ndef get_entity_ids(\n entity_type=\"stock\",\n entity_schema: TradableEntity = None,\n exchanges=None,\n codes=None,\n provider=None,\n filters=None,\n entity_ids=None,\n):\n \"\"\"\n get entity ids by the arguments\n\n :param entity_type:\n :param entity_schema:\n :param exchanges:\n :param codes:\n :param provider:\n :param filters:\n :param entity_ids:\n :return:\n \"\"\"\n df = get_entities(\n entity_type=entity_type,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n provider=provider,\n filters=filters,\n entity_ids=entity_ids,\n )\n if pd_is_not_null(df):\n return df[\"entity_id\"].to_list()\n return None\n\n\nif __name__ == \"__main__\":\n print(get_entities(entity_type=\"block\"))\n\n\n# the __all__ is generated\n__all__ = [\n \"get_db_engine\",\n \"get_providers\",\n \"get_schemas\",\n \"get_db_session\",\n \"get_db_session_factory\",\n \"get_entity_schema\",\n \"get_schema_by_name\",\n \"get_schema_columns\",\n \"common_filter\",\n \"del_data\",\n \"get_by_id\",\n \"get_data\",\n \"data_exist\",\n \"get_data_count\",\n \"get_group\",\n \"decode_entity_id\",\n \"get_entity_type\",\n \"get_entity_exchange\",\n \"get_entity_code\",\n \"df_to_db\",\n \"get_entities\",\n \"get_entity_ids\",\n]\n"
},
{
"path": "src/zvt/contract/base_service.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nfrom typing import Type, List\n\nfrom zvt.contract.api import del_data, get_db_session\nfrom zvt.contract.zvt_info import StateMixin\nfrom zvt.utils.str_utils import to_snake_str\n\n\nclass StatefulService(object):\n \"\"\"\n Base service with state could be stored in state_schema\n \"\"\"\n\n #: state schema\n state_schema: Type[StateMixin] = None\n\n #: name of the service, default name of class if not set manually\n name = None\n\n def __init__(self) -> None:\n assert self.state_schema is not None\n if self.name is None:\n self.name = to_snake_str(type(self).__name__)\n self.state_session = get_db_session(data_schema=self.state_schema, provider=\"zvt\")\n\n def clear_state_data(self, entity_id=None):\n \"\"\"\n clear state of the entity\n\n :param entity_id: entity id\n \"\"\"\n filters = [self.state_schema.state_name == self.name]\n if entity_id:\n filters = filters + [self.state_schema.entity_id == entity_id]\n del_data(self.state_schema, filters=filters)\n\n def decode_state(self, state: str):\n \"\"\"\n decode state\n\n :param state:\n :return:\n \"\"\"\n\n return json.loads(state, object_hook=self.state_object_hook())\n\n def encode_state(self, state: object):\n \"\"\"\n encode state\n\n :param state:\n :return:\n \"\"\"\n\n return json.dumps(state, cls=self.state_encoder())\n\n def state_object_hook(self):\n return None\n\n def state_encoder(self):\n return None\n\n\nclass OneStateService(StatefulService):\n \"\"\"\n StatefulService which saving all states in one object\n \"\"\"\n\n def __init__(self) -> None:\n super().__init__()\n self.state_domain = self.state_schema.get_by_id(id=self.name)\n if self.state_domain:\n self.state: dict = self.decode_state(self.state_domain.state)\n else:\n self.state = None\n\n def persist_state(self):\n state_str = self.encode_state(self.state)\n if not self.state_domain:\n self.state_domain = self.state_schema(id=self.name, entity_id=self.name, state_name=self.name)\n self.state_domain.state = state_str\n self.state_session.add(self.state_domain)\n self.state_session.commit()\n\n\nclass EntityStateService(StatefulService):\n \"\"\"\n StatefulService which saving one state one entity\n \"\"\"\n\n def __init__(self, entity_ids) -> None:\n super().__init__()\n self.entity_ids = entity_ids\n state_domains: List[StateMixin] = self.state_schema.query_data(\n filters=[self.state_schema.state_name == self.name], entity_ids=self.entity_ids, return_type=\"domain\"\n )\n\n #: entity_id:state\n self.states: dict = {}\n if state_domains:\n for state in state_domains:\n self.states[state.entity_id] = self.decode_state(state.state)\n\n def persist_state(self, entity_id):\n state = self.states.get(entity_id)\n if state:\n domain_id = f\"{self.name}_{entity_id}\"\n state_domain = self.state_schema.get_by_id(domain_id)\n state_str = self.encode_state(state)\n if not state_domain:\n state_domain = self.state_schema(id=domain_id, entity_id=entity_id, state_name=self.name)\n state_domain.state = state_str\n self.state_session.add(state_domain)\n self.state_session.commit()\n\n\n# the __all__ is generated\n__all__ = [\"StatefulService\", \"OneStateService\", \"EntityStateService\"]\n"
},
{
"path": "src/zvt/contract/context.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nZVT global context. Phase 1: storage_backend and route_registry are optional\ninjections; api uses get_storage_backend()/get_route_registry() when not set.\n\"\"\"\nfrom typing import Optional, TYPE_CHECKING\n\nif TYPE_CHECKING:\n from zvt.contract.storage import StorageBackend\n from zvt.contract.route_registry import RouteRegistry\n\n\nclass Registry(object):\n \"\"\"\n Class storing zvt registering meta\n \"\"\"\n\n def __init__(self) -> None:\n #: all registered providers\n self.providers = []\n\n #: all registered entity types(str)\n self.tradable_entity_types = []\n\n #: all entity schemas\n self.tradable_entity_schemas = []\n\n #: all registered schemas\n self.schemas = []\n\n #: tradable entity type -> schema\n self.tradable_schema_map = {}\n\n #: global sessions\n self.sessions = {}\n\n #: provider_dbname -> engine (deprecated: engines now in StorageBackend)\n self.db_engine_map = {}\n\n #: provider_dbname -> session factory (deprecated: now in StorageBackend)\n self.db_session_map = {}\n\n #: provider -> [db_name1,db_name2...]\n self.provider_map_dbnames = {}\n\n #: db_name -> [declarative_base1,declarative_base2...]\n self.dbname_map_base = {}\n\n #: db_name -> [declarative_meta1,declarative_meta2...]\n self.dbname_map_schemas = {}\n\n #: entity_type -> related schemas\n self.entity_map_schemas = {}\n\n #: factor class registry\n self.factor_cls_registry = {}\n\n #: db_names of internal schemas (business logic data, not tied to external data source)\n self.internal_db_names = set()\n\n #: Optional StorageBackend; if set, api uses it instead of get_storage_backend()\n self.storage_backend: Optional[\"StorageBackend\"] = None\n\n #: Optional RouteRegistry; if set, api uses it instead of get_route_registry()\n self.route_registry: Optional[\"RouteRegistry\"] = None\n\n\n#: :class:`~.zvt.contract.context.Registry` instance\nzvt_context = Registry()\n\n\n# the __all__ is generated\n__all__ = [\"Registry\"]\n"
},
{
"path": "src/zvt/contract/data_type.py",
"content": "# -*- coding: utf-8 -*-\n\n\nclass Bean(object):\n def __init__(self) -> None:\n super().__init__()\n self.__dict__\n\n def dict(self):\n return self.__dict__\n\n def from_dct(self, dct: dict):\n if dct:\n for k in dct:\n self.__dict__[k] = dct[k]\n\n\n# the __all__ is generated\n__all__ = [\"Bean\"]\n"
},
{
"path": "src/zvt/contract/drawer.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nfrom enum import Enum\nfrom typing import List, Optional\n\nimport numpy as np\nimport pandas as pd\nimport plotly.graph_objs as go\nfrom plotly.subplots import make_subplots\n\nfrom zvt.contract.api import decode_entity_id\nfrom zvt.contract.data_type import Bean\nfrom zvt.contract.normal_data import NormalData\nfrom zvt.utils.decorator import to_string\nfrom zvt.utils.pd_utils import pd_is_not_null\n\nlogger = logging.getLogger(__name__)\n\n\nclass ChartType(Enum):\n \"\"\"\n Chart type enum\n \"\"\"\n\n #: candlestick chart\n kline = \"kline\"\n #: line chart\n line = \"line\"\n #: area chart\n area = \"area\"\n #: scatter chart\n scatter = \"scatter\"\n #: histogram chart\n histogram = \"histogram\"\n #: pie chart\n pie = \"pie\"\n #: bar chart\n bar = \"bar\"\n\n\n_zvt_chart_type_map_scatter_mode = {ChartType.line: \"lines\", ChartType.area: \"none\", ChartType.scatter: \"markers\"}\n\n\n@to_string\nclass Rect(Bean):\n \"\"\"\n rect struct with left-bottom(x0, y0), right-top(x1, y1)\n \"\"\"\n\n def __init__(self, x0=None, y0=None, x1=None, y1=None) -> None:\n #: left-bottom x0\n self.x0 = x0\n #: left-bottom y0\n self.y0 = y0\n #: right-top x1\n self.x1 = x1\n #: right-top y1\n self.y1 = y1\n\n\nclass Draw(object):\n def draw_kline(\n self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs\n ):\n return self.draw(\n main_chart=ChartType.kline,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw_line(\n self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs\n ):\n return self.draw(\n main_chart=ChartType.line,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw_area(\n self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs\n ):\n return self.draw(\n main_chart=ChartType.area,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw_scatter(\n self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs\n ):\n return self.draw(\n main_chart=ChartType.scatter,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw_histogram(\n self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs\n ):\n return self.draw(\n ChartType.histogram,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw_bar(self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs):\n return self.draw(\n ChartType.bar,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw_pie(self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs):\n return self.draw(\n ChartType.pie,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def draw(\n self,\n main_chart=ChartType.kline,\n sub_chart=\"bar\",\n width=None,\n height=None,\n title=None,\n keep_ui_state=True,\n show=False,\n scale_value=None,\n **kwargs,\n ):\n\n raise NotImplementedError()\n\n def default_layout(self, main_chart=None, width=None, height=None, title=None, keep_ui_state=True, **layout_params):\n if keep_ui_state:\n uirevision = True\n else:\n uirevision = None\n\n if main_chart == ChartType.histogram:\n xaxis = None\n else:\n xaxis = dict(\n linecolor=\"#BCCCDC\",\n showgrid=False,\n showspikes=True, # Show spike line for X-axis\n # Format spike\n spikethickness=2,\n spikedash=\"dot\",\n spikecolor=\"#999999\",\n spikemode=\"across\",\n rangeselector=dict(\n buttons=list(\n [\n dict(count=1, label=\"1m\", step=\"month\", stepmode=\"backward\"),\n dict(count=3, label=\"3m\", step=\"month\", stepmode=\"backward\"),\n dict(count=6, label=\"6m\", step=\"month\", stepmode=\"backward\"),\n dict(count=1, label=\"YTD\", step=\"year\", stepmode=\"todate\"),\n dict(count=1, label=\"1y\", step=\"year\", stepmode=\"backward\"),\n dict(step=\"all\"),\n ]\n )\n ),\n rangeslider=dict(\n visible=True,\n ),\n type=\"date\",\n )\n\n return dict(\n showlegend=True,\n plot_bgcolor=\"#FFF\",\n hovermode=\"x\",\n hoverdistance=100, # Distance to show hover label of data point\n spikedistance=1000, # Distance to show spike\n uirevision=uirevision,\n height=height,\n width=width,\n title=title,\n yaxis=dict(\n autorange=True,\n fixedrange=False,\n zeroline=False,\n linecolor=\"#BCCCDC\",\n showgrid=False,\n ),\n xaxis=xaxis,\n legend_orientation=\"h\",\n hoverlabel={\"namelength\": -1},\n **layout_params,\n )\n\n\nclass Drawable(object):\n def drawer(self):\n drawer = Drawer(\n main_df=self.drawer_main_df(),\n main_data=self.drawer_main_data(),\n factor_df_list=self.drawer_factor_df_list(),\n factor_data_list=self.drawer_factor_data_list(),\n sub_df_list=self.drawer_sub_df_list(),\n sub_data_list=self.drawer_sub_data_list(),\n sub_col_chart=self.drawer_sub_col_chart(),\n annotation_df=self.drawer_annotation_df(),\n rects=self.drawer_rects(),\n )\n return drawer\n\n def draw(\n self,\n main_chart=ChartType.kline,\n width=None,\n height=None,\n title=None,\n keep_ui_state=True,\n show=False,\n scale_value=None,\n **kwargs,\n ):\n return self.drawer().draw(\n main_chart=main_chart,\n width=width,\n height=height,\n title=title,\n keep_ui_state=keep_ui_state,\n show=show,\n scale_value=scale_value,\n **kwargs,\n )\n\n def drawer_main_df(self) -> Optional[pd.DataFrame]:\n return None\n\n def drawer_main_data(self) -> Optional[NormalData]:\n return None\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n return None\n\n def drawer_factor_data_list(self) -> Optional[List[NormalData]]:\n return None\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n return None\n\n def drawer_sub_data_list(self) -> Optional[List[NormalData]]:\n return None\n\n def drawer_annotation_df(self) -> Optional[pd.DataFrame]:\n return None\n\n def drawer_rects(self) -> Optional[List[Rect]]:\n return None\n\n def drawer_sub_col_chart(self) -> Optional[dict]:\n return None\n\n\nclass StackedDrawer(Draw):\n def __init__(self, *drawers) -> None:\n super().__init__()\n assert len(drawers) > 1\n self.drawers: List[Drawer] = drawers\n\n def make_y_layout(self, index, total, start_index=1, domain_range=(0, 1)):\n part = (domain_range[1] - domain_range[0]) / total\n\n if index == 1:\n yaxis = \"yaxis\"\n y = \"y\"\n else:\n yaxis = f\"yaxis{index}\"\n y = f\"y{index}\"\n\n return (\n yaxis,\n y,\n dict(\n anchor=\"x\",\n autorange=True,\n fixedrange=False,\n zeroline=False,\n linecolor=\"#BCCCDC\",\n showgrid=False,\n domain=[\n domain_range[0] + part * (index - start_index),\n domain_range[0] + part * (index - start_index + 1),\n ],\n ),\n )\n\n def draw(\n self,\n main_chart=ChartType.kline,\n sub_chart=\"bar\",\n width=None,\n height=None,\n title=None,\n keep_ui_state=True,\n show=False,\n scale_value=None,\n **kwargs,\n ):\n stacked_fig = go.Figure()\n\n total = len(self.drawers)\n start = 1\n domain_range = (0, 1)\n for drawer in self.drawers:\n if drawer.has_sub_plot():\n domain_range = (0.2, 1)\n start = 2\n break\n for index, drawer in enumerate(self.drawers, start=start):\n traces, sub_traces = drawer.make_traces(\n main_chart=main_chart, sub_chart=sub_chart, scale_value=scale_value, **kwargs\n )\n\n # fix sub traces as the bottom\n if sub_traces:\n yaxis, y, layout = self.make_y_layout(index=1, total=1, domain_range=(0, 0.2))\n # update sub_traces with yaxis\n for trace in sub_traces:\n trace.yaxis = y\n stacked_fig.add_traces(sub_traces)\n stacked_fig.layout[yaxis] = layout\n\n # make y layouts\n yaxis, y, layout = self.make_y_layout(\n index=index, total=total, start_index=start, domain_range=domain_range\n )\n\n stacked_fig.layout[yaxis] = layout\n\n # update traces with yaxis\n for trace in traces:\n trace.yaxis = y\n stacked_fig.add_traces(traces)\n\n # update shapes with yaxis\n if drawer.rects:\n for rect in drawer.rects:\n stacked_fig.add_shape(\n type=\"rect\",\n x0=rect.x0,\n y0=rect.y0,\n x1=rect.x1,\n y1=rect.y1,\n line=dict(color=\"RoyalBlue\", width=1),\n # fillcolor=\"LightSkyBlue\",\n yref=y,\n )\n\n # annotations\n if pd_is_not_null(drawer.annotation_df):\n stacked_fig.layout[\"annotations\"] = annotations(drawer.annotation_df, yref=y)\n\n stacked_fig.update_layout(\n self.default_layout(\n main_chart=main_chart, width=width, height=height, title=title, keep_ui_state=keep_ui_state\n )\n )\n\n if show:\n stacked_fig.show()\n else:\n return stacked_fig\n\n\nclass Drawer(Draw):\n def __init__(\n self,\n main_df: pd.DataFrame = None,\n factor_df_list: List[pd.DataFrame] = None,\n sub_df_list: pd.DataFrame = None,\n main_data: NormalData = None,\n factor_data_list: List[NormalData] = None,\n sub_data_list: NormalData = None,\n sub_col_chart: Optional[dict] = None,\n rects: List[Rect] = None,\n annotation_df: pd.DataFrame = None,\n scale_value: int = None,\n ) -> None:\n \"\"\"\n\n :param main_df: df for main chart\n :param factor_df_list: list of factor df on main chart\n :param sub_df_list: df for sub chart under main chart\n :param main_data: NormalData wrap main_df,use either\n :param factor_data_list: list of NormalData wrap factor_df,use either\n :param sub_data_list: NormalData wrap sub_df,use either\n :param annotation_df:\n \"\"\"\n\n #: 主图数据\n if main_data is None:\n main_data = NormalData(main_df)\n self.main_data: NormalData = main_data\n\n #: 主图因子\n if not factor_data_list and factor_df_list:\n factor_data_list = []\n for df in factor_df_list:\n factor_data_list.append(NormalData(df))\n #: 每一个df可能有多个column, 代表多个指标，对于连续型的，可以放在一个df里面\n #: 对于离散型的，比如一些特定模式的连线，放在多个df里面较好，因为index不同\n self.factor_data_list: List[NormalData] = factor_data_list\n\n #: 副图数据\n if not sub_data_list and sub_df_list:\n sub_data_list = []\n for df in sub_df_list:\n sub_data_list.append(NormalData(df))\n #: 每一个df可能有多个column, 代表多个指标，对于连续型的，可以放在一个df里面\n #: 对于离散型的，比如一些特定模式的连线，放在多个df里面较好，因为index不同\n self.sub_data_list: List[NormalData] = sub_data_list\n\n #: 幅图col对应的图形，line or bar\n self.sub_col_chart = sub_col_chart\n\n #: 主图的标记数据\n self.annotation_df = annotation_df\n\n #: list of rect\n self.rects = rects\n\n self.scale_value = scale_value\n\n def add_factor_df(self, df: pd.DataFrame):\n self.add_factor_data(NormalData(df))\n\n def add_factor_data(self, data: NormalData):\n if not self.factor_data_list:\n self.factor_data_list = []\n self.factor_data_list.append(data)\n\n def add_sub_df(self, df: pd.DataFrame):\n self.add_sub_data(NormalData(df))\n\n def add_sub_data(self, data: NormalData):\n if not self.sub_data_list:\n self.sub_data_list = []\n self.sub_data_list.append(data)\n\n def has_sub_plot(self):\n return self.sub_data_list is not None and not self.sub_data_list[0].empty()\n\n def make_traces(self, main_chart=ChartType.kline, sub_chart=\"bar\", yaxis=\"y\", scale_value=None, **kwargs):\n traces = []\n sub_traces = []\n\n for entity_id, df in self.main_data.entity_map_df.items():\n df = df.select_dtypes(np.number)\n df = df.copy()\n if scale_value:\n for col in df.columns:\n first = None\n for i in range(0, len(df)):\n first = df[col][i]\n if first != 0:\n break\n if first == 0:\n continue\n scale = scale_value / first\n df[col] = df[col] * scale\n code = entity_id\n try:\n _, _, code = decode_entity_id(entity_id)\n except Exception:\n pass\n\n # 构造主图\n if main_chart == ChartType.bar:\n for col in df.columns:\n trace_name = \"{}_{}\".format(code, col)\n ydata = df[col].values.tolist()\n traces.append(go.Bar(x=df.index, y=ydata, name=trace_name, yaxis=yaxis, **kwargs))\n elif main_chart == ChartType.kline:\n trace_name = \"{}_kdata\".format(code)\n trace = go.Candlestick(\n x=df.index,\n open=df[\"open\"],\n close=df[\"close\"],\n low=df[\"low\"],\n high=df[\"high\"],\n name=trace_name,\n yaxis=yaxis,\n **kwargs,\n )\n traces.append(trace)\n elif main_chart in [ChartType.scatter, ChartType.line, ChartType.area]:\n mode = _zvt_chart_type_map_scatter_mode.get(main_chart)\n for col in df.columns:\n trace_name = \"{}_{}\".format(code, col)\n ydata = df[col].values.tolist()\n traces.append(go.Scatter(x=df.index, y=ydata, mode=mode, name=trace_name, yaxis=yaxis, **kwargs))\n elif main_chart == ChartType.histogram:\n for col in df.columns:\n trace_name = \"{}_{}\".format(code, col)\n x = df[col].tolist()\n trace = go.Histogram(x=x, name=trace_name, **kwargs)\n traces.append(trace)\n annotation = [\n dict(\n entity_id=entity_id,\n timestamp=x[-1],\n value=0,\n flag=f\"{trace_name}:{x[-1]}\",\n )\n ]\n annotation_df = pd.DataFrame.from_records(annotation, index=[\"entity_id\", \"timestamp\"])\n if pd_is_not_null(self.annotation_df):\n self.annotation_df = pd.concat([self.annotation_df, annotation_df])\n else:\n self.annotation_df = annotation_df\n elif main_chart == ChartType.pie:\n for _, row in df.iterrows():\n traces.append(go.Pie(name=entity_id, labels=df.columns.tolist(), values=row.tolist(), **kwargs))\n else:\n assert False\n\n # 构造主图指标\n if self.factor_data_list:\n for factor_data in self.factor_data_list:\n if not factor_data.empty():\n factor_df = factor_data.entity_map_df.get(entity_id)\n factor_df = factor_df.select_dtypes(np.number)\n if pd_is_not_null(factor_df):\n for col in factor_df.columns:\n trace_name = \"{}_{}\".format(code, col)\n ydata = factor_df[col].values.tolist()\n\n line = go.Scatter(\n x=factor_df.index, y=ydata, mode=\"lines\", name=trace_name, yaxis=yaxis, **kwargs\n )\n traces.append(line)\n\n # 构造幅图\n if self.has_sub_plot():\n for sub_data in self.sub_data_list:\n sub_df = sub_data.entity_map_df.get(entity_id)\n if pd_is_not_null(sub_df):\n sub_df = sub_df.select_dtypes(np.number)\n for col in sub_df.columns:\n trace_name = \"{}_{}\".format(code, col)\n ydata = sub_df[col].values.tolist()\n\n def color(i):\n if i > 0:\n return \"red\"\n else:\n return \"green\"\n\n colors = [color(i) for i in ydata]\n\n the_sub_chart = None\n if self.sub_col_chart is not None:\n the_sub_chart = self.sub_col_chart.get(col)\n if not the_sub_chart:\n the_sub_chart = sub_chart\n\n if the_sub_chart == ChartType.line:\n sub_trace = go.Scatter(\n x=sub_df.index, y=ydata, name=trace_name, yaxis=\"y2\", marker=dict(color=colors)\n )\n else:\n sub_trace = go.Bar(\n x=sub_df.index, y=ydata, name=trace_name, yaxis=\"y2\", marker=dict(color=colors)\n )\n sub_traces.append(sub_trace)\n\n return traces, sub_traces\n\n def add_rects(self, fig, yaxis=\"y\"):\n if self.rects:\n for rect in self.rects:\n fig.add_shape(\n type=\"rect\",\n x0=rect.x0,\n y0=rect.y0,\n x1=rect.x1,\n y1=rect.y1,\n line=dict(color=\"RoyalBlue\", width=1),\n # fillcolor=\"LightSkyBlue\"\n )\n fig.update_shapes(dict(xref=\"x\", yref=yaxis))\n\n def draw(\n self,\n main_chart=ChartType.kline,\n sub_chart=\"bar\",\n width=None,\n height=None,\n title=None,\n keep_ui_state=True,\n show=False,\n scale_value=None,\n **kwargs,\n ):\n yaxis = \"y\"\n traces, sub_traces = self.make_traces(\n main_chart=main_chart, sub_chart=sub_chart, yaxis=yaxis, scale_value=scale_value, **kwargs\n )\n\n if sub_traces:\n fig = make_subplots(rows=2, cols=1, row_heights=[0.8, 0.2], vertical_spacing=0.08, shared_xaxes=True)\n fig.add_traces(traces, rows=[1] * len(traces), cols=[1] * len(traces))\n fig.add_traces(sub_traces, rows=[2] * len(sub_traces), cols=[1] * len(sub_traces))\n else:\n fig = go.Figure()\n fig.add_traces(traces)\n\n # 绘制矩形\n self.add_rects(fig, yaxis=yaxis)\n\n fig.update_layout(\n self.default_layout(\n main_chart=main_chart, width=width, height=height, title=title, keep_ui_state=keep_ui_state\n )\n )\n\n if sub_traces:\n fig.update_layout(xaxis_rangeslider_visible=False)\n fig.update_layout(xaxis2_rangeslider_visible=True, xaxis2_rangeslider_thickness=0.1)\n # 绘制标志\n if pd_is_not_null(self.annotation_df):\n fig.layout[\"annotations\"] = annotations(self.annotation_df, yref=yaxis)\n\n if show:\n fig.show()\n else:\n return fig\n\n def draw_table(self, width=None, height=None, title=None, keep_ui_state=True, **kwargs):\n cols = self.main_data.data_df.index.names + self.main_data.data_df.columns.tolist()\n\n index1 = self.main_data.data_df.index.get_level_values(0).tolist()\n index2 = self.main_data.data_df.index.get_level_values(1).tolist()\n values = [index1] + [index2] + [self.main_data.data_df[col] for col in self.main_data.data_df.columns]\n\n data = go.Table(\n header=dict(\n values=cols,\n fill_color=[\"#000080\", \"#000080\"] + [\"#0066cc\"] * len(self.main_data.data_df.columns),\n align=\"left\",\n font=dict(color=\"white\", size=13),\n ),\n cells=dict(values=values, fill=dict(color=\"#F5F8FF\"), align=\"left\"),\n **kwargs,\n )\n\n fig = go.Figure()\n fig.add_traces([data])\n fig.update_layout(self.default_layout(width=width, height=height, title=title, keep_ui_state=keep_ui_state))\n\n fig.show()\n\n\ndef annotations(annotation_df: pd.DataFrame, yref=\"y\"):\n \"\"\"\n annotation_df format::\n\n value flag color\n entity_id timestamp\n\n :param annotation_df:\n :param yref: specific yaxis e.g, y,y2,y3\n :return:\n \"\"\"\n\n if pd_is_not_null(annotation_df):\n annotations = []\n for trace_name, df in annotation_df.groupby(level=0):\n if pd_is_not_null(df):\n for (_, timestamp), item in df.iterrows():\n if \"color\" in item:\n color = item[\"color\"]\n else:\n color = \"#ec0000\"\n\n value = round(item[\"value\"], 2)\n annotations.append(\n dict(\n x=timestamp,\n y=value,\n xref=\"x\",\n yref=yref,\n text=item[\"flag\"],\n showarrow=True,\n align=\"center\",\n arrowhead=2,\n arrowsize=1,\n arrowwidth=2,\n # arrowcolor='#030813',\n ax=-10,\n ay=-30,\n bordercolor=\"#c7c7c7\",\n borderwidth=1,\n bgcolor=color,\n opacity=0.8,\n )\n )\n return annotations\n return None\n\n\n# the __all__ is generated\n__all__ = [\"ChartType\", \"Rect\", \"Draw\", \"Drawable\", \"StackedDrawer\", \"Drawer\", \"annotations\"]\n"
},
{
"path": "src/zvt/contract/factor.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport logging\nimport time\nfrom enum import Enum\nfrom typing import List, Union, Optional, Type\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract import zvt_context\nfrom zvt.contract.api import get_data, df_to_db, del_data\nfrom zvt.contract.base_service import EntityStateService\nfrom zvt.contract.reader import DataReader, DataListener\nfrom zvt.contract.schema import Mixin, TradableEntity\nfrom zvt.contract.zvt_info import FactorState\nfrom zvt.utils.pd_utils import pd_is_not_null, drop_continue_duplicate, is_filter_result_df, is_score_result_df\nfrom zvt.utils.str_utils import to_snake_str\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass TargetType(Enum):\n positive = \"positive\"\n negative = \"negative\"\n keep = \"keep\"\n\n\nclass Indicator(object):\n def __init__(self) -> None:\n self.logger = logging.getLogger(self.__class__.__name__)\n self.indicators = []\n\n\nclass Transformer(Indicator):\n def __init__(self) -> None:\n super().__init__()\n\n def transform(self, input_df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n input_df format::\n\n col1 col2 col3 ...\n entity_id timestamp\n 1.2 0.5 0.3 ...\n 1.0 0.7 0.2 ...\n\n the return result would change the columns and keep the format\n\n :param input_df:\n :return:\n \"\"\"\n g = input_df.groupby(level=0)\n if len(g.groups) == 1:\n entity_id = input_df.index[0][0]\n\n df = input_df.reset_index(level=0, drop=True)\n ret_df = self.transform_one(entity_id=entity_id, df=df)\n ret_df[\"entity_id\"] = entity_id\n\n return ret_df.set_index(\"entity_id\", append=True).swaplevel(0, 1)\n else:\n return g.apply(lambda x: self.transform_one(x.index[0][0], x.reset_index(level=0, drop=True)))\n\n def transform_one(self, entity_id: str, df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n df format::\n\n col1 col2 col3 ...\n timestamp\n 1.2 0.5 0.3 ...\n 1.0 0.7 0.2 ...\n\n the return result would change the columns and keep the format\n\n :param entity_id:\n :param df:\n :return:\n \"\"\"\n return df\n\n\nclass Accumulator(Indicator):\n def __init__(self, acc_window: int = 1) -> None:\n \"\"\"\n\n :param acc_window: the window size of acc for computing,default is 1\n \"\"\"\n super().__init__()\n self.acc_window = acc_window\n\n def acc(self, input_df: pd.DataFrame, acc_df: pd.DataFrame, states: dict) -> (pd.DataFrame, dict):\n \"\"\"\n\n :param input_df: new input\n :param acc_df: previous result\n :param states: current states of the entity\n :return: new result and states\n \"\"\"\n g = input_df.groupby(level=0)\n if len(g.groups) == 1:\n entity_id = input_df.index[0][0]\n\n df = input_df.reset_index(level=0, drop=True)\n if pd_is_not_null(acc_df) and (entity_id == acc_df.index[0][0]):\n acc_one_df = acc_df.reset_index(level=0, drop=True)\n else:\n acc_one_df = None\n ret_df, state = self.acc_one(entity_id=entity_id, df=df, acc_df=acc_one_df, state=states.get(entity_id))\n if pd_is_not_null(ret_df):\n ret_df[\"entity_id\"] = entity_id\n ret_df = ret_df.set_index(\"entity_id\", append=True).swaplevel(0, 1)\n ret_df[\"entity_id\"] = entity_id\n return ret_df, {entity_id: state}\n return None, {entity_id: state}\n else:\n new_states = {}\n\n def cal_acc(x):\n entity_id = x.index[0][0]\n if pd_is_not_null(acc_df):\n acc_g = acc_df.groupby(level=0)\n acc_one_df = None\n if entity_id in acc_g.groups:\n acc_one_df = acc_g.get_group(entity_id)\n if pd_is_not_null(acc_one_df):\n acc_one_df = acc_one_df.reset_index(level=0, drop=True)\n else:\n acc_one_df = None\n\n one_result, state = self.acc_one(\n entity_id=entity_id,\n df=x.reset_index(level=0, drop=True),\n acc_df=acc_one_df,\n state=states.get(x.index[0][0]),\n )\n\n new_states[entity_id] = state\n return one_result\n\n ret_df = g.apply(lambda x: cal_acc(x))\n return ret_df, new_states\n\n def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame, state: dict) -> (pd.DataFrame, dict):\n \"\"\"\n df format::\n\n col1 col2 col3 ...\n timestamp\n 1.2 0.5 0.3 ...\n 1.0 0.7 0.2 ...\n\n the new result and state\n\n :param df: current input df\n :param entity_id: current computing entity_id\n :param acc_df: current result of the entity_id\n :param state: current state of the entity_id\n :return: new result and state of the entity_id\n \"\"\"\n return acc_df, state\n\n\nclass Scorer(object):\n def __init__(self) -> None:\n self.logger = logging.getLogger(self.__class__.__name__)\n\n def score(self, input_df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n\n :param input_df: current input df\n :return: df with normal score\n \"\"\"\n return input_df\n\n\ndef _register_class(target_class):\n if target_class.__name__ not in (\"Factor\", \"FilterFactor\", \"ScoreFactor\", \"StateFactor\"):\n zvt_context.factor_cls_registry[target_class.__name__] = target_class\n\n\nclass FactorMeta(type):\n def __new__(meta, name, bases, class_dict):\n cls = type.__new__(meta, name, bases, class_dict)\n _register_class(cls)\n return cls\n\n\nclass Factor(DataReader, EntityStateService, DataListener):\n #: Schema for storing states\n state_schema = FactorState\n #: define the schema for persist,its columns should be same as indicators in transformer or accumulator\n factor_schema: Type[Mixin] = None\n\n #: transformer for this factor if not passed as __init__ argument\n transformer: Transformer = None\n #: accumulator for this factor if not passed as __init__ argument\n accumulator: Accumulator = None\n\n def __init__(\n self,\n data_schema: Type[Mixin],\n entity_schema: Type[TradableEntity] = None,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n ) -> None:\n \"\"\"\n :param keep_all_timestamp:\n :param fill_method:\n :param effective_number:\n :param transformer:\n :param accumulator:\n :param need_persist: whether persist factor\n :param only_compute_factor: only compute factor nor result\n :param factor_name:\n :param clear_state:\n :param only_load_factor: only load factor and compute result\n \"\"\"\n self.only_load_factor = only_load_factor\n\n #: define unique name of your factor if you want to keep factor state\n #: the factor state is defined by factor_name and entity_id\n if not factor_name:\n self.name = to_snake_str(type(self).__name__)\n else:\n self.name = factor_name\n\n DataReader.__init__(\n self,\n data_schema,\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n )\n\n EntityStateService.__init__(self, entity_ids=entity_ids)\n\n self.clear_state = clear_state\n\n self.keep_all_timestamp = keep_all_timestamp\n self.fill_method = fill_method\n self.effective_number = effective_number\n\n if transformer:\n self.transformer = transformer\n else:\n self.transformer = self.__class__.transformer\n\n if accumulator:\n self.accumulator = accumulator\n else:\n self.accumulator = self.__class__.accumulator\n\n self.need_persist = need_persist\n self.only_compute_factor = only_compute_factor\n\n #: 中间结果，不持久化\n #: data_df->pipe_df\n self.pipe_df: pd.DataFrame = None\n\n #: 计算因子的结果，可持久化,通过对pipe_df的计算得到\n #: pipe_df->factor_df\n self.factor_df: pd.DataFrame = None\n\n #: result_df是用于选股的标准df,通过对factor_df的计算得到\n #: factor_df->result_df\n self.result_df: pd.DataFrame = None\n\n if self.clear_state:\n self.clear_state_data()\n elif self.need_persist or self.only_load_factor:\n self.load_factor()\n\n #: 根据已经计算的factor_df和computing_window来保留data_df\n #: 因为读取data_df的目的是为了计算factor_df,选股和回测只依赖factor_df\n #: 所以如果有持久化的factor_df,只需保留需要用于计算的data_df即可\n if pd_is_not_null(self.data_df) and self.computing_window:\n dfs = []\n for entity_id, df in self.data_df.groupby(level=0):\n latest_laved = get_data(\n provider=\"zvt\",\n data_schema=self.factor_schema,\n entity_id=entity_id,\n order=self.factor_schema.timestamp.desc(),\n limit=1,\n index=[self.category_field, self.time_field],\n return_type=\"domain\",\n )\n if latest_laved:\n df1 = df[df.timestamp < latest_laved[0].timestamp].iloc[-self.computing_window :]\n if pd_is_not_null(df1):\n df = df[df.timestamp >= df1.iloc[0].timestamp]\n dfs.append(df)\n\n self.data_df = pd.concat(dfs)\n\n self.register_data_listener(self)\n\n #: the compute logic is not triggered from load data\n #: for the case:1)load factor from db 2)compute the result\n if self.only_load_factor:\n self.compute()\n\n def load_data(self):\n if self.only_load_factor:\n return\n super().load_data()\n\n def load_factor(self):\n if self.only_compute_factor:\n #: 如果只是为了计算因子，只需要读取acc_window的factor_df\n if self.accumulator is not None:\n self.factor_df = self.load_window_df(\n provider=\"zvt\", data_schema=self.factor_schema, window=self.accumulator.acc_window\n )\n else:\n self.factor_df = get_data(\n provider=\"zvt\",\n data_schema=self.factor_schema,\n start_timestamp=self.start_timestamp,\n entity_ids=self.entity_ids,\n end_timestamp=self.end_timestamp,\n index=[self.category_field, self.time_field],\n )\n\n self.decode_factor_df(self.factor_df)\n\n def decode_factor_df(self, df):\n col_map_object_hook = self.factor_col_map_object_hook()\n if pd_is_not_null(df) and col_map_object_hook:\n for col in col_map_object_hook:\n if col in df.columns:\n df[col] = df[col].apply(\n lambda x: json.loads(x, object_hook=col_map_object_hook.get(col)) if x else None\n )\n\n def factor_col_map_object_hook(self) -> dict:\n \"\"\"\n\n :return:{col:object_hook}\n \"\"\"\n return {}\n\n def clear_state_data(self, entity_id=None):\n super().clear_state_data(entity_id=entity_id)\n if entity_id:\n del_data(self.factor_schema, filters=[self.factor_schema.entity_id == entity_id], provider=\"zvt\")\n else:\n del_data(self.factor_schema, provider=\"zvt\")\n\n def pre_compute(self):\n if not self.only_load_factor and not pd_is_not_null(self.pipe_df):\n self.pipe_df = self.data_df\n\n def do_compute(self):\n self.logger.info(\"compute factor start\")\n self.compute_factor()\n self.logger.info(\"compute factor finish\")\n\n self.logger.info(\"compute result start\")\n self.compute_result()\n self.logger.info(\"compute result finish\")\n\n def compute_factor(self):\n if self.only_load_factor:\n return\n #: 无状态的转换运算\n if pd_is_not_null(self.data_df) and self.transformer:\n self.pipe_df = self.transformer.transform(self.data_df)\n else:\n self.pipe_df = self.data_df\n\n #: 有状态的累加运算\n if pd_is_not_null(self.pipe_df) and self.accumulator:\n self.factor_df, self.states = self.accumulator.acc(self.pipe_df, self.factor_df, self.states)\n else:\n self.factor_df = self.pipe_df\n\n def compute_result(self):\n if pd_is_not_null(self.factor_df):\n cols = []\n if is_filter_result_df(self.factor_df):\n cols.append(\"filter_result\")\n if is_score_result_df(self.factor_df):\n cols.append(\"score_result\")\n\n if cols:\n self.result_df = self.factor_df[cols]\n\n def after_compute(self):\n if self.only_load_factor:\n return\n if self.keep_all_timestamp:\n self.fill_gap()\n\n if self.need_persist and pd_is_not_null(self.factor_df):\n self.persist_factor()\n\n def compute(self):\n self.pre_compute()\n\n self.logger.info(f\"[[[ ~~~~~~~~factor:{self.name} ~~~~~~~~]]]\")\n self.logger.info(\"do_compute start\")\n start_time = time.time()\n self.do_compute()\n cost_time = time.time() - start_time\n self.logger.info(\"do_compute finished,cost_time:{}s\".format(cost_time))\n\n self.logger.info(\"after_compute start\")\n start_time = time.time()\n self.after_compute()\n cost_time = time.time() - start_time\n self.logger.info(\"after_compute finished,cost_time:{}s\".format(cost_time))\n self.logger.info(f\"[[[ ^^^^^^^^factor:{self.name} ^^^^^^^^]]]\")\n\n def drawer_main_df(self) -> Optional[pd.DataFrame]:\n if self.only_load_factor:\n return self.factor_df\n return self.data_df\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n if (self.transformer is not None or self.accumulator is not None) and pd_is_not_null(self.factor_df):\n indicators = None\n if self.transformer is not None:\n indicators = self.transformer.indicators\n elif self.accumulator is not None:\n indicators = self.accumulator.indicators\n\n if indicators:\n return [self.factor_df[indicators]]\n else:\n return [self.factor_df]\n return None\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n if (self.transformer is not None or self.accumulator is not None) and pd_is_not_null(self.result_df):\n return [self.result_df]\n return None\n\n def drawer_annotation_df(self) -> Optional[pd.DataFrame]:\n def order_type_flag(order_type):\n if order_type is None:\n return None\n if order_type:\n return \"B\"\n if not order_type:\n return \"S\"\n\n def order_type_color(order_type):\n if order_type:\n return \"#ec0000\"\n else:\n return \"#00da3c\"\n\n if is_filter_result_df(self.result_df):\n annotation_df = self.result_df[[\"filter_result\"]].copy()\n annotation_df = annotation_df[~annotation_df[\"filter_result\"].isna()]\n annotation_df = drop_continue_duplicate(annotation_df, \"filter_result\")\n annotation_df[\"value\"] = self.factor_df.loc[annotation_df.index][\"close\"]\n annotation_df[\"flag\"] = annotation_df[\"filter_result\"].apply(lambda x: order_type_flag(x))\n annotation_df[\"color\"] = annotation_df[\"filter_result\"].apply(lambda x: order_type_color(x))\n return annotation_df\n\n def fill_gap(self):\n #: 该操作较慢，只适合做基本面的运算\n idx = pd.date_range(self.start_timestamp, self.end_timestamp)\n new_index = pd.MultiIndex.from_product(\n [self.result_df.index.levels[0], idx], names=[self.category_field, self.time_field]\n )\n self.result_df = self.result_df.loc[~self.result_df.index.duplicated(keep=\"first\")]\n self.result_df = self.result_df.reindex(new_index)\n self.result_df = self.result_df.groupby(level=0).fillna(method=self.fill_method, limit=self.effective_number)\n\n def add_entities(self, entity_ids):\n if (self.entity_ids and entity_ids) and (set(self.entity_ids) == set(entity_ids)):\n self.logger.info(f\"current: {self.entity_ids}\")\n self.logger.info(f\"refresh: {entity_ids}\")\n return\n new_entity_ids = None\n if entity_ids:\n new_entity_ids = list(set(entity_ids) - set(self.entity_ids))\n self.entity_ids = list(set(self.entity_ids + entity_ids))\n\n if new_entity_ids:\n self.logger.info(f\"added new entity: {new_entity_ids}\")\n if not self.only_load_factor:\n new_data_df = self.data_schema.query_data(\n entity_ids=new_entity_ids,\n provider=self.provider,\n columns=self.columns,\n start_timestamp=self.start_timestamp,\n end_timestamp=self.end_timestamp,\n filters=self.filters,\n order=self.order,\n limit=self.limit,\n level=self.level,\n index=[self.category_field, self.time_field],\n time_field=self.time_field,\n )\n self.data_df = pd.concat([self.data_df, new_data_df], sort=False)\n self.data_df.sort_index(level=[0, 1], inplace=True)\n\n new_factor_df = get_data(\n provider=\"zvt\",\n data_schema=self.factor_schema,\n start_timestamp=self.start_timestamp,\n entity_ids=new_entity_ids,\n end_timestamp=self.end_timestamp,\n index=[self.category_field, self.time_field],\n )\n self.decode_factor_df(new_factor_df)\n\n self.factor_df = pd.concat([self.factor_df, new_factor_df], sort=False)\n self.factor_df.sort_index(level=[0, 1], inplace=True)\n\n def on_data_loaded(self, data: pd.DataFrame):\n self.compute()\n\n def on_data_changed(self, data: pd.DataFrame):\n \"\"\"\n overwrite it for computing after data added\n\n :param data:\n \"\"\"\n self.compute()\n\n def on_entity_data_changed(self, entity, added_data: pd.DataFrame):\n \"\"\"\n overwrite it for computing after entity data added\n\n :param entity:\n :param added_data:\n \"\"\"\n pass\n\n def persist_factor(self):\n df = self.factor_df.copy()\n #: encode json columns\n if pd_is_not_null(df) and self.factor_col_map_object_hook():\n for col in self.factor_col_map_object_hook():\n if col in df.columns:\n df[col] = df[col].apply(lambda x: json.dumps(x, cls=self.state_encoder()))\n\n if self.states:\n g = df.groupby(level=0)\n for entity_id in self.states:\n state = self.states[entity_id]\n try:\n if state:\n self.persist_state(entity_id=entity_id)\n if entity_id in g.groups:\n df_to_db(\n df=df.loc[(entity_id,)], data_schema=self.factor_schema, provider=\"zvt\", force_update=False\n )\n except Exception as e:\n self.logger.error(f\"{self.name} {entity_id} save state error\")\n self.logger.exception(e)\n #: clear them if error happen\n self.clear_state_data(entity_id)\n else:\n df_to_db(df=df, data_schema=self.factor_schema, provider=\"zvt\", force_update=False)\n\n def get_filter_df(self):\n if is_filter_result_df(self.result_df):\n return self.result_df[[\"filter_result\"]]\n\n def get_score_df(self):\n if is_score_result_df(self.result_df):\n return self.result_df[[\"score_result\"]]\n\n def get_trading_signal_df(self):\n df = self.result_df[[\"filter_result\"]].copy()\n df = df[~df[\"filter_result\"].isna()]\n df = drop_continue_duplicate(df, \"filter_result\")\n return df\n\n def get_targets(\n self,\n timestamp=None,\n start_timestamp=None,\n end_timestamp=None,\n target_type: TargetType = TargetType.positive,\n positive_threshold=0.8,\n negative_threshold=-0.8,\n ):\n if timestamp and (start_timestamp or end_timestamp):\n raise ValueError(\"Use timestamp or (start_timestamp, end_timestamp)\")\n # select by filter\n filter_df = self.get_filter_df()\n selected_df = None\n target_df = None\n if pd_is_not_null(filter_df):\n if target_type == TargetType.positive:\n selected_df = filter_df[filter_df[\"filter_result\"] == True]\n elif target_type == TargetType.negative:\n selected_df = filter_df[filter_df[\"filter_result\"] == False]\n else:\n selected_df = filter_df[filter_df[\"filter_result\"].isna()]\n\n # select by score\n score_df = self.get_score_df()\n if pd_is_not_null(score_df):\n if pd_is_not_null(selected_df):\n # filter at first\n score_df = score_df.loc[selected_df.index, :]\n if target_type == TargetType.positive:\n selected_df = score_df[score_df[\"score_result\"] >= positive_threshold]\n elif target_type == TargetType.negative:\n selected_df = score_df[score_df[\"score_result\"] <= negative_threshold]\n else:\n selected_df = score_df[\n (score_df[\"score_result\"] > negative_threshold) & (score_df[\"score\"] < positive_threshold)\n ]\n\n self.logger.debug(selected_df)\n\n if pd_is_not_null(selected_df):\n selected_df = selected_df.reset_index(level=\"entity_id\")\n if timestamp:\n if to_pd_timestamp(timestamp) in selected_df.index:\n target_df = selected_df.loc[[to_pd_timestamp(timestamp)], [\"entity_id\"]]\n else:\n target_df = selected_df.loc[\n slice(to_pd_timestamp(start_timestamp), to_pd_timestamp(end_timestamp)), [\"entity_id\"]\n ]\n\n if pd_is_not_null(target_df):\n return target_df[\"entity_id\"].tolist()\n return []\n\n\nclass ScoreFactor(Factor):\n scorer: Scorer = None\n\n def compute_result(self):\n super().compute_result()\n if pd_is_not_null(self.factor_df) and self.scorer:\n self.result_df = self.scorer.score(self.factor_df)\n\n\n# the __all__ is generated\n__all__ = [\"TargetType\", \"Indicator\", \"Transformer\", \"Accumulator\", \"Scorer\", \"FactorMeta\", \"Factor\", \"ScoreFactor\"]\n"
},
{
"path": "src/zvt/contract/model.py",
"content": "# -*- coding: utf-8 -*-\nfrom datetime import datetime\n\nfrom pydantic import BaseModel, ConfigDict\n\n\nclass CustomModel(BaseModel):\n model_config = ConfigDict(from_attributes=True, allow_inf_nan=True)\n\n\nclass MixinModel(CustomModel):\n id: str\n entity_id: str\n timestamp: datetime\n\n\n# the __all__ is generated\n__all__ = [\"CustomModel\", \"MixinModel\"]\n"
},
{
"path": "src/zvt/contract/normal_data.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.utils.pd_utils import pd_is_not_null, fill_with_same_index, normal_index_df, is_normal_df\n\n\nclass NormalData(object):\n table_type_sample = None\n\n def __init__(self, df, category_field=\"entity_id\", time_field=\"timestamp\", fill_index: bool = False) -> None:\n self.data_df = df\n self.category_field = category_field\n self.time_field = time_field\n self.fill_index = fill_index\n\n self.entity_ids = []\n self.df_list = []\n self.entity_map_df = {}\n\n self.normalize()\n\n def normalize(self):\n \"\"\"\n normalize data_df to::\n\n col1 col2 col3\n entity_id timestamp\n\n \"\"\"\n if pd_is_not_null(self.data_df):\n if not is_normal_df(self.data_df):\n self.data_df = normal_index_df(self.data_df, self.category_field, self.time_field)\n\n self.entity_ids = self.data_df.index.levels[0].to_list()\n\n for entity_id in self.entity_ids:\n df = self.data_df.loc[(entity_id,)]\n self.df_list.append(df)\n self.entity_map_df[entity_id] = df\n\n if len(self.df_list) > 1 and self.fill_index:\n self.df_list = fill_with_same_index(df_list=self.df_list)\n\n def empty(self):\n return not pd_is_not_null(self.data_df)\n\n\n# the __all__ is generated\n__all__ = [\"NormalData\"]\n"
},
{
"path": "src/zvt/contract/reader.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import List, Union, Type, Optional\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import get_entities\nfrom zvt.contract.drawer import Drawable\nfrom zvt.contract.schema import Mixin, TradableEntity\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp\n\n\nclass DataListener(object):\n def on_data_loaded(self, data: pd.DataFrame) -> object:\n \"\"\"\n\n :param data:\n \"\"\"\n raise NotImplementedError\n\n def on_data_changed(self, data: pd.DataFrame) -> object:\n \"\"\"\n\n :param data:\n \"\"\"\n raise NotImplementedError\n\n def on_entity_data_changed(self, entity: str, added_data: pd.DataFrame) -> object:\n \"\"\"\n\n :param entity: the entity\n :param added_data: the data added for the entity\n \"\"\"\n pass\n\n\nclass DataReader(Drawable):\n logger = logging.getLogger(__name__)\n\n def __init__(\n self,\n data_schema: Type[Mixin],\n entity_schema: Type[TradableEntity] = None,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = now_pd_timestamp(),\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: IntervalLevel = None,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n ) -> None:\n self.logger = logging.getLogger(self.__class__.__name__)\n\n self.data_schema = data_schema\n self.entity_schema = entity_schema\n self.provider = provider\n self.entity_provider = entity_provider\n self.start_timestamp = start_timestamp\n self.end_timestamp = end_timestamp\n self.start_timestamp = to_pd_timestamp(self.start_timestamp)\n self.end_timestamp = to_pd_timestamp(self.end_timestamp)\n self.exchanges = exchanges\n self.codes = codes\n self.entity_ids = entity_ids\n\n # 转换成标准entity_id\n if entity_schema and not self.entity_ids:\n df = get_entities(\n entity_schema=entity_schema, provider=self.entity_provider, exchanges=self.exchanges, codes=self.codes\n )\n if pd_is_not_null(df):\n self.entity_ids = df[\"entity_id\"].to_list()\n\n self.filters = filters\n self.order = order\n self.limit = limit\n\n if level:\n self.level = IntervalLevel(level)\n else:\n self.level = level\n\n self.category_field = category_field\n self.time_field = time_field\n self.computing_window = keep_window\n\n self.category_col = eval(\"self.data_schema.{}\".format(self.category_field))\n self.time_col = eval(\"self.data_schema.{}\".format(self.time_field))\n\n self.columns = columns\n\n if self.columns:\n # always add category_column and time_field for normalizing\n self.columns = list(set(self.columns) | {self.category_field, self.time_field})\n\n self.data_listeners: List[DataListener] = []\n\n self.data_df: pd.DataFrame = None\n\n self.load_data()\n\n def load_window_df(self, provider, data_schema, window):\n window_df = None\n\n dfs = []\n for entity_id in self.entity_ids:\n df = data_schema.query_data(\n provider=provider,\n index=[self.category_field, self.time_field],\n order=data_schema.timestamp.desc(),\n entity_id=entity_id,\n limit=window,\n )\n if pd_is_not_null(df):\n dfs.append(df)\n if dfs:\n window_df = pd.concat(dfs)\n window_df = window_df.sort_index(level=[0, 1])\n return window_df\n\n def load_data(self):\n self.logger.info(\"load_data start\")\n start_time = time.time()\n params = dict(\n entity_size=len(self.entity_ids) if self.entity_ids != None else None,\n provider=self.provider,\n columns=self.columns,\n start_timestamp=self.start_timestamp,\n end_timestamp=self.end_timestamp,\n filters=self.filters,\n order=self.order,\n limit=self.limit,\n level=self.level,\n index=[self.category_field, self.time_field],\n time_field=self.time_field,\n )\n self.logger.info(f\"query_data params:{params}\")\n\n self.data_df = self.data_schema.query_data(\n entity_ids=self.entity_ids,\n provider=self.provider,\n columns=self.columns,\n start_timestamp=self.start_timestamp,\n end_timestamp=self.end_timestamp,\n filters=self.filters,\n order=self.order,\n limit=self.limit,\n level=self.level,\n index=[self.category_field, self.time_field],\n time_field=self.time_field,\n )\n\n cost_time = time.time() - start_time\n self.logger.info(\"load_data finished, cost_time:{}\".format(cost_time))\n\n for listener in self.data_listeners:\n listener.on_data_loaded(self.data_df)\n\n def move_on(self, to_timestamp: Union[str, pd.Timestamp] = None, timeout: int = 20) -> object:\n \"\"\"\n using continual fetching data in realtime\n 1)get the data happened before to_timestamp,if not set,get all the data which means to now\n 2)if computing_window set,the data_df would be cut for saving memory\n\n\n :param to_timestamp:\n :type to_timestamp:\n :param timeout:\n :type timeout: int\n :return:\n :rtype:\n \"\"\"\n\n if not pd_is_not_null(self.data_df):\n self.load_data()\n return\n\n start_time = time.time()\n\n has_got = []\n dfs = []\n changed = False\n while True:\n for entity_id, df in self.data_df.groupby(level=0):\n if entity_id in has_got:\n continue\n\n recorded_timestamp = df.index.levels[1].max()\n\n #: move_on读取数据，表明之前的数据已经处理完毕，只需要保留computing_window的数据\n if self.computing_window:\n df = df.iloc[-self.computing_window :]\n\n added_filter = [self.category_col == entity_id, self.time_col > recorded_timestamp]\n if self.filters:\n filters = self.filters + added_filter\n else:\n filters = added_filter\n\n added_df = self.data_schema.query_data(\n provider=self.provider,\n columns=self.columns,\n end_timestamp=to_timestamp,\n filters=filters,\n level=self.level,\n index=[self.category_field, self.time_field],\n )\n\n if pd_is_not_null(added_df):\n self.logger.info(f'got new data:{df.to_json(orient=\"records\", force_ascii=False)}')\n\n for listener in self.data_listeners:\n listener.on_entity_data_changed(entity=entity_id, added_data=added_df)\n #: if got data,just move to another entity_id\n changed = True\n has_got.append(entity_id)\n # df = df.append(added_df, sort=False)\n df = pd.concat([df, added_df], sort=False)\n dfs.append(df)\n else:\n cost_time = time.time() - start_time\n if cost_time > timeout:\n #: if timeout,just add the old data\n has_got.append(entity_id)\n dfs.append(df)\n self.logger.warning(\n \"category:{} level:{} getting data timeout,to_timestamp:{},now:{}\".format(\n entity_id, self.level, to_timestamp, now_pd_timestamp()\n )\n )\n continue\n\n if len(has_got) == len(self.data_df.index.levels[0]):\n break\n\n if dfs:\n self.data_df = pd.concat(dfs, sort=False)\n self.data_df.sort_index(level=[0, 1], inplace=True)\n\n if changed:\n for listener in self.data_listeners:\n listener.on_data_changed(self.data_df)\n\n def register_data_listener(self, listener):\n if listener not in self.data_listeners:\n self.data_listeners.append(listener)\n\n #: notify it once after registered\n if pd_is_not_null(self.data_df):\n listener.on_data_loaded(self.data_df)\n\n def deregister_data_listener(self, listener):\n if listener in self.data_listeners:\n self.data_listeners.remove(listener)\n\n def empty(self):\n return not pd_is_not_null(self.data_df)\n\n def drawer_main_df(self) -> Optional[pd.DataFrame]:\n return self.data_df\n\n\nif __name__ == \"__main__\":\n from zvt.domain import Stock1dKdata, Stock\n\n data_reader = DataReader(\n data_schema=Stock1dKdata,\n entity_schema=Stock,\n codes=[\"002572\", \"000338\"],\n start_timestamp=\"2017-01-01\",\n end_timestamp=\"2019-06-10\",\n )\n\n data_reader.draw(show=True)\n\n\n# the __all__ is generated\n__all__ = [\"DataListener\", \"DataReader\"]\n"
},
{
"path": "src/zvt/contract/recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nimport uuid\nfrom typing import List\n\nimport pandas as pd\nimport requests\nfrom sqlalchemy.orm import Session\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import get_db_session, get_schema_columns\nfrom zvt.contract.api import get_entities, get_data\nfrom zvt.contract.base_service import OneStateService\nfrom zvt.contract.schema import Mixin, TradableEntity\nfrom zvt.contract.utils import is_in_same_interval, evaluate_size_from_timestamp\nfrom zvt.contract.zvt_info import RecorderState\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import (\n to_pd_timestamp,\n TIME_FORMAT_DAY,\n to_date_time_str,\n now_pd_timestamp,\n now_date_time_str,\n)\nfrom zvt.utils.utils import fill_domain_from_dict\n\n\ndef _register_kdata_schemas_for_recorder(cls):\n \"\"\"\n Pre-register concrete kdata schemas (Stock1dKdata, etc.) for recorders that define\n supported_levels. KdataCommon is a placeholder; the real schemas are per (level, adjust_type).\n Must have: entity_schema, data_schema (KdataCommon), supported_levels, provider.\n \"\"\"\n if not (\n hasattr(cls, \"entity_schema\")\n and cls.entity_schema\n and hasattr(cls, \"data_schema\")\n and cls.data_schema\n and cls.data_schema.__name__.endswith(\"KdataCommon\")\n and hasattr(cls, \"supported_levels\")\n and cls.supported_levels\n and hasattr(cls, \"provider\")\n and cls.provider\n ):\n return False\n from zvt.api.kdata import get_kdata_schema\n from zvt.contract import zvt_context\n\n entity_type = cls.entity_schema.__name__.lower()\n adjust_types = getattr(cls, \"supported_adjust_types\", [None])\n for level in cls.supported_levels:\n for adjust_type in adjust_types:\n try:\n schema = get_kdata_schema(entity_type=entity_type, level=level, adjust_type=adjust_type)\n if schema:\n schema.register_recorder_cls(cls.provider, cls)\n db_name = getattr(schema, \"_zvt_db_name\", None)\n if db_name:\n if cls.provider not in zvt_context.providers:\n zvt_context.providers.append(cls.provider)\n if not zvt_context.provider_map_dbnames.get(cls.provider):\n zvt_context.provider_map_dbnames[cls.provider] = []\n if db_name not in zvt_context.provider_map_dbnames[cls.provider]:\n zvt_context.provider_map_dbnames[cls.provider].append(db_name)\n except Exception:\n pass\n return True\n\n\nclass Meta(type):\n def __new__(meta, name, bases, class_dict):\n cls = type.__new__(meta, name, bases, class_dict)\n if hasattr(cls, \"data_schema\") and hasattr(cls, \"provider\"):\n if cls.data_schema and issubclass(cls.data_schema, Mixin):\n if _register_kdata_schemas_for_recorder(cls):\n pass\n else:\n cls.data_schema.register_recorder_cls(cls.provider, cls)\n from zvt.contract import zvt_context\n if cls.provider not in zvt_context.providers:\n zvt_context.providers.append(cls.provider)\n if not zvt_context.provider_map_dbnames.get(cls.provider):\n zvt_context.provider_map_dbnames[cls.provider] = []\n db_name = getattr(cls.data_schema, \"_zvt_db_name\", None)\n if db_name and db_name not in zvt_context.provider_map_dbnames[cls.provider]:\n zvt_context.provider_map_dbnames[cls.provider].append(db_name)\n return cls\n\n\nclass Recorder(OneStateService, metaclass=Meta):\n #: overwrite them to set up the data you want to record\n provider: str = None\n data_schema: Mixin = None\n\n #: original page url\n original_page_url = None\n #: request url\n url = None\n\n state_schema = RecorderState\n\n def __init__(self, force_update: bool = False, sleeping_time: int = 10) -> None:\n super().__init__()\n self.logger = logging.getLogger(self.__class__.__name__)\n\n assert self.provider is not None\n assert self.data_schema is not None\n # Register concrete schema for kdata Recorders that set data_schema in __init__\n # (e.g. via get_kdata_schema); metaclass only runs at class-def time when data_schema may be None\n self.data_schema.register_recorder_cls(self.provider, self.__class__)\n from zvt.contract import zvt_context\n if self.provider not in zvt_context.providers:\n zvt_context.providers.append(self.provider)\n if not zvt_context.provider_map_dbnames.get(self.provider):\n zvt_context.provider_map_dbnames[self.provider] = []\n db_name = getattr(self.data_schema, \"_zvt_db_name\", None)\n if db_name and db_name not in zvt_context.provider_map_dbnames[self.provider]:\n zvt_context.provider_map_dbnames[self.provider].append(db_name)\n if self.provider not in self.data_schema.get_providers():\n self.logger.error(\n f\"provider: {self.provider} is not registered for {self.data_schema}({self.data_schema.get_providers()})\"\n )\n assert False\n\n self.force_update = force_update\n self.sleeping_time = sleeping_time\n\n #: using to do db operations\n self.session = get_db_session(provider=self.provider, data_schema=self.data_schema)\n self.http_session = requests.Session()\n\n def run(self):\n raise NotImplementedError\n\n def sleep(self, seconds=None):\n if seconds:\n sleeping_time = seconds\n else:\n sleeping_time = self.sleeping_time\n\n if sleeping_time and sleeping_time > 0:\n self.logger.info(f\"sleeping {sleeping_time} seconds\")\n time.sleep(self.sleeping_time)\n\n\nclass EntityEventRecorder(Recorder):\n #: overwrite them to fetch the entity list\n entity_provider: str = None\n entity_schema: TradableEntity = None\n\n def __init__(\n self,\n force_update=False,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n end_timestamp=None,\n entity_filters=None,\n ignore_failed=True,\n return_unfinished=False,\n ) -> None:\n \"\"\"\n :param latest_trading_date:\n :param code:\n :param ignore_failed:\n :param entity_filters:\n :param exchanges:\n :param entity_id: for record single entity\n :param entity_ids: set entity_ids or (entity_type,exchanges,codes)\n :param codes:\n :param day_data: one record per day,set to True if you want skip recording it when data of today exist\n :param force_update:\n :param sleeping_time:\n \"\"\"\n super().__init__(force_update=force_update, sleeping_time=sleeping_time)\n\n assert self.entity_provider is not None\n assert self.entity_schema is not None\n\n #: setup the entities you want to record\n self.exchanges = exchanges\n if codes is None and code is not None:\n self.codes = [code]\n else:\n self.codes = codes\n self.day_data = day_data\n self.end_timestamp = end_timestamp\n\n #: set entity_ids or (entity_type,exchanges,codes)\n self.entity_ids = None\n if entity_id:\n self.entity_ids = [entity_id]\n if entity_ids:\n self.entity_ids = entity_ids\n self.entity_filters = entity_filters\n self.ignore_failed = ignore_failed\n self.return_unfinished = return_unfinished\n\n self.entity_session: Session = None\n self.entities: List = None\n self.init_entities()\n\n def init_entities(self):\n \"\"\"\n init the entities which we would record data for\n\n \"\"\"\n if self.entity_provider == self.provider and self.entity_schema == self.data_schema:\n self.entity_session = self.session\n else:\n self.entity_session = get_db_session(provider=self.entity_provider, data_schema=self.entity_schema)\n\n if self.day_data:\n if not self.end_timestamp:\n self.end_timestamp = now_date_time_str()\n df = self.data_schema.query_data(\n start_timestamp=self.end_timestamp, columns=[\"entity_id\", \"timestamp\"], provider=self.provider\n )\n if pd_is_not_null(df):\n entity_ids = df[\"entity_id\"].tolist()\n self.logger.info(f\"ignore entity_ids:{entity_ids}\")\n if self.entity_filters:\n self.entity_filters.append(self.entity_schema.entity_id.notin_(entity_ids))\n else:\n self.entity_filters = [self.entity_schema.entity_id.notin_(entity_ids)]\n\n #: init the entity list\n self.entities = get_entities(\n session=self.entity_session,\n entity_schema=self.entity_schema,\n exchanges=self.exchanges,\n entity_ids=self.entity_ids,\n codes=self.codes,\n return_type=\"domain\",\n provider=self.entity_provider,\n filters=self.entity_filters,\n )\n\n\nclass TimeSeriesDataRecorder(EntityEventRecorder):\n default_size = 2000\n\n def __init__(\n self,\n force_update=False,\n sleeping_time=5,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n return_unfinished=False,\n ) -> None:\n self.start_timestamp = to_pd_timestamp(start_timestamp)\n self.end_timestamp = to_pd_timestamp(end_timestamp)\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code=code,\n codes=codes,\n day_data=day_data,\n end_timestamp=self.end_timestamp,\n entity_filters=entity_filters,\n ignore_failed=ignore_failed,\n return_unfinished=return_unfinished,\n )\n\n self.real_time = real_time\n self.close_hour, self.close_minute = self.entity_schema.get_close_hour_and_minute()\n self.fix_duplicate_way = fix_duplicate_way\n\n def get_latest_saved_record(self, entity):\n order = eval(\"self.data_schema.{}.desc()\".format(self.get_evaluated_time_field()))\n\n records = get_data(\n entity_id=entity.id,\n provider=self.provider,\n data_schema=self.data_schema,\n order=order,\n limit=1,\n return_type=\"domain\",\n session=self.session,\n )\n if records:\n return records[0]\n return None\n\n def evaluate_start_end_size_timestamps(self, entity):\n #: not to list date yet\n if entity.timestamp and (entity.timestamp >= now_pd_timestamp()):\n self.logger.info(\"ignore entity: {} list date: {}\", entity.id, entity.timestamp)\n return entity.timestamp, None, 0, None\n\n latest_saved_record = self.get_latest_saved_record(entity=entity)\n\n if latest_saved_record:\n latest_timestamp = eval(\"latest_saved_record.{}\".format(self.get_evaluated_time_field()))\n else:\n latest_timestamp = entity.timestamp\n\n if not latest_timestamp:\n return self.start_timestamp, self.end_timestamp, self.default_size, None\n\n if self.start_timestamp:\n latest_timestamp = max(latest_timestamp, self.start_timestamp)\n\n size = self.default_size\n if self.end_timestamp:\n if latest_timestamp > self.end_timestamp:\n size = 0\n\n return latest_timestamp, self.end_timestamp, size, None\n\n def get_data_map(self):\n \"\"\"\n {'original_field':('domain_field',transform_func)}\n\n \"\"\"\n return {}\n\n def record(self, entity, start, end, size, timestamps):\n \"\"\"\n implement the recording logic in this method, should return json or domain list\n\n :param entity:\n :type entity:\n :param start:\n :type start:\n :param end:\n :type end:\n :param size:\n :type size:\n :param timestamps:\n :type timestamps:\n \"\"\"\n raise NotImplementedError\n\n def get_evaluated_time_field(self):\n \"\"\"\n the timestamp field for evaluating time range of recorder,used in get_latest_saved_record\n\n \"\"\"\n return \"timestamp\"\n\n def get_original_time_field(self):\n return \"timestamp\"\n\n def generate_domain_id(self, entity, original_data, time_fmt=TIME_FORMAT_DAY):\n \"\"\"\n generate domain id from the entity and original data,the default id meaning:entity + event happen time\n\n :param entity:\n :type entity:\n :param original_data:\n :type original_data:\n :param time_fmt:\n :type time_fmt:\n :return:\n :rtype:\n \"\"\"\n timestamp = to_date_time_str(original_data[self.get_original_time_field()], fmt=time_fmt)\n return \"{}_{}\".format(entity.id, timestamp)\n\n def generate_domain(self, entity, original_data):\n \"\"\"\n generate the data_schema instance using entity and original_data,the original_data is from record result\n\n :param entity:\n :param original_data:\n \"\"\"\n\n got_new_data = False\n\n #: if the domain is directly generated in record method, we just return it\n if isinstance(original_data, self.data_schema):\n got_new_data = True\n return got_new_data, original_data\n\n the_id = self.generate_domain_id(entity, original_data)\n\n #: optional way\n #: item = self.session.query(self.data_schema).get(the_id)\n\n items = get_data(\n data_schema=self.data_schema,\n session=self.session,\n provider=self.provider,\n entity_id=entity.id,\n filters=[self.data_schema.id == the_id],\n return_type=\"domain\",\n )\n\n if items and not self.force_update:\n self.logger.info(\"ignore the data {}:{} saved before\".format(self.data_schema, the_id))\n return got_new_data, None\n\n if not items:\n timestamp_str = original_data[self.get_original_time_field()]\n timestamp = None\n try:\n timestamp = to_pd_timestamp(timestamp_str)\n except Exception as e:\n self.logger.exception(e)\n\n if \"name\" in get_schema_columns(self.data_schema):\n domain_item = self.data_schema(\n id=the_id, code=entity.code, name=entity.name, entity_id=entity.id, timestamp=timestamp\n )\n else:\n domain_item = self.data_schema(id=the_id, code=entity.code, entity_id=entity.id, timestamp=timestamp)\n got_new_data = True\n else:\n domain_item = items[0]\n\n fill_domain_from_dict(domain_item, original_data, self.get_data_map())\n return got_new_data, domain_item\n\n def persist(self, entity, domain_list):\n \"\"\"\n persist the domain list to db\n\n :param entity:\n :param domain_list:\n \"\"\"\n if domain_list:\n try:\n if domain_list[0].timestamp >= domain_list[-1].timestamp:\n first_timestamp = domain_list[-1].timestamp\n last_timestamp = domain_list[0].timestamp\n else:\n first_timestamp = domain_list[0].timestamp\n last_timestamp = domain_list[-1].timestamp\n except:\n first_timestamp = domain_list[0].timestamp\n last_timestamp = domain_list[-1].timestamp\n\n self.logger.info(\n \"persist {} for entity_id:{},time interval:[{},{}]\".format(\n self.data_schema, entity.id, first_timestamp, last_timestamp\n )\n )\n\n self.session.add_all(domain_list)\n self.session.commit()\n\n def on_finish(self):\n try:\n if self.session:\n self.session.close()\n\n if self.entity_session:\n self.entity_session.close()\n if self.http_session:\n self.http_session.close()\n except Exception as e:\n self.logger.error(e)\n\n def on_finish_entity(self, entity):\n pass\n\n def run(self):\n finished_items = []\n unfinished_items = self.entities\n raising_exception = None\n while True:\n count = len(unfinished_items)\n for index, entity_item in enumerate(unfinished_items):\n try:\n self.logger.info(f\"run to {index + 1}/{count}\")\n\n start_timestamp, end_timestamp, size, timestamps = self.evaluate_start_end_size_timestamps(\n entity_item\n )\n size = int(size)\n\n if timestamps:\n self.logger.info(\n \"entity_id:{},evaluate_start_end_size_timestamps result:{},{},{},{}-{}\".format(\n entity_item.id, start_timestamp, end_timestamp, size, timestamps[0], timestamps[-1]\n )\n )\n else:\n self.logger.info(\n \"entity_id:{},evaluate_start_end_size_timestamps result:{},{},{},{}\".format(\n entity_item.id, start_timestamp, end_timestamp, size, timestamps\n )\n )\n\n #: no more to record\n if size == 0:\n finished_items.append(entity_item)\n self.logger.info(\n \"finish recording {} for entity_id:{},latest_timestamp:{}\".format(\n self.data_schema, entity_item.id, start_timestamp\n )\n )\n self.on_finish_entity(entity_item)\n continue\n\n #: sleep for a while to next entity\n if index != 0:\n self.sleep()\n\n original_list = self.record(\n entity_item, start=start_timestamp, end=end_timestamp, size=size, timestamps=timestamps\n )\n\n all_duplicated = True\n\n if original_list:\n domain_list = []\n for original_item in original_list:\n got_new_data, domain_item = self.generate_domain(entity_item, original_item)\n\n if got_new_data:\n all_duplicated = False\n\n #: handle the case generate_domain_id generate duplicate id\n if domain_item:\n duplicate = [item for item in domain_list if item.id == domain_item.id]\n if duplicate:\n #: regenerate the id\n if self.fix_duplicate_way == \"add\":\n domain_item.id = \"{}_{}\".format(domain_item.id, uuid.uuid1())\n #: ignore\n else:\n self.logger.info(f\"ignore original duplicate item:{domain_item.id}\")\n continue\n\n domain_list.append(domain_item)\n\n if domain_list:\n self.persist(entity_item, domain_list)\n else:\n self.logger.info(\"just got {} duplicated data in this cycle\".format(len(original_list)))\n\n #: could not get more data\n entity_finished = False\n if not original_list or all_duplicated:\n #: not realtime\n if not self.real_time:\n entity_finished = True\n\n #: realtime and to the close time\n if self.real_time and (self.close_hour is not None) and (self.close_minute is not None):\n current_timestamp = pd.Timestamp.now()\n if current_timestamp.hour >= self.close_hour:\n if current_timestamp.minute - self.close_minute >= 5:\n self.logger.info(\n \"{} now is the close time:{}\".format(entity_item.id, current_timestamp)\n )\n\n entity_finished = True\n\n #: add finished entity to finished_items\n if entity_finished:\n finished_items.append(entity_item)\n\n latest_saved_record = self.get_latest_saved_record(entity=entity_item)\n if latest_saved_record:\n start_timestamp = eval(\"latest_saved_record.{}\".format(self.get_evaluated_time_field()))\n\n self.logger.info(\n \"finish recording {} for entity_id:{},latest_timestamp:{}\".format(\n self.data_schema, entity_item.id, start_timestamp\n )\n )\n self.on_finish_entity(entity_item)\n continue\n\n except Exception as e:\n self.logger.exception(\n \"recording data for entity_id:{},{},error:{}\".format(entity_item.id, self.data_schema, e)\n )\n raising_exception = e\n if self.return_unfinished:\n self.on_finish()\n unfinished_items = set(unfinished_items) - set(finished_items)\n return [item.entity_id for item in unfinished_items]\n\n finished_items = unfinished_items\n break\n\n unfinished_items = set(unfinished_items) - set(finished_items)\n\n if len(unfinished_items) == 0:\n break\n\n self.on_finish()\n if self.return_unfinished:\n return []\n\n if raising_exception:\n raise raising_exception\n\n\nclass FixedCycleDataRecorder(TimeSeriesDataRecorder):\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n return_unfinished=False,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code=code,\n codes=codes,\n day_data=day_data,\n entity_filters=entity_filters,\n ignore_failed=ignore_failed,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n return_unfinished=return_unfinished,\n )\n\n self.level = IntervalLevel(level)\n self.kdata_use_begin_time = kdata_use_begin_time\n self.one_day_trading_minutes = one_day_trading_minutes\n\n def get_latest_saved_record(self, entity):\n order = eval(\"self.data_schema.{}.desc()\".format(self.get_evaluated_time_field()))\n\n #: 对于k线这种数据，最后一个记录有可能是没完成的，所以取两个\n #: 同一周期内只保留最新的一个数据\n records = get_data(\n entity_id=entity.id,\n provider=self.provider,\n data_schema=self.data_schema,\n order=order,\n limit=2,\n return_type=\"domain\",\n session=self.session,\n level=self.level,\n )\n if records:\n #: delete unfinished kdata\n if len(records) == 2:\n if is_in_same_interval(t1=records[0].timestamp, t2=records[1].timestamp, level=self.level):\n self.session.delete(records[1])\n self.session.flush()\n return records[0]\n return None\n\n def evaluate_start_end_size_timestamps(self, entity):\n #: not to list date yet\n if entity.timestamp and (entity.timestamp >= now_pd_timestamp()):\n return entity.timestamp, None, 0, None\n\n #: get latest record\n latest_saved_record = self.get_latest_saved_record(entity=entity)\n\n if latest_saved_record:\n #: the latest saved timestamp\n latest_saved_timestamp = latest_saved_record.timestamp\n else:\n #: the list date\n latest_saved_timestamp = entity.timestamp\n\n if not latest_saved_timestamp:\n return None, None, self.default_size, None\n\n size = evaluate_size_from_timestamp(\n start_timestamp=latest_saved_timestamp,\n level=self.level,\n one_day_trading_minutes=self.one_day_trading_minutes,\n )\n\n if self.start_timestamp:\n start = max(self.start_timestamp, latest_saved_timestamp)\n else:\n start = latest_saved_timestamp\n\n return start, None, size, None\n\n\nclass TimestampsDataRecorder(TimeSeriesDataRecorder):\n def __init__(\n self,\n force_update=False,\n sleeping_time=5,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code=code,\n codes=codes,\n day_data=day_data,\n entity_filters=entity_filters,\n ignore_failed=ignore_failed,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n self.security_timestamps_map = {}\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n raise NotImplementedError\n\n def evaluate_start_end_size_timestamps(self, entity):\n timestamps = self.security_timestamps_map.get(entity.id)\n if not timestamps:\n timestamps = self.init_timestamps(entity)\n if self.start_timestamp:\n timestamps = [t for t in timestamps if t >= self.start_timestamp]\n\n if self.end_timestamp:\n timestamps = [t for t in timestamps if t <= self.end_timestamp]\n\n self.security_timestamps_map[entity.id] = timestamps\n\n if not timestamps:\n return None, None, 0, timestamps\n\n timestamps.sort()\n\n self.logger.info(\"entity_id:{},timestamps start:{},end:{}\".format(entity.id, timestamps[0], timestamps[-1]))\n\n latest_record = self.get_latest_saved_record(entity=entity)\n\n if latest_record:\n self.logger.info(\"latest record timestamp:{}\".format(latest_record.timestamp))\n timestamps = [t for t in timestamps if t >= latest_record.timestamp]\n\n if timestamps:\n return timestamps[0], timestamps[-1], len(timestamps), timestamps\n return None, None, 0, None\n\n return timestamps[0], timestamps[-1], len(timestamps), timestamps\n\n\n# the __all__ is generated\n__all__ = [\n \"Meta\",\n \"Recorder\",\n \"EntityEventRecorder\",\n \"TimeSeriesDataRecorder\",\n \"FixedCycleDataRecorder\",\n \"TimestampsDataRecorder\",\n]\n"
},
{
"path": "src/zvt/contract/register.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nPhase 3: providers removed from register_schema. Providers come from Recorder registration.\n\"\"\"\nimport logging\nfrom typing import List\n\nimport sqlalchemy\nfrom sqlalchemy import MetaData\nfrom sqlalchemy.ext.declarative import DeclarativeMeta\nfrom sqlalchemy.sql.ddl import CreateTable\nfrom sqlalchemy.sql.expression import text\n\nfrom zvt.contract import zvt_context\nfrom zvt.contract.schema import TradableEntity, Mixin\nfrom zvt.utils.utils import add_to_map_list\n\nlogger = logging.getLogger(__name__)\n\n\ndef ensure_schema_tables_and_indexes(engine, schema_base: DeclarativeMeta, db_name: str):\n \"\"\"Create tables and indexes for schema. Used by lazy init.\"\"\"\n schema_base.metadata.create_all(bind=engine)\n for table_name, table in iter(schema_base.metadata.tables.items()):\n db_meta = MetaData()\n db_meta.reflect(bind=engine)\n db_table = db_meta.tables[table_name]\n existing_columns = [c.name for c in db_table.columns]\n added_columns = [c for c in table.columns if c.name not in existing_columns]\n index_list = []\n with engine.connect() as con:\n if db_name in (\"zvt_info\", \"stock_news\", \"stock_tags\", \"stock_quote\"):\n con.execute(text(\"PRAGMA journal_mode=WAL;\"))\n con.execute(text(\"PRAGMA journal_size_limit=1073741824;\"))\n else:\n con.execute(text(\"PRAGMA journal_mode=DELETE;\"))\n rs = con.execute(text(\"PRAGMA INDEX_LIST('{}')\".format(table_name)))\n for row in rs:\n index_list.append(row[1])\n try:\n if added_columns:\n ddl_c = engine.dialect.ddl_compiler(engine.dialect, CreateTable(table))\n for added_column in added_columns:\n stmt = text(\n f\"ALTER TABLE {table_name} ADD COLUMN {ddl_c.get_column_specification(added_column)}\"\n )\n logger.info(f\"{engine.url} migrations:\\n {stmt}\")\n con.execute(stmt)\n for col in [\n \"timestamp\", \"entity_id\", \"code\", \"report_period\",\n \"created_timestamp\", \"updated_timestamp\",\n ]:\n if col in table.c:\n column = getattr(table.c, col)\n index_name = \"{}_{}_index\".format(table_name, col)\n if index_name not in index_list:\n sqlalchemy.schema.Index(index_name, column).create(engine)\n for cols in [(\"timestamp\", \"entity_id\"), (\"timestamp\", \"code\")]:\n if cols[0] in table.c and cols[1] in table.c:\n column0 = getattr(table.c, cols[0])\n column1 = getattr(table.c, cols[1])\n index_name = \"{}_{}_{}_index\".format(table_name, cols[0], cols[1])\n if index_name not in index_list:\n sqlalchemy.schema.Index(index_name, column0, column1).create(engine)\n except Exception as e:\n logger.error(e)\n\n\ndef register_entity(entity_type: str = None):\n \"\"\"\n function for register entity type\n\n :param entity_type:\n :type entity_type:\n :return:\n :rtype:\n \"\"\"\n\n def register(cls):\n # register the entity\n if issubclass(cls, TradableEntity):\n entity_type_ = entity_type\n if not entity_type:\n entity_type_ = cls.__name__.lower()\n\n if entity_type_ not in zvt_context.tradable_entity_types:\n zvt_context.tradable_entity_types.append(entity_type_)\n zvt_context.tradable_entity_schemas.append(cls)\n zvt_context.tradable_schema_map[entity_type_] = cls\n return cls\n\n return register\n\n\ndef register_schema(\n db_name: str,\n schema_base: DeclarativeMeta,\n entity_type: str = None,\n internal: bool = False,\n):\n \"\"\"\n Register schema. Providers come from Recorder registration (provider_map_recorder).\n Tables/engines are created lazily on first get_db_engine(provider, db_name).\n For schemas without Recorders, add to config: storage.schema_providers.\n\n :param internal: If True, schema is business/internal data, not tied to external data source.\n Internal schemas only care about storage routing, do not participate in\n provider switching (e.g. zvt_info, trader_info, stock_tags).\n \"\"\"\n schemas = []\n for item in schema_base.registry.mappers:\n cls = item.class_\n if type(cls) == DeclarativeMeta:\n if zvt_context.dbname_map_schemas.get(db_name):\n schemas = zvt_context.dbname_map_schemas[db_name]\n zvt_context.schemas.append(cls)\n if issubclass(cls, Mixin):\n cls._zvt_db_name = db_name\n cls._zvt_internal = internal # internal=True: business data, no external data source\n if entity_type:\n add_to_map_list(the_map=zvt_context.entity_map_schemas, key=entity_type, value=cls)\n schemas.append(cls)\n\n zvt_context.dbname_map_schemas[db_name] = schemas\n zvt_context.dbname_map_base[db_name] = schema_base\n if internal:\n zvt_context.internal_db_names.add(db_name)\n\n\n# the __all__ is generated\n__all__ = [\"register_entity\", \"register_schema\", \"ensure_schema_tables_and_indexes\"]\n"
},
{
"path": "src/zvt/contract/route_registry.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nRoute registry: maps (provider, db_name) -> storage_id.\nPhase 1: uses deterministic rule storage_id = \"{provider}_{db_name}\".\nPhase 2: supports config file overrides via zvt_config[\"storage\"][\"storage_routes\"].\n\"\"\"\nfrom typing import Optional, Type\n\n\ndef _get_db_name_from_context(data_schema) -> Optional[str]:\n \"\"\"Resolve db_name for a schema from zvt_context.dbname_map_base.\"\"\"\n from zvt.contract import zvt_context\n for db_name, base in zvt_context.dbname_map_base.items():\n if issubclass(data_schema, base):\n return db_name\n return None\n\n\ndef _get_storage_config() -> dict:\n \"\"\"Load storage config from zvt_config. Lazy import to avoid cycles.\"\"\"\n try:\n from zvt import zvt_config\n return zvt_config.get(\"storage\") or {}\n except Exception:\n return {}\n\n\nclass RouteRegistry:\n \"\"\"\n Maps (provider, db_name) or (provider, data_schema) to storage_id.\n Default: storage_id = \"{provider}_{db_name}\".\n Config override: zvt_config[\"storage\"][\"storage_routes\"][\"provider|db_name\"] = storage_id.\n \"\"\"\n\n def __init__(self) -> None:\n self._route_overrides: dict = {}\n\n def register_route(self, provider: str, db_name: str, storage_id: str) -> None:\n \"\"\"Register explicit route override. Takes precedence over config.\"\"\"\n key = f\"{provider}|{db_name}\"\n self._route_overrides[key] = storage_id\n\n def _get_route_overrides_from_config(self) -> dict:\n \"\"\"Merge config storage_routes with programmatic overrides. Config is loaded once.\"\"\"\n config = _get_storage_config()\n routes = dict(config.get(\"storage_routes\") or {})\n routes.update(self._route_overrides)\n return routes\n\n def get_storage_id(self, provider: str, db_name: str) -> str:\n \"\"\"\n Get storage_id for (provider, db_name).\n Checks: 1) programmatic overrides 2) config storage_routes 3) default \"{provider}_{db_name}\".\n \"\"\"\n overrides = self._get_route_overrides_from_config()\n key = f\"{provider}|{db_name}\"\n if key in overrides:\n return overrides[key]\n return f\"{provider}_{db_name}\"\n\n def get_storage_id_for_schema(\n self, provider: str, data_schema: Type = None, db_name: str = None\n ) -> str:\n \"\"\"\n Get storage_id for (provider, data_schema) or (provider, db_name).\n :param provider: Data provider name.\n :param data_schema: Schema class (used to resolve db_name if db_name is None).\n :param db_name: DB name. If provided, data_schema is ignored.\n :return: storage_id string.\n \"\"\"\n if db_name is None:\n db_name = _get_db_name_from_context(data_schema)\n if db_name is None:\n raise ValueError(f\"Cannot resolve db_name for schema {data_schema}\")\n return self.get_storage_id(provider, db_name)\n\n\n# Default instance; can be replaced for custom routing\n_default_route_registry: Optional[RouteRegistry] = None\n\n\ndef get_route_registry() -> RouteRegistry:\n \"\"\"Get the default route registry.\"\"\"\n global _default_route_registry\n if _default_route_registry is None:\n _default_route_registry = RouteRegistry()\n return _default_route_registry\n\n\ndef set_route_registry(registry: RouteRegistry):\n \"\"\"Replace default route registry. Useful for tests.\"\"\"\n global _default_route_registry\n _default_route_registry = registry\n\n\n__all__ = [\n \"RouteRegistry\",\n \"get_route_registry\",\n \"set_route_registry\",\n]\n"
},
{
"path": "src/zvt/contract/schema.py",
"content": "# -*- coding: utf-8 -*-\nimport inspect\nimport json\nfrom datetime import timedelta\nfrom typing import Dict, List, Union\n\nimport pandas as pd\nimport pkg_resources\nfrom sqlalchemy import Column, String, DateTime, Float\nfrom sqlalchemy.orm import Session\n\nfrom zvt.contract import IntervalLevel\n\n\ndef _get_schema_providers() -> Dict[str, List[str]]:\n \"\"\"Get schema_providers merged with package default (ensures defaults are available).\"\"\"\n default = {}\n try:\n with open(pkg_resources.resource_filename(\"zvt\", \"config.json\")) as f:\n default = (json.load(f).get(\"storage\") or {}).get(\"schema_providers\") or {}\n except Exception:\n pass\n try:\n from zvt import zvt_config\n user = (zvt_config.get(\"storage\") or {}).get(\"schema_providers\") or {}\n default = dict(default)\n default.update(user)\n except Exception:\n pass\n return default\nfrom zvt.utils.time_utils import date_and_time, is_same_date_time, now_pd_timestamp\n\n\nclass Mixin(object):\n \"\"\"\n Base class of schema.\n \"\"\"\n\n #: id\n id = Column(String, primary_key=True)\n #: entity id\n entity_id = Column(String)\n\n #: the meaning could be different for different case,most time it means 'happen time'\n #: Need replace with your timezone\n timestamp = Column(DateTime)\n\n # unix epoch,same meaning with timestamp\n # ts = Column(BIGINT)\n\n @classmethod\n def help(cls):\n print(inspect.getsource(cls))\n\n @classmethod\n def important_cols(cls):\n return []\n\n @classmethod\n def time_field(cls):\n return \"timestamp\"\n\n @classmethod\n def register_recorder_cls(cls, provider, recorder_cls):\n \"\"\"\n register the recorder for the schema\n\n :param provider:\n :param recorder_cls:\n \"\"\"\n # don't make provider_map_recorder as class field,it should be created for the sub class as need\n if not hasattr(cls, \"provider_map_recorder\"):\n cls.provider_map_recorder = {}\n\n if provider not in cls.provider_map_recorder:\n cls.provider_map_recorder[provider] = recorder_cls\n\n @classmethod\n def register_provider(cls, provider):\n \"\"\"(Deprecated) Providers now come from Recorder registration.\"\"\"\n if not hasattr(cls, \"_zvt_providers_override\"):\n cls._zvt_providers_override = []\n if provider not in cls._zvt_providers_override:\n cls._zvt_providers_override.append(provider)\n\n @classmethod\n def is_internal(cls) -> bool:\n \"\"\"True if schema is internal/business data, not tied to external data source.\"\"\"\n return getattr(cls, \"_zvt_internal\", False)\n\n @classmethod\n def get_providers(cls) -> List[str]:\n \"\"\"\n Providers: from provider_map_recorder (Recorder registration), or config storage.schema_providers.\n Only use provider_map_recorder when set on this concrete schema class (cls.__dict__), not inherited\n from a shared base like KdataCommon - otherwise StockQuote would wrongly get Stock1dKdata's providers.\n \"\"\"\n if \"provider_map_recorder\" in cls.__dict__ and cls.provider_map_recorder:\n return list(cls.provider_map_recorder.keys())\n db_name = getattr(cls, \"_zvt_db_name\", None)\n if db_name:\n schema_providers = _get_schema_providers()\n if db_name in schema_providers:\n return schema_providers[db_name]\n return getattr(cls, \"_zvt_providers_override\", []) or []\n\n @classmethod\n def test_data_correctness(cls, provider, data_samples):\n for data in data_samples:\n item = cls.query_data(provider=provider, ids=[data[\"id\"]], return_type=\"dict\")\n print(item)\n for k in data:\n if k == \"timestamp\":\n assert is_same_date_time(item[0][k], data[k])\n else:\n assert item[0][k] == data[k]\n\n @classmethod\n def get_by_id(cls, id, provider_index: int = 0, provider: str = None):\n from .api import get_by_id\n\n if not provider:\n provider = cls.get_providers()[provider_index]\n return get_by_id(data_schema=cls, id=id, provider=provider)\n\n @classmethod\n def query_data(\n cls,\n provider_index: int = 0,\n ids: List[str] = None,\n entity_ids: List[str] = None,\n entity_id: str = None,\n codes: List[str] = None,\n code: str = None,\n level: Union[IntervalLevel, str] = None,\n provider: str = None,\n columns: List = None,\n col_label: dict = None,\n return_type: str = \"df\",\n start_timestamp: Union[pd.Timestamp, str] = None,\n end_timestamp: Union[pd.Timestamp, str] = None,\n filters: List = None,\n session: Session = None,\n order=None,\n limit: int = None,\n distinct=None,\n index: Union[str, list] = None,\n drop_index_col=False,\n time_field: str = \"timestamp\",\n ):\n \"\"\"\n query data by the arguments\n\n :param provider_index:\n :param data_schema:\n :param ids:\n :param entity_ids:\n :param entity_id:\n :param codes:\n :param code:\n :param level:\n :param provider:\n :param columns:\n :param col_label: dict with key(column), value(label)\n :param return_type: df, domain or dict. default is df\n :param start_timestamp:\n :param end_timestamp:\n :param filters:\n :param session:\n :param order:\n :param limit:\n :param index: index field name, str for single index, str list for multiple index\n :param drop_index_col: whether drop the col if it's in index, default False\n :param time_field:\n :return: results basing on return_type.\n \"\"\"\n from .api import get_data\n\n if not provider:\n provider = cls.get_providers()[provider_index]\n return get_data(\n data_schema=cls,\n ids=ids,\n entity_ids=entity_ids,\n entity_id=entity_id,\n codes=codes,\n code=code,\n level=level,\n provider=provider,\n columns=columns,\n col_label=col_label,\n return_type=return_type,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=filters,\n session=session,\n order=order,\n limit=limit,\n index=index,\n distinct=distinct,\n drop_index_col=drop_index_col,\n time_field=time_field,\n )\n\n @classmethod\n def get_storages(\n cls,\n provider: str = None,\n ):\n \"\"\"\n get the storages info\n\n :param provider: provider\n :return: storages\n \"\"\"\n if not provider:\n providers = cls.get_providers()\n else:\n providers = [provider]\n from zvt.contract.api import get_db_engine\n\n engines = []\n for p in providers:\n engines.append(get_db_engine(provider=p, data_schema=cls))\n return engines\n\n @classmethod\n def record_data(\n cls,\n provider_index: int = 0,\n provider: str = None,\n force_update=None,\n sleeping_time=None,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n real_time=None,\n fix_duplicate_way=None,\n start_timestamp=None,\n end_timestamp=None,\n one_day_trading_minutes=None,\n **kwargs,\n ):\n \"\"\"\n record data by the arguments\n\n :param entity_id:\n :param provider_index:\n :param provider:\n :param force_update:\n :param sleeping_time:\n :param exchanges:\n :param entity_ids:\n :param code:\n :param codes:\n :param real_time:\n :param fix_duplicate_way:\n :param start_timestamp:\n :param end_timestamp:\n :param one_day_trading_minutes:\n :param kwargs:\n :return:\n \"\"\"\n if cls.provider_map_recorder:\n print(f\"{cls.__name__} registered recorders:{cls.provider_map_recorder}\")\n\n if provider:\n recorder_class = cls.provider_map_recorder[provider]\n else:\n recorder_class = cls.provider_map_recorder[cls.get_providers()[provider_index]]\n\n # get args for specific recorder class\n from zvt.contract.recorder import TimeSeriesDataRecorder\n\n if issubclass(recorder_class, TimeSeriesDataRecorder):\n args = [\n item\n for item in inspect.getfullargspec(cls.record_data).args\n if item not in (\"cls\", \"provider_index\", \"provider\")\n ]\n else:\n args = [\"force_update\", \"sleeping_time\"]\n\n #: just fill the None arg to kw,so we could use the recorder_class default args\n kw = {}\n for arg in args:\n tmp = eval(arg)\n if tmp is not None:\n kw[arg] = tmp\n\n #: FixedCycleDataRecorder\n from zvt.contract.recorder import FixedCycleDataRecorder\n\n if issubclass(recorder_class, FixedCycleDataRecorder):\n #: contract:\n #: 1)use FixedCycleDataRecorder to record the data with IntervalLevel\n #: 2)the table of schema with IntervalLevel format is {entity}_{level}_[adjust_type]_{event}\n table: str = cls.__tablename__\n try:\n items = table.split(\"_\")\n if len(items) == 4:\n adjust_type = items[2]\n kw[\"adjust_type\"] = adjust_type\n level = IntervalLevel(items[1])\n except:\n #: for other schema not with normal format,but need to calculate size for remaining days\n level = IntervalLevel.LEVEL_1DAY\n\n kw[\"level\"] = level\n\n #: add other custom args\n for k in kwargs:\n kw[k] = kwargs[k]\n\n r = recorder_class(**kw)\n return r.run()\n else:\n r = recorder_class(**kw)\n return r.run()\n else:\n print(f\"no recorders for {cls.__name__}\")\n\n\nclass NormalMixin(Mixin):\n #: the record created time in db\n created_timestamp = Column(DateTime, default=pd.Timestamp.now())\n #: the record updated time in db, some recorder would check it for whether need to refresh\n updated_timestamp = Column(DateTime)\n\n\nclass Entity(Mixin):\n #: 标的类型\n entity_type = Column(String(length=64))\n #: 所属交易所\n exchange = Column(String(length=32))\n #: 编码\n code = Column(String(length=64))\n #: 名字\n name = Column(String(length=128))\n #: 上市日\n list_date = Column(DateTime)\n #: 退市日\n end_date = Column(DateTime)\n\n\nclass TradableEntity(Entity):\n \"\"\"\n tradable entity\n \"\"\"\n\n @classmethod\n def get_timezone(cls):\n \"\"\"\n overwrite it to get the timezone of the entity\n\n :return: pytz timezone\n \"\"\"\n\n return None\n\n @classmethod\n def get_trading_dates(cls, start_date=None, end_date=None):\n \"\"\"\n overwrite it to get the trading dates of the entity\n\n :param start_date:\n :param end_date:\n :return: list of dates\n \"\"\"\n return pd.date_range(start_date, end_date, freq=\"B\")\n\n @classmethod\n def get_trading_intervals(cls, include_bidding_time=False):\n \"\"\"\n overwrite it to get the trading intervals of the entity\n\n :return: list of time intervals, in format [(start,end)]\n \"\"\"\n if include_bidding_time:\n return [(\"09:15\", \"11:30\"), (\"13:00\", \"15:00\")]\n else:\n return [(\"09:30\", \"11:30\"), (\"13:00\", \"15:00\")]\n\n @classmethod\n def in_real_trading_time(cls, timestamp=None, include_bidding_time=True):\n if not timestamp:\n timestamp = now_pd_timestamp(tz=cls.get_timezone())\n else:\n timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone())\n for open_close in cls.get_trading_intervals(include_bidding_time=include_bidding_time):\n open_time = date_and_time(the_date=timestamp.date(), the_time=open_close[0], tz=cls.get_timezone())\n close_time = date_and_time(the_date=timestamp.date(), the_time=open_close[1], tz=cls.get_timezone())\n if open_time <= timestamp <= close_time:\n return True\n else:\n continue\n return False\n\n @classmethod\n def before_trading_time(cls, timestamp=None):\n if not timestamp:\n timestamp = now_pd_timestamp(tz=cls.get_timezone())\n else:\n timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone())\n open_time = date_and_time(\n the_date=timestamp.date(),\n the_time=cls.get_trading_intervals(include_bidding_time=True)[0][0],\n tz=cls.get_timezone(),\n )\n return timestamp < open_time\n\n @classmethod\n def after_trading_time(cls, timestamp=None):\n if not timestamp:\n timestamp = now_pd_timestamp(tz=cls.get_timezone())\n else:\n timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone())\n close_time = date_and_time(\n the_date=timestamp.date(),\n the_time=cls.get_trading_intervals(include_bidding_time=True)[-1][1],\n tz=cls.get_timezone(),\n )\n return timestamp > close_time\n\n @classmethod\n def in_trading_time(cls, timestamp=None):\n if not timestamp:\n timestamp = now_pd_timestamp(tz=cls.get_timezone())\n else:\n timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone())\n open_time = date_and_time(\n the_date=timestamp.date(),\n the_time=cls.get_trading_intervals(include_bidding_time=True)[0][0],\n tz=cls.get_timezone(),\n )\n close_time = date_and_time(\n the_date=timestamp.date(),\n the_time=cls.get_trading_intervals(include_bidding_time=True)[-1][1],\n tz=cls.get_timezone(),\n )\n return open_time <= timestamp <= close_time\n\n @classmethod\n def get_close_hour_and_minute(cls):\n hour, minute = cls.get_trading_intervals()[-1][1].split(\":\")\n return int(hour), int(minute)\n\n @classmethod\n def get_interval_timestamps(cls, start_date, end_date, level: IntervalLevel):\n \"\"\"\n generate the timestamps for the level\n\n :param start_date:\n :param end_date:\n :param level:\n \"\"\"\n\n for current_date in cls.get_trading_dates(start_date=start_date, end_date=end_date):\n if level == IntervalLevel.LEVEL_1DAY:\n yield current_date\n elif level == IntervalLevel.LEVEL_1WEEK:\n if current_date.weekday() == 4:\n yield current_date\n else:\n start_end_list = cls.get_trading_intervals()\n\n for start_end in start_end_list:\n start = start_end[0]\n end = start_end[1]\n\n current_timestamp = date_and_time(the_date=current_date, the_time=start)\n end_timestamp = date_and_time(the_date=current_date, the_time=end)\n\n while current_timestamp <= end_timestamp:\n yield current_timestamp\n current_timestamp = current_timestamp + timedelta(minutes=level.to_minute())\n\n @classmethod\n def is_open_timestamp(cls, timestamp):\n timestamp = pd.Timestamp(timestamp)\n return is_same_date_time(\n timestamp,\n date_and_time(the_date=timestamp.date(), the_time=cls.get_trading_intervals()[0][0]),\n )\n\n @classmethod\n def is_close_timestamp(cls, timestamp):\n timestamp = pd.Timestamp(timestamp)\n return is_same_date_time(\n timestamp,\n date_and_time(the_date=timestamp.date(), the_time=cls.get_trading_intervals()[-1][1]),\n )\n\n @classmethod\n def is_finished_kdata_timestamp(cls, timestamp: pd.Timestamp, level: IntervalLevel):\n \"\"\"\n :param timestamp: the timestamp could be recorded in kdata of the level\n :type timestamp: pd.Timestamp\n :param level:\n :type level: zvt.domain.common.IntervalLevel\n :return:\n :rtype: bool\n \"\"\"\n timestamp = pd.Timestamp(timestamp)\n\n for t in cls.get_interval_timestamps(timestamp.date(), timestamp.date(), level=level):\n if is_same_date_time(t, timestamp):\n return True\n\n return False\n\n @classmethod\n def could_short(cls):\n \"\"\"\n whether could be shorted\n\n :return:\n \"\"\"\n return False\n\n @classmethod\n def get_trading_t(cls):\n \"\"\"\n 0 means t+0\n 1 means t+1\n\n :return:\n \"\"\"\n return 1\n\n\nclass ActorEntity(Entity):\n pass\n\n\nclass NormalEntityMixin(TradableEntity):\n #: the record created time in db\n created_timestamp = Column(DateTime, default=pd.Timestamp.now())\n #: the record updated time in db, some recorder would check it for whether need to refresh\n updated_timestamp = Column(DateTime)\n\n\nclass Portfolio(TradableEntity):\n \"\"\"\n composition of tradable entities\n \"\"\"\n\n @classmethod\n def get_stocks(\n cls,\n code=None,\n codes=None,\n ids=None,\n timestamp=now_pd_timestamp(),\n provider=None,\n ):\n \"\"\"\n the publishing policy of portfolio positions is different for different types,\n overwrite this function for get the holding stocks in specific date\n\n :param code: portfolio(etf/block/index...) code\n :param codes: portfolio(etf/block/index...) codes\n :param ids: portfolio(etf/block/index...) ids\n :param timestamp: the date of the holding stocks\n :param provider: the data provider\n :return:\n \"\"\"\n from zvt.contract.api import get_schema_by_name\n\n schema_str = f\"{cls.__name__}Stock\"\n portfolio_stock = get_schema_by_name(schema_str)\n return portfolio_stock.query_data(provider=provider, code=code, codes=codes, timestamp=timestamp, ids=ids)\n\n\n#: 组合(Fund,Etf,Index,Block等)和个股(Stock)的关系 应该继承自该类\n#: 该基础类可以这样理解:\n#: entity为组合本身,其包含了stock这种entity,timestamp为持仓日期,从py的\"你知道你在干啥\"的哲学出发，不加任何约束\nclass PortfolioStock(Mixin):\n #: portfolio标的类型\n entity_type = Column(String(length=64))\n #: portfolio所属交易所\n exchange = Column(String(length=32))\n #: portfolio编码\n code = Column(String(length=64))\n #: portfolio名字\n name = Column(String(length=128))\n\n stock_id = Column(String)\n stock_code = Column(String(length=64))\n stock_name = Column(String(length=128))\n\n\n#: 支持时间变化,报告期标的调整\nclass PortfolioStockHistory(PortfolioStock):\n #: 报告期,season1,half_year,season3,year\n report_period = Column(String(length=32))\n #: 3-31,6-30,9-30,12-31\n report_date = Column(DateTime)\n\n #: 占净值比例\n proportion = Column(Float)\n #: 持有股票的数量\n shares = Column(Float)\n #: 持有股票的市值\n market_cap = Column(Float)\n\n\n#: 交易标的和参与者的关系应该继承自该类, meet,遇见,恰如其分的诠释参与者和交易标的的关系\n#: 市场就是参与者与交易标的的关系，类的命名规范为{Entity}{relation}{Entity}，entity_id代表\"所\"为的entity,\"受\"者entity以具体类别的id命名\n#: 比如StockTopTenHolder:TradableMeetActor中entity_id和actor_id,分别代表股票和股东\nclass TradableMeetActor(Mixin):\n #: tradable code\n code = Column(String(length=64))\n #: tradable name\n name = Column(String(length=128))\n\n actor_id = Column(String)\n actor_type = Column(String)\n actor_code = Column(String(length=64))\n actor_name = Column(String(length=128))\n\n\n#: 也可以\"所\"为参与者，\"受\"为标的\nclass ActorMeetTradable(Mixin):\n #: actor code\n code = Column(String(length=64))\n #: actor name\n name = Column(String(length=128))\n\n tradable_id = Column(String)\n tradable_type = Column(String)\n tradable_code = Column(String(length=64))\n tradable_name = Column(String(length=128))\n\n\n# the __all__ is generated\n__all__ = [\n \"Mixin\",\n \"NormalMixin\",\n \"Entity\",\n \"TradableEntity\",\n \"ActorEntity\",\n \"NormalEntityMixin\",\n \"Portfolio\",\n \"PortfolioStock\",\n \"PortfolioStockHistory\",\n \"TradableMeetActor\",\n \"ActorMeetTradable\",\n]\n"
},
{
"path": "src/zvt/contract/storage.py",
"content": "# -*- coding: utf-8 -*-\n\"\"\"\nStorage abstraction for ZVT.\nPhase 1: StorageBackend interface + SqliteStorageBackend with existing path logic.\nPhase 2: path_template and base_path from config.\n\"\"\"\nimport json\nimport logging\nimport os\nfrom abc import ABC, abstractmethod\nfrom typing import Optional\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.engine import Engine\nfrom sqlalchemy.orm import sessionmaker\n\nlogger = logging.getLogger(__name__)\n\n\ndef _default_data_path() -> str:\n \"\"\"Get data path from zvt_env, with fallback for tests.\"\"\"\n try:\n from zvt import zvt_env\n return zvt_env.get(\"data_path\", \".\")\n except Exception:\n return \".\"\n\n\ndef _get_storage_config() -> dict:\n \"\"\"Load storage config from zvt_config. Lazy import to avoid cycles.\"\"\"\n try:\n from zvt import zvt_config\n return zvt_config.get(\"storage\") or {}\n except Exception:\n return {}\n\n\nclass StorageBackend(ABC):\n \"\"\"\n Abstract storage backend. Decouples physical storage from domain/read/record logic.\n \"\"\"\n\n @abstractmethod\n def get_engine(self, storage_id: str, data_path: Optional[str] = None) -> Engine:\n \"\"\"\n Get or create SQLAlchemy engine for the given storage_id.\n :param storage_id: Logical storage identifier (e.g. \"em_stock_meta\").\n :param data_path: Base path for data files. If None, uses zvt_env[\"data_path\"].\n :return: Engine instance.\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def get_session_factory(self, storage_id: str, data_path: Optional[str] = None):\n \"\"\"\n Get or create sessionmaker for the given storage_id.\n Must be configured with engine before use (caller's responsibility, e.g. in register_schema).\n :param storage_id: Logical storage identifier.\n :param data_path: Base path for data files.\n :return: sessionmaker instance.\n \"\"\"\n raise NotImplementedError\n\n\nclass SqliteStorageBackend(StorageBackend):\n \"\"\"\n SQLite storage backend. Default path: {data_path}/{provider}/{provider}_{db_name}.db\n Config (zvt_config[\"storage\"]):\n - base_path: override data path\n - path_template: format string with {base_path}, {provider}, {db_name}, {storage_id}\n \"\"\"\n\n def __init__(self, path_template: Optional[str] = None, base_path: Optional[str] = None):\n \"\"\"\n :param path_template: Optional. Overrides config. Placeholders: {base_path}, {provider}, {db_name}, {storage_id}\n :param base_path: Optional. Overrides config.\n \"\"\"\n self._path_template_override = path_template\n self._base_path_override = base_path\n self._engine_map = {}\n self._session_factory_map = {}\n\n def _get_path_template(self) -> Optional[str]:\n \"\"\"Path template: constructor override > config > None (use default).\"\"\"\n if self._path_template_override is not None:\n return self._path_template_override\n config = _get_storage_config()\n return config.get(\"path_template\")\n\n def _get_base_path(self, data_path: Optional[str]) -> str:\n \"\"\"Base path: param > constructor override > config > zvt_env.\"\"\"\n if data_path is not None:\n return data_path\n if self._base_path_override is not None:\n return self._base_path_override\n config = _get_storage_config()\n cfg_path = config.get(\"base_path\")\n if cfg_path is not None:\n return cfg_path\n return _default_data_path()\n\n def _storage_id_to_path(self, storage_id: str, data_path: str) -> str:\n \"\"\"\n Compute SQLite file path from storage_id.\n storage_id format: \"{provider}_{db_name}\" (e.g. \"em_stock_meta\").\n Default path: {data_path}/{provider}/{provider}_{db_name}.db\n \"\"\"\n if \"_\" not in storage_id:\n raise ValueError(f\"Invalid storage_id: {storage_id}, expected '{{provider}}_{{db_name}}'\")\n provider, db_name = storage_id.split(\"_\", 1)\n path_template = self._get_path_template()\n if path_template:\n return path_template.format(\n base_path=data_path, provider=provider, db_name=db_name, storage_id=storage_id\n )\n provider_dir = os.path.join(data_path, provider)\n if not os.path.exists(provider_dir):\n os.makedirs(provider_dir)\n return os.path.join(provider_dir, f\"{provider}_{db_name}.db?check_same_thread=False\")\n\n def get_engine(self, storage_id: str, data_path: Optional[str] = None) -> Engine:\n data_path = self._get_base_path(data_path)\n if storage_id in self._engine_map:\n return self._engine_map[storage_id]\n db_path = self._storage_id_to_path(storage_id, data_path)\n url = \"sqlite:///\" + db_path\n engine = create_engine(\n url, echo=False, json_serializer=lambda obj: json.dumps(obj, ensure_ascii=False)\n )\n self._engine_map[storage_id] = engine\n return engine\n\n def get_session_factory(self, storage_id: str, data_path: Optional[str] = None):\n data_path = self._get_base_path(data_path)\n if storage_id in self._session_factory_map:\n return self._session_factory_map[storage_id]\n fac = sessionmaker()\n self._session_factory_map[storage_id] = fac\n return fac\n\n# Default instance; can be replaced for testing or custom backends\n_default_storage_backend: Optional[StorageBackend] = None\n\n\ndef get_storage_backend() -> StorageBackend:\n \"\"\"Get the default storage backend. Creates SqliteStorageBackend on first use.\"\"\"\n global _default_storage_backend\n if _default_storage_backend is None:\n _default_storage_backend = SqliteStorageBackend()\n return _default_storage_backend\n\n\ndef set_storage_backend(backend: StorageBackend):\n \"\"\"Replace default storage backend. Useful for tests.\"\"\"\n global _default_storage_backend\n _default_storage_backend = backend\n\n\n__all__ = [\n \"StorageBackend\",\n \"SqliteStorageBackend\",\n \"get_storage_backend\",\n \"set_storage_backend\",\n]\n"
},
{
"path": "src/zvt/contract/utils.py",
"content": "# -*- coding: utf-8 -*-\nimport math\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\ndef is_in_same_interval(t1: pd.Timestamp, t2: pd.Timestamp, level: IntervalLevel):\n t1 = to_pd_timestamp(t1)\n t2 = to_pd_timestamp(t2)\n if level == IntervalLevel.LEVEL_1WEEK:\n return t1.week == t2.week\n if level == IntervalLevel.LEVEL_1MON:\n return t1.month == t2.month\n\n return level.floor_timestamp(t1) == level.floor_timestamp(t2)\n\n\ndef evaluate_size_from_timestamp(\n start_timestamp, level: IntervalLevel, one_day_trading_minutes, end_timestamp: pd.Timestamp = None\n):\n \"\"\"\n given from timestamp,level,one_day_trading_minutes,this func evaluate size of kdata to current.\n it maybe a little bigger than the real size for fetching all the kdata.\n\n :param start_timestamp:\n :type start_timestamp: pd.Timestamp\n :param level:\n :type level: IntervalLevel\n :param one_day_trading_minutes:\n :type one_day_trading_minutes: int\n \"\"\"\n if not end_timestamp:\n end_timestamp = pd.Timestamp.now()\n else:\n end_timestamp = to_pd_timestamp(end_timestamp)\n\n time_delta = end_timestamp - to_pd_timestamp(start_timestamp)\n\n one_day_trading_seconds = one_day_trading_minutes * 60\n\n if level == IntervalLevel.LEVEL_1DAY:\n return time_delta.days + 1\n\n if level == IntervalLevel.LEVEL_1WEEK:\n return int(math.ceil(time_delta.days / 7)) + 1\n\n if level == IntervalLevel.LEVEL_1MON:\n return int(math.ceil(time_delta.days / 30)) + 1\n\n if time_delta.days > 0:\n seconds = (time_delta.days + 1) * one_day_trading_seconds\n return int(math.ceil(seconds / level.to_second())) + 1\n else:\n seconds = time_delta.total_seconds()\n return min(int(math.ceil(seconds / level.to_second())) + 1, one_day_trading_seconds / level.to_second() + 1)\n\n\ndef next_timestamp_on_level(current_timestamp: pd.Timestamp, level: IntervalLevel) -> pd.Timestamp:\n current_timestamp = to_pd_timestamp(current_timestamp)\n return current_timestamp + pd.Timedelta(seconds=level.to_second())\n\n\ndef is_finished_kdata_timestamp(timestamp, level: IntervalLevel):\n timestamp = to_pd_timestamp(timestamp)\n if level.floor_timestamp(timestamp) == timestamp:\n return True\n return False\n"
},
{
"path": "src/zvt/contract/zvt_info.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Text\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.contract.schema import Mixin\n\nZvtInfoBase = declarative_base()\n\n\nclass StateMixin(Mixin):\n #: the unique name of the service, e.g. recorder,factor,tag\n state_name = Column(String(length=128))\n\n #: json string\n state = Column(Text())\n\n\nclass RecorderState(ZvtInfoBase, StateMixin):\n \"\"\"\n Schema for storing recorder state\n \"\"\"\n\n __tablename__ = \"recorder_state\"\n\n\nclass TaggerState(ZvtInfoBase, StateMixin):\n \"\"\"\n Schema for storing tagger state\n \"\"\"\n\n __tablename__ = \"tagger_state\"\n\n\nclass FactorState(ZvtInfoBase, StateMixin):\n \"\"\"\n Schema for storing factor state\n \"\"\"\n\n __tablename__ = \"factor_state\"\n\n\nregister_schema(db_name=\"zvt_info\", schema_base=ZvtInfoBase, internal=True)\n\n\n# the __all__ is generated\n__all__ = [\"StateMixin\", \"RecorderState\", \"TaggerState\", \"FactorState\"]\n"
},
{
"path": "src/zvt/domain/__init__.py",
"content": "# -*- coding: utf-8 -*-\nimport enum\n\n\nclass BlockCategory(enum.Enum):\n #: 行业版块\n industry = \"industry\"\n #: 概念版块\n concept = \"concept\"\n #: 区域版块\n area = \"area\"\n\n\nclass IndexCategory(enum.Enum):\n #: 中国指数提供商：\n #: 中证指数公司 http://www.csindex.com.cn/zh-CN\n #: 上证指数(上交所标的) 中证指数(沪深)\n\n #: 国证指数公司 http://www.cnindex.com.cn/index.html\n #: 深证指数(深交所标的) 国证指数(沪深)\n\n #: 规模指数\n #: 常见的上证指数，深证指数等\n scope = \"scope\"\n #: 行业指数\n industry = \"industry\"\n #: 风格指数\n style = \"style\"\n #: 主题指数\n #\n #: 策略指数\n #\n #: 综合指数\n #\n #: 债券指数\n #\n #: 基金指数\n fund = \"fund\"\n #: 定制指数\n #\n #: 人民币指数\n #\n #: 跨境指数\n #\n #: 其他指数\n\n\nclass ReportPeriod(enum.Enum):\n # 有些基金的2，4季报只有10大持仓，半年报和年报有详细持仓，需要区别对待\n season1 = \"season1\"\n season2 = \"season2\"\n season3 = \"season3\"\n season4 = \"season4\"\n half_year = \"half_year\"\n year = \"year\"\n\n\n# 用于区分不同的财务指标\nclass CompanyType(enum.Enum):\n qiye = \"qiye\"\n baoxian = \"baoxian\"\n yinhang = \"yinhang\"\n quanshang = \"quanshang\"\n\n\nCHINA_FUTURE_CODE_MAP_NAME = {\n \"I\": \"铁矿石\",\n \"RB\": \"螺纹钢\",\n \"HC\": \"热轧卷板\",\n \"SS\": \"不锈钢\",\n \"SF\": \"硅铁\",\n \"SM\": \"锰硅\",\n \"WR\": \"线材\",\n \"CU\": \"沪铜\",\n \"AL\": \"沪铝\",\n \"ZN\": \"沪锌\",\n \"PB\": \"沪铅\",\n \"NI\": \"沪镍\",\n \"SN\": \"沪锡\",\n \"BC\": \"国际铜\",\n \"AU\": \"沪金\",\n \"AG\": \"沪银\",\n \"A\": \"豆一\",\n \"B\": \"豆二\",\n \"Y\": \"豆油\",\n \"M\": \"豆粕\",\n \"RS\": \"菜籽\",\n \"OI\": \"菜油\",\n \"RM\": \"菜粕\",\n \"P\": \"棕榈油\",\n \"C\": \"玉米\",\n \"CS\": \"玉米淀粉\",\n \"JD\": \"鸡蛋\",\n \"CF\": \"一号棉花\",\n \"CY\": \"棉纱\",\n \"SR\": \"白糖\",\n \"AP\": \"苹果\",\n \"CJ\": \"红枣\",\n \"PK\": \"花生\",\n \"PM\": \"普麦\",\n \"WH\": \"强麦\",\n \"RR\": \"粳米\",\n \"JR\": \"粳稻\",\n \"RI\": \"早籼稻\",\n \"LR\": \"晚籼稻\",\n \"LH\": \"生猪\",\n \"SC\": \"原油\",\n \"FU\": \"燃油\",\n \"PG\": \"LPG\",\n \"LU\": \"低硫燃油\",\n \"BU\": \"石油沥青\",\n \"MA\": \"甲醇\",\n \"EG\": \"乙二醇\",\n \"L\": \"聚乙烯\",\n \"TA\": \"PTA\",\n \"V\": \"聚氯乙烯\",\n \"PP\": \"聚丙烯\",\n \"EB\": \"苯乙烯\",\n \"SA\": \"纯碱\",\n \"FG\": \"玻璃\",\n \"UR\": \"尿素\",\n \"RU\": \"橡胶\",\n \"NR\": \"20号胶\",\n \"SP\": \"纸浆\",\n \"FB\": \"纤维板\",\n \"BB\": \"胶合板\",\n \"PF\": \"短纤\",\n \"JM\": \"焦煤\",\n \"J\": \"焦炭\",\n \"ZC\": \"动力煤\",\n \"IC\": \"中证500指数\",\n \"IF\": \"沪深300指数\",\n \"IH\": \"上证50指数\",\n \"T\": \"10年期国债期货\",\n \"TF\": \"5年期国债期货\",\n \"TS\": \"2年期国债期货\",\n}\n\n\ndef get_future_name(code):\n simple_code = code[:-4]\n return f\"{CHINA_FUTURE_CODE_MAP_NAME[simple_code]}{code[-4:]}\"\n\n\n# the __all__ is generated\n__all__ = [\"BlockCategory\", \"IndexCategory\", \"ReportPeriod\", \"CompanyType\", \"get_future_name\"]\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule misc\nfrom .misc import *\nfrom .misc import __all__ as _misc_all\n\n__all__ += _misc_all\n\n# import all from submodule quotes\nfrom .quotes import *\nfrom .quotes import __all__ as _quotes_all\n\n__all__ += _quotes_all\n\n# import all from submodule meta\nfrom .meta import *\nfrom .meta import __all__ as _meta_all\n\n__all__ += _meta_all\n\n# import all from submodule fundamental\nfrom .fundamental import *\nfrom .fundamental import __all__ as _fundamental_all\n\n__all__ += _fundamental_all\n\n# import all from submodule macro\nfrom .macro import *\nfrom .macro import __all__ as _macro_all\n\n__all__ += _macro_all\n\n# import all from submodule actor\nfrom .actor import *\nfrom .actor import __all__ as _actor_all\n\n__all__ += _actor_all\n\n# import all from submodule emotion\nfrom .emotion import *\nfrom .emotion import __all__ as _emotion_all\n\n__all__ += _emotion_all\n"
},
{
"path": "src/zvt/domain/actor/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule stock_actor\nfrom .stock_actor import *\nfrom .stock_actor import __all__ as _stock_actor_all\n\n__all__ += _stock_actor_all\n\n# import all from submodule actor_meta\nfrom .actor_meta import *\nfrom .actor_meta import __all__ as _actor_meta_all\n\n__all__ += _actor_meta_all\n"
},
{
"path": "src/zvt/domain/actor/actor_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.contract.schema import ActorEntity\n\nActorMetaBase = declarative_base()\n\n\n#: 参与者\nclass ActorMeta(ActorMetaBase, ActorEntity):\n __tablename__ = \"actor_meta\"\n\n\nregister_schema(db_name=\"actor_meta\", schema_base=ActorMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"ActorMeta\"]\n"
},
{
"path": "src/zvt/domain/actor/stock_actor.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, DateTime, Float, Boolean, Integer\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.contract.schema import TradableMeetActor\n\nStockActorBase = declarative_base()\n\n\nclass StockTopTenFreeHolder(StockActorBase, TradableMeetActor):\n __tablename__ = \"stock_top_ten_free_holder\"\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 持股数\n holding_numbers = Column(Float)\n #: 持股比例\n holding_ratio = Column(Float)\n #: 持股市值\n holding_values = Column(Float)\n\n\nclass StockTopTenHolder(StockActorBase, TradableMeetActor):\n __tablename__ = \"stock_top_ten_holder\"\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 持股数\n holding_numbers = Column(Float)\n #: 持股比例\n holding_ratio = Column(Float)\n #: 持股市值\n holding_values = Column(Float)\n\n\nclass StockInstitutionalInvestorHolder(StockActorBase, TradableMeetActor):\n __tablename__ = \"stock_institutional_investor_holder\"\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 持股数\n holding_numbers = Column(Float)\n #: 持股比例\n holding_ratio = Column(Float)\n #: 持股市值\n holding_values = Column(Float)\n\n\nclass StockActorSummary(StockActorBase, TradableMeetActor):\n __tablename__ = \"stock_actor_summary\"\n #: tradable code\n code = Column(String(length=64))\n #: tradable name\n name = Column(String(length=128))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 变动比例\n change_ratio = Column(Float)\n #: 是否完成\n is_complete = Column(Boolean)\n #: 持股市值\n actor_type = Column(String)\n actor_count = Column(Integer)\n\n #: 持股数\n holding_numbers = Column(Float)\n #: 持股比例\n holding_ratio = Column(Float)\n #: 持股市值\n holding_values = Column(Float)\n\n\nregister_schema(db_name=\"stock_actor\", schema_base=StockActorBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"StockTopTenFreeHolder\", \"StockTopTenHolder\", \"StockInstitutionalInvestorHolder\", \"StockActorSummary\"]\n"
},
{
"path": "src/zvt/domain/emotion/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule emotion\nfrom .emotion import *\nfrom .emotion import __all__ as _emotion_all\n\n__all__ += _emotion_all\n"
},
{
"path": "src/zvt/domain/emotion/emotion.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Integer, DateTime, Boolean, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nEmotionBase = declarative_base()\n\n\nclass LimitUpInfo(EmotionBase, Mixin):\n __tablename__ = \"limit_up_info\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n #: 是否新股\n is_new = Column(Boolean)\n #: 是否回封，是就是打开过，否相反\n is_again_limit = Column(Boolean)\n #: 涨停打开次数,0代表封住就没开板\n open_count = Column(Integer)\n #: 首次封板时间\n first_limit_up_time = Column(DateTime)\n #: 最后封板时间\n last_limit_up_time = Column(DateTime)\n #: 涨停类型:换手板，一字板\n limit_up_type = Column(String)\n #: 封单金额\n order_amount = Column(String)\n #: 最近一年封板成功率\n success_rate = Column(Float)\n #: 流通市值\n currency_value = Column(Float)\n #: 涨幅\n change_pct = Column(Float)\n #: 换手率\n turnover_rate = Column(Float)\n #: 涨停原因\n reason = Column(String)\n #: 几天几板\n high_days = Column(String)\n #: 最近几板，不一定是连板\n high_days_count = Column(Integer)\n\n\nclass LimitDownInfo(EmotionBase, Mixin):\n __tablename__ = \"limit_down_info\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n #: 是否新股\n is_new = Column(Boolean)\n #: 是否回封，是就是打开过，否相反\n is_again_limit = Column(Boolean)\n #: 流通市值\n currency_value = Column(Float)\n #: 涨幅\n change_pct = Column(Float)\n #: 换手率\n turnover_rate = Column(Float)\n\n\nclass Emotion(EmotionBase, Mixin):\n __tablename__ = \"emotion\"\n #: 涨停数量\n limit_up_count = Column(Integer)\n #: 炸板数\n limit_up_open_count = Column(Integer)\n #: 涨停封板成功率\n limit_up_success_rate = Column(Float)\n\n #: 连板高度\n max_height = Column(Integer)\n #: 连板数x个数 相加\n continuous_power = Column(Integer)\n\n #: 跌停数量\n limit_down_count = Column(Integer)\n #: 跌停打开\n limit_down_open_count = Column(Integer)\n #: 跌停封板成功率\n limit_down_success_rate = Column(Float)\n\n\nregister_schema(db_name=\"emotion\", schema_base=EmotionBase)\n\n\n# the __all__ is generated\n__all__ = [\"LimitUpInfo\", \"LimitDownInfo\", \"Emotion\"]\n"
},
{
"path": "src/zvt/domain/fundamental/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule dividend_financing\nfrom .dividend_financing import *\nfrom .dividend_financing import __all__ as _dividend_financing_all\n\n__all__ += _dividend_financing_all\n\n# import all from submodule finance\nfrom .finance import *\nfrom .finance import __all__ as _finance_all\n\n__all__ += _finance_all\n\n# import all from submodule trading\nfrom .trading import *\nfrom .trading import __all__ as _trading_all\n\n__all__ += _trading_all\n\n# import all from submodule valuation\nfrom .valuation import *\nfrom .valuation import __all__ as _valuation_all\n\n__all__ += _valuation_all\n"
},
{
"path": "src/zvt/domain/fundamental/dividend_financing.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, DateTime, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nDividendFinancingBase = declarative_base()\n\n\nclass DividendFinancing(DividendFinancingBase, Mixin):\n __tablename__ = \"dividend_financing\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n #: 分红总额\n dividend_money = Column(Float)\n\n #: 新股\n ipo_issues = Column(Float)\n ipo_raising_fund = Column(Float)\n\n #: 增发\n spo_issues = Column(Float)\n spo_raising_fund = Column(Float)\n #: 配股\n rights_issues = Column(Float)\n rights_raising_fund = Column(Float)\n\n\nclass DividendDetail(DividendFinancingBase, Mixin):\n __tablename__ = \"dividend_detail\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n #: 公告日\n announce_date = Column(DateTime)\n #: 股权登记日\n record_date = Column(DateTime)\n #: 除权除息日\n dividend_date = Column(DateTime)\n\n #: 方案\n dividend = Column(String(length=128))\n\n\nclass SpoDetail(DividendFinancingBase, Mixin):\n __tablename__ = \"spo_detail\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n spo_issues = Column(Float)\n spo_price = Column(Float)\n spo_raising_fund = Column(Float)\n\n\nclass RightsIssueDetail(DividendFinancingBase, Mixin):\n __tablename__ = \"rights_issue_detail\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n #: 配股\n rights_issues = Column(Float)\n rights_issue_price = Column(Float)\n rights_raising_fund = Column(Float)\n\n\nregister_schema(\n db_name=\"dividend_financing\", schema_base=DividendFinancingBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"DividendFinancing\", \"DividendDetail\", \"SpoDetail\", \"RightsIssueDetail\"]\n"
},
{
"path": "src/zvt/domain/fundamental/finance.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, DateTime, Float, Integer\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nFinanceBase = declarative_base()\n\n\nclass BalanceSheet(FinanceBase, Mixin):\n @classmethod\n def important_cols(cls):\n return [\n \"total_assets\",\n \"total_liabilities\",\n \"equity\",\n \"cash_and_cash_equivalents\",\n \"accounts_receivable\",\n \"inventories\",\n \"goodwill\",\n ]\n\n __tablename__ = \"balance_sheet\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 流动资产\n #\n #: 货币资金\n cash_and_cash_equivalents = Column(Float)\n #: 应收票据\n note_receivable = Column(Float)\n #: 应收账款\n accounts_receivable = Column(Float)\n #: 预付款项\n advances_to_suppliers = Column(Float)\n #: 其他应收款\n other_receivables = Column(Float)\n #: 存货\n inventories = Column(Float)\n #: 一年内到期的非流动资产\n current_portion_of_non_current_assets = Column(Float)\n #: 其他流动资产\n other_current_assets = Column(Float)\n #: 流动资产合计\n total_current_assets = Column(Float)\n #: 非流动资产\n #\n #: 可供出售金融资产\n fi_assets_saleable = Column(Float)\n #: 长期应收款\n long_term_receivables = Column(Float)\n #: 长期股权投资\n long_term_equity_investment = Column(Float)\n #: 投资性房地产\n real_estate_investment = Column(Float)\n #: 固定资产\n fixed_assets = Column(Float)\n #: 在建工程\n construction_in_process = Column(Float)\n #: 无形资产\n intangible_assets = Column(Float)\n #: 商誉\n goodwill = Column(Float)\n #: 长期待摊费用\n long_term_prepaid_expenses = Column(Float)\n #: 递延所得税资产\n deferred_tax_assets = Column(Float)\n #: 其他非流动资产\n other_non_current_assets = Column(Float)\n #: 非流动资产合计\n total_non_current_assets = Column(Float)\n #: 资产总计\n total_assets = Column(Float)\n #: 流动负债\n #\n #: 短期借款\n short_term_borrowing = Column(Float)\n #: 吸收存款及同业存放\n accept_money_deposits = Column(Float)\n #: 应付账款\n accounts_payable = Column(Float)\n #: 预收款项\n advances_from_customers = Column(Float)\n #: 应付职工薪酬\n employee_benefits_payable = Column(Float)\n #: 应交税费\n taxes_payable = Column(Float)\n #: 应付利息\n interest_payable = Column(Float)\n #: 其他应付款\n other_payable = Column(Float)\n #: 一年内到期的非流动负债\n current_portion_of_non_current_liabilities = Column(Float)\n #: 其他流动负债\n other_current_liabilities = Column(Float)\n #: 流动负债合计\n total_current_liabilities = Column(Float)\n #: 非流动负债\n #\n #: 长期借款\n long_term_borrowing = Column(Float)\n #: 长期应付款\n long_term_payable = Column(Float)\n #: 递延收益\n deferred_revenue = Column(Float)\n #: 递延所得税负债\n deferred_tax_liabilities = Column(Float)\n #: 其他非流动负债\n other_non_current_liabilities = Column(Float)\n #: 非流动负债合计\n total_non_current_liabilities = Column(Float)\n #: 负债合计\n total_liabilities = Column(Float)\n #: 所有者权益(或股东权益)\n #\n #: 实收资本（或股本）\n capital = Column(Float)\n #: 资本公积\n capital_reserve = Column(Float)\n #: 专项储备\n special_reserve = Column(Float)\n #: 盈余公积\n surplus_reserve = Column(Float)\n #: 未分配利润\n undistributed_profits = Column(Float)\n #: 归属于母公司股东权益合计\n equity = Column(Float)\n #: 少数股东权益\n equity_as_minority_interest = Column(Float)\n #: 股东权益合计\n total_equity = Column(Float)\n #: 负债和股东权益合计\n total_liabilities_and_equity = Column(Float)\n\n #: 银行相关\n #: 资产\n #: 现金及存放中央银行款项\n fi_cash_and_deposit_in_central_bank = Column(Float)\n #: 存放同业款项\n fi_deposit_in_other_fi = Column(Float)\n #: 贵金属\n fi_expensive_metals = Column(Float)\n #: 拆出资金\n fi_lending_to_other_fi = Column(Float)\n #: 以公允价值计量且其变动计入当期损益的金融资产\n fi_financial_assets_effect_current_income = Column(Float)\n #: 衍生金融资产\n fi_financial_derivative_asset = Column(Float)\n #: 买入返售金融资产\n fi_buying_sell_back_fi__asset = Column(Float)\n #: 应收账款\n #\n #: 应收利息\n fi_interest_receivable = Column(Float)\n #: 发放贷款及垫款\n fi_disbursing_loans_and_advances = Column(Float)\n #: 可供出售金融资产\n #\n #: 持有至到期投资\n fi_held_to_maturity_investment = Column(Float)\n #: 应收款项类投资\n fi_account_receivable_investment = Column(Float)\n #: 投资性房地产\n #\n #: 固定资产\n #\n #: 无形资产\n #\n #: 商誉\n #\n #: 递延所得税资产\n #\n #: 其他资产\n fi_other_asset = Column(Float)\n #: 资产总计\n #\n\n #: 负债\n #\n #: 向中央银行借款\n fi_borrowings_from_central_bank = Column(Float)\n #: 同业和其他金融机构存放款项\n fi_deposit_from_other_fi = Column(Float)\n #: 拆入资金\n fi_borrowings_from_fi = Column(Float)\n #: 以公允价值计量且其变动计入当期损益的金融负债\n fi_financial_liability_effect_current_income = Column(Float)\n #: 衍生金融负债\n fi_financial_derivative_liability = Column(Float)\n #: 卖出回购金融资产款\n fi_sell_buy_back_fi_asset = Column(Float)\n #: 吸收存款\n fi_savings_absorption = Column(Float)\n #: 存款证及应付票据\n fi_notes_payable = Column(Float)\n #: 应付职工薪酬\n #\n #: 应交税费\n #\n #: 应付利息\n #\n #: 预计负债\n fi_estimated_liabilities = Column(Float)\n #: 应付债券\n fi_bond_payable = Column(Float)\n #: 其他负债\n fi_other_liability = Column(Float)\n #: 负债合计\n #\n\n #: 所有者权益(或股东权益)\n #: 股本\n fi_capital = Column(Float)\n #: 其他权益工具\n fi_other_equity_instruments = Column(Float)\n #: 其中:优先股\n fi_preferred_stock = Column(Float)\n #: 资本公积\n #\n #: 盈余公积\n #\n #: 一般风险准备\n fi_generic_risk_reserve = Column(Float)\n #: 未分配利润\n #\n #: 归属于母公司股东权益合计\n #\n #: 股东权益合计\n #\n #: 负债及股东权益总计\n\n #: 券商相关\n #: 资产\n #\n #: 货币资金\n #\n #: 其中: 客户资金存款\n fi_client_fund = Column(Float)\n #: 结算备付金\n fi_deposit_reservation_for_balance = Column(Float)\n #: 其中: 客户备付金\n fi_client_deposit_reservation_for_balance = Column(Float)\n #: 融出资金\n fi_margin_out_fund = Column(Float)\n #: 以公允价值计量且其变动计入当期损益的金融资产\n #\n #: 衍生金融资产\n #\n #: 买入返售金融资产\n #\n #: 应收利息\n #\n #: 应收款项\n fi_receivables = Column(Float)\n #: 存出保证金\n fi_deposit_for_recognizance = Column(Float)\n #: 可供出售金融资产\n #\n #: 持有至到期投资\n #\n #: 长期股权投资\n #\n #: 固定资产\n #\n #: 在建工程\n #\n #: 无形资产\n #\n #: 商誉\n #\n #: 递延所得税资产\n #\n #: 其他资产\n #\n #: 资产总计\n #\n #: 负债\n #\n #: 短期借款\n #\n #: 拆入资金\n #\n #: 以公允价值计量且其变动计入当期损益的金融负债\n #\n #: 衍生金融负债\n #\n #: 卖出回购金融资产款\n #\n #: 代理买卖证券款\n fi_receiving_as_agent = Column(Float)\n #: 应付账款\n #\n #: 应付职工薪酬\n #\n #: 应交税费\n #\n #: 应付利息\n #\n #: 应付短期融资款\n fi_short_financing_payable = Column(Float)\n #: 预计负债\n #\n #: 应付债券\n #\n #: 递延所得税负债\n #\n #: 其他负债\n #\n #: 负债合计\n #\n #: 所有者权益(或股东权益)\n #\n #: 股本\n #\n #: 资本公积\n #\n #: 其他权益工具\n #\n #: 盈余公积\n #\n #: 一般风险准备\n #\n #: 交易风险准备\n fi_trade_risk_reserve = Column(Float)\n #: 未分配利润\n #\n #: 归属于母公司股东权益合计\n #\n #: 少数股东权益\n #\n #: 股东权益合计\n #\n #: 负债和股东权益总计\n\n #: 保险相关\n\n #: 资产\n #: 应收保费\n fi_premiums_receivable = Column(Float)\n #: 应收分保账款\n fi_reinsurance_premium_receivable = Column(Float)\n #: 应收分保合同准备金\n fi_reinsurance_contract_reserve = Column(Float)\n #: 保户质押贷款\n fi_policy_pledge_loans = Column(Float)\n #: 发放贷款及垫款\n #: 定期存款\n fi_time_deposit = Column(Float)\n #: 可供出售金融资产\n #\n #: 持有至到期投资\n #\n #: 应收款项类投资\n #\n #: 应收账款\n #\n #: 长期股权投资\n #\n #: 存出资本保证金\n fi_deposit_for_capital_recognizance = Column(Float)\n #: 投资性房地产\n #\n #: 固定资产\n #\n #: 无形资产\n #\n #: 商誉\n #\n #: 递延所得税资产\n #\n #: 其他资产\n #\n #: 独立账户资产\n fi_capital_in_independent_accounts = Column(Float)\n #: 资产总计\n #\n #: 负债\n #\n #: 短期借款\n #\n #: 同业及其他金融机构存放款项\n #\n #: 拆入资金\n #\n #: 以公允价值计量且其变动计入当期损益的金融负债\n #\n #: 衍生金融负债\n #\n #: 卖出回购金融资产款\n #\n #: 吸收存款\n #\n #: 代理买卖证券款\n #\n #: 应付账款\n #\n #: 预收账款\n fi_advance_from_customers = Column(Float)\n #: 预收保费\n fi_advance_premium = Column(Float)\n #: 应付手续费及佣金\n fi_fees_and_commissions_payable = Column(Float)\n #: 应付分保账款\n fi_dividend_payable_for_reinsurance = Column(Float)\n #: 应付职工薪酬\n #\n #: 应交税费\n #\n #: 应付利息\n #\n #: 预计负债\n #\n #: 应付赔付款\n fi_claims_payable = Column(Float)\n #: 应付保单红利\n fi_policy_holder_dividend_payable = Column(Float)\n #: 保户储金及投资款\n fi_policy_holder_deposits_and_investment_funds = Column(Float)\n #: 保险合同准备金\n fi_contract_reserve = Column(Float)\n #: 长期借款\n #\n #: 应付债券\n #\n #: 递延所得税负债\n #\n #: 其他负债\n #\n #: 独立账户负债\n fi_independent_liability = Column(Float)\n #: 负债合计\n #\n #: 所有者权益(或股东权益)\n #\n #: 股本\n #\n #: 资本公积\n #\n #: 盈余公积\n #\n #: 一般风险准备\n #\n #: 未分配利润\n #\n #: 归属于母公司股东权益总计\n #\n #: 少数股东权益\n #\n #: 股东权益合计\n #\n #: 负债和股东权益总计\n\n\nclass IncomeStatement(FinanceBase, Mixin):\n @classmethod\n def important_cols(cls):\n return [\n \"operating_income\",\n \"investment_income\",\n \"total_operating_costs\",\n \"total_profits\",\n \"sales_costs\",\n \"managing_costs\",\n \"financing_costs\",\n ]\n\n __tablename__ = \"income_statement\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 营业总收入\n #\n #: 营业收入\n operating_income = Column(Float)\n #: 营业总成本\n total_operating_costs = Column(Float)\n #: 营业成本\n operating_costs = Column(Float)\n #: 研发费用\n rd_costs = Column(Float)\n #: 提取保险合同准备金净额\n net_change_in_insurance_contract_reserves = Column(Float)\n #: 营业税金及附加\n business_taxes_and_surcharges = Column(Float)\n #: 销售费用\n sales_costs = Column(Float)\n #: 管理费用\n managing_costs = Column(Float)\n #: 财务费用\n financing_costs = Column(Float)\n #: 资产减值损失\n assets_devaluation = Column(Float)\n #: 其他经营收益\n #\n #: 加: 投资收益\n investment_income = Column(Float)\n #: 其中: 对联营企业和合营企业的投资收益\n investment_income_from_related_enterprise = Column(Float)\n #: 营业利润\n operating_profit = Column(Float)\n #: 加: 营业外收入\n non_operating_income = Column(Float)\n #: 减: 营业外支出\n non_operating_costs = Column(Float)\n #: 其中: 非流动资产处置净损失\n loss_on_disposal_non_current_asset = Column(Float)\n\n #: 利润总额\n total_profits = Column(Float)\n #: 减: 所得税费用\n tax_expense = Column(Float)\n #: 净利润\n net_profit = Column(Float)\n #: 其中: 归属于母公司股东的净利润\n net_profit_as_parent = Column(Float)\n #: 少数股东损益\n net_profit_as_minority_interest = Column(Float)\n #: 扣除非经常性损益后的净利润\n deducted_net_profit = Column(Float)\n #: 每股收益\n #: 基本每股收益\n eps = Column(Float)\n #: 稀释每股收益\n diluted_eps = Column(Float)\n #: 其他综合收益\n other_comprehensive_income = Column(Float)\n #: 归属于母公司股东的其他综合收益\n other_comprehensive_income_as_parent = Column(Float)\n #: 归属于少数股东的其他综合收益\n other_comprehensive_income_as_minority_interest = Column(Float)\n #: 综合收益总额\n total_comprehensive_income = Column(Float)\n #: 归属于母公司所有者的综合收益总额\n total_comprehensive_income_as_parent = Column(Float)\n #: 归属于少数股东的综合收益总额\n total_comprehensive_income_as_minority_interest = Column(Float)\n\n #: 银行相关\n #: 利息净收入\n fi_net_interest_income = Column(Float)\n #: 其中:利息收入\n fi_interest_income = Column(Float)\n #: 利息支出\n fi_interest_expenses = Column(Float)\n #: 手续费及佣金净收入\n fi_net_incomes_from_fees_and_commissions = Column(Float)\n #: 其中:手续费及佣金收入\n fi_incomes_from_fees_and_commissions = Column(Float)\n #: 手续费及佣金支出\n fi_expenses_for_fees_and_commissions = Column(Float)\n #: 公允价值变动收益\n fi_income_from_fair_value_change = Column(Float)\n #: 汇兑收益\n fi_income_from_exchange = Column(Float)\n #: 其他业务收入\n fi_other_income = Column(Float)\n #: 业务及管理费\n fi_operate_and_manage_expenses = Column(Float)\n\n #: 保险相关\n #: 已赚保费\n fi_net_income_from_premium = Column(Float)\n #: 其中:保险业务收入\n fi_income_from_premium = Column(Float)\n #: 分保费收入\n fi_income_from_reinsurance_premium = Column(Float)\n #: 减:分出保费\n fi_reinsurance_premium = Column(Float)\n #: 提取未到期责任准备金\n fi_undue_duty_reserve = Column(Float)\n #: 银行业务利息净收入\n fi_net_income_from_bank_interest = Column(Float)\n #: 其中:银行业务利息收入\n fi_income_from_bank_interest = Column(Float)\n #: 银行业务利息支出\n fi_expenses_for_bank_interest = Column(Float)\n #: 非保险业务手续费及佣金净收入\n fi_net_incomes_from_fees_and_commissions_of_non_insurance = Column(Float)\n #: 非保险业务手续费及佣金收入\n fi_incomes_from_fees_and_commissions_of_non_insurance = Column(Float)\n #: 非保险业务手续费及佣金支出\n fi_expenses_for_fees_and_commissions_of_non_insurance = Column(Float)\n #: 退保金\n fi_insurance_surrender_costs = Column(Float)\n #: 赔付支出\n fi_insurance_claims_expenses = Column(Float)\n #: 减:摊回赔付支出\n fi_amortized_insurance_claims_expenses = Column(Float)\n #: 提取保险责任准备金\n fi_insurance_duty_reserve = Column(Float)\n #: 减:摊回保险责任准备金\n fi_amortized_insurance_duty_reserve = Column(Float)\n #: 保单红利支出\n fi_dividend_expenses_to_insured = Column(Float)\n #: 分保费用\n fi_reinsurance_expenses = Column(Float)\n #: 减:摊回分保费用\n fi_amortized_reinsurance_expenses = Column(Float)\n #: 其他业务成本\n fi_other_op_expenses = Column(Float)\n\n #: 券商相关\n #: 手续费及佣金净收入\n #\n #: 其中:代理买卖证券业务净收入\n fi_net_incomes_from_trading_agent = Column(Float)\n #: 证券承销业务净收入\n fi_net_incomes_from_underwriting = Column(Float)\n #: 受托客户资产管理业务净收入\n fi_net_incomes_from_customer_asset_management = Column(Float)\n #: 手续费及佣金净收入其他项目\n fi_fees_from_other = Column(Float)\n #: 公允价值变动收益\n #\n #: 其中:可供出售金融资产公允价值变动损益\n fi_income_from_fair_value_change_of_fi_salable = Column(Float)\n\n\nclass CashFlowStatement(FinanceBase, Mixin):\n @classmethod\n def important_cols(cls):\n return [\"net_op_cash_flows\", \"net_investing_cash_flows\", \"net_financing_cash_flows\", \"cash\"]\n\n __tablename__ = \"cash_flow_statement\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n #: 经营活动产生的现金流量\n #\n #: 销售商品、提供劳务收到的现金\n cash_from_selling = Column(Float)\n\n #: 收到的税费返还\n tax_refund = Column(Float)\n\n #: 收到其他与经营活动有关的现金\n cash_from_other_op = Column(Float)\n\n #: 经营活动现金流入小计\n total_op_cash_inflows = Column(Float)\n\n #: 购买商品、接受劳务支付的现金\n cash_to_goods_services = Column(Float)\n #: 支付给职工以及为职工支付的现金\n cash_to_employees = Column(Float)\n #: 支付的各项税费\n taxes_and_surcharges = Column(Float)\n #: 支付其他与经营活动有关的现金\n cash_to_other_related_op = Column(Float)\n #: 经营活动现金流出小计\n total_op_cash_outflows = Column(Float)\n\n #: 经营活动产生的现金流量净额\n net_op_cash_flows = Column(Float)\n\n #: 投资活动产生的现金流量\n\n #: 收回投资收到的现金\n cash_from_disposal_of_investments = Column(Float)\n #: 取得投资收益收到的现金\n cash_from_returns_on_investments = Column(Float)\n #: 处置固定资产、无形资产和其他长期资产收回的现金净额\n cash_from_disposal_fixed_intangible_assets = Column(Float)\n #: 处置子公司及其他营业单位收到的现金净额\n cash_from_disposal_subsidiaries = Column(Float)\n\n #: 收到其他与投资活动有关的现金\n cash_from_other_investing = Column(Float)\n\n #: 投资活动现金流入小计\n total_investing_cash_inflows = Column(Float)\n\n #: 购建固定资产、无形资产和其他长期资产支付的现金\n cash_to_acquire_fixed_intangible_assets = Column(Float)\n #: 投资支付的现金\n cash_to_investments = Column(Float)\n\n #: 取得子公司及其他营业单位支付的现金净额\n cash_to_acquire_subsidiaries = Column(Float)\n\n #: 支付其他与投资活动有关的现金\n cash_to_other_investing = Column(Float)\n\n #: 投资活动现金流出小计\n total_investing_cash_outflows = Column(Float)\n\n #: 投资活动产生的现金流量净额\n net_investing_cash_flows = Column(Float)\n\n #: 筹资活动产生的现金流量\n #\n #: 吸收投资收到的现金\n cash_from_accepting_investment = Column(Float)\n #: 子公司吸收少数股东投资收到的现金\n cash_from_subsidiaries_accepting_minority_interest = Column(Float)\n\n #: 取得借款收到的现金\n cash_from_borrowings = Column(Float)\n #: 发行债券收到的现金\n cash_from_issuing_bonds = Column(Float)\n #: 收到其他与筹资活动有关的现金\n cash_from_other_financing = Column(Float)\n\n #: 筹资活动现金流入小计\n total_financing_cash_inflows = Column(Float)\n\n #: 偿还债务支付的现金\n cash_to_repay_borrowings = Column(Float)\n\n #: 分配股利、利润或偿付利息支付的现金\n cash_to_pay_interest_dividend = Column(Float)\n\n #: 子公司支付给少数股东的股利、利润\n cash_to_pay_subsidiaries_minority_interest = Column(Float)\n\n #: 支付其他与筹资活动有关的现金\n cash_to_other_financing = Column(Float)\n #: 筹资活动现金流出小计\n total_financing_cash_outflows = Column(Float)\n\n #: 筹资活动产生的现金流量净额\n net_financing_cash_flows = Column(Float)\n #: 汇率变动对现金及现金等价物的影响\n foreign_exchange_rate_effect = Column(Float)\n #: 现金及现金等价物净增加额\n net_cash_increase = Column(Float)\n #: 加: 期初现金及现金等价物余额\n cash_at_beginning = Column(Float)\n #: 期末现金及现金等价物余额\n cash = Column(Float)\n\n #: 银行相关\n #: 客户存款和同业及其他金融机构存放款项净增加额\n fi_deposit_increase = Column(Float)\n #: 向中央银行借款净增加额\n fi_borrow_from_central_bank_increase = Column(Float)\n #: 存放中央银行和同业款项及其他金融机构净减少额\n fi_deposit_in_others_decrease = Column(Float)\n #: 拆入资金及卖出回购金融资产款净增加额\n fi_borrowing_and_sell_repurchase_increase = Column(Float)\n #: 其中:卖出回购金融资产款净增加额\n fi_sell_repurchase_increase = Column(Float)\n #: 拆出资金及买入返售金融资产净减少额\n fi_lending_and_buy_repurchase_decrease = Column(Float)\n #: 其中:拆出资金净减少额\n fi_lending_decrease = Column(Float)\n #: 买入返售金融资产净减少额\n fi_buy_repurchase_decrease = Column(Float)\n #: 收取的利息、手续费及佣金的现金\n fi_cash_from_interest_commission = Column(Float)\n #: 客户贷款及垫款净增加额\n fi_loan_advance_increase = Column(Float)\n #: 存放中央银行和同业及其他金融机构款项净增加额\n fi_deposit_in_others_increase = Column(Float)\n #: 拆出资金及买入返售金融资产净增加额\n fi_lending_and_buy_repurchase_increase = Column(Float)\n #: 其中:拆出资金净增加额\n fi_lending_increase = Column(Float)\n #: 拆入资金及卖出回购金融资产款净减少额\n fi_borrowing_and_sell_repurchase_decrease = Column(Float)\n #: 其中:拆入资金净减少额\n fi_borrowing_decrease = Column(Float)\n #: 卖出回购金融资产净减少额\n fi_sell_repurchase_decrease = Column(Float)\n #: 支付利息、手续费及佣金的现金\n fi_cash_to_interest_commission = Column(Float)\n #: 应收账款净增加额\n fi_account_receivable_increase = Column(Float)\n #: 偿付债券利息支付的现金\n fi_cash_to_pay_interest = Column(Float)\n\n #: 保险相关\n #: 收到原保险合同保费取得的现金\n fi_cash_from_premium_of_original = Column(Float)\n #: 保户储金及投资款净增加额\n fi_insured_deposit_increase = Column(Float)\n #: 银行及证券业务卖出回购资金净增加额\n fi_bank_broker_sell_repurchase_increase = Column(Float)\n #: 银行及证券业务买入返售资金净减少额\n fi_bank_broker_buy_repurchase_decrease = Column(Float)\n #: 支付原保险合同赔付等款项的现金\n fi_cash_to_insurance_claim = Column(Float)\n #: 支付再保险业务现金净额\n fi_cash_to_reinsurance = Column(Float)\n #: 银行业务及证券业务拆借资金净减少额\n fi_lending_decrease = Column(Float)\n #: 银行业务及证券业务卖出回购资金净减少额\n fi_bank_broker_sell_repurchase_decrease = Column(Float)\n #: 支付保单红利的现金\n fi_cash_to_dividends = Column(Float)\n #: 保户质押贷款净增加额\n fi_insured_pledge_loans_increase = Column(Float)\n #: 收购子公司及其他营业单位支付的现金净额\n fi_cash_to_acquire_subsidiaries = Column(Float)\n #: 处置子公司及其他营业单位流出的现金净额\n fi_cash_to_disposal_subsidiaries = Column(Float)\n #: 支付卖出回购金融资产款现金净额\n fi_cash_to_sell_repurchase = Column(Float)\n\n #: 券商相关\n #: 拆入资金净增加额\n fi_borrowing_increase = Column(Float)\n #: 代理买卖证券收到的现金净额\n fi_cash_from_trading_agent = Column(Float)\n #: 回购业务资金净增加额\n fi_cash_from_repurchase_increase = Column(Float)\n #: 处置交易性金融资产的净减少额\n fi_disposal_trade_asset_decrease = Column(Float)\n #: 回购业务资金净减少额\n fi_repurchase_decrease = Column(Float)\n #: 代理买卖证券支付的现金净额（净减少额）\n fi_cash_to_agent_trade = Column(Float)\n\n\n#: 主要财务指标\n\n\nclass FinanceFactor(FinanceBase, Mixin):\n @classmethod\n def important_cols(cls):\n return [\n \"basic_eps\",\n \"total_op_income\",\n \"net_profit\",\n \"op_income_growth_yoy\",\n \"net_profit_growth_yoy\",\n \"roe\",\n \"rota\",\n \"gross_profit_margin\",\n \"net_margin\",\n ]\n\n __tablename__ = \"finance_factor\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n #: 每股指标\n #\n #: 基本每股收益(元)\n basic_eps = Column(Float)\n #: 扣非每股收益(元)\n deducted_eps = Column(Float)\n #: 稀释每股收益(元)\n diluted_eps = Column(Float)\n #: 每股净资产(元)\n bps = Column(Float)\n #: 每股资本公积(元)\n capital_reserve_ps = Column(Float)\n #: 每股未分配利润(元)\n undistributed_profit_ps = Column(Float)\n #: 每股经营现金流(元)\n op_cash_flow_ps = Column(Float)\n #: 成长能力指标\n #\n #: 营业总收入(元)\n total_op_income = Column(Float)\n #: 毛利润(元)\n gross_profit = Column(Float)\n #: 归属净利润(元)\n net_profit = Column(Float)\n #: 扣非净利润(元)\n deducted_net_profit = Column(Float)\n #: 营业总收入同比增长\n op_income_growth_yoy = Column(Float)\n #: 归属净利润同比增长\n net_profit_growth_yoy = Column(Float)\n #: 扣非净利润同比增长\n deducted_net_profit_growth_yoy = Column(Float)\n #: 营业总收入滚动环比增长\n op_income_growth_qoq = Column(Float)\n #: 归属净利润滚动环比增长\n net_profit_growth_qoq = Column(Float)\n #: 扣非净利润滚动环比增长\n deducted_net_profit_growth_qoq = Column(Float)\n #: 盈利能力指标\n #\n #: 净资产收益率(加权)\n roe = Column(Float)\n #: 净资产收益率(扣非/加权)\n deducted_roe = Column(Float)\n #: 总资产收益率(加权)\n rota = Column(Float)\n #: 毛利率\n gross_profit_margin = Column(Float)\n #: 净利率\n net_margin = Column(Float)\n #: 收益质量指标\n #\n #: 预收账款/营业收入\n advance_receipts_per_op_income = Column(Float)\n #: 销售净现金流/营业收入\n sales_net_cash_flow_per_op_income = Column(Float)\n #: 经营净现金流/营业收入\n op_net_cash_flow_per_op_income = Column(Float)\n #: 实际税率\n actual_tax_rate = Column(Float)\n #: 财务风险指标\n #\n #: 流动比率\n current_ratio = Column(Float)\n #: 速动比率\n quick_ratio = Column(Float)\n #: 现金流量比率\n cash_flow_ratio = Column(Float)\n #: 资产负债率\n debt_asset_ratio = Column(Float)\n #: 权益乘数\n em = Column(Float)\n #: 产权比率\n equity_ratio = Column(Float)\n #: 营运能力指标(一般企业)\n #\n #: 总资产周转天数(天)\n total_assets_turnover_days = Column(Integer)\n #: 存货周转天数(天)\n inventory_turnover_days = Column(Integer)\n #: 应收账款周转天数(天)\n receivables_turnover_days = Column(Integer)\n #: 总资产周转率(次)\n total_assets_turnover = Column(Float)\n #: 存货周转率(次)\n inventory_turnover = Column(Float)\n #: 应收账款周转率(次)\n receivables_turnover = Column(Float)\n\n #: 专项指标(银行)\n #\n #: 存款总额\n fi_total_deposit = Column(Float)\n #: 贷款总额\n fi_total_loan = Column(Float)\n #: 存贷款比例\n fi_loan_deposit_ratio = Column(Float)\n #: 资本充足率\n fi_capital_adequacy_ratio = Column(Float)\n #: 核心资本充足率\n fi_core_capital_adequacy_ratio = Column(Float)\n #: 不良贷款率\n fi_npl_ratio = Column(Float)\n #: 不良贷款拨备覆盖率\n fi_npl_provision_coverage = Column(Float)\n #: 资本净额\n fi_net_capital = Column(Float)\n #: 专项指标(保险)\n #\n #: 总投资收益率\n insurance_roi = Column(Float)\n #: 净投资收益率\n insurance_net_investment_yield = Column(Float)\n #: 已赚保费\n insurance_earned_premium = Column(Float)\n #: 赔付支出\n insurance_payout = Column(Float)\n #: 退保率\n insurance_surrender_rate = Column(Float)\n #: 偿付能力充足率\n insurance_solvency_adequacy_ratio = Column(Float)\n #: 专项指标(券商)\n #\n #: 净资本\n broker_net_capital = Column(Float)\n #: 净资产\n broker_net_assets = Column(Float)\n #: 净资本/净资产\n broker_net_capital_assets_ratio = Column(Float)\n #: 自营固定收益类证券规模/净资本\n broker_self_operated_fixed_income_securities_net_capital_ratio = Column(Float)\n\n\nregister_schema(db_name=\"finance\", schema_base=FinanceBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"BalanceSheet\", \"IncomeStatement\", \"CashFlowStatement\", \"FinanceFactor\"]\n"
},
{
"path": "src/zvt/domain/fundamental/trading.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nTradingBase = declarative_base()\n\n\nclass ManagerTrading(TradingBase, Mixin):\n __tablename__ = \"manager_trading\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n #: 日期 变动人 变动数量(股) 交易均价(元) 结存股票(股) 交易方式 董监高管 高管职位 与高管关系\n #: 2017-08-11 韦春 200 9.16 -- 竞价交易 刘韬 高管 兄弟姐妹\n\n #: 变动人\n trading_person = Column(String(length=32))\n #: 变动数量\n volume = Column(Float)\n #: 交易均价\n price = Column(Float)\n #: 结存股票\n holding = Column(Float)\n #: 交易方式\n trading_way = Column(String(length=32))\n #: 董监高管\n manager = Column(String(length=32))\n #: 高管职位\n manager_position = Column(String(length=32))\n #: 与高管关系\n relationship_with_manager = Column(String(length=32))\n\n\nclass HolderTrading(TradingBase, Mixin):\n __tablename__ = \"holder_trading\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n #: 股东名称\n holder_name = Column(String(length=32))\n #: 变动数量\n volume = Column(Float)\n #: 变动比例\n change_pct = Column(Float)\n #: 变动后持股比例\n holding_pct = Column(Float)\n\n\nclass BigDealTrading(TradingBase, Mixin):\n __tablename__ = \"big_deal_trading\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n #: 成交额\n turnover = Column(Float)\n #: 成交价\n price = Column(Float)\n #: 卖出营业部\n sell_broker = Column(String(length=128))\n #: 买入营业部\n buy_broker = Column(String(length=128))\n #: 折/溢价率\n compare_rate = Column(Float)\n\n\nclass MarginTrading(TradingBase, Mixin):\n __tablename__ = \"margin_trading\"\n code = Column(String(length=32))\n\n #: 融资余额(元）\n fin_value = Column(Float)\n #: 融资买入额（元）\n fin_buy_value = Column(Float)\n #: 融资偿还额（元）\n fin_refund_value = Column(Float)\n #: 融券余量（股）\n sec_value = Column(Float)\n #: 融券卖出量（股）\n sec_sell_value = Column(Float)\n #: 融券偿还量（股）\n sec_refund_value = Column(Float)\n #: 融资融券余额（元）\n fin_sec_value = Column(Float)\n\n\nclass DragonAndTiger(TradingBase, Mixin):\n __tablename__ = \"dragon_and_tiger\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: 异动原因\n reason = Column(String(length=128))\n #: 成交额\n turnover = Column(Float)\n #: 涨幅\n change_pct = Column(Float)\n #: 净买入\n net_in = Column(Float)\n\n #: 买入营业部\n dep1 = Column(String(length=128))\n dep1_in = Column(Float)\n dep1_out = Column(Float)\n dep1_rate = Column(Float)\n\n dep2 = Column(String(length=128))\n dep2_in = Column(Float)\n dep2_out = Column(Float)\n dep2_rate = Column(Float)\n\n dep3 = Column(String(length=128))\n dep3_in = Column(Float)\n dep3_out = Column(Float)\n dep3_rate = Column(Float)\n\n dep4 = Column(String(length=128))\n dep4_in = Column(Float)\n dep4_out = Column(Float)\n dep4_rate = Column(Float)\n\n dep5 = Column(String(length=128))\n dep5_in = Column(Float)\n dep5_out = Column(Float)\n dep5_rate = Column(Float)\n\n #: 卖出营业部\n dep_1 = Column(String(length=128))\n dep_1_in = Column(Float)\n dep_1_out = Column(Float)\n dep_1_rate = Column(Float)\n\n dep_2 = Column(String(length=128))\n dep_2_in = Column(Float)\n dep_2_out = Column(Float)\n dep_2_rate = Column(Float)\n\n dep_3 = Column(String(length=128))\n dep_3_in = Column(Float)\n dep_3_out = Column(Float)\n dep_3_rate = Column(Float)\n\n dep_4 = Column(String(length=128))\n dep_4_in = Column(Float)\n dep_4_out = Column(Float)\n dep_4_rate = Column(Float)\n\n dep_5 = Column(String(length=128))\n dep_5_in = Column(Float)\n dep_5_out = Column(Float)\n dep_5_rate = Column(Float)\n\n\nregister_schema(\n db_name=\"trading\", schema_base=TradingBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"ManagerTrading\", \"HolderTrading\", \"BigDealTrading\", \"MarginTrading\", \"DragonAndTiger\"]\n"
},
{
"path": "src/zvt/domain/fundamental/valuation.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nValuationBase = declarative_base()\n\n\nclass StockValuation(ValuationBase, Mixin):\n __tablename__ = \"stock_valuation\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n #: 总股本(股)\n capitalization = Column(Float)\n #: 公司已发行的普通股股份总数(包含A股，B股和H股的总股本)\n circulating_cap = Column(Float)\n #: 市值\n market_cap = Column(Float)\n #: 流通市值\n circulating_market_cap = Column(Float)\n #: 换手率\n turnover_ratio = Column(Float)\n #: 静态pe\n pe = Column(Float)\n #: 动态pe\n pe_ttm = Column(Float)\n #: 市净率\n pb = Column(Float)\n #: 市销率\n ps = Column(Float)\n #: 市现率\n pcf = Column(Float)\n\n\nclass EtfValuation(ValuationBase, Mixin):\n __tablename__ = \"etf_valuation\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n #: 静态pe\n pe = Column(Float)\n #: 加权\n pe1 = Column(Float)\n #: 动态pe\n pe_ttm = Column(Float)\n #: 加权\n pe_ttm1 = Column(Float)\n #: 市净率\n pb = Column(Float)\n #: 加权\n pb1 = Column(Float)\n #: 市销率\n ps = Column(Float)\n #: 加权\n ps1 = Column(Float)\n #: 市现率\n pcf = Column(Float)\n #: 加权\n pcf1 = Column(Float)\n\n\nregister_schema(db_name=\"valuation\", schema_base=ValuationBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"StockValuation\", \"EtfValuation\"]\n"
},
{
"path": "src/zvt/domain/macro/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule monetary\nfrom .monetary import *\nfrom .monetary import __all__ as _monetary_all\n\n__all__ += _monetary_all\n\n# import all from submodule macro\nfrom .macro import *\nfrom .macro import __all__ as _macro_all\n\n__all__ += _macro_all\n"
},
{
"path": "src/zvt/domain/macro/macro.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Float, BIGINT\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nMacroBase = declarative_base()\n\n\nclass Economy(MacroBase, Mixin):\n # https://datatopics.worldbank.org/world-development-indicators//themes/economy.html\n __tablename__ = \"economy\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n population = Column(BIGINT)\n\n gdp = Column(Float)\n gdp_per_capita = Column(Float)\n gdp_per_employed = Column(Float)\n gdp_growth = Column(Float)\n agriculture_growth = Column(Float)\n industry_growth = Column(Float)\n manufacturing_growth = Column(Float)\n service_growth = Column(Float)\n consumption_growth = Column(Float)\n capital_growth = Column(Float)\n exports_growth = Column(Float)\n imports_growth = Column(Float)\n\n gni = Column(Float)\n gni_per_capita = Column(Float)\n\n gross_saving = Column(Float)\n cpi = Column(Float)\n unemployment_rate = Column(Float)\n fdi_of_gdp = Column(Float)\n\n\nregister_schema(db_name=\"macro\", schema_base=MacroBase)\n\n\n# the __all__ is generated\n__all__ = [\"Economy\"]\n"
},
{
"path": "src/zvt/domain/macro/monetary.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nMonetaryBase = declarative_base()\n\n\nclass TreasuryYield(MonetaryBase, Mixin):\n __tablename__ = \"treasury_yield\"\n\n code = Column(String(length=32))\n\n # 2年期\n yield_2 = Column(Float)\n # 5年期\n yield_5 = Column(Float)\n # 10年期\n yield_10 = Column(Float)\n # 30年期\n yield_30 = Column(Float)\n\n\nregister_schema(db_name=\"monetary\", schema_base=MonetaryBase)\n\n\n# the __all__ is generated\n__all__ = [\"TreasuryYield\"]\n"
},
{
"path": "src/zvt/domain/meta/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule blockus_meta\nfrom .blockus_meta import *\nfrom .blockus_meta import __all__ as _blockus_meta_all\n\n__all__ += _blockus_meta_all\n\n# import all from submodule stockus_meta\nfrom .stockus_meta import *\nfrom .stockus_meta import __all__ as _stockus_meta_all\n\n__all__ += _stockus_meta_all\n\n# import all from submodule indexhk_meta\nfrom .indexhk_meta import *\nfrom .indexhk_meta import __all__ as _indexhk_meta_all\n\n__all__ += _indexhk_meta_all\n\n# import all from submodule stockhk_meta\nfrom .stockhk_meta import *\nfrom .stockhk_meta import __all__ as _stockhk_meta_all\n\n__all__ += _stockhk_meta_all\n\n# import all from submodule indexus_meta\nfrom .indexus_meta import *\nfrom .indexus_meta import __all__ as _indexus_meta_all\n\n__all__ += _indexus_meta_all\n\n# import all from submodule country_meta\nfrom .country_meta import *\nfrom .country_meta import __all__ as _country_meta_all\n\n__all__ += _country_meta_all\n\n# import all from submodule cbond_meta\nfrom .cbond_meta import *\nfrom .cbond_meta import __all__ as _cbond_meta_all\n\n__all__ += _cbond_meta_all\n\n# import all from submodule index_meta\nfrom .index_meta import *\nfrom .index_meta import __all__ as _index_meta_all\n\n__all__ += _index_meta_all\n\n# import all from submodule future_meta\nfrom .future_meta import *\nfrom .future_meta import __all__ as _future_meta_all\n\n__all__ += _future_meta_all\n\n# import all from submodule etf_meta\nfrom .etf_meta import *\nfrom .etf_meta import __all__ as _etf_meta_all\n\n__all__ += _etf_meta_all\n\n# import all from submodule currency_meta\nfrom .currency_meta import *\nfrom .currency_meta import __all__ as _currency_meta_all\n\n__all__ += _currency_meta_all\n\n# import all from submodule stock_meta\nfrom .stock_meta import *\nfrom .stock_meta import __all__ as _stock_meta_all\n\n__all__ += _stock_meta_all\n\n# import all from submodule block_meta\nfrom .block_meta import *\nfrom .block_meta import __all__ as _block_meta_all\n\n__all__ += _block_meta_all\n\n# import all from submodule fund_meta\nfrom .fund_meta import *\nfrom .fund_meta import __all__ as _fund_meta_all\n\n__all__ += _fund_meta_all\n"
},
{
"path": "src/zvt/domain/meta/block_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio, PortfolioStock\nfrom zvt.contract.register import register_schema, register_entity\n\nBlockMetaBase = declarative_base()\n\n\n#: 板块\n@register_entity(entity_type=\"block\")\nclass Block(BlockMetaBase, Portfolio):\n __tablename__ = \"block\"\n\n #: 板块类型，行业(industry),概念(concept)\n category = Column(String(length=64))\n\n\nclass BlockStock(BlockMetaBase, PortfolioStock):\n __tablename__ = \"block_stock\"\n\n\nregister_schema(db_name=\"block_meta\", schema_base=BlockMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Block\", \"BlockStock\"]\n"
},
{
"path": "src/zvt/domain/meta/blockus_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio, PortfolioStock\nfrom zvt.contract.register import register_schema, register_entity\n\nBlockusMetaBase = declarative_base()\n\n\n#: 板块\n@register_entity(entity_type=\"blockus\")\nclass Blockus(BlockusMetaBase, Portfolio):\n __tablename__ = \"block\"\n\n #: 板块类型，行业(industry),概念(concept)\n category = Column(String(length=64))\n\n\nclass BlockusStockus(BlockusMetaBase, PortfolioStock):\n __tablename__ = \"blockus_stockus\"\n\n\nregister_schema(db_name=\"blockus_meta\", schema_base=BlockusMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Blockus\", \"BlockusStockus\"]\n"
},
{
"path": "src/zvt/domain/meta/cbond_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.register import register_schema, register_entity\n\nCBondBase = declarative_base()\n\n\n#: 美股\n@register_entity(entity_type=\"cbond\")\nclass CBond(CBondBase, TradableEntity):\n __tablename__ = \"cbond\"\n\n\nregister_schema(db_name=\"cbond_meta\", schema_base=CBondBase)\n\n\n# the __all__ is generated\n__all__ = [\"CBond\"]\n"
},
{
"path": "src/zvt/domain/meta/country_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema, register_entity\nfrom zvt.contract.schema import TradableEntity\n\nCountryMetaBase = declarative_base()\n\n\n@register_entity(entity_type=\"country\")\nclass Country(CountryMetaBase, TradableEntity):\n __tablename__ = \"country\"\n\n #: 区域\n #: region\n region = Column(String(length=128))\n #: 首都\n #: capital city\n capital_city = Column(String(length=128))\n #: 收入水平\n #: income level\n income_level = Column(String(length=64))\n #: 贷款类型\n #: lending type\n lending_type = Column(String(length=64))\n #: 经度\n #: longitude\n longitude = Column(Float)\n #: 纬度\n #: latitude\n latitude = Column(Float)\n\n\nregister_schema(db_name=\"country_meta\", schema_base=CountryMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Country\"]\n"
},
{
"path": "src/zvt/domain/meta/currency_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema, register_entity\nfrom zvt.contract.schema import TradableEntity\n\nCurrencyMetaBase = declarative_base()\n\n\n@register_entity(entity_type=\"currency\")\nclass Currency(CurrencyMetaBase, TradableEntity):\n __tablename__ = \"currency\"\n\n\nregister_schema(db_name=\"currency_meta\", schema_base=CurrencyMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Currency\"]\n"
},
{
"path": "src/zvt/domain/meta/etf_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio, PortfolioStockHistory\nfrom zvt.contract.register import register_schema, register_entity\nfrom zvt.utils.time_utils import now_pd_timestamp\n\nEtfMetaBase = declarative_base()\n\n\n#: etf\n@register_entity(entity_type=\"etf\")\nclass Etf(EtfMetaBase, Portfolio):\n __tablename__ = \"etf\"\n category = Column(String(length=64))\n\n @classmethod\n def get_stocks(cls, code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None):\n from zvt.api.portfolio import get_etf_stocks\n\n return get_etf_stocks(code=code, codes=codes, ids=ids, timestamp=timestamp, provider=provider)\n\n\nclass EtfStock(EtfMetaBase, PortfolioStockHistory):\n __tablename__ = \"etf_stock\"\n\n\nregister_schema(db_name=\"etf_meta\", schema_base=EtfMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Etf\", \"EtfStock\"]\n"
},
{
"path": "src/zvt/domain/meta/fund_meta.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Integer\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio, PortfolioStockHistory\nfrom zvt.contract.register import register_entity, register_schema\nfrom zvt.utils.time_utils import now_pd_timestamp\n\nFundMetaBase = declarative_base()\n\n\n#: 个股\n@register_entity(entity_type=\"fund\")\nclass Fund(FundMetaBase, Portfolio):\n __tablename__ = \"fund\"\n #: 基金管理人\n advisor = Column(String(length=100))\n #: 基金托管人\n trustee = Column(String(length=100))\n\n #: 编码\t基金运作方式\n #: 401001\t开放式基金\n #: 401002\t封闭式基金\n #: 401003\tQDII\n #: 401004\tFOF\n #: 401005\tETF\n #: 401006\tLOF\n #: 基金运作方式编码\n operate_mode_id = Column(Integer)\n #: 基金运作方式\n operate_mode = Column(String(length=32))\n\n #: 编码\t基金类别\n #: 402001\t股票型\n #: 402002\t货币型\n #: 402003\t债券型\n #: 402004\t混合型\n #: 402005\t基金型\n #: 402006\t贵金属\n #: 402007\t封闭式\n #: 投资标的类型编码\n underlying_asset_type_id = Column(Integer)\n #: 投资标的类型\n underlying_asset_type = Column(String(length=32))\n\n @classmethod\n def get_stocks(cls, code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None):\n from zvt.api.portfolio import get_fund_stocks\n\n return get_fund_stocks(code=code, codes=codes, ids=ids, timestamp=timestamp, provider=provider)\n\n\nclass FundStock(FundMetaBase, PortfolioStockHistory):\n __tablename__ = \"fund_stock\"\n\n\nregister_schema(db_name=\"fund_meta\", schema_base=FundMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Fund\", \"FundStock\"]\n"
},
{
"path": "src/zvt/domain/meta/future_meta.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema, register_entity\nfrom zvt.contract.schema import TradableEntity\n\nFutureMetaBase = declarative_base()\n\n\n@register_entity(entity_type=\"future\")\nclass Future(FutureMetaBase, TradableEntity):\n __tablename__ = \"future\"\n\n\nregister_schema(db_name=\"future_meta\", schema_base=FutureMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Future\"]\n"
},
{
"path": "src/zvt/domain/meta/index_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio, PortfolioStockHistory\nfrom zvt.contract.register import register_schema, register_entity\n\nIndexMetaBase = declarative_base()\n\n\n#: 指数\n@register_entity(entity_type=\"index\")\nclass Index(IndexMetaBase, Portfolio):\n __tablename__ = \"index\"\n\n #: 发布商\n publisher = Column(String(length=64))\n #: 类别\n #: see IndexCategory\n category = Column(String(length=64))\n #: 基准点数\n base_point = Column(Float)\n\n\nclass IndexStock(IndexMetaBase, PortfolioStockHistory):\n __tablename__ = \"index_stock\"\n\n\nregister_schema(db_name=\"index_meta\", schema_base=IndexMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Index\", \"IndexStock\"]\n"
},
{
"path": "src/zvt/domain/meta/indexhk_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio\nfrom zvt.contract.register import register_schema, register_entity\n\nIndexhkMetaBase = declarative_base()\n\n\n#: 港股指数\n@register_entity(entity_type=\"indexhk\")\nclass Indexhk(IndexhkMetaBase, Portfolio):\n __tablename__ = \"indexhk\"\n\n #: 发布商\n publisher = Column(String(length=64))\n #: 类别\n #: see IndexCategory\n category = Column(String(length=64))\n #: 基准点数\n base_point = Column(Float)\n\n\nregister_schema(db_name=\"indexhk_meta\", schema_base=IndexhkMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Indexhk\"]\n"
},
{
"path": "src/zvt/domain/meta/indexus_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Portfolio\nfrom zvt.contract.register import register_schema, register_entity\n\nIndexusMetaBase = declarative_base()\n\n\n#: 美股指数\n@register_entity(entity_type=\"indexus\")\nclass Indexus(IndexusMetaBase, Portfolio):\n __tablename__ = \"indexus\"\n\n #: 发布商\n publisher = Column(String(length=64))\n #: 类别\n #: see IndexCategory\n category = Column(String(length=64))\n #: 基准点数\n base_point = Column(Float)\n\n\nregister_schema(db_name=\"indexus_meta\", schema_base=IndexusMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Indexus\"]\n"
},
{
"path": "src/zvt/domain/meta/stock_meta.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String, DateTime, BigInteger, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.register import register_schema, register_entity\n\nStockMetaBase = declarative_base()\n\n\n#: 个股\n@register_entity(entity_type=\"stock\")\nclass Stock(StockMetaBase, TradableEntity):\n __tablename__ = \"stock\"\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n\n #: 股东上次更新时间\n holder_modified_date = Column(DateTime)\n #: 控股股东\n controlling_holder = Column(String)\n #: 实际控制人\n controlling_holder_parent = Column(String)\n #: 前十大股东占比\n top_ten_ratio = Column(Float)\n\n\n#: 个股详情\nclass StockDetail(StockMetaBase, TradableEntity):\n __tablename__ = \"stock_detail\"\n\n #: 所属行业\n industries = Column(String)\n #: 行业指数\n industry_indices = Column(String)\n #: 所属板块\n concept_indices = Column(String)\n #: 所属区域\n area_indices = Column(String)\n\n #: 成立日期\n date_of_establishment = Column(DateTime)\n #: 公司简介\n profile = Column(String(length=1024))\n #: 主营业务\n main_business = Column(String(length=512))\n #: 发行量(股)\n issues = Column(BigInteger)\n #: 发行价格\n price = Column(Float)\n #: 募资净额(元)\n raising_fund = Column(Float)\n #: 发行市盈率\n issue_pe = Column(Float)\n #: 网上中签率\n net_winning_rate = Column(Float)\n\n\nregister_schema(\n db_name=\"stock_meta\", schema_base=StockMetaBase\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock\", \"StockDetail\"]\n"
},
{
"path": "src/zvt/domain/meta/stockhk_meta.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Boolean, Float, String\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.register import register_schema, register_entity\n\nStockhkMetaBase = declarative_base()\n\n\n#: 港股\n@register_entity(entity_type=\"stockhk\")\nclass Stockhk(StockhkMetaBase, TradableEntity):\n __tablename__ = \"stockhk\"\n #: 是否属于港股通\n south = Column(Boolean)\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n #: 所属行业\n industry = Column(String)\n\n @classmethod\n def get_trading_t(cls):\n \"\"\"\n 0 means t+0\n 1 means t+1\n\n :return:\n \"\"\"\n return 0\n\n @classmethod\n def get_trading_intervals(cls, include_bidding_time=False):\n \"\"\"\n overwrite it to get the trading intervals of the entity\n\n :return: list of time intervals, in format [(start,end)]\n \"\"\"\n if include_bidding_time:\n return [(\"09:15\", \"12:00\"), (\"13:00\", \"16:00\")]\n else:\n return [(\"09:30\", \"12:00\"), (\"13:00\", \"16:00\")]\n\n\nregister_schema(db_name=\"stockhk_meta\", schema_base=StockhkMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Stockhk\"]\n"
},
{
"path": "src/zvt/domain/meta/stockus_meta.py",
"content": "# -*- coding: utf-8 -*-\nimport pytz\nfrom sqlalchemy import Column, Float, String\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.register import register_schema, register_entity\n\nStockusMetaBase = declarative_base()\n\n\n#: 美股\n@register_entity(entity_type=\"stockus\")\nclass Stockus(StockusMetaBase, TradableEntity):\n __tablename__ = \"stockus\"\n\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n #: 所属行业\n industry = Column(String)\n\n @classmethod\n def get_timezone(cls):\n return pytz.timezone(\"America/New_York\")\n\n @classmethod\n def get_trading_t(cls):\n \"\"\"\n 0 means t+0\n 1 means t+1\n\n :return:\n \"\"\"\n return 0\n\n @classmethod\n def get_trading_intervals(cls, include_bidding_time=False):\n \"\"\"\n overwrite it to get the trading intervals of the entity\n\n :return: list of time intervals, in format [(start,end)]\n \"\"\"\n return [(\"09:30\", \"16:00\")]\n\n\nregister_schema(db_name=\"stockus_meta\", schema_base=StockusMetaBase)\n\n\n# the __all__ is generated\n__all__ = [\"Stockus\"]\n"
},
{
"path": "src/zvt/domain/misc/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule overall\nfrom .overall import *\nfrom .overall import __all__ as _overall_all\n\n__all__ += _overall_all\n\n# import all from submodule money_flow\nfrom .money_flow import *\nfrom .money_flow import __all__ as _money_flow_all\n\n__all__ += _money_flow_all\n\n# import all from submodule holder\nfrom .holder import *\nfrom .holder import __all__ as _holder_all\n\n__all__ += _holder_all\n\n# import all from submodule stock_news\nfrom .stock_news import *\nfrom .stock_news import __all__ as _stock_news_all\n\n__all__ += _stock_news_all\n"
},
{
"path": "src/zvt/domain/misc/holder.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, DateTime, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nHolderBase = declarative_base()\n\n\nclass HkHolder(HolderBase, Mixin):\n __tablename__ = \"hk_holder\"\n #: 股票代码\n code = Column(String(length=32))\n #: 股票名称\n name = Column(String(length=32))\n\n #: 市场通编码\t三种类型：310001-沪股通，310002-深股通，310005-港股通\n holder_code = Column(String(length=32))\n #: 市场通名称\t三种类型：沪股通，深股通，港股通\n holder_name = Column(String(length=32))\n\n #: 持股数量\n share_number = Column(Float)\n #: 持股比例\n share_ratio = Column(Float)\n\n\nclass TopTenTradableHolder(HolderBase, Mixin):\n __tablename__ = \"top_ten_tradable_holder\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 股东代码\n holder_code = Column(String(length=32))\n #: 股东名称\n holder_name = Column(String(length=32))\n #: 持股数\n shareholding_numbers = Column(Float)\n #: 持股比例\n shareholding_ratio = Column(Float)\n #: 变动\n change = Column(Float)\n #: 变动比例\n change_ratio = Column(Float)\n\n\nclass TopTenHolder(HolderBase, Mixin):\n __tablename__ = \"top_ten_holder\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 股东代码\n holder_code = Column(String(length=32))\n #: 股东名称\n holder_name = Column(String(length=32))\n #: 持股数\n shareholding_numbers = Column(Float)\n #: 持股比例\n shareholding_ratio = Column(Float)\n #: 变动\n change = Column(Float)\n #: 变动比例\n change_ratio = Column(Float)\n\n\nclass InstitutionalInvestorHolder(HolderBase, Mixin):\n __tablename__ = \"institutional_investor_holder\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n\n report_period = Column(String(length=32))\n report_date = Column(DateTime)\n\n #: 机构类型\n institutional_investor_type = Column(String(length=64))\n #: 股东代码\n holder_code = Column(String(length=32))\n #: 股东名称\n holder_name = Column(String(length=32))\n #: 持股数\n shareholding_numbers = Column(Float)\n #: 持股比例\n shareholding_ratio = Column(Float)\n\n\nregister_schema(db_name=\"holder\", schema_base=HolderBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"HkHolder\", \"TopTenTradableHolder\", \"TopTenHolder\", \"InstitutionalInvestorHolder\"]\n"
},
{
"path": "src/zvt/domain/misc/money_flow.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nMoneyFlowBase = declarative_base()\n\n\n#: 板块资金流向\n\n\nclass BlockMoneyFlow(MoneyFlowBase, Mixin):\n __tablename__ = \"block_money_flow\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: 收盘价\n close = Column(Float)\n change_pct = Column(Float)\n turnover_rate = Column(Float)\n\n #: 净流入\n net_inflows = Column(Float)\n #: 净流入率\n net_inflow_rate = Column(Float)\n\n #: 主力=超大单+大单\n net_main_inflows = Column(Float)\n net_main_inflow_rate = Column(Float)\n #: 超大单\n net_huge_inflows = Column(Float)\n net_huge_inflow_rate = Column(Float)\n #: 大单\n net_big_inflows = Column(Float)\n net_big_inflow_rate = Column(Float)\n\n #: 中单\n net_medium_inflows = Column(Float)\n net_medium_inflow_rate = Column(Float)\n #: 小单\n net_small_inflows = Column(Float)\n net_small_inflow_rate = Column(Float)\n\n\nclass StockMoneyFlow(MoneyFlowBase, Mixin):\n __tablename__ = \"stock_money_flow\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: 收盘价\n close = Column(Float)\n change_pct = Column(Float)\n turnover_rate = Column(Float)\n\n #: 净流入\n net_inflows = Column(Float)\n #: 净流入率\n net_inflow_rate = Column(Float)\n\n #: 主力=超大单+大单\n net_main_inflows = Column(Float)\n net_main_inflow_rate = Column(Float)\n #: 超大单\n net_huge_inflows = Column(Float)\n net_huge_inflow_rate = Column(Float)\n #: 大单\n net_big_inflows = Column(Float)\n net_big_inflow_rate = Column(Float)\n\n #: 中单\n net_medium_inflows = Column(Float)\n net_medium_inflow_rate = Column(Float)\n #: 小单\n net_small_inflows = Column(Float)\n net_small_inflow_rate = Column(Float)\n\n\nclass IndexMoneyFlow(MoneyFlowBase, Mixin):\n __tablename__ = \"index_money_flow\"\n\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: 净流入\n net_inflows = Column(Float)\n #: 净流入率\n net_inflow_rate = Column(Float)\n\n #: 主力=超大单+大单\n net_main_inflows = Column(Float)\n net_main_inflow_rate = Column(Float)\n #: 超大单\n net_huge_inflows = Column(Float)\n net_huge_inflow_rate = Column(Float)\n #: 大单\n net_big_inflows = Column(Float)\n net_big_inflow_rate = Column(Float)\n\n #: 中单\n net_medium_inflows = Column(Float)\n net_medium_inflow_rate = Column(Float)\n #: 小单\n net_small_inflows = Column(Float)\n net_small_inflow_rate = Column(Float)\n\n\nregister_schema(db_name=\"money_flow\", schema_base=MoneyFlowBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"BlockMoneyFlow\", \"StockMoneyFlow\", \"IndexMoneyFlow\"]\n"
},
{
"path": "src/zvt/domain/misc/overall.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, Float\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nOverallBase = declarative_base()\n\n\n#: 市场整体估值\n\n\nclass StockSummary(OverallBase, Mixin):\n __tablename__ = \"stock_summary\"\n\n provider = Column(String(length=32))\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n total_value = Column(Float)\n total_tradable_vaule = Column(Float)\n pe = Column(Float)\n pb = Column(Float)\n volume = Column(Float)\n turnover = Column(Float)\n turnover_rate = Column(Float)\n\n\n#: 融资融券概况\n\n\nclass MarginTradingSummary(OverallBase, Mixin):\n __tablename__ = \"margin_trading_summary\"\n provider = Column(String(length=32))\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: 融资余额\n margin_value = Column(Float)\n #: 买入额\n margin_buy = Column(Float)\n\n #: 融券余额\n short_value = Column(Float)\n #: 卖出量\n short_volume = Column(Float)\n\n #: 融资融券余额\n total_value = Column(Float)\n\n\n#: 北向/南向成交概况\n\n\nclass CrossMarketSummary(OverallBase, Mixin):\n __tablename__ = \"cross_market_summary\"\n provider = Column(String(length=32))\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n buy_amount = Column(Float)\n buy_volume = Column(Float)\n sell_amount = Column(Float)\n sell_volume = Column(Float)\n quota_daily = Column(Float)\n quota_daily_balance = Column(Float)\n\n\nregister_schema(db_name=\"overall\", schema_base=OverallBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"StockSummary\", \"MarginTradingSummary\", \"CrossMarketSummary\"]\n"
},
{
"path": "src/zvt/domain/misc/stock_news.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, JSON, Boolean, DateTime, Integer\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nNewsBase = declarative_base()\n\n\nclass StockNews(NewsBase, Mixin):\n __tablename__ = \"stock_news\"\n\n #: 新闻编号\n news_code = Column(String)\n #: 新闻地址\n news_url = Column(String)\n #: 新闻标题\n news_title = Column(String)\n #: 新闻内容\n news_content = Column(String)\n #: 新闻解读\n news_analysis = Column(JSON)\n #: 用户设置为忽略\n ignore_by_user = Column(Boolean, default=False)\n\n\nclass StockHotTopic(NewsBase, Mixin):\n __tablename__ = \"stock_hot_topic\"\n\n #: 出现时间\n created_timestamp = Column(DateTime)\n #: 热度排行\n position = Column(Integer)\n #: 相关标的\n entity_ids = Column(JSON)\n\n #: 新闻编号\n news_code = Column(String)\n #: 新闻标题\n news_title = Column(String)\n #: 新闻内容\n news_content = Column(String)\n #: 新闻解读\n news_analysis = Column(JSON)\n\n\nregister_schema(db_name=\"stock_news\", schema_base=NewsBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"StockNews\", \"StockHotTopic\"]\n"
},
{
"path": "src/zvt/domain/quotes/__init__.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import String, Column, Float, Integer, JSON, Boolean\n\nfrom zvt.contract import Mixin\n\n\nclass KdataCommon(Mixin):\n provider = Column(String(length=32))\n code = Column(String(length=32))\n name = Column(String(length=32))\n # Enum constraint is not extendable\n # level = Column(Enum(IntervalLevel, values_callable=enum_value))\n level = Column(String(length=32))\n\n # 开盘价\n open = Column(Float)\n # 收盘价\n close = Column(Float)\n # 最高价\n high = Column(Float)\n # 最低价\n low = Column(Float)\n # 成交量\n volume = Column(Float)\n # 成交金额\n turnover = Column(Float)\n # 涨跌幅\n change_pct = Column(Float)\n # 换手率\n turnover_rate = Column(Float)\n\n\nclass TickCommon(Mixin):\n #: UNIX时间戳\n time = Column(Integer)\n #: 开盘价\n open = Column(Float)\n #: 收盘价/当前价格\n close = Column(Float)\n #: 最高价\n high = Column(Float)\n #: 最低价\n low = Column(Float)\n #: 成交量\n volume = Column(Float)\n #: 成交金额\n turnover = Column(Float)\n #: 委卖价\n ask_price = Column(Float)\n #: 委买价\n bid_price = Column(Float)\n #: 委卖量\n ask_vol = Column(JSON)\n #: 委买量\n bid_vol = Column(JSON)\n #: 成交笔数\n transaction_num = Column(Integer)\n\n\nclass BlockKdataCommon(KdataCommon):\n pass\n\n\nclass IndexKdataCommon(KdataCommon):\n pass\n\n\nclass IndexhkKdataCommon(KdataCommon):\n pass\n\n\nclass IndexusKdataCommon(KdataCommon):\n pass\n\n\nclass EtfKdataCommon(KdataCommon):\n turnover_rate = Column(Float)\n\n # ETF 累计净值（货币 ETF 为七日年化)\n cumulative_net_value = Column(Float)\n\n\nclass StockKdataCommon(KdataCommon):\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n\n\nclass StockusKdataCommon(KdataCommon):\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n\n\nclass StockhkKdataCommon(KdataCommon):\n pass\n\n\n# future common kdata\nclass FutureKdataCommon(KdataCommon):\n #: 持仓量\n interest = Column(Float)\n #: 结算价\n settlement = Column(Float)\n #: 涨跌幅(按收盘价)\n # change_pct = Column(Float)\n #: 涨跌幅(按结算价)\n change_pct1 = Column(Float)\n\n\nclass CurrencyKdataCommon(KdataCommon):\n #: 持仓量\n interest = Column(Float)\n #: 结算价\n settlement = Column(Float)\n #: 涨跌幅(按收盘价)\n # change_pct = Column(Float)\n #: 涨跌幅(按结算价)\n change_pct1 = Column(Float)\n\n\n# the __all__ is generated\n__all__ = [\n \"KdataCommon\",\n \"TickCommon\",\n \"BlockKdataCommon\",\n \"IndexKdataCommon\",\n \"IndexhkKdataCommon\",\n \"IndexusKdataCommon\",\n \"EtfKdataCommon\",\n \"StockKdataCommon\",\n \"StockusKdataCommon\",\n \"StockhkKdataCommon\",\n \"FutureKdataCommon\",\n \"CurrencyKdataCommon\",\n]\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule indexhk\nfrom .indexhk import *\nfrom .indexhk import __all__ as _indexhk_all\n\n__all__ += _indexhk_all\n\n# import all from submodule trade_day\nfrom .trade_day import *\nfrom .trade_day import __all__ as _trade_day_all\n\n__all__ += _trade_day_all\n\n# import all from submodule indexus\nfrom .indexus import *\nfrom .indexus import __all__ as _indexus_all\n\n__all__ += _indexus_all\n\n# import all from submodule stockhk\nfrom .stockhk import *\nfrom .stockhk import __all__ as _stockhk_all\n\n__all__ += _stockhk_all\n\n# import all from submodule stockus\nfrom .stockus import *\nfrom .stockus import __all__ as _stockus_all\n\n__all__ += _stockus_all\n\n# import all from submodule index\nfrom .index import *\nfrom .index import __all__ as _index_all\n\n__all__ += _index_all\n\n# import all from submodule etf\nfrom .etf import *\nfrom .etf import __all__ as _etf_all\n\n__all__ += _etf_all\n\n# import all from submodule stock\nfrom .stock import *\nfrom .stock import __all__ as _stock_all\n\n__all__ += _stock_all\n\n# import all from submodule currency\nfrom .currency import *\nfrom .currency import __all__ as _currency_all\n\n__all__ += _currency_all\n\n# import all from submodule future\nfrom .future import *\nfrom .future import __all__ as _future_all\n\n__all__ += _future_all\n\n# import all from submodule block\nfrom .block import *\nfrom .block import __all__ as _block_all\n\n__all__ += _block_all\n"
},
{
"path": "src/zvt/domain/quotes/block/__init__.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule block_1d_kdata\nfrom .block_1d_kdata import *\nfrom .block_1d_kdata import __all__ as _block_1d_kdata_all\n\n__all__ += _block_1d_kdata_all\n\n# import all from submodule block_1wk_kdata\nfrom .block_1wk_kdata import *\nfrom .block_1wk_kdata import __all__ as _block_1wk_kdata_all\n\n__all__ += _block_1wk_kdata_all\n\n# import all from submodule block_1mon_kdata\nfrom .block_1mon_kdata import *\nfrom .block_1mon_kdata import __all__ as _block_1mon_kdata_all\n\n__all__ += _block_1mon_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/block/block_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import BlockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Block1dKdata(KdataBase, BlockKdataCommon):\n __tablename__ = \"block_1d_kdata\"\n\n\nregister_schema(db_name=\"block_1d_kdata\", schema_base=KdataBase, entity_type=\"block\")\n\n\n# the __all__ is generated\n__all__ = [\"Block1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/block/block_1mon_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import BlockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Block1monKdata(KdataBase, BlockKdataCommon):\n __tablename__ = \"block_1mon_kdata\"\n\n\nregister_schema(db_name=\"block_1mon_kdata\", schema_base=KdataBase, entity_type=\"block\")\n\n\n# the __all__ is generated\n__all__ = [\"Block1monKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/block/block_1wk_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import BlockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Block1wkKdata(KdataBase, BlockKdataCommon):\n __tablename__ = \"block_1wk_kdata\"\n\n\nregister_schema(db_name=\"block_1wk_kdata\", schema_base=KdataBase, entity_type=\"block\")\n\n\n# the __all__ is generated\n__all__ = [\"Block1wkKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/currency/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule currency_1d_kdata\nfrom .currency_1d_kdata import *\nfrom .currency_1d_kdata import __all__ as _currency_1d_kdata_all\n\n__all__ += _currency_1d_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/currency/currency_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import CurrencyKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Currency1dKdata(KdataBase, CurrencyKdataCommon):\n __tablename__ = \"currency_1d_kdata\"\n\n\nregister_schema(db_name=\"currency_1d_kdata\", schema_base=KdataBase, entity_type=\"currency\")\n\n\n# the __all__ is generated\n__all__ = [\"Currency1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/etf/__init__.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule etf_1d_kdata\nfrom .etf_1d_kdata import *\nfrom .etf_1d_kdata import __all__ as _etf_1d_kdata_all\n\n__all__ += _etf_1d_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/etf/etf_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import EtfKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Etf1dKdata(KdataBase, EtfKdataCommon):\n __tablename__ = \"etf_1d_kdata\"\n\n\nregister_schema(db_name=\"etf_1d_kdata\", schema_base=KdataBase, entity_type=\"etf\")\n\n\n# the __all__ is generated\n__all__ = [\"Etf1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/future/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule future_1d_kdata\nfrom .future_1d_kdata import *\nfrom .future_1d_kdata import __all__ as _future_1d_kdata_all\n\n__all__ += _future_1d_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/future/future_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import FutureKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Future1dKdata(KdataBase, FutureKdataCommon):\n __tablename__ = \"future_1d_kdata\"\n\n\nregister_schema(db_name=\"future_1d_kdata\", schema_base=KdataBase, entity_type=\"future\")\n\n\n# the __all__ is generated\n__all__ = [\"Future1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/index/__init__.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule index_1d_kdata\nfrom .index_1d_kdata import *\nfrom .index_1d_kdata import __all__ as _index_1d_kdata_all\n\n__all__ += _index_1d_kdata_all\n\n# import all from submodule index_1m_kdata\nfrom .index_1m_kdata import *\nfrom .index_1m_kdata import __all__ as _index_1m_kdata_all\n\n__all__ += _index_1m_kdata_all\n\n# import all from submodule index_1wk_kdata\nfrom .index_1wk_kdata import *\nfrom .index_1wk_kdata import __all__ as _index_1wk_kdata_all\n\n__all__ += _index_1wk_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/index/index_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import IndexKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Index1dKdata(KdataBase, IndexKdataCommon):\n __tablename__ = \"index_1d_kdata\"\n\n\nregister_schema(db_name=\"index_1d_kdata\", schema_base=KdataBase, entity_type=\"index\")\n\n\n# the __all__ is generated\n__all__ = [\"Index1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/index/index_1m_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import IndexKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Index1mKdata(KdataBase, IndexKdataCommon, TradableEntity):\n __tablename__ = \"index_1m_kdata\"\n\n\nregister_schema(db_name=\"index_1m_kdata\", schema_base=KdataBase, entity_type=\"index\")\n\n\n# the __all__ is generated\n__all__ = [\"Index1mKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/index/index_1wk_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import IndexKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Index1wkKdata(KdataBase, IndexKdataCommon):\n __tablename__ = \"index_1wk_kdata\"\n\n\nregister_schema(db_name=\"index_1wk_kdata\", schema_base=KdataBase, entity_type=\"index\")\n\n\n# the __all__ is generated\n__all__ = [\"Index1wkKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/indexhk/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule indexhk_1d_kdata\nfrom .indexhk_1d_kdata import *\nfrom .indexhk_1d_kdata import __all__ as _indexhk_1d_kdata_all\n\n__all__ += _indexhk_1d_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/indexhk/indexhk_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import IndexhkKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Indexhk1dKdata(KdataBase, IndexhkKdataCommon):\n __tablename__ = \"indexhk_1d_kdata\"\n\n\nregister_schema(db_name=\"indexhk_1d_kdata\", schema_base=KdataBase, entity_type=\"indexhk\")\n\n\n# the __all__ is generated\n__all__ = [\"Indexhk1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/indexus/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule indexus_1d_kdata\nfrom .indexus_1d_kdata import *\nfrom .indexus_1d_kdata import __all__ as _indexus_1d_kdata_all\n\n__all__ += _indexus_1d_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/indexus/indexus_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import IndexusKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Indexus1dKdata(KdataBase, IndexusKdataCommon):\n __tablename__ = \"indexus_1d_kdata\"\n\n\nregister_schema(db_name=\"indexus_1d_kdata\", schema_base=KdataBase, entity_type=\"indexus\")\n\n\n# the __all__ is generated\n__all__ = [\"Indexus1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/__init__.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule stock_1h_kdata\nfrom .stock_1h_kdata import *\nfrom .stock_1h_kdata import __all__ as _stock_1h_kdata_all\n\n__all__ += _stock_1h_kdata_all\n\n# import all from submodule stock_15m_hfq_kdata\nfrom .stock_15m_hfq_kdata import *\nfrom .stock_15m_hfq_kdata import __all__ as _stock_15m_hfq_kdata_all\n\n__all__ += _stock_15m_hfq_kdata_all\n\n# import all from submodule stock_1wk_kdata\nfrom .stock_1wk_kdata import *\nfrom .stock_1wk_kdata import __all__ as _stock_1wk_kdata_all\n\n__all__ += _stock_1wk_kdata_all\n\n# import all from submodule stock_15m_kdata\nfrom .stock_15m_kdata import *\nfrom .stock_15m_kdata import __all__ as _stock_15m_kdata_all\n\n__all__ += _stock_15m_kdata_all\n\n# import all from submodule stock_1m_hfq_kdata\nfrom .stock_1m_hfq_kdata import *\nfrom .stock_1m_hfq_kdata import __all__ as _stock_1m_hfq_kdata_all\n\n__all__ += _stock_1m_hfq_kdata_all\n\n# import all from submodule stock_4h_hfq_kdata\nfrom .stock_4h_hfq_kdata import *\nfrom .stock_4h_hfq_kdata import __all__ as _stock_4h_hfq_kdata_all\n\n__all__ += _stock_4h_hfq_kdata_all\n\n# import all from submodule stock_5m_hfq_kdata\nfrom .stock_5m_hfq_kdata import *\nfrom .stock_5m_hfq_kdata import __all__ as _stock_5m_hfq_kdata_all\n\n__all__ += _stock_5m_hfq_kdata_all\n\n# import all from submodule stock_5m_kdata\nfrom .stock_5m_kdata import *\nfrom .stock_5m_kdata import __all__ as _stock_5m_kdata_all\n\n__all__ += _stock_5m_kdata_all\n\n# import all from submodule stock_1d_kdata\nfrom .stock_1d_kdata import *\nfrom .stock_1d_kdata import __all__ as _stock_1d_kdata_all\n\n__all__ += _stock_1d_kdata_all\n\n# import all from submodule stock_30m_hfq_kdata\nfrom .stock_30m_hfq_kdata import *\nfrom .stock_30m_hfq_kdata import __all__ as _stock_30m_hfq_kdata_all\n\n__all__ += _stock_30m_hfq_kdata_all\n\n# import all from submodule stock_1mon_hfq_kdata\nfrom .stock_1mon_hfq_kdata import *\nfrom .stock_1mon_hfq_kdata import __all__ as _stock_1mon_hfq_kdata_all\n\n__all__ += _stock_1mon_hfq_kdata_all\n\n# import all from submodule stock_1wk_hfq_kdata\nfrom .stock_1wk_hfq_kdata import *\nfrom .stock_1wk_hfq_kdata import __all__ as _stock_1wk_hfq_kdata_all\n\n__all__ += _stock_1wk_hfq_kdata_all\n\n# import all from submodule stock_1mon_kdata\nfrom .stock_1mon_kdata import *\nfrom .stock_1mon_kdata import __all__ as _stock_1mon_kdata_all\n\n__all__ += _stock_1mon_kdata_all\n\n# import all from submodule stock_1h_hfq_kdata\nfrom .stock_1h_hfq_kdata import *\nfrom .stock_1h_hfq_kdata import __all__ as _stock_1h_hfq_kdata_all\n\n__all__ += _stock_1h_hfq_kdata_all\n\n# import all from submodule stock_1m_kdata\nfrom .stock_1m_kdata import *\nfrom .stock_1m_kdata import __all__ as _stock_1m_kdata_all\n\n__all__ += _stock_1m_kdata_all\n\n# import all from submodule stock_4h_kdata\nfrom .stock_4h_kdata import *\nfrom .stock_4h_kdata import __all__ as _stock_4h_kdata_all\n\n__all__ += _stock_4h_kdata_all\n\n# import all from submodule stock_1d_hfq_kdata\nfrom .stock_1d_hfq_kdata import *\nfrom .stock_1d_hfq_kdata import __all__ as _stock_1d_hfq_kdata_all\n\n__all__ += _stock_1d_hfq_kdata_all\n\n# import all from submodule stock_quote\nfrom .stock_quote import *\nfrom .stock_quote import __all__ as _stock_quote_all\n\n__all__ += _stock_quote_all\n\n# import all from submodule stock_30m_kdata\nfrom .stock_30m_kdata import *\nfrom .stock_30m_kdata import __all__ as _stock_30m_kdata_all\n\n__all__ += _stock_30m_kdata_all\n\n# import all from submodule stock_quote_log\nfrom .stock_quote_log import *\nfrom .stock_quote_log import __all__ as _stock_quote_log_all\n\n__all__ += _stock_quote_log_all\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_15m_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock15mHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_15m_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_15m_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock15mHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_15m_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock15mKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_15m_kdata\"\n\n\nregister_schema(\n db_name=\"stock_15m_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock15mKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1d_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1dHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1d_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1d_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1dHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1dKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1d_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1d_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1h_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1hHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1h_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1h_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1hHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1h_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1hKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1h_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1h_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1hKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1m_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1mHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1m_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1m_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1mHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1m_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1mKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1m_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1m_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1mKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1mon_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1monHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1mon_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1mon_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1monHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1mon_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1monKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1mon_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1mon_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1monKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1wk_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1wkHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1wk_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1wk_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1wkHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_1wk_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock1wkKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_1wk_kdata\"\n\n\nregister_schema(\n db_name=\"stock_1wk_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1wkKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_30m_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock30mHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_30m_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_30m_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock30mHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_30m_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock30mKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_30m_kdata\"\n\n\nregister_schema(\n db_name=\"stock_30m_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock30mKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_4h_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock4hHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_4h_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_4h_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock4hHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_4h_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock4hKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_4h_kdata\"\n\n\nregister_schema(\n db_name=\"stock_4h_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock4hKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_5m_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock5mHfqKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_5m_hfq_kdata\"\n\n\nregister_schema(\n db_name=\"stock_5m_hfq_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock5mHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_5m_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stock5mKdata(KdataBase, StockKdataCommon):\n __tablename__ = \"stock_5m_kdata\"\n\n\nregister_schema(\n db_name=\"stock_5m_kdata\", schema_base=KdataBase, entity_type=\"stock\"\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock5mKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_quote.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import String, Column, Float, Integer, Boolean\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nStockQuoteBase = declarative_base()\n\n\nclass StockQuote(StockQuoteBase, StockKdataCommon):\n __tablename__ = \"stock_quote\"\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 封涨停金额\n # limit_up_amount = Column(Float)\n #: 封跌停金额\n # limit_down_amount = Column(Float)\n #: 5挡卖单金额\n # ask_amount = Column(Float)\n #: 5挡买单金额\n # bid_amount = Column(Float)\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n\n\nclass Stock1mQuote(StockQuoteBase, Mixin):\n __tablename__ = \"stock_1m_quote\"\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 均价\n avg_price = Column(Float)\n # 涨跌幅\n change_pct = Column(Float)\n # 成交量\n volume = Column(Float)\n # 成交金额\n turnover = Column(Float)\n # 换手率\n turnover_rate = Column(Float)\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 冲涨停\n near_limit_up = Column(Boolean)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n\n\nregister_schema(db_name=\"stock_quote\", schema_base=StockQuoteBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"StockQuote\", \"Stock1mQuote\"]\n"
},
{
"path": "src/zvt/domain/quotes/stock/stock_quote_log.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Float, Integer, Boolean\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockKdataCommon\n\nStockQuoteLogBase = declarative_base()\n\n\nclass StockQuoteLog(StockQuoteLogBase, StockKdataCommon):\n __tablename__ = \"stock_quote_log\"\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 冲涨停\n near_limit_up = Column(Boolean)\n #: 封涨停金额\n # limit_up_amount = Column(Float)\n #: 封跌停金额\n # limit_down_amount = Column(Float)\n #: 5挡卖单金额\n # ask_amount = Column(Float)\n #: 5挡买单金额\n # bid_amount = Column(Float)\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n\n\nregister_schema(db_name=\"stock_quote_log\", schema_base=StockQuoteLogBase, entity_type=\"stock\")\n\n\n# the __all__ is generated\n__all__ = [\"StockQuoteLog\"]\n"
},
{
"path": "src/zvt/domain/quotes/stockhk/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule stockhk_1d_kdata\nfrom .stockhk_1d_kdata import *\nfrom .stockhk_1d_kdata import __all__ as _stockhk_1d_kdata_all\n\n__all__ += _stockhk_1d_kdata_all\n\n# import all from submodule stockhk_1d_hfq_kdata\nfrom .stockhk_1d_hfq_kdata import *\nfrom .stockhk_1d_hfq_kdata import __all__ as _stockhk_1d_hfq_kdata_all\n\n__all__ += _stockhk_1d_hfq_kdata_all\n\n# import all from submodule stockhk_quote\nfrom .stockhk_quote import *\nfrom .stockhk_quote import __all__ as _stockhk_quote_all\n\n__all__ += _stockhk_quote_all\n"
},
{
"path": "src/zvt/domain/quotes/stockhk/stockhk_1d_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockhkKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stockhk1dHfqKdata(KdataBase, StockhkKdataCommon):\n __tablename__ = \"stockhk_1d_hfq_kdata\"\n\n\nregister_schema(db_name=\"stockhk_1d_hfq_kdata\", schema_base=KdataBase, entity_type=\"stockhk\")\n\n\n# the __all__ is generated\n__all__ = [\"Stockhk1dHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stockhk/stockhk_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockhkKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stockhk1dKdata(KdataBase, StockhkKdataCommon):\n __tablename__ = \"stockhk_1d_kdata\"\n\n\nregister_schema(db_name=\"stockhk_1d_kdata\", schema_base=KdataBase, entity_type=\"stockhk\")\n\n\n# the __all__ is generated\n__all__ = [\"Stockhk1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stockhk/stockhk_quote.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import String, Column, Float, Integer, Boolean\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockhkKdataCommon\n\nStockhkQuoteBase = declarative_base()\n\n\nclass StockhkQuote(StockhkQuoteBase, StockhkKdataCommon):\n __tablename__ = \"stockhk_quote\"\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 封涨停金额\n limit_up_amount = Column(Float)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n #: 封跌停金额\n limit_down_amount = Column(Float)\n #: 5挡卖单金额\n ask_amount = Column(Float)\n #: 5挡买单金额\n bid_amount = Column(Float)\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n\n\nclass Stockhk1mQuote(StockhkQuoteBase, Mixin):\n __tablename__ = \"stockhk_1m_quote\"\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 均价\n avg_price = Column(Float)\n # 涨跌幅\n change_pct = Column(Float)\n # 成交量\n volume = Column(Float)\n # 成交金额\n turnover = Column(Float)\n # 换手率\n turnover_rate = Column(Float)\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n\n\nregister_schema(db_name=\"stockhk_quote\", schema_base=StockhkQuoteBase, entity_type=\"stockhk\")\n\n\n# the __all__ is generated\n__all__ = [\"StockhkQuote\", \"Stockhk1mQuote\"]\n"
},
{
"path": "src/zvt/domain/quotes/stockus/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule stockus_1d_kdata\nfrom .stockus_1d_kdata import *\nfrom .stockus_1d_kdata import __all__ as _stockus_1d_kdata_all\n\n__all__ += _stockus_1d_kdata_all\n\n# import all from submodule stockus_quote\nfrom .stockus_quote import *\nfrom .stockus_quote import __all__ as _stockus_quote_all\n\n__all__ += _stockus_quote_all\n\n# import all from submodule stockus_1d_hfq_kdata\nfrom .stockus_1d_hfq_kdata import *\nfrom .stockus_1d_hfq_kdata import __all__ as _stockus_1d_hfq_kdata_all\n\n__all__ += _stockus_1d_hfq_kdata_all\n"
},
{
"path": "src/zvt/domain/quotes/stockus/stockus_1d_hfq_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockusKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stockus1dHfqKdata(KdataBase, StockusKdataCommon):\n __tablename__ = \"stockus_1d_hfq_kdata\"\n\n\nregister_schema(db_name=\"stockus_1d_hfq_kdata\", schema_base=KdataBase, entity_type=\"stockus\")\n\n\n# the __all__ is generated\n__all__ = [\"Stockus1dHfqKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stockus/stockus_1d_kdata.py",
"content": "# -*- coding: utf-8 -*-\n# this file is generated by gen_kdata_schema function, dont't change it\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockusKdataCommon\n\nKdataBase = declarative_base()\n\n\nclass Stockus1dKdata(KdataBase, StockusKdataCommon):\n __tablename__ = \"stockus_1d_kdata\"\n\n\nregister_schema(db_name=\"stockus_1d_kdata\", schema_base=KdataBase, entity_type=\"stockus\")\n\n\n# the __all__ is generated\n__all__ = [\"Stockus1dKdata\"]\n"
},
{
"path": "src/zvt/domain/quotes/stockus/stockus_quote.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import String, Column, Float, Integer, Boolean\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\nfrom zvt.domain.quotes import StockusKdataCommon\n\nStockusQuoteBase = declarative_base()\n\n\nclass StockusQuote(StockusQuoteBase, StockusKdataCommon):\n __tablename__ = \"stockus_quote\"\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 封涨停金额\n limit_up_amount = Column(Float)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n #: 封跌停金额\n limit_down_amount = Column(Float)\n #: 5挡卖单金额\n ask_amount = Column(Float)\n #: 5挡买单金额\n bid_amount = Column(Float)\n #: 流通市值\n float_cap = Column(Float)\n #: 总市值\n total_cap = Column(Float)\n\n\nclass Stockus1mQuote(StockusQuoteBase, Mixin):\n __tablename__ = \"stockus_1m_quote\"\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n #: UNIX时间戳\n time = Column(Integer)\n #: 最新价\n price = Column(Float)\n #: 均价\n avg_price = Column(Float)\n # 涨跌幅\n change_pct = Column(Float)\n # 成交量\n volume = Column(Float)\n # 成交金额\n turnover = Column(Float)\n # 换手率\n turnover_rate = Column(Float)\n #: 是否涨停\n is_limit_up = Column(Boolean)\n #: 是否跌停\n is_limit_down = Column(Boolean)\n\n\nregister_schema(db_name=\"stockus_quote\", schema_base=StockusQuoteBase, entity_type=\"stockus\")\n\n\n# the __all__ is generated\n__all__ = [\"StockusQuote\", \"Stockus1mQuote\"]\n"
},
{
"path": "src/zvt/domain/quotes/trade_day.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nTradeDayBase = declarative_base()\n\n\nclass StockTradeDay(TradeDayBase, Mixin):\n __tablename__ = \"stock_trade_day\"\n\n\nregister_schema(db_name=\"trade_day\", schema_base=TradeDayBase)\n\n\n# the __all__ is generated\n__all__ = [\"StockTradeDay\"]\n"
},
{
"path": "src/zvt/factors/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule algorithm\nfrom .algorithm import *\nfrom .algorithm import __all__ as _algorithm_all\n\n__all__ += _algorithm_all\n\n# import all from submodule top_stocks\nfrom .top_stocks import *\nfrom .top_stocks import __all__ as _top_stocks_all\n\n__all__ += _top_stocks_all\n\n# import all from submodule ma\nfrom .ma import *\nfrom .ma import __all__ as _ma_all\n\n__all__ += _ma_all\n\n# import all from submodule transformers\nfrom .transformers import *\nfrom .transformers import __all__ as _transformers_all\n\n__all__ += _transformers_all\n\n# import all from submodule macd\nfrom .macd import *\nfrom .macd import __all__ as _macd_all\n\n__all__ += _macd_all\n\n# import all from submodule zen\nfrom .zen import *\nfrom .zen import __all__ as _zen_all\n\n__all__ += _zen_all\n\n# import all from submodule technical_factor\nfrom .technical_factor import *\nfrom .technical_factor import __all__ as _technical_factor_all\n\n__all__ += _technical_factor_all\n\n# import all from submodule fundamental\nfrom .fundamental import *\nfrom .fundamental import __all__ as _fundamental_all\n\n__all__ += _fundamental_all\n\n# import all from submodule factor_service\nfrom .factor_service import *\nfrom .factor_service import __all__ as _factor_service_all\n\n__all__ += _factor_service_all\n\n# import all from submodule factor_models\nfrom .factor_models import *\nfrom .factor_models import __all__ as _factor_models_all\n\n__all__ += _factor_models_all\n\n# import all from submodule target_selector\nfrom .target_selector import *\nfrom .target_selector import __all__ as _target_selector_all\n\n__all__ += _target_selector_all\n\n# import all from submodule shape\nfrom .shape import *\nfrom .shape import __all__ as _shape_all\n\n__all__ += _shape_all\n"
},
{
"path": "src/zvt/factors/algorithm.py",
"content": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\n\nfrom zvt.contract.factor import Scorer, Transformer\nfrom zvt.utils.pd_utils import normal_index_df, group_by_entity_id, normalize_group_compute_result\n\n\ndef ma(s: pd.Series, window: int = 5) -> pd.Series:\n \"\"\"\n\n :param s:\n :param window:\n :return:\n \"\"\"\n return s.rolling(window=window, min_periods=window).mean()\n\n\ndef ema(s: pd.Series, window: int = 12) -> pd.Series:\n return s.ewm(span=window, adjust=False, min_periods=window).mean()\n\n\ndef live_or_dead(x):\n if x:\n return 1\n else:\n return -1\n\n\ndef macd(\n s: pd.Series,\n slow: int = 26,\n fast: int = 12,\n n: int = 9,\n return_type: str = \"df\",\n normal: bool = False,\n count_live_dead: bool = False,\n):\n # 短期均线\n ema_fast = ema(s, window=fast)\n # 长期均线\n ema_slow = ema(s, window=slow)\n\n # 短期均线 - 长期均线 = 趋势的力度\n diff: pd.Series = ema_fast - ema_slow\n # 力度均线\n dea: pd.Series = diff.ewm(span=n, adjust=False).mean()\n\n # 力度 的变化\n m: pd.Series = (diff - dea) * 2\n\n # normal it\n if normal:\n diff = diff / s\n dea = dea / s\n m = m / s\n\n if count_live_dead:\n live = (diff > dea).apply(lambda x: live_or_dead(x))\n bull = (diff > 0) & (dea > 0)\n live_count = live * (live.groupby((live != live.shift()).cumsum()).cumcount() + 1)\n\n if return_type == \"se\":\n if count_live_dead:\n return diff, dea, m, live, bull, live_count\n return diff, dea, m\n else:\n if count_live_dead:\n return pd.DataFrame(\n {\"diff\": diff, \"dea\": dea, \"macd\": m, \"live\": live, \"bull\": bull, \"live_count\": live_count}\n )\n return pd.DataFrame({\"diff\": diff, \"dea\": dea, \"macd\": m})\n\n\ndef point_in_range(point: float, range: tuple):\n \"\"\"\n\n :param point: one point\n :param range: (start,end)\n :return:\n \"\"\"\n return range[0] <= point <= range[1]\n\n\ndef intersect_ranges(range_list):\n if len(range_list) == 1:\n return range_list[0]\n\n result = intersect(range_list[0], range_list[1])\n for range_i in range_list[2:]:\n result = intersect(result, range_i)\n return result\n\n\ndef combine(range_a, range_b):\n if intersect(range_a, range_b):\n return min(range_a[0], range_b[0]), max(range_a[1], range_b[1])\n return None\n\n\ndef distance(range_a, range_b, use_max=False):\n if use_max:\n # 上升\n if range_b[0] >= range_a[1]:\n return (range_b[1] - range_a[0]) / range_a[0]\n\n # 下降\n if range_b[1] <= range_a[0]:\n return (range_b[0] - range_a[1]) / range_a[1]\n else:\n middle_start = (range_a[0] + range_a[1]) / 2\n middle_end = (range_b[0] + range_b[1]) / 2\n\n return (middle_end - middle_start) / middle_start\n\n\ndef intersect(range_a, range_b):\n \"\"\"\n range_a and range_b with format (start,end) in y axis\n\n :param range_a:\n :param range_b:\n :return:\n \"\"\"\n if not range_a or not range_b:\n return None\n # 包含\n if point_in_range(range_a[0], range_b) and point_in_range(range_a[1], range_b):\n return range_a\n if point_in_range(range_b[0], range_a) and point_in_range(range_b[1], range_a):\n return range_b\n\n if point_in_range(range_a[0], range_b):\n return range_a[0], range_b[1]\n\n if point_in_range(range_b[0], range_a):\n return range_b[0], range_a[1]\n return None\n\n\nclass RankScorer(Scorer):\n def __init__(self, ascending=True) -> None:\n self.ascending = ascending\n\n def score(self, input_df) -> pd.DataFrame:\n result_df = input_df.groupby(level=1).rank(ascending=self.ascending, pct=True)\n return result_df\n\n\nclass MaTransformer(Transformer):\n def __init__(self, windows=None, cal_change_pct=False) -> None:\n super().__init__()\n if windows is None:\n windows = [5, 10]\n self.windows = windows\n self.cal_change_pct = cal_change_pct\n\n def transform(self, input_df: pd.DataFrame) -> pd.DataFrame:\n if self.cal_change_pct:\n group_pct = group_by_entity_id(input_df[\"close\"]).pct_change()\n input_df[\"change_pct\"] = normalize_group_compute_result(group_pct)\n\n for window in self.windows:\n col = \"ma{}\".format(window)\n self.indicators.append(col)\n\n group_ma = group_by_entity_id(input_df[\"close\"]).rolling(window=window, min_periods=window).mean()\n input_df[col] = normalize_group_compute_result(group_ma)\n\n return input_df\n\n def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n transform_one would not take effects if transform was implemented.\n Just show how to implement it here, most of time you should overwrite transform directly for performance.\n\n :param entity_id:\n :param df:\n :return:\n \"\"\"\n if self.cal_change_pct:\n df[\"change_pct\"] = df[\"close\"].pct_change()\n\n for window in self.windows:\n col = \"ma{}\".format(window)\n self.indicators.append(col)\n\n df[col] = df[\"close\"].rolling(window=window, min_periods=window).mean()\n\n return df\n\n\nclass IntersectTransformer(Transformer):\n def __init__(self, kdata_overlap=0) -> None:\n super().__init__()\n self.kdata_overlap = kdata_overlap\n\n def transform(self, input_df) -> pd.DataFrame:\n \"\"\"\n\n :param input_df:\n :return:\n \"\"\"\n if self.kdata_overlap > 0:\n # 没有重叠，区间就是(0,0)\n input_df[\"overlap\"] = [(0, 0)] * len(input_df.index)\n\n def cal_overlap(s):\n high = input_df.loc[s.index, \"high\"]\n low = input_df.loc[s.index, \"low\"]\n intersection = intersect_ranges(list(zip(low.to_list(), high.to_list())))\n if intersection:\n # 设置column overlap为intersection,即重叠区间\n input_df.at[s.index[-1], \"overlap\"] = intersection\n return 0\n\n input_df[[\"high\", \"low\"]].groupby(level=0).rolling(\n window=self.kdata_overlap, min_periods=self.kdata_overlap\n ).apply(cal_overlap, raw=False)\n\n return input_df\n\n\nclass MaAndVolumeTransformer(Transformer):\n def __init__(self, windows=None, vol_windows=None, kdata_overlap=0) -> None:\n super().__init__()\n if vol_windows is None:\n vol_windows = [30]\n if windows is None:\n windows = [5, 10]\n self.windows = windows\n self.vol_windows = vol_windows\n self.kdata_overlap = kdata_overlap\n\n def transform(self, input_df) -> pd.DataFrame:\n for window in self.windows:\n col = f\"ma{window}\"\n self.indicators.append(col)\n\n ma_df = input_df[\"close\"].groupby(level=0).rolling(window=window, min_periods=window).mean()\n ma_df = ma_df.reset_index(level=0, drop=True)\n input_df[col] = ma_df\n\n for vol_window in self.vol_windows:\n col = \"vol_ma{}\".format(vol_window)\n\n vol_ma_df = input_df[\"volume\"].groupby(level=0).rolling(window=vol_window, min_periods=vol_window).mean()\n vol_ma_df = vol_ma_df.reset_index(level=0, drop=True)\n input_df[col] = vol_ma_df\n\n if self.kdata_overlap > 0:\n input_df[\"overlap\"] = [(0, 0)] * len(input_df.index)\n\n def cal_overlap(s):\n high = input_df.loc[s.index, \"high\"]\n low = input_df.loc[s.index, \"low\"]\n intersection = intersect_ranges(list(zip(low.to_list(), high.to_list())))\n if intersection:\n input_df.at[s.index[-1], \"overlap\"] = intersection\n return 0\n\n input_df[[\"high\", \"low\"]].groupby(level=0).rolling(\n window=self.kdata_overlap, min_periods=self.kdata_overlap\n ).apply(cal_overlap, raw=False)\n\n return input_df\n\n\nclass MacdTransformer(Transformer):\n def __init__(self, slow=26, fast=12, n=9, normal=False, count_live_dead=False) -> None:\n super().__init__()\n self.slow = slow\n self.fast = fast\n self.n = n\n self.normal = normal\n self.count_live_dead = count_live_dead\n\n self.indicators.append(\"diff\")\n self.indicators.append(\"dea\")\n self.indicators.append(\"macd\")\n\n def transform(self, input_df) -> pd.DataFrame:\n macd_df = input_df.groupby(level=0)[\"close\"].apply(\n lambda x: macd(\n x,\n slow=self.slow,\n fast=self.fast,\n n=self.n,\n return_type=\"df\",\n normal=self.normal,\n count_live_dead=self.count_live_dead,\n )\n )\n macd_df = macd_df.reset_index(level=0, drop=True)\n input_df = pd.concat([input_df, macd_df], axis=1, sort=False, verify_integrity=True)\n return input_df\n\n def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame:\n print(f\"transform_one {entity_id} {df}\")\n return macd(\n df[\"close\"],\n slow=self.slow,\n fast=self.fast,\n n=self.n,\n return_type=\"df\",\n normal=self.normal,\n count_live_dead=self.count_live_dead,\n )\n\n\nclass QuantileScorer(Scorer):\n def __init__(self, score_levels=[0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]) -> None:\n self.score_levels = score_levels\n\n def score(self, input_df):\n self.score_levels.sort(reverse=True)\n\n quantile_df = input_df.groupby(level=1).quantile(self.score_levels)\n quantile_df.index.names = [self.time_field, \"score_result\"]\n\n self.logger.info(\"factor:{},quantile:\\n{}\".format(self.factor_name, quantile_df))\n\n result_df = input_df.copy()\n result_df.reset_index(inplace=True, level=\"entity_id\")\n result_df[\"quantile\"] = None\n for timestamp in quantile_df.index.levels[0]:\n length = len(result_df.loc[result_df.index == timestamp, \"quantile\"])\n result_df.loc[result_df.index == timestamp, \"quantile\"] = [quantile_df.loc[timestamp].to_dict()] * length\n\n self.logger.info(\"factor:{},df with quantile:\\n{}\".format(self.factor_name, result_df))\n\n # result_df = result_df.set_index(['entity_id'], append=True)\n # result_df = result_df.sort_index(level=[0, 1])\n #\n # self.logger.info(result_df)\n #\n def calculate_score(df, factor_name, quantile):\n original_value = df[factor_name]\n score_map = quantile.get(factor_name)\n min_score = self.score_levels[-1]\n\n if original_value < score_map.get(min_score):\n return 0\n\n for score in self.score_levels[:-1]:\n if original_value >= score_map.get(score):\n return score\n\n for factor in input_df.columns.to_list():\n result_df[factor] = result_df.apply(lambda x: calculate_score(x, factor, x[\"quantile\"]), axis=1)\n\n result_df = result_df.reset_index()\n result_df = normal_index_df(result_df)\n result_df = result_df.loc[:, self.factors]\n\n result_df = result_df.loc[~result_df.index.duplicated(keep=\"first\")]\n\n self.logger.info(\"factor:{},df:\\n{}\".format(self.factor_name, result_df))\n\n return result_df\n\n\n# the __all__ is generated\n__all__ = [\n \"ma\",\n \"ema\",\n \"live_or_dead\",\n \"macd\",\n \"point_in_range\",\n \"intersect_ranges\",\n \"combine\",\n \"distance\",\n \"intersect\",\n \"RankScorer\",\n \"MaTransformer\",\n \"IntersectTransformer\",\n \"MaAndVolumeTransformer\",\n \"MacdTransformer\",\n \"QuantileScorer\",\n]\n"
},
{
"path": "src/zvt/factors/factor_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom datetime import datetime\nfrom typing import List, Optional\n\nfrom pydantic import BaseModel, Field\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.trader import TradingSignalType\nfrom zvt.utils.time_utils import date_time_by_interval, current_date\n\n\nclass FactorRequestModel(BaseModel):\n factor_name: str\n entity_ids: Optional[List[str]]\n data_provider: str = Field(default=\"em\")\n start_timestamp: datetime = Field(default=date_time_by_interval(current_date(), -365))\n level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)\n\n\nclass TradingSignalModel(BaseModel):\n entity_id: str\n happen_timestamp: datetime\n due_timestamp: datetime\n trading_level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)\n trading_signal_type: TradingSignalType\n position_pct: Optional[float] = Field(default=0.2)\n order_amount: Optional[float] = Field(default=None)\n order_money: Optional[float] = Field(default=None)\n\n\nclass FactorResultModel(BaseModel):\n entity_ids: Optional[List[str]]\n tag_reason: str\n\n\n# the __all__ is generated\n__all__ = [\"FactorRequestModel\", \"TradingSignalModel\", \"FactorResultModel\"]\n"
},
{
"path": "src/zvt/factors/factor_service.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.contract import zvt_context\nfrom zvt.domain import Stock\nfrom zvt.factors.factor_models import FactorRequestModel\nfrom zvt.factors.technical_factor import TechnicalFactor\nfrom zvt.trader import TradingSignalType\n\n\ndef query_factor_result(factor_request_model: FactorRequestModel):\n factor_name = factor_request_model.factor_name\n entity_ids = factor_request_model.entity_ids\n level = factor_request_model.level\n\n factor: TechnicalFactor = zvt_context.factor_cls_registry[factor_name](\n provider=\"em\",\n entity_provider=\"em\",\n entity_schema=Stock,\n entity_ids=entity_ids,\n level=level,\n start_timestamp=factor_request_model.start_timestamp,\n )\n df = factor.get_trading_signal_df()\n df = df.reset_index(drop=False)\n\n def to_trading_signal(order_type):\n if order_type is None:\n return None\n if order_type:\n return TradingSignalType.open_long\n if not order_type:\n return TradingSignalType.close_long\n\n df = df.rename(columns={\"timestamp\": \"happen_timestamp\"})\n df[\"due_timestamp\"] = df[\"happen_timestamp\"] + pd.Timedelta(seconds=level.to_second())\n df[\"trading_signal_type\"] = df[\"filter_result\"].apply(lambda x: to_trading_signal(x))\n\n print(df)\n return df.to_dict(orient=\"records\")\n\n\n# the __all__ is generated\n__all__ = [\"query_factor_result\"]\n"
},
{
"path": "src/zvt/factors/fundamental/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule finance_factor\nfrom .finance_factor import *\nfrom .finance_factor import __all__ as _finance_factor_all\n\n__all__ += _finance_factor_all\n"
},
{
"path": "src/zvt/factors/fundamental/finance_factor.py",
"content": "# -*- coding: utf-8 -*-\nimport operator\nfrom itertools import accumulate\nfrom typing import List, Union, Type\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, Mixin, TradableEntity\nfrom zvt.contract.factor import Factor, Transformer, Accumulator\nfrom zvt.domain import FinanceFactor, BalanceSheet, Stock\n\n\nclass FinanceBaseFactor(Factor):\n def __init__(\n self,\n data_schema: Type[Mixin] = FinanceFactor,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = None,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n ) -> None:\n if not columns:\n columns = data_schema.important_cols()\n super().__init__(\n data_schema,\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n )\n\n\nclass GoodCompanyFactor(FinanceBaseFactor):\n def __init__(\n self,\n data_schema: Type[Mixin] = FinanceFactor,\n entity_schema: TradableEntity = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = (\n FinanceFactor.roe,\n FinanceFactor.op_income_growth_yoy,\n FinanceFactor.net_profit_growth_yoy,\n FinanceFactor.report_period,\n FinanceFactor.op_net_cash_flow_per_op_income,\n FinanceFactor.sales_net_cash_flow_per_op_income,\n FinanceFactor.current_ratio,\n FinanceFactor.debt_asset_ratio,\n ),\n filters: List = (\n FinanceFactor.roe >= 0.02,\n FinanceFactor.op_income_growth_yoy >= 0.05,\n FinanceFactor.net_profit_growth_yoy >= 0.05,\n FinanceFactor.op_net_cash_flow_per_op_income >= 0.1,\n FinanceFactor.sales_net_cash_flow_per_op_income >= 0.3,\n FinanceFactor.current_ratio >= 1,\n FinanceFactor.debt_asset_ratio <= 0.5,\n ),\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = True,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n window=\"1095d\",\n count=8,\n col_period_threshold={\"roe\": 0.02},\n ) -> None:\n self.window = window\n self.count = count\n\n # 对于根据年度计算才有意义的指标，比如roe,我们会对不同季度的值区别处理,传入的参数为季度值\n self.col_period_threshold = col_period_threshold\n if self.col_period_threshold:\n if \"report_period\" not in columns and (data_schema.report_period not in columns):\n columns.append(data_schema.report_period)\n\n self.logger.info(f\"using data_schema:{data_schema.__name__}\")\n\n super().__init__(\n data_schema,\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n )\n\n def compute_factor(self):\n def filter_df(df):\n se = pd.Series(index=df.index)\n for index, row in df.iterrows():\n\n if row.report_period == \"year\":\n mul = 4\n elif row.report_period == \"season3\":\n mul = 3\n elif row.report_period == \"half_year\":\n mul = 2\n else:\n mul = 1\n\n filters = []\n for col in self.col_period_threshold:\n col_se = eval(f\"row.{col}\")\n filters.append(col_se >= mul * self.col_period_threshold[col])\n se[index] = list(accumulate(filters, func=operator.__and__))[-1]\n\n return se\n\n if self.col_period_threshold:\n self.factor_df = self.data_df.loc[lambda df: filter_df(df), :]\n\n self.factor_df = pd.DataFrame(index=self.data_df.index, columns=[\"count\"], data=1)\n\n self.factor_df = self.factor_df.reset_index(level=1)\n\n self.factor_df = self.factor_df.groupby(level=0).rolling(window=self.window, on=self.time_field).count()\n\n self.factor_df = self.factor_df.reset_index(level=0, drop=True)\n self.factor_df = self.factor_df.set_index(self.time_field, append=True)\n\n self.factor_df = self.factor_df.loc[(slice(None), slice(self.start_timestamp, self.end_timestamp)), :]\n\n self.logger.info(\"factor:{},factor_df:\\n{}\".format(self.name, self.factor_df))\n\n def compute_result(self):\n self.result_df = self.factor_df.apply(lambda x: x >= self.count)\n self.result_df.columns = [\"filter_score\"]\n\n self.logger.info(\"factor:{},result_df:\\n{}\".format(self.name, self.result_df))\n\n\nif __name__ == \"__main__\":\n # f1 = GoodCompanyFactor(keep_all_timestamp=False)\n # print(f1.result_df)\n\n # 高股息 低应收\n factor2 = GoodCompanyFactor(\n data_schema=BalanceSheet,\n columns=[BalanceSheet.accounts_receivable],\n filters=[BalanceSheet.accounts_receivable <= 0.2 * BalanceSheet.total_current_assets],\n keep_all_timestamp=False,\n col_period_threshold=None,\n )\n print(factor2.result_df)\n\n\n# the __all__ is generated\n__all__ = [\"FinanceBaseFactor\", \"GoodCompanyFactor\"]\n"
},
{
"path": "src/zvt/factors/ma/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule ma_stats_factor\nfrom .ma_stats_factor import *\nfrom .ma_stats_factor import __all__ as _ma_stats_factor_all\n\n__all__ += _ma_stats_factor_all\n\n# import all from submodule top_bottom_factor\nfrom .top_bottom_factor import *\nfrom .top_bottom_factor import __all__ as _top_bottom_factor_all\n\n__all__ += _top_bottom_factor_all\n\n# import all from submodule ma_factor\nfrom .ma_factor import *\nfrom .ma_factor import __all__ as _ma_factor_all\n\n__all__ += _ma_factor_all\n\n# import all from submodule domain\nfrom .domain import *\nfrom .domain import __all__ as _domain_all\n\n__all__ += _domain_all\n"
},
{
"path": "src/zvt/factors/ma/domain/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule stock_1d_ma_stats_factor\nfrom .stock_1d_ma_stats_factor import *\nfrom .stock_1d_ma_stats_factor import __all__ as _stock_1d_ma_stats_factor_all\n\n__all__ += _stock_1d_ma_stats_factor_all\n\n# import all from submodule stock_1d_ma_factor\nfrom .stock_1d_ma_factor import *\nfrom .stock_1d_ma_factor import __all__ as _stock_1d_ma_factor_all\n\n__all__ += _stock_1d_ma_factor_all\n\n# import all from submodule common\nfrom .common import *\nfrom .common import __all__ as _common_all\n\n__all__ += _common_all\n"
},
{
"path": "src/zvt/factors/ma/domain/common.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Float, Integer\n\nfrom zvt.contract import Mixin\n\n\nclass MaStatsFactorCommon(Mixin):\n open = Column(Float)\n close = Column(Float)\n high = Column(Float)\n low = Column(Float)\n turnover = Column(Float)\n\n ma5 = Column(Float)\n ma10 = Column(Float)\n\n ma34 = Column(Float)\n ma55 = Column(Float)\n ma89 = Column(Float)\n ma144 = Column(Float)\n\n ma120 = Column(Float)\n ma250 = Column(Float)\n\n vol_ma30 = Column(Float)\n\n live = Column(Integer)\n count = Column(Integer)\n distance = Column(Float)\n area = Column(Float)\n\n\n# the __all__ is generated\n__all__ = [\"MaStatsFactorCommon\"]\n"
},
{
"path": "src/zvt/factors/ma/domain/stock_1d_ma_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Float, String\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nStock1dMaFactorBase = declarative_base()\n\n\nclass Stock1dMaFactor(Stock1dMaFactorBase, Mixin):\n __tablename__ = \"Stock1dMaFactor\"\n\n level = Column(String(length=32))\n code = Column(String(length=32))\n name = Column(String(length=32))\n\n open = Column(Float)\n close = Column(Float)\n high = Column(Float)\n low = Column(Float)\n\n ma5 = Column(Float)\n ma10 = Column(Float)\n\n ma34 = Column(Float)\n ma55 = Column(Float)\n ma89 = Column(Float)\n ma144 = Column(Float)\n\n ma120 = Column(Float)\n ma250 = Column(Float)\n\n\nregister_schema(db_name=\"stock_1d_ma_factor\", schema_base=Stock1dMaFactorBase, internal=True)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1dMaFactor\"]\n"
},
{
"path": "src/zvt/factors/ma/domain/stock_1d_ma_stats_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.factors.ma.domain.common import MaStatsFactorCommon\n\nStock1dMaStatsFactorBase = declarative_base()\n\n\nclass Stock1dMaStatsFactor(Stock1dMaStatsFactorBase, MaStatsFactorCommon):\n __tablename__ = \"stock_1d_ma_stats_factor\"\n\n\nregister_schema(db_name=\"stock_1d_ma_stats_factor\", schema_base=Stock1dMaStatsFactorBase, internal=True)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1dMaStatsFactor\"]\n"
},
{
"path": "src/zvt/factors/ma/ma_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Union, Type\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, TradableEntity, AdjustType\nfrom zvt.contract.api import get_schema_by_name\nfrom zvt.contract.factor import Accumulator\nfrom zvt.contract.factor import Transformer\nfrom zvt.domain import Stock\nfrom zvt.factors.algorithm import MaTransformer, MaAndVolumeTransformer\nfrom zvt.factors.technical_factor import TechnicalFactor\n\n\ndef get_ma_factor_schema(entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY):\n if type(level) == str:\n level = IntervalLevel(level)\n\n schema_str = \"{}{}MaFactor\".format(entity_type.capitalize(), level.value.capitalize())\n\n return get_schema_by_name(schema_str)\n\n\nclass MaFactor(TechnicalFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n windows=None,\n ) -> None:\n if need_persist:\n self.factor_schema = get_ma_factor_schema(entity_type=entity_schema.__name__, level=level)\n\n if not windows:\n windows = [5, 10, 34, 55, 89, 144, 120, 250]\n self.windows = windows\n transformer: Transformer = MaTransformer(windows=windows)\n\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n None,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n\nclass CrossMaFactor(MaFactor):\n def compute_result(self):\n super().compute_result()\n cols = [f\"ma{window}\" for window in self.windows]\n s = self.factor_df[cols[0]] > self.factor_df[cols[1]]\n current_col = cols[1]\n for col in cols[2:]:\n s = s & (self.factor_df[current_col] > self.factor_df[col])\n current_col = col\n\n print(self.factor_df[s])\n self.result_df = s.to_frame(name=\"filter_result\")\n\n\nclass VolumeUpMaFactor(TechnicalFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n windows=None,\n vol_windows=None,\n turnover_threshold=300000000,\n turnover_rate_threshold=0.02,\n up_intervals=40,\n over_mode=\"and\",\n ) -> None:\n if not windows:\n windows = [250]\n if not vol_windows:\n vol_windows = [30]\n\n self.windows = windows\n self.vol_windows = vol_windows\n self.turnover_threshold = turnover_threshold\n self.turnover_rate_threshold = turnover_rate_threshold\n self.up_intervals = up_intervals\n self.over_mode = over_mode\n\n transformer: Transformer = MaAndVolumeTransformer(windows=windows, vol_windows=vol_windows)\n\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n def compute_result(self):\n super().compute_result()\n\n # 价格刚上均线\n cols = [f\"ma{window}\" for window in self.windows]\n filter_up = (self.factor_df[\"close\"] > self.factor_df[cols[0]]) & (\n self.factor_df[\"close\"] < 1.15 * self.factor_df[cols[0]]\n )\n for col in cols[1:]:\n if self.over_mode == \"and\":\n filter_up = filter_up & (\n (self.factor_df[\"close\"] > self.factor_df[col])\n & (self.factor_df[\"close\"] < 1.1 * self.factor_df[col])\n )\n else:\n filter_up = filter_up | (\n (self.factor_df[\"close\"] > self.factor_df[col])\n & (self.factor_df[\"close\"] < 1.1 * self.factor_df[col])\n )\n # 放量\n if self.vol_windows:\n vol_cols = [f\"vol_ma{window}\" for window in self.vol_windows]\n filter_vol = self.factor_df[\"volume\"] > 2 * self.factor_df[vol_cols[0]]\n for col in vol_cols[1:]:\n filter_vol = filter_vol & (self.factor_df[\"volume\"] > 2 * self.factor_df[col])\n\n # 成交额，换手率过滤\n filter_turnover = (self.factor_df[\"turnover\"] > self.turnover_threshold) & (\n self.factor_df[\"turnover_rate\"] > self.turnover_rate_threshold\n )\n s = filter_up & filter_vol & filter_turnover\n\n # 突破后的时间周期 up_intervals\n s[s == False] = None\n s = s.groupby(level=0).fillna(method=\"ffill\", limit=self.up_intervals)\n s[s.isna()] = False\n\n # 还在均线附近\n # 1)刚突破\n # 2)突破后，回调到附近\n filter_result = filter_up & s & filter_turnover\n\n self.result_df = filter_result.to_frame(name=\"filter_result\")\n # self.result_df = self.result_df.replace(False, None)\n\n\nclass CrossMaVolumeFactor(VolumeUpMaFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n windows=[5, 10, 250],\n vol_windows=None,\n turnover_threshold=300000000,\n turnover_rate_threshold=0.02,\n up_intervals=40,\n over_mode=\"and\",\n ) -> None:\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n windows,\n vol_windows,\n turnover_threshold,\n turnover_rate_threshold,\n up_intervals,\n over_mode,\n )\n\n def compute_result(self):\n # 均线多头排列\n cols = [f\"ma{window}\" for window in self.windows]\n filter_se = self.factor_df[cols[0]] > self.factor_df[cols[1]]\n current_col = cols[1]\n for col in cols[2:]:\n filter_se = filter_se & (self.factor_df[current_col] > self.factor_df[col])\n current_col = col\n\n filter_se = filter_se & (self.factor_df[\"turnover\"] > self.turnover_threshold)\n self.result_df = filter_se.to_frame(name=\"filter_result\")\n # self.result_df = self.result_df.replace(False, None)\n\n\nif __name__ == \"__main__\":\n provider = \"em\"\n target_date = get_latest_kdata_date(entity_type=\"stock\", provider=provider, adjust_type=AdjustType.qfq)\n factor = VolumeUpMaFactor(\n entity_schema=Stock,\n entity_provider=provider,\n provider=provider,\n entity_ids=None,\n start_timestamp=date_time_by_interval(target_date, -600),\n end_timestamp=target_date,\n adjust_type=AdjustType.qfq,\n windows=[120, 250],\n over_mode=\"or\",\n up_intervals=60,\n turnover_threshold=300000000,\n turnover_rate_threshold=0.02,\n )\n\n stocks = factor.get_targets(timestamp=target_date, target_type=TargetType.positive) # factor = CrossMaVolumeFactor(\n print(stocks)\n # entity_provider=\"em\",\n # provider=\"em\",\n # entity_ids=[\"stock_sz_000338\"],\n # start_timestamp=\"2020-01-01\",\n # end_timestamp=now_pd_timestamp(),\n # need_persist=False,\n # )\n # factor.drawer().draw(show=True)\n\n\n# the __all__ is generated\n__all__ = [\"get_ma_factor_schema\", \"MaFactor\", \"CrossMaFactor\", \"VolumeUpMaFactor\", \"CrossMaVolumeFactor\"]\n"
},
{
"path": "src/zvt/factors/ma/ma_stats_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Union, Type, Optional\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, TradableEntity, AdjustType\nfrom zvt.contract.api import get_schema_by_name\nfrom zvt.contract.factor import Accumulator\nfrom zvt.domain import Stock\nfrom zvt.factors.algorithm import live_or_dead\nfrom zvt.factors.technical_factor import TechnicalFactor\nfrom zvt.utils.pd_utils import pd_is_not_null\n\n\ndef get_ma_stats_factor_schema(entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY):\n if type(level) == str:\n level = IntervalLevel(level)\n\n schema_str = \"{}{}MaStatsFactor\".format(entity_type.capitalize(), level.value.capitalize())\n\n return get_schema_by_name(schema_str)\n\n\nclass MaStatsAccumulator(Accumulator):\n def __init__(self, acc_window: int = 250, windows=None, vol_windows=None) -> None:\n super().__init__(acc_window)\n self.windows = windows\n self.vol_windows = vol_windows\n\n def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame, state: dict) -> (pd.DataFrame, dict):\n self.logger.info(f\"acc_one:{entity_id}\")\n if pd_is_not_null(acc_df):\n df = df[df.index > acc_df.index[-1]]\n if pd_is_not_null(df):\n self.logger.info(f'compute from {df.iloc[0][\"timestamp\"]}')\n acc_df = pd.concat([acc_df, df])\n else:\n self.logger.info(\"no need to compute\")\n return acc_df, state\n else:\n acc_df = df\n\n for window in self.windows:\n col = \"ma{}\".format(window)\n self.indicators.append(col)\n\n ma_df = acc_df[\"close\"].rolling(window=window, min_periods=window).mean()\n acc_df[col] = ma_df\n\n acc_df[\"live\"] = (acc_df[\"ma5\"] > acc_df[\"ma10\"]).apply(lambda x: live_or_dead(x))\n acc_df[\"distance\"] = (acc_df[\"ma5\"] - acc_df[\"ma10\"]) / acc_df[\"close\"]\n\n live = acc_df[\"live\"]\n acc_df[\"count\"] = live * (live.groupby((live != live.shift()).cumsum()).cumcount() + 1)\n\n acc_df[\"bulk\"] = (live != live.shift()).cumsum()\n area_df = acc_df[[\"distance\", \"bulk\"]]\n acc_df[\"area\"] = area_df.groupby(\"bulk\").cumsum()\n\n for vol_window in self.vol_windows:\n col = \"vol_ma{}\".format(vol_window)\n self.indicators.append(col)\n\n vol_ma_df = acc_df[\"turnover\"].rolling(window=vol_window, min_periods=vol_window).mean()\n acc_df[col] = vol_ma_df\n\n acc_df = acc_df.set_index(\"timestamp\", drop=False)\n return acc_df, state\n\n\nclass MaStatsFactor(TechnicalFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n need_persist: bool = True,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n windows=None,\n vol_windows=None,\n ) -> None:\n if need_persist:\n self.factor_schema = get_ma_stats_factor_schema(entity_type=entity_schema.__name__, level=level)\n\n if not windows:\n windows = [5, 10, 34, 55, 89, 144, 120, 250]\n self.windows = windows\n\n if not vol_windows:\n vol_windows = [30]\n self.vol_windows = vol_windows\n\n columns: List = [\"id\", \"entity_id\", \"timestamp\", \"level\", \"open\", \"close\", \"high\", \"low\", \"turnover\"]\n\n accumulator: Accumulator = MaStatsAccumulator(windows=self.windows, vol_windows=self.vol_windows)\n\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n None,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n\nclass TFactor(MaStatsFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n need_persist: bool = True,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = True,\n adjust_type: Union[AdjustType, str] = None,\n windows=None,\n vol_windows=None,\n ) -> None:\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n windows,\n vol_windows,\n )\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n return [self.factor_df[[\"area\"]]]\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n return [self.factor_df[[\"ma5\", \"ma10\"]]]\n\n\nif __name__ == \"__main__\":\n codes = [\"000338\"]\n\n f = TFactor(codes=codes, only_load_factor=False)\n\n # distribute(f.factor_df[['area']],'area')\n f.draw(show=True)\n\n\n# the __all__ is generated\n__all__ = [\"get_ma_stats_factor_schema\", \"MaStatsAccumulator\", \"MaStatsFactor\", \"TFactor\"]\n"
},
{
"path": "src/zvt/factors/ma/top_bottom_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Union\n\nimport pandas as pd\n\nfrom zvt.contract import AdjustType\nfrom zvt.contract import IntervalLevel, TradableEntity\nfrom zvt.contract.drawer import Drawer\nfrom zvt.contract.factor import Accumulator\nfrom zvt.contract.factor import Transformer\nfrom zvt.contract.reader import DataReader\nfrom zvt.domain import Stock, Stock1dKdata\nfrom zvt.factors.technical_factor import TechnicalFactor\nfrom zvt.utils.time_utils import now_pd_timestamp\n\n\nclass TopBottomTransformer(Transformer):\n def __init__(self, window=20) -> None:\n super().__init__()\n self.window = window\n\n def transform(self, input_df) -> pd.DataFrame:\n top_df = input_df[\"high\"].groupby(level=0).rolling(window=self.window, min_periods=self.window).max()\n top_df = top_df.reset_index(level=0, drop=True)\n input_df[\"top\"] = top_df\n\n bottom_df = input_df[\"low\"].groupby(level=0).rolling(window=self.window, min_periods=self.window).min()\n bottom_df = bottom_df.reset_index(level=0, drop=True)\n input_df[\"bottom\"] = bottom_df\n\n return input_df\n\n\nclass TopBottomFactor(TechnicalFactor):\n def __init__(\n self,\n entity_schema: TradableEntity = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = [\"id\", \"entity_id\", \"timestamp\", \"level\", \"open\", \"close\", \"high\", \"low\"],\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n window=30,\n ) -> None:\n\n transformer = TopBottomTransformer(window=window)\n\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n\nif __name__ == \"__main__\":\n factor = TopBottomFactor(\n codes=[\"601318\"],\n start_timestamp=\"2005-01-01\",\n end_timestamp=now_pd_timestamp(),\n level=IntervalLevel.LEVEL_1DAY,\n window=120,\n )\n print(factor.factor_df)\n\n data_reader1 = DataReader(data_schema=Stock1dKdata, entity_schema=Stock, codes=[\"601318\"])\n\n drawer = Drawer(main_df=data_reader1.data_df, factor_df_list=[factor.factor_df[[\"top\", \"bottom\"]]])\n drawer.draw_kline(show=True)\n\n\n# the __all__ is generated\n__all__ = [\"TopBottomTransformer\", \"TopBottomFactor\"]\n"
},
{
"path": "src/zvt/factors/macd/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule macd_factor\nfrom .macd_factor import *\nfrom .macd_factor import __all__ as _macd_factor_all\n\n__all__ += _macd_factor_all\n"
},
{
"path": "src/zvt/factors/macd/macd_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Optional\n\nimport numpy as np\nimport pandas as pd\n\nfrom zvt.factors.algorithm import MacdTransformer\nfrom zvt.factors.technical_factor import TechnicalFactor\n\n\nclass MacdFactor(TechnicalFactor):\n transformer = MacdTransformer(count_live_dead=True)\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n return None\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n return [self.factor_df[[\"diff\", \"dea\", \"macd\"]]]\n\n def drawer_sub_col_chart(self) -> Optional[dict]:\n return {\"diff\": \"line\", \"dea\": \"line\", \"macd\": \"bar\"}\n\n\nclass BullFactor(MacdFactor):\n def compute_result(self):\n super().compute_result()\n self.result_df = self.factor_df[\"bull\"].to_frame(name=\"filter_result\")\n\n\nclass KeepBullFactor(BullFactor):\n keep_window = 10\n\n def compute_result(self):\n super().compute_result()\n df = (\n self.result_df[\"filter_result\"]\n .groupby(level=0)\n .rolling(window=self.keep_window, min_periods=self.keep_window)\n .apply(lambda x: np.logical_and.reduce(x))\n )\n df = df.reset_index(level=0, drop=True)\n self.result_df[\"filter_result\"] = df\n\n\n# 金叉 死叉 持续时间 切换点\nclass LiveOrDeadFactor(MacdFactor):\n pattern = [-5, 1]\n\n def compute_result(self):\n super().compute_result()\n self.factor_df[\"pre\"] = self.factor_df[\"live_count\"].shift()\n s = (self.factor_df[\"pre\"] <= self.pattern[0]) & (self.factor_df[\"live_count\"] >= self.pattern[1])\n self.result_df = s.to_frame(name=\"filter_result\")\n\n\nclass GoldCrossFactor(MacdFactor):\n def compute_result(self):\n super().compute_result()\n s = self.factor_df[\"live\"] == 1\n self.result_df = s.to_frame(name=\"filter_result\")\n\n\nif __name__ == \"__main__\":\n f = GoldCrossFactor(provider=\"em\", entity_provider=\"em\", entity_ids=[\"stock_sz_000338\"])\n f.drawer().draw(show=True)\n\n\n# the __all__ is generated\n__all__ = [\"MacdFactor\", \"BullFactor\", \"KeepBullFactor\", \"LiveOrDeadFactor\", \"GoldCrossFactor\"]\n"
},
{
"path": "src/zvt/factors/shape.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport logging\nfrom enum import Enum\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.contract.data_type import Bean\nfrom zvt.contract.drawer import Rect\nfrom zvt.factors.algorithm import intersect\nfrom zvt.utils.time_utils import TIME_FORMAT_ISO8601, to_date_time_str\n\nlogger = logging.getLogger(__name__)\n\n\nclass Direction(Enum):\n up = \"up\"\n down = \"down\"\n\n def opposite(self):\n if self == Direction.up:\n return Direction.down\n if self == Direction.down:\n return Direction.up\n\n\nclass Fenxing(Bean):\n def __init__(self, state, kdata, index) -> None:\n self.state = state\n self.kdata = kdata\n self.index = index\n\n\ndef fenxing_power(left, middle, right, fenxing=\"tmp_ding\"):\n if fenxing == \"tmp_ding\":\n a = middle[\"high\"] - middle[\"close\"]\n b = middle[\"high\"] - left[\"high\"]\n c = middle[\"high\"] - right[\"high\"]\n return -(a + b + c) / middle[\"close\"]\n if fenxing == \"tmp_di\":\n a = abs(middle[\"low\"] - middle[\"close\"])\n b = abs(middle[\"low\"] - left[\"low\"])\n c = abs(middle[\"low\"] - right[\"low\"])\n return (a + b + c) / middle[\"close\"]\n\n\ndef a_include_b(a: pd.Series, b: pd.Series) -> bool:\n \"\"\"\n kdata a includes kdata b\n\n :param a:\n :param b:\n :return:\n \"\"\"\n return (a[\"high\"] >= b[\"high\"]) and (a[\"low\"] <= b[\"low\"])\n\n\ndef get_direction(kdata, pre_kdata, current=Direction.up) -> Direction:\n if is_up(kdata, pre_kdata):\n return Direction.up\n if is_down(kdata, pre_kdata):\n return Direction.down\n\n return current\n\n\ndef is_up(kdata, pre_kdata):\n return kdata[\"high\"] > pre_kdata[\"high\"]\n\n\ndef is_down(kdata, pre_kdata):\n return kdata[\"low\"] < pre_kdata[\"low\"]\n\n\ndef handle_first_fenxing(one_df, step=11):\n if step >= len(one_df):\n logger.info(f\"coult not get fenxing by step {step}, len {len(one_df)}\")\n return None, None, None, None\n\n logger.info(f\"try to get first fenxing by step {step}\")\n\n df = one_df.iloc[:step]\n ding_kdata = df[df[\"high\"].max() == df[\"high\"]]\n ding_index = int(ding_kdata.index[-1])\n\n di_kdata = df[df[\"low\"].min() == df[\"low\"]]\n di_index = int(di_kdata.index[-1])\n\n # 确定第一个分型\n if abs(ding_index - di_index) >= 4:\n if ding_index > di_index:\n fenxing = \"bi_di\"\n fenxing_index = di_index\n one_df.loc[di_index, \"bi_di\"] = True\n # 确定第一个分型后，开始遍历的位置\n start_index = ding_index\n # 目前的笔的方向，up代表寻找 can_ding;down代表寻找can_di\n direction = Direction.up\n interval = ding_index - di_index\n else:\n fenxing = \"bi_ding\"\n fenxing_index = ding_index\n one_df.loc[ding_index, \"bi_ding\"] = True\n start_index = di_index\n direction = Direction.down\n interval = di_index - ding_index\n return (\n Fenxing(\n state=fenxing,\n index=fenxing_index,\n kdata={\n \"low\": float(one_df.loc[fenxing_index][\"low\"]),\n \"high\": float(one_df.loc[fenxing_index][\"high\"]),\n },\n ),\n start_index,\n direction,\n interval,\n )\n else:\n logger.info(\"need add step\")\n return handle_first_fenxing(one_df, step=step + 1)\n\n\ndef handle_zhongshu(points: list, acc_df, end_index, zhongshu_col=\"zhongshu\", zhongshu_change_col=\"zhongshu_change\"):\n zhongshu = None\n zhongshu_change = None\n interval = None\n\n if len(points) == 4:\n x1 = points[0][0]\n x2 = points[3][0]\n\n interval = points[3][2] - points[0][2]\n\n if points[0][1] < points[1][1]:\n # 向下段\n range = intersect((points[0][1], points[1][1]), (points[2][1], points[3][1]))\n if range:\n y1, y2 = range\n # 记录中枢\n zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2)\n zhongshu_change = abs(y1 - y2) / y1\n acc_df.loc[end_index, zhongshu_col] = zhongshu\n acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change\n points = points[-1:]\n else:\n points = points[1:]\n else:\n # 向上段\n range = intersect((points[1][1], points[0][1]), (points[3][1], points[2][1]))\n if range:\n y1, y2 = range\n # 记录中枢\n zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2)\n zhongshu_change = abs(y1 - y2) / y1\n\n acc_df.loc[end_index, zhongshu_col] = zhongshu\n acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change\n points = points[-1:]\n else:\n points = points[1:]\n return points, zhongshu, zhongshu_change, interval\n\n\ndef handle_duan(fenxing_list: List[Fenxing], pre_duan_state=\"yi\"):\n state = fenxing_list[0].state\n # 1笔区间\n bi1_start = fenxing_list[0].kdata\n bi1_end = fenxing_list[1].kdata\n # 3笔区间\n bi3_start = fenxing_list[2].kdata\n bi3_end = fenxing_list[3].kdata\n\n if state == \"bi_ding\":\n # 向下段,下-上-下\n\n # 第一笔区间\n range1 = (bi1_end[\"low\"], bi1_start[\"high\"])\n # 第三笔区间\n range3 = (bi3_end[\"low\"], bi3_start[\"high\"])\n\n # 1,3有重叠，认为第一个段出现\n if intersect(range1, range3):\n return \"down\"\n\n else:\n # 向上段，上-下-上\n\n # 第一笔区间\n range1 = (bi1_start[\"low\"], bi1_end[\"high\"])\n # 第三笔区间\n range3 = (bi3_start[\"low\"], bi3_end[\"high\"])\n\n # 1,3有重叠，认为第一个段出现\n if intersect(range1, range3):\n return \"up\"\n\n return pre_duan_state\n\n\ndef handle_including(one_df, index, kdata, pre_index, pre_kdata, tmp_direction: Direction):\n # 改kdata\n if a_include_b(kdata, pre_kdata):\n # 长的kdata变短\n if tmp_direction == Direction.up:\n one_df.loc[index, \"low\"] = pre_kdata[\"low\"]\n else:\n one_df.loc[index, \"high\"] = pre_kdata[\"high\"]\n # 改pre_kdata\n elif a_include_b(pre_kdata, kdata):\n # 长的pre_kdata变短\n if tmp_direction == Direction.down:\n one_df.loc[pre_index, \"low\"] = kdata[\"low\"]\n else:\n one_df.loc[pre_index, \"high\"] = kdata[\"high\"]\n\n\nclass FactorStateEncoder(json.JSONEncoder):\n def default(self, object):\n if isinstance(object, pd.Series):\n return object.to_dict()\n elif isinstance(object, pd.Timestamp):\n return to_date_time_str(object, fmt=TIME_FORMAT_ISO8601)\n elif isinstance(object, Enum):\n return object.value\n elif isinstance(object, Bean):\n return object.dict()\n else:\n return super().default(object)\n\n\ndef decode_rect(dct):\n return Rect(x0=dct[\"x0\"], y0=dct[\"y0\"], x1=dct[\"x1\"], y1=dct[\"y1\"])\n\n\ndef decode_fenxing(dct):\n return Fenxing(state=dct[\"state\"], kdata=dct[\"kdata\"], index=dct[\"index\"])\n\n\n# the __all__ is generated\n__all__ = [\n \"Direction\",\n \"Fenxing\",\n \"fenxing_power\",\n \"a_include_b\",\n \"get_direction\",\n \"is_up\",\n \"is_down\",\n \"handle_first_fenxing\",\n \"handle_zhongshu\",\n \"handle_duan\",\n \"handle_including\",\n \"FactorStateEncoder\",\n \"decode_rect\",\n \"decode_fenxing\",\n]\n"
},
{
"path": "src/zvt/factors/target_selector.py",
"content": "import operator\nfrom enum import Enum\nfrom itertools import accumulate\nfrom typing import List, Optional\n\nimport pandas as pd\nfrom pandas import DataFrame\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.factor import Factor\nfrom zvt.domain.meta.stock_meta import Stock\nfrom zvt.utils.pd_utils import index_df, pd_is_not_null, is_filter_result_df, is_score_result_df\nfrom zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp\n\n\nclass TradeType(Enum):\n # open_long 代表开多，并应该平掉相应标的的空单\n open_long = \"open_long\"\n # open_short 代表开空，并应该平掉相应标的的多单\n open_short = \"open_short\"\n # keep 代表保持现状，跟主动开仓有区别，有时有仓位是可以保持的，但不适合开新的仓\n keep = \"keep\"\n\n\nclass SelectMode(Enum):\n condition_and = \"condition_and\"\n condition_or = \"condition_or\"\n\n\nclass TargetSelector(object):\n def __init__(\n self,\n entity_ids=None,\n entity_schema=Stock,\n exchanges=None,\n codes=None,\n start_timestamp=None,\n end_timestamp=None,\n long_threshold=0.8,\n short_threshold=0.2,\n level=IntervalLevel.LEVEL_1DAY,\n provider=None,\n select_mode: SelectMode = SelectMode.condition_and,\n ) -> None:\n self.entity_ids = entity_ids\n self.entity_schema = entity_schema\n self.exchanges = exchanges\n self.codes = codes\n self.provider = provider\n self.select_mode = select_mode\n\n if start_timestamp:\n self.start_timestamp = to_pd_timestamp(start_timestamp)\n if end_timestamp:\n self.end_timestamp = to_pd_timestamp(end_timestamp)\n else:\n self.end_timestamp = now_pd_timestamp()\n\n self.long_threshold = long_threshold\n self.short_threshold = short_threshold\n self.level = level\n\n self.factors: List[Factor] = []\n self.filter_result = None\n self.score_result = None\n\n self.open_long_df: Optional[DataFrame] = None\n self.open_short_df: Optional[DataFrame] = None\n self.keep_df: Optional[DataFrame] = None\n\n self.init_factors(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n level=self.level,\n )\n\n def init_factors(self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, level):\n pass\n\n def add_factor(self, factor: Factor):\n self.check_factor(factor)\n self.factors.append(factor)\n return self\n\n def check_factor(self, factor: Factor):\n assert factor.level == self.level\n\n def move_on(self, to_timestamp=None, kdata_use_begin_time=False, timeout=20):\n if self.factors:\n for factor in self.factors:\n factor.move_on(to_timestamp, timeout=timeout)\n\n self.run()\n\n def run(self):\n \"\"\" \"\"\"\n if self.factors:\n filters = []\n scores = []\n for factor in self.factors:\n if is_filter_result_df(factor.result_df):\n df = factor.result_df[[\"filter_result\"]]\n if pd_is_not_null(df):\n df.columns = [\"score\"]\n filters.append(df)\n else:\n raise Exception(\"no data for factor:{},{}\".format(factor.name, factor))\n if is_score_result_df(factor.result_df):\n df = factor.result_df[[\"score_result\"]]\n if pd_is_not_null(df):\n df.columns = [\"score\"]\n scores.append(df)\n else:\n raise Exception(\"no data for factor:{},{}\".format(factor.name, factor))\n\n if filters:\n if self.select_mode == SelectMode.condition_and:\n self.filter_result = list(accumulate(filters, func=operator.__and__))[-1]\n else:\n self.filter_result = list(accumulate(filters, func=operator.__or__))[-1]\n\n if scores:\n self.score_result = list(accumulate(scores, func=operator.__add__))[-1] / len(scores)\n\n self.generate_targets()\n\n def get_targets(self, timestamp, trade_type: TradeType = TradeType.open_long) -> List[str]:\n if trade_type == TradeType.open_long:\n df = self.open_long_df\n elif trade_type == TradeType.open_short:\n df = self.open_short_df\n elif trade_type == TradeType.keep:\n df = self.keep_df\n else:\n assert False\n\n if pd_is_not_null(df):\n if timestamp in df.index:\n target_df = df.loc[[to_pd_timestamp(timestamp)], :]\n return target_df[\"entity_id\"].tolist()\n return []\n\n def get_targets_between(\n self, start_timestamp, end_timestamp, trade_type: TradeType = TradeType.open_long\n ) -> List[str]:\n if trade_type == TradeType.open_long:\n df = self.open_long_df\n elif trade_type == TradeType.open_short:\n df = self.open_short_df\n elif trade_type == TradeType.keep:\n df = self.keep_df\n else:\n assert False\n\n if pd_is_not_null(df):\n index = pd.date_range(start_timestamp, end_timestamp, freq=self.level.to_pd_freq())\n return list(set(df.loc[df.index & index][\"entity_id\"].tolist()))\n return []\n\n def get_open_long_targets(self, timestamp):\n return self.get_targets(timestamp=timestamp, trade_type=TradeType.open_long)\n\n def get_open_short_targets(self, timestamp):\n return self.get_targets(timestamp=timestamp, trade_type=TradeType.open_short)\n\n # overwrite it to generate targets\n def generate_targets(self):\n keep_result = pd.DataFrame()\n long_result = pd.DataFrame()\n short_result = pd.DataFrame()\n\n if pd_is_not_null(self.filter_result):\n keep_result = self.filter_result[self.filter_result[\"score\"].isna()]\n long_result = self.filter_result[self.filter_result[\"score\"] == True]\n short_result = self.filter_result[self.filter_result[\"score\"] == False]\n\n if pd_is_not_null(self.score_result):\n score_keep_result = self.score_result[\n (self.score_result[\"score\"] > self.short_threshold) & (self.score_result[\"score\"] < self.long_threshold)\n ]\n if pd_is_not_null(keep_result):\n keep_result = score_keep_result.loc[keep_result.index, :]\n else:\n keep_result = score_keep_result\n\n score_long_result = self.score_result[self.score_result[\"score\"] >= self.long_threshold]\n if pd_is_not_null(long_result):\n long_result = score_long_result.loc[long_result.index, :]\n else:\n long_result = score_long_result\n\n score_short_result = self.score_result[self.score_result[\"score\"] <= self.short_threshold]\n if pd_is_not_null(short_result):\n short_result = score_short_result.loc[short_result.index, :]\n else:\n short_result = score_short_result\n\n self.keep_df = self.normalize_result_df(keep_result)\n self.open_long_df = self.normalize_result_df(long_result)\n self.open_short_df = self.normalize_result_df(short_result)\n\n def get_result_df(self):\n return self.open_long_df\n\n def normalize_result_df(self, df):\n if pd_is_not_null(df):\n df = df.reset_index()\n df = index_df(df)\n df = df.sort_values(by=[\"score\", \"entity_id\"])\n return df\n\n\n# the __all__ is generated\n__all__ = [\"TradeType\", \"SelectMode\", \"TargetSelector\"]\n"
},
{
"path": "src/zvt/factors/technical_factor.py",
"content": "from typing import List, Union, Type, Optional\n\nimport pandas as pd\n\nfrom zvt.api.kdata import get_kdata_schema, default_adjust_type\nfrom zvt.contract import IntervalLevel, TradableEntity, AdjustType\nfrom zvt.contract.factor import Factor, Transformer, Accumulator, FactorMeta\nfrom zvt.domain import Stock\n\n\nclass TechnicalFactor(Factor, metaclass=FactorMeta):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n ) -> None:\n if columns is None:\n columns = [\n \"id\",\n \"entity_id\",\n \"timestamp\",\n \"level\",\n \"open\",\n \"close\",\n \"high\",\n \"low\",\n \"volume\",\n \"turnover\",\n \"turnover_rate\",\n ]\n\n # 股票默认使用后复权\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=entity_schema.__name__)\n\n self.adjust_type = adjust_type\n self.data_schema = get_kdata_schema(entity_schema.__name__, level=level, adjust_type=adjust_type)\n\n if not factor_name:\n if type(level) == str:\n factor_name = f\"{type(self).__name__.lower()}_{level}\"\n else:\n factor_name = f\"{type(self).__name__.lower()}_{level.value}\"\n\n super().__init__(\n self.data_schema,\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n )\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n return [self.factor_df[[\"volume\"]]]\n\n\n# the __all__ is generated\n__all__ = [\"TechnicalFactor\"]\n"
},
{
"path": "src/zvt/factors/top_stocks.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport logging\nfrom typing import List\n\nfrom sqlalchemy import Column, String, Integer\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.api.kdata import get_trade_dates, get_kdata_schema\nfrom zvt.api.selector import (\n get_entity_ids_by_filter,\n get_limit_up_stocks,\n get_mini_and_small_stock,\n get_middle_and_big_stock,\n get_recent_active_stocks,\n get_recent_trending_stocks,\n)\nfrom zvt.api.stats import get_top_performance_entities_by_periods, TopType\nfrom zvt.contract import Mixin, AdjustType\nfrom zvt.contract.api import get_db_session\nfrom zvt.contract.factor import TargetType\nfrom zvt.contract.register import register_schema\nfrom zvt.domain import Stock\nfrom zvt.factors.ma.ma_factor import VolumeUpMaFactor\nfrom zvt.utils.time_utils import (\n date_time_by_interval,\n to_pd_timestamp,\n to_date_time_str,\n TIME_FORMAT_DAY,\n next_date,\n)\n\nlogger = logging.getLogger(__name__)\n\nTopStocksBase = declarative_base()\n\n\nclass TopStocks(TopStocksBase, Mixin):\n __tablename__ = \"top_stocks\"\n entity_type = Column(String(length=64))\n\n short_count = Column(Integer)\n short_stocks = Column(String(length=2048))\n\n long_count = Column(Integer)\n long_stocks = Column(String(length=2048))\n\n small_vol_up_count = Column(Integer)\n small_vol_up_stocks = Column(String(length=2048))\n\n big_vol_up_count = Column(Integer)\n big_vol_up_stocks = Column(String(length=2048))\n\n all_stocks_count = Column(Integer)\n\n\nregister_schema(db_name=\"top_stocks\", schema_base=TopStocksBase, internal=True)\n\n\ndef compute_vol_up_stocks(target_date, provider=\"em\", adjust_type=AdjustType.qfq, stock_type=\"small\", entity_ids=None):\n if stock_type == \"small\":\n current_entity_pool = get_mini_and_small_stock(\n timestamp=target_date, provider=provider, adjust_type=adjust_type\n )\n turnover_threshold = 500000000\n turnover_rate_threshold = 0.02\n elif stock_type == \"big\":\n current_entity_pool = get_middle_and_big_stock(\n timestamp=target_date, provider=provider, adjust_type=adjust_type\n )\n turnover_threshold = 500000000\n turnover_rate_threshold = 0.01\n else:\n assert False\n\n if entity_ids:\n current_entity_pool = set(current_entity_pool) & set(entity_ids)\n\n kdata_schema = get_kdata_schema(entity_type=\"stock\", level=\"1d\", adjust_type=adjust_type)\n filters = [\n kdata_schema.timestamp == to_pd_timestamp(target_date),\n kdata_schema.turnover >= turnover_threshold,\n kdata_schema.turnover_rate >= turnover_rate_threshold,\n ]\n kdata_df = kdata_schema.query_data(\n provider=provider, filters=filters, columns=[\"entity_id\", \"timestamp\"], index=\"entity_id\"\n )\n if current_entity_pool:\n current_entity_pool = set(current_entity_pool) & set(kdata_df.index.tolist())\n else:\n current_entity_pool = kdata_df.index.tolist()\n\n factor = VolumeUpMaFactor(\n entity_schema=Stock,\n entity_provider=provider,\n provider=provider,\n entity_ids=current_entity_pool,\n start_timestamp=date_time_by_interval(target_date, -600),\n end_timestamp=target_date,\n adjust_type=adjust_type,\n windows=[120, 250],\n over_mode=\"or\",\n up_intervals=60,\n turnover_threshold=turnover_threshold,\n turnover_rate_threshold=turnover_rate_threshold,\n )\n\n stocks = factor.get_targets(timestamp=target_date, target_type=TargetType.positive)\n return stocks\n\n\ndef compute_top_stocks(start_date, entity_type=\"stock\", provider=\"em\", force_update=False, adjust_type=AdjustType.qfq):\n session = get_db_session(provider=\"zvt\", data_schema=TopStocks)\n if force_update:\n session.query(TopStocks).filter(TopStocks.timestamp >= start_date).filter(\n TopStocks.entity_type == entity_type\n ).delete()\n else:\n latest = TopStocks.query_data(\n limit=1,\n filters=[TopStocks.entity_type == entity_type],\n order=TopStocks.timestamp.desc(),\n return_type=\"domain\",\n )\n if latest:\n start_date = next_date(the_time=to_date_time_str(latest[0].timestamp, fmt=TIME_FORMAT_DAY))\n\n trade_days = get_trade_dates(entity_type=entity_type, start=start_date)\n\n for target_date in trade_days:\n top_stocks = TopStocks(\n entity_type=entity_type,\n entity_id=f\"{entity_type}_top\",\n id=f\"{entity_type}_top_{target_date}\",\n timestamp=target_date,\n )\n\n entity_ids = get_entity_ids_by_filter(\n entity_type=entity_type,\n target_date=target_date,\n provider=\"em\",\n )\n\n turnover_threshold = 0\n if entity_type == \"stockus\" or entity_type == \"stockhk\":\n turnover_threshold = 10000000\n\n short_selected, short_period = get_top_performance_entities_by_periods(\n entity_provider=\"em\",\n data_provider=provider,\n target_date=target_date,\n periods=[*range(1, 20)],\n ignore_new_stock=False,\n ignore_st=False,\n entity_ids=entity_ids,\n entity_type=entity_type,\n adjust_type=adjust_type,\n top_count=30,\n turnover_threshold=turnover_threshold,\n turnover_rate_threshold=0,\n return_type=TopType.positive,\n )\n if entity_type == \"stock\":\n limit_up_stocks = get_limit_up_stocks(timestamp=target_date)\n logger.info(f\"limit_up_stocks got: {len(limit_up_stocks)}\")\n\n recent_active_stocks = get_recent_active_stocks(adjust_type=adjust_type, provider=provider, recent_days=10)\n logger.info(f\"recent_active_stocks got: {len(recent_active_stocks)}\")\n\n recent_trending_stocks = get_recent_trending_stocks(\n adjust_type=adjust_type, provider=provider, recent_days=20\n )\n logger.info(f\"recent_trending_stocks got: {len(recent_trending_stocks)}\")\n\n short_selected = list(set(short_selected + limit_up_stocks + recent_active_stocks + recent_trending_stocks))\n top_stocks.short_count = len(short_selected)\n top_stocks.short_stocks = json.dumps(short_selected, ensure_ascii=False)\n\n long_period_start = short_period + 1\n long_selected, long_period = get_top_performance_entities_by_periods(\n entity_provider=\"em\",\n data_provider=provider,\n target_date=target_date,\n periods=[*range(long_period_start, long_period_start + 30)],\n ignore_new_stock=False,\n ignore_st=False,\n entity_ids=entity_ids,\n entity_type=entity_type,\n adjust_type=adjust_type,\n top_count=30,\n turnover_threshold=turnover_threshold,\n turnover_rate_threshold=0,\n return_type=TopType.positive,\n )\n top_stocks.long_count = len(long_selected)\n top_stocks.long_stocks = json.dumps(long_selected, ensure_ascii=False)\n\n if entity_type == \"stock\":\n small_vol_up_stocks = compute_vol_up_stocks(\n target_date=target_date, provider=provider, stock_type=\"small\", entity_ids=entity_ids\n )\n top_stocks.small_vol_up_count = len(small_vol_up_stocks)\n top_stocks.small_vol_up_stocks = json.dumps(small_vol_up_stocks, ensure_ascii=False)\n\n big_vol_up_stocks = compute_vol_up_stocks(\n target_date=target_date, provider=provider, stock_type=\"big\", entity_ids=entity_ids\n )\n top_stocks.big_vol_up_count = len(big_vol_up_stocks)\n top_stocks.big_vol_up_stocks = json.dumps(big_vol_up_stocks, ensure_ascii=False)\n\n top_stocks.all_stocks_count = len(entity_ids)\n\n session.add(top_stocks)\n session.commit()\n\n\ndef get_top_stocks(target_date, entity_type=\"stock\", return_type=\"short\"):\n datas: List[TopStocks] = TopStocks.query_data(\n filters=[TopStocks.entity_type == entity_type, TopStocks.timestamp == to_pd_timestamp(target_date)],\n return_type=\"domain\",\n )\n stocks = []\n if datas:\n assert len(datas) == 1\n top_stock = datas[0]\n if return_type == \"all\":\n short_stocks = json.loads(top_stock.short_stocks) if top_stock.short_stocks else []\n long_stocks = json.loads(top_stock.long_stocks) if top_stock.long_stocks else []\n small_vol_up_stocks = json.loads(top_stock.small_vol_up_stocks) if top_stock.small_vol_up_stocks else []\n big_vol_up_stocks = json.loads(top_stock.big_vol_up_stocks) if top_stock.big_vol_up_stocks else []\n all_stocks = list(set(short_stocks + long_stocks + small_vol_up_stocks + big_vol_up_stocks))\n return all_stocks\n elif return_type == \"short\":\n stocks = json.loads(top_stock.short_stocks) if top_stock.short_stocks else []\n elif return_type == \"long\":\n stocks = json.loads(top_stock.long_stocks) if top_stock.long_stocks else []\n elif return_type == \"small_vol_up\":\n stocks = json.loads(top_stock.small_vol_up_stocks) if top_stock.small_vol_up_stocks else []\n elif return_type == \"big_vol_up\":\n stocks = json.loads(top_stock.big_vol_up_stocks) if top_stock.big_vol_up_stocks else []\n else:\n assert False\n return stocks\n\n\nif __name__ == \"__main__\":\n print(get_top_stocks(entity_type=\"stockus\", target_date=\"2025-07-25\", return_type=\"all\"))\n\n# the __all__ is generated\n__all__ = [\n \"TopStocks\",\n \"compute_vol_up_stocks\",\n \"compute_top_stocks\",\n \"get_top_stocks\",\n]\n"
},
{
"path": "src/zvt/factors/transformers.py",
"content": "# -*- coding: utf-8 -*-\nimport numpy as np\nimport pandas as pd\n\nfrom zvt.contract.factor import Transformer\nfrom zvt.factors.algorithm import MaTransformer\nfrom zvt.factors.technical_factor import TechnicalFactor\nfrom zvt.utils.pd_utils import group_by_entity_id, normalize_group_compute_result, merge_filter_result\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\ndef _cal_state(s, df, pre, interval, col):\n assert len(s) == pre + interval\n s = df.loc[s.index, :]\n pre_df: pd.DataFrame = s.iloc[:pre, :]\n recent_df: pd.DataFrame = s.iloc[-interval:, :]\n if pre_df.isnull().values.any() or recent_df.isnull().values.any():\n return np.nan\n pre_result = np.logical_and.reduce(pre_df[\"close\"] > pre_df[col])\n recent_result = np.logical_and.reduce(recent_df[\"close\"] < recent_df[col])\n if pre_result and recent_result:\n return True\n return np.nan\n\n\nclass CrossMaTransformer(MaTransformer):\n def __init__(self, windows=None, cal_change_pct=False) -> None:\n super().__init__(windows, cal_change_pct)\n\n def transform(self, input_df: pd.DataFrame) -> pd.DataFrame:\n input_df = super().transform(input_df)\n cols = [f\"ma{window}\" for window in self.windows]\n s = input_df[cols[0]] > input_df[cols[1]]\n current_col = cols[1]\n for col in cols[2:]:\n s = s & (input_df[current_col] > input_df[col])\n current_col = col\n input_df[\"filter_result\"] = s\n return input_df\n\n\nclass SpecificTransformer(Transformer):\n def __init__(self, buy_timestamp, sell_timestamp) -> None:\n self.buy_timestamp = to_pd_timestamp(buy_timestamp)\n self.sell_timestamp = to_pd_timestamp(sell_timestamp)\n\n def transform(self, input_df: pd.DataFrame) -> pd.DataFrame:\n s = input_df[input_df.get_level_values[\"timestamp\"] == self.buy_timestamp]\n s[s == False] = None\n s[input_df.get_level_values[\"timestamp\"] == self.sell_timestamp] = False\n input_df[\"filter_result\"] = s\n return input_df\n\n\nclass FallBelowTransformer(Transformer):\n def __init__(self, window=10, interval=3) -> None:\n super().__init__()\n self.window = window\n self.interval = interval\n\n def transform(self, input_df: pd.DataFrame) -> pd.DataFrame:\n col = f\"ma{self.window}\"\n if col not in input_df.columns:\n group_result = (\n group_by_entity_id(input_df[\"close\"]).rolling(window=self.window, min_periods=self.window).mean()\n )\n group_result = normalize_group_compute_result(group_result=group_result)\n input_df[col] = group_result\n\n # 连续3(interval)日收在10(window)日线下\n s = input_df[\"close\"] < input_df[col]\n s = (\n group_by_entity_id(s)\n .rolling(window=self.interval, min_periods=self.interval)\n .apply(lambda x: np.logical_and.reduce(x))\n )\n s = normalize_group_compute_result(group_result=s)\n # 构造卖点\n s[s == False] = None\n s[s == True] = False\n input_df = merge_filter_result(input_df=input_df, filter_result=s)\n\n return input_df\n\n\nif __name__ == \"__main__\":\n # df = Stock1dHfqKdata.query_data(codes=[\"000338\"], index=[\"entity_id\", \"timestamp\"])\n # df = FallBelowTransformer().transform(df)\n # print(df[\"filter_result\"])\n TechnicalFactor(transformer=SpecificTransformer(timestamp=\"2020-03-01\"))\n\n\n# the __all__ is generated\n__all__ = [\"CrossMaTransformer\", \"SpecificTransformer\", \"FallBelowTransformer\"]\n"
},
{
"path": "src/zvt/factors/zen/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule zen_factor\nfrom .zen_factor import *\nfrom .zen_factor import __all__ as _zen_factor_all\n\n__all__ += _zen_factor_all\n\n# import all from submodule base_factor\nfrom .base_factor import *\nfrom .base_factor import __all__ as _base_factor_all\n\n__all__ += _base_factor_all\n\n# import all from submodule domain\nfrom .domain import *\nfrom .domain import __all__ as _domain_all\n\n__all__ += _domain_all\n"
},
{
"path": "src/zvt/factors/zen/base_factor.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport logging\nfrom enum import Enum\nfrom typing import List\nfrom typing import Union, Optional, Type\n\nimport numpy as np\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.api import get_schema_by_name\nfrom zvt.contract.data_type import Bean\nfrom zvt.contract.drawer import Rect\nfrom zvt.contract.factor import Accumulator\nfrom zvt.contract.factor import Transformer\nfrom zvt.domain import Stock, Index, Index1dKdata\nfrom zvt.factors.algorithm import intersect, combine\nfrom zvt.factors.shape import (\n Fenxing,\n Direction,\n handle_first_fenxing,\n decode_rect,\n get_direction,\n handle_including,\n fenxing_power,\n handle_duan,\n)\nfrom zvt.factors.technical_factor import TechnicalFactor\nfrom zvt.utils.decorator import to_string\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import TIME_FORMAT_ISO8601, to_date_time_str\n\nlogger = logging.getLogger(__name__)\n\n\nclass FactorStateEncoder(json.JSONEncoder):\n def default(self, object):\n if isinstance(object, pd.Series):\n return object.to_dict()\n elif isinstance(object, pd.Timestamp):\n return to_date_time_str(object, fmt=TIME_FORMAT_ISO8601)\n elif isinstance(object, Enum):\n return object.value\n elif isinstance(object, Bean):\n return object.dict()\n else:\n return super().default(object)\n\n\ndef get_zen_factor_schema(entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY):\n if type(level) == str:\n level = IntervalLevel(level)\n\n # z factor schema rule\n # 1)name:{SecurityType.value.capitalize()}{IntervalLevel.value.upper()}ZFactor\n schema_str = \"{}{}ZenFactor\".format(entity_type.capitalize(), level.value.capitalize())\n\n return get_schema_by_name(schema_str)\n\n\n@to_string\nclass ZenState(Bean):\n def __init__(self, state: dict = None) -> None:\n super().__init__()\n\n if not state:\n state = dict()\n\n # 用于计算未完成段的分型\n self.fenxing_list = state.get(\"fenxing_list\", [])\n fenxing_list = [Fenxing(item[\"state\"], item[\"kdata\"], item[\"index\"]) for item in self.fenxing_list]\n self.fenxing_list = fenxing_list\n\n # 目前的方向\n if state.get(\"direction\"):\n self.direction = Direction(state.get(\"direction\"))\n else:\n self.direction = None\n\n # 候选分型(candidate)\n self.can_fenxing = state.get(\"can_fenxing\")\n self.can_fenxing_index = state.get(\"can_fenxing_index\")\n # 反方向count\n self.opposite_count = state.get(\"opposite_count\", 0)\n # 目前段的方向\n self.current_duan_state = state.get(\"current_duan_state\", \"yi\")\n\n # 记录用于计算中枢的段\n # list of (timestamp,value)\n self.duans = state.get(\"duans\", [])\n self.bis = state.get(\"bis\", [])\n\n # 前一个点\n self.pre_bi = state.get(\"pre_bi\")\n self.pre_duan = state.get(\"pre_duan\")\n\n # 目前的merge_zhongshu\n self.merge_zhongshu = state.get(\"merge_zhongshu\")\n self.merge_zhongshu_level = state.get(\"merge_zhongshu_level\")\n self.merge_zhongshu_interval = state.get(\"merge_zhongshu_interval\")\n\n\ndef handle_zhongshu(\n points: list,\n acc_df,\n end_index,\n zhongshu_col=\"zhongshu\",\n zhongshu_change_col=\"zhongshu_change\",\n):\n zhongshu = None\n zhongshu_change = None\n interval = None\n\n if len(points) == 4:\n x1 = points[0][0]\n x2 = points[3][0]\n\n interval = points[3][2] - points[0][2]\n\n if points[0][1] < points[1][1]:\n # 向下段\n range = intersect((points[0][1], points[1][1]), (points[2][1], points[3][1]))\n if range:\n y1, y2 = range\n # 记录中枢\n zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2)\n zhongshu_change = abs(y1 - y2) / abs(y1)\n acc_df.loc[end_index, zhongshu_col] = zhongshu\n acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change\n points = points[-1:]\n else:\n points = points[1:]\n else:\n # 向上段\n range = intersect((points[1][1], points[0][1]), (points[3][1], points[2][1]))\n if range:\n y1, y2 = range\n # 记录中枢\n zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2)\n zhongshu_change = abs(y1 - y2) / abs(y1)\n\n acc_df.loc[end_index, zhongshu_col] = zhongshu\n acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change\n points = points[-1:]\n else:\n points = points[1:]\n return points, zhongshu, zhongshu_change, interval\n\n\nclass ZenAccumulator(Accumulator):\n def __init__(self, acc_window: int = 1) -> None:\n \"\"\"\n 算法和概念\n <实体> 某种状态的k线\n [实体] 连续实体排列\n\n 两k线的关系有三种: 上涨，下跌，包含\n 上涨: k线高点比之前高，低点比之前高\n 下跌: k线低点比之前低，高点比之前低\n 包含: k线高点比之前高，低点比之前低;反方向，即被包含\n 处理包含关系，长的k线缩短，上涨时，低点取max(low1,low2)；下跌时，高点取min(high1,high2)\n\n 第一个顶(底)分型: 出现连续4根下跌(上涨)k线\n 之后开始寻找 候选底(顶)分型，寻找的过程中有以下状态\n\n <临时顶>: 中间k线比两边的高点高,是一条特定的k线\n <临时底>: 中间k线比两边的高点高,是一条特定的k线\n\n <候选顶分型>: 连续的<临时顶>取最大\n <候选底分型>: 连续的<临时底>取最小\n 任何时刻只能有一个候选，其之前是一个确定的分型\n\n <上升k线>:\n <下降k线>:\n <连接k线>: 分型之间的k线都可以认为是连接k线，以上为演化过程的中间态\n distance(<候选顶分型>, <连接k线>)>=4 则 <候选顶分型> 变成顶分型\n distance(<候选底分型>, <连接k线>)>=4 则 <候选底分型> 变成底分型\n\n <顶分型><连接k线><候选底分型>\n <底分型><连接k线><候选顶分型>\n \"\"\"\n super().__init__(acc_window)\n\n def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame, state: dict) -> (pd.DataFrame, dict):\n self.logger.info(f\"acc_one:{entity_id}\")\n if pd_is_not_null(acc_df):\n df = df[df.index > acc_df.index[-1]]\n if pd_is_not_null(df):\n self.logger.info(f'compute from {df.iloc[0][\"timestamp\"]}')\n # 遍历的开始位置\n start_index = len(acc_df)\n\n acc_df = pd.concat([acc_df, df])\n\n zen_state = ZenState(state)\n\n acc_df = acc_df.reset_index(drop=True)\n current_interval = acc_df.iloc[start_index - 1][\"current_interval\"]\n else:\n self.logger.info(\"no need to compute\")\n return acc_df, state\n else:\n acc_df = df\n # 笔的底\n acc_df[\"bi_di\"] = False\n # 笔的顶\n acc_df[\"bi_ding\"] = False\n # 记录笔顶/底分型的值，bi_di取low,bi_ding取high,其他为None,绘图时取有值的连线即为 笔\n acc_df[\"bi_value\"] = np.nan\n # 笔的变化\n acc_df[\"bi_change\"] = np.nan\n # 笔的斜率\n acc_df[\"bi_slope\"] = np.nan\n # 持续的周期\n acc_df[\"bi_interval\"] = np.nan\n\n # 记录临时分型，不变\n acc_df[\"tmp_ding\"] = False\n acc_df[\"tmp_di\"] = False\n # 分型的力度\n acc_df[\"fenxing_power\"] = np.nan\n\n # 目前分型确定的方向\n acc_df[\"current_direction\"] = None\n acc_df[\"current_change\"] = np.nan\n acc_df[\"current_interval\"] = np.nan\n acc_df[\"current_slope\"] = np.nan\n # 最近的一个笔中枢\n # acc_df['current_zhongshu'] = np.nan\n acc_df[\"current_zhongshu_change\"] = np.nan\n acc_df[\"current_zhongshu_y0\"] = np.nan\n acc_df[\"current_zhongshu_y1\"] = np.nan\n\n acc_df[\"current_merge_zhongshu_change\"] = np.nan\n acc_df[\"current_merge_zhongshu_y0\"] = np.nan\n acc_df[\"current_merge_zhongshu_y1\"] = np.nan\n acc_df[\"current_merge_zhongshu_level\"] = np.nan\n acc_df[\"current_merge_zhongshu_interval\"] = np.nan\n\n # 目前走势的临时方向 其跟direction的的关系 确定了下一个分型\n acc_df[\"tmp_direction\"] = None\n acc_df[\"opposite_change\"] = np.nan\n acc_df[\"opposite_interval\"] = np.nan\n acc_df[\"opposite_slope\"] = np.nan\n\n acc_df[\"duan_state\"] = \"yi\"\n\n # 段的底\n acc_df[\"duan_di\"] = False\n # 段的顶\n acc_df[\"duan_ding\"] = False\n # 记录段顶/底的值，为duan_di时取low,为duan_ding时取high,其他为None,绘图时取有值的连线即为 段\n acc_df[\"duan_value\"] = np.nan\n # 段的变化\n acc_df[\"duan_change\"] = np.nan\n # 段的斜率\n acc_df[\"duan_slope\"] = np.nan\n # 持续的周期\n acc_df[\"duan_interval\"] = np.nan\n\n # 记录在确定中枢的最后一个段的终点x1，值为Rect(x0,y0,x1,y1)\n acc_df[\"zhongshu\"] = None\n acc_df[\"zhongshu_change\"] = np.nan\n\n acc_df[\"bi_zhongshu\"] = None\n acc_df[\"bi_zhongshu_change\"] = np.nan\n\n acc_df[\"merge_zhongshu\"] = None\n acc_df[\"merge_zhongshu_change\"] = np.nan\n acc_df[\"merge_zhongshu_level\"] = np.nan\n acc_df[\"merge_zhongshu_interval\"] = np.nan\n\n acc_df = acc_df.reset_index(drop=True)\n\n zen_state = ZenState(\n dict(\n fenxing_list=[],\n direction=None,\n can_fenxing=None,\n can_fenxing_index=None,\n opposite_count=0,\n current_duan_state=\"yi\",\n duans=[],\n pre_bi=None,\n pre_duan=None,\n merge_zhongshu=None,\n )\n )\n\n zen_state.fenxing_list: List[Fenxing] = []\n\n # 取前11条k线，至多出现一个顶分型+底分型\n # 注:只是一种方便的确定第一个分型的办法，有了第一个分型，后面的处理就比较统一\n # start_index 为遍历开始的位置\n # direction为一个确定分型后的方向，即顶分型后为:down，底分型后为:up\n fenxing, start_index, direction, current_interval = handle_first_fenxing(acc_df, step=11)\n if not fenxing:\n return None, None\n\n zen_state.fenxing_list.append(fenxing)\n zen_state.direction = direction\n\n # list of (timestamp,value)\n zen_state.duans = []\n zen_state.bis = []\n\n pre_kdata = acc_df.iloc[start_index - 1]\n pre_index = start_index - 1\n\n tmp_direction = zen_state.direction\n current_merge_zhongshu = decode_rect(zen_state.merge_zhongshu) if zen_state.merge_zhongshu else None\n current_merge_zhongshu_change = None\n current_merge_zhongshu_interval = zen_state.merge_zhongshu_interval\n current_merge_zhongshu_level = zen_state.merge_zhongshu_level\n\n current_zhongshu = None\n current_zhongshu_change = None\n for index, kdata in acc_df.iloc[start_index:].iterrows():\n # print(f'timestamp: {kdata.timestamp}')\n # 临时方向\n tmp_direction = get_direction(kdata, pre_kdata, current=tmp_direction)\n\n # current states\n current_interval = current_interval + 1\n if zen_state.direction == Direction.up:\n pre_value = acc_df.loc[zen_state.fenxing_list[0].index, \"low\"]\n current_value = kdata[\"high\"]\n else:\n pre_value = acc_df.loc[zen_state.fenxing_list[0].index, \"high\"]\n current_value = kdata[\"low\"]\n acc_df.loc[index, \"current_direction\"] = zen_state.direction.value\n acc_df.loc[index, \"current_interval\"] = current_interval\n change = (current_value - pre_value) / abs(pre_value)\n acc_df.loc[index, \"current_change\"] = change\n acc_df.loc[index, \"current_slope\"] = change / current_interval\n if current_zhongshu:\n # acc_df.loc[index, 'current_zhongshu'] = current_zhongshu\n acc_df.loc[index, \"current_zhongshu_y0\"] = current_zhongshu.y0\n acc_df.loc[index, \"current_zhongshu_y1\"] = current_zhongshu.y1\n acc_df.loc[index, \"current_zhongshu_change\"] = current_zhongshu_change\n else:\n # acc_df.loc[index, 'current_zhongshu'] = acc_df.loc[index - 1, 'current_zhongshu']\n acc_df.loc[index, \"current_zhongshu_y0\"] = acc_df.loc[index - 1, \"current_zhongshu_y0\"]\n acc_df.loc[index, \"current_zhongshu_y1\"] = acc_df.loc[index - 1, \"current_zhongshu_y1\"]\n acc_df.loc[index, \"current_zhongshu_change\"] = acc_df.loc[index - 1, \"current_zhongshu_change\"]\n\n if current_merge_zhongshu:\n # acc_df.loc[index, 'current_merge_zhongshu'] = current_merge_zhongshu\n acc_df.loc[index, \"current_merge_zhongshu_y0\"] = current_merge_zhongshu.y0\n acc_df.loc[index, \"current_merge_zhongshu_y1\"] = current_merge_zhongshu.y1\n acc_df.loc[index, \"current_merge_zhongshu_change\"] = current_merge_zhongshu_change\n acc_df.loc[index, \"current_merge_zhongshu_level\"] = current_merge_zhongshu_level\n acc_df.loc[index, \"current_merge_zhongshu_interval\"] = current_merge_zhongshu_interval\n else:\n # acc_df.loc[index, 'current_merge_zhongshu'] = acc_df.loc[index - 1, 'current_merge_zhongshu']\n acc_df.loc[index, \"current_merge_zhongshu_y0\"] = acc_df.loc[index - 1, \"current_merge_zhongshu_y0\"]\n acc_df.loc[index, \"current_merge_zhongshu_y1\"] = acc_df.loc[index - 1, \"current_merge_zhongshu_y1\"]\n acc_df.loc[index, \"current_merge_zhongshu_change\"] = acc_df.loc[\n index - 1, \"current_merge_zhongshu_change\"\n ]\n acc_df.loc[index, \"current_merge_zhongshu_level\"] = acc_df.loc[\n index - 1, \"current_merge_zhongshu_level\"\n ]\n acc_df.loc[index, \"current_merge_zhongshu_interval\"] = acc_df.loc[\n index - 1, \"current_merge_zhongshu_interval\"\n ]\n\n # 处理包含关系\n handle_including(\n one_df=acc_df,\n index=index,\n kdata=kdata,\n pre_index=pre_index,\n pre_kdata=pre_kdata,\n tmp_direction=tmp_direction,\n )\n\n # 根据方向，寻找对应的分型 和 段\n if zen_state.direction == Direction.up:\n tmp_fenxing_col = \"tmp_ding\"\n fenxing_col = \"bi_ding\"\n else:\n tmp_fenxing_col = \"tmp_di\"\n fenxing_col = \"bi_di\"\n\n # 方向一致，延续中\n if tmp_direction == zen_state.direction:\n zen_state.opposite_count = 0\n # 反向，寻找反 分型\n else:\n zen_state.opposite_count = zen_state.opposite_count + 1\n\n # opposite states\n current_interval = zen_state.opposite_count\n if tmp_direction == Direction.up:\n pre_value = acc_df.loc[index - zen_state.opposite_count, \"low\"]\n current_value = kdata[\"high\"]\n else:\n pre_value = acc_df.loc[index - zen_state.opposite_count, \"high\"]\n current_value = kdata[\"low\"]\n acc_df.loc[index, \"tmp_direction\"] = tmp_direction.value\n acc_df.loc[index, \"opposite_interval\"] = current_interval\n change = (current_value - pre_value) / abs(pre_value)\n acc_df.loc[index, \"opposite_change\"] = change\n acc_df.loc[index, \"opposite_slope\"] = change / current_interval\n\n # 第一次反向\n if zen_state.opposite_count == 1:\n acc_df.loc[pre_index, tmp_fenxing_col] = True\n acc_df.loc[pre_index, \"fenxing_power\"] = fenxing_power(\n acc_df.loc[pre_index - 1],\n pre_kdata,\n kdata,\n fenxing=tmp_fenxing_col,\n )\n\n if zen_state.can_fenxing is not None:\n # 候选底分型\n if tmp_direction == Direction.up:\n # 取小的\n if pre_kdata[\"low\"] <= zen_state.can_fenxing[\"low\"]:\n zen_state.can_fenxing = pre_kdata[[\"low\", \"high\"]]\n zen_state.can_fenxing_index = pre_index\n\n # 候选顶分型\n else:\n # 取大的\n if pre_kdata[\"high\"] >= zen_state.can_fenxing[\"high\"]:\n zen_state.can_fenxing = pre_kdata[[\"low\", \"high\"]]\n zen_state.can_fenxing_index = pre_index\n else:\n zen_state.can_fenxing = pre_kdata[[\"low\", \"high\"]]\n zen_state.can_fenxing_index = pre_index\n\n # 分型确立\n if zen_state.can_fenxing is not None:\n if zen_state.opposite_count >= 4 or (index - zen_state.can_fenxing_index >= 8):\n acc_df.loc[zen_state.can_fenxing_index, fenxing_col] = True\n\n # 记录笔的值\n if fenxing_col == \"bi_ding\":\n bi_value = acc_df.loc[zen_state.can_fenxing_index, \"high\"]\n else:\n bi_value = acc_df.loc[zen_state.can_fenxing_index, \"low\"]\n acc_df.loc[zen_state.can_fenxing_index, \"bi_value\"] = bi_value\n\n # 计算笔斜率\n if zen_state.pre_bi:\n change = (bi_value - zen_state.pre_bi[1]) / abs(zen_state.pre_bi[1])\n interval = zen_state.can_fenxing_index - zen_state.pre_bi[0]\n bi_slope = change / interval\n acc_df.loc[zen_state.can_fenxing_index, \"bi_change\"] = change\n acc_df.loc[zen_state.can_fenxing_index, \"bi_slope\"] = bi_slope\n acc_df.loc[zen_state.can_fenxing_index, \"bi_interval\"] = interval\n\n # 记录用于计算笔中枢的笔\n zen_state.bis.append(\n (\n acc_df.loc[zen_state.can_fenxing_index, \"timestamp\"],\n bi_value,\n zen_state.can_fenxing_index,\n )\n )\n\n # 计算笔中枢，当下来说这个 中枢 是确定的，并且是不可变的\n # 但标记的点为 过去，注意在回测时最近的一个中枢可能用到未来函数，前一个才是 已知的\n # 所以记了一个 current_zhongshu_y0 current_zhongshu_y1 这个是可直接使用的\n end_index = zen_state.can_fenxing_index\n\n (\n zen_state.bis,\n current_zhongshu,\n current_zhongshu_change,\n current_zhongshu_interval,\n ) = handle_zhongshu(\n points=zen_state.bis,\n acc_df=acc_df,\n end_index=end_index,\n zhongshu_col=\"bi_zhongshu\",\n zhongshu_change_col=\"bi_zhongshu_change\",\n )\n\n if not current_merge_zhongshu:\n current_merge_zhongshu = current_zhongshu\n current_merge_zhongshu_change = current_zhongshu_change\n current_merge_zhongshu_level = 1\n current_merge_zhongshu_interval = current_zhongshu_interval\n else:\n if current_zhongshu:\n range_a = (\n current_merge_zhongshu.y0,\n current_merge_zhongshu.y1,\n )\n range_b = (current_zhongshu.y0, current_zhongshu.y1)\n combine_range = combine(range_a, range_b)\n if combine_range:\n y0 = combine_range[0]\n y1 = combine_range[1]\n current_merge_zhongshu = Rect(\n x0=current_merge_zhongshu.x0,\n x1=current_zhongshu.x1,\n y0=y0,\n y1=y1,\n )\n current_merge_zhongshu_change = abs(y0 - y1) / abs(y0)\n current_merge_zhongshu_level = current_merge_zhongshu_level + 1\n current_merge_zhongshu_interval = (\n current_merge_zhongshu_interval + current_zhongshu_interval\n )\n else:\n current_merge_zhongshu = current_zhongshu\n current_merge_zhongshu_change = current_zhongshu_change\n current_merge_zhongshu_level = 1\n current_merge_zhongshu_interval = current_zhongshu_interval\n\n acc_df.loc[end_index, \"merge_zhongshu\"] = current_merge_zhongshu\n acc_df.loc[end_index, \"merge_zhongshu_change\"] = current_merge_zhongshu_change\n acc_df.loc[end_index, \"merge_zhongshu_level\"] = current_merge_zhongshu_level\n acc_df.loc[end_index, \"merge_zhongshu_interval\"] = current_merge_zhongshu_interval\n\n zen_state.merge_zhongshu = current_merge_zhongshu\n zen_state.merge_zhongshu_interval = current_merge_zhongshu_interval\n zen_state.merge_zhongshu_level = current_merge_zhongshu_level\n\n zen_state.pre_bi = (zen_state.can_fenxing_index, bi_value)\n\n zen_state.opposite_count = 0\n zen_state.direction = zen_state.direction.opposite()\n zen_state.can_fenxing = None\n\n # 确定第一个段\n if zen_state.fenxing_list != None:\n zen_state.fenxing_list.append(\n Fenxing(\n state=fenxing_col,\n kdata={\n \"low\": float(acc_df.loc[zen_state.can_fenxing_index][\"low\"]),\n \"high\": float(acc_df.loc[zen_state.can_fenxing_index][\"high\"]),\n },\n index=zen_state.can_fenxing_index,\n )\n )\n\n if len(zen_state.fenxing_list) == 4:\n duan_state = handle_duan(\n fenxing_list=zen_state.fenxing_list,\n pre_duan_state=zen_state.current_duan_state,\n )\n\n change = duan_state != zen_state.current_duan_state\n\n if change:\n zen_state.current_duan_state = duan_state\n\n # 确定状态\n acc_df.loc[\n zen_state.fenxing_list[0].index : zen_state.fenxing_list[-1].index,\n \"duan_state\",\n ] = zen_state.current_duan_state\n\n duan_index = zen_state.fenxing_list[0].index\n if zen_state.current_duan_state == \"up\":\n acc_df.loc[duan_index, \"duan_di\"] = True\n duan_value = acc_df.loc[duan_index, \"low\"]\n else:\n duan_index = zen_state.fenxing_list[0].index\n acc_df.loc[duan_index, \"duan_ding\"] = True\n duan_value = acc_df.loc[duan_index, \"high\"]\n # 记录段的值\n acc_df.loc[duan_index, \"duan_value\"] = duan_value\n\n # 计算段斜率\n if zen_state.pre_duan:\n change = (duan_value - zen_state.pre_duan[1]) / abs(zen_state.pre_duan[1])\n interval = duan_index - zen_state.pre_duan[0]\n duan_slope = change / interval\n acc_df.loc[duan_index, \"duan_change\"] = change\n acc_df.loc[duan_index, \"duan_slope\"] = duan_slope\n acc_df.loc[duan_index, \"duan_interval\"] = interval\n\n zen_state.pre_duan = (duan_index, duan_value)\n\n # 记录用于计算中枢的段\n zen_state.duans.append(\n (\n acc_df.loc[duan_index, \"timestamp\"],\n duan_value,\n duan_index,\n )\n )\n\n # 计算中枢\n zen_state.duans, _, _, _ = handle_zhongshu(\n points=zen_state.duans,\n acc_df=acc_df,\n end_index=duan_index,\n zhongshu_col=\"zhongshu\",\n zhongshu_change_col=\"zhongshu_change\",\n )\n\n # 只留最后一个\n zen_state.fenxing_list = zen_state.fenxing_list[-1:]\n else:\n # 保持之前的状态并踢出候选\n acc_df.loc[zen_state.fenxing_list[0].index, \"duan_state\"] = (\n zen_state.current_duan_state\n )\n zen_state.fenxing_list = zen_state.fenxing_list[1:]\n\n pre_kdata = kdata\n pre_index = index\n\n acc_df = acc_df.set_index(\"timestamp\", drop=False)\n return acc_df, zen_state\n\n\nclass ZenFactor(TechnicalFactor):\n accumulator = ZenAccumulator()\n\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = False,\n adjust_type: Union[AdjustType, str] = None,\n ) -> None:\n self.factor_schema = get_zen_factor_schema(entity_type=entity_schema.__name__, level=level)\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n def factor_col_map_object_hook(self) -> dict:\n return {\n \"zhongshu\": decode_rect,\n \"bi_zhongshu\": decode_rect,\n \"merge_zhongshu\": decode_rect,\n }\n\n def state_encoder(self):\n return FactorStateEncoder\n\n def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]:\n bi_value = self.factor_df[[\"bi_value\"]].dropna()\n # duan_value = self.factor_df[['duan_value']].dropna()\n return [bi_value]\n\n def drawer_rects(self) -> List[Rect]:\n df1 = self.factor_df[[\"merge_zhongshu\"]].dropna()\n return df1[\"merge_zhongshu\"].tolist()\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n # bi_slope = self.factor_df[['bi_slope']].dropna()\n # duan_slope = self.factor_df[['duan_slope']].dropna()\n # power = self.factor_df[['fenxing_power']].dropna()\n # zhongshu_change = self.factor_df[['zhongshu_change']].dropna()\n # return [bi_slope, duan_slope, power, zhongshu_change]\n # change1 = self.factor_df[['current_merge_zhongshu_level']].dropna()\n # change2 = self.factor_df[['opposite_change']].dropna()\n current_slope = self.factor_df[[\"current_slope\"]].dropna()\n return [current_slope]\n\n\nif __name__ == \"__main__\":\n entity_ids = [\"index_sh_000001\"]\n Index1dKdata.record_data(entity_ids=entity_ids)\n\n f = ZenFactor(\n entity_schema=Index,\n entity_ids=entity_ids,\n need_persist=False,\n provider=\"em\",\n entity_provider=\"exchange\",\n )\n f.draw(show=True)\n\n\n# the __all__ is generated\n__all__ = [\"FactorStateEncoder\", \"get_zen_factor_schema\", \"ZenState\", \"handle_zhongshu\", \"ZenAccumulator\", \"ZenFactor\"]\n"
},
{
"path": "src/zvt/factors/zen/domain/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule index_1d_zen_factor\nfrom .index_1d_zen_factor import *\nfrom .index_1d_zen_factor import __all__ as _index_1d_zen_factor_all\n\n__all__ += _index_1d_zen_factor_all\n\n# import all from submodule stock_1wk_zen_factor\nfrom .stock_1wk_zen_factor import *\nfrom .stock_1wk_zen_factor import __all__ as _stock_1wk_zen_factor_all\n\n__all__ += _stock_1wk_zen_factor_all\n\n# import all from submodule common\nfrom .common import *\nfrom .common import __all__ as _common_all\n\n__all__ += _common_all\n\n# import all from submodule stock_1d_zen_factor\nfrom .stock_1d_zen_factor import *\nfrom .stock_1d_zen_factor import __all__ as _stock_1d_zen_factor_all\n\n__all__ += _stock_1d_zen_factor_all\n"
},
{
"path": "src/zvt/factors/zen/domain/common.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Float, String, Boolean, Integer\n\nfrom zvt.contract import Mixin\n\n\nclass ZenFactorCommon(Mixin):\n level = Column(String(length=32))\n # 开盘价\n open = Column(Float)\n # 收盘价\n close = Column(Float)\n # 最高价\n high = Column(Float)\n # 最低价\n low = Column(Float)\n # 成交量\n volume = Column(Float)\n # 成交金额\n turnover = Column(Float)\n\n # 笔的底\n bi_di = Column(Boolean)\n # 笔的顶\n bi_ding = Column(Boolean)\n # 记录笔顶/底分型的值，bi_di取low,bi_ding取high,其他为None,绘图时取有值的连线即为 笔\n bi_value = Column(Float)\n # 笔的变化\n bi_change = Column(Float)\n # 笔的斜率\n bi_slope = Column(Float)\n # 持续的周期\n bi_interval = Column(Integer)\n\n # 记录临时分型，不变\n tmp_ding = Column(Boolean)\n tmp_di = Column(Boolean)\n # 分型的力度\n fenxing_power = Column(Float)\n\n # 目前分型确定的方向\n current_direction = Column(String(length=16))\n current_change = Column(Float)\n current_interval = Column(Integer)\n current_slope = Column(Float)\n # 最近的一个笔中枢\n # current_zhongshu = Column(String(length=512))\n current_zhongshu_y0 = Column(Float)\n current_zhongshu_y1 = Column(Float)\n current_zhongshu_change = Column(Float)\n\n current_merge_zhongshu_y0 = Column(Float)\n current_merge_zhongshu_y1 = Column(Float)\n current_merge_zhongshu_change = Column(Float)\n current_merge_zhongshu_level = Column(Integer)\n current_merge_zhongshu_interval = Column(Integer)\n\n # 目前走势的临时方向 其跟direction的的关系 确定了下一个分型\n tmp_direction = Column(String(length=16))\n # 已经确定分型，目前反向才有值\n opposite_change = Column(Float)\n opposite_slope = Column(Float)\n opposite_interval = Column(Integer)\n\n duan_state = Column(String(length=32))\n\n # 段的底\n duan_di = Column(Boolean)\n # 段的顶\n duan_ding = Column(Boolean)\n # 记录段顶/底的值，为duan_di时取low,为duan_ding时取high,其他为None,绘图时取有值的连线即为 段\n duan_value = Column(Float)\n # 段的变化\n duan_change = Column(Float)\n # 段的斜率\n duan_slope = Column(Float)\n # 持续的周期\n duan_interval = Column(Integer)\n\n # 记录在确定中枢的最后一个段的终点x1，值为Rect(x0,y0,x1,y1)\n zhongshu = Column(String(length=512))\n zhongshu_change = Column(Float)\n\n # 记录在确定中枢的最后一个笔的终点x1，值为Rect(x0,y0,x1,y1)\n bi_zhongshu = Column(String(length=512))\n bi_zhongshu_change = Column(Float)\n\n # 从前往后，合并相邻的有重叠的笔中枢\n merge_zhongshu = Column(String(length=512))\n merge_zhongshu_change = Column(Float)\n merge_zhongshu_level = Column(Integer)\n merge_zhongshu_interval = Column(Integer)\n\n\n# the __all__ is generated\n__all__ = [\"ZenFactorCommon\"]\n"
},
{
"path": "src/zvt/factors/zen/domain/index_1d_zen_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy.ext.declarative import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.factors.zen.domain.common import ZenFactorCommon\n\nIndex1dZenFactorBase = declarative_base()\n\n\nclass Index1dZenFactor(Index1dZenFactorBase, ZenFactorCommon):\n __tablename__ = \"index_1d_zen_factor\"\n\n\nregister_schema(db_name=\"index_1d_zen_factor\", schema_base=Index1dZenFactorBase, entity_type=\"index\", internal=True)\n\n\n# the __all__ is generated\n__all__ = [\"Index1dZenFactor\"]\n"
},
{
"path": "src/zvt/factors/zen/domain/stock_1d_zen_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy.ext.declarative import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.factors.zen.domain.common import ZenFactorCommon\n\nStock1dZenFactorBase = declarative_base()\n\n\nclass Stock1dZenFactor(Stock1dZenFactorBase, ZenFactorCommon):\n __tablename__ = \"stock_1d_zen_factor\"\n\n\nregister_schema(db_name=\"stock_1d_zen_factor\", schema_base=Stock1dZenFactorBase, entity_type=\"stock\", internal=True)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1dZenFactor\"]\n"
},
{
"path": "src/zvt/factors/zen/domain/stock_1wk_zen_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy.ext.declarative import declarative_base\n\nfrom zvt.contract.register import register_schema\nfrom zvt.factors.zen.domain.common import ZenFactorCommon\n\nStock1wkZenFactorBase = declarative_base()\n\n\nclass Stock1wkZenFactor(Stock1wkZenFactorBase, ZenFactorCommon):\n __tablename__ = \"stock_1wk_zen_factor\"\n\n\nregister_schema(\n db_name=\"stock_1wk_zen_factor\", schema_base=Stock1wkZenFactorBase, entity_type=\"stock\", internal=True\n)\n\n\n# the __all__ is generated\n__all__ = [\"Stock1wkZenFactor\"]\n"
},
{
"path": "src/zvt/factors/zen/zen_factor.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport math\nfrom enum import Enum\nfrom typing import List, Optional\nfrom typing import Union, Type\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.data_type import Bean\nfrom zvt.contract.drawer import Rect\nfrom zvt.contract.factor import Accumulator\nfrom zvt.contract.factor import Transformer\nfrom zvt.domain import Stock\nfrom zvt.factors.algorithm import distance, intersect\nfrom zvt.factors.zen.base_factor import ZenFactor\nfrom zvt.utils.pd_utils import (\n group_by_entity_id,\n normalize_group_compute_result,\n pd_is_not_null,\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass ZhongshuRange(Enum):\n # <=0.4\n small = \"small\"\n # >0.4\n big = \"big\"\n\n @classmethod\n def of(cls, change):\n if change <= 0.4:\n return ZhongshuRange.small\n else:\n return ZhongshuRange.big\n\n\nclass ZhongshuLevel(Enum):\n # level <= 3\n level1 = \"level1\"\n # 3 < level <=7\n level2 = \"level2\"\n # level > 7\n level3 = \"level3\"\n\n @classmethod\n def of(cls, level):\n if level <= 3:\n return ZhongshuLevel.level1\n elif level <= 7:\n return ZhongshuLevel.level2\n else:\n return ZhongshuLevel.level3\n\n\nclass ZhongshuDistance(Enum):\n big_up = \"big_up\"\n big_down = \"big_down\"\n small_up = \"small_up\"\n small_down = \"small_down\"\n\n @classmethod\n def of(cls, d):\n if d is None or math.isnan(d) or d == 0:\n zhongshu_distance = None\n elif d <= -0.5:\n zhongshu_distance = ZhongshuDistance.big_down\n elif d < 0:\n zhongshu_distance = ZhongshuDistance.small_down\n elif d <= 0.5:\n zhongshu_distance = ZhongshuDistance.small_up\n else:\n zhongshu_distance = ZhongshuDistance.big_up\n return zhongshu_distance\n\n\nclass Zhongshu(object):\n def __str__(self) -> str:\n if self.zhongshu_distance:\n d = self.zhongshu_distance.value\n else:\n d = None\n return f\"{self.zhongshu_range.value},{self.zhongshu_level.value},{d}\"\n\n def __eq__(self, o: object) -> bool:\n if isinstance(o, self.__class__):\n return (\n self.zhongshu_range == o.zhongshu_range\n and self.zhongshu_level == o.zhongshu_level\n and self.zhongshu_distance == o.zhongshu_distance\n )\n return False\n\n def __init__(\n self,\n zhongshu_range: ZhongshuRange,\n zhongshu_level: ZhongshuLevel,\n zhongshu_distance: ZhongshuDistance,\n ) -> None:\n self.zhongshu_range = zhongshu_range\n self.zhongshu_level = zhongshu_level\n self.zhongshu_distance = zhongshu_distance\n\n\ndef category_zen_state():\n all_states = []\n\n for zhongshu_range in ZhongshuRange:\n for zhongshu_level in ZhongshuLevel:\n for distance in ZhongshuDistance:\n pass\n\n\nclass ZenState(Bean):\n def __eq__(self, o: object) -> bool:\n if isinstance(o, self.__class__):\n return self.zhongshu_list == o.zhongshu_list\n\n def __str__(self) -> str:\n return \",\".join([f\"{elem}\" for elem in self.zhongshu_list])\n\n def __init__(self, zhongshu_state_list: List) -> None:\n self.zhongshu_list: List[Zhongshu] = []\n self.zhongshu_state_list = zhongshu_state_list\n\n pre_range = None\n for zhongshu_state in zhongshu_state_list:\n current_range = (zhongshu_state[0], zhongshu_state[1])\n d = None\n if pre_range is None:\n pre_range = current_range\n else:\n d = distance(pre_range, current_range)\n pre_range = current_range\n change = zhongshu_state[2]\n level = zhongshu_state[3]\n\n zhongshu_range = ZhongshuRange.of(change=change)\n zhongshu_level = ZhongshuLevel.of(level=level)\n zhongshu_distance = ZhongshuDistance.of(d=d)\n\n zhongshu = Zhongshu(\n zhongshu_range=zhongshu_range,\n zhongshu_level=zhongshu_level,\n zhongshu_distance=zhongshu_distance,\n )\n\n self.zhongshu_list.append(zhongshu)\n\n\ndef cal_distance(s):\n d_list = []\n current_range = None\n print(s)\n for idx, row in s.items():\n d = None\n if row is not None:\n if current_range is None:\n current_range = row\n else:\n d = distance((current_range.y0, current_range.y1), (row.y0, row.y1))\n current_range = row\n d_list.append(d)\n return pd.Series(index=s.index, data=d_list)\n\n\ndef cal_zen_state(s):\n zen_states = []\n zhongshu_state_list = []\n current_zhongshu_state = None\n for idx, row in s.items():\n # row\n # 0 current_merge_zhongshu_y0\n # 1 current_merge_zhongshu_y1\n # 2 current_merge_zhongshu_change\n # 3 current_merge_zhongshu_level\n # 4 current_merge_zhongshu_interval\n if row[0] is not None and not math.isnan(row[0]):\n if current_zhongshu_state != row:\n # 相同的中枢，保留最近的(包含关系时产生)\n if current_zhongshu_state != None and intersect(\n (current_zhongshu_state[0], current_zhongshu_state[1]),\n (row[0], row[1]),\n ):\n zhongshu_state_list = zhongshu_state_list[:-1]\n\n # 最多保留最近5个\n zhongshu_state_list = zhongshu_state_list[-4:] + [row]\n current_zhongshu_state = row\n\n if len(zhongshu_state_list) == 5:\n zen_states.append(ZenState(zhongshu_state_list))\n else:\n zen_states.append(None)\n return pd.Series(index=s.index, data=zen_states)\n\n\ndef good_state(zen_state: ZenState):\n if zen_state:\n zhongshu0 = zen_state.zhongshu_list[0]\n zhongshu1 = zen_state.zhongshu_list[1]\n zhongshu2 = zen_state.zhongshu_list[2]\n zhongshu3 = zen_state.zhongshu_list[3]\n zhongshu4 = zen_state.zhongshu_list[4]\n\n # 没大涨过\n if ZhongshuDistance.big_up not in (\n zhongshu1.zhongshu_distance,\n zhongshu2.zhongshu_distance,\n zhongshu3.zhongshu_distance,\n zhongshu4.zhongshu_distance,\n ):\n if ZhongshuRange.big not in (\n zhongshu3.zhongshu_range,\n zhongshu4.zhongshu_range,\n ):\n # 最近一个窄幅震荡\n if ZhongshuRange.small == zhongshu4.zhongshu_range and ZhongshuLevel.level1 != zhongshu4.zhongshu_level:\n return True\n\n return False\n\n\ndef trending_state(zen_state: ZenState):\n if zen_state:\n zhongshu0 = zen_state.zhongshu_list[0]\n zhongshu1 = zen_state.zhongshu_list[1]\n zhongshu2 = zen_state.zhongshu_list[2]\n zhongshu3 = zen_state.zhongshu_list[3]\n zhongshu4 = zen_state.zhongshu_list[4]\n\n # 没大涨过\n if ZhongshuDistance.big_up not in (\n zhongshu1.zhongshu_distance,\n zhongshu2.zhongshu_distance,\n zhongshu3.zhongshu_distance,\n ):\n if ZhongshuRange.big not in (\n zhongshu3.zhongshu_range,\n zhongshu4.zhongshu_range,\n ):\n # 最近一个窄幅震荡\n if ZhongshuRange.small == zhongshu4.zhongshu_range and ZhongshuLevel.level1 == zhongshu4.zhongshu_level:\n return True\n\n return False\n\n\nclass TrendingFactor(ZenFactor):\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = True,\n adjust_type: Union[AdjustType, str] = None,\n ) -> None:\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n def compute_result(self):\n super().compute_result()\n if pd_is_not_null(self.factor_df):\n df = self.factor_df.apply(\n lambda x: (\n x[\"current_merge_zhongshu_y0\"],\n x[\"current_merge_zhongshu_y1\"],\n x[\"current_merge_zhongshu_change\"],\n x[\"current_merge_zhongshu_level\"],\n x[\"current_merge_zhongshu_interval\"],\n ),\n axis=1,\n )\n\n state_df = group_by_entity_id(df).apply(cal_zen_state)\n print(self.factor_df)\n print(state_df)\n self.factor_df[\"zen_state\"] = normalize_group_compute_result(state_df)\n self.factor_df[\"good_state\"] = self.factor_df[\"zen_state\"].apply(good_state)\n\n s = self.factor_df[\"good_state\"]\n self.result_df = s.to_frame(name=\"filter_result\")\n\n\nclass ShakingFactor(ZenFactor):\n # 震荡区间\n shaking_range = 0.5\n\n def __init__(\n self,\n entity_schema: Type[TradableEntity] = Stock,\n provider: str = None,\n entity_provider: str = None,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n limit: int = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n category_field: str = \"entity_id\",\n time_field: str = \"timestamp\",\n keep_window: int = None,\n keep_all_timestamp: bool = False,\n fill_method: str = \"ffill\",\n effective_number: int = None,\n transformer: Transformer = None,\n accumulator: Accumulator = None,\n need_persist: bool = False,\n only_compute_factor: bool = False,\n factor_name: str = None,\n clear_state: bool = False,\n only_load_factor: bool = True,\n adjust_type: Union[AdjustType, str] = None,\n ) -> None:\n super().__init__(\n entity_schema,\n provider,\n entity_provider,\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n columns,\n filters,\n order,\n limit,\n level,\n category_field,\n time_field,\n keep_window,\n keep_all_timestamp,\n fill_method,\n effective_number,\n transformer,\n accumulator,\n need_persist,\n only_compute_factor,\n factor_name,\n clear_state,\n only_load_factor,\n adjust_type,\n )\n\n def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]:\n df1 = self.factor_df[[\"current_merge_zhongshu_y1\"]].dropna()\n df2 = self.factor_df[[\"current_merge_zhongshu_y0\"]].dropna()\n return [df1, df2]\n\n def drawer_rects(self) -> List[Rect]:\n return super().drawer_rects()\n\n def compute_result(self):\n super().compute_result()\n # 窄幅震荡\n s1 = self.factor_df[\"current_merge_zhongshu_change\"] <= self.shaking_range\n # 中枢级别\n s2 = self.factor_df[\"current_merge_zhongshu_level\"] >= 2\n s3 = self.factor_df[\"current_merge_zhongshu_interval\"] >= 120\n\n # 中枢上缘\n s4 = self.factor_df[\"close\"] <= 1.1 * self.factor_df[\"current_merge_zhongshu_y1\"]\n s5 = self.factor_df[\"close\"] >= 0.9 * self.factor_df[\"current_merge_zhongshu_y1\"]\n\n # 中枢下缘\n s6 = self.factor_df[\"close\"] <= 1.1 * self.factor_df[\"current_merge_zhongshu_y0\"]\n s7 = self.factor_df[\"close\"] >= 0.9 * self.factor_df[\"current_merge_zhongshu_y0\"]\n\n s = s1 & s2 & s3 & ((s4 & s5) | (s6 & s7))\n # s = s.groupby(level=0).apply(drop_continue_duplicate)\n if s.index.nlevels == 3:\n s = s.reset_index(level=0, drop=True)\n\n self.result_df = s.to_frame(name=\"filter_result\")\n print(self.result_df)\n\n\nif __name__ == \"__main__\":\n entity_ids = [\"stock_sz_000338\"]\n\n f = ZenFactor(\n provider=\"em\",\n entity_schema=Stock,\n entity_ids=entity_ids,\n need_persist=True,\n )\n f.draw(show=True)\n\n\n# the __all__ is generated\n__all__ = [\n \"ZhongshuRange\",\n \"ZhongshuLevel\",\n \"ZhongshuDistance\",\n \"Zhongshu\",\n \"category_zen_state\",\n \"ZenState\",\n \"cal_distance\",\n \"cal_zen_state\",\n \"good_state\",\n \"trending_state\",\n \"TrendingFactor\",\n \"ShakingFactor\",\n]\n"
},
{
"path": "src/zvt/fill_project.py",
"content": "# script to auto generate some files\nfrom zvt.autocode.generator import gen_kdata_schema, gen_exports\nfrom zvt.contract import AdjustType\nfrom zvt.contract import IntervalLevel\n\n\ndef gen_kdata_schemas():\n \"\"\"\n generate kdata(OHLC) schemas for tradable entity\n\n \"\"\"\n # A股行情\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"stock\",\n levels=[\n level for level in IntervalLevel if level not in (IntervalLevel.LEVEL_L2_QUOTE, IntervalLevel.LEVEL_TICK)\n ],\n adjust_types=[None, AdjustType.hfq],\n entity_in_submodule=True,\n )\n # 中国期货\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"future\",\n levels=[IntervalLevel.LEVEL_1DAY],\n entity_in_submodule=True,\n )\n\n # 美股\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"stockus\",\n levels=[IntervalLevel.LEVEL_1DAY],\n adjust_types=[None, AdjustType.hfq],\n entity_in_submodule=True,\n )\n # 美指\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"indexus\",\n levels=[IntervalLevel.LEVEL_1DAY],\n entity_in_submodule=True,\n )\n\n # 港股\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"stockhk\",\n levels=[IntervalLevel.LEVEL_1DAY],\n adjust_types=[None, AdjustType.hfq],\n entity_in_submodule=True,\n )\n\n # 板块行情\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"block\",\n levels=[IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK, IntervalLevel.LEVEL_1MON],\n entity_in_submodule=True,\n )\n\n # A股指数行情\n gen_kdata_schema(\n pkg=\"zvt\",\n entity_type=\"index\",\n levels=[IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK],\n entity_in_submodule=True,\n )\n\n # etf行情\n gen_kdata_schema(\n pkg=\"zvt\", entity_type=\"etf\", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True\n )\n\n # currency行情\n gen_kdata_schema(\n pkg=\"zvt\", entity_type=\"currency\", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True\n )\n\n\nif __name__ == \"__main__\":\n # gen_exports(\"api\")\n # gen_exports(\"broker\")\n # gen_exports(\"common\")\n # gen_exports(\"contract\", export_from_package=True, export_modules=[\"schema\"])\n gen_exports(\"domain\", export_from_package=True)\n # gen_exports(\"factors\", export_from_package=True)\n # gen_exports(\"trading\")\n\n # gen_exports(\"ml\")\n # gen_exports(\"utils\", export_from_package=False, export_var=True)\n # gen_exports('informer')\n # gen_exports('trader')\n # gen_exports('autocode')\n # gen_exports(\"zhdate\")\n gen_exports(\"recorders\", export_from_package=True, exclude_modules=[\"qmt\"])\n # gen_exports(\"tag\", export_from_package=False)\n # gen_exports(\"sso\", export_from_package=False)\n # gen_kdata_schemas()\n # zip_dir(ZVT_TEST_DATA_PATH, zip_file_name=DATA_SAMPLE_ZIP_PATH)\n"
},
{
"path": "src/zvt/informer/__init__.py",
"content": "# -*- coding: utf-8 -*-\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule informer\nfrom .informer import *\nfrom .informer import __all__ as _informer_all\n\n__all__ += _informer_all\n"
},
{
"path": "src/zvt/informer/inform_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport eastmoneypy\nimport requests\n\nfrom zvt import zvt_config\nfrom zvt.contract.api import get_entities\nfrom zvt.informer import EmailInformer\n\n\ndef inform_email(entity_ids, entity_type, target_date, title, provider):\n msg = \"no targets\"\n if entity_ids:\n entities = get_entities(provider=provider, entity_type=entity_type, entity_ids=entity_ids, return_type=\"domain\")\n assert len(entities) == len(entity_ids)\n\n infos = [f\"{entity.name}({entity.code})\" for entity in entities]\n msg = \"\\n\".join(infos) + \"\\n\"\n\n EmailInformer().send_message(zvt_config[\"email_username\"], f\"{target_date} {title}\", msg)\n\n\ndef add_to_eastmoney(codes, group, entity_type=\"stock\", over_write=True, headers_list=None):\n if headers_list is None:\n headers_list = [None]\n\n for headers in headers_list:\n with requests.Session() as session:\n group_id = eastmoneypy.get_group_id(group, session=session, headers=headers)\n\n need_create_group = False\n\n if not group_id:\n need_create_group = True\n\n if group_id and over_write:\n eastmoneypy.del_group(group_name=group, session=session, headers=headers)\n need_create_group = True\n\n codes = set(codes)\n if need_create_group:\n result = eastmoneypy.create_group(group_name=group, session=session, headers=headers)\n group_id = result[\"gid\"]\n else:\n current_codes = eastmoneypy.list_entities(group_id=group_id, session=session, headers=headers)\n if current_codes:\n codes = codes - set(current_codes)\n\n for code in codes:\n eastmoneypy.add_to_group(\n code=code, entity_type=entity_type, group_id=group_id, session=session, headers=headers\n )\n\n\ndef clean_eastmoney_groups(keep, headers_list=None):\n if headers_list is None:\n headers_list = [None]\n\n for headers in headers_list:\n if keep is None:\n keep = [\"自选股\"]\n with requests.Session() as session:\n groups = eastmoneypy.get_groups(session=session, headers=headers)\n groups_to_clean = [group[\"gid\"] for group in groups if group[\"gname\"] not in keep]\n for gid in groups_to_clean:\n eastmoneypy.del_group(group_id=gid, session=session, headers=headers)\n\n\ndef delete_eastmoney_group(group_name, headers_list=None):\n if headers_list is None:\n headers_list = [None]\n for headers in headers_list:\n with requests.Session() as session:\n eastmoneypy.del_group(group_name=group_name, session=session, headers=headers)\n\n\n# the __all__ is generated\n__all__ = [\"inform_email\", \"add_to_eastmoney\", \"clean_eastmoney_groups\", \"delete_eastmoney_group\"]\n"
},
{
"path": "src/zvt/informer/informer.py",
"content": "# -*- coding: utf-8 -*-\nimport email\nimport json\nimport logging\nimport smtplib\nfrom email.header import Header\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nimport requests\n\nfrom zvt import zvt_config\n\nlogger = logging.getLogger(__name__)\n\n\nclass Informer(object):\n def send_message(self, to_user, title, body, **kwargs):\n pass\n\n\nclass EmailInformer(Informer):\n def __init__(self, ssl=True) -> None:\n super().__init__()\n self.ssl = ssl\n\n def send_message_(self, to_user, title, body, **kwargs):\n if (\n not zvt_config[\"smtp_host\"]\n or not zvt_config[\"smtp_port\"]\n or not zvt_config[\"email_username\"]\n or not zvt_config[\"email_password\"]\n ):\n logger.warning(f\"Please set smtp_host/smtp_port/email_username/email_password in ~/zvt-home/config.json\")\n return\n host = zvt_config[\"smtp_host\"]\n port = zvt_config[\"smtp_port\"]\n\n smtp_client = None\n try:\n if self.ssl:\n try:\n smtp_client = smtplib.SMTP_SSL(host=host, port=port)\n except:\n smtp_client = smtplib.SMTP_SSL()\n else:\n try:\n smtp_client = smtplib.SMTP(host=host, port=port)\n except:\n smtp_client = smtplib.SMTP()\n\n smtp_client.connect(host=host, port=port)\n smtp_client.login(zvt_config[\"email_username\"], zvt_config[\"email_password\"])\n msg = MIMEMultipart(\"alternative\")\n msg[\"Subject\"] = Header(title).encode()\n msg[\"From\"] = \"{} <{}>\".format(Header(\"zvt\").encode(), zvt_config[\"email_username\"])\n if type(to_user) is list:\n msg[\"To\"] = \", \".join(to_user)\n else:\n msg[\"To\"] = to_user\n msg[\"Message-id\"] = email.utils.make_msgid()\n msg[\"Date\"] = email.utils.formatdate()\n\n plain_text = MIMEText(body, _subtype=\"plain\", _charset=\"UTF-8\")\n msg.attach(plain_text)\n smtp_client.sendmail(zvt_config[\"email_username\"], to_user, msg.as_string())\n except Exception as e:\n logger.exception(\"send email failed\", e)\n finally:\n if smtp_client:\n try:\n smtp_client.quit()\n except Exception as e:\n logger.exception(\"smtp_client quit failed\", e)\n\n def send_message(self, to_user, title, body, sub_size=20, with_sender=True, **kwargs):\n if type(to_user) is list and sub_size:\n size = len(to_user)\n if size >= sub_size:\n step_size = int(size / sub_size)\n if size % sub_size:\n step_size = step_size + 1\n else:\n step_size = 1\n\n for step in range(step_size):\n sub_to_user = to_user[sub_size * step : sub_size * (step + 1)]\n if with_sender:\n sub_to_user.append(zvt_config[\"email_username\"])\n self.send_message_(sub_to_user, title, body, **kwargs)\n else:\n self.send_message_(to_user, title, body, **kwargs)\n\n\nclass WechatInformer(Informer):\n GET_TOKEN_URL = \"https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid={}&secret={}\".format(\n zvt_config[\"wechat_app_id\"], zvt_config[\"wechat_app_secrect\"]\n )\n\n GET_TEMPLATE_URL = \"https://api.weixin.qq.com/cgi-bin/template/get_all_private_template?access_token={}\"\n SEND_MSG_URL = \"https://api.weixin.qq.com/cgi-bin/message/template/send?access_token={}\"\n\n token = None\n\n def __init__(self) -> None:\n self.refresh_token()\n\n def refresh_token(self):\n resp = requests.get(self.GET_TOKEN_URL)\n logger.info(\"refresh_token resp.status_code:{}, resp.text:{}\".format(resp.status_code, resp.text))\n\n if resp.status_code == 200 and resp.json() and \"access_token\" in resp.json():\n self.token = resp.json()[\"access_token\"]\n else:\n logger.exception(\"could not refresh_token\")\n\n def send_price_notification(self, to_user, security_name, current_price, change_pct):\n the_json = self._format_price_notification(to_user, security_name, current_price, change_pct)\n the_data = json.dumps(the_json, ensure_ascii=False).encode(\"utf-8\")\n\n resp = requests.post(self.SEND_MSG_URL.format(self.token), the_data)\n\n logger.info(\"send_price_notification resp:{}\".format(resp.text))\n\n if resp.json() and resp.json()[\"errcode\"] == 0:\n logger.info(\"send_price_notification to user:{} data:{} success\".format(to_user, the_json))\n\n def _format_price_notification(self, to_user, security_name, current_price, change_pct):\n if change_pct > 0:\n title = \"吃肉喝汤\"\n else:\n title = \"关灯吃面\"\n\n # 先固定一个template\n\n # {\n # \"template_id\": \"mkqi-L1h56mH637vLXiuS_ulLTs1byDYYgLBbSXQ65U\",\n # \"title\": \"涨跌幅提醒\",\n # \"primary_industry\": \"金融业\",\n # \"deputy_industry\": \"证券|基金|理财|信托\",\n # \"content\": \"{{first.DATA}}\\n股票名：{{keyword1.DATA}}\\n最新价：{{keyword2.DATA}}\\n涨跌幅：{{keyword3.DATA}}\\n{{remark.DATA}}\",\n # \"example\": \"您好，腾新控股最新价130.50元，上涨达到设置的3.2%\\r\\n股票名：腾讯控股（00700）\\r\\n最新价：130.50元\\r\\n涨跌幅：+3.2%\\r\\n点击查看最新实时行情。\"\n # }\n\n template_id = \"mkqi-L1h56mH637vLXiuS_ulLTs1byDYYgLBbSXQ65U\"\n the_json = {\n \"touser\": to_user,\n \"template_id\": template_id,\n \"url\": \"http://www.foolcage.com\",\n \"data\": {\n \"first\": {\"value\": title, \"color\": \"#173177\"},\n \"keyword1\": {\"value\": security_name, \"color\": \"#173177\"},\n \"keyword2\": {\"value\": current_price, \"color\": \"#173177\"},\n \"keyword3\": {\"value\": \"{:.2%}\".format(change_pct), \"color\": \"#173177\"},\n \"remark\": {\"value\": \"会所嫩模 Or 下海干活?\", \"color\": \"#173177\"},\n },\n }\n\n return the_json\n\n\nclass QiyeWechatBot(Informer):\n def __init__(self, token=None) -> None:\n self.token = token\n\n def send_message(self, content):\n if not self.token:\n if not zvt_config[\"qiye_wechat_bot_token\"]:\n logger.warning(f\"Please set qiye_wechat_bot_token in ~/zvt-home/config.json\")\n return\n self.token = zvt_config[\"qiye_wechat_bot_token\"]\n\n msg = {\n \"msgtype\": \"text\",\n \"text\": {\"content\": content},\n }\n requests.post(f\"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key={self.token}\", json=msg)\n\n\nif __name__ == \"__main__\":\n # weixin_action = WechatInformer()\n # weixin_action.send_price_notification(to_user='oRvNP0XIb9G3g6a-2fAX9RHX5--Q', security_name='BTC/USDT',\n # current_price=1000, change_pct='0.5%')\n bot = QiyeWechatBot()\n bot.send_message(content=\"test\")\n# the __all__ is generated\n__all__ = [\"Informer\", \"EmailInformer\", \"WechatInformer\"]\n"
},
{
"path": "src/zvt/main.py",
"content": "import dash_bootstrap_components as dbc\nfrom dash import html\nfrom dash.dependencies import Input, Output\n\nfrom zvt.ui import zvt_app\nfrom zvt.ui.apps import factor_app\n\n\ndef serve_layout():\n layout = html.Div(\n children=[\n # banner\n html.Div(className=\"zvt-banner\", children=html.H2(className=\"h2-title\", children=\"ZVT\")),\n dbc.CardHeader(\n dbc.Tabs(\n [dbc.Tab(label=\"factor\", tab_id=\"tab-factor\", label_style={}, tab_style={\"width\": \"100px\"})],\n id=\"card-tabs\",\n active_tab=\"tab-factor\",\n )\n ),\n dbc.CardBody(html.P(id=\"card-content\", className=\"card-text\")),\n ]\n )\n\n return layout\n\n\n@zvt_app.callback(Output(\"card-content\", \"children\"), [Input(\"card-tabs\", \"active_tab\")])\ndef tab_content(active_tab):\n if \"tab-factor\" == active_tab:\n return factor_app.factor_layout()\n\n\nzvt_app.layout = serve_layout\n\n\ndef main():\n # init_plugins()\n zvt_app.run_server(debug=True, host=\"0.0.0.0\")\n # zvt_app.run_server()\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "src/zvt/misc/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/misc/constants.py",
"content": "# -*- coding: utf-8 -*-\nCHINESEYEARCODE = [\n 19416,\n 19168,\n 42352,\n 21717,\n 53856,\n 55632,\n 91476,\n 22176,\n 39632,\n 21970,\n 19168,\n 42422,\n 42192,\n 53840,\n 119381,\n 46400,\n 54944,\n 44450,\n 38320,\n 84343,\n 18800,\n 42160,\n 46261,\n 27216,\n 27968,\n 109396,\n 11104,\n 38256,\n 21234,\n 18800,\n 25958,\n 54432,\n 59984,\n 92821,\n 23248,\n 11104,\n 100067,\n 37600,\n 116951,\n 51536,\n 54432,\n 120998,\n 46416,\n 22176,\n 107956,\n 9680,\n 37584,\n 53938,\n 43344,\n 46423,\n 27808,\n 46416,\n 86869,\n 19872,\n 42416,\n 83315,\n 21168,\n 43432,\n 59728,\n 27296,\n 44710,\n 43856,\n 19296,\n 43748,\n 42352,\n 21088,\n 62051,\n 55632,\n 23383,\n 22176,\n 38608,\n 19925,\n 19152,\n 42192,\n 54484,\n 53840,\n 54616,\n 46400,\n 46752,\n 103846,\n 38320,\n 18864,\n 43380,\n 42160,\n 45690,\n 27216,\n 27968,\n 44870,\n 43872,\n 38256,\n 19189,\n 18800,\n 25776,\n 29859,\n 59984,\n 27480,\n 23232,\n 43872,\n 38613,\n 37600,\n 51552,\n 55636,\n 54432,\n 55888,\n 30034,\n 22176,\n 43959,\n 9680,\n 37584,\n 51893,\n 43344,\n 46240,\n 47780,\n 44368,\n 21977,\n 19360,\n 42416,\n 86390,\n 21168,\n 43312,\n 31060,\n 27296,\n 44368,\n 23378,\n 19296,\n 42726,\n 42208,\n 53856,\n 60005,\n 54576,\n 23200,\n 30371,\n 38608,\n 19195,\n 19152,\n 42192,\n 118966,\n 53840,\n 54560,\n 56645,\n 46496,\n 22224,\n 21938,\n 18864,\n 42359,\n 42160,\n 43600,\n 111189,\n 27936,\n 44448,\n 84835,\n 37744,\n 18936,\n 18800,\n 25776,\n 92326,\n 59984,\n 27296,\n 108228,\n 43744,\n 37600,\n 53987,\n 51552,\n 54615,\n 54432,\n 55888,\n 23893,\n 22176,\n 42704,\n 21972,\n 21200,\n 43448,\n 43344,\n 46240,\n 46758,\n 44368,\n 21920,\n 43940,\n 42416,\n 21168,\n 45683,\n 26928,\n 29495,\n 27296,\n 44368,\n 84821,\n 19296,\n 42352,\n 21732,\n 53600,\n 59752,\n 54560,\n 55968,\n 92838,\n 22224,\n 19168,\n 43476,\n 41680,\n 53584,\n 62034,\n 54560,\n]\n\"\"\"\n从1900年到2100年的农历月份数据代码 20位二进制代码表示一个年份的数据。\n\n前四位0:表示闰月为29天，1:表示闰月为30天\n中间12位：从左起表示1-12月每月的大小，1为30天，0为29天\n最后四位：表示闰月的月份，0表示当年无闰月\n\n前四位和最后四位应该结合使用，如果最后四位为0，则不考虑前四位\n例： \n1901年代码为 19168，转成二进制为 0b100101011100000, 最后四位为0，当年无闰月，月份数据为 010010101110 分别代表12月的大小情况\n1903年代码为 21717，转成二进制为 0b101010011010101，最后四位为5，当年为闰五月，首四位为0，闰月为29天，月份数据为 010101001101 分别代表12月的大小情况\n\n\"\"\"\n\nCHINESENEWYEAR = [\n \"19000131\",\n \"19010219\",\n \"19020208\",\n \"19030129\",\n \"19040216\",\n \"19050204\",\n \"19060125\",\n \"19070213\",\n \"19080202\",\n \"19090122\",\n \"19100210\",\n \"19110130\",\n \"19120218\",\n \"19130206\",\n \"19140126\",\n \"19150214\",\n \"19160203\",\n \"19170123\",\n \"19180211\",\n \"19190201\",\n \"19200220\",\n \"19210208\",\n \"19220128\",\n \"19230216\",\n \"19240205\",\n \"19250124\",\n \"19260213\",\n \"19270202\",\n \"19280123\",\n \"19290210\",\n \"19300130\",\n \"19310217\",\n \"19320206\",\n \"19330126\",\n \"19340214\",\n \"19350204\",\n \"19360124\",\n \"19370211\",\n \"19380131\",\n \"19390219\",\n \"19400208\",\n \"19410127\",\n \"19420215\",\n \"19430205\",\n \"19440125\",\n \"19450213\",\n \"19460202\",\n \"19470122\",\n \"19480210\",\n \"19490129\",\n \"19500217\",\n \"19510206\",\n \"19520127\",\n \"19530214\",\n \"19540203\",\n \"19550124\",\n \"19560212\",\n \"19570131\",\n \"19580218\",\n \"19590208\",\n \"19600128\",\n \"19610215\",\n \"19620205\",\n \"19630125\",\n \"19640213\",\n \"19650202\",\n \"19660121\",\n \"19670209\",\n \"19680130\",\n \"19690217\",\n \"19700206\",\n \"19710127\",\n \"19720215\",\n \"19730203\",\n \"19740123\",\n \"19750211\",\n \"19760131\",\n \"19770218\",\n \"19780207\",\n \"19790128\",\n \"19800216\",\n \"19810205\",\n \"19820125\",\n \"19830213\",\n \"19840202\",\n \"19850220\",\n \"19860209\",\n \"19870129\",\n \"19880217\",\n \"19890206\",\n \"19900127\",\n \"19910215\",\n \"19920204\",\n \"19930123\",\n \"19940210\",\n \"19950131\",\n \"19960219\",\n \"19970207\",\n \"19980128\",\n \"19990216\",\n \"20000205\",\n \"20010124\",\n \"20020212\",\n \"20030201\",\n \"20040122\",\n \"20050209\",\n \"20060129\",\n \"20070218\",\n \"20080207\",\n \"20090126\",\n \"20100214\",\n \"20110203\",\n \"20120123\",\n \"20130210\",\n \"20140131\",\n \"20150219\",\n \"20160208\",\n \"20170128\",\n \"20180216\",\n \"20190205\",\n \"20200125\",\n \"20210212\",\n \"20220201\",\n \"20230122\",\n \"20240210\",\n \"20250129\",\n \"20260217\",\n \"20270206\",\n \"20280126\",\n \"20290213\",\n \"20300203\",\n \"20310123\",\n \"20320211\",\n \"20330131\",\n \"20340219\",\n \"20350208\",\n \"20360128\",\n \"20370215\",\n \"20380204\",\n \"20390124\",\n \"20400212\",\n \"20410201\",\n \"20420122\",\n \"20430210\",\n \"20440130\",\n \"20450217\",\n \"20460206\",\n \"20470126\",\n \"20480214\",\n \"20490202\",\n \"20500123\",\n \"20510211\",\n \"20520201\",\n \"20530219\",\n \"20540208\",\n \"20550128\",\n \"20560215\",\n \"20570204\",\n \"20580124\",\n \"20590212\",\n \"20600202\",\n \"20610121\",\n \"20620209\",\n \"20630129\",\n \"20640217\",\n \"20650205\",\n \"20660126\",\n \"20670214\",\n \"20680203\",\n \"20690123\",\n \"20700211\",\n \"20710131\",\n \"20720219\",\n \"20730207\",\n \"20740127\",\n \"20750215\",\n \"20760205\",\n \"20770124\",\n \"20780212\",\n \"20790202\",\n \"20800122\",\n \"20810209\",\n \"20820129\",\n \"20830217\",\n \"20840206\",\n \"20850126\",\n \"20860214\",\n \"20870203\",\n \"20880124\",\n \"20890210\",\n \"20900130\",\n \"20910218\",\n \"20920207\",\n \"20930127\",\n \"20940215\",\n \"20950205\",\n \"20960125\",\n \"20970212\",\n \"20980201\",\n \"20990121\",\n \"21000209\",\n]\n\"\"\"\n从1900年，至2100年每年的农历春节的公历日期\n\"\"\"\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/misc/misc_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom datetime import datetime\n\nfrom zvt.contract.model import CustomModel\n\n\nclass TimeMessage(CustomModel):\n # 时间\n timestamp: datetime\n # 信息\n message: str\n\n\n# the __all__ is generated\n__all__ = [\"TimeMessage\"]\n"
},
{
"path": "src/zvt/misc/misc_service.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.misc.zhdate import ZhDate\nfrom zvt.utils.time_utils import to_pd_timestamp, current_date, count_interval\n\n\ndef holiday_distance(timestamp=None, consider_future_days=15):\n if not timestamp:\n the_date = current_date()\n else:\n the_date = to_pd_timestamp(timestamp)\n\n # 业绩预告\n month = the_date.month\n\n infos = [f\"今天是{the_date.date()}\"]\n if month == 12:\n infos.append(\"业绩预告期，注意排雷\")\n\n # 元旦\n new_year = to_pd_timestamp(f\"{the_date.year + 1}-01-01\")\n distance = count_interval(the_date, new_year)\n if 0 < distance < consider_future_days:\n infos.append(f\"距离元旦还有{distance}天\")\n if month in (1, 2):\n # 春节\n zh_date = ZhDate(lunar_year=the_date.year, lunar_month=1, lunar_day=1)\n spring_date = zh_date.newyear\n distance = count_interval(the_date, spring_date)\n if 0 < distance < consider_future_days:\n infos.append(f\"距离春节还有{distance}天\")\n\n # 两会\n # 三月初\n lianghui = to_pd_timestamp(f\"{the_date.year}-03-01\")\n distance = count_interval(the_date, lianghui)\n if 0 < distance < consider_future_days:\n infos.append(f\"距离两会还有{distance}天\")\n\n # 年报发布\n if month in (3, 4):\n infos.append(\"年报发布期，注意排雷\")\n\n # 五一\n if month == 4:\n wuyi = to_pd_timestamp(f\"{the_date.year}-05-01\")\n distance = count_interval(the_date, wuyi)\n if 0 < distance < consider_future_days:\n infos.append(f\"距离五一还有{distance}天\")\n\n # 业绩发布\n if month in (7, 8):\n infos.append(\"半年报发布期，注意排雷\")\n\n if month == 9:\n # 国庆\n shiyi = to_pd_timestamp(f\"{the_date.year}-10-01\")\n distance = count_interval(the_date, shiyi)\n if 0 < distance < consider_future_days:\n infos.append(f\"距离国庆还有{distance}天\")\n\n msg = \"，\".join(infos)\n return msg\n\n\ndef get_time_message():\n return {\"timestamp\": current_date(), \"message\": holiday_distance()}\n\n\nif __name__ == \"__main__\":\n print(get_time_message())\n\n# the __all__ is generated\n__all__ = [\"holiday_distance\", \"get_time_message\"]\n"
},
{
"path": "src/zvt/misc/zhdate.py",
"content": "\"\"\"\n-*- coding: utf-8 -*-\nthanks to https://github.com/CutePandaSh/zhdate\n\"\"\"\nfrom datetime import datetime, timedelta\nfrom itertools import accumulate\n\nfrom zvt.misc.constants import CHINESEYEARCODE, CHINESENEWYEAR\n\n\nclass ZhDate:\n def __init__(self, lunar_year, lunar_month, lunar_day, leap_month=False):\n \"\"\"初始化函数\n\n Arguments:\n lunar_year {int} -- 农历年\n lunar_month {int} -- 农历月份\n lunar_day {int} -- 农历日\n\n Keyword Arguments:\n leap_month {bool} -- 是否是在农历闰月中 (default: {False})\n \"\"\"\n self.lunar_year = lunar_year\n self.lunar_month = lunar_month\n self.lunar_day = lunar_day\n self.leap_month = leap_month\n self.year_code = CHINESEYEARCODE[self.lunar_year - 1900]\n self.newyear = datetime.strptime(CHINESENEWYEAR[self.lunar_year - 1900], \"%Y%m%d\")\n if not ZhDate.validate(lunar_year, lunar_month, lunar_day, leap_month):\n raise TypeError(\"农历日期不支持所谓“{}”，超出农历1900年1月1日至2100年12月29日，或日期不存在\".format(self))\n\n def to_datetime(self):\n \"\"\"农历日期转换称公历日期\n\n Returns:\n datetime -- 当前农历对应的公历日期\n \"\"\"\n return self.newyear + timedelta(days=self.__days_passed())\n\n @staticmethod\n def from_datetime(dt):\n \"\"\"静态方法，从公历日期生成农历日期\n\n Arguments:\n dt {datetime} -- 公历的日期\n\n Returns:\n ZhDate -- 生成的农历日期对象\n \"\"\"\n lunar_year = dt.year\n # 如果还没有到农历正月初一 农历年份减去1\n lunar_year -= (datetime.strptime(CHINESENEWYEAR[lunar_year - 1900], \"%Y%m%d\") - dt).total_seconds() > 0\n # 当时农历新年时的日期对象\n newyear_dt = datetime.strptime(CHINESENEWYEAR[lunar_year - 1900], \"%Y%m%d\")\n # 查询日期距离当年的春节差了多久\n days_passed = (dt - newyear_dt).days\n # 被查询日期的年份码\n year_code = CHINESEYEARCODE[lunar_year - 1900]\n # 取得本年的月份列表\n month_days = ZhDate.decode(year_code)\n\n for pos, days in enumerate(accumulate(month_days)):\n if days_passed + 1 <= days:\n month = pos + 1\n lunar_day = month_days[pos] - (days - days_passed) + 1\n break\n\n leap_month = False\n if (year_code & 0xF) == 0 or month <= (year_code & 0xF):\n lunar_month = month\n else:\n lunar_month = month - 1\n\n if (year_code & 0xF) != 0 and month == (year_code & 0xF) + 1:\n leap_month = True\n\n return ZhDate(lunar_year, lunar_month, lunar_day, leap_month)\n\n @staticmethod\n def today():\n return ZhDate.from_datetime(datetime.now())\n\n def __days_passed(self):\n \"\"\"私有方法，计算当前农历日期和当年农历新年之间的天数差值\n\n Returns:\n int -- 差值天数\n \"\"\"\n month_days = ZhDate.decode(self.year_code)\n # 当前农历年的闰月，为0表示无润叶\n month_leap = self.year_code & 0xF\n\n # 当年无闰月，或者有闰月但是当前月小于闰月\n if (month_leap == 0) or (self.lunar_month < month_leap):\n days_passed_month = sum(month_days[: self.lunar_month - 1])\n # 当前不是闰月，并且当前月份和闰月相同\n elif (not self.leap_month) and (self.lunar_month == month_leap):\n days_passed_month = sum(month_days[: self.lunar_month - 1])\n else:\n days_passed_month = sum(month_days[: self.lunar_month])\n\n return days_passed_month + self.lunar_day - 1\n\n def chinese(self):\n ZHNUMS = \"〇一二三四五六七八九十\"\n zh_year = \"\"\n for i in range(0, 4):\n zh_year += ZHNUMS[int(str(self.lunar_year)[i])]\n\n if self.leap_month:\n zh_month = \"闰\"\n else:\n zh_month = \"\"\n\n if self.lunar_month == 1:\n zh_month += \"正\"\n elif self.lunar_month == 12:\n zh_month += \"腊\"\n elif self.lunar_month <= 10:\n zh_month += ZHNUMS[self.lunar_month]\n else:\n zh_month += \"十{}\".format(ZHNUMS[self.lunar_month - 10])\n\n if self.lunar_day <= 10:\n zh_day = \"初{}\".format(ZHNUMS[self.lunar_day])\n elif self.lunar_day < 20:\n zh_day = \"十{}\".format(ZHNUMS[self.lunar_day - 10])\n elif self.lunar_day == 20:\n zh_day = \"二十\"\n elif self.lunar_day < 30:\n zh_day = \"廿{}\".format(ZHNUMS[self.lunar_day - 20])\n else:\n zh_day = \"三十\"\n\n year_tiandi = ZhDate.__tiandi(self.lunar_year - 1900 + 36)\n\n shengxiao = \"鼠牛虎兔龙蛇马羊猴鸡狗猪\"\n\n return \"{}年{}月{} {}{}年\".format(zh_year, zh_month, zh_day, year_tiandi, shengxiao[(self.lunar_year - 1900) % 12])\n\n def __str__(self):\n \"\"\"打印字符串的方法\n\n Returns:\n str -- 标准格式农历日期字符串\n \"\"\"\n return \"农历{}年{}{}月{}日\".format(self.lunar_year, \"闰\" if self.leap_month else \"\", self.lunar_month, self.lunar_day)\n\n def __repr__(self):\n return self.__str__()\n\n def __eq__(self, another):\n if not isinstance(another, ZhDate):\n raise TypeError(\"比较必须都是ZhDate类型\")\n cond1 = self.lunar_year == another.lunar_year\n cond2 = self.lunar_month == another.lunar_month\n cond3 = self.lunar_day == another.lunar_day\n cond4 = self.leap_month == another.leap_month\n return cond1 and cond2 and cond3 and cond4\n\n def __add__(self, another):\n if not isinstance(another, int):\n raise TypeError(\"加法只支持整数天数相加\")\n return ZhDate.from_datetime(self.to_datetime() + timedelta(days=another))\n\n def __sub__(self, another):\n if isinstance(another, int):\n return ZhDate.from_datetime(self.to_datetime() - timedelta(days=another))\n elif isinstance(another, ZhDate):\n return (self.to_datetime() - another.to_datetime()).days\n elif isinstance(another, datetime):\n return (self.to_datetime() - another).days\n else:\n raise TypeError(\"减法只支持整数，ZhDate, Datetime类型\")\n\n \"\"\"\n 以下为帮助函数\n \"\"\"\n\n @staticmethod\n def __tiandi(anum):\n tian = \"甲乙丙丁戊己庚辛壬癸\"\n di = \"子丑寅卯辰巳午未申酉戌亥\"\n return \"{}{}\".format(tian[anum % 10], di[anum % 12])\n\n @staticmethod\n def validate(year, month, day, leap):\n \"\"\"农历日期校验\n\n Arguments:\n year {int} -- 农历年份\n month {int} -- 农历月份\n day {int} -- 农历日期\n leap {bool} -- 农历是否为闰月日期\n\n Returns:\n bool -- 校验是否通过\n \"\"\"\n # 年份低于1900，大于2100，或者月份不属于 1-12，或者日期不属于 1-30，返回校验失败\n if not (1900 <= year <= 2100 and 1 <= month <= 12 and 1 <= day <= 30):\n return False\n\n year_code = CHINESEYEARCODE[year - 1900]\n\n # 有闰月标志\n if leap:\n if (year_code & 0xF) != month: # 年度闰月和校验闰月不一致的话，返回校验失败\n return False\n elif day == 30: # 如果日期是30的话，直接返回年度代码首位是否为1，即闰月是否为大月\n return (year_code >> 16) == 1\n else: # 年度闰月和当前月份相同，日期不为30的情况，返回通过\n return True\n elif day <= 29: # 非闰月，并且日期小于等于29，返回通过\n return True\n else: # 非闰月日期为30，返回年度代码中的月份位是否为1，即是否为大月\n return ((year_code >> (12 - month) + 4) & 1) == 1\n\n @staticmethod\n def decode(year_code):\n \"\"\"解析年度农历代码函数\n\n Arguments:\n year_code {int} -- 从年度代码数组中获取的代码整数\n\n Returns:\n list[int, ] -- 当前年度代码解析以后形成的每月天数数组，已将闰月嵌入对应位置，即有闰月的年份返回的列表长度为13，否则为12\n \"\"\"\n # 请问您为什么不在这么重要的地方写注释？\n month_days = []\n for i in range(4, 16):\n # 向右移动相应的位数\n # 1 这个数只有一位，与任何数进行 按位与 都只能获得其\n # 从后往前第一位，对！是获得这一位\n month_days.insert(0, 30 if (year_code >> i) & 1 else 29)\n\n # 0xf 即 15 即二进制的 1111\n # 所以 1111 与任何数进行 按位与\n # 都将获得其最后四位，对！是获得这最后四位\n # 后四位非0则表示有闰月（多一月），则插入一次月份\n # 而首四位表示闰月的天数\n if year_code & 0xF:\n month_days.insert((year_code & 0xF), 30 if year_code >> 16 else 29)\n\n # 返回一个列表\n return month_days\n\n @staticmethod\n def month_days(year):\n \"\"\"根据年份返回当前农历月份天数list\n\n Arguments:\n year {int} -- 1900到2100的之间的整数\n\n Returns:\n [int] -- 农历年份所对应的农历月份天数列表\n \"\"\"\n return ZhDate.decode(CHINESEYEARCODE[year - 1900])\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/ml/__init__.py",
"content": "# -*- coding: utf-8 -*-\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule lables\nfrom .lables import *\nfrom .lables import __all__ as _lables_all\n\n__all__ += _lables_all\n\n# import all from submodule ml\nfrom .ml import *\nfrom .ml import __all__ as _ml_all\n\n__all__ += _ml_all\n"
},
{
"path": "src/zvt/ml/lables.py",
"content": "# -*- coding: utf-8 -*-\nfrom enum import Enum\n\n\nclass BehaviorCategory(Enum):\n # 上涨\n up = 1\n # 下跌\n down = -1\n\n\nclass RelativePerformance(Enum):\n # 表现比90%好\n best = 0.9\n ordinary = 0.5\n poor = 0\n\n\n# the __all__ is generated\n__all__ = [\"BehaviorCategory\", \"RelativePerformance\"]\n"
},
{
"path": "src/zvt/ml/ml.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nfrom typing import Union, Type, List\n\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression, SGDRegressor\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom zvt.api.kdata import default_adjust_type, get_kdata\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.drawer import Drawer\nfrom zvt.domain import Stock\nfrom zvt.factors.transformers import MaTransformer\nfrom zvt.ml.lables import RelativePerformance, BehaviorCategory\nfrom zvt.utils.pd_utils import group_by_entity_id, normalize_group_compute_result, pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\ndef cal_change(s: pd.Series, predict_range):\n return s.pct_change(periods=-predict_range)\n\n\ndef cal_behavior_cls(s: pd.Series, predict_range):\n return s.pct_change(periods=-predict_range).apply(\n lambda x: BehaviorCategory.up.value if x > 0 else BehaviorCategory.down.value\n )\n\n\ndef cal_predict(s: pd.Series, predict_range):\n return s.shift(periods=-predict_range)\n\n\ndef cal_relative_performance(s: pd.Series):\n if s >= RelativePerformance.best.value:\n return RelativePerformance.best\n if s >= RelativePerformance.ordinary.value:\n return RelativePerformance.ordinary\n if s >= RelativePerformance.poor.value:\n return RelativePerformance.poor\n\n\nclass MLMachine(object):\n entity_schema: Type[TradableEntity] = None\n\n def __init__(\n self,\n entity_ids: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = \"2015-01-01\",\n end_timestamp: Union[str, pd.Timestamp] = \"2021-12-01\",\n predict_start_timestamp: Union[str, pd.Timestamp] = \"2021-06-01\",\n predict_steps: int = 20,\n level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY,\n adjust_type: Union[AdjustType, str] = None,\n data_provider: str = None,\n label_method: str = \"raw\",\n ) -> None:\n \"\"\"\n\n :param entity_ids:\n :param start_timestamp:\n :param end_timestamp:\n :param predict_start_timestamp:\n :param predict_steps:\n :param level:\n :param adjust_type:\n :param data_provider:\n :param label_method: raw, change, or behavior_cls\n \"\"\"\n super().__init__()\n self.entity_ids = entity_ids\n self.start_timestamp = to_pd_timestamp(start_timestamp)\n self.end_timestamp = to_pd_timestamp(end_timestamp)\n self.predict_start_timestamp = to_pd_timestamp(predict_start_timestamp)\n assert self.start_timestamp < self.predict_start_timestamp < self.end_timestamp\n self.predict_steps = predict_steps\n\n self.level = level\n if not adjust_type:\n adjust_type = default_adjust_type(entity_type=self.entity_schema.__name__)\n self.adjust_type = adjust_type\n\n self.data_provider = data_provider\n self.label_method = label_method\n\n self.kdata_df = self.build_kdata()\n if not pd_is_not_null(self.kdata_df):\n logger.error(\"not kdta\")\n assert False\n\n self.feature_df = self.build_feature(self.entity_ids, self.start_timestamp, self.end_timestamp)\n # drop na in feature\n self.feature_df = self.feature_df.dropna()\n self.feature_names = list(set(self.feature_df.columns) - {\"entity_id\", \"timestamp\"})\n self.feature_df = self.feature_df.loc[:, self.feature_names]\n\n self.label_ser = self.build_label()\n # keep same index with feature df\n self.label_ser = self.label_ser.loc[self.feature_df.index]\n self.label_name = self.label_ser.name\n\n self.training_X, self.training_y, self.testing_X, self.testing_y = self.split_data()\n\n logger.info(self.training_X)\n logger.info(self.training_y)\n\n self.model = None\n self.pred_y = None\n\n def split_data(self):\n training_x = self.feature_df[self.feature_df.index.get_level_values(\"timestamp\") < self.predict_start_timestamp]\n training_y = self.label_ser[self.label_ser.index.get_level_values(\"timestamp\") < self.predict_start_timestamp]\n\n testing_x = self.feature_df[self.feature_df.index.get_level_values(\"timestamp\") >= self.predict_start_timestamp]\n testing_y = self.label_ser[self.label_ser.index.get_level_values(\"timestamp\") >= self.predict_start_timestamp]\n return training_x, training_y, testing_x, testing_y\n\n def build_kdata(self):\n columns = [\"entity_id\", \"timestamp\", \"close\"]\n return get_kdata(\n entity_ids=self.entity_ids,\n start_timestamp=self.start_timestamp,\n end_timestamp=self.end_timestamp,\n columns=columns,\n level=self.level,\n adjust_type=self.adjust_type,\n provider=self.data_provider,\n index=[\"entity_id\", \"timestamp\"],\n drop_index_col=True,\n )\n\n def build_label(self):\n label_name = f\"y_{self.predict_steps}\"\n if self.label_method == \"raw\":\n y = (\n group_by_entity_id(self.kdata_df[\"close\"])\n .apply(lambda x: cal_predict(x, self.predict_steps))\n .rename(label_name)\n )\n elif self.label_method == \"change\":\n y = (\n group_by_entity_id(self.kdata_df[\"close\"])\n .apply(lambda x: cal_change(x, self.predict_steps))\n .rename(label_name)\n )\n elif self.label_method == \"behavior_cls\":\n y = (\n group_by_entity_id(self.kdata_df[\"close\"])\n .apply(lambda x: cal_behavior_cls(x, self.predict_steps))\n .rename(label_name)\n )\n else:\n assert False\n y = normalize_group_compute_result(y)\n\n return y\n\n def train(self, model=LinearRegression(), **params):\n self.model = model.fit(self.training_X, self.training_y, **params)\n return self.model\n\n def draw_result(self, entity_id):\n if self.label_method == \"raw\":\n df = self.kdata_df.loc[[entity_id], [\"close\"]].copy()\n\n pred_df = self.pred_y.to_frame(name=\"pred_close\")\n pred_df = pred_df.loc[[entity_id], :].shift(self.predict_steps)\n\n drawer = Drawer(\n main_df=df,\n factor_df_list=[pred_df],\n )\n drawer.draw_line(show=True)\n else:\n pred_df = self.pred_y.to_frame(name=\"pred_result\").loc[[entity_id], :]\n df = self.testing_y.to_frame(name=\"real_result\").loc[[entity_id], :].join(pred_df, how=\"outer\")\n\n drawer = Drawer(main_df=df)\n drawer.draw_table()\n\n def predict(self):\n predictions = self.model.predict(self.testing_X)\n self.pred_y = pd.Series(data=predictions, index=self.testing_y.index)\n # explained_variance_score(self.testing_y, self.pred_y)\n # mean_squared_error(self.testing_y, self.pred_y)\n\n def build_feature(\n self, entity_ids: List[str], start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp\n ) -> pd.DataFrame:\n \"\"\"\n result df format\n col1 col2 col3 ...\n entity_id timestamp\n 1.2 0.5 0.3 ...\n 1.0 0.7 0.2 ...\n\n :param entity_ids: entity id list\n :param start_timestamp:\n :param end_timestamp:\n :rtype: pd.DataFrame\n \"\"\"\n raise NotImplementedError\n\n\nclass StockMLMachine(MLMachine):\n entity_schema = Stock\n\n\nclass MaStockMLMachine(StockMLMachine):\n def build_feature(\n self, entity_ids: List[str], start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp\n ) -> pd.DataFrame:\n \"\"\"\n\n :param entity_ids:\n :param start_timestamp:\n :param end_timestamp:\n :return:\n \"\"\"\n t = MaTransformer(windows=[5, 10, 120, 250])\n df = t.transform(self.kdata_df)\n return df\n\n\nif __name__ == \"__main__\":\n machine = MaStockMLMachine(entity_ids=[\"stock_sz_000001\"])\n reg = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))\n machine.train(model=reg)\n machine.predict()\n machine.draw_result(entity_id=\"stock_sz_000001\")\n\n# the __all__ is generated\n__all__ = [\n \"cal_change\",\n \"cal_behavior_cls\",\n \"cal_predict\",\n \"cal_relative_performance\",\n \"MLMachine\",\n \"StockMLMachine\",\n \"MaStockMLMachine\",\n]\n"
},
{
"path": "src/zvt/plugin.py",
"content": "# -*- coding: utf-8 -*-\nimport argparse\n\nfrom zvt.autocode import gen_exports\nfrom zvt.autocode.generator import gen_plugin_project\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--entity\", help=\"entity name\", default=\"future\")\n parser.add_argument(\"--prefix\", help=\"project prefix\", default=\"zvt\")\n parser.add_argument(\"--dir\", help=\"project directory\", default=\".\")\n parser.add_argument(\"--providers\", help=\"providers\", default=[\"joinquant\"], nargs=\"+\")\n\n args = parser.parse_args()\n\n dir_path = args.dir\n entity = args.entity\n providers = args.providers\n prefix = args.prefix\n gen_plugin_project(prefix=prefix, dir_path=dir_path, entity_type=entity, providers=providers)\n\n\ndef export():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dir\", help=\"export directory\", default=\".\")\n args = parser.parse_args()\n dir_path = args.dir\n gen_exports(dir_path=dir_path)\n\n\nif __name__ == \"__main__\":\n gen_plugin_project(dir_path=\"../../../\", entity_type=\"macro\", providers=[\"zvt\"])\n main()\n"
},
{
"path": "src/zvt/recorders/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\nCHINA_STOCK_MAIN_INDEX = [\n # # 聚宽编码\n # # 市场通编码\t市场通名称\n # # 310001\t沪股通\n # # 310002\t深股通\n # # 310003\t港股通（沪）\n # # 310004\t港股通（深）\n {\n \"id\": \"index_cn_310001\",\n \"entity_id\": \"index_cn_310001\",\n \"code\": \"310001\",\n \"name\": \"沪股通\",\n \"timestamp\": \"2014-11-17\",\n \"exchange\": \"cn\",\n \"entity_type\": \"index\",\n \"category\": \"other\",\n },\n {\n \"id\": \"index_cn_310002\",\n \"entity_id\": \"index_cn_310002\",\n \"code\": \"310002\",\n \"name\": \"深股通\",\n \"timestamp\": \"2014-11-17\",\n \"exchange\": \"cn\",\n \"entity_type\": \"index\",\n \"category\": \"other\",\n },\n {\n \"id\": \"index_cn_310003\",\n \"entity_id\": \"index_cn_310003\",\n \"code\": \"310003\",\n \"name\": \"港股通（沪）\",\n \"timestamp\": \"2014-11-17\",\n \"exchange\": \"cn\",\n \"entity_type\": \"index\",\n \"category\": \"other\",\n },\n {\n \"id\": \"index_cn_310004\",\n \"entity_id\": \"index_cn_310004\",\n \"code\": \"310004\",\n \"name\": \"港股通（深）\",\n \"timestamp\": \"2014-11-17\",\n \"exchange\": \"cn\",\n \"entity_type\": \"index\",\n \"category\": \"other\",\n },\n]\n\n\ndef init_main_index(provider=\"exchange\"):\n from zvt.utils.time_utils import to_pd_timestamp\n import pandas as pd\n from zvt.contract.api import df_to_db\n from zvt.domain.meta import Index\n\n for item in CHINA_STOCK_MAIN_INDEX:\n item[\"timestamp\"] = to_pd_timestamp(item[\"timestamp\"])\n df = pd.DataFrame(CHINA_STOCK_MAIN_INDEX)\n # print(df)\n df_to_db(df=df, data_schema=Index, provider=provider, force_update=False)\n\n\n# the __all__ is generated\n__all__ = [\"init_main_index\"]\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule consts\nfrom .consts import *\nfrom .consts import __all__ as _consts_all\n\n__all__ += _consts_all\n\n# import all from submodule jqka\nfrom .jqka import *\nfrom .jqka import __all__ as _jqka_all\n\n__all__ += _jqka_all\n\n# import all from submodule eastmoney\nfrom .eastmoney import *\nfrom .eastmoney import __all__ as _eastmoney_all\n\n__all__ += _eastmoney_all\n\n# import all from submodule em\nfrom .em import *\nfrom .em import __all__ as _em_all\n\n__all__ += _em_all\n\n# import all from submodule exchange\nfrom .exchange import *\nfrom .exchange import __all__ as _exchange_all\n\n__all__ += _exchange_all\n\n# import all from submodule wb\nfrom .wb import *\nfrom .wb import __all__ as _wb_all\n\n__all__ += _wb_all\n\n# import all from submodule joinquant\nfrom .joinquant import *\nfrom .joinquant import __all__ as _joinquant_all\n\n__all__ += _joinquant_all\n\n# import all from submodule sina\nfrom .sina import *\nfrom .sina import __all__ as _sina_all\n\n__all__ += _sina_all\n\n# Recorders must be imported before schema use (providers derived from Recorder registration)\ninit_main_index(provider=\"exchange\")\n"
},
{
"path": "src/zvt/recorders/consts.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.utils.utils import chrome_copy_header_to_dict\n\nSSE_KDATA_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nHost: yunhq.sse.com.cn:32041\nConnection: keep-alive\nPragma: no-cache\nCache-Control: no-cache\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36\nAccept: */*\nReferer: http://www.sse.com.cn/market/price/trends/\nAccept-Encoding: gzip, deflate\nAccept-Language: zh-CN,zh;q=0.8,en;q=0.6\nCookie: yfx_c_g_u_id_10000042=_ck17072000172016360411059933357; yfx_f_l_v_t_10000042=f_t_1500481040618__r_t_1507560823182__v_t_1507561607501__r_c_1; VISITED_MENU=%5B%228451%22%2C%228453%22%5D\n\"\"\"\n)\n\nDEFAULT_SH_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nAccept: */*\nAccept-Encoding: gzip, deflate\nAccept-Language: zh-CN,zh;q=0.9,en;q=0.8\nConnection: keep-alive\nCookie: yfx_c_g_u_id_10000042=_ck21052517524516575117884257529; yfx_f_l_v_t_10000042=f_t_1621936365643__r_t_1621936365643__v_t_1621936365643__r_c_0; VISITED_MENU=%5B%228466%22%2C%228528%22%5D; JSESSIONID=3C970EA11443D2B7F3844558174C2CDB\nHost: query.sse.com.cn\nReferer: http://www.sse.com.cn/\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36\n\"\"\"\n)\n\nDEFAULT_SZ_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nAccept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\nAccept-Encoding:gzip, deflate, sdch\nAccept-Language:zh-CN,zh;q=0.8,en;q=0.6\nConnection:keep-alive\nHost:www.szse.cn\nReferer:http://www.szse.cn/main/marketdata/jypz/colist/\nUpgrade-Insecure-Requests:1\nUser-Agent:Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.76 Mobile Safari/537.36\n\"\"\"\n)\n\nDEFAULT_TICK_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nHost: market.finance.sina.com.cn\nAccept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\nAccept-Encoding: gzip, deflate, sdch\nAccept-Language: zh-CN,zh;q=0.8,en;q=0.6\nCookie: U_TRS1=000000b3.fffc2f53.566f784b.2900c0a6; UOR=www.baidu.com,blog.sina.com.cn,; SINAGLOBAL=182.139.185.185_1450145868.176138; vjuids=-fde6fe2f2.151a36dec26.0.113c0d8; SGUID=1450145869010_36029656; Apache=182.139.185.132_1457087046.693218; ULV=1457087047846:3:2:2:182.139.185.132_1457087046.693218:1457087047557; U_TRS2=0000009f.95a9161b.57038c9e.11b3a69f; SessionID=e2ajs2t3k4emag633adfjho861; SINA_FINANCE_SELECT_TYPE=stock; SCF=AhFAeZgjMw_ozKbe2MXZtX2fG8ZrLemvX24bd57wciL98hfpOY_4hN8aYqvvaxZDbINdQq3XqZSrS-ubJo-5VUI.; sso_info=v02m6alo5qztbaYloWum6akpp2WpaSPk4S1jpOYsYyjlLONg5DA; SUP=cv%3D1%26bt%3D1470117772%26et%3D1470204172%26d%3D40c3%26i%3De2a8%26us%3D0%26vf%3D0%26vt%3D0%26ac%3D2%26st%3D0%26lt%3D7%26uid%3D1596125344%26user%3Djackofxuan%2540gmail.com%26ag%3D4%26name%3Djackofxuan%2540gmail.com%26nick%3Dvaanni%26sex%3D%26ps%3D0%26email%3D%26dob%3D%26ln%3D%26os%3D%26fmp%3D%26lcp%3D2011-12-26%252017%253A00%253A23; __utma=269849203.900451459.1468839849.1468839849.1471431141.2; __utmc=269849203; __utmz=269849203.1471431141.2.2.utmcsr=finance.sina.com.cn|utmccn=(referral)|utmcmd=referral|utmcct=/; SINABLOGNUINFO=1596125344.5f22f0a0.vannii; SINA_FINANCE=%3A1596125344%3A4; hk_visited_stocks=02338; WEB2_APACHE2_GD=1401fbd37fe42dfbc571af9dacb5e317; _s_upa=25; lxlrtst=1475225080_o; SR_SEL=1_511; SINA_PORTFOLIO=sh601600%2Csh600089%2Csz000528%2Csh600150%2Csz000338%2Csh600031%2Csh600199%2Csh000001%2Csz399001%2Csz000778%2Csh600315%2Csh600570%2Csh601608%2Csz000099%2Csh600439%2Csz000521%2Csz300276%2Csz000783%2Csh600396%2Csz300336%2Csz000751%2Csz000848%2Csh600711%2Csh600880%2Csh600362%2Csh600060%2Csz000680%2Csz002051; ArtiFSize=14; visited_uss=gb_goog%7Cgb_sina%7Cgb_.dji; FINA_DMHQ=0; hqEtagMode=1; VISITED_FUTURE=hf_CL_0%2Chf_GC_0%2Chf_SI_0%2Chf_CAD_0%2CCF1609_1%2CRB0_1%2Chf_DJS_0%2CCFF_RE_IF1606_2%2CCFF_RE_IF1510_2%2CCFF_RE_IC1606_2%2CRM0_1%2CRB1610_1; lxlrttp=1476092678; FINA_V5_HQ=0; SUB=_2A251BPWoDeTxGedL4lQQ8ivPzziIHXVWcGBgrDV_PUNbm9BeLXLzkW8iLp81bNoV5si_9RFc9X4XY1wBcg..; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9W5J7vXaOMNYJQ0d27EZPWIF5NHD95QpSK.ceKzfe0BXWs4Dqcj.i--fi-zRiKnEi--fiKLhi-iWi--Xi-isi-88i--Ri-2piKyh; ALF=1507965304; FIN_ALL_VISITED=sz000338%2Csh000001%2Csz000680%2Csz399006%2Csh600028%2Csh600315%2Csh600031%2Csh601608%2Csh601038%2Csh601628%2CCL%2Csh600150%2Csz300369%2Csz300079%2CAGTD%2Csz300336%2Csz300276%2Cgoog%2Csina%2CGC%2CSI%2Csh600004%2Csh600000%2Csz300374%2Csh600199%2Csh600439%2Csz000783%2Csz000912%2CCAD%2Csz002628%2Csh600362%2Csz002051%2Csz000930%2Csz000528%2Csh603568%2Csh600060%2Csh600635%2Csz150266%2Csz000651%2Csz000099%2Csh600570%2Csz000002%2Csh600048%2Csz000839%2Csz002223%2Csh600880%2Csz000521%2Csz000878%2Csz399001%2Csh600396%2Csz000751%2Csh600016%2Csz000915%2Csh600779%2Csh600497%2Csh601600%2Csh600111%2Csh600550%2Csz000426%2Csz002237; FINA_LV2_S_2=1596125344; FINA_LV2_S_2_B=0; rotatecount=1; vjlast=1476433318; FINA_V_S_2=sz000338,sh000001,sz000680,sz399006,sh600028,sh600315,sh600031,sh601608,sh601038,sh601628,sh600150,sz300369,sz300079,sz300336,sz300276,sh600004,sh600000,sz300374,sh600199,sh600439\nReferer:market.finance.sina.com.cn\nUpgrade-Insecure-Requests: 1\nUser-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36\n\"\"\"\n)\n\nDEFAULT_KDATA_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nAccept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\nAccept-Encoding:gzip, deflate, sdch\nAccept-Language:zh-CN,zh;q=0.8,en;q=0.6\nCache-Control:max-age=0\nConnection:keep-alive\nHost:vip.stock.finance.sina.com.cn\nReferer:vip.stock.finance.sina.com.cn\nUpgrade-Insecure-Requests:1\nUser-Agent:Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.76 Mobile Safari/537.36\n\"\"\"\n)\n\nTONGHUASHUN_GN_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nAccept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\nAccept-Encoding:gzip, deflate, sdch\nAccept-Language:zh-CN,zh;q=0.8,en;q=0.6\nCache-Control:max-age=0\nConnection:keep-alive\nCookie:Hm_lvt_ab89213e83c551bf095446c08bf64988=1477116434; Hm_lpvt_ab89213e83c551bf095446c08bf64988=1477116434; historystock=600126; spversion=20130314; Hm_lvt_78c58f01938e4d85eaf619eae71b4ed1=1477105757,1477116434; Hm_lpvt_78c58f01938e4d85eaf619eae71b4ed1=1477362626\nHost:q.10jqka.com.cn\nUpgrade-Insecure-Requests:1\nUser-Agent:Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.76 Mobile Safari/537.36\n\"\"\"\n)\n\nTONGHUASHUN_KDATA_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nAccept-Encoding: gzip, deflate\nAccept-Language: zh-CN,zh;q=0.8,en;q=0.6\nUser-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36\nAccept: */*\nReferer: http://stockpage.10jqka.com.cn/HQ_v4.html\nCookie: __utma=156575163.1843700306.1488352720.1499234323.1502172029.4; __utmc=156575163; __utmz=156575163.1488352720.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); spversion=20130314; Hm_lvt_78c58f01938e4d85eaf619eae71b4ed1=1507300869; Hm_lpvt_78c58f01938e4d85eaf619eae71b4ed1=1508464591; historystock=603189%7C*%7C300295%7C*%7C600839%7C*%7C000338%7C*%7C002194; log=; v=AREjaxfGLPcJoUDd5wHRp1QiKRaufoSAL_MpD_OlDDL35T_CO86VwL9CPa2D\nConnection: keep-alive\n\"\"\"\n)\n\nDEFAULT_BALANCE_SHEET_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nHost: money.finance.sina.com.cn\nUser-Agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:54.0) Gecko/20100101 Firefox/54.0\nAccept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\nAccept-Language: en-US,en;q=0.5\nAccept-Encoding: gzip, deflate\nReferer: http://vip.stock.finance.sina.com.cn/corp/go.php/vFD_CashFlow/stockid/000338/ctrl/part/displaytype/4.phtml\nCookie: U_TRS1=000000be.c95848c3.59817e10.a54886e2; U_TRS2=000000be.c96a48c3.59817e10.a91795e2; UOR=,vip.stock.finance.sina.com.cn,; ULV=1501658645426:2:2:2:182.148.114.190_1501658642.469995:1501658642409; SINAGLOBAL=182.148.114.190_1501658642.469991; Apache=182.148.114.190_1501658642.469995; _s_upa=1; SUB=_2A250hQ5nDeRhGedL4lQQ8ivPzziIHXVX83ivrDV_PUNbm9BeLXigkW8-niaOks2yNkw8lYo-TvoqGk6nRA..; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9W5J7vXaOMNYJQ0d27EZPWIF5NHD95QpSK.ceKzfe0BXWs4Dqcj.i--fi-zRiKnEi--fiKLhi-iWi--Xi-isi-88i--Ri-2piKyh; SCF=At4whqZZyjTBTvcLfR0tyqIpfHUX2VOK-qvBVHkbyahiCVcr4-8NjJQGHwCaTtkQJ0SPmrzvZARwtEkL1I_46z8.; ALF=1533194679; sso_info=v02m6alo5qztbaYloWum6akpp2WpaSPk4S1jpOYsYyjlLONg5DA; FINANCE2=f7634b1d12920e2763ffc0dc463ef6bb\nConnection: keep-alive\nUpgrade-Insecure-Requests: 1\n\"\"\"\n)\n\nDEFAULT_SH_SUMMARY_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nHost: query.sse.com.cn\nConnection: keep-alive\nUser-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36\nAccept: */*\nReferer: http://www.sse.com.cn/market/stockdata/overview/day/\nAccept-Encoding: gzip, deflate\nAccept-Language: zh-CN,zh;q=0.8,en;q=0.6\nCookie: yfx_c_g_u_id_10000042=_ck17122009304714819234313401740; VISITED_COMPANY_CODE=%5B%22000016%22%5D; VISITED_INDEX_CODE=%5B%22000016%22%5D; yfx_f_l_v_t_10000042=f_t_1513733447386__r_t_1515716891222__v_t_1515721033042__r_c_3; VISITED_MENU=%5B%228464%22%2C%229666%22%2C%229668%22%2C%229669%22%2C%228454%22%2C%228460%22%2C%229665%22%2C%228459%22%2C%229692%22%2C%228451%22%2C%228466%22%5D\n\"\"\"\n)\n\nDEFAULT_SH_ETF_LIST_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nHost: query.sse.com.cn\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\nAccept: */*\nReferer: http://www.sse.com.cn/assortment/fund/etf/list/\nAccept-Encoding: gzip, deflate\nAccept-Language: zh-CN,zh;q=0.9\nCookie: yfx_c_g_u_id_10000042=_ck19062609443812815766114343798; VISITED_COMPANY_CODE=%5B%22510300%22%5D; VISITED_FUND_CODE=%5B%22510300%22%5D; VISITED_MENU=%5B%228307%22%2C%228823%22%2C%228547%22%2C%228556%22%2C%228549%22%2C%2210848%22%2C%228550%22%5D; yfx_f_l_v_t_10000042=f_t_1561513478278__r_t_1561692626758__v_t_1561695738302__r_c_1\nConnection: keep-alive\n\"\"\"\n)\n\nEASTMONEY_ETF_NET_VALUE_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\nReferer: http://fund.eastmoney.com/\n\"\"\"\n)\n\nDEFAULT_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\n\"\"\"\n)\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/recorders/eastmoney/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule common\nfrom .common import *\nfrom .common import __all__ as _common_all\n\n__all__ += _common_all\n\n# import all from submodule trading\nfrom .trading import *\nfrom .trading import __all__ as _trading_all\n\n__all__ += _trading_all\n\n# import all from submodule holder\nfrom .holder import *\nfrom .holder import __all__ as _holder_all\n\n__all__ += _holder_all\n\n# import all from submodule finance\nfrom .finance import *\nfrom .finance import __all__ as _finance_all\n\n__all__ += _finance_all\n\n# import all from submodule meta\nfrom .meta import *\nfrom .meta import __all__ as _meta_all\n\n__all__ += _meta_all\n\n# import all from submodule dividend_financing\nfrom .dividend_financing import *\nfrom .dividend_financing import __all__ as _dividend_financing_all\n\n__all__ += _dividend_financing_all\n"
},
{
"path": "src/zvt/recorders/eastmoney/common.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nimport requests\n\nfrom zvt.contract.api import get_data_count, get_data\nfrom zvt.contract.recorder import TimestampsDataRecorder, TimeSeriesDataRecorder\nfrom zvt.domain import CompanyType\nfrom zvt.domain.meta.stock_meta import StockDetail\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\nclass ApiWrapper(object):\n def request(self, url=None, method=\"post\", param=None, path_fields=None):\n raise NotImplementedError\n\n\ndef get_fc(security_item):\n if security_item.exchange == \"sh\":\n fc = \"{}01\".format(security_item.code)\n if security_item.exchange == \"sz\":\n fc = \"{}02\".format(security_item.code)\n\n return fc\n\n\ndef get_company_type(stock_domain: StockDetail):\n industries = stock_domain.industries.split(\",\")\n if (\"银行\" in industries) or (\"信托\" in industries):\n return CompanyType.yinhang\n if \"保险\" in industries:\n return CompanyType.baoxian\n if \"证券\" in industries:\n return CompanyType.quanshang\n return CompanyType.qiye\n\n\ndef company_type_flag(security_item):\n try:\n company_type = get_company_type(security_item)\n\n if company_type == CompanyType.qiye:\n return \"4\"\n if company_type == CompanyType.quanshang:\n return \"1\"\n if company_type == CompanyType.baoxian:\n return \"2\"\n if company_type == CompanyType.yinhang:\n return \"3\"\n except Exception as e:\n logger.warning(e)\n\n param = {\"color\": \"w\", \"fc\": get_fc(security_item)}\n\n resp = requests.post(\"https://emh5.eastmoney.com/api/CaiWuFenXi/GetCompanyType\", json=param)\n\n ct = resp.json().get(\"Result\").get(\"CompanyType\")\n\n logger.warning(\"{} not catching company type:{}\".format(security_item, ct))\n\n return ct\n\n\ndef call_eastmoney_api(url=None, method=\"post\", param=None, path_fields=None):\n if method == \"post\":\n resp = requests.post(url, json=param)\n\n resp.encoding = \"utf-8\"\n\n try:\n origin_result = resp.json().get(\"Result\")\n except Exception as e:\n logger.exception(\"code:{},content:{}\".format(resp.status_code, resp.text))\n raise e\n\n if path_fields:\n the_data = get_from_path_fields(origin_result, path_fields)\n if not the_data:\n logger.warning(\n \"url:{},param:{},origin_result:{},could not get data for nested_fields:{}\".format(\n url, param, origin_result, path_fields\n )\n )\n return the_data\n\n return origin_result\n\n\ndef get_from_path_fields(the_json, path_fields):\n the_data = the_json.get(path_fields[0])\n if the_data:\n for field in path_fields[1:]:\n the_data = the_data.get(field)\n if not the_data:\n return None\n return the_data\n\n\nclass EastmoneyApiWrapper(ApiWrapper):\n def request(self, url=None, method=\"post\", param=None, path_fields=None):\n return call_eastmoney_api(url=url, method=method, param=param, path_fields=path_fields)\n\n\nclass BaseEastmoneyRecorder(object):\n request_method = \"post\"\n path_fields = None\n api_wrapper = EastmoneyApiWrapper()\n\n def generate_request_param(self, security_item, start, end, size, timestamp):\n raise NotImplementedError\n\n def record(self, entity_item, start, end, size, timestamps):\n if timestamps:\n original_list = []\n for the_timestamp in timestamps:\n param = self.generate_request_param(entity_item, start, end, size, the_timestamp)\n tmp_list = self.api_wrapper.request(\n url=self.url, param=param, method=self.request_method, path_fields=self.path_fields\n )\n self.logger.info(\n \"record {} for entity_id:{},timestamp:{}\".format(self.data_schema, entity_item.id, the_timestamp)\n )\n # fill timestamp field\n for tmp in tmp_list:\n tmp[self.get_evaluated_time_field()] = the_timestamp\n original_list += tmp_list\n if len(original_list) == 50:\n break\n return original_list\n\n else:\n param = self.generate_request_param(entity_item, start, end, size, None)\n return self.api_wrapper.request(\n url=self.url, param=param, method=self.request_method, path_fields=self.path_fields\n )\n\n\nclass EastmoneyTimestampsDataRecorder(BaseEastmoneyRecorder, TimestampsDataRecorder):\n entity_provider = \"eastmoney\"\n entity_schema = StockDetail\n\n provider = \"eastmoney\"\n\n timestamps_fetching_url = None\n timestamp_list_path_fields = None\n timestamp_path_fields = None\n\n def init_timestamps(self, entity):\n param = {\"color\": \"w\", \"fc\": get_fc(entity)}\n\n timestamp_json_list = call_eastmoney_api(\n url=self.timestamps_fetching_url, path_fields=self.timestamp_list_path_fields, param=param\n )\n\n if self.timestamp_path_fields and timestamp_json_list:\n timestamps = [get_from_path_fields(data, self.timestamp_path_fields) for data in timestamp_json_list]\n return [to_pd_timestamp(t) for t in timestamps]\n return []\n\n\nclass EastmoneyPageabeDataRecorder(BaseEastmoneyRecorder, TimeSeriesDataRecorder):\n entity_provider = \"eastmoney\"\n entity_schema = StockDetail\n\n provider = \"eastmoney\"\n\n page_url = None\n\n def get_remote_count(self, security_item):\n param = {\"color\": \"w\", \"fc\": get_fc(security_item), \"pageNum\": 1, \"pageSize\": 1}\n return call_eastmoney_api(self.page_url, param=param, path_fields=[\"TotalCount\"])\n\n def evaluate_start_end_size_timestamps(self, entity):\n remote_count = self.get_remote_count(entity)\n\n if remote_count == 0:\n return None, None, 0, None\n\n # get local count\n local_count = get_data_count(\n data_schema=self.data_schema, session=self.session, filters=[self.data_schema.entity_id == entity.id]\n )\n # FIXME:the > case\n if local_count >= remote_count:\n return None, None, 0, None\n\n return None, None, remote_count - local_count, None\n\n def generate_request_param(self, security_item, start, end, size, timestamp):\n return {\n \"color\": \"w\",\n \"fc\": get_fc(security_item),\n \"pageNum\": 1,\n # just get more for some fixed data\n \"pageSize\": size + 10,\n }\n\n\nclass EastmoneyMoreDataRecorder(BaseEastmoneyRecorder, TimeSeriesDataRecorder):\n entity_provider = \"eastmoney\"\n entity_schema = StockDetail\n\n provider = \"eastmoney\"\n\n def get_remote_latest_record(self, security_item):\n param = {\"color\": \"w\", \"fc\": get_fc(security_item), \"pageNum\": 1, \"pageSize\": 1}\n results = call_eastmoney_api(self.url, param=param, path_fields=self.path_fields)\n _, result = self.generate_domain(security_item, results[0])\n return result\n\n def evaluate_start_end_size_timestamps(self, entity):\n # get latest record\n latest_record = get_data(\n entity_id=entity.id,\n provider=self.provider,\n data_schema=self.data_schema,\n order=self.data_schema.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n session=self.session,\n )\n if latest_record:\n remote_record = self.get_remote_latest_record(entity)\n if not remote_record or (latest_record[0].id == remote_record.id):\n return None, None, 0, None\n else:\n return None, None, 10, None\n\n return None, None, 1000, None\n\n def generate_request_param(self, security_item, start, end, size, timestamp):\n return {\"color\": \"w\", \"fc\": get_fc(security_item), \"pageNum\": 1, \"pageSize\": size}\n\n\n# the __all__ is generated\n__all__ = [\n \"ApiWrapper\",\n \"get_fc\",\n \"get_company_type\",\n \"company_type_flag\",\n \"call_eastmoney_api\",\n \"get_from_path_fields\",\n \"EastmoneyApiWrapper\",\n \"BaseEastmoneyRecorder\",\n \"EastmoneyTimestampsDataRecorder\",\n \"EastmoneyPageabeDataRecorder\",\n \"EastmoneyMoreDataRecorder\",\n]\n"
},
{
"path": "src/zvt/recorders/eastmoney/dividend_financing/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule eastmoney_dividend_detail_recorder\nfrom .eastmoney_dividend_detail_recorder import *\nfrom .eastmoney_dividend_detail_recorder import __all__ as _eastmoney_dividend_detail_recorder_all\n\n__all__ += _eastmoney_dividend_detail_recorder_all\n\n# import all from submodule eastmoney_rights_issue_detail_recorder\nfrom .eastmoney_rights_issue_detail_recorder import *\nfrom .eastmoney_rights_issue_detail_recorder import __all__ as _eastmoney_rights_issue_detail_recorder_all\n\n__all__ += _eastmoney_rights_issue_detail_recorder_all\n\n# import all from submodule eastmoney_dividend_financing_recorder\nfrom .eastmoney_dividend_financing_recorder import *\nfrom .eastmoney_dividend_financing_recorder import __all__ as _eastmoney_dividend_financing_recorder_all\n\n__all__ += _eastmoney_dividend_financing_recorder_all\n\n# import all from submodule eastmoney_spo_detail_recorder\nfrom .eastmoney_spo_detail_recorder import *\nfrom .eastmoney_spo_detail_recorder import __all__ as _eastmoney_spo_detail_recorder_all\n\n__all__ += _eastmoney_spo_detail_recorder_all\n"
},
{
"path": "src/zvt/recorders/eastmoney/dividend_financing/eastmoney_dividend_detail_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import DividendDetail\nfrom zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass DividendDetailRecorder(EastmoneyPageabeDataRecorder):\n data_schema = DividendDetail\n\n url = \"https://emh5.eastmoney.com/api/FenHongRongZi/GetFenHongSongZhuanList\"\n page_url = url\n path_fields = [\"FenHongSongZhuanList\"]\n\n def get_original_time_field(self):\n return \"GongGaoRiQi\"\n\n def get_data_map(self):\n return {\n # 公告日\n \"announce_date\": (\"GongGaoRiQi\", to_pd_timestamp),\n # 股权登记日\n \"record_date\": (\"GuQuanDengJiRi\", to_pd_timestamp),\n # 除权除息日\n \"dividend_date\": (\"ChuQuanChuXiRi\", to_pd_timestamp),\n # 方案\n \"dividend\": (\"FengHongFangAn\", str),\n }\n\n\nif __name__ == \"__main__\":\n # init_log('dividend_detail.log')\n\n recorder = DividendDetailRecorder(codes=[\"601318\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"DividendDetailRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/dividend_financing/eastmoney_dividend_financing_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain.fundamental.dividend_financing import DividendFinancing\nfrom zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder\nfrom zvt.utils.utils import second_item_to_float\n\n\nclass DividendFinancingRecorder(EastmoneyPageabeDataRecorder):\n data_schema = DividendFinancing\n\n url = \"https://emh5.eastmoney.com/api/FenHongRongZi/GetLiNianFenHongRongZiList\"\n page_url = url\n path_fields = [\"LiNianFenHongRongZiList\"]\n\n def get_original_time_field(self):\n return \"ShiJian\"\n\n def get_data_map(self):\n return {\n # 分红总额\n \"dividend_money\": (\"FenHongZongE\", second_item_to_float),\n # 新股\n \"ipo_issues\": (\"XinGu\", second_item_to_float),\n # 增发\n \"spo_issues\": (\"ZengFa\", second_item_to_float),\n # 配股\n \"rights_issues\": (\"PeiFa\", second_item_to_float),\n }\n\n def on_finish(self):\n try:\n code_security = {}\n for item in self.entities:\n code_security[item.code] = item\n\n need_fill_items = DividendFinancing.query_data(\n provider=self.provider,\n codes=list(code_security.keys()),\n return_type=\"domain\",\n session=self.session,\n filters=[DividendFinancing.ipo_raising_fund.is_(None), DividendFinancing.ipo_issues != 0],\n )\n\n for need_fill_item in need_fill_items:\n if need_fill_item:\n need_fill_item.ipo_raising_fund = code_security[item.code].raising_fund\n self.session.commit()\n except Exception as e:\n self.logger.exception(e)\n\n super().on_finish()\n\n\nif __name__ == \"__main__\":\n # init_log('dividend_financing.log')\n\n recorder = DividendFinancingRecorder(codes=[\"000999\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"DividendFinancingRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/dividend_financing/eastmoney_rights_issue_detail_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.consts import SAMPLE_STOCK_CODES\nfrom zvt.domain import RightsIssueDetail, DividendFinancing\nfrom zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import now_pd_timestamp\nfrom zvt.utils.utils import to_float\n\n\nclass RightsIssueDetailRecorder(EastmoneyPageabeDataRecorder):\n data_schema = RightsIssueDetail\n\n url = \"https://emh5.eastmoney.com/api/FenHongRongZi/GetPeiGuMingXiList\"\n page_url = url\n path_fields = [\"PeiGuMingXiList\"]\n\n def get_original_time_field(self):\n return \"PeiGuGongGaoRi\"\n\n def get_data_map(self):\n return {\n \"rights_issues\": (\"ShiJiPeiGu\", to_float),\n \"rights_issue_price\": (\"PeiGuJiaGe\", to_float),\n \"rights_raising_fund\": (\"ShiJiMuJi\", to_float),\n }\n\n def on_finish(self):\n last_year = str(now_pd_timestamp().year)\n codes = [item.code for item in self.entities]\n need_filleds = DividendFinancing.query_data(\n provider=self.provider,\n codes=codes,\n return_type=\"domain\",\n session=self.session,\n filters=[DividendFinancing.rights_raising_fund.is_(None)],\n end_timestamp=last_year,\n )\n\n for item in need_filleds:\n df = RightsIssueDetail.query_data(\n provider=self.provider,\n entity_id=item.entity_id,\n columns=[RightsIssueDetail.timestamp, RightsIssueDetail.rights_raising_fund],\n start_timestamp=item.timestamp,\n end_timestamp=\"{}-12-31\".format(item.timestamp.year),\n )\n if pd_is_not_null(df):\n item.rights_raising_fund = df[\"rights_raising_fund\"].sum()\n self.session.commit()\n\n super().on_finish()\n\n\nif __name__ == \"__main__\":\n # init_log('rights_issue.log')\n\n recorder = RightsIssueDetailRecorder(codes=SAMPLE_STOCK_CODES)\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"RightsIssueDetailRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/dividend_financing/eastmoney_spo_detail_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import SpoDetail, DividendFinancing\nfrom zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import now_pd_timestamp\nfrom zvt.utils.utils import to_float\n\n\nclass SPODetailRecorder(EastmoneyPageabeDataRecorder):\n data_schema = SpoDetail\n\n url = \"https://emh5.eastmoney.com/api/FenHongRongZi/GetZengFaMingXiList\"\n page_url = url\n path_fields = [\"ZengFaMingXiList\"]\n\n def get_original_time_field(self):\n return \"ZengFaShiJian\"\n\n def get_data_map(self):\n return {\n \"spo_issues\": (\"ShiJiZengFa\", to_float),\n \"spo_price\": (\"ZengFaJiaGe\", to_float),\n \"spo_raising_fund\": (\"ShiJiMuJi\", to_float),\n }\n\n def on_finish(self):\n last_year = str(now_pd_timestamp().year)\n codes = [item.code for item in self.entities]\n need_filleds = DividendFinancing.query_data(\n provider=self.provider,\n codes=codes,\n return_type=\"domain\",\n session=self.session,\n filters=[DividendFinancing.spo_raising_fund.is_(None)],\n end_timestamp=last_year,\n )\n\n for item in need_filleds:\n df = SpoDetail.query_data(\n provider=self.provider,\n entity_id=item.entity_id,\n columns=[SpoDetail.timestamp, SpoDetail.spo_raising_fund],\n start_timestamp=item.timestamp,\n end_timestamp=\"{}-12-31\".format(item.timestamp.year),\n )\n if pd_is_not_null(df):\n item.spo_raising_fund = df[\"spo_raising_fund\"].sum()\n self.session.commit()\n super().on_finish()\n\n\nif __name__ == \"__main__\":\n # init_log('spo_detail.log')\n\n recorder = SPODetailRecorder(codes=[\"000999\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"SPODetailRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/finance/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule eastmoney_cash_flow_recorder\nfrom .eastmoney_cash_flow_recorder import *\nfrom .eastmoney_cash_flow_recorder import __all__ as _eastmoney_cash_flow_recorder_all\n\n__all__ += _eastmoney_cash_flow_recorder_all\n\n# import all from submodule eastmoney_income_statement_recorder\nfrom .eastmoney_income_statement_recorder import *\nfrom .eastmoney_income_statement_recorder import __all__ as _eastmoney_income_statement_recorder_all\n\n__all__ += _eastmoney_income_statement_recorder_all\n\n# import all from submodule eastmoney_finance_factor_recorder\nfrom .eastmoney_finance_factor_recorder import *\nfrom .eastmoney_finance_factor_recorder import __all__ as _eastmoney_finance_factor_recorder_all\n\n__all__ += _eastmoney_finance_factor_recorder_all\n\n# import all from submodule base_china_stock_finance_recorder\nfrom .base_china_stock_finance_recorder import *\nfrom .base_china_stock_finance_recorder import __all__ as _base_china_stock_finance_recorder_all\n\n__all__ += _base_china_stock_finance_recorder_all\n\n# import all from submodule eastmoney_balance_sheet_recorder\nfrom .eastmoney_balance_sheet_recorder import *\nfrom .eastmoney_balance_sheet_recorder import __all__ as _eastmoney_balance_sheet_recorder_all\n\n__all__ += _eastmoney_balance_sheet_recorder_all\n"
},
{
"path": "src/zvt/recorders/eastmoney/finance/base_china_stock_finance_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom jqdatapy.api import get_fundamentals, get_query_count\n\nfrom zvt.api.utils import to_report_period_type\nfrom zvt.contract.api import get_data\nfrom zvt.domain import FinanceFactor, ReportPeriod\nfrom zvt.recorders.eastmoney.common import (\n company_type_flag,\n get_fc,\n EastmoneyTimestampsDataRecorder,\n call_eastmoney_api,\n get_from_path_fields,\n)\nfrom zvt.recorders.joinquant.common import to_jq_entity_id\nfrom zvt.utils.pd_utils import index_df\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str, to_pd_timestamp\n\n\ndef to_jq_report_period(timestamp):\n the_date = to_pd_timestamp(timestamp)\n report_period = to_report_period_type(timestamp)\n if report_period == ReportPeriod.year.value:\n return \"{}\".format(the_date.year)\n if report_period == ReportPeriod.season1.value:\n return \"{}q1\".format(the_date.year)\n if report_period == ReportPeriod.half_year.value:\n return \"{}q2\".format(the_date.year)\n if report_period == ReportPeriod.season3.value:\n return \"{}q3\".format(the_date.year)\n\n assert False\n\n\nclass BaseChinaStockFinanceRecorder(EastmoneyTimestampsDataRecorder):\n finance_report_type = None\n data_type = 1\n\n timestamps_fetching_url = \"https://emh5.eastmoney.com/api/CaiWuFenXi/GetCompanyReportDateList\"\n timestamp_list_path_fields = [\"CompanyReportDateList\"]\n timestamp_path_fields = [\"ReportDate\"]\n\n def __init__(\n self,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n force_update=False,\n sleeping_time=5,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n\n try:\n self.logger.info(f\"joinquant query count:{get_query_count()}\")\n self.fetch_jq_timestamp = True\n except Exception as e:\n self.fetch_jq_timestamp = False\n self.logger.warning(\n f\"joinquant account not ok,the timestamp(publish date) for finance would be not correct. {e}\"\n )\n\n def init_timestamps(self, entity):\n param = {\"color\": \"w\", \"fc\": get_fc(entity), \"DataType\": self.data_type}\n\n if self.finance_report_type == \"LiRunBiaoList\" or self.finance_report_type == \"XianJinLiuLiangBiaoList\":\n param[\"ReportType\"] = 1\n\n timestamp_json_list = call_eastmoney_api(\n url=self.timestamps_fetching_url, path_fields=self.timestamp_list_path_fields, param=param\n )\n\n if self.timestamp_path_fields:\n timestamps = [get_from_path_fields(data, self.timestamp_path_fields) for data in timestamp_json_list]\n\n return [to_pd_timestamp(t) for t in timestamps]\n\n def generate_request_param(self, security_item, start, end, size, timestamps):\n if len(timestamps) <= 10:\n param = {\n \"color\": \"w\",\n \"fc\": get_fc(security_item),\n \"corpType\": company_type_flag(security_item),\n # 0 means get all types\n \"reportDateType\": 0,\n \"endDate\": \"\",\n \"latestCount\": size,\n }\n else:\n param = {\n \"color\": \"w\",\n \"fc\": get_fc(security_item),\n \"corpType\": company_type_flag(security_item),\n # 0 means get all types\n \"reportDateType\": 0,\n \"endDate\": to_date_time_str(timestamps[10]),\n \"latestCount\": 10,\n }\n\n if self.finance_report_type == \"LiRunBiaoList\" or self.finance_report_type == \"XianJinLiuLiangBiaoList\":\n param[\"reportType\"] = 1\n\n return param\n\n def generate_path_fields(self, security_item):\n comp_type = company_type_flag(security_item)\n\n if comp_type == \"3\":\n return [\"{}_YinHang\".format(self.finance_report_type)]\n elif comp_type == \"2\":\n return [\"{}_BaoXian\".format(self.finance_report_type)]\n elif comp_type == \"1\":\n return [\"{}_QuanShang\".format(self.finance_report_type)]\n elif comp_type == \"4\":\n return [\"{}_QiYe\".format(self.finance_report_type)]\n\n def record(self, entity, start, end, size, timestamps):\n # different with the default timestamps handling\n param = self.generate_request_param(entity, start, end, size, timestamps)\n self.logger.info(\"request param:{}\".format(param))\n\n return self.api_wrapper.request(\n url=self.url, param=param, method=self.request_method, path_fields=self.generate_path_fields(entity)\n )\n\n def get_original_time_field(self):\n return \"ReportDate\"\n\n def fill_timestamp_with_jq(self, security_item, the_data):\n # get report published date from jq\n try:\n df = get_fundamentals(\n table=\"indicator\",\n code=to_jq_entity_id(security_item),\n columns=\"pubDate\",\n date=to_jq_report_period(the_data.report_date),\n count=None,\n parse_dates=[\"pubDate\"],\n )\n if pd_is_not_null(df):\n the_data.timestamp = to_pd_timestamp(df[\"pubDate\"][0])\n self.logger.info(\n \"jq fill {} {} timestamp:{} for report_date:{}\".format(\n self.data_schema, security_item.id, the_data.timestamp, the_data.report_date\n )\n )\n self.session.commit()\n except Exception as e:\n self.logger.error(f\"Failed to fill timestamp(publish date) for finance data from joinquant {e}\")\n\n def on_finish_entity(self, entity):\n super().on_finish_entity(entity)\n\n if not self.fetch_jq_timestamp:\n return\n\n # fill the timestamp for report published date\n the_data_list = get_data(\n data_schema=self.data_schema,\n provider=self.provider,\n entity_id=entity.id,\n order=self.data_schema.timestamp.asc(),\n return_type=\"domain\",\n session=self.session,\n filters=[\n self.data_schema.timestamp == self.data_schema.report_date,\n self.data_schema.timestamp >= to_pd_timestamp(\"2005-01-01\"),\n ],\n )\n if the_data_list:\n if self.data_schema == FinanceFactor:\n for the_data in the_data_list:\n self.fill_timestamp_with_jq(entity, the_data)\n else:\n df = FinanceFactor.query_data(\n entity_id=entity.id,\n columns=[FinanceFactor.timestamp, FinanceFactor.report_date, FinanceFactor.id],\n filters=[\n FinanceFactor.timestamp != FinanceFactor.report_date,\n FinanceFactor.timestamp >= to_pd_timestamp(\"2005-01-01\"),\n FinanceFactor.report_date >= the_data_list[0].report_date,\n FinanceFactor.report_date <= the_data_list[-1].report_date,\n ],\n )\n\n if pd_is_not_null(df):\n index_df(df, index=\"report_date\", time_field=\"report_date\")\n\n for the_data in the_data_list:\n if (df is not None) and (not df.empty) and the_data.report_date in df.index:\n the_data.timestamp = df.at[the_data.report_date, \"timestamp\"]\n self.logger.info(\n \"db fill {} {} timestamp:{} for report_date:{}\".format(\n self.data_schema, entity.id, the_data.timestamp, the_data.report_date\n )\n )\n self.session.commit()\n else:\n # self.logger.info(\n # 'waiting jq fill {} {} timestamp:{} for report_date:{}'.format(self.data_schema,\n # security_item.id,\n # the_data.timestamp,\n # the_data.report_date))\n\n self.fill_timestamp_with_jq(entity, the_data)\n\n\n# the __all__ is generated\n__all__ = [\"to_jq_report_period\", \"BaseChinaStockFinanceRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/finance/eastmoney_balance_sheet_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.utils import to_report_period_type\nfrom zvt.domain import BalanceSheet\nfrom zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder\nfrom zvt.utils.time_utils import to_pd_timestamp\nfrom zvt.utils.utils import add_func_to_value, first_item_to_float\n\nbalance_sheet_map = {\n # 流动资产\n #\n # 货币资金\n \"cash_and_cash_equivalents\": \"Monetaryfund\",\n # 应收票据\n \"note_receivable\": \"Billrec\",\n # 应收账款\n \"accounts_receivable\": \"Accountrec\",\n # 预付款项\n \"advances_to_suppliers\": \"Advancepay\",\n # 其他应收款\n \"other_receivables\": \"Otherrec\",\n # 存货\n \"inventories\": \"Inventory\",\n # 一年内到期的非流动资产\n \"current_portion_of_non_current_assets\": \"Nonlassetoneyear\",\n # 其他流动资产\n \"other_current_assets\": \"Otherlasset\",\n # 流动资产合计\n \"total_current_assets\": \"Sumlasset\",\n # 非流动资产\n #\n # 可供出售金融资产\n \"fi_assets_saleable\": \"Saleablefasset\",\n # 长期应收款\n \"long_term_receivables\": \"Ltrec\",\n # 长期股权投资\n \"long_term_equity_investment\": \"Ltequityinv\",\n # 投资性房地产\n \"real_estate_investment\": \"Estateinvest\",\n # 固定资产\n \"fixed_assets\": \"Fixedasset\",\n # 在建工程\n \"construction_in_process\": \"Constructionprogress\",\n # 无形资产\n \"intangible_assets\": \"Intangibleasset\",\n # 商誉\n \"goodwill\": \"Goodwill\",\n # 长期待摊费用\n \"long_term_prepaid_expenses\": \"Ltdeferasset\",\n # 递延所得税资产\n \"deferred_tax_assets\": \"Deferincometaxasset\",\n # 其他非流动资产\n \"other_non_current_assets\": \"Othernonlasset\",\n # 非流动资产合计\n \"total_non_current_assets\": \"Sumnonlasset\",\n # 资产总计\n \"total_assets\": \"Sumasset\",\n # 流动负债\n #\n # 短期借款\n \"short_term_borrowing\": \"Stborrow\",\n # 吸收存款及同业存放\n \"accept_money_deposits\": \"Deposit\",\n # 应付账款\n \"accounts_payable\": \"Accountpay\",\n # 预收款项\n \"advances_from_customers\": \"Advancereceive\",\n # 应付职工薪酬\n \"employee_benefits_payable\": \"Salarypay\",\n # 应交税费\n \"taxes_payable\": \"Taxpay\",\n # 应付利息\n \"interest_payable\": \"Interestpay\",\n # 其他应付款\n \"other_payable\": \"Otherpay\",\n # 一年内到期的非流动负债\n \"current_portion_of_non_current_liabilities\": \"Nonlliaboneyear\",\n # 其他流动负债\n \"other_current_liabilities\": \"Otherlliab\",\n # 流动负债合计\n \"total_current_liabilities\": \"Sumlliab\",\n # 非流动负债\n #\n # 长期借款\n \"long_term_borrowing\": \"Ltborrow\",\n # 长期应付款\n \"long_term_payable\": \"Ltaccountpay\",\n # 递延收益\n \"deferred_revenue\": \"Deferincome\",\n # 递延所得税负债\n \"deferred_tax_liabilities\": \"Deferincometaxliab\",\n # 其他非流动负债\n \"other_non_current_liabilities\": \"Othernonlliab\",\n # 非流动负债合计\n \"total_non_current_liabilities\": \"Sumnonlliab\",\n # 负债合计\n \"total_liabilities\": \"Sumliab\",\n # 所有者权益(或股东权益)\n #\n # 实收资本（或股本）\n \"capital\": \"Sharecapital\",\n # 资本公积\n \"capital_reserve\": \"Capitalreserve\",\n # 专项储备\n \"special_reserve\": \"Specialreserve\",\n # 盈余公积\n \"surplus_reserve\": \"Surplusreserve\",\n # 未分配利润\n \"undistributed_profits\": \"Retainedearning\",\n # 归属于母公司股东权益合计\n \"equity\": \"Sumparentequity\",\n # 少数股东权益\n \"equity_as_minority_interest\": \"Minorityequity\",\n # 股东权益合计\n \"total_equity\": \"Sumshequity\",\n # 负债和股东权益合计\n \"total_liabilities_and_equity\": \"Sumliabshequity\",\n # 银行相关\n # 资产\n # 现金及存放中央银行款项\n \"fi_cash_and_deposit_in_central_bank\": \"Cashanddepositcbank\",\n # 存放同业款项\n \"fi_deposit_in_other_fi\": \"Depositinfi\",\n # 贵金属\n \"fi_expensive_metals\": \"Preciousmetal\",\n # 拆出资金\n \"fi_lending_to_other_fi\": \"Lendfund\",\n # 以公允价值计量且其变动计入当期损益的金融资产\n \"fi_financial_assets_effect_current_income\": \"Fvaluefasset\",\n # 衍生金融资产\n \"fi_financial_derivative_asset\": \"Derivefasset\",\n # 买入返售金融资产\n \"fi_buying_sell_back_fi__asset\": \"Buysellbackfasset\",\n # 应收账款\n #\n # 应收利息\n \"fi_interest_receivable\": \"Interestrec\",\n # 发放贷款及垫款\n \"fi_disbursing_loans_and_advances\": \"Loanadvances\",\n # 可供出售金融资产\n #\n # 持有至到期投资\n \"fi_held_to_maturity_investment\": \"Heldmaturityinv\",\n # 应收款项类投资\n \"fi_account_receivable_investment\": \"Investrec\",\n # 投资性房地产\n #\n # 固定资产\n #\n # 无形资产\n #\n # 商誉\n #\n # 递延所得税资产\n #\n # 其他资产\n \"fi_other_asset\": \"Otherasset\",\n # 资产总计\n #\n # 负债\n #\n # 向中央银行借款\n \"fi_borrowings_from_central_bank\": \"Borrowfromcbank\",\n # 同业和其他金融机构存放款项\n \"fi_deposit_from_other_fi\": \"Fideposit\",\n # 拆入资金\n \"fi_borrowings_from_fi\": \"Borrowfund\",\n # 以公允价值计量且其变动计入当期损益的金融负债\n \"fi_financial_liability_effect_current_income\": \"Fvaluefliab\",\n # 衍生金融负债\n \"fi_financial_derivative_liability\": \"Derivefliab\",\n # 卖出回购金融资产款\n \"fi_sell_buy_back_fi_asset\": \"Sellbuybackfasset\",\n # 吸收存款\n \"fi_savings_absorption\": \"Acceptdeposit\",\n # 存款证及应付票据\n \"fi_notes_payable\": \"Cdandbillrec\",\n # 应付职工薪酬\n #\n # 应交税费\n #\n # 应付利息\n #\n # 预计负债\n \"fi_estimated_liabilities\": \"Anticipateliab\",\n # 应付债券\n \"fi_bond_payable\": \"Bondpay\",\n # 其他负债\n \"fi_other_liability\": \"Otherliab\",\n # 负债合计\n #\n # 所有者权益(或股东权益)\n # 股本\n \"fi_capital\": \"Shequity\",\n # 其他权益工具\n \"fi_other_equity_instruments\": \"Otherequity\",\n # 其中:优先股\n \"fi_preferred_stock\": \"Preferredstock\",\n # 资本公积\n #\n # 盈余公积\n #\n # 一般风险准备\n \"fi_generic_risk_reserve\": \"Generalriskprepare\",\n # 未分配利润\n #\n # 归属于母公司股东权益合计\n #\n # 股东权益合计\n #\n # 负债及股东权益总计\n # 券商相关\n # 资产\n #\n # 货币资金\n #\n # 其中: 客户资金存款\n \"fi_client_fund\": \"Clientfund\",\n # 结算备付金\n \"fi_deposit_reservation_for_balance\": \"Settlementprovision\",\n # 其中: 客户备付金\n \"fi_client_deposit_reservation_for_balance\": \"Clientprovision\",\n # 融出资金\n \"fi_margin_out_fund\": \"Marginoutfund\",\n # 以公允价值计量且其变动计入当期损益的金融资产\n #\n # 衍生金融资产\n #\n # 买入返售金融资产\n #\n # 应收利息\n #\n # 应收款项\n \"fi_receivables\": \"Receivables\",\n # 存出保证金\n \"fi_deposit_for_recognizance\": \"Gdepositpay\",\n # 可供出售金融资产\n #\n # 持有至到期投资\n #\n # 长期股权投资\n #\n # 固定资产\n #\n # 在建工程\n #\n # 无形资产\n #\n # 商誉\n #\n # 递延所得税资产\n #\n # 其他资产\n #\n # 资产总计\n #\n # 负债\n #\n # 短期借款\n #\n # 拆入资金\n #\n # 以公允价值计量且其变动计入当期损益的金融负债\n #\n # 衍生金融负债\n #\n # 卖出回购金融资产款\n #\n # 代理买卖证券款\n \"fi_receiving_as_agent\": \"Agenttradesecurity\",\n # 应付账款\n #\n # 应付职工薪酬\n #\n # 应交税费\n #\n # 应付利息\n #\n # 应付短期融资款\n \"fi_short_financing_payable\": \"Shortfinancing\",\n # 预计负债\n #\n # 应付债券\n #\n # 递延所得税负债\n #\n # 其他负债\n #\n # 负债合计\n #\n # 所有者权益(或股东权益)\n #\n # 股本\n #\n # 资本公积\n #\n # 其他权益工具\n #\n # 盈余公积\n #\n # 一般风险准备\n #\n # 交易风险准备\n \"fi_trade_risk_reserve\": \"Traderiskprepare\",\n # 未分配利润\n #\n # 归属于母公司股东权益合计\n #\n # 少数股东权益\n #\n # 股东权益合计\n #\n # 负债和股东权益总计\n # 保险相关\n # 应收保费\n \"fi_premiums_receivable\": \"Premiumrec\",\n \"fi_reinsurance_premium_receivable\": \"Rirec\",\n # 应收分保合同准备金\n \"fi_reinsurance_contract_reserve\": \"Ricontactreserverec\",\n # 保户质押贷款\n \"fi_policy_pledge_loans\": \"Insuredpledgeloan\",\n # 定期存款\n \"fi_time_deposit\": \"Tdeposit\",\n # 可供出售金融资产\n #\n # 持有至到期投资\n #\n # 应收款项类投资\n #\n # 应收账款\n #\n # 长期股权投资\n #\n # 存出资本保证金\n \"fi_deposit_for_capital_recognizance\": \"Capitalgdepositpay\",\n # 投资性房地产\n #\n # 固定资产\n #\n # 无形资产\n #\n # 商誉\n #\n # 递延所得税资产\n #\n # 其他资产\n #\n # 独立账户资产\n \"fi_capital_in_independent_accounts\": \"Independentasset\",\n # 资产总计\n #\n # 负债\n #\n # 短期借款\n #\n # 同业及其他金融机构存放款项\n #\n # 拆入资金\n #\n # 以公允价值计量且其变动计入当期损益的金融负债\n #\n # 衍生金融负债\n #\n # 卖出回购金融资产款\n #\n # 吸收存款\n #\n # 代理买卖证券款\n #\n # 应付账款\n #\n # 预收账款\n \"fi_advance_from_customers\": \"Advancerec\",\n # 预收保费\n \"fi_advance_premium\": \"Premiumadvance\",\n # 应付手续费及佣金\n \"fi_fees_and_commissions_payable\": \"Commpay\",\n # 应付分保账款\n \"fi_dividend_payable_for_reinsurance\": \"Ripay\",\n # 应付职工薪酬\n #\n # 应交税费\n #\n # 应付利息\n #\n # 预计负债\n #\n # 应付赔付款\n \"fi_claims_payable\": \"Claimpay\",\n # 应付保单红利\n \"fi_policy_holder_dividend_payable\": \"Policydivipay\",\n # 保户储金及投资款\n \"fi_policy_holder_deposits_and_investment_funds\": \"Insureddepositinv\",\n # 保险合同准备金\n \"fi_contract_reserve\": \"Contactreserve\",\n # 长期借款\n #\n # 应付债券\n #\n # 递延所得税负债\n #\n # 其他负债\n #\n # 独立账户负债\n \"fi_independent_liability\": \"Independentliab\",\n # 负债合计\n #\n # 所有者权益(或股东权益)\n #\n # 股本\n #\n # 资本公积\n #\n # 盈余公积\n #\n # 一般风险准备\n #\n # 未分配利润\n #\n # 归属于母公司股东权益总计\n #\n # 少数股东权益\n #\n # 股东权益合计\n #\n # 负债和股东权益总计\n}\n\nadd_func_to_value(balance_sheet_map, first_item_to_float)\nbalance_sheet_map[\"report_period\"] = (\"ReportDate\", to_report_period_type)\nbalance_sheet_map[\"report_date\"] = (\"ReportDate\", to_pd_timestamp)\n\n\nclass ChinaStockBalanceSheetRecorder(BaseChinaStockFinanceRecorder):\n data_schema = BalanceSheet\n\n url = \"https://emh5.eastmoney.com/api/CaiWuFenXi/GetZiChanFuZhaiBiaoList\"\n finance_report_type = \"ZiChanFuZhaiBiaoList\"\n data_type = 3\n\n def get_data_map(self):\n return balance_sheet_map\n\n\nif __name__ == \"__main__\":\n # init_log('blance_sheet.log')\n recorder = ChinaStockBalanceSheetRecorder(codes=[\"002572\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaStockBalanceSheetRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/finance/eastmoney_cash_flow_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.utils import to_report_period_type\nfrom zvt.domain import CashFlowStatement\nfrom zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder\nfrom zvt.utils.time_utils import to_pd_timestamp\nfrom zvt.utils.utils import add_func_to_value, first_item_to_float\n\ncash_flow_map = {\n # 经营活动产生的现金流量\n #\n # 销售商品、提供劳务收到的现金\n \"cash_from_selling\": \"Salegoodsservicerec\",\n # 收到的税费返还\n \"tax_refund\": \"Taxreturnrec\",\n # 收到其他与经营活动有关的现金\n \"cash_from_other_op\": \"Otheroperaterec\",\n # 经营活动现金流入小计\n \"total_op_cash_inflows\": \"Sumoperateflowin\",\n # 购买商品、接受劳务支付的现金\n \"cash_to_goods_services\": \"Buygoodsservicepay\",\n # 支付给职工以及为职工支付的现金\n \"cash_to_employees\": \"Employeepay\",\n # 支付的各项税费\n \"taxes_and_surcharges\": \"Taxpay\",\n # 支付其他与经营活动有关的现金\n \"cash_to_other_related_op\": \"Otheroperatepay\",\n # 经营活动现金流出小计\n \"total_op_cash_outflows\": \"Sumoperateflowout\",\n # 经营活动产生的现金流量净额\n \"net_op_cash_flows\": \"Netoperatecashflow\",\n # 投资活动产生的现金流量\n # 收回投资收到的现金\n \"cash_from_disposal_of_investments\": \"Disposalinvrec\",\n # 取得投资收益收到的现金\n \"cash_from_returns_on_investments\": \"Invincomerec\",\n # 处置固定资产、无形资产和其他长期资产收回的现金净额\n \"cash_from_disposal_fixed_intangible_assets\": \"Dispfilassetrec\",\n # 处置子公司及其他营业单位收到的现金净额\n \"cash_from_disposal_subsidiaries\": \"Dispsubsidiaryrec\",\n # 收到其他与投资活动有关的现金\n \"cash_from_other_investing\": \"Otherinvrec\",\n # 投资活动现金流入小计\n \"total_investing_cash_inflows\": \"Suminvflowin\",\n # 购建固定资产、无形资产和其他长期资产支付的现金\n \"cash_to_acquire_fixed_intangible_assets\": \"Buyfilassetpay\",\n # 投资支付的现金\n \"cash_to_investments\": \"Invpay\",\n # 取得子公司及其他营业单位支付的现金净额\n \"cash_to_acquire_subsidiaries\": \"Getsubsidiarypay\",\n # 支付其他与投资活动有关的现金\n \"cash_to_other_investing\": \"Otherinvpay\",\n # 投资活动现金流出小计\n \"total_investing_cash_outflows\": \"Suminvflowout\",\n # 投资活动产生的现金流量净额\n \"net_investing_cash_flows\": \"Netinvcashflow\",\n # 筹资活动产生的现金流量\n #\n # 吸收投资收到的现金\n \"cash_from_accepting_investment\": \"Acceptinvrec\",\n # 子公司吸收少数股东投资收到的现金\n \"cash_from_subsidiaries_accepting_minority_interest\": \"Subsidiaryaccept\",\n # 取得借款收到的现金\n \"cash_from_borrowings\": \"Loanrec\",\n # 发行债券收到的现金\n \"cash_from_issuing_bonds\": \"Issuebondrec\",\n # 收到其他与筹资活动有关的现金\n \"cash_from_other_financing\": \"Otherfinarec\",\n # 筹资活动现金流入小计\n \"total_financing_cash_inflows\": \"Sumfinaflowin\",\n # 偿还债务支付的现金\n \"cash_to_repay_borrowings\": \"Repaydebtpay\",\n # 分配股利、利润或偿付利息支付的现金\n \"cash_to_pay_interest_dividend\": \"Diviprofitorintpay\",\n # 子公司支付给少数股东的股利、利润\n \"cash_to_pay_subsidiaries_minority_interest\": \"Subsidiarypay\",\n # 支付其他与筹资活动有关的现金\n \"cash_to_other_financing\": \"Otherfinapay\",\n # 筹资活动现金流出小计\n \"total_financing_cash_outflows\": \"Sumfinaflowout\",\n # 筹资活动产生的现金流量净额\n \"net_financing_cash_flows\": \"Netfinacashflow\",\n # 汇率变动对现金及现金等价物的影响\n \"foreign_exchange_rate_effect\": \"Effectexchangerate\",\n # 现金及现金等价物净增加额\n \"net_cash_increase\": \"Nicashequi\",\n # 加: 期初现金及现金等价物余额\n \"cash_at_beginning\": \"Cashequibeginning\",\n # 期末现金及现金等价物余额\n \"cash\": \"Cashequiending\",\n # 银行相关\n # 客户存款和同业及其他金融机构存放款项净增加额\n \"fi_deposit_increase\": \"Nideposit\",\n # 向中央银行借款净增加额\n \"fi_borrow_from_central_bank_increase\": \"Niborrowfromcbank\",\n # 存放中央银行和同业款项及其他金融机构净减少额\n \"fi_deposit_in_others_decrease\": \"Nddepositincbankfi\",\n # 拆入资金及卖出回购金融资产款净增加额\n \"fi_borrowing_and_sell_repurchase_increase\": \"Niborrowsellbuyback\",\n # 其中:卖出回购金融资产款净增加额\n \"fi_sell_repurchase_increase\": \"Nisellbuybackfasset\",\n # 拆出资金及买入返售金融资产净减少额\n \"fi_lending_and_buy_repurchase_decrease\": \"Ndlendbuysellback\",\n # 其中:拆出资金净减少额\n \"fi_lending_decrease\": \"Ndlendfund\",\n # 买入返售金融资产净减少额\n \"fi_buy_repurchase_decrease\": \"Ndbuysellbackfasset\",\n # 收取的利息、手续费及佣金的现金\n \"fi_cash_from_interest_commission\": \"Intandcommrec\",\n # 客户贷款及垫款净增加额\n \"fi_loan_advance_increase\": \"Niloanadvances\",\n # 存放中央银行和同业及其他金融机构款项净增加额\n \"fi_deposit_in_others_increase\": \"Nidepositincbankfi\",\n # 拆出资金及买入返售金融资产净增加额\n \"fi_lending_and_buy_repurchase_increase\": \"Nilendsellbuyback\",\n # 其中:拆出资金净增加额\n \"fi_lending_increase\": \"Nilendfund\",\n # 拆入资金及卖出回购金融资产款净减少额\n \"fi_borrowing_and_sell_repurchase_decrease\": \"Ndborrowsellbuyback\",\n # 其中:拆入资金净减少额\n \"fi_borrowing_decrease\": \"Ndborrowfund\",\n # 卖出回购金融资产净减少额\n \"fi_sell_repurchase_decrease\": \"Ndsellbuybackfasset\",\n # 支付利息、手续费及佣金的现金\n \"fi_cash_to_interest_commission\": \"Intandcommpay\",\n # 应收账款净增加额\n \"fi_account_receivable_increase\": \"Niaccountrec\",\n # 偿付债券利息支付的现金\n \"fi_cash_to_pay_interest\": \"Bondintpay\",\n # 保险相关\n # 收到原保险合同保费取得的现金\n \"fi_cash_from_premium_of_original\": \"Premiumrec\",\n # 保户储金及投资款净增加额\n \"fi_insured_deposit_increase\": \"Niinsureddepositinv\",\n # 银行及证券业务卖出回购资金净增加额\n \"fi_bank_broker_sell_repurchase_increase\": \"Nisellbuyback\",\n # 银行及证券业务买入返售资金净减少额\n \"fi_bank_broker_buy_repurchase_decrease\": \"Ndbuysellback\",\n # 支付原保险合同赔付等款项的现金\n \"fi_cash_to_insurance_claim\": \"Indemnitypay\",\n # 支付再保险业务现金净额\n \"fi_cash_to_reinsurance\": \"Netripay\",\n # 银行业务及证券业务拆借资金净减少额\n \"fi_lending_decrease\": \"Ndlendfund\",\n # 银行业务及证券业务卖出回购资金净减少额\n \"fi_bank_broker_sell_repurchase_decrease\": \"Ndsellbuyback\",\n # 支付保单红利的现金\n \"fi_cash_to_dividends\": \"Divipay\",\n # 保户质押贷款净增加额\n \"fi_insured_pledge_loans_increase\": \"Niinsuredpledgeloan\",\n # 收购子公司及其他营业单位支付的现金净额\n \"fi_cash_to_acquire_subsidiaries\": \"Buysubsidiarypay\",\n # 处置子公司及其他营业单位流出的现金净额\n \"fi_cash_to_disposal_subsidiaries\": \"Dispsubsidiarypay\",\n # 支付卖出回购金融资产款现金净额\n \"fi_cash_to_sell_repurchase\": \"Netsellbuybackfassetpay\",\n # 券商相关\n # 拆入资金净增加额\n \"fi_borrowing_increase\": \"Niborrowfund\",\n # 代理买卖证券收到的现金净额\n \"fi_cash_from_trading_agent\": \"Agenttradesecurityrec\",\n # 回购业务资金净增加额\n \"fi_cash_from_repurchase_increase\": \"Nibuybackfund\",\n # 处置交易性金融资产的净减少额\n \"fi_disposal_trade_asset_decrease\": \"Nddisptradefasset\",\n # 回购业务资金净减少额\n \"fi_repurchase_decrease\": \"Ndbuybackfund\",\n # 代理买卖证券支付的现金净额（净减少额）\n \"fi_cash_to_agent_trade\": \"Agenttradesecuritypay\",\n}\n\nadd_func_to_value(cash_flow_map, first_item_to_float)\ncash_flow_map[\"report_period\"] = (\"ReportDate\", to_report_period_type)\ncash_flow_map[\"report_date\"] = (\"ReportDate\", to_pd_timestamp)\n\n\nclass ChinaStockCashFlowRecorder(BaseChinaStockFinanceRecorder):\n data_schema = CashFlowStatement\n\n url = \"https://emh5.eastmoney.com/api/CaiWuFenXi/GetXianJinLiuLiangBiaoList\"\n finance_report_type = \"XianJinLiuLiangBiaoList\"\n data_type = 4\n\n def get_data_map(self):\n return cash_flow_map\n\n\nif __name__ == \"__main__\":\n # init_log('cash_flow.log')\n recorder = ChinaStockCashFlowRecorder(codes=[\"002572\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaStockCashFlowRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/finance/eastmoney_finance_factor_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.utils import to_report_period_type\nfrom zvt.domain import FinanceFactor\nfrom zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder\nfrom zvt.utils.time_utils import to_pd_timestamp\nfrom zvt.utils.utils import add_func_to_value, to_float\n\nfinance_factor_map = {\n # 基本每股收益(元)\n \"basic_eps\": \"Epsjb\",\n # 扣非每股收益(元)\n \"deducted_eps\": \"Epskcjb\",\n # 稀释每股收益(元)\n \"diluted_eps\": \"Epsxs\",\n # 每股净资产(元)\n \"bps\": \"Bps\",\n # 每股资本公积(元)\n \"capital_reserve_ps\": \"Mgzbgj\",\n # 每股未分配利润(元)\n \"undistributed_profit_ps\": \"Mgwfplr\",\n # 每股经营现金流(元)\n \"op_cash_flow_ps\": \"Mgjyxjje\",\n # 成长能力指标\n #\n # 营业总收入(元)\n \"total_op_income\": \"Totalincome\",\n # 毛利润(元)\n \"gross_profit\": \"Grossprofit\",\n # 归属净利润(元)\n \"net_profit\": \"Parentnetprofit\",\n # 扣非净利润(元)\n \"deducted_net_profit\": \"Bucklenetprofit\",\n # 营业总收入同比增长\n \"op_income_growth_yoy\": \"Totalincomeyoy\",\n # 归属净利润同比增长\n \"net_profit_growth_yoy \": \"Parentnetprofityoy\",\n # 扣非净利润同比增长\n \"deducted_net_profit_growth_yoy\": \"Bucklenetprofityoy\",\n # 营业总收入滚动环比增长\n \"op_income_growth_qoq\": \"Totalincomerelativeratio\",\n # 归属净利润滚动环比增长\n \"net_profit_growth_qoq\": \"Parentnetprofitrelativeratio\",\n # 扣非净利润滚动环比增长\n \"deducted_net_profit_growth_qoq\": \"Bucklenetprofitrelativeratio\",\n # 盈利能力指标\n #\n # 净资产收益率(加权)\n \"roe\": \"Roejq\",\n # 净资产收益率(扣非/加权)\n \"deducted_roe\": \"Roekcjq\",\n # 总资产收益率(加权)\n \"rota\": \"Allcapitalearningsrate\",\n # 毛利率\n \"gross_profit_margin\": \"Grossmargin\",\n # 净利率\n \"net_margin\": \"Netinterest\",\n # 收益质量指标\n #\n # 预收账款/营业收入\n \"advance_receipts_per_op_income\": \"Accountsrate\",\n # 销售净现金流/营业收入\n \"sales_net_cash_flow_per_op_income\": \"Salesrate\",\n # 经营净现金流/营业收入\n \"op_net_cash_flow_per_op_income\": \"Operatingrate\",\n # 实际税率\n \"actual_tax_rate\": \"Taxrate\",\n # 财务风险指标\n #\n # 流动比率\n \"current_ratio\": \"Liquidityratio\",\n # 速动比率\n \"quick_ratio\": \"Quickratio\",\n # 现金流量比率\n \"cash_flow_ratio\": \"Cashflowratio\",\n # 资产负债率\n \"debt_asset_ratio\": \"Assetliabilityratio\",\n # 权益乘数\n \"em\": \"Equitymultiplier\",\n # 产权比率\n \"equity_ratio\": \"Equityratio\",\n # 营运能力指标(一般企业)\n #\n # 总资产周转天数(天)\n \"total_assets_turnover_days\": \"Totalassetsdays\",\n # 存货周转天数(天)\n \"inventory_turnover_days\": \"Inventorydays\",\n # 应收账款周转天数(天)\n \"receivables_turnover_days\": \"Accountsreceivabledays\",\n # 总资产周转率(次)\n \"total_assets_turnover\": \"Totalassetrate\",\n # 存货周转率(次)\n \"inventory_turnover\": \"Inventoryrate\",\n # 应收账款周转率(次)\n \"receivables_turnover\": \"Accountsreceiveablerate\",\n # 专项指标(银行)\n #\n # 存款总额\n \"fi_total_deposit\": \"Totaldeposit\",\n # 贷款总额\n \"fi_total_loan\": \"Totalloan\",\n # 存贷款比例\n \"fi_loan_deposit_ratio\": \"Depositloanratio\",\n # 资本充足率\n \"fi_capital_adequacy_ratio\": \"Capitaladequacyratio\",\n # 核心资本充足率\n \"fi_core_capital_adequacy_ratio\": \"Corecapitaladequacyratio\",\n # 不良贷款率\n \"fi_npl_ratio\": \"Nplratio\",\n # 不良贷款拨备覆盖率\n \"fi_npl_provision_coverage\": \"Nplprovisioncoverage\",\n # 资本净额\n \"fi_net_capital\": \"Netcapital_b\",\n # 专项指标(保险)\n #\n # 总投资收益率\n \"insurance_roi\": \"Tror\",\n # 净投资收益率\n \"insurance_net_investment_yield\": \"Nror\",\n # 已赚保费\n \"insurance_earned_premium\": \"Eapre\",\n # 赔付支出\n \"insurance_payout\": \"Comexpend\",\n # 退保率\n \"insurance_surrender_rate\": \"Surrate\",\n # 偿付能力充足率\n \"insurance_solvency_adequacy_ratio\": \"Solvenra\",\n # 专项指标(券商)\n #\n # 净资本\n \"broker_net_capital\": \"Netcapital\",\n # 净资产\n \"broker_net_assets\": \"Netassets\",\n # 净资本/净资产\n \"broker_net_capital_assets_ratio\": \"Captialrate\",\n # 自营固定收益类证券规模/净资本\n \"broker_self_operated_fixed_income_securities_net_capital_ratio\": \"Incomesizerate\",\n}\n\nadd_func_to_value(finance_factor_map, to_float)\nfinance_factor_map[\"report_period\"] = (\"ReportDate\", to_report_period_type)\nfinance_factor_map[\"report_date\"] = (\"ReportDate\", to_pd_timestamp)\n\n\nclass ChinaStockFinanceFactorRecorder(BaseChinaStockFinanceRecorder):\n url = \"https://emh5.eastmoney.com/api/CaiWuFenXi/GetZhuYaoZhiBiaoList\"\n finance_report_type = \"ZhuYaoZhiBiaoList\"\n\n data_schema = FinanceFactor\n data_type = 1\n\n def get_data_map(self):\n return finance_factor_map\n\n\nif __name__ == \"__main__\":\n # init_log('finance_factor.log')\n recorder = ChinaStockFinanceFactorRecorder(codes=[\"000001\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaStockFinanceFactorRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/finance/eastmoney_income_statement_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.utils import to_report_period_type\nfrom zvt.domain import IncomeStatement\nfrom zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder\nfrom zvt.utils.time_utils import to_pd_timestamp\nfrom zvt.utils.utils import add_func_to_value, first_item_to_float\n\nincome_statement_map = {\n # 营业总收入\n #\n # 营业收入\n \"operating_income\": \"Operatereve\",\n # 营业总成本\n \"total_operating_costs\": \"Totaloperateexp\",\n # 营业成本\n \"operating_costs\": \"Operateexp\",\n # 研发费用\n \"rd_costs\": \"Rdexp\",\n # 提取保险合同准备金净额\n \"net_change_in_insurance_contract_reserves\": \"Netcontactreserve\",\n # 营业税金及附加\n \"business_taxes_and_surcharges\": \"Operatetax\",\n # 销售费用\n \"sales_costs\": \"Saleexp\",\n # 管理费用\n \"managing_costs\": \"Manageexp\",\n # 财务费用\n \"financing_costs\": \"Financeexp\",\n # 资产减值损失\n \"assets_devaluation\": \"Assetdevalueloss\",\n # 其他经营收益\n #\n # 加: 投资收益\n \"investment_income\": \"Investincome\",\n # 其中: 对联营企业和合营企业的投资收益\n \"investment_income_from_related_enterprise\": \"Investjointincome\",\n # 营业利润\n \"operating_profit\": \"Operateprofit\",\n # 加: 营业外收入\n \"non_operating_income\": \"Nonoperatereve\",\n # 减: 营业外支出\n \"non_operating_costs\": \"Nonoperateexp\",\n # 其中: 非流动资产处置净损失\n \"loss_on_disposal_non_current_asset\": \"Nonlassetnetloss\",\n # 利润总额\n \"total_profits\": \"Sumprofit\",\n # 减: 所得税费用\n \"tax_expense\": \"Incometax\",\n # 净利润\n \"net_profit\": \"Netprofit\",\n # 其中: 归属于母公司股东的净利润\n \"net_profit_as_parent\": \"Parentnetprofit\",\n # 少数股东损益\n \"net_profit_as_minority_interest\": \"Minorityincome\",\n # 扣除非经常性损益后的净利润\n \"deducted_net_profit\": \"Kcfjcxsyjlr\",\n # 每股收益\n # 基本每股收益\n \"eps\": \"Basiceps\",\n # 稀释每股收益\n \"diluted_eps\": \"Dilutedeps\",\n # 其他综合收益\n \"other_comprehensive_income\": \"Othercincome\",\n # 归属于母公司股东的其他综合收益\n \"other_comprehensive_income_as_parent\": \"Parentothercincome\",\n # 归属于少数股东的其他综合收益\n \"other_comprehensive_income_as_minority_interest\": \"Minorityothercincome\",\n # 综合收益总额\n \"total_comprehensive_income\": \"Sumcincome\",\n # 归属于母公司所有者的综合收益总额\n \"total_comprehensive_income_as_parent\": \"Parentcincome\",\n # 归属于少数股东的综合收益总额\n \"total_comprehensive_income_as_minority_interest\": \"Minoritycincome\",\n # 银行相关\n # 利息净收入\n \"fi_net_interest_income\": \"Intnreve\",\n # 其中:利息收入\n \"fi_interest_income\": \"Intreve\",\n # 利息支出\n \"fi_interest_expenses\": \"Intexp\",\n # 手续费及佣金净收入\n \"fi_net_incomes_from_fees_and_commissions\": \"Commnreve\",\n # 其中:手续费及佣金收入\n \"fi_incomes_from_fees_and_commissions\": \"Commreve\",\n # 手续费及佣金支出\n \"fi_expenses_for_fees_and_commissions\": \"Commexp\",\n # 公允价值变动收益\n \"fi_income_from_fair_value_change\": \"Fvalueincome\",\n # 汇兑收益\n \"fi_income_from_exchange\": \"Exchangeincome\",\n # 其他业务收入\n \"fi_other_income\": \"Otherreve\",\n # 业务及管理费\n \"fi_operate_and_manage_expenses\": \"Operatemanageexp\",\n # 保险相关\n # 已赚保费\n \"fi_net_income_from_premium\": \"Premiumearned\",\n # 其中:保险业务收入\n \"fi_income_from_premium\": \"Insurreve\",\n # 分保费收入\n \"fi_income_from_reinsurance_premium\": \"Rireve\",\n # 减:分出保费\n \"fi_reinsurance_premium\": \"Ripremium\",\n # 提取未到期责任准备金\n \"fi_undue_duty_reserve\": \"Unduereserve\",\n # 银行业务利息净收入\n \"fi_net_income_from_bank_interest\": \"Bankintnreve\",\n # 其中:银行业务利息收入\n \"fi_income_from_bank_interest\": \"Bankintreve\",\n # 银行业务利息支出\n \"fi_expenses_for_bank_interest\": \"Bankintexp\",\n # 非保险业务手续费及佣金净收入\n \"fi_net_incomes_from_fees_and_commissions_of_non_insurance\": \"Ninsurcommnreve\",\n # 非保险业务手续费及佣金收入\n \"fi_incomes_from_fees_and_commissions_of_non_insurance\": \"Ninsurcommreve\",\n # 非保险业务手续费及佣金支出\n \"fi_expenses_for_fees_and_commissions_of_non_insurance\": \"Ninsurcommexp\",\n # 退保金\n \"fi_insurance_surrender_costs\": \"Surrenderpremium\",\n # 赔付支出\n \"fi_insurance_claims_expenses\": \"Indemnityexp\",\n # 减:摊回赔付支出\n \"fi_amortized_insurance_claims_expenses\": \"Amortiseindemnityexp\",\n # 提取保险责任准备金\n \"fi_insurance_duty_reserve\": \"Dutyreserve\",\n # 减:摊回保险责任准备金\n \"fi_amortized_insurance_duty_reserve\": \"Amortisedutyreserve\",\n # 保单红利支出\n \"fi_dividend_expenses_to_insured\": \"Policydiviexp\",\n # 分保费用\n \"fi_reinsurance_expenses\": \"Riexp\",\n # 减:摊回分保费用\n \"fi_amortized_reinsurance_expenses\": \"Amortiseriexp\",\n # 其他业务成本\n \"fi_other_op_expenses\": \"Otherexp\",\n # 券商相关\n # 手续费及佣金净收入\n #\n # 其中:代理买卖证券业务净收入\n \"fi_net_incomes_from_trading_agent\": \"Agenttradesecurity\",\n # 证券承销业务净收入\n \"fi_net_incomes_from_underwriting\": \"Securityuw\",\n # 受托客户资产管理业务净收入\n \"fi_net_incomes_from_customer_asset_management\": \"Clientassetmanage\",\n # 手续费及佣金净收入其他项目\n \"fi_fees_from_other\": \"Commnreveother\",\n # 公允价值变动收益\n #\n # 其中:可供出售金融资产公允价值变动损益\n \"fi_income_from_fair_value_change_of_fi_salable\": \"Fvalueosalable\",\n}\n\nadd_func_to_value(income_statement_map, first_item_to_float)\nincome_statement_map[\"report_period\"] = (\"ReportDate\", to_report_period_type)\nincome_statement_map[\"report_date\"] = (\"ReportDate\", to_pd_timestamp)\n\n\nclass ChinaStockIncomeStatementRecorder(BaseChinaStockFinanceRecorder):\n data_schema = IncomeStatement\n\n url = \"https://emh5.eastmoney.com/api/CaiWuFenXi/GetLiRunBiaoList\"\n finance_report_type = \"LiRunBiaoList\"\n\n data_type = 2\n\n def get_data_map(self):\n return income_statement_map\n\n\nif __name__ == \"__main__\":\n # init_log('income_statement.log')\n recorder = ChinaStockIncomeStatementRecorder(codes=[\"002572\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaStockIncomeStatementRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/holder/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule eastmoney_top_ten_tradable_holder_recorder\nfrom .eastmoney_top_ten_tradable_holder_recorder import *\nfrom .eastmoney_top_ten_tradable_holder_recorder import __all__ as _eastmoney_top_ten_tradable_holder_recorder_all\n\n__all__ += _eastmoney_top_ten_tradable_holder_recorder_all\n\n# import all from submodule eastmoney_stock_actor_recorder\nfrom .eastmoney_stock_actor_recorder import *\nfrom .eastmoney_stock_actor_recorder import __all__ as _eastmoney_stock_actor_recorder_all\n\n__all__ += _eastmoney_stock_actor_recorder_all\n\n# import all from submodule eastmoney_top_ten_holder_recorder\nfrom .eastmoney_top_ten_holder_recorder import *\nfrom .eastmoney_top_ten_holder_recorder import __all__ as _eastmoney_top_ten_holder_recorder_all\n\n__all__ += _eastmoney_top_ten_holder_recorder_all\n"
},
{
"path": "src/zvt/recorders/eastmoney/holder/eastmoney_stock_actor_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport requests\n\nfrom zvt.api.utils import get_recent_report_date\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.actor.actor_meta import ActorMeta\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass EastmoneyActorRecorder(Recorder):\n name = \"eastmoney_actor_recorder\"\n provider = \"eastmoney\"\n data_schema = ActorMeta\n\n url = \"https://datacenter.eastmoney.com/securities/api/data/v1/get?reportName=RPT_FREEHOLDERS_BASIC_INFO&columns=HOLDER_NAME,END_DATE,HOLDER_NEW,HOLDER_NUM,HOLDER_CODE"eColumns=&filter=(END_DATE='{}')&pageNumber={}&pageSize={}&sortTypes=-1,-1&sortColumns=HOLDER_NUM,HOLDER_NAME&source=SECURITIES&client=SW\"\n\n start = \"2016-03-31\"\n\n def get_data(self, end_date, pn, ps):\n resp = requests.get(url=self.url.format(end_date, pn, ps))\n return resp.json()\n\n def run(self):\n current_date = get_recent_report_date()\n pn = 1\n ps = 2000\n\n while to_pd_timestamp(current_date) >= to_pd_timestamp(self.start):\n if not self.state:\n current_date = get_recent_report_date()\n result = self.get_data(end_date=current_date, pn=pn, ps=ps)\n print(result)\n self.state = {\"end_date\": current_date, \"pages\": result[\"result\"][\"pages\"], \"pn\": pn, \"ps\": ps}\n self.persist_state(\"stock_sz_000001\", self.state)\n else:\n if self.state[\"pn\"] >= self.state[\"pages\"]:\n current_date = get_recent_report_date(the_date=self.state[\"end_date\"], step=1)\n pn = pn\n ps = ps\n else:\n pn = self.state[\"pn\"] + 1\n ps = self.state[\"ps\"]\n current_date = self.state[\"end_date\"]\n\n result = self.get_data(end_date=current_date, pn=pn, ps=ps)\n print(result)\n self.state = {\"end_date\": current_date, \"pages\": result[\"result\"][\"pages\"], \"pn\": pn, \"ps\": ps}\n self.persist_state(\"stock_sz_000001\", self.state)\n\n\nif __name__ == \"__main__\":\n EastmoneyActorRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"EastmoneyActorRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/holder/eastmoney_top_ten_holder_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.utils import to_report_period_type\nfrom zvt.domain.misc.holder import TopTenHolder\nfrom zvt.recorders.eastmoney.common import EastmoneyTimestampsDataRecorder, get_fc\nfrom zvt.utils.time_utils import to_date_time_str, to_pd_timestamp\nfrom zvt.utils.utils import to_float\n\n\nclass TopTenHolderRecorder(EastmoneyTimestampsDataRecorder):\n provider = \"eastmoney\"\n data_schema = TopTenHolder\n\n url = \"https://emh5.eastmoney.com/api/GuBenGuDong/GetShiDaGuDong\"\n path_fields = [\"ShiDaGuDongList\"]\n\n timestamps_fetching_url = \"https://emh5.eastmoney.com/api/GuBenGuDong/GetFirstRequest2Data\"\n timestamp_list_path_fields = [\"SDGDBGQ\", \"ShiDaGuDongBaoGaoQiList\"]\n timestamp_path_fields = [\"BaoGaoQi\"]\n\n def get_data_map(self):\n return {\n \"report_period\": (\"timestamp\", to_report_period_type),\n \"report_date\": (\"timestamp\", to_pd_timestamp),\n # 股东代码\n \"holder_code\": (\"GuDongDaiMa\", str),\n # 股东名称\n \"holder_name\": (\"GuDongMingCheng\", str),\n # 持股数\n \"shareholding_numbers\": (\"ChiGuShu\", to_float),\n # 持股比例\n \"shareholding_ratio\": (\"ChiGuBiLi\", to_float),\n # 变动\n \"change\": (\"ZengJian\", to_float),\n # 变动比例\n \"change_ratio\": (\"BianDongBiLi\", to_float),\n }\n\n def generate_request_param(self, security_item, start, end, size, timestamp):\n return {\"color\": \"w\", \"fc\": get_fc(security_item), \"BaoGaoQi\": to_date_time_str(timestamp)}\n\n def generate_domain_id(self, entity, original_data):\n the_name = original_data.get(\"GuDongMingCheng\")\n timestamp = original_data[self.get_original_time_field()]\n the_id = \"{}_{}_{}\".format(entity.id, timestamp, the_name)\n return the_id\n\n\nif __name__ == \"__main__\":\n # init_log('top_ten_holder.log')\n\n TopTenHolderRecorder(codes=[\"002572\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"TopTenHolderRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/holder/eastmoney_top_ten_tradable_holder_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import TopTenTradableHolder\nfrom zvt.recorders.eastmoney.holder.eastmoney_top_ten_holder_recorder import TopTenHolderRecorder\n\n\nclass TopTenTradableHolderRecorder(TopTenHolderRecorder):\n provider = \"eastmoney\"\n data_schema = TopTenTradableHolder\n\n url = \"https://emh5.eastmoney.com/api/GuBenGuDong/GetShiDaLiuTongGuDong\"\n path_fields = [\"ShiDaLiuTongGuDongList\"]\n timestamps_fetching_url = \"https://emh5.eastmoney.com/api/GuBenGuDong/GetFirstRequest2Data\"\n timestamp_list_path_fields = [\"SDLTGDBGQ\", \"ShiDaLiuTongGuDongBaoGaoQiList\"]\n timestamp_path_fields = [\"BaoGaoQi\"]\n\n\nif __name__ == \"__main__\":\n # init_log('top_ten_tradable_holder.log')\n\n TopTenTradableHolderRecorder(codes=[\"002572\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"TopTenTradableHolderRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/meta/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule eastmoney_block_meta_recorder\nfrom .eastmoney_block_meta_recorder import *\nfrom .eastmoney_block_meta_recorder import __all__ as _eastmoney_block_meta_recorder_all\n\n__all__ += _eastmoney_block_meta_recorder_all\n\n# import all from submodule eastmoney_stock_meta_recorder\nfrom .eastmoney_stock_meta_recorder import *\nfrom .eastmoney_stock_meta_recorder import __all__ as _eastmoney_stock_meta_recorder_all\n\n__all__ += _eastmoney_stock_meta_recorder_all\n"
},
{
"path": "src/zvt/recorders/eastmoney/meta/eastmoney_block_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\nimport requests\n\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder, TimeSeriesDataRecorder\nfrom zvt.domain import BlockStock, BlockCategory, Block\nfrom zvt.recorders.consts import DEFAULT_HEADER\nfrom zvt.utils.time_utils import now_pd_timestamp\nfrom zvt.utils.utils import json_callback_param\n\n\nclass EastmoneyBlockRecorder(Recorder):\n provider = \"eastmoney\"\n data_schema = Block\n\n # 用于抓取行业/概念/地域列表\n category_map_url = {\n BlockCategory.industry: \"https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._BKHY&sty=DCRRBKCPAL&st=(ChangePercent)&sr=-1&p=1&ps=200&lvl=&cb=jsonp_F1A61014DE5E45B7A50068EA290BC918&token=4f1862fc3b5e77c150a2b985b12db0fd&_=08766\",\n BlockCategory.concept: \"https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._BKGN&sty=DCRRBKCPAL&st=(ChangePercent)&sr=-1&p=1&ps=300&lvl=&cb=jsonp_3071689CC1E6486A80027D69E8B33F26&token=4f1862fc3b5e77c150a2b985b12db0fd&_=08251\",\n # BlockCategory.area: 'https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._BKDY&sty=DCRRBKCPAL&st=(ChangePercent)&sr=-1&p=1&ps=200&lvl=&cb=jsonp_A597D4867B3D4659A203AADE5B3B3AD5&token=4f1862fc3b5e77c150a2b985b12db0fd&_=02443'\n }\n\n def run(self):\n for category, url in self.category_map_url.items():\n resp = requests.get(url, headers=DEFAULT_HEADER)\n results = json_callback_param(resp.text)\n the_list = []\n for result in results:\n items = result.split(\",\")\n code = items[1]\n name = items[2]\n entity_id = f\"block_cn_{code}\"\n the_list.append(\n {\n \"id\": entity_id,\n \"entity_id\": entity_id,\n \"entity_type\": \"block\",\n \"exchange\": \"cn\",\n \"code\": code,\n \"name\": name,\n \"category\": category.value,\n }\n )\n if the_list:\n df = pd.DataFrame.from_records(the_list)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=self.force_update)\n self.logger.info(f\"finish record eastmoney blocks:{category.value}\")\n\n\nclass EastmoneyBlockStockRecorder(TimeSeriesDataRecorder):\n entity_provider = \"eastmoney\"\n entity_schema = Block\n\n provider = \"eastmoney\"\n data_schema = BlockStock\n\n # 用于抓取行业包含的股票\n category_stocks_url = \"https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.{}{}&sty=SFCOO&st=(Close)&sr=-1&p=1&ps=300&cb=jsonp_B66B5BAA1C1B47B5BB9778045845B947&token=7bc05d0d4c3c22ef9fca8c2a912d779c\"\n\n def record(self, entity, start, end, size, timestamps):\n resp = requests.get(self.category_stocks_url.format(entity.code, \"1\"), headers=DEFAULT_HEADER)\n try:\n results = json_callback_param(resp.text)\n the_list = []\n for result in results:\n items = result.split(\",\")\n stock_code = items[1]\n stock_id = china_stock_code_to_id(stock_code)\n block_id = entity.id\n\n the_list.append(\n {\n \"id\": \"{}_{}\".format(block_id, stock_id),\n \"entity_id\": block_id,\n \"entity_type\": \"block\",\n \"exchange\": entity.exchange,\n \"code\": entity.code,\n \"name\": entity.name,\n \"timestamp\": now_pd_timestamp(),\n \"stock_id\": stock_id,\n \"stock_code\": stock_code,\n \"stock_name\": items[2],\n }\n )\n if the_list:\n df = pd.DataFrame.from_records(the_list)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n\n self.logger.info(\"finish recording block:{},{}\".format(entity.category, entity.name))\n\n except Exception as e:\n self.logger.error(\"error:,resp.text:\", e, resp.text)\n self.sleep()\n\n\nif __name__ == \"__main__\":\n # init_log('china_stock_category.log')\n EastmoneyBlockRecorder().run()\n\n recorder = EastmoneyBlockStockRecorder(code=\"BK1144\")\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EastmoneyBlockRecorder\", \"EastmoneyBlockStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/meta/eastmoney_stock_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport requests\n\nfrom zvt.contract.api import get_entities\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.stock_meta import StockDetail, Stock\nfrom zvt.recorders.exchange.exchange_stock_meta_recorder import ExchangeStockMetaRecorder\nfrom zvt.utils.time_utils import to_pd_timestamp\nfrom zvt.utils.utils import to_float, pct_to_float\n\n\nclass EastmoneyStockRecorder(ExchangeStockMetaRecorder):\n data_schema = Stock\n provider = \"eastmoney\"\n\n\nclass EastmoneyStockDetailRecorder(Recorder):\n provider = \"eastmoney\"\n data_schema = StockDetail\n\n def __init__(self, force_update=False, sleeping_time=5, code=None, codes=None) -> None:\n super().__init__(force_update, sleeping_time)\n\n # get list at first\n EastmoneyStockRecorder().run()\n\n if codes is None and code is not None:\n self.codes = [code]\n else:\n self.codes = codes\n filters = None\n if not self.force_update:\n filters = [StockDetail.profile.is_(None)]\n self.entities = get_entities(\n session=self.session,\n entity_schema=StockDetail,\n exchanges=None,\n codes=self.codes,\n filters=filters,\n return_type=\"domain\",\n provider=self.provider,\n )\n\n def run(self):\n for security_item in self.entities:\n assert isinstance(security_item, StockDetail)\n\n if security_item.exchange == \"sh\":\n fc = \"{}01\".format(security_item.code)\n if security_item.exchange == \"sz\":\n fc = \"{}02\".format(security_item.code)\n\n # 基本资料\n # param = {\"color\": \"w\", \"fc\": fc, \"SecurityCode\": \"SZ300059\"}\n\n securities_code = f\"{security_item.code}.{security_item.exchange.upper()}\"\n param = {\n \"type\": \"RPT_F10_ORG_BASICINFO\",\n \"sty\": \"ORG_PROFIE,MAIN_BUSINESS,FOUND_DATE,EM2016,BLGAINIAN,REGIONBK\",\n \"filter\": f\"(SECUCODE=\\\"{securities_code}\\\")\",\n \"client\": \"app\",\n \"source\": \"SECURITIES\",\n \"pageNumber\": 1,\n \"pageSize\": 1\n }\n resp = requests.get(\"https://datacenter.eastmoney.com/securities/api/data/get\", params=param)\n resp.encoding = \"utf8\"\n\n resp_json = resp.json()[\"result\"][\"data\"][0]\n\n security_item.profile = resp_json[\"ORG_PROFIE\"]\n security_item.main_business = resp_json[\"MAIN_BUSINESS\"]\n security_item.date_of_establishment = to_pd_timestamp(resp_json[\"FOUND_DATE\"])\n\n # 关联行业\n industries = \",\".join(resp_json[\"EM2016\"].split(\"-\"))\n security_item.industries = industries\n\n # 关联概念\n security_item.concept_indices = resp_json[\"BLGAINIAN\"]\n\n # 关联地区\n security_item.area_indices = resp_json[\"REGIONBK\"]\n\n self.sleep()\n\n # 发行相关\n param = {\n \"reportName\": \"RPT_F10_ORG_ISSUEINFO\",\n \"columns\": \"AFTER_ISSUE_PE,ISSUE_PRICE,TOTAL_ISSUE_NUM,NET_RAISE_FUNDS,ONLINE_ISSUE_LWR\",\n \"filter\": f\"(SECUCODE=\\\"{securities_code}\\\")(TYPENEW=\\\"4\\\")\",\n \"client\": \"app\",\n \"source\": \"SECURITIES\",\n \"pageNumber\": 1,\n \"pageSize\": 1\n }\n resp = requests.get(\"https://datacenter.eastmoney.com/securities/api/data/v1/get\", params=param)\n resp.encoding = \"utf8\"\n\n resp_json = resp.json()[\"result\"][\"data\"][0]\n\n security_item.issue_pe = resp_json[\"AFTER_ISSUE_PE\"]\n security_item.price = resp_json[\"ISSUE_PRICE\"]\n security_item.issues = resp_json[\"TOTAL_ISSUE_NUM\"]\n security_item.raising_fund = resp_json.get(\"NET_RAISE_FUNDS\")\n security_item.net_winning_rate = resp_json[\"ONLINE_ISSUE_LWR\"]\n\n self.session.commit()\n\n self.logger.info(\"finish recording stock meta for:{}\".format(security_item.code))\n\n self.sleep()\n\n\nif __name__ == \"__main__\":\n # init_log('china_stock_meta.log')\n\n recorder = EastmoneyStockRecorder()\n recorder.run()\n StockDetail.record_data(codes=[\"000338\", \"000777\"], provider=\"eastmoney\")\n\n\n# the __all__ is generated\n__all__ = [\"EastmoneyStockRecorder\", \"EastmoneyStockDetailRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/trading/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule eastmoney_manager_trading_recorder\nfrom .eastmoney_manager_trading_recorder import *\nfrom .eastmoney_manager_trading_recorder import __all__ as _eastmoney_manager_trading_recorder_all\n\n__all__ += _eastmoney_manager_trading_recorder_all\n\n# import all from submodule eastmoney_holder_trading_recorder\nfrom .eastmoney_holder_trading_recorder import *\nfrom .eastmoney_holder_trading_recorder import __all__ as _eastmoney_holder_trading_recorder_all\n\n__all__ += _eastmoney_holder_trading_recorder_all\n"
},
{
"path": "src/zvt/recorders/eastmoney/trading/eastmoney_holder_trading_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import HolderTrading\nfrom zvt.recorders.eastmoney.common import EastmoneyMoreDataRecorder\nfrom zvt.utils.utils import to_float\n\n\nclass HolderTradingRecorder(EastmoneyMoreDataRecorder):\n data_schema = HolderTrading\n\n url = \"https://emh5.eastmoney.com/api/JiaoYiShuJu/GetGuDongZengJian\"\n path_fields = [\"GuDongZengJianList\"]\n\n def get_original_time_field(self):\n return \"RiQi\"\n\n def get_data_map(self):\n return {\n \"holder_name\": (\"GuDongMingCheng\", str),\n \"volume\": (\"BianDongShuLiang\", to_float),\n \"change_pct\": (\"BianDongBiLi\", to_float),\n \"holding_pct\": (\"BianDongHouChiGuBiLi\", to_float),\n }\n\n def generate_domain_id(self, entity, original_data):\n the_name = original_data.get(\"GuDongMingCheng\")\n timestamp = original_data[self.get_original_time_field()]\n the_id = \"{}_{}_{}\".format(entity.id, timestamp, the_name)\n return the_id\n\n\nif __name__ == \"__main__\":\n # init_log('holder_trading.log')\n\n recorder = HolderTradingRecorder(codes=[\"002572\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"HolderTradingRecorder\"]\n"
},
{
"path": "src/zvt/recorders/eastmoney/trading/eastmoney_manager_trading_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import ManagerTrading\nfrom zvt.recorders.eastmoney.common import EastmoneyMoreDataRecorder\nfrom zvt.utils.utils import to_float\n\n\nclass ManagerTradingRecorder(EastmoneyMoreDataRecorder):\n data_schema = ManagerTrading\n\n url = \"https://emh5.eastmoney.com/api/JiaoYiShuJu/GetGaoGuanZengJian\"\n path_fields = [\"GaoGuanZengJianList\"]\n\n def get_original_time_field(self):\n return \"RiQi\"\n\n def get_data_map(self):\n return {\n \"trading_person\": (\"BianDongRen\", str),\n \"volume\": (\"BianDongShuLiang\", to_float),\n \"price\": (\"JiaoYiJunJia\", to_float),\n \"holding\": (\"BianDongHouShuLiang\", to_float),\n \"trading_way\": (\"JiaoYiTuJing\", str),\n \"manager\": (\"GaoGuanMingCheng\", str),\n \"manager_position\": (\"GaoGuanZhiWei\", str),\n \"relationship_with_manager\": (\"GaoGuanGuanXi\", str),\n }\n\n def generate_domain_id(self, entity, original_data):\n the_name = original_data.get(\"BianDongRen\")\n timestamp = original_data[self.get_original_time_field()]\n the_id = \"{}_{}_{}\".format(entity.id, timestamp, the_name)\n return the_id\n\n\nif __name__ == \"__main__\":\n # init_log('manager_trading.log')\n\n recorder = ManagerTradingRecorder(codes=[\"002572\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"ManagerTradingRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule quotes\nfrom .quotes import *\nfrom .quotes import __all__ as _quotes_all\n\n__all__ += _quotes_all\n\n# import all from submodule trading\nfrom .trading import *\nfrom .trading import __all__ as _trading_all\n\n__all__ += _trading_all\n\n# import all from submodule actor\nfrom .actor import *\nfrom .actor import __all__ as _actor_all\n\n__all__ += _actor_all\n\n# import all from submodule em_api\nfrom .em_api import *\nfrom .em_api import __all__ as _em_api_all\n\n__all__ += _em_api_all\n\n# import all from submodule macro\nfrom .macro import *\nfrom .macro import __all__ as _macro_all\n\n__all__ += _macro_all\n\n# import all from submodule meta\nfrom .meta import *\nfrom .meta import __all__ as _meta_all\n\n__all__ += _meta_all\n\n# import all from submodule misc\nfrom .misc import *\nfrom .misc import __all__ as _misc_all\n\n__all__ += _misc_all\n"
},
{
"path": "src/zvt/recorders/em/actor/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule em_stock_top_ten_recorder\nfrom .em_stock_top_ten_recorder import *\nfrom .em_stock_top_ten_recorder import __all__ as _em_stock_top_ten_recorder_all\n\n__all__ += _em_stock_top_ten_recorder_all\n\n# import all from submodule em_stock_top_ten_free_recorder\nfrom .em_stock_top_ten_free_recorder import *\nfrom .em_stock_top_ten_free_recorder import __all__ as _em_stock_top_ten_free_recorder_all\n\n__all__ += _em_stock_top_ten_free_recorder_all\n\n# import all from submodule em_stock_actor_summary_recorder\nfrom .em_stock_actor_summary_recorder import *\nfrom .em_stock_actor_summary_recorder import __all__ as _em_stock_actor_summary_recorder_all\n\n__all__ += _em_stock_actor_summary_recorder_all\n\n# import all from submodule em_stock_ii_recorder\nfrom .em_stock_ii_recorder import *\nfrom .em_stock_ii_recorder import __all__ as _em_stock_ii_recorder_all\n\n__all__ += _em_stock_ii_recorder_all\n"
},
{
"path": "src/zvt/recorders/em/actor/em_stock_actor_summary_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.utils import to_report_period_type, value_to_pct\nfrom zvt.contract import ActorType\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Stock\nfrom zvt.domain.actor.stock_actor import StockActorSummary\nfrom zvt.recorders.em.em_api import get_ii_holder_report_dates, actor_type_to_org_type, get_ii_summary\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str\n\n\n# [{'CHANGE_RATIO': -1.045966694333,\n# 'IS_COMPLETE': '1',\n# 'ORG_TYPE': '07',\n# 'REPORT_DATE': '2021-03-31 00:00:00',\n# 'SECUCODE': '000338.SZ',\n# 'SECURITY_CODE': '000338',\n# 'TOTAL_FREE_SHARES': 2598718411,\n# 'TOTAL_MARKET_CAP': 49999342227.64,\n# 'TOTAL_ORG_NUM': 5,\n# 'TOTAL_SHARES_RATIO': 29.51742666}]\n\n\nclass EMStockActorSummaryRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"em\"\n data_schema = StockActorSummary\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n result = get_ii_holder_report_dates(code=entity_item.code)\n if result:\n return [to_pd_timestamp(item[\"REPORT_DATE\"]) for item in result]\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n self.logger.info(f\"to {entity.code} {the_date}\")\n for actor_type in ActorType:\n if actor_type == ActorType.private_equity or actor_type == ActorType.individual:\n continue\n result = get_ii_summary(\n code=entity.code, report_date=the_date, org_type=actor_type_to_org_type(actor_type)\n )\n if result:\n summary_list = [\n {\n \"id\": f\"{entity.entity_id}_{the_date}_{actor_type.value}\",\n \"entity_id\": entity.entity_id,\n \"timestamp\": timestamp,\n \"code\": entity.code,\n \"name\": entity.name,\n \"actor_type\": actor_type.value,\n \"actor_count\": item[\"TOTAL_ORG_NUM\"],\n \"report_date\": timestamp,\n \"report_period\": to_report_period_type(timestamp),\n \"change_ratio\": value_to_pct(item[\"CHANGE_RATIO\"], default=1),\n \"is_complete\": item[\"IS_COMPLETE\"],\n \"holding_numbers\": item[\"TOTAL_FREE_SHARES\"],\n \"holding_ratio\": value_to_pct(item[\"TOTAL_SHARES_RATIO\"], default=0),\n \"holding_values\": item[\"TOTAL_MARKET_CAP\"],\n }\n for item in result\n ]\n df = pd.DataFrame.from_records(summary_list)\n df_to_db(\n data_schema=self.data_schema,\n df=df,\n provider=self.provider,\n force_update=True,\n drop_duplicates=True,\n )\n\n\nif __name__ == \"__main__\":\n EMStockActorSummaryRecorder(codes=[\"000338\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockActorSummaryRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/actor/em_stock_ii_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.utils import to_report_period_type, value_to_pct\nfrom zvt.contract import ActorType\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Stock, ActorMeta\nfrom zvt.domain.actor.stock_actor import StockInstitutionalInvestorHolder\nfrom zvt.recorders.em.em_api import get_ii_holder_report_dates, get_ii_holder, actor_type_to_org_type\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str\n\n\n# {'END_DATE': '2021-03-31 00:00:00',\n# 'HOLDER_CODE': '10015776',\n# 'HOLDER_CODE_OLD': '80010104',\n# 'HOLDER_NAME': '香港中央结算代理人有限公司',\n# 'HOLDER_RANK': 1,\n# 'HOLD_NUM': 1938664086,\n# 'HOLD_NUM_RATIO': 24.44,\n# 'HOLD_RATIO_QOQ': '0.04093328',\n# 'IS_HOLDORG': '1',\n# 'SECUCODE': '000338.SZ'}\n\n# {'END_DATE': '2021-03-31 00:00:00',\n# 'FREE_HOLDNUM_RATIO': 0.631949916991,\n# 'FREE_RATIO_QOQ': '-5.33046217',\n# 'HOLDER_CODE': '161606',\n# 'HOLDER_CODE_OLD': '161606',\n# 'HOLDER_NAME': '交通银行-融通行业景气证券投资基金',\n# 'HOLDER_RANK': 10,\n# 'HOLD_NUM': 39100990,\n# 'IS_HOLDORG': '1',\n# 'SECUCODE': '000338.SZ'}\n\n\nclass EMStockIIRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"em\"\n data_schema = StockInstitutionalInvestorHolder\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n result = get_ii_holder_report_dates(code=entity_item.code)\n if result:\n return [to_pd_timestamp(item[\"REPORT_DATE\"]) for item in result]\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n self.logger.info(f\"to {entity.code} {the_date}\")\n for actor_type in ActorType:\n if actor_type == ActorType.private_equity or actor_type == ActorType.individual:\n continue\n result = get_ii_holder(\n code=entity.code, report_date=the_date, org_type=actor_type_to_org_type(actor_type)\n )\n if result:\n holders = [\n {\n \"id\": f'{entity.entity_id}_{the_date}_{actor_type.value}_cn_{item[\"HOLDER_CODE\"]}',\n \"entity_id\": entity.entity_id,\n \"timestamp\": timestamp,\n \"code\": entity.code,\n \"name\": entity.name,\n \"actor_id\": f'{actor_type.value}_cn_{item[\"HOLDER_CODE\"]}',\n \"actor_type\": actor_type.value,\n \"actor_code\": item[\"HOLDER_CODE\"],\n \"actor_name\": f'{item[\"HOLDER_NAME\"]}',\n \"report_date\": timestamp,\n \"report_period\": to_report_period_type(timestamp),\n \"holding_numbers\": item[\"TOTAL_SHARES\"],\n \"holding_ratio\": value_to_pct(item[\"FREESHARES_RATIO\"], 0),\n \"holding_values\": item[\"HOLD_VALUE\"],\n }\n for item in result\n ]\n df = pd.DataFrame.from_records(holders)\n df_to_db(\n data_schema=self.data_schema,\n df=df,\n provider=self.provider,\n force_update=True,\n drop_duplicates=True,\n )\n\n # save the actors\n actors = [\n {\n \"id\": f'{actor_type.value}_cn_{item[\"HOLDER_CODE\"]}',\n \"entity_id\": f'{actor_type.value}_cn_{item[\"HOLDER_CODE\"]}',\n \"entity_type\": actor_type.value,\n \"exchange\": \"cn\",\n \"code\": item[\"HOLDER_CODE\"],\n \"name\": f'{item[\"HOLDER_NAME\"]}',\n }\n for item in result\n ]\n df1 = pd.DataFrame.from_records(actors)\n df_to_db(\n data_schema=ActorMeta, df=df1, provider=self.provider, force_update=False, drop_duplicates=True\n )\n\n\nif __name__ == \"__main__\":\n EMStockIIRecorder(codes=[\"000562\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockIIRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/actor/em_stock_top_ten_free_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.utils import to_report_period_type, value_to_pct\nfrom zvt.contract import ActorType\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Stock, ActorMeta\nfrom zvt.domain.actor.stock_actor import StockTopTenFreeHolder, StockInstitutionalInvestorHolder\nfrom zvt.recorders.em.em_api import get_holder_report_dates, get_free_holders\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str\n\n\nclass EMStockTopTenFreeRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"em\"\n data_schema = StockTopTenFreeHolder\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n result = get_holder_report_dates(code=entity_item.code)\n if result:\n return [to_pd_timestamp(item[\"END_DATE\"]) for item in result]\n\n def on_finish_entity(self, entity):\n super().on_finish_entity(entity)\n holders = StockTopTenFreeHolder.query_data(\n entity_id=entity.id,\n filters=[StockTopTenFreeHolder.holding_values == None],\n session=self.session,\n return_type=\"domain\",\n )\n for holder in holders:\n ii = StockInstitutionalInvestorHolder.query_data(\n entity_id=entity.id,\n filters=[\n StockInstitutionalInvestorHolder.holding_values > 1,\n StockInstitutionalInvestorHolder.holding_ratio > 0.01,\n StockInstitutionalInvestorHolder.timestamp == holder.timestamp,\n ],\n limit=1,\n return_type=\"domain\",\n )\n if ii:\n holder.holding_values = holder.holding_ratio * ii[0].holding_values / ii[0].holding_ratio\n self.session.commit()\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n result = get_free_holders(code=entity.code, end_date=the_date)\n if result:\n holders = []\n new_actors = []\n for item in result:\n # {'END_DATE': '2021-03-31 00:00:00',\n # 'FREE_HOLDNUM_RATIO': 0.631949916991,\n # 'FREE_RATIO_QOQ': '-5.33046217',\n # 'HOLDER_CODE': '161606',\n # 'HOLDER_CODE_OLD': '161606',\n # 'HOLDER_NAME': '交通银行-融通行业景气证券投资基金',\n # 'HOLDER_RANK': 10,\n # 'HOLD_NUM': 39100990,\n # 'IS_HOLDORG': '1',\n # 'SECUCODE': '000338.SZ'}\n # 机构\n if item[\"IS_HOLDORG\"] == \"1\":\n domains: List[ActorMeta] = ActorMeta.query_data(\n filters=[ActorMeta.code == item[\"HOLDER_CODE\"]], return_type=\"domain\"\n )\n if not domains:\n actor_type = ActorType.corporation.value\n actor = ActorMeta(\n entity_id=f'{actor_type}_cn_{item[\"HOLDER_CODE\"]}',\n id=f'{actor_type}_cn_{item[\"HOLDER_CODE\"]}',\n entity_type=actor_type,\n exchange=\"cn\",\n code=item[\"HOLDER_CODE\"],\n name=item[\"HOLDER_NAME\"],\n )\n else:\n actor = domains[0]\n else:\n actor_type = ActorType.individual.value\n actor = ActorMeta(\n entity_id=f'{actor_type}_cn_{item[\"HOLDER_NAME\"]}',\n id=f'{actor_type}_cn_{item[\"HOLDER_NAME\"]}',\n entity_type=actor_type,\n exchange=\"cn\",\n code=item[\"HOLDER_NAME\"],\n name=item[\"HOLDER_NAME\"],\n )\n new_actors.append(actor.__dict__)\n holder = {\n \"id\": f\"{entity.entity_id}_{the_date}_{actor.entity_id}\",\n \"entity_id\": entity.entity_id,\n \"timestamp\": timestamp,\n \"code\": entity.code,\n \"name\": entity.name,\n \"actor_id\": actor.entity_id,\n \"actor_type\": actor.entity_type,\n \"actor_code\": actor.code,\n \"actor_name\": actor.name,\n \"report_date\": timestamp,\n \"report_period\": to_report_period_type(timestamp),\n \"holding_numbers\": item[\"HOLD_NUM\"],\n \"holding_ratio\": value_to_pct(item[\"FREE_HOLDNUM_RATIO\"], 0),\n }\n holders.append(holder)\n if holders:\n df = pd.DataFrame.from_records(holders)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n if new_actors:\n df = pd.DataFrame.from_records(new_actors)\n df_to_db(data_schema=ActorMeta, df=df, provider=self.provider, force_update=False)\n\n\nif __name__ == \"__main__\":\n EMStockTopTenFreeRecorder(codes=[\"000338\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockTopTenFreeRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/actor/em_stock_top_ten_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.utils import to_report_period_type, value_to_pct\nfrom zvt.contract import ActorType\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Stock, ActorMeta\nfrom zvt.domain.actor.stock_actor import StockTopTenHolder, StockInstitutionalInvestorHolder\nfrom zvt.recorders.em.em_api import get_holder_report_dates, get_holders\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str\n\n\nclass EMStockTopTenRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"em\"\n data_schema = StockTopTenHolder\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n result = get_holder_report_dates(code=entity_item.code)\n if result:\n return [to_pd_timestamp(item[\"END_DATE\"]) for item in result]\n\n def on_finish_entity(self, entity):\n super().on_finish_entity(entity)\n holders = StockTopTenHolder.query_data(\n entity_id=entity.id,\n filters=[StockTopTenHolder.holding_values == None],\n session=self.session,\n return_type=\"domain\",\n )\n for holder in holders:\n ii = StockInstitutionalInvestorHolder.query_data(\n entity_id=entity.id,\n filters=[\n StockInstitutionalInvestorHolder.holding_values > 1,\n StockInstitutionalInvestorHolder.holding_ratio > 0.01,\n StockInstitutionalInvestorHolder.timestamp == holder.timestamp,\n ],\n limit=1,\n return_type=\"domain\",\n )\n if ii:\n holder.holding_values = holder.holding_ratio * ii[0].holding_values / ii[0].holding_ratio\n self.session.commit()\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n result = get_holders(code=entity.code, end_date=the_date)\n if result:\n holders = []\n new_actors = []\n for item in result:\n # 机构\n if item[\"IS_HOLDORG\"] == \"1\":\n domains: List[ActorMeta] = ActorMeta.query_data(\n filters=[ActorMeta.code == item[\"HOLDER_CODE\"]], return_type=\"domain\"\n )\n if not domains:\n actor_type = ActorType.corporation.value\n actor = ActorMeta(\n entity_id=f'{actor_type}_cn_{item[\"HOLDER_CODE\"]}',\n id=f'{actor_type}_cn_{item[\"HOLDER_CODE\"]}',\n entity_type=actor_type,\n exchange=\"cn\",\n code=item[\"HOLDER_CODE\"],\n name=item[\"HOLDER_NAME\"],\n )\n else:\n actor = domains[0]\n else:\n actor_type = ActorType.individual.value\n actor = ActorMeta(\n entity_id=f'{actor_type}_cn_{item[\"HOLDER_NAME\"]}',\n id=f'{actor_type}_cn_{item[\"HOLDER_NAME\"]}',\n entity_type=actor_type,\n exchange=\"cn\",\n code=item[\"HOLDER_NAME\"],\n name=item[\"HOLDER_NAME\"],\n )\n new_actors.append(actor.__dict__)\n holder = {\n \"id\": f\"{entity.entity_id}_{the_date}_{actor.entity_id}\",\n \"entity_id\": entity.entity_id,\n \"timestamp\": timestamp,\n \"code\": entity.code,\n \"name\": entity.name,\n \"actor_id\": actor.entity_id,\n \"actor_type\": actor.entity_type,\n \"actor_code\": actor.code,\n \"actor_name\": actor.name,\n \"report_date\": timestamp,\n \"report_period\": to_report_period_type(timestamp),\n \"holding_numbers\": item[\"HOLD_NUM\"],\n \"holding_ratio\": value_to_pct(item[\"HOLD_NUM_RATIO\"], default=0),\n }\n holders.append(holder)\n if holders:\n df = pd.DataFrame.from_records(holders)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n if new_actors:\n df = pd.DataFrame.from_records(new_actors)\n df_to_db(data_schema=ActorMeta, df=df, provider=self.provider, force_update=False)\n\n\nif __name__ == \"__main__\":\n EMStockTopTenRecorder(codes=[\"000002\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockTopTenRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/em_api.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport random\nimport time\nfrom typing import Union\n\nimport demjson3\nimport pandas as pd\nimport requests\nimport sqlalchemy\nfrom requests import Session\n\nfrom zvt import zvt_config\nfrom zvt.api.kdata import generate_kdata_id\nfrom zvt.api.utils import value_to_pct, china_stock_code_to_id\nfrom zvt.contract import (\n ActorType,\n AdjustType,\n IntervalLevel,\n Exchange,\n TradableType,\n get_entity_exchanges,\n tradable_type_map_exchanges,\n)\nfrom zvt.contract.api import decode_entity_id, df_to_db\nfrom zvt.domain import BlockCategory, StockHotTopic\nfrom zvt.recorders.consts import DEFAULT_HEADER\nfrom zvt.utils.time_utils import (\n to_pd_timestamp,\n now_timestamp_ms,\n to_date_time_str,\n current_date,\n now_pd_timestamp,\n)\nfrom zvt.utils.utils import to_float, json_callback_param, chrome_copy_header_to_dict\n\nlogger = logging.getLogger(__name__)\n\nEM_TOKEN_HEADER = None\nif zvt_config[\"em_header\"]:\n try:\n EM_TOKEN_HEADER = chrome_copy_header_to_dict(zvt_config[\"em_header\"])\n except Exception as e:\n logger.error(e)\n\nif not EM_TOKEN_HEADER:\n EM_TOKEN_HEADER = DEFAULT_HEADER\n\n\n# 获取中美国债收益率\ndef get_treasury_yield(pn=1, ps=2000, fetch_all=True):\n results = get_em_data(\n request_type=\"RPTA_WEB_TREASURYYIELD\",\n source=None,\n fields=\"ALL\",\n sort_by=\"SOLAR_DATE\",\n sort=\"desc\",\n pn=pn,\n ps=ps,\n fetch_all=fetch_all,\n )\n yields = []\n for item in results:\n date = item[\"SOLAR_DATE\"]\n # 中国\n yields.append(\n {\n \"id\": f\"country_galaxy_CN_{to_date_time_str(date)}\",\n \"entity_id\": \"country_galaxy_CN\",\n \"timestamp\": to_pd_timestamp(date),\n \"code\": \"CN\",\n \"yield_2\": item.get(\"EMM00588704\"),\n \"yield_5\": item.get(\"EMM00166462\"),\n \"yield_10\": item.get(\"EMM00166466\"),\n \"yield_30\": item.get(\"EMM00166469\"),\n }\n )\n yields.append(\n {\n \"id\": f\"country_galaxy_US_{to_date_time_str(date)}\",\n \"entity_id\": \"country_galaxy_US\",\n \"timestamp\": to_pd_timestamp(date),\n \"code\": \"US\",\n \"yield_2\": item.get(\"EMG00001306\"),\n \"yield_5\": item.get(\"EMG00001308\"),\n \"yield_10\": item.get(\"EMG00001310\"),\n \"yield_30\": item.get(\"EMG00001312\"),\n }\n )\n return yields\n\n\n# 机构持仓日期\ndef get_ii_holder_report_dates(code):\n return get_em_data(\n request_type=\"RPT_F10_MAIN_ORGHOLD\",\n fields=\"REPORT_DATE,IS_COMPLETE\",\n filters=generate_filters(code=code),\n sort_by=\"REPORT_DATE\",\n sort=\"desc\",\n )\n\n\ndef get_dragon_and_tiger_list(start_date, end_date=None):\n start_date = to_date_time_str(start_date)\n if not end_date:\n end_date = now_timestamp_ms()\n end_date = to_date_time_str(end_date)\n return get_em_data(\n request_type=\"RPT_DAILYBILLBOARD_DETAILS\",\n fields=\"ALL\",\n source=\"DataCenter\",\n filters=f\"(TRADE_DATE>='{start_date}')(TRADE_DATE<='{end_date}')\",\n sort_by=\"TRADE_DATE,SECURITY_CODE\",\n sort=\"asc,asc\",\n )\n\n\n# 龙虎榜\ndef get_dragon_and_tiger(code, start_date=None):\n return get_em_data(\n request_type=\"RPT_OPERATEDEPT_TRADE\",\n fields=\"TRADE_ID,TRADE_DATE,EXPLANATION,SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,ACCUM_AMOUNT,CHANGE_RATE,NET_BUY,BUY_BUY_TOTAL,BUY_SELL_TOTAL,BUY_RATIO_TOTAL,SELL_BUY_TOTAL,SELL_SELL_TOTAL,SELL_RATIO_TOTAL,TRADE_DIRECTION,RANK,OPERATEDEPT_NAME,BUY_AMT_REAL,SELL_AMT_REAL,BUY_RATIO,SELL_RATIO,BUY_TOTAL,SELL_TOTAL,BUY_TOTAL_NET,SELL_TOTAL_NET,NET\",\n filters=generate_filters(code=code, trade_date=start_date, field_op={\"trade_date\": \">=\"}),\n params='(groupField=TRADE_ID)(groupedFields=TRADE_DIRECTION,RANK,OPERATEDEPT_NAME,BUY_AMT_REAL,SELL_AMT_REAL,BUY_RATIO,SELL_RATIO,NET\")(groupListName=\"LIST\")',\n sort_by=\"TRADE_DATE,RANK\",\n sort=\"asc,asc\",\n )\n\n\n# 十大股东持仓日期\ndef get_holder_report_dates(code):\n return get_em_data(\n request_type=\"RPT_F10_EH_HOLDERSDATE\",\n fields=\"END_DATE,IS_DEFAULT,IS_REPORTDATE\",\n filters=generate_filters(code=code),\n sort_by=\"END_DATE\",\n sort=\"desc\",\n )\n\n\n# 十大流通股东日期\ndef get_free_holder_report_dates(code):\n return get_em_data(\n request_type=\"RPT_F10_EH_FREEHOLDERSDATE\",\n fields=\"END_DATE,IS_DEFAULT,IS_REPORTDATE\",\n filters=generate_filters(code=code),\n sort_by=\"END_DATE\",\n sort=\"desc\",\n )\n\n\n# https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_EH_RELATION&sty=SECUCODE%2CHOLDER_NAME%2CRELATED_RELATION%2CHOLD_RATIO&filter=(SECUCODE%3D%22601162.SH%22)&client=APP&source=SECURITIES&p=1&ps=200&rdm=rnd_01BE6995104944ED99B70EEB7FFC0353&v=012649539724458458\n# https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FREE_TOTALHOLDNUM&sty=SECUCODE%2CSECURITY_CODE%2CEND_DATE%2CHOLD_NUM_COUNT%2CHOLD_RATIO_COUNT%2CHOLD_RATIO_CHANGE&filter=(SECUCODE%3D%22601162.SH%22)(END_DATE%3D%272024-09-30%27)&client=APP&source=SECURITIES&p=1&ps=200&sr=1&st=&rdm=rnd_FA1943FA30474E3AA0CCF206EA1B5749&v=032098454407366983\ndef get_controlling_shareholder(code):\n return get_em_data(\n request_type=\"RPT_F10_EH_RELATION\",\n fields=\"SECUCODE,CHOLDER_NAME,CRELATED_RELATION,CHOLD_RATIO\",\n filters=generate_filters(code=code),\n )\n\n\n# 机构持仓\ndef get_ii_holder(code, report_date, org_type):\n return get_em_data(\n request_type=\"RPT_MAIN_ORGHOLDDETAIL\",\n fields=\"SECURITY_CODE,REPORT_DATE,HOLDER_CODE,HOLDER_NAME,TOTAL_SHARES,HOLD_VALUE,FREESHARES_RATIO,ORG_TYPE,SECUCODE,FUND_DERIVECODE\",\n filters=generate_filters(code=code, report_date=report_date, org_type=org_type),\n )\n\n\n# 机构持仓汇总\ndef get_ii_summary(code, report_date, org_type):\n return get_em_data(\n request_type=\"RPT_F10_MAIN_ORGHOLDDETAILS\",\n fields=\"SECURITY_CODE,SECUCODE,REPORT_DATE,ORG_TYPE,TOTAL_ORG_NUM,TOTAL_FREE_SHARES,TOTAL_MARKET_CAP,TOTAL_SHARES_RATIO,CHANGE_RATIO,IS_COMPLETE\",\n filters=generate_filters(code=code, report_date=report_date, org_type=org_type),\n )\n\n\ndef get_free_holders(code, end_date):\n return get_em_data(\n request_type=\"RPT_F10_EH_FREEHOLDERS\",\n fields=\"SECUCODE,END_DATE,HOLDER_NAME,HOLDER_CODE,HOLDER_CODE_OLD,HOLD_NUM,FREE_HOLDNUM_RATIO,FREE_RATIO_QOQ,IS_HOLDORG,HOLDER_RANK\",\n filters=generate_filters(code=code, end_date=end_date),\n sort_by=\"HOLDER_RANK\",\n )\n\n\ndef get_top_ten_free_holder_stats(code):\n datas = get_holder_report_dates(code=code)\n if datas:\n end_date = to_date_time_str(datas[0][\"END_DATE\"])\n holders = get_em_data(\n request_type=\"RPT_F10_FREE_TOTALHOLDNUM\",\n fields=\"SECUCODE,SECURITY_CODE,END_DATE,HOLD_NUM_COUNT,HOLD_RATIO_COUNT,HOLD_RATIO_CHANGE,\",\n filters=generate_filters(code=code, end_date=end_date),\n )\n if holders:\n holder = holders[0]\n ratio = 0\n change = 0\n try:\n if holder[\"HOLD_RATIO_COUNT\"]:\n ratio = holder[\"HOLD_RATIO_COUNT\"] / 100\n if holder[\"HOLD_RATIO_CHANGE\"]:\n change = holder[\"HOLD_RATIO_CHANGE\"] / 100\n except Exception as e:\n logger.warning(f\"Wrong holder {holder}\", e)\n\n return {\n \"code\": code,\n \"timestamp\": end_date,\n \"ratio\": ratio,\n \"change\": change,\n }\n\n\ndef get_controlling_shareholder(code):\n holders = get_em_data(\n request_type=\"RPT_F10_EH_RELATION\",\n fields=\"SECUCODE,HOLDER_NAME,RELATED_RELATION,HOLD_RATIO\",\n filters=generate_filters(code=code),\n )\n\n if holders:\n control = {\"ratio\": 0}\n\n for holder in holders:\n if holder[\"RELATED_RELATION\"] == \"控股股东\":\n control[\"holder\"] = holder[\"HOLDER_NAME\"]\n elif holder[\"RELATED_RELATION\"] == \"实际控制人\":\n control[\"parent\"] = holder[\"HOLDER_NAME\"]\n if holder[\"HOLD_RATIO\"]:\n control[\"ratio\"] = control[\"ratio\"] + holder[\"HOLD_RATIO\"]\n return control\n\n\ndef get_holders(code, end_date):\n return get_em_data(\n request_type=\"RPT_F10_EH_HOLDERS\",\n fields=\"SECUCODE,END_DATE,HOLDER_NAME,HOLDER_CODE,HOLDER_CODE_OLD,HOLD_NUM,HOLD_NUM_RATIO,HOLD_RATIO_QOQ,HOLDER_RANK,IS_HOLDORG\",\n filters=generate_filters(code=code, end_date=end_date),\n sort_by=\"HOLDER_RANK\",\n )\n\n\ndef get_basic_info(entity_id, session=None):\n entity_type, exchange, code = decode_entity_id(entity_id)\n if entity_type == \"stock\":\n request_type = \"RPT_F10_ORG_BASICINFO\"\n fields = \"SECUCODE,LISTING_DATE,SECURITY_CODE,SECURITY_NAME_ABBR,ORG_NAME,FORMERNAME,CSRC_INDUSTRY_NAME,STR_CODEH,STR_NAMEH,STR_CODEA,STR_NAMEA,STR_CODEB,STR_NAMEB,REGIONBK,EM2016,BLGAINIAN,CHAIRMAN,LEGAL_PERSON,PRESIDENT,SECRETARY,FOUND_DATE,REG_CAPITAL,TOTAL_NUM,TATOLNUMBER,ORG_TEL,ORG_EMAIL,ORG_WEB,ADDRESS,REG_ADDRESS,ORG_PROFIE,MAIN_BUSINESS,SECURITY_TYPE_CODE,CURRENCY,ACCOUNT_FIRM,LEGAL_ADVISER,EXPAND_NAME_ABBR,ORG_PROFILE\"\n filters = generate_filters(code=code, exchange=exchange.upper())\n elif entity_type == \"stockhk\":\n request_type = \"RPT_HKF10_INFO_ORGPROFILE;RPT_HKF10_INFO_SECURITYINFO\"\n fields = \"SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,ORG_NAME,CHAIRMAN,MAIN_BUSINESS,BELONG_INDUSTRY,BELONG_INDUSTRY,REG_PLACE,REG_CAPITAL,FOUND_DATE,LISTING_DATE,@SECURITY_INNER_CODE;@SECURITY_INNER_CODE,TRADE_UNIT,PAR_VALUE,ISSUE_PRICE,ISSUE_NUM\"\n filters = generate_filters(code=code, exchange=\"HK\")\n elif entity_type == \"stockus\":\n if exchange == \"nasdaq\":\n exchange_code = \"O\"\n elif exchange == \"nyse\":\n exchange_code = \"N\"\n else:\n raise Exception(f\"unknow exchange: {exchange}\")\n\n request_type = \"RPT_USF10_INFO_SECURITYINFO;RPT_USF10_INFO_ORGPROFILE\"\n fields = \"SECUCODE,SECURITY_CODE,SECURITY_TYPE,LISTING_DATE,TRADE_MARKET,ISSUE_PRICE,ISSUE_NUM,@SECUCODE;@SECUCODE,ORG_NAME,ORG_EN_ABBR,BELONG_INDUSTRY,FOUND_DATE,CHAIRMAN,ADDRESS,ORG_WEB\"\n filters = generate_filters(code=code, exchange=exchange_code)\n else:\n raise Exception(f\"unknow entity_type: {entity_type}\")\n\n datas = get_em_data(\n session=session,\n request_type=request_type,\n fields=fields,\n filters=filters,\n )\n if datas:\n return datas[0]\n return None\n\n\ndef _order_param(order: str):\n if order:\n orders = order.split(\",\")\n return \",\".join([\"1\" if item == \"asc\" else \"-1\" for item in orders])\n return order\n\n\ndef get_url(\n request_type=None,\n fields=None,\n request_type_param_name=\"type\",\n fields_param_name=\"sty\",\n source=\"SECURITIES\",\n filters=None,\n order_by=\"\",\n order=\"asc\",\n pn=1,\n ps=2000,\n pn_param_name=\"p\",\n ps_param_name=\"ps\",\n params=None,\n):\n # 根据 url 映射如下\n # type=RPT_F10_MAIN_ORGHOLDDETAILS\n # sty=SECURITY_CODE,SECUCODE,REPORT_DATE,ORG_TYPE,TOTAL_ORG_NUM,TOTAL_FREE_SHARES,TOTAL_MARKET_CAP,TOTAL_SHARES_RATIO,CHANGE_RATIO,IS_COMPLETE\n # filter=(SECUCODE=\"000338.SZ\")(REPORT_DATE=\\'2021-03-31\\')(ORG_TYPE=\"01\")\n # sr=1\n # st=\n sr = _order_param(order=order)\n v = random.randint(1000000000000000, 9000000000000000)\n\n if filters or source:\n url = f\"https://datacenter.eastmoney.com/securities/api/data/get?{request_type_param_name}={request_type}&{fields_param_name}={fields}&filter={filters}&client=APP&source={source}&{pn_param_name}={pn}&{ps_param_name}={ps}&sr={sr}&st={order_by}&v=0{v}\"\n else:\n url = f\"https://datacenter.eastmoney.com/api/data/get?{request_type_param_name}={request_type}&{fields_param_name}={fields}&st={order_by}&sr={sr}&{pn_param_name}={pn}&{ps_param_name}={ps}&_={now_timestamp_ms()}\"\n\n if params:\n url = url + f\"¶ms={params}\"\n\n return url\n\n\ndef get_exchange(code):\n code_ = int(code)\n if 800000 >= code_ >= 600000:\n return \"SH\"\n elif code_ >= 400000:\n return \"BJ\"\n else:\n return \"SZ\"\n\n\ndef actor_type_to_org_type(actor_type: ActorType):\n if actor_type == ActorType.raised_fund:\n return \"01\"\n if actor_type == ActorType.qfii:\n return \"02\"\n if actor_type == ActorType.social_security:\n return \"03\"\n if actor_type == ActorType.broker:\n return \"04\"\n if actor_type == ActorType.insurance:\n return \"05\"\n if actor_type == ActorType.trust:\n return \"06\"\n if actor_type == ActorType.corporation:\n return \"07\"\n assert False\n\n\ndef generate_filters(\n code=None, exchange=None, trade_date=None, report_date=None, end_date=None, org_type=None, field_op: dict = None\n):\n args = [\n item for item in locals().items() if item[1] and (item[0] not in (\"code\", \"exchange\", \"org_type\", \"field_op\"))\n ]\n\n result = \"\"\n if code:\n if exchange:\n result += f'(SECUCODE=\"{code}.{exchange}\")'\n else:\n result += f'(SECUCODE=\"{code}.{get_exchange(code)}\")'\n if org_type:\n result += f'(ORG_TYPE=\"{org_type}\")'\n\n for arg in args:\n field = arg[0]\n value = arg[1]\n if field_op:\n op = field_op.get(field, \"=\")\n else:\n op = \"=\"\n result += f\"({field.upper()}{op}'{value}')\"\n\n return result\n\n\ndef get_em_data(\n request_type,\n fields,\n request_type_param_name=\"type\",\n fields_param_name=\"sty\",\n session=None,\n source=\"SECURITIES\",\n filters=None,\n sort_by=\"\",\n sort=\"asc\",\n pn=1,\n ps=2000,\n pn_param_name=\"p\",\n ps_param_name=\"ps\",\n fetch_all=True,\n fetch_count=1,\n params=None,\n):\n url = get_url(\n request_type=request_type,\n fields=fields,\n request_type_param_name=request_type_param_name,\n fields_param_name=fields_param_name,\n source=source,\n filters=filters,\n order_by=sort_by,\n order=sort,\n pn=pn,\n ps=ps,\n pn_param_name=pn_param_name,\n ps_param_name=ps_param_name,\n params=params,\n )\n if session:\n resp = session.get(url)\n else:\n resp = requests.get(url)\n if resp.status_code == 200:\n json_result = resp.json()\n resp.close()\n\n if json_result:\n if json_result.get(\"result\"):\n data: list = json_result[\"result\"][\"data\"]\n need_next = pn < json_result[\"result\"][\"pages\"]\n elif json_result.get(\"data\"):\n data: list = json_result[\"data\"]\n need_next = json_result[\"hasNext\"] == 1\n else:\n data = []\n need_next = False\n if fetch_all or fetch_count - 1 > 0:\n if need_next:\n next_data = get_em_data(\n session=session,\n request_type=request_type,\n fields=fields,\n request_type_param_name=request_type_param_name,\n fields_param_name=fields_param_name,\n source=source,\n filters=filters,\n sort_by=sort_by,\n sort=sort,\n pn=pn + 1,\n ps=ps,\n pn_param_name=pn_param_name,\n ps_param_name=ps_param_name,\n fetch_all=fetch_all,\n fetch_count=fetch_count - 1,\n params=params,\n )\n if next_data:\n data = data + next_data\n return data\n else:\n return data\n else:\n return data\n else:\n return data\n return None\n raise RuntimeError(f\"request em data code: {resp.status_code}, error: {resp.text}\")\n\n\n# quote\n# url = 'https://push2his.eastmoney.com/api/qt/stock/kline/get?'\n# 日线 klt=101\n# 周线 klt=102\n# 月线 klt=103\n#\n# limit lmt=2000\n#\n# 结束时间 end=20500000\n#\n# 复权 fqt 0 不复权 1 前复权 2 后复权\n# iscca\n#\n# 字段\n# f51,f52,f53,f54,f55,\n# timestamp,open,close,high,low\n# f56,f57,f58,f59,f60,f61,f62,f63,f64\n# volume,turnover,震幅,change_pct,change,turnover_rate\n# 深圳\n# secid=0.399001&klt=101&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\n# secid=0.399001&klt=102&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\n# secid=0.000338&klt=101&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\n#\n# 港股\n# secid=116.01024&klt=102&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\n# 美股\n# secid=106.BABA&klt=102&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\n#\n# 上海\n# secid=1.512660&klt=101&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\ndef get_kdata(entity_id, session=None, level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.qfq, limit=10000):\n entity_type, exchange, code = decode_entity_id(entity_id)\n level = IntervalLevel(level)\n\n sec_id = to_em_sec_id(entity_id)\n fq_flag = to_em_fq_flag(adjust_type)\n level_flag = to_em_level_flag(level)\n # f131 结算价\n # f133 持仓\n # 目前未获取\n url = f\"https://push2his.eastmoney.com/api/qt/stock/kline/get?secid={sec_id}&klt={level_flag}&fqt={fq_flag}&lmt={limit}&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1\"\n\n if session:\n resp = session.get(url, headers=DEFAULT_HEADER)\n else:\n resp = requests.get(url, headers=DEFAULT_HEADER)\n resp.raise_for_status()\n results = resp.json()\n resp.close()\n data = results[\"data\"]\n\n kdatas = []\n\n if data:\n klines = data[\"klines\"]\n name = data[\"name\"]\n\n for result in klines:\n # \"2000-01-28,1005.26,1012.56,1173.12,982.13,3023326,3075552000.00\"\n # \"2021-08-27,19.39,20.30,20.30,19.25,1688497,3370240912.00,5.48,6.01,1.15,3.98,0,0,0\"\n # time,open,close,high,low,volume,turnover\n # \"2022-04-13,10708,10664,10790,10638,402712,43124771328,1.43,0.57,60,0.00,4667112399583576064,4690067230254170112,1169270784\"\n fields = result.split(\",\")\n the_timestamp = to_pd_timestamp(fields[0])\n\n the_id = generate_kdata_id(entity_id=entity_id, timestamp=the_timestamp, level=level)\n\n open = to_float(fields[1])\n close = to_float(fields[2])\n high = to_float(fields[3])\n low = to_float(fields[4])\n volume = to_float(fields[5])\n turnover = to_float(fields[6])\n # 7 振幅\n change_pct = value_to_pct(to_float(fields[8]))\n # 9 变动\n turnover_rate = value_to_pct(to_float(fields[10]))\n\n kdatas.append(\n dict(\n id=the_id,\n timestamp=the_timestamp,\n entity_id=entity_id,\n provider=\"em\",\n code=code,\n name=name,\n level=level.value,\n open=open,\n close=close,\n high=high,\n low=low,\n volume=volume,\n turnover=turnover,\n turnover_rate=turnover_rate,\n change_pct=change_pct,\n )\n )\n if kdatas:\n df = pd.DataFrame.from_records(kdatas)\n return df\n\n\ndef get_basic_info1(entity_id):\n entity_type, exchange, code = decode_entity_id(entity_id)\n if entity_type == \"stock\":\n url = \"https://emh5.eastmoney.com/api/GongSiGaiKuang/GetJiBenZiLiao\"\n result_field = \"JiBenZiLiao\"\n elif entity_type == \"stockus\":\n url = \"https://emh5.eastmoney.com/api/MeiGu/GaiKuang/GetZhengQuanZiLiao\"\n result_field = \"ZhengQuanZiLiao\"\n elif entity_type == \"stockhk\":\n url = \"https://emh5.eastmoney.com/api/GangGu/GaiKuang/GetZhengQuanZiLiao\"\n result_field = \"ZhengQuanZiLiao\"\n else:\n assert False\n\n data = {\"fc\": to_em_fc(entity_id=entity_id), \"color\": \"w\"}\n resp = requests.post(url=url, json=data, headers=DEFAULT_HEADER)\n resp.encoding = \"utf-8\"\n resp.raise_for_status()\n resp.close()\n\n return resp.json()[\"Result\"][result_field]\n\n\ndef get_future_list():\n # 主连\n url = f\"https://futsseapi.eastmoney.com/list/filter/2?fid=sp_all&mktid=0&typeid=0&pageSize=1000&pageIndex=0&callbackName=jQuery34106875017735118845_1649736551642&sort=asc&orderBy=idx&_={now_timestamp_ms()}\"\n resp = requests.get(url, headers=DEFAULT_HEADER)\n resp.raise_for_status()\n result = json_callback_param(resp.text)\n resp.close()\n # [['DCE', 'im'], ['SHFE', 'rbm'], ['SHFE', 'hcm'], ['SHFE', 'ssm'], ['CZCE', 'SFM'], ['CZCE', 'SMM'], ['SHFE', 'wrm'], ['SHFE', 'cum'], ['SHFE', 'alm'], ['SHFE', 'znm'], ['SHFE', 'pbm'], ['SHFE', 'nim'], ['SHFE', 'snm'], ['INE', 'bcm'], ['SHFE', 'aum'], ['SHFE', 'agm'], ['DCE', 'am'], ['DCE', 'bm'], ['DCE', 'ym'], ['DCE', 'mm'], ['CZCE', 'RSM'], ['CZCE', 'OIM'], ['CZCE', 'RMM'], ['DCE', 'pm'], ['DCE', 'cm'], ['DCE', 'csm'], ['DCE', 'jdm'], ['CZCE', 'CFM'], ['CZCE', 'CYM'], ['CZCE', 'SRM'], ['CZCE', 'APM'], ['CZCE', 'CJM'], ['CZCE', 'PKM'], ['CZCE', 'PMM'], ['CZCE', 'WHM'], ['DCE', 'rrm'], ['CZCE', 'JRM'], ['CZCE', 'RIM'], ['CZCE', 'LRM'], ['DCE', 'lhm'], ['INE', 'scm'], ['SHFE', 'fum'], ['DCE', 'pgm'], ['INE', 'lum'], ['SHFE', 'bum'], ['CZCE', 'MAM'], ['DCE', 'egm'], ['DCE', 'lm'], ['CZCE', 'TAM'], ['DCE', 'vm'], ['DCE', 'ppm'], ['DCE', 'ebm'], ['CZCE', 'SAM'], ['CZCE', 'FGM'], ['CZCE', 'URM'], ['SHFE', 'rum'], ['INE', 'nrm'], ['SHFE', 'spm'], ['DCE', 'fbm'], ['DCE', 'bbm'], ['CZCE', 'PFM'], ['DCE', 'jmm'], ['DCE', 'jm'], ['CZCE', 'ZCM'], ['8', '060120'], ['8', '040120'], ['8', '070120'], ['8', '110120'], ['8', '050120'], ['8', '130120']]\n futures = []\n for item in result[\"list\"]:\n entity = {}\n entity[\"exchange\"], entity[\"code\"] = item[\"uid\"].split(\"|\")\n\n # {'8', 'CZCE', 'DCE', 'INE', 'SHFE'}\n if entity[\"exchange\"] == \"8\":\n entity[\"exchange\"] = \"cffex\"\n entity[\"code\"] = to_zvt_code(entity[\"code\"])\n else:\n try:\n entity[\"exchange\"] = Exchange(entity[\"exchange\"].lower()).value\n if entity[\"code\"][-1].lower() == \"m\":\n entity[\"code\"] = entity[\"code\"][:-1]\n else:\n assert False\n entity[\"code\"] = entity[\"code\"].upper()\n except Exception as e:\n logger.error(f\"wrong item: {item}\", e)\n continue\n\n entity[\"entity_type\"] = \"future\"\n entity[\"name\"] = item[\"name\"]\n entity[\"id\"] = f\"future_{entity['exchange']}_{entity['code']}\"\n entity[\"entity_id\"] = entity[\"id\"]\n futures.append(entity)\n df = pd.DataFrame.from_records(data=futures)\n return df\n\n\ndef _calculate_limit(row):\n code = row[\"code\"]\n change_pct = row[\"change_pct\"]\n if code.startswith((\"83\", \"87\", \"88\", \"889\", \"82\", \"920\")):\n return change_pct >= 0.29, change_pct <= -0.29\n elif code.startswith(\"300\") or code.startswith(\"301\") or code.startswith(\"688\"):\n return change_pct >= 0.19, change_pct <= -0.19\n else:\n return change_pct > 0.09, change_pct < -0.09\n\n\ndef get_stock_turnover():\n sz_url = \"https://push2his.eastmoney.com/api/qt/stock/trends2/get?fields1=f1,f2&fields2=f51,f57&ut=fa5fd1943c7b386f172d6893dbfba10b&iscr=0&iscca=0&secid=0.399001&time=0&ndays=2\"\n resp = requests.get(sz_url, headers=DEFAULT_HEADER)\n\n resp.raise_for_status()\n\n data = resp.json()[\"data\"][\"trends\"]\n resp.close()\n return data\n\n\ndef get_top_tradable_list(entity_type, fields, limit, entity_flag, pn=1, exchange=None, return_quote=False):\n logger.info(f\"get_top_tradable_list {entity_type} exchange: {exchange} return_quote: {return_quote} to pn: {pn}\")\n url = f\"https://push2.eastmoney.com/api/qt/clist/get?np=1&fltt=2&invt=2&fields={fields}&pn={pn}&pz={limit}&fid=f3&po=1&{entity_flag}&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1&cb=cbCallbackMore&&callback=jQuery34109676853980006124_{now_timestamp_ms() - 1}&_={now_timestamp_ms()}\"\n resp = requests.get(url, headers=EM_TOKEN_HEADER)\n\n resp.raise_for_status()\n\n result = json_callback_param(resp.text)\n resp.close()\n\n if not result[\"data\"]:\n return None\n\n total = result[\"data\"][\"total\"]\n data = result[\"data\"][\"diff\"]\n\n if pn != 1:\n return data\n\n data_size = len(data)\n\n if total != data_size and limit > data_size:\n pn_size = int(total / data_size)\n if total % data_size:\n pn_size = pn_size + 1\n\n while pn < pn_size:\n pn = pn + 1\n logger.info(f\"sleep 3 seconds to request {pn}/{pn_size}\")\n time.sleep(3)\n\n append_data = get_top_tradable_list(\n entity_type=entity_type,\n fields=fields,\n limit=limit,\n entity_flag=entity_flag,\n pn=pn,\n exchange=exchange,\n return_quote=return_quote,\n )\n if append_data:\n data = data + append_data\n\n df = pd.DataFrame.from_records(data=data)\n\n if return_quote:\n df = df[[\"f12\", \"f13\", \"f14\", \"f2\", \"f3\", \"f5\", \"f8\", \"f6\", \"f15\", \"f16\", \"f17\", \"f20\", \"f21\"]]\n df.columns = [\n \"code\",\n \"market_code\",\n \"name\",\n \"price\",\n \"change_pct\",\n \"volume\",\n \"turnover_rate\",\n \"turnover\",\n \"high\",\n \"low\",\n \"open\",\n \"total_cap\",\n \"float_cap\",\n ]\n\n df = df.dropna()\n df = df[df.change_pct != \"-\"]\n df = df[df.turnover_rate != \"-\"]\n df = df[df.turnover != \"-\"]\n df = df[df.turnover != 0]\n\n df = df.astype({\"change_pct\": \"float\", \"turnover_rate\": \"float\", \"turnover\": \"float\", \"volume\": \"float\"})\n\n df[\"change_pct\"] = df[\"change_pct\"] / 100\n df[\"turnover_rate\"] = df[\"turnover_rate\"] / 100\n\n df[[\"is_limit_up\", \"is_limit_down\"]] = df.apply(lambda row: _calculate_limit(row), axis=1, result_type=\"expand\")\n df[\"entity_id\"] = df[[\"market_code\", \"code\"]].apply(\n lambda row: market_code_to_entity_id(market=row[\"market_code\"], code=row[\"code\"]), axis=1\n )\n else:\n if entity_type in (TradableType.stock, TradableType.stockhk, TradableType.stockus):\n df = df[[\"f12\", \"f13\", \"f14\", \"f20\", \"f21\", \"f9\", \"f23\"]]\n df.columns = [\"code\", \"exchange\", \"name\", \"total_cap\", \"float_cap\", \"pe\", \"pb\"]\n df[[\"total_cap\", \"float_cap\", \"pe\", \"pb\"]] = df[[\"total_cap\", \"float_cap\", \"pe\", \"pb\"]].apply(\n pd.to_numeric, errors=\"coerce\"\n )\n else:\n df = df[[\"f12\", \"f13\", \"f14\"]]\n df.columns = [\"code\", \"exchange\", \"name\"]\n if exchange:\n df[\"exchange\"] = exchange.value\n df[\"entity_type\"] = entity_type.value\n df[\"id\"] = df[[\"entity_type\", \"exchange\", \"code\"]].apply(lambda x: \"_\".join(x.astype(str)), axis=1)\n df[\"entity_id\"] = df[\"id\"]\n\n return df\n\n\ndef get_top_stocks(limit=100):\n # 沪深和北交所\n entity_flag = \"fs=m:0+t:6+f:!2,m:0+t:13+f:!2,m:0+t:80+f:!2,m:1+t:2+f:!2,m:1+t:23+f:!2,m:0+t:81+s:2048\"\n\n fields = \"f2,f3,f5,f6,f8,f12,f13,f14,f15,f16,f17,f20,f21\"\n return get_top_tradable_list(\n entity_type=TradableType.stock, fields=fields, limit=limit, entity_flag=entity_flag, return_quote=True\n )\n\n\ndef get_top_stockhks(limit=20, hk_south=False):\n if hk_south:\n entity_flag = \"fs=b:DLMK0144,b:DLMK0146\"\n else:\n entity_flag = f\"fs=m:116+t:3,m:116+t:4\"\n fields = \"f2,f3,f5,f6,f8,f12,f13,f14,f15,f16,f17,f20,f21\"\n return get_top_tradable_list(\n entity_type=TradableType.stockhk, fields=fields, limit=limit, entity_flag=entity_flag, return_quote=True\n )\n\n\ndef get_top_stockuss(limit=50):\n entity_flag = \"fs=m:105,m:106\"\n fields = \"f2,f3,f5,f6,f8,f12,f13,f14,f15,f16,f17,f20,f21\"\n return get_top_tradable_list(\n entity_type=TradableType.stockus, fields=fields, limit=limit, entity_flag=entity_flag, return_quote=True\n )\n\n\ndef get_tradable_list(\n entity_type: Union[TradableType, str] = \"stock\",\n exchange: Union[Exchange, str] = None,\n limit: int = 10000,\n hk_south=False,\n block_category=BlockCategory.concept,\n):\n entity_type = TradableType(entity_type)\n if entity_type == TradableType.future:\n return get_future_list()\n\n exchanges = get_entity_exchanges(entity_type=entity_type)\n\n if exchange is not None:\n assert exchange in exchanges\n exchanges = [exchange]\n\n dfs = []\n for exchange in exchanges:\n exchange = Exchange(exchange)\n ex_flag = to_em_entity_flag(exchange=exchange)\n entity_flag = f\"fs=m:{ex_flag}\"\n\n if entity_type == TradableType.index:\n if exchange == Exchange.sh:\n entity_flag = \"fs=i:1.000001,i:1.000002,i:1.000003,i:1.000009,i:1.000010,i:1.000011,i:1.000012,i:1.000016,i:1.000300,i:1.000903,i:1.000905,i:1.000906,i:1.000688,i:1.000852,i:2.932000\"\n if exchange == Exchange.sz:\n entity_flag = \"fs=i:0.399001,i:0.399002,i:0.399003,i:0.399004,i:0.399005,i:0.399006,i:0.399100,i:0.399106,i:0.399305,i:0.399550\"\n elif entity_type == TradableType.currency:\n entity_flag = \"fs=m:119,m:120\"\n elif entity_type == TradableType.indexus:\n # 纳斯达克，道琼斯，标普500，美元指数\n entity_flag = \"fs=i:100.NDX,i:100.DJIA,i:100.SPX,i:100.UDI\"\n elif entity_type == TradableType.blockus:\n # 美股板块\n entity_flag = \"fs=m:202\"\n elif entity_type == TradableType.cbond:\n if exchange == Exchange.sz:\n entity_flag = \"fs=m:0+e:11\"\n elif exchange == Exchange.sh:\n entity_flag = \"fs=m:1+e:11\"\n else:\n assert False\n elif entity_type == TradableType.indexhk:\n entity_flag = \"fs=i:305.HSI\"\n # m为交易所代码，t为交易类型\n elif entity_type in [TradableType.block, TradableType.stock, TradableType.stockus, TradableType.stockhk]:\n if exchange == Exchange.sh:\n # t=2 主板\n # t=23 科创板\n entity_flag = \"fs=m:1+t:2,m:1+t:23\"\n if exchange == Exchange.sz:\n # t=6 主板\n # t=80 创业板\n entity_flag = \"fs=m:0+t:6,m:0+t:13,m:0+t:80\"\n if exchange == Exchange.bj:\n entity_flag = \"fs=m:0+t:81+s:2048\"\n if exchange == Exchange.hk:\n if hk_south:\n # 港股通\n entity_flag = \"fs=b:DLMK0144,b:DLMK0146\"\n else:\n # t=3 主板\n # t=4 创业板\n entity_flag = f\"fs=m:116+t:3,m:116+t:4\"\n if exchange == Exchange.nasdaq:\n # t=1\n # t=3 中概股\n entity_flag = f\"fs=m:105+t:1,m:105+t:3\"\n if exchange == Exchange.nyse:\n # t=1\n # t=3 中概股\n entity_flag = f\"fs=m:106+t:1,m:105+t:3\"\n if exchange == Exchange.cn:\n if block_category == BlockCategory.industry:\n entity_flag = entity_flag + \"+t:2\"\n elif block_category == BlockCategory.concept:\n entity_flag = entity_flag + \"+t:3\"\n else:\n assert False\n\n # f2, f3, f4, f12, f13, f14, f19, f111, f148\n fields = \"f1,f2,f3,f4,f12,f13,f14\"\n if entity_type in (TradableType.stock, TradableType.stockhk, TradableType.stockus):\n # 市值,流通市值,pe,pb\n fields = fields + \",f20,f21,f9,f23\"\n\n df = get_top_tradable_list(\n entity_type=entity_type, fields=fields, limit=limit, entity_flag=entity_flag, exchange=exchange\n )\n if entity_type == TradableType.block:\n df[\"category\"] = block_category.value\n\n dfs.append(df)\n\n return pd.concat(dfs)\n\n\ndef get_block_stocks(block_id, name=\"\", session=None):\n entity_type, exchange, code = decode_entity_id(block_id)\n category_stocks_url = f\"http://48.push2.eastmoney.com/api/qt/clist/get?cb=jQuery11240710111145777397_{now_timestamp_ms() - 1}&pn=1&pz=1000&po=1&np=1&ut=bd1d9ddb04089700cf9c27f6f7426281&fltt=2&invt=2&wbp2u=4668014655929990|0|1|0|web&fid=f3&fs=b:{code}+f:!50&fields=f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152,f45&_={now_timestamp_ms()}\"\n if session:\n resp = session.get(category_stocks_url, headers=DEFAULT_HEADER)\n else:\n resp = requests.get(category_stocks_url, headers=DEFAULT_HEADER)\n\n data = json_callback_param(resp.text)[\"data\"]\n the_list = []\n if data:\n results = data[\"diff\"]\n for result in results:\n stock_code = result[\"f12\"]\n stock_name = result[\"f14\"]\n stock_id = china_stock_code_to_id(stock_code)\n\n the_list.append(\n {\n \"id\": \"{}_{}\".format(block_id, stock_id),\n \"entity_id\": block_id,\n \"entity_type\": \"block\",\n \"exchange\": exchange,\n \"code\": code,\n \"name\": name,\n \"timestamp\": current_date(),\n \"stock_id\": stock_id,\n \"stock_code\": stock_code,\n \"stock_name\": stock_name,\n }\n )\n return the_list\n\n\ndef market_code_to_entity_id(market, code):\n if market in (0, 1):\n return china_stock_code_to_id(code)\n elif market == 105:\n return f\"stockus_nasdaq_{code}\"\n elif market == 106:\n return f\"stockus_nyse_{code}\"\n elif market == 116:\n return f\"stockhk_hk_{code}\"\n else:\n for exchange, flag in exchange_map_em_flag.items():\n if flag == market:\n for entity_type, exchanges in tradable_type_map_exchanges.items():\n if exchange in exchanges:\n return f\"{entity_type.value}_{exchange.value}_{code}\"\n return code\n\n\ndef get_hot_topic(session: Session = None):\n url = \"https://emcreative.eastmoney.com/FortuneApi/GuBaApi/common\"\n data = {\n \"url\": \"newctopic/api/Topic/HomeTopicRead?deviceid=IPHONE&version=10001000&product=Guba&plat=Iphone&p=1&ps=20&needPkPost=true\",\n \"type\": \"get\",\n \"parm\": \"\",\n }\n logger.debug(f\"get hot topic from: {url}\")\n if session:\n resp = session.post(url=url, json=data, headers=DEFAULT_HEADER)\n else:\n resp = requests.post(url=url, json=data, headers=DEFAULT_HEADER)\n\n if resp.status_code == 200:\n data_list = resp.json().get(\"re\")\n if data_list:\n hot_topics = []\n for position, data in enumerate(data_list):\n if data[\"stockList\"]:\n entity_ids = [\n market_code_to_entity_id(market=stock[\"qMarket\"], code=stock[\"qCode\"])\n for stock in data[\"stockList\"]\n ]\n else:\n entity_ids = []\n topic_id = data[\"topicid\"]\n entity_id = f\"hot_topic_{topic_id}\"\n hot_topics.append(\n {\n \"id\": entity_id,\n \"entity_id\": entity_id,\n \"timestamp\": now_pd_timestamp(),\n \"created_timestamp\": to_pd_timestamp(data[\"cTime\"]),\n \"position\": position,\n \"entity_ids\": entity_ids,\n \"news_code\": topic_id,\n \"news_title\": data[\"name\"],\n \"news_content\": data[\"summary\"],\n }\n )\n return hot_topics\n\n logger.error(f\"request em data code: {resp.status_code}, error: {resp.text}\")\n\n\ndef record_hot_topic():\n hot_topics = get_hot_topic()\n logger.debug(hot_topics)\n if hot_topics:\n df = pd.DataFrame.from_records(hot_topics)\n df_to_db(\n df=df, data_schema=StockHotTopic, provider=\"em\", force_update=True, dtype={\"entity_ids\": sqlalchemy.JSON}\n )\n\n\ndef get_news(entity_id, ps=200, index=1, start_timestamp=None, session=None, latest_code=None):\n sec_id = to_em_sec_id(entity_id=entity_id)\n url = f\"https://np-listapi.eastmoney.com/comm/wap/getListInfo?cb=callback&client=wap&type=1&mTypeAndCode={sec_id}&pageSize={ps}&pageIndex={index}&callback=jQuery1830017478247906740352_{now_timestamp_ms() - 1}&_={now_timestamp_ms()}\"\n logger.debug(f\"get news from: {url}\")\n if session:\n resp = session.get(url)\n else:\n resp = requests.get(url)\n # {\n # \"Art_ShowTime\": \"2022-02-11 14:29:25\",\n # \"Art_Image\": \"\",\n # \"Art_MediaName\": \"每日经济新闻\",\n # \"Art_Code\": \"202202112274017262\",\n # \"Art_Title\": \"潍柴动力：巴拉德和锡里斯不纳入合并财务报表范围\",\n # \"Art_SortStart\": \"1644560965017262\",\n # \"Art_VideoCount\": 0,\n # \"Art_OriginUrl\": \"http://finance.eastmoney.com/news/1354,202202112274017262.html\",\n # \"Art_Url\": \"http://finance.eastmoney.com/a/202202112274017262.html\",\n # }\n if resp.status_code == 200:\n json_text = resp.text[resp.text.index(\"(\") + 1 : resp.text.rindex(\")\")]\n if \"list\" in demjson3.decode(json_text)[\"data\"]:\n json_result = demjson3.decode(json_text)[\"data\"][\"list\"]\n resp.close()\n if json_result:\n news = [\n {\n \"id\": f'{entity_id}_{item.get(\"Art_ShowTime\", \"\")}',\n \"entity_id\": entity_id,\n \"timestamp\": to_pd_timestamp(item.get(\"Art_ShowTime\", \"\")),\n \"news_code\": item.get(\"Art_Code\", \"\"),\n \"news_url\": item.get(\"Art_Url\", \"\"),\n \"news_title\": item.get(\"Art_Title\", \"\"),\n \"ignore_by_user\": False,\n }\n for index, item in enumerate(json_result)\n if not start_timestamp\n or (\n (to_pd_timestamp(item[\"Art_ShowTime\"]) >= start_timestamp)\n and (item.get(\"Art_Code\", \"\") != latest_code)\n )\n ]\n if len(news) < len(json_result):\n return news\n next_data = get_news(entity_id=entity_id, ps=ps, index=index + 1)\n if next_data:\n return news + next_data\n else:\n return news\n else:\n return None\n\n logger.error(f\"request em data code: {resp.status_code}, error: {resp.text}\")\n\n\n# utils to transform zvt entity to em entity\ndef to_em_fc(entity_id):\n entity_type, exchange, code = decode_entity_id(entity_id)\n if entity_type == \"stock\":\n if exchange == \"sh\":\n return f\"{code}01\"\n if exchange == \"sz\":\n return f\"{code}02\"\n\n if entity_type == \"stockhk\":\n return code\n\n if entity_type == \"stockus\":\n if exchange == \"nyse\":\n return f\"{code}.N\"\n if exchange == \"nasdaq\":\n return f\"{code}.O\"\n\n\nexchange_map_em_flag = {\n #: 深证交易所\n Exchange.sz: 0,\n #: 上证交易所\n Exchange.sh: 1,\n #: 北交所\n Exchange.bj: 0,\n #: 纳斯达克\n Exchange.nasdaq: 105,\n #: 纽交所\n Exchange.nyse: 106,\n #: 中国金融期货交易所\n Exchange.cffex: 8,\n #: 上海期货交易所\n Exchange.shfe: 113,\n #: 大连商品交易所\n Exchange.dce: 114,\n #: 郑州商品交易所\n Exchange.czce: 115,\n #: 上海国际能源交易中心\n Exchange.ine: 142,\n #: 港交所\n Exchange.hk: 116,\n #: 中国行业/概念板块\n Exchange.cn: 90,\n #: 美国指数\n Exchange.us: 100,\n #: 汇率\n Exchange.forex: 119,\n}\n\n\ndef to_em_entity_flag(exchange: Union[Exchange, str]):\n exchange = Exchange(exchange)\n return exchange_map_em_flag.get(exchange, exchange)\n\n\ndef to_em_fq_flag(adjust_type: AdjustType):\n adjust_type = AdjustType(adjust_type)\n if adjust_type == AdjustType.bfq:\n return 0\n if adjust_type == AdjustType.qfq:\n return 1\n if adjust_type == AdjustType.hfq:\n return 2\n\n\ndef to_em_level_flag(level: IntervalLevel):\n level = IntervalLevel(level)\n if level == IntervalLevel.LEVEL_1MIN:\n return 1\n elif level == IntervalLevel.LEVEL_5MIN:\n return 5\n elif level == IntervalLevel.LEVEL_15MIN:\n return 15\n elif level == IntervalLevel.LEVEL_30MIN:\n return 30\n elif level == IntervalLevel.LEVEL_1HOUR:\n return 60\n elif level == IntervalLevel.LEVEL_1DAY:\n return 101\n elif level == IntervalLevel.LEVEL_1WEEK:\n return 102\n elif level == IntervalLevel.LEVEL_1MON:\n return 103\n\n assert False\n\n\ndef to_em_sec_id(entity_id):\n entity_type, exchange, code = decode_entity_id(entity_id)\n # 主力合约\n if entity_type == \"future\" and code[-1].isalpha():\n code = code + \"m\"\n if entity_type == \"currency\" and \"CNYC\" in code:\n return f\"120.{code}\"\n if entity_type == \"indexhk\":\n return f\"100.{code}\"\n return f\"{to_em_entity_flag(exchange)}.{code}\"\n\n\ndef to_zvt_code(code):\n # ('中证当月连续', '8|060120'),\n # ('沪深当月连续', '8|040120'),\n # ('上证当月连续', '8|070120'),\n # ('十债当季连续', '8|110120'),\n # ('五债当季连续', '8|050120'),\n # ('二债当季连续', '8|130120')]\n if code == \"060120\":\n return \"IC\"\n elif code == \"040120\":\n return \"IF\"\n elif code == \"070120\":\n return \"IH\"\n elif code == \"110120\":\n return \"T\"\n elif code == \"050120\":\n return \"TF\"\n elif code == \"130120\":\n return \"TS\"\n return code\n\n\nif __name__ == \"__main__\":\n print(get_tradable_list(entity_type=\"stockhk\"))\n\n\n# the __all__ is generated\n__all__ = [\n \"get_treasury_yield\",\n \"get_ii_holder_report_dates\",\n \"get_dragon_and_tiger_list\",\n \"get_dragon_and_tiger\",\n \"get_holder_report_dates\",\n \"get_free_holder_report_dates\",\n \"get_controlling_shareholder\",\n \"get_ii_holder\",\n \"get_ii_summary\",\n \"get_free_holders\",\n \"get_top_ten_free_holder_stats\",\n \"get_controlling_shareholder\",\n \"get_holders\",\n \"get_basic_info\",\n \"get_url\",\n \"get_exchange\",\n \"actor_type_to_org_type\",\n \"generate_filters\",\n \"get_em_data\",\n \"get_kdata\",\n \"get_basic_info1\",\n \"get_future_list\",\n \"get_stock_turnover\",\n \"get_top_tradable_list\",\n \"get_top_stocks\",\n \"get_top_stockhks\",\n \"get_top_stockuss\",\n \"get_tradable_list\",\n \"get_block_stocks\",\n \"market_code_to_entity_id\",\n \"get_hot_topic\",\n \"record_hot_topic\",\n \"get_news\",\n \"to_em_fc\",\n \"to_em_entity_flag\",\n \"to_em_fq_flag\",\n \"to_em_level_flag\",\n \"to_em_sec_id\",\n \"to_zvt_code\",\n]\n"
},
{
"path": "src/zvt/recorders/em/macro/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule em_treasury_yield_recorder\nfrom .em_treasury_yield_recorder import *\nfrom .em_treasury_yield_recorder import __all__ as _em_treasury_yield_recorder_all\n\n__all__ += _em_treasury_yield_recorder_all\n"
},
{
"path": "src/zvt/recorders/em/macro/em_treasury_yield_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Country\nfrom zvt.domain.macro.monetary import TreasuryYield\nfrom zvt.recorders.em import em_api\n\n\nclass EMTreasuryYieldRecorder(FixedCycleDataRecorder):\n entity_schema = Country\n data_schema = TreasuryYield\n entity_provider = \"wb\"\n provider = \"em\"\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n return_unfinished=False,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n None,\n None,\n None,\n None,\n [\"CN\"],\n True,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n def record(self, entity, start, end, size, timestamps):\n # record before\n if start:\n result = em_api.get_treasury_yield(pn=1, ps=size, fetch_all=False)\n else:\n result = em_api.get_treasury_yield(fetch_all=True)\n if result:\n df = pd.DataFrame.from_records(result)\n df_to_db(\n data_schema=self.data_schema,\n df=df,\n provider=self.provider,\n force_update=True,\n drop_duplicates=True,\n )\n\n\nif __name__ == \"__main__\":\n r = EMTreasuryYieldRecorder()\n r.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMTreasuryYieldRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule em_index_meta_recorder\nfrom .em_index_meta_recorder import *\nfrom .em_index_meta_recorder import __all__ as _em_index_meta_recorder_all\n\n__all__ += _em_index_meta_recorder_all\n\n# import all from submodule em_future_meta_recorder\nfrom .em_future_meta_recorder import *\nfrom .em_future_meta_recorder import __all__ as _em_future_meta_recorder_all\n\n__all__ += _em_future_meta_recorder_all\n\n# import all from submodule em_indexus_meta_recorder\nfrom .em_indexus_meta_recorder import *\nfrom .em_indexus_meta_recorder import __all__ as _em_indexus_meta_recorder_all\n\n__all__ += _em_indexus_meta_recorder_all\n\n# import all from submodule em_indexhk_meta_recorder\nfrom .em_indexhk_meta_recorder import *\nfrom .em_indexhk_meta_recorder import __all__ as _em_indexhk_meta_recorder_all\n\n__all__ += _em_indexhk_meta_recorder_all\n\n# import all from submodule em_cbond_meta_recorder\nfrom .em_cbond_meta_recorder import *\nfrom .em_cbond_meta_recorder import __all__ as _em_cbond_meta_recorder_all\n\n__all__ += _em_cbond_meta_recorder_all\n\n# import all from submodule em_stockhk_meta_recorder\nfrom .em_stockhk_meta_recorder import *\nfrom .em_stockhk_meta_recorder import __all__ as _em_stockhk_meta_recorder_all\n\n__all__ += _em_stockhk_meta_recorder_all\n\n# import all from submodule em_stockus_meta_recorder\nfrom .em_stockus_meta_recorder import *\nfrom .em_stockus_meta_recorder import __all__ as _em_stockus_meta_recorder_all\n\n__all__ += _em_stockus_meta_recorder_all\n\n# import all from submodule em_stock_meta_recorder\nfrom .em_stock_meta_recorder import *\nfrom .em_stock_meta_recorder import __all__ as _em_stock_meta_recorder_all\n\n__all__ += _em_stock_meta_recorder_all\n\n# import all from submodule em_currency_meta_recorder\nfrom .em_currency_meta_recorder import *\nfrom .em_currency_meta_recorder import __all__ as _em_currency_meta_recorder_all\n\n__all__ += _em_currency_meta_recorder_all\n\n# import all from submodule em_block_meta_recorder\nfrom .em_block_meta_recorder import *\nfrom .em_block_meta_recorder import __all__ as _em_block_meta_recorder_all\n\n__all__ += _em_block_meta_recorder_all\n"
},
{
"path": "src/zvt/recorders/em/meta/em_block_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder, TimeSeriesDataRecorder\nfrom zvt.domain import Block, BlockCategory, BlockStock\nfrom zvt.recorders.em import em_api\n\n\nclass EMBlockRecorder(Recorder):\n provider = \"em\"\n data_schema = Block\n\n def run(self):\n for block_category in [BlockCategory.concept, BlockCategory.industry]:\n df = em_api.get_tradable_list(entity_type=\"block\", block_category=block_category, limit=100)\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nclass EMBlockStockRecorder(TimeSeriesDataRecorder):\n entity_provider = \"em\"\n entity_schema = Block\n\n provider = \"em\"\n data_schema = BlockStock\n\n def record(self, entity, start, end, size, timestamps):\n the_list = em_api.get_block_stocks(entity.id, entity.name)\n if the_list:\n df = pd.DataFrame.from_records(the_list)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n self.logger.info(\"finish recording block:{},{}\".format(entity.category, entity.name))\n self.sleep()\n\n\nif __name__ == \"__main__\":\n recorder = EMBlockStockRecorder(day_data=True, sleeping_time=0)\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMBlockRecorder\", \"EMBlockStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_cbond_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.cbond_meta import CBond\nfrom zvt.recorders.em import em_api\n\n\nclass EMCBondRecorder(Recorder):\n provider = \"em\"\n data_schema = CBond\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"cbond\")\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = EMCBondRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMCBondRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_currency_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.currency_meta import Currency\nfrom zvt.recorders.em import em_api\n\n\nclass EMCurrencyRecorder(Recorder):\n provider = \"em\"\n data_schema = Currency\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"currency\")\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = EMCurrencyRecorder(force_update=True)\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMCurrencyRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_future_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import Future\nfrom zvt.recorders.em import em_api\n\n\nclass EMFutureRecorder(Recorder):\n provider = \"em\"\n data_schema = Future\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"future\")\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = EMFutureRecorder(force_update=True)\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMFutureRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_index_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import Index\nfrom zvt.recorders.em import em_api\n\n\nclass EMIndexRecorder(Recorder):\n provider = \"em\"\n data_schema = Index\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"index\", limit=100)\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = EMIndexRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMIndexRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_indexhk_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.indexhk_meta import Indexhk\nfrom zvt.recorders.em import em_api\n\n\nclass EMIndexhkRecorder(Recorder):\n provider = \"em\"\n data_schema = Indexhk\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"indexhk\")\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = EMIndexhkRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMIndexhkRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_indexus_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.indexus_meta import Indexus\nfrom zvt.recorders.em import em_api\n\n\nclass EMIndexusRecorder(Recorder):\n provider = \"em\"\n data_schema = Indexus\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"indexus\")\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = EMIndexusRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMIndexusRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_stock_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nfrom zvt.contract import Exchange\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import Stock\nfrom zvt.recorders.em import em_api\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass EMStockRecorder(Recorder):\n provider = \"em\"\n data_schema = Stock\n\n def run(self):\n for exchange in [Exchange.sh, Exchange.sz, Exchange.bj]:\n df = em_api.get_tradable_list(entity_type=\"stock\", exchange=exchange, limit=100)\n\n if pd_is_not_null(df):\n df[\"total_cap\"] = df[\"total_cap\"].fillna(0)\n df[\"float_cap\"] = df[\"float_cap\"].fillna(0)\n\n self.logger.info(df.head())\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n for item in df[[\"id\", \"name\", \"total_cap\", \"float_cap\"]].values.tolist():\n entity_id = item[0]\n datas: List[Stock] = Stock.query_data(\n entity_id=entity_id, return_type=\"domain\", session=self.session\n )\n\n if datas:\n entity_domain = datas[0]\n\n if \"退\" in entity_domain.name:\n self.logger.info(f\"Ignore: {entity_domain.entity_id} {entity_domain.name} {item}\")\n continue\n\n entity_domain.name = item[1]\n entity_domain.total_cap = item[2]\n entity_domain.float_cap = item[3]\n\n if not entity_domain.list_date:\n basic_info = em_api.get_basic_info(entity_id=entity_id, session=self.http_session)\n self.logger.info(f\"basic info: {basic_info}\")\n if basic_info:\n entity_domain.timestamp = to_pd_timestamp(basic_info.get(\"LISTING_DATE\"))\n entity_domain.list_date = to_pd_timestamp(basic_info.get(\"LISTING_DATE\"))\n else:\n entity_domain.name = entity_domain.name + \"退市\"\n self.session.add(entity_domain)\n self.session.commit()\n\n\nif __name__ == \"__main__\":\n recorder = EMStockRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_stockhk_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.stockhk_meta import Stockhk\nfrom zvt.recorders.em import em_api\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass EMStockhkRecorder(Recorder):\n provider = \"em\"\n data_schema = Stockhk\n record_south_only = True\n\n def run(self):\n df_south = em_api.get_tradable_list(entity_type=\"stockhk\", hk_south=True)\n\n if pd_is_not_null(df_south):\n df_south = df_south.set_index(\"code\", drop=False)\n df_south[\"total_cap\"] = df_south[\"total_cap\"].fillna(0)\n df_south[\"float_cap\"] = df_south[\"float_cap\"].fillna(0)\n df_south[\"south\"] = True\n df_to_db(df=df_south, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n if not self.record_south_only:\n df = em_api.get_tradable_list(entity_type=\"stockhk\")\n\n if pd_is_not_null(df):\n self.logger.info(f\"stockhk count: {len(df)}\")\n self.logger.info(df.head())\n\n df = df.set_index(\"code\", drop=False)\n df[\"total_cap\"] = df[\"total_cap\"].fillna(0)\n df[\"float_cap\"] = df[\"float_cap\"].fillna(0)\n\n df_other = df.loc[~df.index.isin(df_south.index)].copy()\n df_other[\"south\"] = False\n\n df_to_db(\n df=df_other,\n data_schema=self.data_schema,\n provider=self.provider,\n force_update=self.force_update,\n )\n else:\n df = df_south\n\n for item in df[[\"id\", \"name\", \"total_cap\", \"float_cap\"]].values.tolist():\n entity_id = item[0]\n datas: List[Stockhk] = Stockhk.query_data(\n entity_id=entity_id, return_type=\"domain\", session=self.session\n )\n\n if datas:\n entity_domain = datas[0]\n\n if \"退市\" in entity_domain.name:\n self.logger.info(f\"Ignore: {entity_domain.entity_id} {entity_domain.name} {item}\")\n continue\n\n entity_domain.name = item[1]\n entity_domain.total_cap = item[2]\n entity_domain.float_cap = item[3]\n\n if not entity_domain.list_date or not entity_domain.industry:\n basic_info = em_api.get_basic_info(entity_id=entity_id, session=self.http_session)\n self.logger.info(f\"basic info: {basic_info}\")\n if basic_info:\n entity_domain.industry = basic_info.get(\"BELONG_INDUSTRY\")\n entity_domain.timestamp = to_pd_timestamp(basic_info.get(\"LISTING_DATE\"))\n entity_domain.list_date = to_pd_timestamp(basic_info.get(\"LISTING_DATE\"))\n else:\n entity_domain.name = entity_domain.name + \"退市\"\n self.session.add(entity_domain)\n self.session.commit()\n\n\nif __name__ == \"__main__\":\n recorder = EMStockhkRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockhkRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/meta/em_stockus_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.stockus_meta import Stockus\nfrom zvt.recorders.em import em_api\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass EMStockusRecorder(Recorder):\n provider = \"em\"\n data_schema = Stockus\n\n def run(self):\n df = em_api.get_tradable_list(entity_type=\"stockus\")\n\n if pd_is_not_null(df):\n self.logger.info(f\"stockus count: {len(df)}\")\n self.logger.info(df.head())\n\n df[\"total_cap\"] = df[\"total_cap\"].fillna(0)\n df[\"float_cap\"] = df[\"float_cap\"].fillna(0)\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n for item in df[[\"id\", \"name\", \"total_cap\", \"float_cap\"]].values.tolist():\n entity_id = item[0]\n datas: List[Stockus] = Stockus.query_data(\n entity_id=entity_id, return_type=\"domain\", session=self.session\n )\n\n if datas:\n entity_domain = datas[0]\n\n if \"退市\" in entity_domain.name:\n self.logger.info(f\"Ignore: {entity_domain.entity_id} {entity_domain.name} {item}\")\n continue\n\n entity_domain.name = item[1]\n entity_domain.total_cap = item[2]\n entity_domain.float_cap = item[3]\n\n if not entity_domain.industry or not entity_domain.list_date:\n basic_info = em_api.get_basic_info(entity_id=entity_id, session=self.http_session)\n self.logger.info(f\"basic info: {basic_info}\")\n if basic_info:\n entity_domain.industry = basic_info.get(\"BELONG_INDUSTRY\")\n entity_domain.timestamp = to_pd_timestamp(basic_info.get(\"LISTING_DATE\"))\n entity_domain.list_date = to_pd_timestamp(basic_info.get(\"LISTING_DATE\"))\n else:\n entity_domain.name = entity_domain.name + \"退市\"\n self.session.add(entity_domain)\n self.session.commit()\n\n\nif __name__ == \"__main__\":\n recorder = EMStockusRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockusRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/misc/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule em_stock_news_recorder\nfrom .em_stock_news_recorder import *\nfrom .em_stock_news_recorder import __all__ as _em_stock_news_recorder_all\n\n__all__ += _em_stock_news_recorder_all\n"
},
{
"path": "src/zvt/recorders/em/misc/em_stock_news_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.contract.api import df_to_db, decode_entity_id\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Stock, Stockus, Stockhk\nfrom zvt.domain.misc.stock_news import StockNews\nfrom zvt.recorders.em import em_api\nfrom zvt.utils.time_utils import count_interval, now_pd_timestamp, recent_year_date\n\n\nclass EMStockNewsRecorder(FixedCycleDataRecorder):\n original_page_url = \"https://wap.eastmoney.com/quote/stock/0.002572.html\"\n url = \"https://np-listapi.eastmoney.com/comm/wap/getListInfo?cb=callback&client=wap&type=1&mTypeAndCode=0.002572&pageSize=200&pageIndex={}&callback=jQuery1830017478247906740352_1644568731256&_=1644568879493\"\n\n entity_schema = Stock\n data_schema = StockNews\n entity_provider = \"em\"\n provider = \"em\"\n\n def init_entities(self):\n if self.entity_ids:\n entity_type, _, _ = decode_entity_id(self.entity_ids[0])\n if entity_type == \"stockus\":\n self.entity_schema = Stockus\n elif entity_type == \"stockhk\":\n self.entity_schema = Stockhk\n\n super().init_entities()\n\n def record(self, entity, start, end, size, timestamps):\n from_date = recent_year_date()\n if not start or (start < from_date):\n start = from_date\n\n if count_interval(start, now_pd_timestamp()) <= 30:\n ps = 30\n else:\n ps = 200\n\n latest_news: StockNews = self.get_latest_saved_record(entity=entity)\n\n news = em_api.get_news(\n session=self.http_session,\n entity_id=entity.id,\n ps=ps,\n start_timestamp=start,\n latest_code=latest_news.news_code if latest_news else None,\n )\n if news:\n df = pd.DataFrame.from_records(news)\n self.logger.info(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n # df = Stock.query_data(filters=[Stock.exchange == \"bj\"], provider=\"em\")\n # entity_ids = df[\"entity_id\"].tolist()\n r = EMStockNewsRecorder(entity_ids=[\"stock_sh_600345\"], sleeping_time=0)\n r.run()\n\n\n# the __all__ is generated\n__all__ = [\"EMStockNewsRecorder\"]\n"
},
{
"path": "src/zvt/recorders/em/quotes/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule em_kdata_recorder\nfrom .em_kdata_recorder import *\nfrom .em_kdata_recorder import __all__ as _em_kdata_recorder_all\n\n__all__ += _em_kdata_recorder_all\n"
},
{
"path": "src/zvt/recorders/em/quotes/em_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.api.kdata import get_kdata_schema, get_kdata\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import (\n Stock,\n Index,\n Block,\n StockKdataCommon,\n IndexKdataCommon,\n StockhkKdataCommon,\n StockusKdataCommon,\n BlockKdataCommon,\n Indexus,\n IndexusKdataCommon,\n Future,\n FutureKdataCommon,\n Currency,\n CurrencyKdataCommon,\n IndexhkKdataCommon,\n)\nfrom zvt.domain.meta.indexhk_meta import Indexhk\nfrom zvt.domain.meta.stockhk_meta import Stockhk\nfrom zvt.domain.meta.stockus_meta import Stockus\nfrom zvt.recorders.em import em_api\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import count_interval, now_pd_timestamp, current_date\n\n\nclass BaseEMStockKdataRecorder(FixedCycleDataRecorder):\n default_size = 50000\n entity_provider: str = \"em\"\n\n provider = \"em\"\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n adjust_type=AdjustType.qfq,\n return_unfinished=False,\n ) -> None:\n level = IntervalLevel(level)\n self.adjust_type = AdjustType(adjust_type)\n self.entity_type = self.entity_schema.__name__.lower()\n\n self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=self.adjust_type)\n\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n def need_redownload_qfq(self, entity_id, df):\n if self.adjust_type == AdjustType.qfq and pd_is_not_null(df):\n datas = get_kdata(\n entity_id=entity_id,\n provider=self.provider,\n limit=1,\n level=self.level,\n adjust_type=self.adjust_type,\n order=self.data_schema.timestamp.desc(),\n return_type=\"domain\",\n )\n if datas:\n latest_kdata = datas[0]\n check_df = df[df[\"timestamp\"] == latest_kdata.timestamp]\n if pd_is_not_null(check_df):\n old = latest_kdata.close\n new = check_df.iloc[0, :][\"close\"]\n # 相同时间的close不同，表明前复权需要重新计算\n if round(old, 2) != round(new, 2):\n # 删掉重新获取\n self.session.query(self.data_schema).filter(self.data_schema.entity_id == entity_id).delete()\n return True\n else:\n return False\n else:\n self.logger.warning(f\"no data found for {entity_id} at {latest_kdata.timestamp}, 前复权检查失败\")\n return False\n\n def record(self, entity, start, end, size, timestamps):\n df = em_api.get_kdata(\n session=self.http_session, entity_id=entity.id, limit=size, adjust_type=self.adjust_type, level=self.level\n )\n\n if self.need_redownload_qfq(entity.id, df):\n df = em_api.get_kdata(\n session=self.http_session,\n entity_id=entity.id,\n limit=self.default_size,\n adjust_type=self.adjust_type,\n level=self.level,\n )\n\n delisted = False\n if pd_is_not_null(df):\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n latest_timestamp = df.iloc[-1, :][\"timestamp\"]\n days = count_interval(latest_timestamp, now_pd_timestamp())\n if days > 200:\n delisted = True\n else:\n self.logger.info(f\"no kdata for {entity.id}\")\n\n if delisted and (\"退市\" not in entity.name):\n entity.name = entity.name + \"退市\"\n self.logger.info(f\"set {entity.id} name as {entity.name}\")\n self.entity_session.add(entity)\n self.entity_session.commit()\n\n def on_finish_entity(self, entity):\n # fill timestamp\n if not entity.timestamp or not entity.list_date:\n # get the first\n kdatas = self.data_schema.query_data(\n provider=self.provider,\n entity_id=entity.id,\n order=self.data_schema.timestamp.asc(),\n limit=1,\n return_type=\"domain\",\n )\n if kdatas:\n timestamp = kdatas[0].timestamp\n\n self.logger.info(f\"fill {entity.name} list_date as {timestamp}\")\n\n if not entity.timestamp:\n entity.timestamp = timestamp\n if not entity.list_date:\n entity.list_date = timestamp\n self.entity_session.add(entity)\n self.entity_session.commit()\n\n\nclass EMStockKdataRecorder(BaseEMStockKdataRecorder):\n entity_schema = Stock\n data_schema = StockKdataCommon\n supported_levels = [\n level for level in IntervalLevel if level not in (IntervalLevel.LEVEL_L2_QUOTE, IntervalLevel.LEVEL_TICK)\n ]\n supported_adjust_types = [AdjustType.qfq, AdjustType.hfq]\n\n def on_finish_entity(self, entity):\n super().on_finish_entity(entity)\n # fill holder\n if not entity.holder_modified_date or (count_interval(entity.holder_modified_date, now_pd_timestamp()) > 30):\n holder = em_api.get_controlling_shareholder(code=entity.code)\n if holder:\n entity.controlling_holder = holder.get(\"holder\")\n if holder.get(\"parent\"):\n entity.controlling_holder_parent = holder.get(\"parent\")\n else:\n entity.controlling_holder_parent = holder.get(\"holder\")\n entity.holder_modified_date = current_date()\n self.entity_session.add(entity)\n self.entity_session.commit()\n holder_stats = em_api.get_top_ten_free_holder_stats(code=entity.code)\n if holder_stats:\n entity.top_ten_ratio = holder_stats.get(\"ratio\")\n entity.holder_modified_date = current_date()\n self.entity_session.add(entity)\n self.entity_session.commit()\n\n\nclass EMStockusKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Stockus\n data_schema = StockusKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n supported_adjust_types = [AdjustType.qfq, AdjustType.hfq]\n\n\nclass EMStockhkKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Stockhk\n data_schema = StockhkKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n supported_adjust_types = [AdjustType.qfq, AdjustType.hfq]\n\n\nclass EMIndexhkKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Indexhk\n data_schema = IndexhkKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n\n\nclass EMIndexKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Index\n data_schema = IndexKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK]\n\n\nclass EMIndexusKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Indexus\n data_schema = IndexusKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n\n\nclass EMBlockKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Block\n data_schema = BlockKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK, IntervalLevel.LEVEL_1MON]\n\n\nclass EMFutureKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Future\n data_schema = FutureKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n\n\nclass EMCurrencyKdataRecorder(BaseEMStockKdataRecorder):\n entity_provider = \"em\"\n entity_schema = Currency\n data_schema = CurrencyKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n\n\nif __name__ == \"__main__\":\n normal_ids = get_entity_ids_by_filter(entity_type=\"indexhk\", provider=\"em\")\n\n recorder = EMIndexhkKdataRecorder(\n entity_ids=normal_ids,\n level=IntervalLevel.LEVEL_1DAY,\n sleeping_time=2,\n adjust_type=AdjustType.qfq,\n day_data=True,\n )\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\n \"BaseEMStockKdataRecorder\",\n \"EMStockKdataRecorder\",\n \"EMStockusKdataRecorder\",\n \"EMStockhkKdataRecorder\",\n \"EMIndexhkKdataRecorder\",\n \"EMIndexKdataRecorder\",\n \"EMIndexusKdataRecorder\",\n \"EMBlockKdataRecorder\",\n \"EMFutureKdataRecorder\",\n \"EMCurrencyKdataRecorder\",\n]\n"
},
{
"path": "src/zvt/recorders/em/trading/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule em_dragon_and_tiger_recorder\nfrom .em_dragon_and_tiger_recorder import *\nfrom .em_dragon_and_tiger_recorder import __all__ as _em_dragon_and_tiger_recorder_all\n\n__all__ += _em_dragon_and_tiger_recorder_all\n"
},
{
"path": "src/zvt/recorders/em/trading/em_dragon_and_tiger_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import DragonAndTiger\nfrom zvt.recorders.em import em_api\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, TIME_FORMAT_DAY, date_time_by_interval, current_date\n\n\nclass EMDragonAndTigerRecorder(Recorder):\n provider = \"em\"\n data_schema = DragonAndTiger\n\n def run(self):\n latest_infos = DragonAndTiger.query_data(\n provider=self.provider, order=DragonAndTiger.timestamp.desc(), limit=1, return_type=\"domain\"\n )\n if latest_infos:\n start_date = latest_infos[0].timestamp\n else:\n start_date = date_time_by_interval(current_date(), -10)\n\n dragon_list = em_api.get_dragon_and_tiger_list(start_date=start_date)\n\n entity_list = [{\"code\": data[\"SECURITY_CODE\"], \"exchange\": data[\"MARKET\"].lower()} for data in dragon_list]\n df = pd.DataFrame(entity_list)\n unique_df = df.drop_duplicates(subset=[\"code\"])\n unique_list = unique_df.to_dict(\"records\")\n\n self.logger.info(\"Get dragon and tiger for entities: %s\", unique_list)\n for entity in unique_list:\n code = entity[\"code\"]\n entity_id = f\"stock_{entity['exchange']}_{entity['code']}\"\n\n datas = em_api.get_dragon_and_tiger(code=entity[\"code\"], start_date=start_date)\n if datas:\n records = []\n for data in datas:\n timestamp = to_pd_timestamp(data[\"TRADE_DATE\"])\n record = {\n \"id\": f\"{entity_id}_{data['TRADE_ID']}_{to_date_time_str(timestamp, fmt=TIME_FORMAT_DAY)}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"code\": code,\n \"name\": data[\"SECURITY_NAME_ABBR\"],\n \"reason\": data[\"EXPLANATION\"],\n \"turnover\": data[\"ACCUM_AMOUNT\"],\n \"change_pct\": data[\"CHANGE_RATE\"],\n \"net_in\": data[\"NET_BUY\"],\n }\n\n # 营业部列表\n deps = data[\"LIST\"]\n for dep in deps:\n flag = \"\" if dep[\"TRADE_DIRECTION\"] == \"0\" else \"_\"\n rank = dep[\"RANK\"]\n dep_name = f\"dep{flag}{rank}\"\n dep_in = f\"{dep_name}_in\"\n dep_out = f\"{dep_name}_out\"\n dep_rate = f\"{dep_name}_rate\"\n\n record[dep_name] = dep[\"OPERATEDEPT_NAME\"]\n record[dep_in] = dep[\"BUY_AMT_REAL\"]\n record[dep_out] = dep[\"SELL_AMT_REAL\"]\n record[dep_rate] = (dep[\"BUY_RATIO\"] if dep[\"BUY_RATIO\"] else 0) - (\n dep[\"SELL_RATIO\"] if dep[\"SELL_RATIO\"] else 0\n )\n\n records.append(record)\n df = pd.DataFrame.from_records(records)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n else:\n self.logger.info(f\"no data for {entity_id}\")\n\n\nif __name__ == \"__main__\":\n EMDragonAndTigerRecorder(sleeping_time=2).run()\n\n\n# the __all__ is generated\n__all__ = [\"EMDragonAndTigerRecorder\"]\n"
},
{
"path": "src/zvt/recorders/exchange/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule api\nfrom .api import *\nfrom .api import __all__ as _api_all\n\n__all__ += _api_all\n\n# import all from submodule exchange_index_recorder\nfrom .exchange_index_recorder import *\nfrom .exchange_index_recorder import __all__ as _exchange_index_recorder_all\n\n__all__ += _exchange_index_recorder_all\n\n# import all from submodule exchange_stock_meta_recorder\nfrom .exchange_stock_meta_recorder import *\nfrom .exchange_stock_meta_recorder import __all__ as _exchange_stock_meta_recorder_all\n\n__all__ += _exchange_stock_meta_recorder_all\n\n# import all from submodule exchange_stock_summary_recorder\nfrom .exchange_stock_summary_recorder import *\nfrom .exchange_stock_summary_recorder import __all__ as _exchange_stock_summary_recorder_all\n\n__all__ += _exchange_stock_summary_recorder_all\n\n# import all from submodule exchange_etf_meta_recorder\nfrom .exchange_etf_meta_recorder import *\nfrom .exchange_etf_meta_recorder import __all__ as _exchange_etf_meta_recorder_all\n\n__all__ += _exchange_etf_meta_recorder_all\n\n# import all from submodule exchange_index_stock_recorder\nfrom .exchange_index_stock_recorder import *\nfrom .exchange_index_stock_recorder import __all__ as _exchange_index_stock_recorder_all\n\n__all__ += _exchange_index_stock_recorder_all\n"
},
{
"path": "src/zvt/recorders/exchange/api/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule cs_index_stock_api\nfrom .cs_index_stock_api import *\nfrom .cs_index_stock_api import __all__ as _cs_index_stock_api_all\n\n__all__ += _cs_index_stock_api_all\n\n# import all from submodule cn_index_stock_api\nfrom .cn_index_stock_api import *\nfrom .cn_index_stock_api import __all__ as _cn_index_stock_api_all\n\n__all__ += _cn_index_stock_api_all\n\n# import all from submodule cn_index_api\nfrom .cn_index_api import *\nfrom .cn_index_api import __all__ as _cn_index_api_all\n\n__all__ += _cn_index_api_all\n\n# import all from submodule cs_index_api\nfrom .cs_index_api import *\nfrom .cs_index_api import __all__ as _cs_index_api_all\n\n__all__ += _cs_index_api_all\n"
},
{
"path": "src/zvt/recorders/exchange/api/cn_index_api.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\n\nimport pandas as pd\nimport requests\n\nfrom zvt.domain import IndexCategory\nfrom zvt.recorders.consts import DEFAULT_HEADER\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\noriginal_page_url = \"http://www.cnindex.com.cn/zh_indices/sese/index.html?act_menu=1&index_type=-1\"\nurl = \"http://www.cnindex.com.cn/index/indexList?channelCode={}&rows=1000&pageNum=1\"\n\n# 中证指数 抓取 风格指数 行业指数 规模指数 基金指数\ncni_category_map_url = {\n IndexCategory.style: url.format(\"202\"),\n IndexCategory.industry: url.format(\"201\"),\n IndexCategory.scope: url.format(\"200\"),\n IndexCategory.fund: url.format(\"207\"),\n}\n\n# 深证指数 只取规模指数\nsz_category_map_url = {\n IndexCategory.scope: url.format(\"100\"),\n}\n\n\ndef _get_resp_data(resp: requests.Response):\n resp.raise_for_status()\n return resp.json()[\"data\"]\n\n\ndef get_cn_index(index_type=\"cni\", category=IndexCategory.style):\n if index_type == \"cni\":\n category_map_url = cni_category_map_url\n elif index_type == \"sz\":\n category_map_url = sz_category_map_url\n else:\n logger.error(f\"not support index_type: {index_type}\")\n assert False\n\n requests_session = requests.Session()\n\n url = category_map_url.get(category)\n\n resp = requests_session.get(url, headers=DEFAULT_HEADER)\n\n results = _get_resp_data(resp)[\"rows\"]\n # e.g\n # amount: 277743699997.9\n # closeingPoint: 6104.7592\n # docchannel: 1039\n # freeMarketValue: 10794695531696.15\n # id: 142\n # indexcode: \"399370\"\n # indexename: \"CNI Growth\"\n # indexfullcname: \"国证1000成长指数\"\n # indexfullename: \"CNI 1000 Growth Index\"\n # indexname: \"国证成长\"\n # indexsource: \"1\"\n # indextype: \"202\"\n # pb: 5.34\n # peDynamic: 29.8607\n # peStatic: 33.4933\n # percent: 0.0022\n # prefixmonth: null\n # realtimemarket: \"1\"\n # remark: \"\"\n # sampleshowdate: null\n # samplesize: 332\n # showcnindex: \"1\"\n # totalMarketValue: 23113641352198.32\n the_list = []\n\n logger.info(f\"category: {category} \")\n logger.info(f\"results: {results} \")\n for i, result in enumerate(results):\n logger.info(f\"to {i}/{len(results)}\")\n code = result[\"indexcode\"]\n info_resp = requests_session.get(f\"http://www.cnindex.com.cn/index-intro?indexcode={code}\")\n # fbrq: \"2010-01-04\"\n # jd: 1000\n # jr: \"2002-12-31\"\n # jsfs: \"自由流通市值\"\n # jsjj: \"国证成长由国证1000指数样本股中成长风格突出的股票组成，为投资者提供更丰富的指数化投资工具。\"\n # qzsx: null\n # typl: 2\n # xyfw: \"沪深A股\"\n # xygz: \"在国证1000指数样本股中，选取主营业务收入增长率、净利润增长率和净资产收益率综合排名前332只\"\n index_info = _get_resp_data(info_resp)\n name = result[\"indexname\"]\n entity_id = f\"index_sz_{code}\"\n index_item = {\n \"id\": entity_id,\n \"entity_id\": entity_id,\n \"timestamp\": to_pd_timestamp(index_info[\"jr\"]),\n \"entity_type\": \"index\",\n \"exchange\": \"sz\",\n \"code\": code,\n \"name\": name,\n \"category\": category.value,\n \"list_date\": to_pd_timestamp(index_info[\"fbrq\"]),\n \"base_point\": index_info[\"jd\"],\n \"publisher\": \"cnindex\",\n }\n logger.info(index_item)\n the_list.append(index_item)\n time.sleep(3)\n if the_list:\n return pd.DataFrame.from_records(the_list)\n\n\nif __name__ == \"__main__\":\n df = get_cn_index()\n print(df)\n\n\n# the __all__ is generated\n__all__ = [\"get_cn_index\"]\n"
},
{
"path": "src/zvt/recorders/exchange/api/cn_index_stock_api.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nimport pandas as pd\nimport requests\n\nfrom zvt.api.utils import china_stock_code_to_id, value_to_pct, value_multiply\nfrom zvt.recorders.consts import DEFAULT_HEADER\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, TIME_FORMAT_MON\n\nlogger = logging.getLogger(__name__)\n\noriginal_page_url = \"http://www.cnindex.com.cn/module/index-detail.html?act_menu=1&indexCode=399001\"\nurl = \"http://www.cnindex.com.cn/sample-detail/detail?indexcode={}&dateStr={}&pageNum=1&rows=5000\"\n\n\ndef _get_resp_data(resp: requests.Response):\n resp.raise_for_status()\n return resp.json()[\"data\"]\n\n\ndef get_cn_index_stock(code, timestamp, name=None):\n entity_type = \"index\"\n exchange = \"sz\"\n entity_id = f\"{entity_type}_{exchange}_{code}\"\n data_str = to_date_time_str(timestamp, fmt=TIME_FORMAT_MON)\n resp = requests.get(url.format(code, data_str), headers=DEFAULT_HEADER)\n data = _get_resp_data(resp)\n if not data:\n return\n results = _get_resp_data(resp)[\"rows\"]\n\n the_list = []\n for result in results:\n # date: 1614268800000\n # dateStr: \"2021-02-26\"\n # freeMarketValue: 10610.8\n # indexcode: \"399370\"\n # market: null\n # seccode: \"600519\"\n # secname: \"贵州茅台\"\n # totalMarketValue: 26666.32\n # trade: \"主要消费\"\n # weight: 10.01\n stock_code = result[\"seccode\"]\n stock_name = result[\"secname\"]\n stock_id = china_stock_code_to_id(stock_code)\n\n the_list.append(\n {\n \"id\": \"{}_{}_{}\".format(entity_id, result[\"dateStr\"], stock_id),\n \"entity_id\": entity_id,\n \"entity_type\": entity_type,\n \"exchange\": exchange,\n \"code\": code,\n \"name\": name,\n \"timestamp\": to_pd_timestamp(result[\"dateStr\"]),\n \"stock_id\": stock_id,\n \"stock_code\": stock_code,\n \"stock_name\": stock_name,\n \"proportion\": value_to_pct(result[\"weight\"], 0),\n \"market_cap\": value_multiply(result[\"freeMarketValue\"], 100000000, 0),\n }\n )\n if the_list:\n df = pd.DataFrame.from_records(the_list)\n return df\n\n\nif __name__ == \"__main__\":\n df = get_cn_index_stock(timestamp=\"2021-08-01\", code=\"399370\", name=\"国证成长\")\n print(df)\n\n\n# the __all__ is generated\n__all__ = [\"get_cn_index_stock\"]\n"
},
{
"path": "src/zvt/recorders/exchange/api/cs_index_api.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nimport pandas as pd\nimport requests\n\nfrom zvt.domain import IndexCategory\nfrom zvt.recorders.consts import DEFAULT_HEADER\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\noriginal_page_url = \"https://www.csindex.com.cn/zh-CN#/indices/family/list?index_series=2\"\n\nurl = \"https://www.csindex.com.cn/csindex-home/index-list/query-index-item\"\n\nindex_category_map = {IndexCategory.scope: \"17\", IndexCategory.industry: \"18\", IndexCategory.style: \"19\"}\n\n\ndef _get_resp_data(resp: requests.Response):\n resp.raise_for_status()\n return resp.json()[\"data\"]\n\n\ndef _get_params(index_type, category: IndexCategory):\n if index_type == \"csi\":\n index_series = [\"1\"]\n elif index_type == \"sh\":\n index_series = [\"2\"]\n else:\n logger.warning(f\"not support index type: {index_type}\")\n assert False\n index_classify = index_category_map.get(category)\n\n return {\n \"sorter\": {\"sortField\": \"index_classify\", \"sortOrder\": \"asc\"},\n \"pager\": {\"pageNum\": 1, \"pageSize\": 10},\n \"indexFilter\": {\n \"ifCustomized\": None,\n \"ifTracked\": None,\n \"ifWeightCapped\": None,\n \"indexCompliance\": None,\n \"hotSpot\": None,\n \"indexClassify\": [index_classify],\n \"currency\": None,\n \"region\": None,\n \"indexSeries\": index_series,\n \"undefined\": None,\n },\n }\n\n\ndef get_cs_index(index_type=\"sh\"):\n if index_type == \"csi\":\n category_list = [IndexCategory.scope, IndexCategory.industry, IndexCategory.style]\n elif index_type == \"sh\":\n category_list = [IndexCategory.scope]\n else:\n logger.warning(f\"not support index type: {index_type}\")\n assert False\n\n requests_session = requests.Session()\n\n for category in category_list:\n data = _get_params(index_type=index_type, category=category)\n print(data)\n resp = requests_session.post(url, headers=DEFAULT_HEADER, json=data)\n\n print(resp)\n results = _get_resp_data(resp)\n the_list = []\n\n logger.info(f\"category: {category} \")\n logger.info(f\"results: {results} \")\n for i, result in enumerate(results):\n logger.info(f\"to {i}/{len(results)}\")\n code = result[\"indexCode\"]\n\n info_url = f\"https://www.csindex.com.cn/csindex-home/indexInfo/index-basic-info/{code}\"\n info = _get_resp_data(requests_session.get(info_url))\n\n name = result[\"indexName\"]\n entity_id = f\"index_sh_{code}\"\n index_item = {\n \"id\": entity_id,\n \"entity_id\": entity_id,\n \"timestamp\": to_pd_timestamp(info[\"basicDate\"]),\n \"entity_type\": \"index\",\n \"exchange\": \"sh\",\n \"code\": code,\n \"name\": name,\n \"category\": category.value,\n \"list_date\": to_pd_timestamp(result[\"publishDate\"]),\n \"base_point\": info[\"basicIndex\"],\n \"publisher\": \"csindex\",\n }\n logger.info(index_item)\n the_list.append(index_item)\n if the_list:\n return pd.DataFrame.from_records(the_list)\n\n\nif __name__ == \"__main__\":\n df = get_cs_index()\n print(df)\n\n\n# the __all__ is generated\n__all__ = [\"get_cs_index\"]\n"
},
{
"path": "src/zvt/recorders/exchange/api/cs_index_stock_api.py",
"content": "# -*- coding: utf-8 -*-\nimport io\nimport logging\n\nimport pandas as pd\nimport requests\n\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.recorders.consts import DEFAULT_HEADER\nfrom zvt.utils.time_utils import now_pd_timestamp\n\nlogger = logging.getLogger(__name__)\n\noriginal_page_url = \"http://www.csindex.com.cn/zh-CN/downloads/indices\"\nurl = \"http://www.csindex.com.cn/uploads/file/autofile/cons/{}cons.xls\"\n\n\ndef get_cs_index_stock(code, timestamp, name=None):\n entity_type = \"index\"\n exchange = \"sh\"\n entity_id = f\"{entity_type}_{exchange}_{code}\"\n\n response = requests.get(url.format(code), headers=DEFAULT_HEADER)\n response.raise_for_status()\n\n df = pd.read_excel(io.BytesIO(response.content))\n\n df = df[[\"日期Date\", \"成分券代码Constituent Code\", \"成分券名称Constituent Name\"]].rename(\n columns={\"日期Date\": \"timestamp\", \"成分券代码Constituent Code\": \"stock_code\", \"成分券名称Constituent Name\": \"stock_name\"}\n )\n\n df[\"entity_id\"] = entity_id\n df[\"entity_type\"] = \"index\"\n df[\"exchange\"] = \"sh\"\n df[\"code\"] = code\n df[\"name\"] = name\n df[\"stock_id\"] = df[\"stock_code\"].apply(lambda x: china_stock_code_to_id(str(x)))\n # id format: {entity_id}_{timestamp}_{stock_id}\n df[\"id\"] = df[[\"entity_id\", \"timestamp\", \"stock_id\"]].apply(lambda x: \"_\".join(x.astype(str)), axis=1)\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n\n return df\n\n\nif __name__ == \"__main__\":\n df = get_cs_index_stock(code=\"000001\", name=\"上证指数\", timestamp=now_pd_timestamp())\n print(df)\n\n\n# the __all__ is generated\n__all__ = [\"get_cs_index_stock\"]\n"
},
{
"path": "src/zvt/recorders/exchange/exchange_etf_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport io\nimport re\n\nimport demjson3\nimport pandas as pd\nimport requests\n\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import EtfStock, Etf\nfrom zvt.recorders.consts import DEFAULT_SH_ETF_LIST_HEADER\nfrom zvt.utils.time_utils import now_pd_timestamp\n\n\nclass ChinaETFListSpider(Recorder):\n data_schema = EtfStock\n\n def __init__(self, force_update=False, sleeping_time=10.0, provider=\"exchange\") -> None:\n self.provider = provider\n super().__init__(force_update, sleeping_time)\n\n def run(self):\n # 抓取沪市 ETF 列表\n url = \"http://query.sse.com.cn/commonQuery.do?sqlId=COMMON_SSE_ZQPZ_ETFLB_L_NEW\"\n response = requests.get(url, headers=DEFAULT_SH_ETF_LIST_HEADER)\n response_dict = demjson3.decode(response.text)\n\n df = pd.DataFrame(response_dict.get(\"result\", []))\n self.persist_etf_list(df, exchange=\"sh\")\n self.logger.info(\"沪市 ETF 列表抓取完成...\")\n\n # 抓取沪市 ETF 成分股\n self.download_sh_etf_component(df)\n self.logger.info(\"沪市 ETF 成分股抓取完成...\")\n\n # 抓取深市 ETF 列表\n url = \"http://www.szse.cn/api/report/ShowReport?SHOWTYPE=xlsx&CATALOGID=1945\"\n response = requests.get(url)\n\n df = pd.read_excel(io.BytesIO(response.content), dtype=str)\n self.persist_etf_list(df, exchange=\"sz\")\n self.logger.info(\"深市 ETF 列表抓取完成...\")\n\n # 抓取深市 ETF 成分股\n self.download_sz_etf_component(df)\n self.logger.info(\"深市 ETF 成分股抓取完成...\")\n\n def persist_etf_list(self, df: pd.DataFrame, exchange: str):\n if df is None:\n return\n\n df = df.copy()\n if exchange == \"sh\":\n df = df[[\"FUND_ID\", \"FUND_NAME\"]]\n elif exchange == \"sz\":\n df = df[[\"证券代码\", \"证券简称\"]]\n\n df.columns = [\"code\", \"name\"]\n df[\"id\"] = df[\"code\"].apply(lambda code: f\"etf_{exchange}_{code}\")\n df[\"entity_id\"] = df[\"id\"]\n df[\"exchange\"] = exchange\n df[\"entity_type\"] = \"etf\"\n df[\"category\"] = \"etf\"\n\n df = df.dropna(axis=0, how=\"any\")\n df = df.drop_duplicates(subset=\"id\", keep=\"last\")\n\n df_to_db(df=df, data_schema=Etf, provider=self.provider, force_update=False)\n\n def download_sh_etf_component(self, df: pd.DataFrame):\n query_url = (\n \"http://query.sse.com.cn/infodisplay/queryConstituentStockInfo.do?\" \"isPagination=false&type={}&etfClass={}\"\n )\n\n etf_df = df[(df[\"ETF_CLASS\"] == \"1\") | (df[\"ETF_CLASS\"] == \"2\")]\n etf_df = self.populate_sh_etf_type(etf_df)\n\n for _, etf in etf_df.iterrows():\n url = query_url.format(etf[\"ETF_TYPE\"], etf[\"ETF_CLASS\"])\n response = requests.get(url, headers=DEFAULT_SH_ETF_LIST_HEADER)\n response_dict = demjson3.decode(response.text)\n response_df = pd.DataFrame(response_dict.get(\"result\", []))\n\n etf_code = etf[\"FUND_ID\"]\n etf_id = f\"etf_sh_{etf_code}\"\n response_df = response_df[[\"instrumentId\", \"instrumentName\"]].copy()\n response_df.rename(columns={\"instrumentId\": \"stock_code\", \"instrumentName\": \"stock_name\"}, inplace=True)\n\n response_df[\"entity_id\"] = etf_id\n response_df[\"entity_type\"] = \"etf\"\n response_df[\"exchange\"] = \"sh\"\n response_df[\"code\"] = etf_code\n response_df[\"name\"] = etf[\"FUND_NAME\"]\n response_df[\"timestamp\"] = now_pd_timestamp()\n\n response_df[\"stock_id\"] = response_df[\"stock_code\"].apply(lambda code: china_stock_code_to_id(code))\n response_df[\"id\"] = response_df[\"stock_id\"].apply(lambda x: f\"{etf_id}_{x}\")\n\n df_to_db(data_schema=self.data_schema, df=response_df, provider=self.provider)\n self.logger.info(f'{etf[\"FUND_NAME\"]} - {etf_code} 成分股抓取完成...')\n\n self.sleep()\n\n def download_sz_etf_component(self, df: pd.DataFrame):\n query_url = \"http://vip.stock.finance.sina.com.cn/corp/go.php/vII_NewestComponent/indexid/{}.phtml\"\n\n self.parse_sz_etf_underlying_index(df)\n for _, etf in df.iterrows():\n underlying_index = etf[\"拟合指数\"]\n etf_code = etf[\"证券代码\"]\n\n if len(underlying_index) == 0:\n self.logger.info(f'{etf[\"证券简称\"]} - {etf_code} 非 A 股市场指数，跳过...')\n continue\n\n url = query_url.format(underlying_index)\n response = requests.get(url)\n response.encoding = \"gbk\"\n\n try:\n dfs = pd.read_html(response.text, header=1)\n except ValueError as error:\n self.logger.error(f\"HTML parse error: {error}, response: {response.text}\")\n continue\n\n if len(dfs) < 4:\n continue\n\n response_df = dfs[3].copy()\n response_df = response_df.dropna(axis=1, how=\"any\")\n response_df[\"品种代码\"] = response_df[\"品种代码\"].apply(lambda x: f\"{x:06d}\")\n\n etf_id = f\"etf_sz_{etf_code}\"\n response_df = response_df[[\"品种代码\", \"品种名称\"]].copy()\n response_df.rename(columns={\"品种代码\": \"stock_code\", \"品种名称\": \"stock_name\"}, inplace=True)\n\n response_df[\"entity_id\"] = etf_id\n response_df[\"entity_type\"] = \"etf\"\n response_df[\"exchange\"] = \"sz\"\n response_df[\"code\"] = etf_code\n response_df[\"name\"] = etf[\"证券简称\"]\n response_df[\"timestamp\"] = now_pd_timestamp()\n\n response_df[\"stock_id\"] = response_df[\"stock_code\"].apply(lambda code: china_stock_code_to_id(code))\n response_df[\"id\"] = response_df[\"stock_id\"].apply(lambda x: f\"{etf_id}_{x}\")\n\n df_to_db(data_schema=self.data_schema, df=response_df, provider=self.provider)\n self.logger.info(f'{etf[\"证券简称\"]} - {etf_code} 成分股抓取完成...')\n\n self.sleep()\n\n @staticmethod\n def populate_sh_etf_type(df: pd.DataFrame):\n \"\"\"\n 填充沪市 ETF 代码对应的 TYPE 到列表数据中\n :param df: ETF 列表数据\n :return: 包含 ETF 对应 TYPE 的列表数据\n \"\"\"\n query_url = (\n \"http://query.sse.com.cn/infodisplay/queryETFNewAllInfo.do?\"\n \"isPagination=false&type={}&pageHelp.pageSize=25\"\n )\n\n type_df = pd.DataFrame()\n for etf_class in [1, 2]:\n url = query_url.format(etf_class)\n response = requests.get(url, headers=DEFAULT_SH_ETF_LIST_HEADER)\n response_dict = demjson3.decode(response.text)\n response_df = pd.DataFrame(response_dict.get(\"result\", []))\n response_df = response_df[[\"fundid1\", \"etftype\"]]\n\n type_df = pd.concat([type_df, response_df])\n\n result_df = df.copy()\n result_df = result_df.sort_values(by=\"FUND_ID\").reset_index(drop=True)\n type_df = type_df.sort_values(by=\"fundid1\").reset_index(drop=True)\n\n result_df[\"ETF_TYPE\"] = type_df[\"etftype\"]\n\n return result_df\n\n @staticmethod\n def parse_sz_etf_underlying_index(df: pd.DataFrame):\n \"\"\"\n 解析深市 ETF 对应跟踪的指数代码\n :param df: ETF 列表数据\n :return: 解析完成 ETF 对应指数代码的列表数据\n \"\"\"\n\n def parse_index(text):\n if len(text) == 0:\n return \"\"\n\n result = re.search(r\"(\\d+).*\", text)\n if result is None:\n return \"\"\n else:\n return result.group(1)\n\n df[\"拟合指数\"] = df[\"拟合指数\"].apply(parse_index)\n\n\n__all__ = [\"ChinaETFListSpider\"]\n\nif __name__ == \"__main__\":\n spider = ChinaETFListSpider(provider=\"exchange\")\n spider.run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaETFListSpider\"]\n"
},
{
"path": "src/zvt/recorders/exchange/exchange_index_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import Index\nfrom zvt.recorders.exchange.api import cn_index_api, cs_index_api\n\n\nclass ExchangeIndexRecorder(Recorder):\n provider = \"exchange\"\n data_schema = Index\n\n def run(self):\n # 深圳\n self.record_cn_index(\"sz\")\n # 国证\n self.record_cn_index(\"cni\")\n\n # 上海\n self.record_cs_index(\"sh\")\n # 中证\n self.record_cs_index(\"csi\")\n\n # 中证，上海\n def record_cs_index(self, index_type):\n df = cs_index_api.get_cs_index(index_type=index_type)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n self.logger.info(f\"finish record {index_type} index\")\n\n # 国证，深圳\n def record_cn_index(self, index_type):\n if index_type == \"cni\":\n category_map_url = cn_index_api.cni_category_map_url\n elif index_type == \"sz\":\n category_map_url = cn_index_api.sz_category_map_url\n else:\n self.logger.error(f\"not support index_type: {index_type}\")\n assert False\n\n for category, _ in category_map_url.items():\n df = cn_index_api.get_cn_index(index_type=index_type, category=category)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n self.logger.info(f\"finish record {index_type} index:{category.value}\")\n\n\nif __name__ == \"__main__\":\n # init_log('china_stock_category.log')\n ExchangeIndexRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"ExchangeIndexRecorder\"]\n"
},
{
"path": "src/zvt/recorders/exchange/exchange_index_stock_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Index, IndexStock\nfrom zvt.recorders.exchange.api import cs_index_stock_api, cn_index_stock_api\nfrom zvt.utils.time_utils import pre_month_start_date\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass ExchangeIndexStockRecorder(TimestampsDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"exchange\"\n data_schema = IndexStock\n\n def __init__(\n self,\n force_update=False,\n sleeping_time=5,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n record_history=False,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n )\n self.record_history = record_history\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n last_valid_date = pre_month_start_date()\n if self.record_history:\n # 每个月记录一次\n return [to_pd_timestamp(item) for item in pd.date_range(entity_item.list_date, last_valid_date, freq=\"M\")]\n else:\n return [last_valid_date]\n\n def record(self, entity, start, end, size, timestamps):\n if entity.publisher == \"cnindex\":\n for timestamp in timestamps:\n df = cn_index_stock_api.get_cn_index_stock(code=entity.code, timestamp=timestamp, name=entity.name)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n elif entity.publisher == \"csindex\":\n # cs index not support history data\n df = cs_index_stock_api.get_cs_index_stock(code=entity.code, timestamp=None, name=entity.name)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n\n\nif __name__ == \"__main__\":\n # ExchangeIndexMetaRecorder().run()\n ExchangeIndexStockRecorder(codes=[\"399370\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"ExchangeIndexStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/exchange/exchange_stock_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport io\n\nimport pandas as pd\nimport requests\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import Stock, StockDetail\nfrom zvt.recorders.consts import DEFAULT_SH_HEADER, DEFAULT_SZ_HEADER\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\nclass ExchangeStockMetaRecorder(Recorder):\n data_schema = Stock\n provider = \"exchange\"\n\n original_page_url = \"http://www.sse.com.cn/assortment/stock/list/share/\"\n\n def run(self):\n url = (\n \"http://query.sse.com.cn/security/stock/downloadStockListFile.do?csrcCode=&stockCode=&areaName=&stockType=1\"\n )\n resp = requests.get(url, headers=DEFAULT_SH_HEADER)\n self.download_stock_list(response=resp, exchange=\"sh\")\n\n url = (\n \"http://query.sse.com.cn/security/stock/downloadStockListFile.do?csrcCode=&stockCode=&areaName=&stockType=8\"\n )\n resp = requests.get(url, headers=DEFAULT_SH_HEADER)\n self.download_stock_list(response=resp, exchange=\"sh\")\n\n url = \"http://www.szse.cn/api/report/ShowReport?SHOWTYPE=xlsx&CATALOGID=1110&TABKEY=tab1&random=0.20932135244582617\"\n resp = requests.get(url, headers=DEFAULT_SZ_HEADER)\n self.download_stock_list(response=resp, exchange=\"sz\")\n\n def download_stock_list(self, response, exchange):\n df = None\n if exchange == \"sh\":\n df = pd.read_csv(\n io.BytesIO(response.content),\n sep=\"\\s+\",\n encoding=\"GB2312\",\n dtype=str,\n parse_dates=[\"上市日期\"],\n date_format=\"%Y-m-d\",\n on_bad_lines=\"skip\",\n )\n print(df)\n if df is not None:\n df = df.loc[:, [\"公司代码\", \"公司简称\", \"上市日期\"]]\n\n elif exchange == \"sz\":\n df = pd.read_excel(\n io.BytesIO(response.content),\n sheet_name=\"A股列表\",\n dtype=str,\n parse_dates=[\"A股上市日期\"],\n date_format=\"%Y-m-d\",\n )\n if df is not None:\n df = df.loc[:, [\"A股代码\", \"A股简称\", \"A股上市日期\"]]\n\n if df is not None:\n df.columns = [\"code\", \"name\", \"list_date\"]\n\n df = df.dropna(subset=[\"code\"])\n\n # handle the dirty data\n # 600996,贵广网络,2016-12-26,2016-12-26,sh,stock,stock_sh_600996,,次新股,贵州,,\n df.loc[df[\"code\"] == \"600996\", \"list_date\"] = \"2016-12-26\"\n print(df[df[\"list_date\"] == \"-\"])\n print(df[\"list_date\"])\n df[\"list_date\"] = df[\"list_date\"].apply(lambda x: to_pd_timestamp(x))\n df[\"exchange\"] = exchange\n df[\"entity_type\"] = \"stock\"\n df[\"id\"] = df[[\"entity_type\", \"exchange\", \"code\"]].apply(lambda x: \"_\".join(x.astype(str)), axis=1)\n df[\"entity_id\"] = df[\"id\"]\n df[\"timestamp\"] = df[\"list_date\"]\n df = df.dropna(axis=0, how=\"any\")\n df = df.drop_duplicates(subset=(\"id\"), keep=\"last\")\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=False)\n # persist StockDetail too\n df_to_db(df=df, data_schema=StockDetail, provider=self.provider, force_update=False)\n self.logger.info(df.tail())\n self.logger.info(\"persist stock list successs\")\n\n\n__all__ = [\"ExchangeStockMetaRecorder\"]\n\nif __name__ == \"__main__\":\n recorder = ExchangeStockMetaRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"ExchangeStockMetaRecorder\"]\n"
},
{
"path": "src/zvt/recorders/exchange/exchange_stock_summary_recorder.py",
"content": "import demjson3\nimport pandas as pd\nimport requests\n\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Index\nfrom zvt.domain.misc import StockSummary\nfrom zvt.recorders.consts import DEFAULT_SH_SUMMARY_HEADER\nfrom zvt.utils.time_utils import to_date_time_str\nfrom zvt.utils.utils import to_float\n\n\nclass ExchangeStockSummaryRecorder(TimestampsDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"exchange\"\n data_schema = StockSummary\n\n original_page_url = \"http://www.sse.com.cn/market/stockdata/overview/day/\"\n\n url = \"http://query.sse.com.cn/marketdata/tradedata/queryTradingByProdTypeData.do?jsonCallBack=jsonpCallback30731&searchDate={}&prodType=gp&_=1515717065511\"\n\n def __init__(\n self,\n force_update=False,\n sleeping_time=5,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n [\"000001\"],\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n )\n\n def init_timestamps(self, entity):\n return pd.date_range(start=entity.timestamp, end=pd.Timestamp.now(), freq=\"B\").tolist()\n\n def record(self, entity, start, end, size, timestamps):\n json_results = []\n for timestamp in timestamps:\n timestamp_str = to_date_time_str(timestamp)\n url = self.url.format(timestamp_str)\n response = requests.get(url=url, headers=DEFAULT_SH_SUMMARY_HEADER)\n\n results = demjson3.decode(response.text[response.text.index(\"(\") + 1 : response.text.index(\")\")])[\"result\"]\n result = [result for result in results if result[\"productType\"] == \"1\"]\n if result and len(result) == 1:\n result_json = result[0]\n # 有些较老的数据不存在,默认设为0.0\n json_results.append(\n {\n \"provider\": \"exchange\",\n \"timestamp\": timestamp,\n \"name\": \"上证指数\",\n \"pe\": to_float(result_json[\"profitRate\"], 0.0),\n \"total_value\": to_float(result_json[\"marketValue1\"] + \"亿\", 0.0),\n \"total_tradable_vaule\": to_float(result_json[\"negotiableValue1\"] + \"亿\", 0.0),\n \"volume\": to_float(result_json[\"trdVol1\"] + \"万\", 0.0),\n \"turnover\": to_float(result_json[\"trdAmt1\"] + \"亿\", 0.0),\n \"turnover_rate\": to_float(result_json[\"exchangeRate\"], 0.0),\n }\n )\n\n if len(json_results) > 30:\n return json_results\n\n return json_results\n\n def get_data_map(self):\n return None\n\n\nif __name__ == \"__main__\":\n ExchangeStockSummaryRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"ExchangeStockSummaryRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule quotes\nfrom .quotes import *\nfrom .quotes import __all__ as _quotes_all\n\n__all__ += _quotes_all\n\n# import all from submodule common\nfrom .common import *\nfrom .common import __all__ as _common_all\n\n__all__ += _common_all\n\n# import all from submodule fundamental\nfrom .fundamental import *\nfrom .fundamental import __all__ as _fundamental_all\n\n__all__ += _fundamental_all\n\n# import all from submodule misc\nfrom .misc import *\nfrom .misc import __all__ as _misc_all\n\n__all__ += _misc_all\n\n# import all from submodule meta\nfrom .meta import *\nfrom .meta import __all__ as _meta_all\n\n__all__ += _meta_all\n\n# import all from submodule overall\nfrom .overall import *\nfrom .overall import __all__ as _overall_all\n\n__all__ += _overall_all\n"
},
{
"path": "src/zvt/recorders/joinquant/common.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel\nfrom zvt.domain import ReportPeriod\n\n\ndef to_jq_trading_level(trading_level: IntervalLevel):\n if trading_level < IntervalLevel.LEVEL_1HOUR:\n return trading_level.value\n\n if trading_level == IntervalLevel.LEVEL_1HOUR:\n return \"60m\"\n if trading_level == IntervalLevel.LEVEL_4HOUR:\n return \"240m\"\n if trading_level == IntervalLevel.LEVEL_1DAY:\n return \"1d\"\n if trading_level == IntervalLevel.LEVEL_1WEEK:\n return \"1w\"\n if trading_level == IntervalLevel.LEVEL_1MON:\n return \"1M\"\n\n\ndef to_jq_entity_id(security_item):\n if security_item.entity_type == \"stock\" or security_item.entity_type == \"index\":\n if security_item.exchange == \"sh\":\n return \"{}.XSHG\".format(security_item.code)\n if security_item.exchange == \"sz\":\n return \"{}.XSHE\".format(security_item.code)\n\n\ndef to_entity_id(jq_code: str, entity_type):\n try:\n code, exchange = jq_code.split(\".\")\n if exchange == \"XSHG\":\n exchange = \"sh\"\n elif exchange == \"XSHE\":\n exchange = \"sz\"\n except:\n code = jq_code\n exchange = \"sz\"\n\n return f\"{entity_type}_{exchange}_{code}\"\n\n\ndef jq_to_report_period(jq_report_type):\n if jq_report_type == \"第一季度\":\n return ReportPeriod.season1.value\n if jq_report_type == \"第二季度\":\n return ReportPeriod.season2.value\n if jq_report_type == \"第三季度\":\n return ReportPeriod.season3.value\n if jq_report_type == \"第四季度\":\n return ReportPeriod.season4.value\n if jq_report_type == \"半年度\":\n return ReportPeriod.half_year.value\n if jq_report_type == \"年度\":\n return ReportPeriod.year.value\n assert False\n\n\n# the __all__ is generated\n__all__ = [\"to_jq_trading_level\", \"to_jq_entity_id\", \"to_entity_id\", \"jq_to_report_period\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/fundamental/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule jq_margin_trading_recorder\nfrom .jq_margin_trading_recorder import *\nfrom .jq_margin_trading_recorder import __all__ as _jq_margin_trading_recorder_all\n\n__all__ += _jq_margin_trading_recorder_all\n\n# import all from submodule jq_stock_valuation_recorder\nfrom .jq_stock_valuation_recorder import *\nfrom .jq_stock_valuation_recorder import __all__ as _jq_stock_valuation_recorder_all\n\n__all__ += _jq_stock_valuation_recorder_all\n\n# import all from submodule jq_etf_valuation_recorder\nfrom .jq_etf_valuation_recorder import *\nfrom .jq_etf_valuation_recorder import __all__ as _jq_etf_valuation_recorder_all\n\n__all__ += _jq_etf_valuation_recorder_all\n"
},
{
"path": "src/zvt/recorders/joinquant/fundamental/jq_etf_valuation_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.api.portfolio import get_etf_stocks\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import StockValuation, Etf, EtfValuation\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import now_pd_timestamp\n\n\nclass JqChinaEtfValuationRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Etf\n\n # 数据来自jq\n provider = \"joinquant\"\n\n data_schema = EtfValuation\n\n def record(self, entity, start, end, size, timestamps):\n if not end:\n end = now_pd_timestamp()\n\n date_range = pd.date_range(start=start, end=end, freq=\"1D\").tolist()\n for date in date_range:\n # etf包含的个股和比例\n etf_stock_df = get_etf_stocks(code=entity.code, timestamp=date, provider=self.provider)\n\n if pd_is_not_null(etf_stock_df):\n all_pct = etf_stock_df[\"proportion\"].sum()\n\n if all_pct >= 1.2 or all_pct <= 0.8:\n self.logger.error(f\"ignore etf:{entity.id} date:{date} proportion sum:{all_pct}\")\n break\n\n etf_stock_df.set_index(\"stock_id\", inplace=True)\n\n # 个股的估值数据\n stock_valuation_df = StockValuation.query_data(\n entity_ids=etf_stock_df.index.to_list(),\n filters=[StockValuation.timestamp == date],\n index=\"entity_id\",\n )\n\n if pd_is_not_null(stock_valuation_df):\n stock_count = len(etf_stock_df)\n valuation_count = len(stock_valuation_df)\n\n self.logger.info(\n f\"etf:{entity.id} date:{date} stock count: {stock_count},\" f\"valuation count:{valuation_count}\"\n )\n\n pct = abs(stock_count - valuation_count) / stock_count\n\n if pct >= 0.2:\n self.logger.error(f\"ignore etf:{entity.id} date:{date} pct:{pct}\")\n break\n\n se = pd.Series(\n {\n \"id\": \"{}_{}\".format(entity.id, date),\n \"entity_id\": entity.id,\n \"timestamp\": date,\n \"code\": entity.code,\n \"name\": entity.name,\n }\n )\n for col in [\"pe\", \"pe_ttm\", \"pb\", \"ps\", \"pcf\"]:\n # PE=P/E\n # 这里的算法为：将其价格都设为PE,那么Earning为1(亏钱为-1)，结果为 总价格(PE)/总Earning\n\n value = 0\n price = 0\n\n # 权重估值\n positive_df = stock_valuation_df[[col]][stock_valuation_df[col] > 0]\n positive_df[\"count\"] = 1\n positive_df = positive_df.multiply(etf_stock_df[\"proportion\"], axis=\"index\")\n if pd_is_not_null(positive_df):\n value = positive_df[\"count\"].sum()\n price = positive_df[col].sum()\n\n negative_df = stock_valuation_df[[col]][stock_valuation_df[col] < 0]\n if pd_is_not_null(negative_df):\n negative_df[\"count\"] = 1\n negative_df = negative_df.multiply(etf_stock_df[\"proportion\"], axis=\"index\")\n value = value - negative_df[\"count\"].sum()\n price = price + negative_df[col].sum()\n\n se[f\"{col}1\"] = price / value\n\n # 简单算术平均估值\n positive_df = stock_valuation_df[col][stock_valuation_df[col] > 0]\n positive_count = len(positive_df)\n\n negative_df = stock_valuation_df[col][stock_valuation_df[col] < 0]\n negative_count = len(negative_df)\n\n value = positive_count - negative_count\n price = positive_df.sum() + abs(negative_df.sum())\n\n se[col] = price / value\n df = se.to_frame().T\n\n self.logger.info(df)\n\n df_to_db(\n df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update\n )\n\n return None\n\n\nif __name__ == \"__main__\":\n # 上证50\n JqChinaEtfValuationRecorder(codes=[\"512290\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"JqChinaEtfValuationRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/fundamental/jq_margin_trading_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\nfrom jqdatapy.api import get_mtss\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import Stock, MarginTrading\nfrom zvt.recorders.joinquant.common import to_jq_entity_id\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str, TIME_FORMAT_DAY\n\n\nclass MarginTradingRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Stock\n\n # 数据来自jq\n provider = \"joinquant\"\n\n data_schema = MarginTrading\n\n def record(self, entity, start, end, size, timestamps):\n df = get_mtss(code=to_jq_entity_id(entity), date=to_date_time_str(start))\n\n if pd_is_not_null(df):\n df[\"entity_id\"] = entity.id\n df[\"code\"] = entity.code\n df.rename(columns={\"date\": \"timestamp\"}, inplace=True)\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(\n lambda se: \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_DAY)),\n axis=1,\n )\n\n print(df)\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n return None\n\n\nif __name__ == \"__main__\":\n MarginTradingRecorder(codes=[\"000004\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"MarginTradingRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/fundamental/jq_stock_valuation_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\nfrom jqdatapy.api import get_fundamentals\nfrom pandas._libs.tslibs.timedeltas import Timedelta\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import Stock, StockValuation, Etf\nfrom zvt.recorders.joinquant.common import to_jq_entity_id\nfrom zvt.utils.time_utils import now_pd_timestamp, to_date_time_str, to_pd_timestamp\n\n\nclass JqChinaStockValuationRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Stock\n\n # 数据来自jq\n provider = \"joinquant\"\n\n data_schema = StockValuation\n\n def record(self, entity, start, end, size, timestamps):\n start = max(start, to_pd_timestamp(\"2005-01-01\"))\n end = min(now_pd_timestamp(), start + Timedelta(days=500))\n\n count: Timedelta = end - start\n\n # df = get_fundamentals_continuously(q, end_date=now_time_str(), count=count.days + 1, panel=False)\n df = get_fundamentals(\n table=\"valuation\", code=to_jq_entity_id(entity), date=to_date_time_str(end), count=min(count.days, 500)\n )\n df[\"entity_id\"] = entity.id\n df[\"timestamp\"] = pd.to_datetime(df[\"day\"])\n df[\"code\"] = entity.code\n df[\"name\"] = entity.name\n df[\"id\"] = df[\"timestamp\"].apply(lambda x: \"{}_{}\".format(entity.id, to_date_time_str(x)))\n df = df.rename(\n {\"pe_ratio_lyr\": \"pe\", \"pe_ratio\": \"pe_ttm\", \"pb_ratio\": \"pb\", \"ps_ratio\": \"ps\", \"pcf_ratio\": \"pcf\"},\n axis=\"columns\",\n )\n\n df[\"market_cap\"] = df[\"market_cap\"] * 100000000\n df[\"circulating_market_cap\"] = df[\"circulating_market_cap\"] * 100000000\n df[\"capitalization\"] = df[\"capitalization\"] * 10000\n df[\"circulating_cap\"] = df[\"circulating_cap\"] * 10000\n df[\"turnover_ratio\"] = df[\"turnover_ratio\"] * 0.01\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n return None\n\n\nif __name__ == \"__main__\":\n # 上证50\n df = Etf.get_stocks(code=\"510050\")\n stocks = df.stock_id.tolist()\n print(stocks)\n print(len(stocks))\n\n JqChinaStockValuationRecorder(entity_ids=[\"stock_sz_300999\"], force_update=True).run()\n\n\n# the __all__ is generated\n__all__ = [\"JqChinaStockValuationRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/meta/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule jq_fund_meta_recorder\nfrom .jq_fund_meta_recorder import *\nfrom .jq_fund_meta_recorder import __all__ as _jq_fund_meta_recorder_all\n\n__all__ += _jq_fund_meta_recorder_all\n\n# import all from submodule jq_trade_day_recorder\nfrom .jq_trade_day_recorder import *\nfrom .jq_trade_day_recorder import __all__ as _jq_trade_day_recorder_all\n\n__all__ += _jq_trade_day_recorder_all\n\n# import all from submodule jq_stock_meta_recorder\nfrom .jq_stock_meta_recorder import *\nfrom .jq_stock_meta_recorder import __all__ as _jq_stock_meta_recorder_all\n\n__all__ += _jq_stock_meta_recorder_all\n"
},
{
"path": "src/zvt/recorders/joinquant/meta/jq_fund_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom jqdatapy.api import run_query\n\nfrom zvt.api.portfolio import portfolio_relate_stock\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder, TimeSeriesDataRecorder\nfrom zvt.domain.meta.fund_meta import Fund, FundStock\nfrom zvt.recorders.joinquant.common import to_entity_id, jq_to_report_period\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str, date_time_by_interval, now_pd_timestamp, is_same_date\n\n\nclass JqChinaFundRecorder(Recorder):\n provider = \"joinquant\"\n data_schema = Fund\n\n def run(self):\n # 按不同类别抓取\n # 编码\t基金运作方式\n # 401001\t开放式基金\n # 401002\t封闭式基金\n # 401003\tQDII\n # 401004\tFOF\n # 401005\tETF\n # 401006\tLOF\n for operate_mode_id in (401001, 401002, 401005):\n year_count = 2\n while True:\n latest = Fund.query_data(\n filters=[Fund.operate_mode_id == operate_mode_id],\n order=Fund.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n start_timestamp = \"2000-01-01\"\n if latest:\n start_timestamp = latest[0].timestamp\n\n end_timestamp = min(date_time_by_interval(start_timestamp, 365 * year_count), now_pd_timestamp())\n\n df = run_query(\n table=\"finance.FUND_MAIN_INFO\",\n conditions=f\"operate_mode_id#=#{operate_mode_id}&start_date#>=#{to_date_time_str(start_timestamp)}&start_date#<=#{to_date_time_str(end_timestamp)}\",\n parse_dates=[\"start_date\", \"end_date\"],\n dtype={\"main_code\": str},\n )\n if not pd_is_not_null(df) or (df[\"start_date\"].max().year < end_timestamp.year):\n year_count = year_count + 1\n\n if pd_is_not_null(df):\n df.rename(columns={\"start_date\": \"timestamp\"}, inplace=True)\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"list_date\"] = df[\"timestamp\"]\n df[\"end_date\"] = pd.to_datetime(df[\"end_date\"])\n\n df[\"code\"] = df[\"main_code\"]\n df[\"entity_id\"] = df[\"code\"].apply(lambda x: to_entity_id(entity_type=\"fund\", jq_code=x))\n df[\"id\"] = df[\"entity_id\"]\n df[\"entity_type\"] = \"fund\"\n df[\"exchange\"] = \"sz\"\n df_to_db(df, data_schema=Fund, provider=self.provider, force_update=self.force_update)\n self.logger.info(\n f\"persist fund {operate_mode_id} list success {start_timestamp} to {end_timestamp}\"\n )\n\n if is_same_date(end_timestamp, now_pd_timestamp()):\n break\n\n\nclass JqChinaFundStockRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Fund\n\n provider = \"joinquant\"\n data_schema = FundStock\n\n def init_entities(self):\n # 只抓股票型，混合型并且没退市的持仓,\n self.entities = Fund.query_data(\n entity_ids=self.entity_ids,\n codes=self.codes,\n return_type=\"domain\",\n provider=self.entity_provider,\n filters=[Fund.underlying_asset_type.in_((\"股票型\", \"混合型\")), Fund.end_date.is_(None)],\n )\n\n def record(self, entity, start, end, size, timestamps):\n # 忽略退市的\n if entity.end_date:\n return None\n redundant_times = 1\n while redundant_times > 0:\n df = run_query(\n table=\"finance.FUND_PORTFOLIO_STOCK\",\n conditions=f\"pub_date#>=#{to_date_time_str(start)}&code#=#{entity.code}\",\n parse_dates=None,\n )\n df = df.dropna()\n if pd_is_not_null(df):\n # data format\n # id code period_start period_end pub_date report_type_id report_type rank symbol name shares market_cap proportion\n # 0 8640569 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 1 601318 中国平安 19869239.0 1.361043e+09 7.09\n # 1 8640570 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 2 600519 贵州茅台 921670.0 6.728191e+08 3.50\n # 2 8640571 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 3 600036 招商银行 18918815.0 5.806184e+08 3.02\n # 3 8640572 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 4 601166 兴业银行 22862332.0 3.646542e+08 1.90\n df[\"timestamp\"] = pd.to_datetime(df[\"pub_date\"])\n\n df.rename(columns={\"symbol\": \"stock_code\", \"name\": \"stock_name\"}, inplace=True)\n df[\"proportion\"] = df[\"proportion\"] * 0.01\n\n df = portfolio_relate_stock(df, entity)\n\n df[\"stock_id\"] = df[\"stock_code\"].apply(lambda x: china_stock_code_to_id(x))\n df[\"id\"] = df[[\"entity_id\", \"stock_id\", \"pub_date\", \"id\"]].apply(\n lambda x: \"_\".join(x.astype(str)), axis=1\n )\n df[\"report_date\"] = pd.to_datetime(df[\"period_end\"])\n df[\"report_period\"] = df[\"report_type\"].apply(lambda x: jq_to_report_period(x))\n\n saved = df_to_db(\n df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update\n )\n\n # 取不到非重复的数据\n if saved == 0:\n return None\n\n # self.logger.info(df.tail())\n self.logger.info(\n f\"persist fund {entity.code}({entity.name}) portfolio success {df.iloc[-1]['pub_date']}\"\n )\n latest = df[\"timestamp\"].max()\n\n # 取到了最近两年的数据，再请求一次,确保取完最新的数据\n if latest.year >= now_pd_timestamp().year - 1:\n redundant_times = redundant_times - 1\n start = latest\n else:\n return None\n\n return None\n\n\nif __name__ == \"__main__\":\n # JqChinaFundRecorder().run()\n JqChinaFundStockRecorder(codes=[\"000053\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"JqChinaFundRecorder\", \"JqChinaFundStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/meta/jq_stock_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom jqdatapy.api import get_all_securities, run_query\n\nfrom zvt.api.portfolio import portfolio_relate_stock\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.contract.api import df_to_db, get_entity_exchange, get_entity_code\nfrom zvt.contract.recorder import Recorder, TimeSeriesDataRecorder\nfrom zvt.domain import EtfStock, Stock, Etf, StockDetail\nfrom zvt.recorders.joinquant.common import to_entity_id, jq_to_report_period\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str\n\n\nclass BaseJqChinaMetaRecorder(Recorder):\n provider = \"joinquant\"\n\n def __init__(self, force_update=True, sleeping_time=10) -> None:\n super().__init__(force_update, sleeping_time)\n\n def to_zvt_entity(self, df, entity_type, category=None):\n df = df.set_index(\"code\")\n df.index.name = \"entity_id\"\n df = df.reset_index()\n # 上市日期\n df.rename(columns={\"start_date\": \"timestamp\"}, inplace=True)\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"list_date\"] = df[\"timestamp\"]\n df[\"end_date\"] = pd.to_datetime(df[\"end_date\"])\n\n df[\"entity_id\"] = df[\"entity_id\"].apply(lambda x: to_entity_id(entity_type=entity_type, jq_code=x))\n df[\"id\"] = df[\"entity_id\"]\n df[\"entity_type\"] = entity_type\n df[\"exchange\"] = df[\"entity_id\"].apply(lambda x: get_entity_exchange(x))\n df[\"code\"] = df[\"entity_id\"].apply(lambda x: get_entity_code(x))\n df[\"name\"] = df[\"display_name\"]\n\n if category:\n df[\"category\"] = category\n\n return df\n\n\nclass JqChinaStockRecorder(BaseJqChinaMetaRecorder):\n data_schema = Stock\n\n def run(self):\n # 抓取股票列表\n df_stock = self.to_zvt_entity(get_all_securities(code=\"stock\"), entity_type=\"stock\")\n df_to_db(df_stock, data_schema=Stock, provider=self.provider, force_update=self.force_update)\n # persist StockDetail too\n df_to_db(df=df_stock, data_schema=StockDetail, provider=self.provider, force_update=self.force_update)\n\n # self.logger.info(df_stock)\n self.logger.info(\"persist stock list success\")\n\n\nclass JqChinaEtfRecorder(BaseJqChinaMetaRecorder):\n data_schema = Etf\n\n def run(self):\n # 抓取etf列表\n df_index = self.to_zvt_entity(get_all_securities(code=\"etf\"), entity_type=\"etf\", category=\"etf\")\n df_to_db(df_index, data_schema=Etf, provider=self.provider, force_update=self.force_update)\n\n # self.logger.info(df_index)\n self.logger.info(\"persist etf list success\")\n\n\nclass JqChinaStockEtfPortfolioRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Etf\n\n # 数据来自jq\n provider = \"joinquant\"\n\n data_schema = EtfStock\n\n def record(self, entity, start, end, size, timestamps):\n df = run_query(\n table=\"finance.FUND_PORTFOLIO_STOCK\",\n conditions=f\"pub_date#>=#{to_date_time_str(start)}&code#=#{entity.code}\",\n parse_dates=None,\n )\n if pd_is_not_null(df):\n # id code period_start period_end pub_date report_type_id report_type rank symbol name shares market_cap proportion\n # 0 8640569 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 1 601318 中国平安 19869239.0 1.361043e+09 7.09\n # 1 8640570 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 2 600519 贵州茅台 921670.0 6.728191e+08 3.50\n # 2 8640571 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 3 600036 招商银行 18918815.0 5.806184e+08 3.02\n # 3 8640572 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 4 601166 兴业银行 22862332.0 3.646542e+08 1.90\n df[\"timestamp\"] = pd.to_datetime(df[\"pub_date\"])\n\n df.rename(columns={\"symbol\": \"stock_code\", \"name\": \"stock_name\"}, inplace=True)\n df[\"proportion\"] = df[\"proportion\"] * 0.01\n\n df = portfolio_relate_stock(df, entity)\n\n df[\"stock_id\"] = df[\"stock_code\"].apply(lambda x: china_stock_code_to_id(x))\n df[\"id\"] = df[[\"entity_id\", \"stock_id\", \"pub_date\", \"id\"]].apply(lambda x: \"_\".join(x.astype(str)), axis=1)\n df[\"report_date\"] = pd.to_datetime(df[\"period_end\"])\n df[\"report_period\"] = df[\"report_type\"].apply(lambda x: jq_to_report_period(x))\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n # self.logger.info(df.tail())\n self.logger.info(f\"persist etf {entity.code} portfolio success {df.iloc[-1]['pub_date']}\")\n\n return None\n\n\nif __name__ == \"__main__\":\n # JqChinaEtfRecorder().run()\n JqChinaStockEtfPortfolioRecorder(codes=[\"510050\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"BaseJqChinaMetaRecorder\", \"JqChinaStockRecorder\", \"JqChinaEtfRecorder\", \"JqChinaStockEtfPortfolioRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/meta/jq_trade_day_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom jqdatapy.api import get_trade_days\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import StockTradeDay, Stock\nfrom zvt.utils.time_utils import to_date_time_str\n\n\nclass StockTradeDayRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Stock\n\n provider = \"joinquant\"\n data_schema = StockTradeDay\n\n def __init__(\n self,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n day_data=False,\n force_update=False,\n sleeping_time=5,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n entity_filters=None,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n codes=[\"000001\"],\n day_data=day_data,\n entity_filters=entity_filters,\n ignore_failed=True,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n\n def record(self, entity, start, end, size, timestamps):\n df = pd.DataFrame()\n dates = get_trade_days(date=to_date_time_str(start))\n dates = dates.iloc[:, 0]\n self.logger.info(f\"add dates:{dates}\")\n df[\"timestamp\"] = pd.to_datetime(dates)\n df[\"id\"] = [to_date_time_str(date) for date in dates]\n df[\"entity_id\"] = \"stock_sz_000001\"\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n r = StockTradeDayRecorder()\n r.run()\n\n\n# the __all__ is generated\n__all__ = [\"StockTradeDayRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/misc/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule jq_hk_holder_recorder\nfrom .jq_hk_holder_recorder import *\nfrom .jq_hk_holder_recorder import __all__ as _jq_hk_holder_recorder_all\n\n__all__ += _jq_hk_holder_recorder_all\n\n# import all from submodule jq_index_money_flow_recorder\nfrom .jq_index_money_flow_recorder import *\nfrom .jq_index_money_flow_recorder import __all__ as _jq_index_money_flow_recorder_all\n\n__all__ += _jq_index_money_flow_recorder_all\n\n# import all from submodule jq_stock_money_flow_recorder\nfrom .jq_stock_money_flow_recorder import *\nfrom .jq_stock_money_flow_recorder import __all__ as _jq_stock_money_flow_recorder_all\n\n__all__ += _jq_stock_money_flow_recorder_all\n"
},
{
"path": "src/zvt/recorders/joinquant/misc/jq_hk_holder_recorder.py",
"content": "import pandas as pd\nfrom jqdatapy.api import run_query\n\nfrom zvt.contract.api import df_to_db, get_data\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Index\nfrom zvt.domain.misc.holder import HkHolder\nfrom zvt.recorders.joinquant.common import to_entity_id\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str, TIME_FORMAT_DAY, to_pd_timestamp\n\n\n# 这里选择继承TimestampsDataRecorder是因为\n# 1)时间上就是交易日的列表,这个是可知的，可以以此为增量计算点\n# 2)HkHolder数据结构的设计：\n# 沪股通/深股通 每日 持有 标的(股票)的情况\n# 抓取的角度是entity从Index中获取 沪股通/深股通，然后按 每日 去获取\n\n\nclass JoinquantHkHolderRecorder(TimestampsDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"joinquant\"\n data_schema = HkHolder\n\n def __init__(\n self,\n day_data=False,\n force_update=False,\n sleeping_time=5,\n real_time=False,\n start_timestamp=None,\n end_timestamp=None,\n ) -> None:\n # 聚宽编码\n # 市场通编码\t市场通名称\n # 310001\t沪股通\n # 310002\t深股通\n # 310003\t港股通（沪）\n # 310004\t港股通（深）\n codes = [\"310001\", \"310002\"]\n\n super().__init__(\n force_update,\n sleeping_time,\n [\"cn\"],\n None,\n codes,\n day_data,\n real_time=real_time,\n fix_duplicate_way=\"ignore\",\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n\n def init_timestamps(self, entity):\n # 聚宽数据从2017年3月17开始\n return pd.date_range(start=to_pd_timestamp(\"2017-3-17\"), end=pd.Timestamp.now(), freq=\"B\").tolist()\n\n # 覆盖这个方式是因为，HkHolder里面entity其实是股票，而recorder中entity是 Index类型(沪股通/深股通)\n def get_latest_saved_record(self, entity):\n order = eval(\"self.data_schema.{}.desc()\".format(self.get_evaluated_time_field()))\n\n records = get_data(\n filters=[HkHolder.holder_code == entity.code],\n provider=self.provider,\n data_schema=self.data_schema,\n order=order,\n limit=1,\n return_type=\"domain\",\n session=self.session,\n )\n if records:\n return records[0]\n return None\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n df = run_query(\n table=\"finance.STK_HK_HOLD_INFO\",\n conditions=f\"link_id#=#{entity.code}&day#=#{to_date_time_str(timestamp)}\",\n )\n print(df)\n\n if pd_is_not_null(df):\n df.rename(\n columns={\"day\": \"timestamp\", \"link_id\": \"holder_code\", \"link_name\": \"holder_name\"}, inplace=True\n )\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n\n df[\"entity_id\"] = df[\"code\"].apply(lambda x: to_entity_id(entity_type=\"stock\", jq_code=x))\n df[\"code\"] = df[\"code\"].apply(lambda x: x.split(\".\")[0])\n\n # id格式为:{holder_name}_{entity_id}_{timestamp}\n df[\"id\"] = df[[\"holder_name\", \"entity_id\", \"timestamp\"]].apply(\n lambda se: \"{}_{}_{}\".format(\n se[\"holder_name\"], se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_DAY)\n ),\n axis=1,\n )\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n JoinquantHkHolderRecorder(sleeping_time=10).run()\n\n\n# the __all__ is generated\n__all__ = [\"JoinquantHkHolderRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/misc/jq_index_money_flow_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import IndexMoneyFlow, Index, StockMoneyFlow\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str\n\n\nclass JoinquantIndexMoneyFlowRecorder(FixedCycleDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"joinquant\"\n data_schema = IndexMoneyFlow\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n return_unfinished=False,\n ) -> None:\n # 上证指数，深证成指，创业板指，科创板\n support_codes = [\"000001\", \"399001\", \"399006\", \"000688\"]\n if not codes:\n codes = support_codes\n else:\n codes = list(set(codes) & set(support_codes))\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n def record(self, entity, start, end, size, timestamps):\n # 上证\n if entity.code == \"000001\":\n all_df = StockMoneyFlow.query_data(\n provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.entity_id.like(\"stock_sh%\")]\n )\n # 深证\n elif entity.code == \"399001\":\n all_df = StockMoneyFlow.query_data(\n provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.entity_id.like(\"stock_sz%\")]\n )\n # 创业板\n elif entity.code == \"399006\":\n all_df = StockMoneyFlow.query_data(\n provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.code.like(\"300%\")]\n )\n # 科创板\n elif entity.code == \"000688\":\n all_df = StockMoneyFlow.query_data(\n provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.code.like(\"688%\")]\n )\n\n if pd_is_not_null(all_df):\n g = all_df.groupby(\"timestamp\")\n for timestamp, df in g:\n se = pd.Series(\n {\n \"id\": \"{}_{}\".format(entity.id, to_date_time_str(timestamp)),\n \"entity_id\": entity.id,\n \"timestamp\": timestamp,\n \"code\": entity.code,\n \"name\": entity.name,\n }\n )\n for col in [\n \"net_main_inflows\",\n \"net_huge_inflows\",\n \"net_big_inflows\",\n \"net_medium_inflows\",\n \"net_small_inflows\",\n ]:\n se[col] = df[col].sum()\n\n for col in [\n \"net_main_inflow_rate\",\n \"net_huge_inflow_rate\",\n \"net_big_inflow_rate\",\n \"net_medium_inflow_rate\",\n \"net_small_inflow_rate\",\n ]:\n se[col] = df[col].sum() / len(df)\n\n index_df = se.to_frame().T\n\n self.logger.info(index_df)\n\n df_to_db(\n df=index_df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update\n )\n\n return None\n\n\nif __name__ == \"__main__\":\n JoinquantIndexMoneyFlowRecorder(start_timestamp=\"2020-12-01\").run()\n\n\n# the __all__ is generated\n__all__ = [\"JoinquantIndexMoneyFlowRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/misc/jq_stock_money_flow_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom jqdatapy import get_token, get_money_flow\n\nfrom zvt import zvt_config\nfrom zvt.api.kdata import generate_kdata_id\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import StockMoneyFlow, Stock\nfrom zvt.recorders.joinquant.common import to_jq_entity_id\nfrom zvt.recorders.joinquant.misc.jq_index_money_flow_recorder import JoinquantIndexMoneyFlowRecorder\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import TIME_FORMAT_DAY, to_date_time_str\n\n\nclass JoinquantStockMoneyFlowRecorder(FixedCycleDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Stock\n\n provider = \"joinquant\"\n data_schema = StockMoneyFlow\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n compute_index_money_flow=False,\n return_unfinished=False,\n ) -> None:\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n self.compute_index_money_flow = compute_index_money_flow\n get_token(zvt_config[\"jq_username\"], zvt_config[\"jq_password\"], force=True)\n\n def generate_domain_id(self, entity, original_data):\n return generate_kdata_id(entity_id=entity.id, timestamp=original_data[\"timestamp\"], level=self.level)\n\n def on_finish(self):\n # 根据 个股资金流 计算 大盘资金流\n if self.compute_index_money_flow:\n JoinquantIndexMoneyFlowRecorder().run()\n\n def record(self, entity, start, end, size, timestamps):\n if not self.end_timestamp:\n df = get_money_flow(code=to_jq_entity_id(entity), date=to_date_time_str(start))\n else:\n df = get_money_flow(code=to_jq_entity_id(entity), date=start, end_date=to_date_time_str(self.end_timestamp))\n\n df = df.dropna()\n\n if pd_is_not_null(df):\n df[\"name\"] = entity.name\n df.rename(\n columns={\n \"date\": \"timestamp\",\n \"net_amount_main\": \"net_main_inflows\",\n \"net_pct_main\": \"net_main_inflow_rate\",\n \"net_amount_xl\": \"net_huge_inflows\",\n \"net_pct_xl\": \"net_huge_inflow_rate\",\n \"net_amount_l\": \"net_big_inflows\",\n \"net_pct_l\": \"net_big_inflow_rate\",\n \"net_amount_m\": \"net_medium_inflows\",\n \"net_pct_m\": \"net_medium_inflow_rate\",\n \"net_amount_s\": \"net_small_inflows\",\n \"net_pct_s\": \"net_small_inflow_rate\",\n },\n inplace=True,\n )\n\n # 转换到标准float\n inflows_cols = [\n \"net_main_inflows\",\n \"net_huge_inflows\",\n \"net_big_inflows\",\n \"net_medium_inflows\",\n \"net_small_inflows\",\n ]\n for col in inflows_cols:\n df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n df = df.dropna()\n\n if not pd_is_not_null(df):\n return None\n\n df[inflows_cols] = df[inflows_cols].apply(lambda x: x * 10000)\n\n inflow_rate_cols = [\n \"net_main_inflow_rate\",\n \"net_huge_inflow_rate\",\n \"net_big_inflow_rate\",\n \"net_medium_inflow_rate\",\n \"net_small_inflow_rate\",\n ]\n for col in inflow_rate_cols:\n df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n df = df.dropna()\n if not pd_is_not_null(df):\n return None\n\n df[inflow_rate_cols] = df[inflow_rate_cols].apply(lambda x: x / 100)\n\n # 计算总流入\n df[\"net_inflows\"] = (\n df[\"net_huge_inflows\"] + df[\"net_big_inflows\"] + df[\"net_medium_inflows\"] + df[\"net_small_inflows\"]\n )\n # 计算总流入率\n amount = df[\"net_main_inflows\"] / df[\"net_main_inflow_rate\"]\n df[\"net_inflow_rate\"] = df[\"net_inflows\"] / amount\n\n df[\"entity_id\"] = entity.id\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"provider\"] = \"joinquant\"\n df[\"code\"] = entity.code\n\n def generate_kdata_id(se):\n return \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_DAY))\n\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(generate_kdata_id, axis=1)\n\n df = df.drop_duplicates(subset=\"id\", keep=\"last\")\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n return None\n\n\nif __name__ == \"__main__\":\n JoinquantStockMoneyFlowRecorder(codes=[\"000578\"]).run()\n\n\n# the __all__ is generated\n__all__ = [\"JoinquantStockMoneyFlowRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/overall/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule jq_margin_trading_recorder\nfrom .jq_margin_trading_recorder import *\nfrom .jq_margin_trading_recorder import __all__ as _jq_margin_trading_recorder_all\n\n__all__ += _jq_margin_trading_recorder_all\n\n# import all from submodule jq_stock_summary_recorder\nfrom .jq_stock_summary_recorder import *\nfrom .jq_stock_summary_recorder import __all__ as _jq_stock_summary_recorder_all\n\n__all__ += _jq_stock_summary_recorder_all\n\n# import all from submodule jq_cross_market_recorder\nfrom .jq_cross_market_recorder import *\nfrom .jq_cross_market_recorder import __all__ as _jq_cross_market_recorder_all\n\n__all__ += _jq_cross_market_recorder_all\n"
},
{
"path": "src/zvt/recorders/joinquant/overall/jq_cross_market_recorder.py",
"content": "from jqdatapy.api import run_query\n\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import Index, CrossMarketSummary\nfrom zvt.utils.time_utils import to_date_time_str\nfrom zvt.utils.utils import multiple_number\n\n\nclass CrossMarketSummaryRecorder(TimeSeriesDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Index\n\n provider = \"joinquant\"\n data_schema = CrossMarketSummary\n\n def __init__(self, force_update=False, sleeping_time=5, real_time=False, fix_duplicate_way=\"add\") -> None:\n\n # 聚宽编码\n # 市场通编码\t市场通名称\n # 310001\t沪股通\n # 310002\t深股通\n # 310003\t港股通（沪）\n # 310004\t港股通（深）\n\n codes = [\"310001\", \"310002\", \"310003\", \"310004\"]\n super().__init__(\n force_update,\n sleeping_time,\n [\"cn\"],\n None,\n codes=codes,\n day_data=True,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n )\n\n def init_entities(self):\n super().init_entities()\n\n def record(self, entity, start, end, size, timestamps):\n df = run_query(\n table=\"finance.STK_ML_QUOTA\", conditions=f\"link_id#=#{entity.code}&day#>=#{to_date_time_str(start)}\"\n )\n print(df)\n\n json_results = []\n\n for item in df.to_dict(orient=\"records\"):\n result = {\n \"provider\": self.provider,\n \"timestamp\": item[\"day\"],\n \"name\": entity.name,\n \"buy_amount\": multiple_number(item[\"buy_amount\"], 100000000),\n \"buy_volume\": item[\"buy_volume\"],\n \"sell_amount\": multiple_number(item[\"sell_amount\"], 100000000),\n \"sell_volume\": item[\"sell_volume\"],\n \"quota_daily\": multiple_number(item[\"quota_daily\"], 100000000),\n \"quota_daily_balance\": multiple_number(item[\"quota_daily_balance\"], 100000000),\n }\n\n json_results.append(result)\n\n if len(json_results) < 100:\n self.one_shot = True\n\n return json_results\n\n def get_data_map(self):\n return None\n\n\nif __name__ == \"__main__\":\n CrossMarketSummaryRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"CrossMarketSummaryRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/overall/jq_margin_trading_recorder.py",
"content": "from jqdatapy.api import run_query\n\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import Index, MarginTradingSummary\nfrom zvt.utils.time_utils import to_date_time_str\n\n# 聚宽编码\n# XSHG-上海证券交易所\n# XSHE-深圳证券交易所\n\ncode_map_jq = {\"000001\": \"XSHG\", \"399106\": \"XSHE\"}\n\n\nclass MarginTradingSummaryRecorder(TimeSeriesDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"joinquant\"\n data_schema = MarginTradingSummary\n\n def __init__(\n self,\n force_update=False,\n sleeping_time=5,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n ) -> None:\n # 上海A股,深圳市场\n codes = [\"000001\", \"399106\"]\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n codes=codes,\n day_data=day_data,\n entity_filters=entity_filters,\n ignore_failed=ignore_failed,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n\n def record(self, entity, start, end, size, timestamps):\n jq_code = code_map_jq.get(entity.code)\n\n df = run_query(\n table=\"finance.STK_MT_TOTAL\",\n conditions=f\"exchange_code#=#{jq_code}&date#>=#{to_date_time_str(start)}\",\n parse_dates=[\"date\"],\n )\n print(df)\n\n json_results = []\n\n for item in df.to_dict(orient=\"records\"):\n result = {\n \"provider\": self.provider,\n \"timestamp\": item[\"date\"],\n \"name\": entity.name,\n \"margin_value\": item[\"fin_value\"],\n \"margin_buy\": item[\"fin_buy_value\"],\n \"short_value\": item[\"sec_value\"],\n \"short_volume\": item[\"sec_sell_volume\"],\n \"total_value\": item[\"fin_sec_value\"],\n }\n\n json_results.append(result)\n\n if len(json_results) < 100:\n self.one_shot = True\n\n return json_results\n\n def get_data_map(self):\n return None\n\n\nif __name__ == \"__main__\":\n MarginTradingSummaryRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"MarginTradingSummaryRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/overall/jq_stock_summary_recorder.py",
"content": "from jqdatapy.api import run_query\n\nfrom zvt.contract.recorder import TimeSeriesDataRecorder\nfrom zvt.domain import Index\nfrom zvt.domain import StockSummary\nfrom zvt.utils.time_utils import to_date_time_str\nfrom zvt.utils.utils import multiple_number\n\n# 聚宽编码\n# 322001\t上海市场\n# 322002\t上海A股\n# 322003\t上海B股\n# 322004\t深圳市场\t该市场交易所未公布成交量和成交笔数\n# 322005\t深市主板\n# 322006\t中小企业板\n# 322007\t创业板\n\ncode_map_jq = {\"000001\": \"322002\", \"399106\": \"322004\", \"399001\": \"322005\", \"399005\": \"322006\", \"399006\": \"322007\"}\n\n\nclass StockSummaryRecorder(TimeSeriesDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"joinquant\"\n data_schema = StockSummary\n\n def __init__(\n self,\n force_update=False,\n sleeping_time=5,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"add\",\n start_timestamp=None,\n end_timestamp=None,\n ) -> None:\n # 上海A股,深圳市场,深圳成指,中小板,创业板\n codes = [\"000001\", \"399106\", \"399001\", \"399005\", \"399006\"]\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n codes=codes,\n day_data=day_data,\n entity_filters=entity_filters,\n ignore_failed=ignore_failed,\n real_time=real_time,\n fix_duplicate_way=fix_duplicate_way,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n )\n\n def record(self, entity, start, end, size, timestamps):\n jq_code = code_map_jq.get(entity.code)\n\n df = run_query(\n table=\"finance.STK_EXCHANGE_TRADE_INFO\",\n conditions=f\"exchange_code#=#{jq_code}&date#>=#{to_date_time_str(start)}\",\n parse_dates=[\"date\"],\n )\n print(df)\n\n json_results = []\n\n for item in df.to_dict(orient=\"records\"):\n result = {\n \"provider\": self.provider,\n \"timestamp\": item[\"date\"],\n \"name\": entity.name,\n \"pe\": item[\"pe_average\"],\n \"total_value\": multiple_number(item[\"total_market_cap\"], 100000000),\n \"total_tradable_vaule\": multiple_number(item[\"circulating_market_cap\"], 100000000),\n \"volume\": multiple_number(item[\"volume\"], 10000),\n \"turnover\": multiple_number(item[\"money\"], 100000000),\n \"turnover_rate\": item[\"turnover_ratio\"],\n }\n\n json_results.append(result)\n\n if len(json_results) < 100:\n self.one_shot = True\n\n return json_results\n\n def get_data_map(self):\n return None\n\n\nif __name__ == \"__main__\":\n StockSummaryRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"StockSummaryRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/quotes/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule jq_index_kdata_recorder\nfrom .jq_index_kdata_recorder import *\nfrom .jq_index_kdata_recorder import __all__ as _jq_index_kdata_recorder_all\n\n__all__ += _jq_index_kdata_recorder_all\n\n# import all from submodule jq_stock_kdata_recorder\nfrom .jq_stock_kdata_recorder import *\nfrom .jq_stock_kdata_recorder import __all__ as _jq_stock_kdata_recorder_all\n\n__all__ += _jq_stock_kdata_recorder_all\n"
},
{
"path": "src/zvt/recorders/joinquant/quotes/jq_index_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport argparse\n\nimport pandas as pd\nfrom jqdatapy.api import get_token, get_bars\n\nfrom zvt import init_log, zvt_config\nfrom zvt.api.kdata import generate_kdata_id, get_kdata_schema, get_kdata\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Index, IndexKdataCommon\nfrom zvt.recorders.joinquant.common import to_jq_trading_level, to_jq_entity_id\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str, TIME_FORMAT_DAY, TIME_FORMAT_ISO8601\n\n\nclass JqChinaIndexKdataRecorder(FixedCycleDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Index\n\n # 数据来自jq\n provider = \"joinquant\"\n\n # 只是为了把recorder注册到data_schema\n data_schema = IndexKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK]\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n return_unfinished=False,\n ) -> None:\n level = IntervalLevel(level)\n self.data_schema = get_kdata_schema(entity_type=\"index\", level=level)\n self.jq_trading_level = to_jq_trading_level(level)\n get_token(zvt_config[\"jq_username\"], zvt_config[\"jq_password\"], force=True)\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n def init_entities(self):\n super().init_entities()\n # ignore no data index\n self.entities = [\n entity for entity in self.entities if entity.code not in [\"310001\", \"310002\", \"310003\", \"310004\"]\n ]\n\n def generate_domain_id(self, entity, original_data):\n return generate_kdata_id(entity_id=entity.id, timestamp=original_data[\"timestamp\"], level=self.level)\n\n def record(self, entity, start, end, size, timestamps):\n if not self.end_timestamp:\n df = get_bars(\n to_jq_entity_id(entity),\n count=size,\n unit=self.jq_trading_level,\n # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money']\n )\n else:\n end_timestamp = to_date_time_str(self.end_timestamp)\n df = get_bars(\n to_jq_entity_id(entity),\n count=size,\n unit=self.jq_trading_level,\n # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'],\n end_date=end_timestamp,\n )\n if pd_is_not_null(df):\n df[\"name\"] = entity.name\n df.rename(columns={\"money\": \"turnover\", \"date\": \"timestamp\"}, inplace=True)\n\n df[\"entity_id\"] = entity.id\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"provider\"] = \"joinquant\"\n df[\"level\"] = self.level.value\n df[\"code\"] = entity.code\n\n def generate_kdata_id(se):\n if self.level >= IntervalLevel.LEVEL_1DAY:\n return \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_DAY))\n else:\n return \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_ISO8601))\n\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(generate_kdata_id, axis=1)\n\n df = df.drop_duplicates(subset=\"id\", keep=\"last\")\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n return None\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--level\", help=\"trading level\", default=\"1d\", choices=[item.value for item in IntervalLevel])\n parser.add_argument(\"--codes\", help=\"codes\", default=[\"000001\"], nargs=\"+\")\n\n args = parser.parse_args()\n\n level = IntervalLevel(args.level)\n codes = args.codes\n\n init_log(\"jq_china_stock_{}_kdata.log\".format(args.level))\n JqChinaIndexKdataRecorder(level=level, sleeping_time=0, codes=codes, real_time=False).run()\n\n print(get_kdata(entity_id=\"index_sh_000001\", limit=10))\n\n\n# the __all__ is generated\n__all__ = [\"JqChinaIndexKdataRecorder\"]\n"
},
{
"path": "src/zvt/recorders/joinquant/quotes/jq_stock_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\nfrom jqdatapy.api import get_token, get_bars\n\nfrom zvt import zvt_config\nfrom zvt.api.kdata import generate_kdata_id, get_kdata_schema, get_kdata\nfrom zvt.contract import IntervalLevel, AdjustType, AdjustType\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Stock, StockKdataCommon, Stock1wkHfqKdata\nfrom zvt.recorders.joinquant.common import to_jq_trading_level, to_jq_entity_id\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_date_time_str, now_pd_timestamp, TIME_FORMAT_DAY, TIME_FORMAT_ISO8601\n\n\nclass JqChinaStockKdataRecorder(FixedCycleDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Stock\n\n # 数据来自jq\n provider = \"joinquant\"\n\n # 只是为了把recorder注册到data_schema\n data_schema = StockKdataCommon\n supported_levels = [\n level for level in IntervalLevel if level not in (IntervalLevel.LEVEL_L2_QUOTE, IntervalLevel.LEVEL_TICK)\n ]\n supported_adjust_types = [AdjustType.qfq, AdjustType.hfq]\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n adjust_type=AdjustType.qfq,\n return_unfinished=False,\n ) -> None:\n level = IntervalLevel(level)\n adjust_type = AdjustType(adjust_type)\n self.data_schema = get_kdata_schema(entity_type=\"stock\", level=level, adjust_type=adjust_type)\n self.jq_trading_level = to_jq_trading_level(level)\n\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n self.adjust_type = adjust_type\n\n get_token(zvt_config[\"jq_username\"], zvt_config[\"jq_password\"], force=True)\n\n def init_entities(self):\n super().init_entities()\n # 过滤掉退市的\n self.entities = [\n entity for entity in self.entities if (entity.end_date is None) or (entity.end_date > now_pd_timestamp())\n ]\n\n def generate_domain_id(self, entity, original_data):\n return generate_kdata_id(entity_id=entity.id, timestamp=original_data[\"timestamp\"], level=self.level)\n\n def recompute_qfq(self, entity, qfq_factor, last_timestamp):\n # 重新计算前复权数据\n if qfq_factor != 0:\n kdatas = get_kdata(\n provider=self.provider,\n entity_id=entity.id,\n level=self.level.value,\n order=self.data_schema.timestamp.asc(),\n return_type=\"domain\",\n session=self.session,\n filters=[self.data_schema.timestamp < last_timestamp],\n )\n if kdatas:\n self.logger.info(\"recomputing {} qfq kdata,factor is:{}\".format(entity.code, qfq_factor))\n for kdata in kdatas:\n kdata.open = round(kdata.open * qfq_factor, 2)\n kdata.close = round(kdata.close * qfq_factor, 2)\n kdata.high = round(kdata.high * qfq_factor, 2)\n kdata.low = round(kdata.low * qfq_factor, 2)\n self.session.add_all(kdatas)\n self.session.commit()\n\n def record(self, entity, start, end, size, timestamps):\n if self.adjust_type == AdjustType.hfq:\n fq_ref_date = \"2000-01-01\"\n else:\n fq_ref_date = to_date_time_str(now_pd_timestamp())\n\n if not self.end_timestamp:\n df = get_bars(\n to_jq_entity_id(entity),\n count=size,\n unit=self.jq_trading_level,\n # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'],\n fq_ref_date=fq_ref_date,\n )\n else:\n end_timestamp = to_date_time_str(self.end_timestamp)\n df = get_bars(\n to_jq_entity_id(entity),\n count=size,\n unit=self.jq_trading_level,\n # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'],\n end_date=end_timestamp,\n fq_ref_date=fq_ref_date,\n )\n if pd_is_not_null(df):\n df[\"name\"] = entity.name\n df.rename(columns={\"money\": \"turnover\", \"date\": \"timestamp\"}, inplace=True)\n\n df[\"entity_id\"] = entity.id\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"provider\"] = \"joinquant\"\n df[\"level\"] = self.level.value\n df[\"code\"] = entity.code\n\n # 判断是否需要重新计算之前保存的前复权数据\n if self.adjust_type == AdjustType.qfq:\n check_df = df.head(1)\n check_date = check_df[\"timestamp\"][0]\n current_df = get_kdata(\n entity_id=entity.id,\n provider=self.provider,\n start_timestamp=check_date,\n end_timestamp=check_date,\n limit=1,\n level=self.level,\n adjust_type=self.adjust_type,\n )\n if pd_is_not_null(current_df):\n old = current_df.iloc[0, :][\"close\"]\n new = check_df[\"close\"][0]\n # 相同时间的close不同，表明前复权需要重新计算\n if round(old, 2) != round(new, 2):\n qfq_factor = new / old\n last_timestamp = pd.Timestamp(check_date)\n self.recompute_qfq(entity, qfq_factor=qfq_factor, last_timestamp=last_timestamp)\n\n def generate_kdata_id(se):\n if self.level >= IntervalLevel.LEVEL_1DAY:\n return \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_DAY))\n else:\n return \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"], fmt=TIME_FORMAT_ISO8601))\n\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(generate_kdata_id, axis=1)\n\n df = df.drop_duplicates(subset=\"id\", keep=\"last\")\n\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n return None\n\n\nif __name__ == \"__main__\":\n Stock1wkHfqKdata.record_data(codes=[\"300999\"])\n\n\n# the __all__ is generated\n__all__ = [\"JqChinaStockKdataRecorder\"]\n"
},
{
"path": "src/zvt/recorders/jqka/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule emotion\nfrom .emotion import *\nfrom .emotion import __all__ as _emotion_all\n\n__all__ += _emotion_all\n\n# import all from submodule jqka_api\nfrom .jqka_api import *\nfrom .jqka_api import __all__ as _jqka_api_all\n\n__all__ += _jqka_api_all\n"
},
{
"path": "src/zvt/recorders/jqka/emotion/JqkaEmotionRecorder.py",
"content": "# -*- coding: utf-8 -*-\nimport re\nfrom typing import List\n\nimport pandas as pd\n\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import TimestampsDataRecorder\nfrom zvt.domain import Stock\nfrom zvt.domain.emotion.emotion import LimitUpInfo, LimitDownInfo, Emotion\nfrom zvt.recorders.jqka import jqka_api\nfrom zvt.utils.time_utils import to_date_time_str, date_time_by_interval, current_date, to_pd_timestamp\n\n\ndef _get_high_days_count(high_days_str: str):\n if not high_days_str or (high_days_str == \"首板\"):\n return 1\n pattern = r\"\\d+\"\n result = re.findall(pattern, high_days_str)\n return int(result[-1])\n\n\nclass JqkaLimitUpRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"jqka\"\n data_schema = LimitUpInfo\n\n def init_entities(self):\n # fake entity to for trigger run\n self.entities = [Stock(id=\"stock_sz_000001\")]\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n latest_infos = LimitUpInfo.query_data(\n provider=self.provider, order=LimitUpInfo.timestamp.desc(), limit=1, return_type=\"domain\"\n )\n if latest_infos and not self.force_update:\n start_date = latest_infos[0].timestamp\n else:\n # 最近一年的数据\n start_date = date_time_by_interval(current_date(), -360)\n return pd.date_range(start=start_date, end=pd.Timestamp.now(), freq=\"B\").tolist()\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n self.logger.info(f\"record {self.data_schema} to {the_date}\")\n limit_ups = jqka_api.get_limit_up(date=the_date)\n if limit_ups:\n records = []\n for data in limit_ups:\n entity_id = china_stock_code_to_id(code=data[\"code\"])\n\n first_limit_up_time = current_date()\n try:\n first_limit_up_time = pd.Timestamp.fromtimestamp(int(data[\"first_limit_up_time\"]))\n except Exception:\n pass\n\n last_limit_up_time = current_date()\n try:\n last_limit_up_time = pd.Timestamp.fromtimestamp(int(data[\"last_limit_up_time\"]))\n except Exception:\n pass\n record = {\n \"id\": \"{}_{}\".format(entity_id, the_date),\n \"entity_id\": entity_id,\n \"timestamp\": to_pd_timestamp(the_date),\n \"code\": data[\"code\"],\n \"name\": data[\"name\"],\n \"is_new\": data[\"is_new\"],\n \"is_again_limit\": data[\"is_again_limit\"],\n \"open_count\": data[\"open_num\"] if data[\"open_num\"] else 0,\n \"first_limit_up_time\": first_limit_up_time,\n \"last_limit_up_time\": last_limit_up_time,\n \"limit_up_type\": data[\"limit_up_type\"],\n \"order_amount\": data[\"order_amount\"],\n \"success_rate\": data[\"limit_up_suc_rate\"],\n \"currency_value\": data[\"currency_value\"],\n \"change_pct\": data[\"change_rate\"] / 100,\n \"turnover_rate\": data[\"turnover_rate\"] / 100,\n \"reason\": data[\"reason_type\"],\n \"high_days\": data[\"high_days\"],\n \"high_days_count\": _get_high_days_count(data[\"high_days\"]),\n }\n records.append(record)\n df = pd.DataFrame.from_records(records)\n df_to_db(\n data_schema=self.data_schema,\n df=df,\n provider=self.provider,\n force_update=True,\n drop_duplicates=True,\n )\n\n\nclass JqkaLimitDownRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"jqka\"\n data_schema = LimitDownInfo\n\n def init_entities(self):\n # fake entity to for trigger run\n self.entities = [Stock(id=\"stock_sz_000001\")]\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n latest_infos = LimitDownInfo.query_data(\n provider=self.provider, order=LimitDownInfo.timestamp.desc(), limit=1, return_type=\"domain\"\n )\n if latest_infos and not self.force_update:\n start_date = latest_infos[0].timestamp\n else:\n # 最近一年的数据\n start_date = date_time_by_interval(current_date(), -360)\n return pd.date_range(start=start_date, end=pd.Timestamp.now(), freq=\"B\").tolist()\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n self.logger.info(f\"record {self.data_schema} to {the_date}\")\n limit_downs = jqka_api.get_limit_down(date=the_date)\n if limit_downs:\n records = []\n for idx, data in enumerate(limit_downs):\n entity_id = china_stock_code_to_id(code=data[\"code\"])\n record = {\n \"id\": \"{}_{}\".format(entity_id, the_date),\n \"entity_id\": entity_id,\n \"timestamp\": to_pd_timestamp(the_date),\n \"code\": data[\"code\"],\n \"name\": data[\"name\"],\n \"is_new\": data[\"is_new\"],\n \"is_again_limit\": data[\"is_again_limit\"],\n \"currency_value\": data[\"currency_value\"],\n \"change_pct\": data[\"change_rate\"] / 100,\n \"turnover_rate\": data[\"turnover_rate\"] / 100,\n }\n records.append(record)\n df = pd.DataFrame.from_records(records)\n df_to_db(\n data_schema=self.data_schema,\n df=df,\n provider=self.provider,\n force_update=True,\n drop_duplicates=True,\n )\n\n\ndef _cal_power_and_max_height(continuous_limit_up: dict):\n max_height = 0\n power = 0\n for item in continuous_limit_up:\n if max_height < item[\"height\"]:\n max_height = item[\"height\"]\n power = power + item[\"height\"] * item[\"number\"]\n return max_height, power\n\n\nclass JqkaEmotionRecorder(TimestampsDataRecorder):\n entity_provider = \"em\"\n entity_schema = Stock\n\n provider = \"jqka\"\n data_schema = Emotion\n\n def init_entities(self):\n # fake entity to for trigger run\n self.entities = [Stock(id=\"stock_sz_000001\")]\n\n def init_timestamps(self, entity_item) -> List[pd.Timestamp]:\n latest_infos = Emotion.query_data(\n provider=self.provider, order=Emotion.timestamp.desc(), limit=1, return_type=\"domain\"\n )\n if latest_infos and not self.force_update:\n start_date = latest_infos[0].timestamp\n else:\n # 最近一年的数据\n start_date = date_time_by_interval(current_date(), -365)\n return pd.date_range(start=start_date, end=pd.Timestamp.now(), freq=\"B\").tolist()\n\n def record(self, entity, start, end, size, timestamps):\n for timestamp in timestamps:\n the_date = to_date_time_str(timestamp)\n self.logger.info(f\"record {self.data_schema} to {the_date}\")\n limit_stats = jqka_api.get_limit_stats(date=the_date)\n continuous_limit_up = jqka_api.get_continuous_limit_up(date=the_date)\n max_height, continuous_power = _cal_power_and_max_height(continuous_limit_up=continuous_limit_up)\n\n if limit_stats:\n # 大盘\n entity_id = \"stock_sh_000001\"\n record = {\n \"id\": \"{}_{}\".format(entity_id, the_date),\n \"entity_id\": entity_id,\n \"timestamp\": to_pd_timestamp(the_date),\n \"limit_up_count\": limit_stats[\"limit_up_count\"][\"today\"][\"num\"],\n \"limit_up_open_count\": limit_stats[\"limit_up_count\"][\"today\"][\"open_num\"],\n \"limit_up_success_rate\": limit_stats[\"limit_up_count\"][\"today\"][\"rate\"],\n \"limit_down_count\": limit_stats[\"limit_down_count\"][\"today\"][\"num\"],\n \"limit_down_open_count\": limit_stats[\"limit_down_count\"][\"today\"][\"open_num\"],\n \"limit_down_success_rate\": limit_stats[\"limit_down_count\"][\"today\"][\"rate\"],\n \"max_height\": max_height,\n \"continuous_power\": continuous_power,\n }\n df = pd.DataFrame.from_records([record])\n df_to_db(\n data_schema=self.data_schema,\n df=df,\n provider=self.provider,\n force_update=True,\n drop_duplicates=True,\n )\n\n\nif __name__ == \"__main__\":\n # JqkaLimitDownRecorder().run()\n LimitDownInfo.record_data(start_timestamp=\"2024-02-02\", end_timestamp=\"2024-02-16\", force_update=True)\n\n\n# the __all__ is generated\n__all__ = [\"JqkaLimitUpRecorder\", \"JqkaLimitDownRecorder\", \"JqkaEmotionRecorder\"]\n"
},
{
"path": "src/zvt/recorders/jqka/emotion/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule JqkaEmotionRecorder\nfrom .JqkaEmotionRecorder import *\nfrom .JqkaEmotionRecorder import __all__ as _JqkaEmotionRecorder_all\n\n__all__ += _JqkaEmotionRecorder_all\n"
},
{
"path": "src/zvt/recorders/jqka/jqka_api.py",
"content": "# -*- coding: utf-8 -*-\n\nimport requests\n\nfrom zvt.utils.time_utils import now_timestamp_ms, to_date_time_str, TIME_FORMAT_DAY1\nfrom zvt.utils.utils import chrome_copy_header_to_dict\n\n_JKQA_HEADER = chrome_copy_header_to_dict(\n \"\"\"\nAccept: application/json, text/plain, */*\nAccept-Encoding: gzip, deflate, br\nAccept-Language: zh-CN,zh;q=0.9,en;q=0.8\nConnection: keep-alive\nHost: data.10jqka.com.cn\nReferer: https://data.10jqka.com.cn/datacenterph/limitup/limtupInfo.html?fontzoom=no&client_userid=cA2fp&share_hxapp=gsc&share_action=webpage_share.1&back_source=wxhy\nsec-ch-ua: \"Not_A Brand\";v=\"99\", \"Google Chrome\";v=\"109\", \"Chromium\";v=\"109\"\nsec-ch-ua-mobile: ?1\nsec-ch-ua-platform: \"Android\"\nSec-Fetch-Dest: empty\nSec-Fetch-Mode: cors\nSec-Fetch-Site: same-origin\nUser-Agent: Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Mobile Safari/537.36\n\"\"\"\n)\n\n\ndef get_continuous_limit_up(date: str):\n date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1)\n url = f\"https://data.10jqka.com.cn/dataapi/limit_up/continuous_limit_up?filter=HS,GEM2STAR&date={date_str}\"\n resp = requests.get(url, headers=_JKQA_HEADER)\n if resp.status_code == 200:\n json_result = resp.json()\n if json_result:\n return json_result[\"data\"]\n raise RuntimeError(f\"request jkqa data code: {resp.status_code}, error: {resp.text}\")\n\n\ndef get_limit_stats(date: str):\n date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1)\n url = f\"https://data.10jqka.com.cn/dataapi/limit_up/limit_up_pool?page=1&limit=1&field=199112,10,9001,330323,330324,330325,9002,330329,133971,133970,1968584,3475914,9003,9004&filter=HS,GEM2STAR&date={date_str}&order_field=330324&order_type=0&_={now_timestamp_ms()}\"\n resp = requests.get(url, headers=_JKQA_HEADER)\n if resp.status_code == 200:\n json_result = resp.json()\n if json_result:\n return {\n \"limit_up_count\": json_result[\"data\"][\"limit_up_count\"],\n \"limit_down_count\": json_result[\"data\"][\"limit_down_count\"],\n }\n raise RuntimeError(f\"request jkqa data code: {resp.status_code}, error: {resp.text}\")\n\n\ndef get_limit_up(date: str):\n date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1)\n url = f\"https://data.10jqka.com.cn/dataapi/limit_up/limit_up_pool?field=199112,10,9001,330323,330324,330325,9002,330329,133971,133970,1968584,3475914,9003,9004&filter=HS,GEM2STAR&order_field=199112&order_type=0&date={date_str}\"\n return get_jkqa_data(url=url)\n\n\ndef get_limit_down(date: str):\n date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1)\n url = f\"https://data.10jqka.com.cn/dataapi/limit_up/lower_limit_pool?field=199112,10,9001,330323,330324,330325,9002,330329,133971,133970,1968584,3475914,9003,9004&filter=HS,GEM2STAR&order_field=199112&order_type=0&date={date_str}\"\n return get_jkqa_data(url=url)\n\n\ndef get_jkqa_data(url, pn=1, ps=200, fetch_all=True, headers=_JKQA_HEADER):\n requesting_url = url + f\"&page={pn}&limit={ps}&_={now_timestamp_ms()}\"\n print(requesting_url)\n resp = requests.get(requesting_url, headers=headers)\n if resp.status_code == 200:\n json_result = resp.json()\n if json_result and json_result[\"data\"]:\n data: list = json_result[\"data\"][\"info\"]\n if fetch_all:\n if pn < json_result[\"data\"][\"page\"][\"count\"]:\n next_data = get_jkqa_data(\n pn=pn + 1,\n ps=ps,\n url=url,\n fetch_all=fetch_all,\n )\n if next_data:\n data = data + next_data\n if pn == 1 and len(data) != json_result[\"data\"][\"page\"][\"total\"]:\n raise RuntimeError(\n f\"Assertion failed, the total length of data should be {json_result['data']['page']['total']}, only {len(data)} fetched\"\n )\n return data\n else:\n return data\n else:\n return data\n else:\n return data\n return None\n raise RuntimeError(f\"request jkqa data code: {resp.status_code}, error: {resp.text}\")\n\n\nif __name__ == \"__main__\":\n # result = get_limit_up(date=\"20210716\")\n # print(result)\n # result = get_limit_stats(date=\"20210716\")\n # print(result)\n # result = get_limit_down(date=\"20210716\")\n # print(result)\n result = get_continuous_limit_up(date=\"20210716\")\n print(result)\n\n\n# the __all__ is generated\n__all__ = [\"get_continuous_limit_up\", \"get_limit_stats\", \"get_limit_up\", \"get_limit_down\", \"get_jkqa_data\"]\n"
},
{
"path": "src/zvt/recorders/qmt/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule index\nfrom .index import *\nfrom .index import __all__ as _index_all\n\n__all__ += _index_all\n\n# import all from submodule quotes\nfrom .quotes import *\nfrom .quotes import __all__ as _quotes_all\n\n__all__ += _quotes_all\n\n# import all from submodule meta\nfrom .meta import *\nfrom .meta import __all__ as _meta_all\n\n__all__ += _meta_all\n"
},
{
"path": "src/zvt/recorders/qmt/index/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule qmt_index_recorder\nfrom .qmt_index_recorder import *\nfrom .qmt_index_recorder import __all__ as _qmt_index_recorder_all\n\n__all__ += _qmt_index_recorder_all\n"
},
{
"path": "src/zvt/recorders/qmt/index/qmt_index_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.api.kdata import get_kdata_schema\nfrom zvt.broker.qmt import qmt_quote\nfrom zvt.consts import IMPORTANT_INDEX\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.contract.utils import evaluate_size_from_timestamp\nfrom zvt.domain import Index, IndexKdataCommon\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import TIME_FORMAT_DAY, TIME_FORMAT_MINUTE, current_date, to_date_time_str\n\n\nclass QmtIndexRecorder(FixedCycleDataRecorder):\n provider = \"qmt\"\n # class level kdata schema should always use common\n data_schema = IndexKdataCommon\n entity_provider = \"em\"\n entity_schema = Index\n download_history_data = False\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n return_unfinished=False,\n download_history_data=False,\n ) -> None:\n level = IntervalLevel(level)\n self.entity_type = \"index\"\n self.download_history_data = download_history_data\n\n self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=None)\n\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n self.one_day_trading_minutes = 240\n\n def record(self, entity, start, end, size, timestamps):\n if start and (self.level == IntervalLevel.LEVEL_1DAY):\n start = start.date()\n if not start:\n start = \"2005-01-01\"\n if not end:\n end = current_date()\n\n # 统一高频数据习惯，减小数据更新次数，分钟K线需要直接多读1根K线，以兼容start_timestamp=9:30, end_timestamp=15:00的情况\n if self.level == IntervalLevel.LEVEL_1MIN:\n end += pd.Timedelta(seconds=1)\n\n df = qmt_quote.get_kdata(\n entity_id=entity.id,\n start_timestamp=start,\n end_timestamp=end,\n adjust_type=None,\n level=self.level,\n download_history=self.download_history_data,\n )\n time_str_fmt = TIME_FORMAT_DAY if self.level == IntervalLevel.LEVEL_1DAY else TIME_FORMAT_MINUTE\n if pd_is_not_null(df):\n df[\"entity_id\"] = entity.id\n df[\"timestamp\"] = pd.to_datetime(df.index)\n df[\"id\"] = df.apply(\n lambda row: f\"{row['entity_id']}_{to_date_time_str(row['timestamp'], fmt=time_str_fmt)}\", axis=1\n )\n df[\"provider\"] = \"qmt\"\n df[\"level\"] = self.level.value\n df[\"code\"] = entity.code\n df[\"name\"] = entity.name\n df.rename(columns={\"amount\": \"turnover\"}, inplace=True)\n df[\"change_pct\"] = (df[\"close\"] - df[\"preClose\"]) / df[\"preClose\"]\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n else:\n self.logger.info(f\"no kdata for {entity.id}\")\n\n def evaluate_start_end_size_timestamps(self, entity):\n if self.download_history_data and self.start_timestamp and self.end_timestamp:\n # 历史数据可能碎片化，允许按照实际start和end之间有没有写满数据\n expected_size = evaluate_size_from_timestamp(\n start_timestamp=self.start_timestamp,\n end_timestamp=self.end_timestamp,\n level=self.level,\n one_day_trading_minutes=self.one_day_trading_minutes,\n )\n\n recorded_size = (\n self.session.query(self.data_schema)\n .filter(\n self.data_schema.entity_id == entity.id,\n self.data_schema.timestamp >= self.start_timestamp,\n self.data_schema.timestamp <= self.end_timestamp,\n )\n .count()\n )\n\n if expected_size != recorded_size:\n # print(f\"expected_size: {expected_size}, recorded_size: {recorded_size}\")\n return self.start_timestamp, self.end_timestamp, self.default_size, None\n\n start_timestamp, end_timestamp, size, timestamps = super().evaluate_start_end_size_timestamps(entity)\n # start_timestamp is the last updated timestamp\n if self.end_timestamp is not None:\n if start_timestamp >= self.end_timestamp:\n return start_timestamp, end_timestamp, 0, None\n else:\n size = evaluate_size_from_timestamp(\n start_timestamp=start_timestamp,\n level=self.level,\n one_day_trading_minutes=self.one_day_trading_minutes,\n end_timestamp=self.end_timestamp,\n )\n return start_timestamp, self.end_timestamp, size, timestamps\n\n return start_timestamp, end_timestamp, size, timestamps\n\n # # 中证，上海\n # def record_cs_index(self, index_type):\n # df = cs_index_api.get_cs_index(index_type=index_type)\n # df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n # self.logger.info(f\"finish record {index_type} index\")\n #\n # # 国证，深圳\n # def record_cn_index(self, index_type):\n # if index_type == \"cni\":\n # category_map_url = cn_index_api.cni_category_map_url\n # elif index_type == \"sz\":\n # category_map_url = cn_index_api.sz_category_map_url\n # else:\n # self.logger.error(f\"not support index_type: {index_type}\")\n # assert False\n #\n # for category, _ in category_map_url.items():\n # df = cn_index_api.get_cn_index(index_type=index_type, category=category)\n # df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n # self.logger.info(f\"finish record {index_type} index:{category.value}\")\n\n\nif __name__ == \"__main__\":\n # init_log('china_stock_category.log')\n start_timestamp = pd.Timestamp(\"2024-12-01\")\n end_timestamp = pd.Timestamp(\"2024-12-03\")\n QmtIndexRecorder(\n codes=IMPORTANT_INDEX,\n level=IntervalLevel.LEVEL_1MIN,\n sleeping_time=0,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n download_history_data=True,\n ).run()\n\n\n# the __all__ is generated\n__all__ = [\"QmtIndexRecorder\"]\n"
},
{
"path": "src/zvt/recorders/qmt/meta/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule qmt_stock_meta_recorder\nfrom .qmt_stock_meta_recorder import *\nfrom .qmt_stock_meta_recorder import __all__ as _qmt_stock_meta_recorder_all\n\n__all__ += _qmt_stock_meta_recorder_all\n"
},
{
"path": "src/zvt/recorders/qmt/meta/qmt_stock_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.broker.qmt import qmt_quote\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain import Stock\n\n\nclass QMTStockRecorder(Recorder):\n provider = \"qmt\"\n data_schema = Stock\n\n def run(self):\n df = qmt_quote.get_entity_list()\n self.logger.info(df.tail())\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=True)\n\n\nif __name__ == \"__main__\":\n recorder = QMTStockRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"QMTStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/qmt/quotes/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule qmt_kdata_recorder\nfrom .qmt_kdata_recorder import *\nfrom .qmt_kdata_recorder import __all__ as _qmt_kdata_recorder_all\n\n__all__ += _qmt_kdata_recorder_all\n"
},
{
"path": "src/zvt/recorders/qmt/quotes/qmt_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.api.kdata import get_kdata_schema, get_kdata\nfrom zvt.broker.qmt import qmt_quote\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract.api import df_to_db, get_db_session, get_entities\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import (\n Stock,\n StockKdataCommon,\n)\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import current_date, to_date_time_str, now_date_time_str\n\n\nclass BaseQmtKdataRecorder(FixedCycleDataRecorder):\n default_size = 50000\n entity_provider: str = \"qmt\"\n\n provider = \"qmt\"\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n adjust_type=AdjustType.qfq,\n return_unfinished=False,\n ) -> None:\n level = IntervalLevel(level)\n self.adjust_type = AdjustType(adjust_type)\n self.entity_type = self.entity_schema.__name__.lower()\n\n self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=self.adjust_type)\n\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n def init_entities(self):\n \"\"\"\n init the entities which we would record data for\n\n \"\"\"\n if self.entity_provider == self.provider and self.entity_schema == self.data_schema:\n self.entity_session = self.session\n else:\n self.entity_session = get_db_session(provider=self.entity_provider, data_schema=self.entity_schema)\n\n if self.day_data:\n df = self.data_schema.query_data(\n start_timestamp=now_date_time_str(), columns=[\"entity_id\", \"timestamp\"], provider=self.provider\n )\n if pd_is_not_null(df):\n entity_ids = df[\"entity_id\"].tolist()\n self.logger.info(f\"ignore entity_ids:{entity_ids}\")\n if self.entity_filters:\n self.entity_filters.append(self.entity_schema.entity_id.notin_(entity_ids))\n else:\n self.entity_filters = [self.entity_schema.entity_id.notin_(entity_ids)]\n\n #: init the entity list\n self.entities = get_entities(\n session=self.entity_session,\n entity_schema=self.entity_schema,\n exchanges=self.exchanges,\n entity_ids=self.entity_ids,\n codes=self.codes,\n return_type=\"domain\",\n provider=self.entity_provider,\n filters=self.entity_filters,\n )\n\n def record(self, entity, start, end, size, timestamps):\n if start and (self.level == IntervalLevel.LEVEL_1DAY):\n start = start.date()\n\n # 判断是否需要重新计算之前保存的前复权数据\n if start and (self.adjust_type == AdjustType.qfq):\n check_df = qmt_quote.get_kdata(\n entity_id=entity.id,\n start_timestamp=start,\n end_timestamp=start,\n adjust_type=self.adjust_type,\n level=self.level,\n download_history=False,\n )\n if pd_is_not_null(check_df):\n current_df = get_kdata(\n entity_id=entity.id,\n provider=self.provider,\n start_timestamp=start,\n end_timestamp=start,\n limit=1,\n level=self.level,\n adjust_type=self.adjust_type,\n )\n if pd_is_not_null(current_df):\n old = current_df[\"close\"].iloc[0]\n new = check_df[\"close\"].iloc[0]\n # 相同时间的close不同，表明前复权需要重新计算\n if round(old, 2) != round(new, 2):\n # 删掉重新获取\n self.session.query(self.data_schema).filter(self.data_schema.entity_id == entity.id).delete()\n start = \"2005-01-01\"\n else:\n self.logger.warning(\"前复权检查失败，无法获取存储的最新数据\")\n\n if not start:\n start = \"2005-01-01\"\n if not end:\n end = current_date()\n\n df = qmt_quote.get_kdata(\n entity_id=entity.id,\n start_timestamp=start,\n end_timestamp=end,\n adjust_type=self.adjust_type,\n level=self.level,\n download_history=False,\n )\n if pd_is_not_null(df):\n df[\"entity_id\"] = entity.id\n df[\"timestamp\"] = pd.to_datetime(df.index)\n df[\"id\"] = df.apply(lambda row: f\"{row['entity_id']}_{to_date_time_str(row['timestamp'])}\", axis=1)\n df[\"provider\"] = \"qmt\"\n df[\"level\"] = self.level.value\n df[\"code\"] = entity.code\n df[\"name\"] = entity.name\n df.rename(columns={\"amount\": \"turnover\"}, inplace=True)\n df[\"change_pct\"] = (df[\"close\"] - df[\"preClose\"]) / df[\"preClose\"]\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n else:\n self.logger.info(f\"no kdata for {entity.id}\")\n\n\nclass QMTStockKdataRecorder(BaseQmtKdataRecorder):\n entity_schema = Stock\n data_schema = StockKdataCommon\n supported_levels = [IntervalLevel.LEVEL_1DAY]\n supported_adjust_types = [AdjustType.qfq, AdjustType.hfq]\n\n\nif __name__ == \"__main__\":\n # Stock.record_data(provider=\"qmt\")\n QMTStockKdataRecorder(entity_id=\"stock_sz_301611\", adjust_type=AdjustType.qfq).run()\n\n\n# the __all__ is generated\n__all__ = [\"BaseQmtKdataRecorder\", \"QMTStockKdataRecorder\"]\n"
},
{
"path": "src/zvt/recorders/sina/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule money_flow\nfrom .money_flow import *\nfrom .money_flow import __all__ as _money_flow_all\n\n__all__ += _money_flow_all\n\n# import all from submodule quotes\nfrom .quotes import *\nfrom .quotes import __all__ as _quotes_all\n\n__all__ += _quotes_all\n\n# import all from submodule meta\nfrom .meta import *\nfrom .meta import __all__ as _meta_all\n\n__all__ += _meta_all\n"
},
{
"path": "src/zvt/recorders/sina/meta/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule sina_block_recorder\nfrom .sina_block_recorder import *\nfrom .sina_block_recorder import __all__ as _sina_block_recorder_all\n\n__all__ += _sina_block_recorder_all\n"
},
{
"path": "src/zvt/recorders/sina/meta/sina_block_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport json\n\nimport demjson3\nimport pandas as pd\nimport requests\n\nfrom zvt.api.utils import china_stock_code_to_id\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder, TimeSeriesDataRecorder\nfrom zvt.domain import BlockStock, BlockCategory, Block\nfrom zvt.utils.time_utils import now_pd_timestamp\n\n\nclass SinaBlockRecorder(Recorder):\n provider = \"sina\"\n data_schema = Block\n\n # 用于抓取行业/概念/地域列表\n category_map_url = {\n BlockCategory.industry: \"http://vip.stock.finance.sina.com.cn/q/view/newSinaHy.php\",\n BlockCategory.concept: \"http://money.finance.sina.com.cn/q/view/newFLJK.php?param=class\"\n # StockCategory.area: 'http://money.finance.sina.com.cn/q/view/newFLJK.php?param=area',\n }\n\n def run(self):\n # get stock blocks from sina\n for category, url in self.category_map_url.items():\n resp = requests.get(url)\n resp.encoding = \"GBK\"\n\n tmp_str = resp.text\n json_str = tmp_str[tmp_str.index(\"{\") : tmp_str.index(\"}\") + 1]\n tmp_json = json.loads(json_str)\n\n the_list = []\n\n for code in tmp_json:\n name = tmp_json[code].split(\",\")[1]\n entity_id = f\"block_cn_{code}\"\n the_list.append(\n {\n \"id\": entity_id,\n \"entity_id\": entity_id,\n \"entity_type\": \"block\",\n \"exchange\": \"cn\",\n \"code\": code,\n \"name\": name,\n \"category\": category.value,\n }\n )\n if the_list:\n df = pd.DataFrame.from_records(the_list)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n\n self.logger.info(f\"finish record sina blocks:{category.value}\")\n\n\nclass SinaChinaBlockStockRecorder(TimeSeriesDataRecorder):\n entity_provider = \"sina\"\n entity_schema = Block\n\n provider = \"sina\"\n data_schema = BlockStock\n\n # 用于抓取行业包含的股票\n category_stocks_url = \"http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData?page={}&num=5000&sort=symbol&asc=1&node={}&symbol=&_s_r_a=page\"\n\n def record(self, entity, start, end, size, timestamps):\n for page in range(1, 5):\n resp = requests.get(self.category_stocks_url.format(page, entity.code))\n try:\n if resp.text == \"null\" or resp.text is None:\n break\n category_jsons = demjson3.decode(resp.text)\n the_list = []\n for category in category_jsons:\n stock_code = category[\"code\"]\n stock_id = china_stock_code_to_id(stock_code)\n block_id = entity.id\n the_list.append(\n {\n \"id\": \"{}_{}\".format(block_id, stock_id),\n \"entity_id\": block_id,\n \"entity_type\": \"block\",\n \"exchange\": entity.exchange,\n \"code\": entity.code,\n \"name\": entity.name,\n \"timestamp\": now_pd_timestamp(),\n \"stock_id\": stock_id,\n \"stock_code\": stock_code,\n \"stock_name\": category[\"name\"],\n }\n )\n if the_list:\n df = pd.DataFrame.from_records(the_list)\n df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True)\n\n self.logger.info(\"finish recording BlockStock:{},{}\".format(entity.category, entity.name))\n\n except Exception as e:\n self.logger.error(\"error:,resp.text:\", e, resp.text)\n self.sleep()\n\n\nif __name__ == \"__main__\":\n # init_log('sina_china_stock_category.log')\n SinaBlockRecorder().run()\n recorder = SinaChinaBlockStockRecorder(codes=[\"new_cbzz\"])\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"SinaBlockRecorder\", \"SinaChinaBlockStockRecorder\"]\n"
},
{
"path": "src/zvt/recorders/sina/money_flow/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule sina_stock_money_flow_recorder\nfrom .sina_stock_money_flow_recorder import *\nfrom .sina_stock_money_flow_recorder import __all__ as _sina_stock_money_flow_recorder_all\n\n__all__ += _sina_stock_money_flow_recorder_all\n\n# import all from submodule sina_block_money_flow_recorder\nfrom .sina_block_money_flow_recorder import *\nfrom .sina_block_money_flow_recorder import __all__ as _sina_block_money_flow_recorder_all\n\n__all__ += _sina_block_money_flow_recorder_all\n"
},
{
"path": "src/zvt/recorders/sina/money_flow/sina_block_money_flow_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport time\n\nimport requests\n\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import BlockMoneyFlow, BlockCategory, Block\nfrom zvt.utils.time_utils import to_pd_timestamp\nfrom zvt.utils.utils import to_float\n\n\n# 实时资金流\n# 'http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_bkzj_bk?page=1&num=20&sort=netamount&asc=0&fenlei=1'\n# 'http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_bkzj_bk?page=1&num=20&sort=netamount&asc=0&fenlei=0'\n\n\nclass SinaBlockMoneyFlowRecorder(FixedCycleDataRecorder):\n # entity的信息从哪里来\n entity_provider = \"sina\"\n # entity的schema\n entity_schema = Block\n\n # 记录的信息从哪里来\n provider = \"sina\"\n # 记录的schema\n data_schema = BlockMoneyFlow\n\n url = \"http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_bkzj_zjlrqs?page=1&num={}&sort=opendate&asc=0&bankuai={}%2F{}\"\n\n def generate_url(self, category, code, number):\n if category == BlockCategory.industry.value:\n block = 0\n elif category == BlockCategory.concept.value:\n block = 1\n\n return self.url.format(number, block, code)\n\n def get_data_map(self):\n return {}\n\n def record(self, entity, start, end, size, timestamps):\n url = self.generate_url(category=entity.category, code=entity.code, number=size)\n\n resp = requests.get(url)\n\n opendate = \"opendate\"\n avg_price = \"avg_price\"\n avg_changeratio = \"avg_changeratio\"\n turnover = \"turnover\"\n netamount = \"netamount\"\n ratioamount = \"ratioamount\"\n r0_net = \"r0_net\"\n r0_ratio = \"r0_ratio\"\n r0x_ratio = \"r0x_ratio\"\n cnt_r0x_ratio = \"cnt_r0x_ratio\"\n\n json_list = []\n try:\n json_list = eval(resp.text)\n except Exception as e:\n resp.encoding = \"GBK\"\n self.logger.error(resp.text)\n time.sleep(60 * 5)\n\n result_list = []\n for item in json_list:\n result_list.append(\n {\n \"name\": entity.name,\n \"timestamp\": to_pd_timestamp(item[\"opendate\"]),\n \"close\": to_float(item[\"avg_price\"]),\n \"change_pct\": to_float(item[\"avg_changeratio\"]),\n \"turnover_rate\": to_float(item[\"turnover\"]) / 10000,\n \"net_inflows\": to_float(item[\"netamount\"]),\n \"net_inflow_rate\": to_float(item[\"ratioamount\"]),\n \"net_main_inflows\": to_float(item[\"r0_net\"]),\n \"net_main_inflow_rate\": to_float(item[\"r0_ratio\"]),\n }\n )\n\n return result_list\n\n\nif __name__ == \"__main__\":\n SinaBlockMoneyFlowRecorder(codes=[\"new_fjzz\"]).run()\n # SinaIndexMoneyFlowRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"SinaBlockMoneyFlowRecorder\"]\n"
},
{
"path": "src/zvt/recorders/sina/money_flow/sina_stock_money_flow_recorder.py",
"content": "# -*- coding: utf-8 -*-\nimport time\n\nimport requests\n\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import StockMoneyFlow, Stock, StockTradeDay\nfrom zvt.utils.time_utils import to_pd_timestamp, is_same_date, now_pd_timestamp\nfrom zvt.utils.utils import to_float\n\n\nclass SinaStockMoneyFlowRecorder(FixedCycleDataRecorder):\n entity_provider = \"joinquant\"\n entity_schema = Stock\n\n provider = \"sina\"\n data_schema = StockMoneyFlow\n\n url = \"http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_qsfx_lscjfb?page=1&num={}&sort=opendate&asc=0&daima={}\"\n\n def init_entities(self):\n super().init_entities()\n # 过滤掉退市的\n self.entities = [\n entity for entity in self.entities if (entity.end_date is None) or (entity.end_date > now_pd_timestamp())\n ]\n\n # TODO:more general for the case using StockTradeDay\n def evaluate_start_end_size_timestamps(self, entity):\n start, end, size, timestamps = super().evaluate_start_end_size_timestamps(entity)\n if start:\n trade_day = StockTradeDay.query_data(limit=1, order=StockTradeDay.timestamp.desc(), return_type=\"domain\")\n if trade_day:\n if is_same_date(trade_day[0].timestamp, start):\n size = 0\n return start, end, size, timestamps\n\n def generate_url(self, code, number):\n return self.url.format(number, code)\n\n def get_data_map(self):\n return {}\n\n def record(self, entity, start, end, size, timestamps):\n param = {\n \"url\": self.generate_url(code=\"{}{}\".format(entity.exchange, entity.code), number=size),\n \"security_item\": entity,\n }\n\n resp = requests.get(param[\"url\"])\n # {opendate:\"2019-04-29\",trade:\"10.8700\",changeratio:\"-0.0431338\",turnover:\"74.924\",netamount:\"-2903349.8500\",\n # ratioamount:\"-0.155177\",r0:\"0.0000\",r1:\"2064153.0000\",r2:\"6485031.0000\",r3:\"10622169.2100\",r0_net:\"0.0000\",\n # r1_net:\"2064153.0000\",r2_net:\"-1463770.0000\",r3_net:\"-3503732.8500\"}\n opendate = \"opendate\"\n trade = \"trade\"\n changeratio = \"changeratio\"\n turnover = \"turnover\"\n netamount = \"netamount\"\n ratioamount = \"ratioamount\"\n r0 = \"r0\"\n r1 = \"r1\"\n r2 = \"r2\"\n r3 = \"r3\"\n r0_net = \"r0_net\"\n r1_net = \"r1_net\"\n r2_net = \"r2_net\"\n r3_net = \"r3_net\"\n\n json_list = []\n\n try:\n json_list = eval(resp.text)\n except Exception as e:\n resp.encoding = \"GBK\"\n self.logger.error(resp.text)\n time.sleep(60 * 5)\n\n result_list = []\n for item in json_list:\n amount = to_float(item[\"r0\"]) + to_float(item[\"r1\"]) + to_float(item[\"r2\"]) + to_float(item[\"r3\"])\n\n result = {\n \"timestamp\": to_pd_timestamp(item[\"opendate\"]),\n \"name\": entity.name,\n \"close\": to_float(item[\"trade\"]),\n \"change_pct\": to_float(item[\"changeratio\"]),\n \"turnover_rate\": to_float(item[\"turnover\"]) / 10000,\n \"net_inflows\": to_float(item[\"netamount\"]),\n \"net_inflow_rate\": to_float(item[\"ratioamount\"]),\n # # 主力=超大单+大单\n # net_main_inflows = Column(Float)\n # net_main_inflow_rate = Column(Float)\n # # 超大单\n # net_huge_inflows = Column(Float)\n # net_huge_inflow_rate = Column(Float)\n # # 大单\n # net_big_inflows = Column(Float)\n # net_big_inflow_rate = Column(Float)\n #\n # # 中单\n # net_medium_inflows = Column(Float)\n # net_medium_inflow_rate = Column(Float)\n # # 小单\n # net_small_inflows = Column(Float)\n # net_small_inflow_rate = Column(Float)\n \"net_main_inflows\": to_float(item[\"r0_net\"]) + to_float(item[\"r1_net\"]),\n \"net_huge_inflows\": to_float(item[\"r0_net\"]),\n \"net_big_inflows\": to_float(item[\"r1_net\"]),\n \"net_medium_inflows\": to_float(item[\"r2_net\"]),\n \"net_small_inflows\": to_float(item[\"r3_net\"]),\n }\n\n if amount != 0:\n result[\"net_main_inflow_rate\"] = (to_float(item[\"r0_net\"]) + to_float(item[\"r1_net\"])) / amount\n result[\"net_huge_inflow_rate\"] = to_float(item[\"r0_net\"]) / amount\n result[\"net_big_inflow_rate\"] = to_float(item[\"r1_net\"]) / amount\n result[\"net_medium_inflow_rate\"] = to_float(item[\"r2_net\"]) / amount\n result[\"net_small_inflow_rate\"] = to_float(item[\"r3_net\"]) / amount\n\n result_list.append(result)\n\n return result_list\n\n\nif __name__ == \"__main__\":\n SinaStockMoneyFlowRecorder(codes=[\"000406\"]).run()\n # SinaStockMoneyFlowRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"SinaStockMoneyFlowRecorder\"]\n"
},
{
"path": "src/zvt/recorders/sina/quotes/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule sina_index_kdata_recorder\nfrom .sina_index_kdata_recorder import *\nfrom .sina_index_kdata_recorder import __all__ as _sina_index_kdata_recorder_all\n\n__all__ += _sina_index_kdata_recorder_all\n\n# import all from submodule sina_etf_kdata_recorder\nfrom .sina_etf_kdata_recorder import *\nfrom .sina_etf_kdata_recorder import __all__ as _sina_etf_kdata_recorder_all\n\n__all__ += _sina_etf_kdata_recorder_all\n"
},
{
"path": "src/zvt/recorders/sina/quotes/sina_etf_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport demjson3\nimport pandas as pd\nimport requests\n\nfrom zvt import init_log\nfrom zvt.api.kdata import generate_kdata_id, get_kdata\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Etf, Etf1dKdata\nfrom zvt.recorders.consts import EASTMONEY_ETF_NET_VALUE_HEADER\nfrom zvt.utils.time_utils import to_date_time_str\n\n\nclass ChinaETFDayKdataRecorder(FixedCycleDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Etf\n\n provider = \"sina\"\n data_schema = Etf1dKdata\n url = (\n \"http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.getKLineData?\"\n \"symbol={}{}&scale=240&&datalen={}&ma=no\"\n )\n\n def get_data_map(self):\n return {}\n\n def generate_domain_id(self, entity, original_data):\n return generate_kdata_id(entity_id=entity.id, timestamp=original_data[\"timestamp\"], level=self.level)\n\n def on_finish_entity(self, entity):\n kdatas = get_kdata(\n entity_id=entity.id,\n level=IntervalLevel.LEVEL_1DAY.value,\n order=Etf1dKdata.timestamp.asc(),\n return_type=\"domain\",\n session=self.session,\n filters=[Etf1dKdata.cumulative_net_value.is_(None)],\n )\n\n if kdatas and len(kdatas) > 0:\n start = kdatas[0].timestamp\n end = kdatas[-1].timestamp\n\n # 从东方财富获取基金累计净值\n df = self.fetch_cumulative_net_value(entity, start, end)\n\n if df is not None and not df.empty:\n for kdata in kdatas:\n if kdata.timestamp in df.index:\n kdata.cumulative_net_value = df.loc[kdata.timestamp, \"LJJZ\"]\n kdata.change_pct = df.loc[kdata.timestamp, \"JZZZL\"]\n self.session.commit()\n self.logger.info(f\"{entity.code} - {entity.name}累计净值更新完成...\")\n\n def fetch_cumulative_net_value(self, security_item, start, end) -> pd.DataFrame:\n query_url = (\n \"http://api.fund.eastmoney.com/f10/lsjz?\" \"fundCode={}&pageIndex={}&pageSize=200&startDate={}&endDate={}\"\n )\n\n page = 1\n df = pd.DataFrame()\n while True:\n url = query_url.format(security_item.code, page, to_date_time_str(start), to_date_time_str(end))\n\n response = requests.get(url, headers=EASTMONEY_ETF_NET_VALUE_HEADER)\n response_json = demjson3.decode(response.text)\n response_df = pd.DataFrame(response_json[\"Data\"][\"LSJZList\"])\n\n # 最后一页\n if response_df.empty:\n break\n\n response_df[\"FSRQ\"] = pd.to_datetime(response_df[\"FSRQ\"])\n response_df[\"JZZZL\"] = pd.to_numeric(response_df[\"JZZZL\"], errors=\"coerce\")\n response_df[\"LJJZ\"] = pd.to_numeric(response_df[\"LJJZ\"], errors=\"coerce\")\n response_df = response_df.fillna(0)\n response_df.set_index(\"FSRQ\", inplace=True, drop=True)\n\n df = pd.concat([df, response_df])\n page += 1\n\n self.sleep()\n\n return df\n\n def record(self, entity, start, end, size, timestamps):\n # 此 url 不支持分页，如果超过我们想取的条数，则只能取最大条数\n if start is None or size > self.default_size:\n size = 8000\n\n param = {\"security_item\": entity, \"level\": self.level.value, \"size\": size}\n\n security_item = param[\"security_item\"]\n size = param[\"size\"]\n\n url = ChinaETFDayKdataRecorder.url.format(security_item.exchange, security_item.code, size)\n\n response = requests.get(url)\n response_json = demjson3.decode(response.text)\n\n if response_json is None or len(response_json) == 0:\n return []\n\n df = pd.DataFrame(response_json)\n df.rename(columns={\"day\": \"timestamp\"}, inplace=True)\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"name\"] = security_item.name\n df[\"provider\"] = \"sina\"\n df[\"level\"] = param[\"level\"]\n\n return df.to_dict(orient=\"records\")\n\n\n__all__ = [\"ChinaETFDayKdataRecorder\"]\n\nif __name__ == \"__main__\":\n init_log(\"sina_china_etf_day_kdata.log\")\n ChinaETFDayKdataRecorder(level=IntervalLevel.LEVEL_1DAY).run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaETFDayKdataRecorder\"]\n"
},
{
"path": "src/zvt/recorders/sina/quotes/sina_index_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nimport time\n\nimport pandas as pd\nimport requests\n\nfrom zvt.api.kdata import generate_kdata_id, get_kdata_schema\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Index, IndexKdataCommon\nfrom zvt.utils.time_utils import get_year_quarters, is_same_date\n\n\nclass ChinaIndexDayKdataRecorder(FixedCycleDataRecorder):\n entity_provider = \"exchange\"\n entity_schema = Index\n\n provider = \"sina\"\n data_schema = IndexKdataCommon\n url = \"http://vip.stock.finance.sina.com.cn/corp/go.php/vMS_MarketHistory/stockid/{}/type/S.phtml?year={}&jidu={}\"\n\n def __init__(\n self,\n force_update=True,\n sleeping_time=10,\n exchanges=None,\n entity_id=None,\n entity_ids=None,\n code=None,\n codes=None,\n day_data=False,\n entity_filters=None,\n ignore_failed=True,\n real_time=False,\n fix_duplicate_way=\"ignore\",\n start_timestamp=None,\n end_timestamp=None,\n level=IntervalLevel.LEVEL_1DAY,\n kdata_use_begin_time=False,\n one_day_trading_minutes=24 * 60,\n return_unfinished=False,\n ) -> None:\n level = IntervalLevel(level)\n self.adjust_type = AdjustType.qfq\n self.entity_type = self.entity_schema.__name__.lower()\n\n self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=self.adjust_type)\n\n super().__init__(\n force_update,\n sleeping_time,\n exchanges,\n entity_id,\n entity_ids,\n code,\n codes,\n day_data,\n entity_filters,\n ignore_failed,\n real_time,\n fix_duplicate_way,\n start_timestamp,\n end_timestamp,\n level,\n kdata_use_begin_time,\n one_day_trading_minutes,\n return_unfinished,\n )\n\n def get_data_map(self):\n return {}\n\n def generate_domain_id(self, entity, original_data):\n return generate_kdata_id(entity.id, timestamp=original_data[\"timestamp\"], level=self.level)\n\n def record(self, entity, start, end, size, timestamps):\n the_quarters = get_year_quarters(start)\n if not is_same_date(entity.timestamp, start) and len(the_quarters) > 1:\n the_quarters = the_quarters[1:]\n\n param = {\"security_item\": entity, \"quarters\": the_quarters, \"level\": self.level.value}\n\n security_item = param[\"security_item\"]\n quarters = param[\"quarters\"]\n level = param[\"level\"]\n\n result_df = pd.DataFrame()\n for year, quarter in quarters:\n query_url = self.url.format(security_item.code, year, quarter)\n response = requests.get(query_url)\n response.encoding = \"gbk\"\n\n try:\n dfs = pd.read_html(response.text)\n except ValueError as error:\n self.logger.error(f\"skip ({year}-{quarter:02d}){security_item.code}{security_item.name}({error})\")\n time.sleep(10.0)\n continue\n\n if len(dfs) < 5:\n time.sleep(10.0)\n continue\n\n df = dfs[4].copy()\n df = df.iloc[1:]\n df.columns = [\"timestamp\", \"open\", \"high\", \"close\", \"low\", \"volume\", \"turnover\"]\n df[\"name\"] = security_item.name\n df[\"level\"] = level\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n df[\"provider\"] = \"sina\"\n\n result_df = pd.concat([result_df, df])\n\n self.logger.info(f\"({security_item.code}{security_item.name})({year}-{quarter:02d})\")\n time.sleep(10.0)\n\n result_df = result_df.sort_values(by=\"timestamp\")\n\n return result_df.to_dict(orient=\"records\")\n\n\n__all__ = [\"ChinaIndexDayKdataRecorder\"]\n\nif __name__ == \"__main__\":\n ChinaIndexDayKdataRecorder().run()\n\n\n# the __all__ is generated\n__all__ = [\"ChinaIndexDayKdataRecorder\"]\n"
},
{
"path": "src/zvt/recorders/wb/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule wb_economy_recorder\nfrom .wb_economy_recorder import *\nfrom .wb_economy_recorder import __all__ as _wb_economy_recorder_all\n\n__all__ += _wb_economy_recorder_all\n\n# import all from submodule wb_country_recorder\nfrom .wb_country_recorder import *\nfrom .wb_country_recorder import __all__ as _wb_country_recorder_all\n\n__all__ += _wb_country_recorder_all\n\n# import all from submodule wb_api\nfrom .wb_api import *\nfrom .wb_api import __all__ as _wb_api_all\n\n__all__ += _wb_api_all\n"
},
{
"path": "src/zvt/recorders/wb/wb_api.py",
"content": "# -*- coding: utf-8 -*-\nimport itertools\nimport re\nfrom copy import copy\n\nimport pandas as pd\nimport requests\n\nfrom zvt.contract.api import get_entity_code\nfrom zvt.utils.pd_utils import normal_index_df\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nWORLD_BANK_URL = \"http://api.worldbank.org/v2\"\n\n# thanks to https://github.com/mwouts/world_bank_data\n\n_economy_indicator_map = {\n \"population\": \"SP.POP.TOTL\",\n \"gdp\": \"NY.GDP.MKTP.CD\",\n \"gdp_per_capita\": \"NY.GDP.PCAP.CD\",\n \"gdp_per_employed\": \"SL.GDP.PCAP.EM.KD\",\n \"gdp_growth\": \"NY.GDP.MKTP.KD.ZG\",\n \"agriculture_growth\": \"NV.AGR.TOTL.KD.ZG\",\n \"industry_growth\": \"NV.IND.TOTL.KD.ZG\",\n \"manufacturing_growth\": \"NV.IND.MANF.KD.ZG\",\n \"service_growth\": \"NV.SRV.TOTL.KD.ZG\",\n \"consumption_growth\": \"NE.CON.TOTL.KD.ZG\",\n \"capital_growth\": \"NE.GDI.TOTL.KD.ZG\",\n \"exports_growth\": \"NE.EXP.GNFS.KD.ZG\",\n \"imports_growth\": \"NE.IMP.GNFS.KD.ZG\",\n \"gni\": \"NY.GNP.ATLS.CD\",\n \"gni_per_capita\": \"NY.GNP.PCAP.CD\",\n \"gross_saving\": \"NY.GNS.ICTR.ZS\",\n \"cpi\": \"FP.CPI.TOTL\",\n \"unemployment_rate\": \"SL.UEM.TOTL.ZS\",\n \"fdi_of_gdp\": \"BX.KLT.DINV.WD.GD.ZS\",\n}\n\n\ndef _collapse(values):\n \"\"\"Collapse multiple values to a colon-separated list of values\"\"\"\n if isinstance(values, str):\n return values\n if values is None:\n return \"all\"\n if isinstance(values, list):\n return \";\".join([_collapse(v) for v in values])\n return str(values)\n\n\ndef _extract_preferred_field(data, id_or_value):\n \"\"\"In case the preferred representation of data when the latter has multiple representations\"\"\"\n if not id_or_value:\n return data\n\n if not data:\n return \"\"\n\n if isinstance(data, dict):\n if id_or_value in data:\n return data[id_or_value]\n\n if isinstance(data, list):\n return \",\".join([_extract_preferred_field(i, id_or_value) for i in data])\n\n return data\n\n\ndef _wb_get(paths: dict = None, **kwargs):\n params = copy(kwargs)\n params.setdefault(\"format\", \"json\")\n params.setdefault(\"per_page\", 20000)\n\n url = \"/\".join([WORLD_BANK_URL] + list(itertools.chain.from_iterable([(k, _collapse(paths[k])) for k in paths])))\n\n response = requests.get(url=url, params=params)\n response.raise_for_status()\n try:\n data = response.json()\n except ValueError:\n raise ValueError(\n \"{msg}\\nurl={url}\\nparams={params}\".format(msg=_extract_message(response.text), url=url, params=params)\n )\n if isinstance(data, list) and data and \"message\" in data[0]:\n try:\n msg = data[0][\"message\"][0][\"value\"]\n except (KeyError, IndexError):\n msg = str(msg)\n\n raise ValueError(\"{msg}\\nurl={url}\\nparams={params}\".format(msg=msg, url=url, params=params))\n\n # Redo the request and get the full information when the first response is incomplete\n if isinstance(data, list):\n page_information, data = data\n if \"page\" not in params:\n current_page = 1\n while current_page < int(page_information[\"pages\"]):\n params[\"page\"] = current_page = int(page_information[\"page\"]) + 1\n response = requests.get(url=url, params=params)\n response.raise_for_status()\n page_information, new_data = response.json()\n data.extend(new_data)\n\n if not data:\n raise RuntimeError(\"The request returned no data:\\nurl={url}\\nparams={params}\".format(url=url, params=params))\n\n return data\n\n\ndef _extract_message(msg):\n \"\"\"'\n \n The indicator was not found. It may have been deleted or archived.\n '\"\"\"\n if \"wb:message\" not in msg:\n return msg\n return re.sub(\n re.compile(\".*]*>\", re.DOTALL), \"\", re.sub(re.compile(\".*\", re.DOTALL), \"\", msg)\n )\n\n\ndef _get_meta(name, filters=None, expected=None, **params):\n \"\"\"Request data and return it in the form of a data frame\"\"\"\n filters = _collapse(filters)\n id_or_value = \"value\"\n\n if expected and id_or_value not in expected:\n raise ValueError(\"'id_or_value' should be one of '{}'\".format(\"', '\".join(expected)))\n\n data = _wb_get(paths={name: filters}, **params)\n\n # We get a list (countries) of dictionary (properties)\n columns = data[0].keys()\n records = {}\n\n for col in columns:\n records[col] = [_extract_preferred_field(cnt[col], id_or_value) for cnt in data]\n\n return pd.DataFrame(records, columns=columns)\n\n\ndef get_countries():\n df = _get_meta(\"country\", expected=[\"id\", \"iso2code\", \"value\"])\n\n for col in [\"latitude\", \"longitude\"]:\n df[col] = pd.to_numeric(df[col])\n df.rename(\n columns={\n \"iso2Code\": \"code\",\n \"incomeLevel\": \"income_level\",\n \"lendingType\": \"lending_type\",\n \"capitalCity\": \"capital_city\",\n },\n inplace=True,\n )\n df[\"entity_type\"] = \"country\"\n df[\"exchange\"] = \"galaxy\"\n df[\"entity_id\"] = df[[\"entity_type\", \"exchange\", \"code\"]].apply(lambda x: \"_\".join(x.astype(str)), axis=1)\n df[\"id\"] = df[\"entity_id\"]\n return df\n\n\ndef get_indicators(indicator=None, language=None, id_or_value=None, **params):\n \"\"\"Return a DataFrame that describes one, multiple or all indicators, indexed by the indicator id.\n :param indicator: None (all indicators), the id of an indicator, or a list of multiple ids\n :param language: Desired language\n :param id_or_value: Choose either 'id' or 'value' for columns 'source' and 'topics'\"\"\"\n\n if id_or_value == \"iso2code\":\n id_or_value = \"id\"\n\n return _get_meta(\n \"indicator\", indicator, language=language, id_or_value=id_or_value, expected=[\"id\", \"value\"], **params\n )\n\n\ndef get_indicator_data(indicator, indicator_name=None, country=None, date=None):\n datas = _wb_get(paths={\"country\": country, \"indicator\": indicator}, date=date)\n records = [\n {\n \"code\": item[\"country\"][\"id\"],\n \"timestamp\": to_pd_timestamp(item[\"date\"]),\n item[\"indicator\"][\"id\"] if not indicator_name else indicator_name: item[\"value\"],\n }\n for item in datas\n ]\n df = pd.DataFrame.from_records(data=records)\n df = df.set_index([\"code\", \"timestamp\"])\n return df\n\n\ndef get_regions(region=None, language=None, **params):\n \"\"\"Return a DataFrame that describes one, multiple or all regions, indexed by the region id.\n :param region: None (all regions), the id of a region, or a list of multiple ids\n :param language: Desired language\"\"\"\n return _get_meta(\"region\", region, language, **params)\n\n\ndef get_sources(source=None, language=None, **params):\n \"\"\"Return a DataFrame that describes one, multiple or all sources, indexed by the source id.\n :param source: None (all sources), the id of a source, or a list of multiple ids\n :param language: Desired language\"\"\"\n return _get_meta(\"source\", source, language, **params)\n\n\ndef get_topics(topic=None, language=None, **params):\n \"\"\"Return a DataFrame that describes one, multiple or all sources, indexed by the source id.\n :param topic: None (all topics), the id of a topic, or a list of multiple ids\n :param language: Desired language\"\"\"\n return _get_meta(\"topic\", topic, language, **params)\n\n\ndef get_incomelevels(incomelevel=None, language=None, **params):\n \"\"\"Return a DataFrame that describes one, multiple or all income levels, indexed by the IL id.\n :param incomelevel: None (all income levels), the id of an income level, or a list of multiple ids\n :param language: Desired language\"\"\"\n return _get_meta(\"incomelevel\", incomelevel, language, **params)\n\n\ndef get_lendingtypes(lendingtype=None, language=None, **params):\n \"\"\"Return a DataFrame that describes one, multiple or all lending types, indexed by the LT id.\n :param lendingtype: None (all lending types), the id of a lending type, or a list of multiple ids\n :param language: Desired language\"\"\"\n return _get_meta(\"lendingtype\", lendingtype, language, **params)\n\n\ndef get_economy_data(entity_id, indicators=None, date=None):\n country = get_entity_code(entity_id=entity_id)\n if not indicators:\n indicators = _economy_indicator_map.keys()\n dfs = []\n for indicator in indicators:\n data = get_indicator_data(\n indicator=_economy_indicator_map.get(indicator), indicator_name=indicator, country=country, date=date\n )\n dfs.append(data)\n df = pd.concat(dfs, axis=1)\n df = df.reset_index(drop=False)\n df[\"entity_id\"] = entity_id\n df[\"id\"] = df[[\"entity_id\", \"timestamp\"]].apply(lambda x: \"_\".join(x.astype(str)), axis=1)\n df = normal_index_df(df, drop=False)\n return df\n\n\nif __name__ == \"__main__\":\n # df = get_countries()\n # print(df)\n df = get_economy_data(entity_id=\"country_galaxy_CN\")\n print(df)\n # df = get_sources()\n # print(df)\n\n\n# the __all__ is generated\n__all__ = [\n \"get_countries\",\n \"get_indicators\",\n \"get_indicator_data\",\n \"get_regions\",\n \"get_sources\",\n \"get_topics\",\n \"get_incomelevels\",\n \"get_lendingtypes\",\n \"get_economy_data\",\n]\n"
},
{
"path": "src/zvt/recorders/wb/wb_country_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import Recorder\nfrom zvt.domain.meta.country_meta import Country\nfrom zvt.recorders.wb import wb_api\n\n\nclass WBCountryRecorder(Recorder):\n provider = \"wb\"\n data_schema = Country\n\n def run(self):\n df = wb_api.get_countries()\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n\n\nif __name__ == \"__main__\":\n recorder = WBCountryRecorder()\n recorder.run()\n\n\n# the __all__ is generated\n__all__ = [\"WBCountryRecorder\"]\n"
},
{
"path": "src/zvt/recorders/wb/wb_economy_recorder.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom zvt.contract.api import df_to_db\nfrom zvt.contract.recorder import FixedCycleDataRecorder\nfrom zvt.domain import Country, Economy\nfrom zvt.recorders.wb import wb_api\nfrom zvt.utils.time_utils import current_date\n\n\nclass WBEconomyRecorder(FixedCycleDataRecorder):\n entity_schema = Country\n data_schema = Economy\n entity_provider = \"wb\"\n provider = \"wb\"\n\n def record(self, entity, start, end, size, timestamps):\n date = None\n if start:\n date = f\"{start.year}:{current_date().year}\"\n try:\n df = wb_api.get_economy_data(entity_id=entity.id, date=date)\n df[\"name\"] = entity.name\n df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)\n # 一些地方获取不到数据会报错\n except Exception as e:\n self.logger.warning(f\"Failed to get {entity.name} economy data\", e)\n\n\nif __name__ == \"__main__\":\n entity_ids = [\"country_galaxy_CN\", \"country_galaxy_US\"]\n r = WBEconomyRecorder(entity_ids=entity_ids)\n r.run()\n\n\n# the __all__ is generated\n__all__ = [\"WBEconomyRecorder\"]\n"
},
{
"path": "src/zvt/resources/concept_main_tag_mapping.json",
"content": "{\n \"广电\": \"文化传媒\",\n \"数字阅读\": \"文化传媒\",\n \"网络游戏\": \"文化传媒\",\n \"手游概念\": \"文化传媒\",\n \"影视概念\": \"文化传媒\",\n \"稀土永磁\": \"资源\",\n \"可燃冰\": \"资源\",\n \"油价相关\": \"资源\",\n \"低碳冶金\": \"资源\",\n \"基本金属\": \"资源\",\n \"黄金概念\": \"资源\",\n \"天然气\": \"资源\",\n \"稀缺资源\": \"资源\",\n \"页岩气\": \"资源\",\n \"煤化工\": \"资源\",\n \"油气设服\": \"资源\",\n \"小金属概念\": \"资源\",\n \"新能源\": \"新能源\",\n \"BC电池\": \"新能源\",\n \"TOPCon电池\": \"新能源\",\n \"钒电池\": \"新能源\",\n \"钙钛矿电池\": \"新能源\",\n \"麒麟电池\": \"新能源\",\n \"动力电池回收\": \"新能源\",\n \"钠离子电池\": \"新能源\",\n \"固态电池\": \"新能源\",\n \"刀片电池\": \"新能源\",\n \"HIT电池\": \"新能源\",\n \"燃料电池\": \"新能源\",\n \"锂电池\": \"新能源\",\n \"储能\": \"新能源\",\n \"充电桩\": \"新能源\",\n \"熔盐储能\": \"新能源\",\n \"换电概念\": \"新能源\",\n \"盐湖提锂\": \"新能源\",\n \"风能\": \"新能源\",\n \"太阳能\": \"新能源\",\n \"氢能源\": \"新能源\",\n \"抽水蓄能\": \"新能源\",\n \"光伏建筑一体化\": \"新能源\",\n \"人造太阳\": \"新能源\",\n \"可控核聚变\": \"新能源\",\n \"核能核电\": \"电力\",\n \"绿色电力\": \"电力\",\n \"特高压\": \"电力\",\n \"虚拟电厂\": \"电力\",\n \"智能电网\": \"电力\",\n \"超超临界发电\": \"电力\",\n \"半导体概念\": \"半导体\",\n \"中芯概念\": \"半导体\",\n \"存储芯片\": \"半导体\",\n \"AI芯片\": \"半导体\",\n \"汽车芯片\": \"半导体\",\n \"国产芯片\": \"半导体\",\n \"第三代半导体\": \"半导体\",\n \"第四代半导体\": \"半导体\",\n \"英伟达概念\": \"半导体\",\n \"玻璃基板\": \"半导体\",\n \"高带宽内存\": \"半导体\",\n \"Chiplet概念\": \"半导体\",\n \"光刻胶\": \"半导体\",\n \"氮化镓\": \"半导体\",\n \"EDA概念\": \"半导体\",\n \"IGBT概念\": \"半导体\",\n \"PCB\": \"半导体\",\n \"新能源车\": \"车路云\",\n \"华为汽车\": \"车路云\",\n \"汽车拆解\": \"车路云\",\n \"汽车一体化压铸\": \"车路云\",\n \"小米汽车\": \"车路云\",\n \"汽车热管理\": \"车路云\",\n \"电子后视镜\": \"车路云\",\n \"高压快充\": \"车路云\",\n \"车联网\": \"车路云\",\n \"激光雷达\": \"车路云\",\n \"特斯拉\": \"车路云\",\n \"EDR概念\": \"车路云\",\n \"无人驾驶\": \"车路云\",\n \"ETC\": \"车路云\",\n \"物联网\": \"智能机器\",\n \"2025规划\": \"智能机器\",\n \"智能机器\": \"智能机器\",\n \"工业互联\": \"智能机器\",\n \"轮毂电机\": \"智能机器\",\n \"发电机概念\": \"智能机器\",\n \"同步磁阻电机\": \"智能机器\",\n \"机器人执行器\": \"智能机器\",\n \"新型工业化\": \"智能机器\",\n \"工业母机\": \"智能机器\",\n \"工业4.0\": \"智能机器\",\n \"减速器\": \"智能机器\",\n \"机器人概念\": \"智能机器\",\n \"PLC概念\": \"智能机器\",\n \"机器视觉\": \"智能机器\",\n \"生物医药\": \"医药\",\n \"痘病毒防治\": \"医药\",\n \"地塞米松\": \"医药\",\n \"消毒剂\": \"医药\",\n \"口罩\": \"医药\",\n \"肝素概念\": \"医药\",\n \"健康中国\": \"医药\",\n \"幽门螺杆菌概念\": \"医药\",\n \"代糖概念\": \"医药\",\n \"医疗器械概念\": \"医药\",\n \"生物疫苗\": \"医药\",\n \"维生素\": \"医药\",\n \"注射器概念\": \"医药\",\n \"流感\": \"医药\",\n \"AI制药\": \"医药\",\n \"中药概念\": \"医药\",\n \"减肥药\": \"医药\",\n \"创新药\": \"医药\",\n \"新冠药物\": \"医药\",\n \"长寿药\": \"医药\",\n \"独家药品\": \"医药\",\n \"病毒防治\": \"医药\",\n \"SPD概念\": \"医药\",\n \"辅助生殖\": \"医药\",\n \"肝炎概念\": \"医药\",\n \"蒙脱石散\": \"医药\",\n \"血氧仪\": \"医药\",\n \"熊去氧胆酸\": \"医药\",\n \"抗原检测\": \"医药\",\n \"抗菌面料\": \"医药\",\n \"千金藤素\": \"医药\",\n \"DRG/DIP\": \"医药\",\n \"CRO\": \"医药\",\n \"阿兹海默\": \"医药\",\n \"CAR-T细胞疗法\": \"医药\",\n \"新冠检测\": \"医药\",\n \"青蒿素\": \"医药\",\n \"超级真菌\": \"医药\",\n \"气溶胶检测\": \"医药\",\n \"重组蛋白\": \"医药\",\n \"疫苗冷链\": \"医药\",\n \"精准医疗\": \"医药\",\n \"单抗概念\": \"医药\",\n \"免疫治疗\": \"医药\",\n \"基因测序\": \"医药\",\n \"体外诊断\": \"医药\",\n \"互联医疗\": \"医药\",\n \"人脑工程\": \"医药\",\n \"啤酒概念\": \"大消费\",\n \"进口博览\": \"大消费\",\n \"退税商店\": \"大消费\",\n \"拼多多概念\": \"大消费\",\n \"抖音小店\": \"大消费\",\n \"乳业\": \"大消费\",\n \"C2M概念\": \"大消费\",\n \"调味品概念\": \"大消费\",\n \"毛发医疗\": \"大消费\",\n \"化妆品概念\": \"大消费\",\n \"白酒\": \"大消费\",\n \"医疗美容\": \"大消费\",\n \"户外露营\": \"大消费\",\n \"在线旅游\": \"大消费\",\n \"跨境电商\": \"大消费\",\n \"电商概念\": \"大消费\",\n \"新零售\": \"大消费\",\n \"智能家居\": \"大消费\",\n \"网红直播\": \"大消费\",\n \"免税概念\": \"大消费\",\n \"预制菜概念\": \"大消费\",\n \"培育钻石\": \"大消费\",\n \"婴童概念\": \"大消费\",\n \"托育服务\": \"大消费\",\n \"智慧灯杆\": \"消费电子\",\n \"UWB概念\": \"消费电子\",\n \"电子纸概念\": \"消费电子\",\n \"胎压监测\": \"消费电子\",\n \"3D玻璃\": \"消费电子\",\n \"屏下摄像\": \"消费电子\",\n \"超清视频\": \"消费电子\",\n \"植物照明\": \"消费电子\",\n \"LED\": \"消费电子\",\n \"3D摄像头\": \"消费电子\",\n \"eSIM\": \"消费电子\",\n \"蓝宝石\": \"消费电子\",\n \"无线耳机\": \"消费电子\",\n \"智能穿戴\": \"消费电子\",\n \"AI手机\": \"消费电子\",\n \"AIPC\": \"消费电子\",\n \"柔性屏(折叠屏)\": \"消费电子\",\n \"星闪概念\": \"消费电子\",\n \"传感器\": \"消费电子\",\n \"被动元件\": \"消费电子\",\n \"小米概念\": \"消费电子\",\n \"无线充电\": \"消费电子\",\n \"智能电视\": \"消费电子\",\n \"空间计算\": \"消费电子\",\n \"裸眼3D\": \"消费电子\",\n \"混合现实\": \"消费电子\",\n \"增强现实\": \"消费电子\",\n \"虚拟现实\": \"消费电子\",\n \"MicroLED\": \"消费电子\",\n \"MiniLED\": \"消费电子\",\n \"OLED\": \"消费电子\",\n \"VPN\": \"消费电子\",\n \"IPv6\": \"消费电子\",\n \"WiFi\": \"消费电子\",\n \"毫米波概念\": \"消费电子\",\n \"5G概念\": \"消费电子\",\n \"6G概念\": \"消费电子\",\n \"F5G概念\": \"消费电子\",\n \"量子通信\": \"消费电子\",\n \"化工原料\": \"化工\",\n \"环氧丙烷\": \"化工\",\n \"PVDF概念\": \"化工\",\n \"新材料\": \"化工\",\n \"MLCC\": \"化工\",\n \"碳纤维\": \"化工\",\n \"PEEK材料概念\": \"化工\",\n \"磷化工\": \"化工\",\n \"碳基材料\": \"化工\",\n \"纳米银\": \"化工\",\n \"碳化硅\": \"化工\",\n \"复合集流体\": \"化工\",\n \"有机硅\": \"化工\",\n \"石墨烯\": \"化工\",\n \"氟化工\": \"化工\",\n \"草甘膦\": \"化工\",\n \"钛白粉\": \"化工\",\n \"降解塑料\": \"化工\",\n \"工业气体\": \"化工\",\n \"氦气概念\": \"化工\",\n \"超级电容\": \"化工\",\n \"军民融合\": \"军工\",\n \"海工装备\": \"军工\",\n \"军工\": \"军工\",\n \"航母概念\": \"军工\",\n \"国家安防\": \"军工\",\n \"空间站概念\": \"军工\",\n \"大飞机\": \"军工\",\n \"铜缆高速连接\": \"AI\",\n \"ERP概念\": \"AI\",\n \"数字哨兵\": \"AI\",\n \"电子身份证\": \"AI\",\n \"电子车牌\": \"AI\",\n \"大数据\": \"AI\",\n \"智慧城市\": \"AI\",\n \"云计算\": \"AI\",\n \"国产软件\": \"AI\",\n \"生物识别\": \"AI\",\n \"RCS概念\": \"AI\",\n \"远程办公\": \"AI\",\n \"在线教育\": \"AI\",\n \"百度概念\": \"AI\",\n \"人工智能\": \"AI\",\n \"液冷概念\": \"AI\",\n \"光通信模块\": \"AI\",\n \"CPO概念\": \"AI\",\n \"AI语料\": \"AI\",\n \"Kimi概念\": \"AI\",\n \"Sora概念\": \"AI\",\n \"短剧互动游戏\": \"AI\",\n \"多模态AI\": \"AI\",\n \"数据要素\": \"AI\",\n \"算力概念\": \"AI\",\n \"MLOps概念\": \"AI\",\n \"ChatGPT概念\": \"AI\",\n \"AIGC概念\": \"AI\",\n \"数据确权\": \"AI\",\n \"Web3.0\": \"AI\",\n \"虚拟数字人\": \"AI\",\n \"数字水印\": \"AI\",\n \"数据安全\": \"AI\",\n \"云游戏\": \"AI\",\n \"数字孪生\": \"AI\",\n \"边缘计算\": \"AI\",\n \"数据中心\": \"AI\",\n \"华为概念\": \"AI\",\n \"鸿蒙概念\": \"AI\",\n \"华为欧拉\": \"AI\",\n \"华为昇腾\": \"AI\",\n \"国资云概念\": \"AI\",\n \"东数西算\": \"AI\",\n \"网络安全\": \"AI\",\n \"元宇宙概念\": \"AI\",\n \"NFT概念\": \"AI\",\n \"信创\": \"AI\",\n \"数字经济\": \"AI\",\n \"区块链\": \"AI\",\n \"智慧政务\": \"AI\",\n \"数字货币\": \"AI\",\n \"电子竞技\": \"AI\",\n \"知识产权\": \"AI\",\n \"时空大数据\": \"AI\",\n \"低空经济\": \"低空经济\",\n \"飞行汽车(eVTOL)\": \"低空经济\",\n \"无人机\": \"低空经济\",\n \"建筑节能\": \"房地产\",\n \"REITs概念\": \"房地产\",\n \"租售同权\": \"房地产\",\n \"铁路基建\": \"房地产\",\n \"PPP模式\": \"房地产\",\n \"工程机械概念\": \"房地产\",\n \"新型城镇化\": \"房地产\",\n \"装配建筑\": \"房地产\",\n \"地下管网\": \"房地产\",\n \"民爆概念\": \"房地产\",\n \"参股期货\": \"金融\",\n \"参股券商\": \"金融\",\n \"参股保险\": \"金融\",\n \"跨境支付\": \"金融\",\n \"互联金融\": \"金融\",\n \"券商概念\": \"金融\",\n \"移动支付\": \"金融\",\n \"参股银行\": \"金融\",\n \"粮食概念\": \"农业\",\n \"水产养殖\": \"农业\",\n \"生态农业\": \"农业\",\n \"蝗虫防治\": \"农业\",\n \"农业种植\": \"农业\",\n \"鸡肉概念\": \"农业\",\n \"转基因\": \"农业\",\n \"人造肉\": \"农业\",\n \"食品安全\": \"农业\",\n \"猪肉概念\": \"农业\",\n \"生物质能发电\": \"公用\",\n \"噪声防治\": \"公用\",\n \"土壤修复\": \"公用\",\n \"地热能\": \"公用\",\n \"海绵城市\": \"公用\",\n \"节能环保\": \"公用\",\n \"尾气治理\": \"公用\",\n \"职业教育\": \"公用\",\n \"医废处理\": \"公用\",\n \"快递概念\": \"物流\",\n \"RCEP概念\": \"物流\",\n \"央企改革\": \"国企\",\n \"中特估\": \"国企\",\n \"中字头\": \"国企\",\n \"沪企改革\": \"国企\",\n \"国企改革\": \"国企\",\n \"世界杯\": \"其他\",\n \"东盟自贸区概念\": \"其他\",\n \"娃哈哈概念\": \"其他\",\n \"空气能热泵\": \"其他\",\n \"核酸采样亭\": \"其他\",\n \"中俄贸易概念\": \"其他\",\n \"净水概念\": \"其他\",\n \"京津冀\": \"其他\",\n \"低价股\": \"其他\",\n \"商汤概念\": \"其他\",\n \"粤港自贸\": \"其他\",\n \"土地流转\": \"其他\",\n \"壳资源\": \"其他\",\n \"盲盒经济\": \"其他\",\n \"内贸流通\": \"其他\",\n \"京东金融\": \"其他\",\n \"乡村振兴\": \"其他\",\n \"东北振兴\": \"其他\",\n \"社区团购\": \"其他\",\n \"地摊经济\": \"其他\",\n \"快手概念\": \"其他\",\n \"蚂蚁概念\": \"其他\",\n \"证金持股\": \"其他\",\n \"养老概念\": \"其他\",\n \"冷链物流\": \"其他\",\n \"贬值受益\": \"其他\",\n \"纾困概念\": \"其他\",\n \"阿里概念\": \"其他\",\n \"深圳特区\": \"其他\",\n \"超级品牌\": \"其他\",\n \"中超概念\": \"其他\",\n \"养老金\": \"其他\",\n \"专精特新\": \"其他\",\n \"统一大市场\": \"其他\",\n \"光伏高速公路\": \"其他\",\n \"核污染防治\": \"其他\",\n \"磁悬浮概念\": \"其他\",\n \"垃圾分类\": \"其他\",\n \"电子烟\": \"其他\",\n \"工业大麻\": \"其他\",\n \"全息技术\": \"其他\",\n \"超导概念\": \"其他\",\n \"北交所概念\": \"其他\",\n \"赛马概念\": \"其他\",\n \"体育产业\": \"其他\",\n \"雄安新区\": \"其他\",\n \"共享经济\": \"其他\",\n \"彩票概念\": \"其他\",\n \"苹果概念\": \"其他\",\n \"供销社概念\": \"其他\",\n \"水利建设\": \"其他\",\n \"3D打印\": \"其他\",\n \"创投\": \"其他\",\n \"字节概念\": \"其他\",\n \"海洋经济\": \"其他\",\n \"上海自贸\": \"其他\",\n \"一带一路\": \"其他\",\n \"碳交易\": \"其他\",\n \"宠物经济\": \"其他\",\n \"商业航天\": \"商业航天\",\n \"航天概念\": \"商业航天\",\n \"天基互联\": \"商业航天\",\n \"北斗导航\": \"商业航天\",\n \"通用航空\": \"商业航天\"\n}"
},
{
"path": "src/zvt/resources/hk_industry_main_tag_mapping.json",
"content": "{\n \"地产\": \"房地产\",\n \"建筑\": \"基建\",\n \"工业工程\": \"基建\",\n \"其他金融\": \"金融\",\n \"药品及生物科技\": \"医药\",\n \"医疗保健设备和服务\": \"医药\",\n \"软件服务\": \"AI\",\n \"电讯\": \"AI\",\n \"专业零售\": \"大消费\",\n \"旅游及消闲设施\": \"大消费\",\n \"食物饮品\": \"大消费\",\n \"纺织及服饰\": \"大消费\",\n \"消费者主要零售商\": \"大消费\",\n \"媒体及娱乐\": \"文化传媒\",\n \"家庭电器及用品\": \"消费电子\",\n \"资讯科技器材\": \"消费电子\",\n \"工用运输\": \"物流\",\n \"一般金属及矿石\": \"资源\",\n \"原材料\": \"资源\",\n \"汽车\": \"车路云\",\n \"农业产品\": \"农业\",\n \"煤炭\": \"资源\",\n \"黄金及贵金属\": \"资源\",\n \"石油及天然气\": \"资源\",\n \"工用支援\": \"其他\",\n \"综合企业\": \"其他\",\n \"支援服务\": \"其他\"\n}"
},
{
"path": "src/zvt/resources/industry_main_tag_mapping.json",
"content": "{\n \"风电设备\": \"新能源\",\n \"电池\": \"新能源\",\n \"光伏设备\": \"新能源\",\n \"能源金属\": \"新能源\",\n \"电源设备\": \"新能源\",\n \"半导体\": \"半导体\",\n \"电子化学品\": \"半导体\",\n \"医疗服务\": \"医药\",\n \"中药\": \"医药\",\n \"化学制药\": \"医药\",\n \"生物制品\": \"医药\",\n \"医药商业\": \"医药\",\n \"医疗器械\": \"医药\",\n \"贸易行业\": \"大消费\",\n \"家用轻工\": \"大消费\",\n \"造纸印刷\": \"大消费\",\n \"酿酒行业\": \"大消费\",\n \"珠宝首饰\": \"大消费\",\n \"美容护理\": \"大消费\",\n \"食品饮料\": \"大消费\",\n \"旅游酒店\": \"大消费\",\n \"商业百货\": \"大消费\",\n \"纺织服装\": \"大消费\",\n \"家电行业\": \"大消费\",\n \"航空机场\": \"大消费\",\n \"小金属\": \"资源\",\n \"贵金属\": \"资源\",\n \"有色金属\": \"资源\",\n \"煤炭行业\": \"资源\",\n \"石油行业\": \"资源\",\n \"燃气\": \"资源\",\n \"采掘行业\": \"资源\",\n \"消费电子\": \"消费电子\",\n \"电子元件\": \"消费电子\",\n \"光学光电子\": \"消费电子\",\n \"汽车零部件\": \"车路云\",\n \"汽车服务\": \"车路云\",\n \"汽车整车\": \"车路云\",\n \"交运设备\": \"车路云\",\n \"电机\": \"智能机器\",\n \"通用设备\": \"智能机器\",\n \"专用设备\": \"智能机器\",\n \"仪器仪表\": \"智能机器\",\n \"电网设备\": \"电力\",\n \"电力行业\": \"电力\",\n \"房地产开发\": \"房地产\",\n \"房地产服务\": \"房地产\",\n \"工程建设\": \"房地产\",\n \"水泥建材\": \"房地产\",\n \"装修装饰\": \"房地产\",\n \"装修建材\": \"房地产\",\n \"工程咨询服务\": \"房地产\",\n \"钢铁行业\": \"房地产\",\n \"工程机械\": \"房地产\",\n \"非金属材料\": \"化工\",\n \"包装材料\": \"化工\",\n \"化学制品\": \"化工\",\n \"化肥行业\": \"化工\",\n \"化学原料\": \"化工\",\n \"化纤行业\": \"化工\",\n \"塑料制品\": \"化工\",\n \"玻璃玻纤\": \"化工\",\n \"橡胶制品\": \"化工\",\n \"证券\": \"金融\",\n \"保险\": \"金融\",\n \"银行\": \"金融\",\n \"多元金融\": \"金融\",\n \"通信服务\": \"AI\",\n \"通信设备\": \"AI\",\n \"互联网服务\": \"AI\",\n \"软件开发\": \"AI\",\n \"计算机设备\": \"AI\",\n \"文化传媒\": \"文化传媒\",\n \"教育\": \"文化传媒\",\n \"游戏\": \"文化传媒\",\n \"农牧饲渔\": \"农业\",\n \"农药兽药\": \"农业\",\n \"物流行业\": \"物流\",\n \"航运港口\": \"物流\",\n \"铁路公路\": \"物流\",\n \"航天航空\": \"军工\",\n \"船舶制造\": \"军工\",\n \"环保行业\": \"环保\",\n \"公用事业\": \"其他\",\n \"专业服务\": \"其他\",\n \"综合行业\": \"其他\"\n}"
},
{
"path": "src/zvt/resources/log_conf.yaml",
"content": "version: 1\ndisable_existing_loggers: False\nformatters:\n default:\n # \"()\": uvicorn.logging.DefaultFormatter\n format: '%(asctime)s %(levelname)s %(threadName)s %(message)s'\n access:\n # \"()\": uvicorn.logging.AccessFormatter\n format: '%(asctime)s %(levelname)s %(threadName)s %(message)s'\nhandlers:\n default:\n formatter: default\n class: logging.StreamHandler\n stream: ext://sys.stderr\n file:\n class: logging.handlers.RotatingFileHandler\n formatter: default\n filename: server.log\n maxBytes: 524288000\n level: INFO\n backupCount: 10\n access:\n formatter: access\n class: logging.StreamHandler\n stream: ext://sys.stdout\nloggers:\n uvicorn.error:\n level: INFO\n handlers:\n - default\n propagate: no\n uvicorn.access:\n level: INFO\n handlers:\n - access\n propagate: no\nroot:\n level: INFO\n handlers:\n - default\n - file\n propagate: no"
},
{
"path": "src/zvt/resources/missed_concept.json",
"content": "[\n \"北京冬奥\",\n \"长江三角\",\n \"HS300_\",\n \"股权激励\",\n \"标准普尔\",\n \"预盈预增\",\n \"上证50_\",\n \"预亏预减\",\n \"上证180_\",\n \"滨海新区\",\n \"MSCI中国\",\n \"沪股通\",\n \"转债标的\",\n \"昨日涨停_含一字\",\n \"万达概念\",\n \"成渝特区\",\n \"ST股\",\n \"机构重仓\",\n \"IPO受益\",\n \"参股新三板\",\n \"破净股\",\n \"上证380\",\n \"宁组合\",\n \"茅指数\",\n \"深证100R\",\n \"深股通\",\n \"举牌\",\n \"债转股\",\n \"昨日连板_含一字\",\n \"融资融券\",\n \"央视50_\",\n \"昨日触板\",\n \"科创板做市股\",\n \"QFII重仓\",\n \"科创板做市商\",\n \"独角兽\",\n \"AB股\",\n \"基金重仓\",\n \"富士康\",\n \"创业板综\",\n \"中证500\",\n \"次新股\",\n \"富时罗素\",\n \"百元股\",\n \"创业成份\",\n \"送转预期\",\n \"B股\",\n \"杭州亚运会\",\n \"深成500\",\n \"股权转让\",\n \"社保重仓\",\n \"昨日连板\",\n \"微盘股\",\n \"昨日涨停\",\n \"GDR\",\n \"分拆预期\",\n \"高送转\",\n \"湖北自贸\",\n \"AH股\"\n]"
},
{
"path": "src/zvt/resources/missed_industry.json",
"content": "[]"
},
{
"path": "src/zvt/resources/us_industry_main_tag_mapping.json",
"content": "{\n \"生物科技\": \"医药\",\n \"制药\": \"医药\",\n \"药品零售\": \"医药\",\n \"医疗保健设备\": \"医药\",\n \"医疗保健技术\": \"医药\",\n \"生命科学工具和服务\": \"医药\",\n \"医疗保健用品\": \"医药\",\n \"投资银行业与经纪业\": \"金融\",\n \"区域性银行\": \"金融\",\n \"互助储蓄与抵押信贷金融服务\": \"金融\",\n \"资产管理与托管银行\": \"金融\",\n \"综合金融服务\": \"金融\",\n \"综合性银行\": \"金融\",\n \"财产与意外伤害保险\": \"金融\",\n \"医疗保健房地产投资信托\": \"金融\",\n \"多样化房地产投资信托\": \"金融\",\n \"抵押房地产投资信托\": \"金融\",\n \"特殊金融服务\": \"金融\",\n \"金融交易所和数据\": \"金融\",\n \"消费信贷\": \"金融\",\n \"保险经纪商\": \"金融\",\n \"综合性资本市场\": \"金融\",\n \"人寿与健康保险\": \"金融\",\n \"再保险\": \"金融\",\n \"多元化保险\": \"金融\",\n \"多领域控股\": \"金融\",\n \"餐馆\": \"大消费\",\n \"啤酒酿造商\": \"大消费\",\n \"包装食品与肉类\": \"大消费\",\n \"食品零售\": \"大消费\",\n \"互联网与直销零售\": \"大消费\",\n \"服装、服饰与奢侈品\": \"大消费\",\n \"个人护理用品\": \"大消费\",\n \"食品分销商\": \"大消费\",\n \"服装零售\": \"大消费\",\n \"保健护理服务\": \"大消费\",\n \"消闲用品\": \"大消费\",\n \"软饮料与不含酒精饮料\": \"大消费\",\n \"酒店、度假村与豪华游轮\": \"大消费\",\n \"鞋类\": \"大消费\",\n \"酿酒商与葡萄酒商\": \"大消费\",\n \"综合货品商店\": \"大消费\",\n \"家用器具与特殊消费品\": \"大消费\",\n \"纺织品\": \"大消费\",\n \"日常消费品零售\": \"大消费\",\n \"百货商店\": \"大消费\",\n \"其他专卖店\": \"大消费\",\n \"保健护理产品经销商\": \"大消费\",\n \"经销商\": \"大消费\",\n \"居家用品\": \"大消费\",\n \"保健护理机构\": \"大消费\",\n \"家庭装饰零售\": \"大消费\",\n \"家庭装潢零售\": \"大消费\",\n \"烟草\": \"大消费\",\n \"特殊消费者服务\": \"大消费\",\n \"家庭装饰品\": \"大消费\",\n \"纸制品\": \"大消费\",\n \"机场服务\": \"大消费\",\n \"管理型保健护理\": \"大消费\",\n \"办公服务与用品\": \"大消费\",\n \"消费电子产品\": \"消费电子\",\n \"电子元件\": \"消费电子\",\n \"电脑与电子产品零售\": \"消费电子\",\n \"家用电器\": \"消费电子\",\n \"电气部件与设备\": \"消费电子\",\n \"电子设备和仪器\": \"消费电子\",\n \"电子制造服务\": \"消费电子\",\n \"应用软件\": \"AI\",\n \"互联网服务与基础设施\": \"AI\",\n \"数据处理与外包服务\": \"AI\",\n \"信息科技咨询与其它服务\": \"AI\",\n \"通信设备\": \"AI\",\n \"电脑硬件、储存设备及电脑周边\": \"AI\",\n \"系统软件\": \"AI\",\n \"综合电信业务\": \"AI\",\n \"无线电信业务\": \"AI\",\n \"技术产品经销商\": \"AI\",\n \"半导体产品\": \"半导体\",\n \"半导体材料与设备\": \"半导体\",\n \"石油与天然气的勘探与生产\": \"资源\",\n \"石油与天然气钻井\": \"资源\",\n \"石油天然气设备与服务\": \"资源\",\n \"石油与天然气的储存和运输\": \"资源\",\n \"石油与天然气的炼制和营销\": \"资源\",\n \"综合性石油与天然气企业\": \"资源\",\n \"多种金属与采矿\": \"资源\",\n \"贵重金属与矿石\": \"资源\",\n \"铝\": \"资源\",\n \"黄金\": \"资源\",\n \"煤与消费用燃料\": \"资源\",\n \"燃气公用事业\": \"资源\",\n \"水公用事业\": \"资源\",\n \"钢铁\": \"资源\",\n \"金属、玻璃及塑料器皿\": \"资源\",\n \"电力公用事业\": \"电力\",\n \"新能源发电业者\": \"电力\",\n \"独立电力生产商与能源贸易商\": \"电力\",\n \"重型电气设备\": \"电力\",\n \"航天航空与国防\": \"军工\",\n \"安全和报警服务\": \"军工\",\n \"电影与娱乐\": \"文化传媒\",\n \"赌场与赌博\": \"文化传媒\",\n \"互动媒体与服务\": \"文化传媒\",\n \"出版\": \"文化传媒\",\n \"广告\": \"文化传媒\",\n \"广播\": \"文化传媒\",\n \"教育服务\": \"文化传媒\",\n \"互动家庭娱乐\": \"文化传媒\",\n \"消闲设施\": \"文化传媒\",\n \"调查和咨询服务\": \"文化传媒\",\n \"有线和卫星电视\": \"文化传媒\",\n \"房地产开发\": \"房地产\",\n \"房地产服务\": \"房地产\",\n \"住宅建筑\": \"房地产\",\n \"建筑材料\": \"房地产\",\n \"建筑与工程\": \"房地产\",\n \"建筑产品\": \"房地产\",\n \"建筑机械与重型运输设备\": \"房地产\",\n \"房地产经营公司\": \"房地产\",\n \"零售业房地产投资信托\": \"房地产\",\n \"工业房地产投资信托\": \"房地产\",\n \"酒店及度假村房地产投资信托\": \"房地产\",\n \"特种房地产投资信托\": \"房地产\",\n \"办公房地产投资信托\": \"房地产\",\n \"多样化房地产活动\": \"房地产\",\n \"住宅房地产投资信托\": \"房地产\",\n \"汽车零售\": \"车路云\",\n \"汽车制造商\": \"车路云\",\n \"摩托车制造商\": \"车路云\",\n \"汽车零件与设备\": \"车路云\",\n \"陆运\": \"物流\",\n \"航空货运与物流\": \"物流\",\n \"客运航空公司\": \"物流\",\n \"铁路\": \"物流\",\n \"海上运输\": \"物流\",\n \"公路与铁路\": \"物流\",\n \"海港与服务\": \"物流\",\n \"特种化学制品\": \"化工\",\n \"多种化学制品\": \"化工\",\n \"商品化工\": \"化工\",\n \"化肥与农用药剂\": \"化工\",\n \"轮胎与橡胶\": \"化工\",\n \"工业气体\": \"化工\",\n \"农用农业机械\": \"农业\",\n \"林业产品\": \"农业\",\n \"农产品与服务\": \"农业\",\n \"环境与设施服务\": \"环保\",\n \"贸易公司与经销商\": \"其他\",\n \"工业机械、物料与部件\": \"其他\",\n \"人力资源与就业服务\": \"其他\",\n \"综合支持服务\": \"其他\",\n \"复合型公用事业\": \"其他\",\n \"纸质和塑料包装产品及材料\": \"其他\",\n \"商业印刷\": \"其他\",\n \"工业集团企业\": \"其他\",\n \"非传统电信运营商\": \"其他\"\n}"
},
{
"path": "src/zvt/rest/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "src/zvt/rest/data.py",
"content": "# -*- coding: utf-8 -*-\nfrom fastapi import APIRouter\nfrom fastapi.encoders import jsonable_encoder\n\nimport zvt.contract as contract\nimport zvt.contract.api as contract_api\n\ndata_router = APIRouter(\n prefix=\"/api/data\",\n tags=[\"data\"],\n responses={404: {\"description\": \"Not found\"}},\n)\n\n\n@data_router.get(\n \"/providers\",\n response_model=list,\n)\ndef get_data_providers():\n \"\"\"\n Get data providers\n \"\"\"\n return contract_api.get_providers()\n\n\n@data_router.get(\n \"/schemas\",\n response_model=list,\n)\ndef get_data_schemas(provider):\n \"\"\"\n Get schemas by provider\n \"\"\"\n return [schema.__name__ for schema in contract_api.get_schemas(provider=provider)]\n\n\n@data_router.get(\n \"/query_data\",\n response_model=list,\n)\ndef query_data(provider: str, schema: str):\n \"\"\"\n Get schemas by provider\n \"\"\"\n model: contract.Mixin = contract_api.get_schema_by_name(schema)\n with contract_api.DBSession(provider=provider, data_schema=model)() as session:\n return jsonable_encoder(model.query_data(session=session, limit=100, return_type=\"domain\"))\n"
},
{
"path": "src/zvt/rest/factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nfrom fastapi import APIRouter\n\nfrom zvt.contract import zvt_context\nfrom zvt.factors import factor_service\nfrom zvt.factors.factor_models import FactorRequestModel, TradingSignalModel\n\nfactor_router = APIRouter(\n prefix=\"/api/factor\",\n tags=[\"factor\"],\n responses={404: {\"description\": \"Not found\"}},\n)\n\n\n@factor_router.get(\"/get_factors\", response_model=List[str])\ndef get_factors():\n return [name for name in zvt_context.factor_cls_registry.keys()]\n\n\n@factor_router.post(\"/query_factor_result\", response_model=List[TradingSignalModel])\ndef query_factor_result(factor_request_model: FactorRequestModel):\n return factor_service.query_factor_result(factor_request_model)\n"
},
{
"path": "src/zvt/rest/misc.py",
"content": "# -*- coding: utf-8 -*-\nfrom fastapi import APIRouter\n\nfrom zvt.misc import misc_service\nfrom zvt.misc.misc_models import TimeMessage\n\nmisc_router = APIRouter(\n prefix=\"/api/misc\",\n tags=[\"misc\"],\n responses={404: {\"description\": \"Not found\"}},\n)\n\n\n@misc_router.get(\n \"/time_message\",\n response_model=TimeMessage,\n)\ndef get_time_message():\n \"\"\"\n Get time message\n \"\"\"\n return misc_service.get_time_message()\n"
},
{
"path": "src/zvt/rest/trading.py",
"content": "import platform\nfrom typing import List, Optional\n\nfrom fastapi import APIRouter, HTTPException\nfrom fastapi_pagination import Page\n\nimport zvt.contract.api as contract_api\nimport zvt.trading.trading_service as trading_service\nfrom zvt.common.trading_models import BuyParameter, SellParameter, TradingResult\nfrom zvt.tag.tag_schemas import MainTagInfo\nfrom zvt.trading.trading_models import (\n BuildTradingPlanModel,\n TradingPlanModel,\n QueryTradingPlanModel,\n QueryStockQuoteModel,\n StockQuoteStatsModel,\n QueryStockQuoteSettingModel,\n BuildQueryStockQuoteSettingModel,\n QueryTagQuoteModel,\n TagQuoteStatsModel,\n KdataModel,\n KdataRequestModel,\n TSModel,\n TSRequestModel,\n QuoteStatsModel,\n)\nfrom zvt.trading.trading_schemas import QueryStockQuoteSetting\n\ntrading_router = APIRouter(\n prefix=\"/api/trading\",\n tags=[\"trading\"],\n # dependencies=[Depends(get_current_user)],\n responses={404: {\"description\": \"Not found\"}},\n)\n\n\n@trading_router.post(\"/query_kdata\", response_model=Optional[List[KdataModel]])\ndef query_kdata(kdata_request_model: KdataRequestModel):\n return trading_service.query_kdata(kdata_request_model)\n\n\n@trading_router.post(\"/query_ts\", response_model=Optional[List[TSModel]])\ndef query_kdata(ts_request_model: TSRequestModel):\n return trading_service.query_ts(ts_request_model)\n\n\n@trading_router.get(\"/get_quote_stats\", response_model=Optional[QuoteStatsModel])\ndef get_quote_stats():\n return trading_service.query_quote_stats()\n\n\n@trading_router.get(\"/get_query_stock_quote_setting\", response_model=Optional[QueryStockQuoteSettingModel])\ndef get_query_stock_quote_setting():\n with contract_api.DBSession(provider=\"zvt\", data_schema=QueryStockQuoteSetting)() as session:\n query_setting: List[QueryStockQuoteSetting] = QueryStockQuoteSetting.query_data(\n session=session, return_type=\"domain\"\n )\n df = MainTagInfo.query_data(return_type=\"df\")\n tags = df[\"tag\"].tolist()\n\n if query_setting:\n return QueryStockQuoteSettingModel(stock_pool_name=query_setting[0].stock_pool_name, main_tags=tags)\n return QueryStockQuoteSettingModel(stock_pool_name=\"A股\", main_tags=tags)\n\n\n@trading_router.post(\"/build_query_stock_quote_setting\", response_model=QueryStockQuoteSettingModel)\ndef build_query_stock_quote_setting(build_query_stock_quote_setting_model: BuildQueryStockQuoteSettingModel):\n return trading_service.build_query_stock_quote_setting(build_query_stock_quote_setting_model)\n\n\n@trading_router.post(\"/query_tag_quotes\", response_model=List[TagQuoteStatsModel])\ndef query_tag_quotes(query_tag_quote_model: QueryTagQuoteModel):\n return trading_service.query_tag_quotes(query_tag_quote_model)\n\n\n@trading_router.post(\"/query_stock_quotes\", response_model=Optional[StockQuoteStatsModel])\ndef query_stock_quotes(query_stock_quote_model: QueryStockQuoteModel):\n return trading_service.query_stock_quotes(query_stock_quote_model)\n\n\n@trading_router.post(\"/build_trading_plan\", response_model=TradingPlanModel)\ndef build_trading_plan(build_trading_plan_model: BuildTradingPlanModel):\n return trading_service.build_trading_plan(build_trading_plan_model)\n\n\n@trading_router.post(\"/query_trading_plan\", response_model=Page[TradingPlanModel])\ndef query_trading_plan(query_trading_plan_model: QueryTradingPlanModel):\n return trading_service.query_trading_plan(query_trading_plan_model)\n\n\n@trading_router.get(\"/get_current_trading_plan\", response_model=List[TradingPlanModel])\ndef get_current_trading_plan():\n return trading_service.get_current_trading_plan()\n\n\n@trading_router.get(\"/get_future_trading_plan\", response_model=List[TradingPlanModel])\ndef get_future_trading_plan():\n return trading_service.get_future_trading_plan()\n\n\n@trading_router.post(\"/buy\", response_model=TradingResult)\ndef buy(buy_position_strategy: BuyParameter):\n if platform.system() == \"Windows\":\n from zvt.broker.qmt.context import qmt_context\n\n return qmt_context.qmt_account.buy(buy_position_strategy)\n else:\n raise HTTPException(status_code=500, detail=\"Please use qmt in windows! \")\n\n\n@trading_router.post(\"/sell\", response_model=TradingResult)\ndef sell(sell_position_strategy: SellParameter):\n if platform.system() == \"Windows\":\n from zvt.broker.qmt.context import qmt_context\n\n return qmt_context.qmt_account.sell(sell_position_strategy)\n else:\n raise HTTPException(status_code=500, detail=\"Please use qmt in windows! \")\n"
},
{
"path": "src/zvt/rest/work.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Optional\n\nfrom fastapi import APIRouter\n\nimport zvt.contract.api as contract_api\nimport zvt.tag.tag_service as tag_service\nfrom zvt.domain import Stock\nfrom zvt.tag.common import TagType\nfrom zvt.tag.tag_models import (\n TagInfoModel,\n CreateTagInfoModel,\n StockTagsModel,\n SimpleStockTagsModel,\n SetStockTagsModel,\n CreateStockPoolInfoModel,\n StockPoolInfoModel,\n CreateStockPoolsModel,\n StockPoolsModel,\n QueryStockTagStatsModel,\n StockTagStatsModel,\n QueryStockTagsModel,\n QuerySimpleStockTagsModel,\n ActivateSubTagsResultModel,\n ActivateSubTagsModel,\n BatchSetStockTagsModel,\n StockTagOptions,\n MainTagIndustryRelation,\n MainTagSubTagRelation,\n IndustryInfoModel,\n ChangeMainTagModel,\n BuildMainTagIndustryRelationModel,\n BuildMainTagSubTagRelationModel,\n)\nfrom zvt.tag.tag_schemas import (\n StockTags,\n MainTagInfo,\n SubTagInfo,\n HiddenTagInfo,\n StockPoolInfo,\n StockPools,\n IndustryInfo,\n)\nfrom zvt.utils.time_utils import current_date\n\nwork_router = APIRouter(\n prefix=\"/api/work\",\n tags=[\"work\"],\n # dependencies=[Depends(get_current_user)],\n responses={404: {\"description\": \"Not found\"}},\n)\n\n\n@work_router.post(\"/create_stock_pool_info\", response_model=StockPoolInfoModel)\ndef create_stock_pool_info(create_stock_pool_info_model: CreateStockPoolInfoModel):\n return tag_service.build_stock_pool_info(create_stock_pool_info_model, timestamp=current_date())\n\n\n@work_router.get(\"/get_stock_pool_info\", response_model=List[StockPoolInfoModel])\ndef get_stock_pool_info():\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockPoolInfo)() as session:\n stock_pool_info: List[StockPoolInfo] = StockPoolInfo.query_data(session=session, return_type=\"domain\")\n return stock_pool_info\n\n\n@work_router.post(\"/create_stock_pools\", response_model=StockPoolsModel)\ndef create_stock_pools(create_stock_pools_model: CreateStockPoolsModel):\n return tag_service.build_stock_pool(create_stock_pools_model, current_date())\n\n\n@work_router.delete(\"/delete_stock_pool\", response_model=str)\ndef delete_stock_pool(stock_pool_name: str):\n return tag_service.delete_stock_pool(stock_pool_name=stock_pool_name)\n\n\n@work_router.get(\"/get_stock_pools\", response_model=Optional[StockPoolsModel])\ndef get_stock_pools(stock_pool_name: str):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockPools)() as session:\n stock_pools: List[StockPools] = StockPools.query_data(\n session=session,\n filters=[StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if stock_pools:\n return stock_pools[0]\n return None\n\n\n@work_router.get(\"/get_main_tag_info\", response_model=List[TagInfoModel])\ndef get_main_tag_info():\n \"\"\"\n Get main_tag info\n \"\"\"\n with contract_api.DBSession(provider=\"zvt\", data_schema=MainTagInfo)() as session:\n tags_info: List[MainTagInfo] = MainTagInfo.query_data(session=session, return_type=\"domain\")\n return tags_info\n\n\n@work_router.get(\"/get_sub_tag_info\", response_model=List[TagInfoModel])\ndef get_sub_tag_info():\n \"\"\"\n Get sub_tag info\n \"\"\"\n with contract_api.DBSession(provider=\"zvt\", data_schema=SubTagInfo)() as session:\n tags_info: List[SubTagInfo] = SubTagInfo.query_data(session=session, return_type=\"domain\")\n return tags_info\n\n\n@work_router.get(\"/get_main_tag_sub_tag_relation\", response_model=MainTagSubTagRelation)\ndef get_main_tag_sub_tag_relation(main_tag):\n return tag_service.get_main_tag_sub_tag_relation(main_tag=main_tag)\n\n\n@work_router.get(\"/get_industry_info\", response_model=List[IndustryInfoModel])\ndef get_industry_info():\n \"\"\"\n Get industry info\n \"\"\"\n with contract_api.DBSession(provider=\"zvt\", data_schema=IndustryInfo)() as session:\n industry_info: List[IndustryInfo] = IndustryInfo.query_data(session=session, return_type=\"domain\")\n return industry_info\n\n\n@work_router.get(\"/get_main_tag_industry_relation\", response_model=MainTagIndustryRelation)\ndef get_main_tag_industry_relation(main_tag):\n return tag_service.get_main_tag_industry_relation(main_tag=main_tag)\n\n\n@work_router.get(\"/get_hidden_tag_info\", response_model=List[TagInfoModel])\ndef get_hidden_tag_info():\n \"\"\"\n Get hidden_tag info\n \"\"\"\n with contract_api.DBSession(provider=\"zvt\", data_schema=MainTagInfo)() as session:\n tags_info: List[HiddenTagInfo] = HiddenTagInfo.query_data(session=session, return_type=\"domain\")\n return tags_info\n\n\n@work_router.post(\"/create_main_tag_info\", response_model=TagInfoModel)\ndef create_main_tag_info(tag_info: CreateTagInfoModel):\n return tag_service.create_tag_info(tag_info, tag_type=TagType.main_tag)\n\n\n@work_router.delete(\"/delete_main_tag\", response_model=str)\ndef delete_main_tag(tag: str):\n tag_service.delete_tag(tag=tag, tag_type=TagType.main_tag)\n return \"ok\"\n\n\n@work_router.post(\"/create_sub_tag_info\", response_model=TagInfoModel)\ndef create_sub_tag_info(tag_info: CreateTagInfoModel):\n return tag_service.create_tag_info(tag_info, TagType.sub_tag)\n\n\n@work_router.delete(\"/delete_sub_tag\", response_model=str)\ndef delete_sub_tag(tag: str):\n tag_service.delete_tag(tag=tag, tag_type=TagType.sub_tag)\n return \"ok\"\n\n\n@work_router.post(\"/create_hidden_tag_info\", response_model=TagInfoModel)\ndef create_hidden_tag_info(tag_info: CreateTagInfoModel):\n return tag_service.create_tag_info(tag_info, TagType.hidden_tag)\n\n\n@work_router.delete(\"/delete_hidden_tag\", response_model=str)\ndef delete_hidden_tag(tag: str):\n tag_service.delete_tag(tag=tag, tag_type=TagType.hidden_tag)\n return \"ok\"\n\n\n@work_router.post(\"/query_stock_tags\", response_model=List[StockTagsModel])\ndef query_stock_tags(query_stock_tags_model: QueryStockTagsModel):\n \"\"\"\n Get entity tags\n \"\"\"\n filters = [StockTags.entity_id.in_(query_stock_tags_model.entity_ids)]\n\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n tags: List[StockTags] = StockTags.query_data(\n session=session, filters=filters, return_type=\"domain\", order=StockTags.timestamp.desc()\n )\n tags_dict = {tag.entity_id: tag for tag in tags}\n sorted_tags = [tags_dict[entity_id] for entity_id in query_stock_tags_model.entity_ids]\n return sorted_tags\n\n\n@work_router.post(\"/query_simple_stock_tags\", response_model=List[SimpleStockTagsModel])\ndef query_simple_stock_tags(query_simple_stock_tags_model: QuerySimpleStockTagsModel):\n \"\"\"\n Get simple entity tags\n \"\"\"\n\n entity_ids = query_simple_stock_tags_model.entity_ids\n\n filters = [StockTags.entity_id.in_(entity_ids)]\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n tags: List[dict] = StockTags.query_data(\n session=session, filters=filters, return_type=\"dict\", order=StockTags.timestamp.desc()\n )\n entity_tag_map = {item[\"entity_id\"]: item for item in tags}\n result_tags = []\n stocks = Stock.query_data(provider=\"em\", entity_ids=[tag[\"entity_id\"] for tag in tags], return_type=\"domain\")\n stocks_map = {item.entity_id: item for item in stocks}\n for entity_id in entity_ids:\n tag = entity_tag_map.get(entity_id)\n if not tag:\n continue\n tag[\"name\"] = stocks_map.get(entity_id).name\n if stocks_map.get(entity_id).controlling_holder_parent:\n tag[\"controlling_holder_parent\"] = stocks_map.get(entity_id).controlling_holder_parent\n else:\n tag[\"controlling_holder_parent\"] = stocks_map.get(entity_id).controlling_holder\n tag[\"top_ten_ratio\"] = stocks_map.get(entity_id).top_ten_ratio\n result_tags.append(tag)\n return result_tags\n\n\n@work_router.get(\"/get_stock_tag_options\", response_model=StockTagOptions)\ndef get_stock_tag_options(entity_id: str):\n \"\"\"\n Get stock tag options\n \"\"\"\n return tag_service.get_stock_tag_options(entity_id=entity_id)\n\n\n@work_router.post(\"/set_stock_tags\", response_model=StockTagsModel)\ndef set_stock_tags(set_stock_tags_model: SetStockTagsModel):\n \"\"\"\n Set stock tags\n \"\"\"\n return tag_service.build_stock_tags(\n set_stock_tags_model=set_stock_tags_model, timestamp=current_date(), set_by_user=True\n )\n\n\n@work_router.post(\"/build_stock_tags\", response_model=List[StockTagsModel])\ndef build_stock_tags(set_stock_tags_model_list: List[SetStockTagsModel]):\n \"\"\"\n Set stock tags in batch\n \"\"\"\n return [\n tag_service.build_stock_tags(\n set_stock_tags_model=set_stock_tags_model, timestamp=current_date(), set_by_user=True\n )\n for set_stock_tags_model in set_stock_tags_model_list\n ]\n\n\n@work_router.post(\"/query_stock_tag_stats\", response_model=List[StockTagStatsModel])\ndef query_stock_tag_stats(query_stock_tag_stats_model: QueryStockTagStatsModel):\n \"\"\"\n Get stock tag stats\n \"\"\"\n\n return tag_service.query_stock_tag_stats(query_stock_tag_stats_model=query_stock_tag_stats_model)\n\n\n@work_router.get(\"/get_main_tags_in_stock_pool\", response_model=List[str])\ndef get_main_tags_in_stock_pool(stock_pool_name: str):\n \"\"\"\n Get stock tag stats\n \"\"\"\n\n return tag_service.get_main_tags_in_stock_pool(stock_pool_name)\n\n\n@work_router.post(\"/activate_sub_tags\", response_model=ActivateSubTagsResultModel)\ndef activate_sub_tags(activate_sub_tags_model: ActivateSubTagsModel):\n \"\"\"\n Activate sub tags\n \"\"\"\n\n return tag_service.activate_sub_tags(activate_sub_tags_model=activate_sub_tags_model)\n\n\n@work_router.post(\"/batch_set_stock_tags\", response_model=List[StockTagsModel])\ndef batch_set_stock_tags(batch_set_stock_tags_model: BatchSetStockTagsModel):\n return tag_service.batch_set_stock_tags(batch_set_stock_tags_model=batch_set_stock_tags_model)\n\n\n@work_router.post(\"/build_main_tag_industry_relation\", response_model=str)\ndef build_main_tag_industry_relation(build_relation_model: BuildMainTagIndustryRelationModel):\n tag_service.build_main_tag_industry_relation(build_relation_model=build_relation_model)\n return \"success\"\n\n\n@work_router.post(\"/build_main_tag_sub_tag_relation\", response_model=str)\ndef build_main_tag_sub_tag_relation(build_relation_model: BuildMainTagSubTagRelationModel):\n tag_service.build_main_tag_sub_tag_relation(build_relation_model=build_relation_model)\n return \"ok\"\n\n\n@work_router.post(\"/change_main_tag\", response_model=List[StockTagsModel])\ndef change_main_tag(change_main_tag_model: ChangeMainTagModel):\n return tag_service.change_main_tag(change_main_tag_model=change_main_tag_model)\n"
},
{
"path": "src/zvt/samples/__init__.py",
"content": "# -*- coding: utf-8 -*-\nfrom .stock_traders import *\n"
},
{
"path": "src/zvt/samples/stock_traders.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel\nfrom zvt.factors.ma.ma_factor import CrossMaFactor\nfrom zvt.factors.macd.macd_factor import BullFactor\nfrom zvt.trader.trader import StockTrader\n\n\nclass MyMaTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n return [\n CrossMaFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n windows=[5, 10],\n need_persist=False,\n adjust_type=adjust_type,\n )\n ]\n\n\nclass MyBullTrader(StockTrader):\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n return [\n BullFactor(\n entity_ids=entity_ids,\n entity_schema=entity_schema,\n exchanges=exchanges,\n codes=codes,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n adjust_type=adjust_type,\n )\n ]\n\n\nif __name__ == \"__main__\":\n # single stock with cross ma factor\n MyMaTrader(\n codes=[\"000338\"],\n level=IntervalLevel.LEVEL_1DAY,\n start_timestamp=\"2018-01-01\",\n end_timestamp=\"2019-06-30\",\n trader_name=\"000338_ma_trader\",\n ).run()\n\n # single stock with bull factor\n # MyBullTrader(codes=['000338'], level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01',\n # end_timestamp='2019-06-30', trader_name='000338_bull_trader').run()\n\n # multiple stocks with cross ma factor\n # MyMaTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01',\n # end_timestamp='2019-06-30', trader_name='sample_stocks_ma_trader').run()\n\n # multiple stocks with bull factor\n # MyBullTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01',\n # end_timestamp='2019-06-30', trader_name='sample_stocks_bull_trader').run()\n"
},
{
"path": "src/zvt/sched/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "src/zvt/sched/sched.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport os\n\nfrom apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor\nfrom apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import ZVT_HOME\n\nlogger = logging.getLogger(__name__)\n\njobs_db_path = os.path.join(ZVT_HOME, \"jobs.db\")\n\n\njobstores = {\"default\": SQLAlchemyJobStore(url=f\"sqlite:///{jobs_db_path}\")}\n\nexecutors = {\"default\": ThreadPoolExecutor(20), \"processpool\": ProcessPoolExecutor(5)}\njob_defaults = {\"coalesce\": False, \"max_instances\": 1}\n\nzvt_scheduler = BackgroundScheduler(jobstores=jobstores, executors=executors, job_defaults=job_defaults)\n\n\ndef sched_tasks():\n import platform\n\n if platform.system() == \"Windows\":\n try:\n from zvt.broker.qmt.qmt_quote import record_stock_quote\n\n zvt_scheduler.add_job(func=record_stock_quote, trigger=\"cron\", hour=9, minute=19, day_of_week=\"mon-fri\")\n except Exception as e:\n logger.error(\"QMT not work\", e)\n else:\n logger.warning(\"QMT need run in Windows!\")\n\n zvt_scheduler.start()\n\n\nif __name__ == \"__main__\":\n sched_tasks()\n"
},
{
"path": "src/zvt/tag/__init__.py",
"content": "# -*- coding: utf-8 -*-#\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/tag/ai_suggestion.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport logging\nimport re\nfrom typing import List\n\nimport pandas as pd\nfrom openai import OpenAI\nfrom sqlalchemy import func, or_\n\nimport zvt.contract.api as contract_api\nfrom zvt import zvt_config\nfrom zvt.domain import StockNews, Stock\nfrom zvt.tag.tag_utils import match_tag\nfrom zvt.utils.time_utils import date_time_by_interval, current_date\n\nlogger = logging.getLogger(__name__)\n\n\ndef normalize_tag_suggestions(tag_suggestions):\n for direction in [\"up\", \"down\"]:\n if direction in tag_suggestions:\n for item in tag_suggestions[direction]:\n tag_type, tag = match_tag(item[\"block\"])\n item[\"tag\"] = tag\n item[\"tag_type\"] = tag_type\n if item[\"stocks\"]:\n stocks = Stock.query_data(\n filters=[Stock.name.in_(item[\"stocks\"])], return_type=\"dict\", provider=\"em\"\n )\n if len(stocks) != len(item[\"stocks\"]):\n logger.warning(\n f\"Stocks not found in zvt:{set(item['stocks']) - set([item['name'] for item in stocks])}\"\n )\n item[\"stocks\"] = [{\"entity_id\": item[\"entity_id\"], \"name\": item[\"name\"]} for item in stocks]\n return tag_suggestions\n\n\ndef set_stock_news_tag_suggestions(stock_news, tag_suggestions, session):\n if stock_news.news_analysis:\n stock_news.news_analysis = dict(stock_news.news_analysis)\n else:\n stock_news.news_analysis = {}\n\n result = normalize_tag_suggestions(tag_suggestions)\n logger.debug(result)\n stock_news.news_analysis[\"tag_suggestions\"] = result\n session.add(stock_news)\n session.commit()\n\n\ndef build_tag_suggestions(entity_id):\n with contract_api.DBSession(provider=\"em\", data_schema=StockNews)() as session:\n start_date = date_time_by_interval(current_date(), -30)\n datas: List[StockNews] = StockNews.query_data(\n entity_id=entity_id,\n limit=1,\n order=StockNews.timestamp.desc(),\n filters=[\n StockNews.timestamp >= start_date,\n func.json_extract(StockNews.news_analysis, f'$.\"tag_suggestions\"') != None,\n ],\n return_type=\"domain\",\n )\n if datas:\n latest_data = datas[0]\n else:\n latest_data = None\n\n filters = [\n or_(\n StockNews.news_title.like(\"%上涨%\"),\n StockNews.news_title.like(\"%拉升%\"),\n StockNews.news_title.like(\"%涨停%\"),\n StockNews.news_title.like(\"%下跌%\"),\n StockNews.news_title.like(\"%跌停%\"),\n ),\n StockNews.timestamp >= start_date,\n func.json_extract(StockNews.news_analysis, f'$.\"tag_suggestions\"') == None,\n ]\n if latest_data:\n filters = filters + [\n StockNews.timestamp >= latest_data.timestamp,\n StockNews.news_code != latest_data.news_code,\n ]\n\n stock_news_list: List[StockNews] = StockNews.query_data(\n entity_id=entity_id,\n session=session,\n order=StockNews.news_code.asc(),\n return_type=\"domain\",\n filters=filters,\n )\n\n if not stock_news_list:\n logger.info(\"all stock news has been analyzed\")\n return\n\n example = {\n \"up\": [{\"block\": \"block_a\", \"stocks\": [\"stock_a\", \"stock_b\"]}],\n \"down\": [{\"block\": \"block_b\", \"stocks\": [\"stock_1\", \"stock_2\"]}],\n }\n\n client = OpenAI(\n api_key=zvt_config[\"qwen_api_key\"],\n base_url=\"https://dashscope.aliyuncs.com/compatible-mode/v1\",\n )\n\n for stock_news in stock_news_list:\n # same news\n if latest_data and (stock_news.news_code == latest_data.news_code):\n tag_suggestions = latest_data.news_analysis.get(\"tag_suggestions\")\n if tag_suggestions:\n set_stock_news_tag_suggestions(stock_news, tag_suggestions, session)\n continue\n\n news_title = stock_news.news_title\n news_content = stock_news.news_content\n logger.info(news_title)\n logger.info(news_content)\n\n completion = client.chat.completions.create(\n model=\"qwen-max\",\n messages=[\n {\n \"role\": \"system\",\n \"content\": f\"请从新闻标题和内容中识别是上涨还是下跌，提取相应的板块和个股，按照格式: {example} 输出一个 JSON 对象\",\n },\n {\n \"role\": \"user\",\n \"content\": f\"新闻标题:{news_title}, 新闻内容:{news_content}\",\n },\n ],\n temperature=0.2,\n )\n content = completion.choices[0].message.content\n content = content.replace(\"```json\", \"\")\n content = content.replace(\"```\", \"\")\n content = re.sub(r\"\\s+\", \"\", content)\n logger.info(f\"message content: {content}\")\n tag_suggestions = json.loads(content)\n set_stock_news_tag_suggestions(stock_news, tag_suggestions, session)\n\n\ndef extract_info(tag_dict):\n extracted_info = []\n for key, value in tag_dict.items():\n for item in value:\n extracted_info.append({\"tag\": item[\"tag\"], \"stocks\": [stock[\"name\"] for stock in item[\"stocks\"]]})\n return extracted_info\n\n\ndef build_tag_suggestions_stats():\n with contract_api.DBSession(provider=\"em\", data_schema=StockNews)() as session:\n start_date = date_time_by_interval(current_date(), -10)\n stock_news_list: List[StockNews] = StockNews.query_data(\n session=session,\n order=StockNews.timestamp.desc(),\n distinct=StockNews.news_code,\n return_type=\"dict\",\n filters=[\n StockNews.timestamp >= start_date,\n func.json_extract(StockNews.news_analysis, f'$.\"tag_suggestions\"') != None,\n ],\n )\n datas = []\n for stock_news in stock_news_list:\n tag_suggestions = stock_news[\"news_analysis\"].get(\"tag_suggestions\")\n if tag_suggestions:\n for key in (\"up\", \"down\"):\n suggestions = tag_suggestions.get(key)\n if suggestions:\n datas = datas + [\n {\n \"tag\": item[\"tag\"],\n \"tag_type\": item[\"tag_type\"],\n \"entity_ids\": [stock[\"entity_id\"] for stock in item[\"stocks\"]],\n \"stock_names\": [stock[\"name\"] for stock in item[\"stocks\"]],\n }\n for item in suggestions\n ]\n df = pd.DataFrame.from_records(data=datas)\n grouped_df = (\n df.groupby(\"tag\")\n .agg(\n tag_count=(\"tag\", \"count\"),\n tag_type=(\"tag_type\", \"first\"),\n entity_ids=(\"entity_ids\", \"sum\"),\n stock_names=(\"stock_names\", \"sum\"),\n )\n .reset_index()\n )\n grouped_df[\"entity_ids\"] = grouped_df[\"entity_ids\"].apply(set).apply(list)\n grouped_df[\"stock_names\"] = grouped_df[\"stock_names\"].apply(set).apply(list)\n grouped_df[\"entity_ids_count\"] = grouped_df[\"entity_ids\"].apply(len)\n\n sorted_df = grouped_df.sort_values(by=[\"tag_count\", \"entity_ids_count\"], ascending=[False, False])\n return sorted_df.to_dict(orient=\"records\")\n\n\nif __name__ == \"__main__\":\n build_tag_suggestions_stats()\n"
},
{
"path": "src/zvt/tag/common.py",
"content": "# -*- coding: utf-8 -*-\nfrom enum import Enum\n\n\nclass StockPoolType(Enum):\n system = \"system\"\n custom = \"custom\"\n dynamic = \"dynamic\"\n\n\nclass DynamicPoolType(Enum):\n limit_up = \"limit_up\"\n limit_down = \"limit_down\"\n\n\nclass InsertMode(Enum):\n overwrite = \"overwrite\"\n append = \"append\"\n\n\nclass TagType(Enum):\n #: A tag is a main tag due to its extensive capacity.\n main_tag = \"main_tag\"\n sub_tag = \"sub_tag\"\n hidden_tag = \"hidden_tag\"\n\n\nclass TagStatsQueryType(Enum):\n simple = \"simple\"\n details = \"details\"\n\n\n# the __all__ is generated\n__all__ = [\"StockPoolType\", \"DynamicPoolType\", \"InsertMode\", \"TagType\", \"TagStatsQueryType\"]\n"
},
{
"path": "src/zvt/tag/tag_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import Dict, Union, List, Optional\n\nfrom pydantic import field_validator, Field\nfrom pydantic_core.core_schema import ValidationInfo\n\nfrom zvt.contract.model import MixinModel, CustomModel\nfrom zvt.tag.common import StockPoolType, TagType, TagStatsQueryType, InsertMode\nfrom zvt.tag.tag_utils import get_stock_pool_names\n\n\nclass TagInfoModel(MixinModel):\n tag: str\n tag_reason: Optional[str] = Field(default=None)\n main_tag: Optional[str] = Field(default=None)\n\n\nclass CreateTagInfoModel(CustomModel):\n tag: str\n tag_reason: Optional[str] = Field(default=None)\n\n\nclass IndustryInfoModel(MixinModel):\n industry_name: str\n description: str\n # related main tag\n main_tag: str\n\n\nclass MainTagIndustryRelation(CustomModel):\n main_tag: str\n industry_list: List[str]\n\n\nclass BuildMainTagIndustryRelationModel(CustomModel):\n main_tag: str\n industry_list: List[str]\n activate: bool = Field(default=False)\n\n\nclass MainTagSubTagRelation(CustomModel):\n main_tag: str\n sub_tag_list: List[str]\n\n\nclass BuildMainTagSubTagRelationModel(CustomModel):\n main_tag: str\n sub_tag_list: List[str]\n activate: bool = Field(default=False)\n\n\nclass ChangeMainTagModel(CustomModel):\n current_main_tag: str\n new_main_tag: str\n\n\nclass StockTagsModel(MixinModel):\n main_tag: Optional[str] = Field(default=None)\n main_tag_reason: Optional[str] = Field(default=None)\n main_tags: Dict[str, str]\n\n sub_tag: Optional[str] = Field(default=None)\n sub_tag_reason: Optional[str] = Field(default=None)\n sub_tags: Union[Dict[str, str], None]\n\n active_hidden_tags: Union[Dict[str, str], None]\n hidden_tags: Union[Dict[str, str], None]\n set_by_user: bool = False\n\n\nclass SimpleStockTagsModel(CustomModel):\n entity_id: str\n name: str\n main_tag: Optional[str] = Field(default=None)\n main_tag_reason: Optional[str] = Field(default=None)\n main_tags: Dict[str, str]\n sub_tag: Union[str, None]\n sub_tag_reason: Optional[str] = Field(default=None)\n sub_tags: Union[Dict[str, str], None]\n active_hidden_tags: Union[Dict[str, str], None]\n controlling_holder_parent: Optional[str] = Field(default=None)\n top_ten_ratio: Optional[float] = Field(default=None)\n\n\nclass QueryStockTagsModel(CustomModel):\n entity_ids: List[str]\n\n\nclass QuerySimpleStockTagsModel(CustomModel):\n entity_ids: List[str]\n\n\nclass BatchSetStockTagsModel(CustomModel):\n entity_ids: List[str]\n tag: str\n tag_reason: Optional[str] = Field(default=None)\n tag_type: TagType\n\n\nclass TagParameter(CustomModel):\n main_tag: str\n main_tag_reason: Optional[str] = Field(default=None)\n sub_tag: Optional[str] = Field(default=None)\n sub_tag_reason: Optional[str] = Field(default=None)\n hidden_tag: Optional[str] = Field(default=None)\n hidden_tag_reason: Optional[str] = Field(default=None)\n\n\nclass StockTagOptions(CustomModel):\n main_tag: Optional[str] = Field(default=None)\n sub_tag: Optional[str] = Field(default=None)\n # hidden_tags: Optional[List[str]] = Field(default=None)\n active_hidden_tags: Optional[Dict[str, str]] = Field(default=None)\n main_tag_options: List[CreateTagInfoModel]\n sub_tag_options: List[CreateTagInfoModel]\n hidden_tag_options: List[CreateTagInfoModel]\n\n\nclass SetStockTagsModel(CustomModel):\n entity_id: str\n main_tag: str\n main_tag_reason: Optional[str] = Field(default=None)\n sub_tag: Optional[str] = Field(default=None)\n sub_tag_reason: Optional[str] = Field(default=None)\n active_hidden_tags: Optional[Dict[str, str]] = Field(default=None)\n\n # @field_validator(\"main_tag\")\n # @classmethod\n # def main_tag_must_be_in(cls, v: str) -> str:\n # if v not in get_main_tags():\n # raise ValueError(f\"main_tag: {v} must be created at main_tag_info at first\")\n # return v\n #\n # @field_validator(\"sub_tag\")\n # @classmethod\n # def sub_tag_must_be_in(cls, v: str) -> str:\n # if v and (v not in get_sub_tags()):\n # raise ValueError(f\"sub_tag: {v} must be created at sub_tag_info at first\")\n # return v\n #\n # @field_validator(\"active_hidden_tags\")\n # @classmethod\n # def hidden_tag_must_be_in(cls, v: Union[Dict[str, str], None]) -> Union[Dict[str, str], None]:\n # if v:\n # for item in v.keys():\n # if item not in get_hidden_tags():\n # raise ValueError(f\"hidden_tag: {v} must be created at hidden_tag_info at first\")\n # return v\n\n\nclass StockPoolModel(MixinModel):\n stock_pool_name: str\n entity_ids: List[str]\n\n\nclass StockPoolInfoModel(MixinModel):\n stock_pool_type: StockPoolType\n stock_pool_name: str\n\n\nclass CreateStockPoolInfoModel(CustomModel):\n stock_pool_type: StockPoolType\n stock_pool_name: str\n\n @field_validator(\"stock_pool_name\")\n @classmethod\n def stock_pool_name_existed(cls, v: str) -> str:\n if v in get_stock_pool_names():\n raise ValueError(f\"stock_pool_name: {v} has been used\")\n return v\n\n\nclass StockPoolsModel(MixinModel):\n stock_pool_name: str\n entity_ids: List[str]\n\n\nclass CreateStockPoolsModel(CustomModel):\n entity_type: str = Field(default=\"stock\")\n stock_pool_name: str\n entity_ids: List[str]\n insert_mode: InsertMode = Field(default=InsertMode.overwrite)\n\n # @field_validator(\"stock_pool_name\")\n # @classmethod\n # def stock_pool_name_must_be_in(cls, v: str) -> str:\n # if v:\n # if v not in get_stock_pool_names():\n # raise ValueError(f\"stock_pool_name: {v} must be created at stock_pool_info at first\")\n # return v\n\n\nclass QueryStockTagStatsModel(CustomModel):\n stock_pool_name: Optional[str] = Field(default=None)\n entity_ids: Optional[List[str]] = Field(default=None)\n query_type: Optional[TagStatsQueryType] = Field(default=TagStatsQueryType.details)\n\n @field_validator(\"stock_pool_name\", \"entity_ids\")\n @classmethod\n def phone_or_mobile_must_set_only_one(cls, v, validation_info: ValidationInfo, **kwargs):\n if validation_info.field_name == \"stock_pool_name\":\n other_field = \"entity_ids\"\n else:\n other_field = \"stock_pool_name\"\n\n other_value = kwargs.get(other_field)\n\n if v and other_value:\n raise ValueError(f\"Only one of 'stock_pool_name' or 'entity_ids' should be set.\")\n elif not v and not other_value:\n raise ValueError(\"Either 'stock_pool_name' or 'entity_ids' must be set.\")\n\n return v\n\n @field_validator(\"stock_pool_name\")\n @classmethod\n def stock_pool_name_must_be_in(cls, v: str) -> str:\n if v:\n if v not in get_stock_pool_names():\n raise ValueError(f\"stock_pool_name: {v} not existed\")\n return v\n\n\nclass StockTagDetailsModel(CustomModel):\n entity_id: str\n main_tag: Optional[str] = Field(default=None)\n sub_tag: Optional[str] = Field(default=None)\n hidden_tags: Union[List[str], None]\n\n #: 代码\n code: str\n #: 名字\n name: str\n #: 减持\n recent_reduction: Optional[bool] = Field(default=None)\n #: 增持\n recent_acquisition: Optional[bool] = Field(default=None)\n #: 解禁\n recent_unlock: Optional[bool] = Field(default=None)\n #: 增发配股\n recent_additional_or_rights_issue: Optional[bool] = Field(default=None)\n #: 业绩利好\n recent_positive_earnings_news: Optional[bool] = Field(default=None)\n #: 业绩利空\n recent_negative_earnings_news: Optional[bool] = Field(default=None)\n #: 上榜次数\n recent_dragon_and_tiger_count: Optional[int] = Field(default=None)\n #: 违规行为\n recent_violation_alert: Optional[bool] = Field(default=None)\n #: 利好\n recent_positive_news: Optional[bool] = Field(default=None)\n #: 利空\n recent_negative_news: Optional[bool] = Field(default=None)\n #: 新闻总结\n recent_news_summary: Optional[Dict[str, str]] = Field(default=None)\n\n\nclass StockTagStatsModel(MixinModel):\n main_tag: str\n turnover: Optional[float] = Field(default=None)\n entity_count: Optional[int] = Field(default=None)\n position: Optional[int] = Field(default=None)\n is_main_line: Optional[bool] = Field(default=None)\n main_line_continuous_days: Optional[int] = Field(default=None)\n entity_ids: Optional[List[str]] = Field(default=None)\n stock_details: Optional[List[StockTagDetailsModel]] = Field(default=None)\n\n\nclass ActivateSubTagsModel(CustomModel):\n sub_tags: List[str]\n\n\nclass ActivateSubTagsResultModel(CustomModel):\n tag_entity_ids: Dict[str, Union[List[str], None]]\n\n\n# the __all__ is generated\n__all__ = [\n \"TagInfoModel\",\n \"CreateTagInfoModel\",\n \"StockTagsModel\",\n \"SimpleStockTagsModel\",\n \"QueryStockTagsModel\",\n \"QuerySimpleStockTagsModel\",\n \"BatchSetStockTagsModel\",\n \"TagParameter\",\n \"StockTagOptions\",\n \"SetStockTagsModel\",\n \"StockPoolModel\",\n \"StockPoolInfoModel\",\n \"CreateStockPoolInfoModel\",\n \"StockPoolsModel\",\n \"CreateStockPoolsModel\",\n \"QueryStockTagStatsModel\",\n \"StockTagDetailsModel\",\n \"StockTagStatsModel\",\n \"ActivateSubTagsModel\",\n \"ActivateSubTagsResultModel\",\n]\n"
},
{
"path": "src/zvt/tag/tag_schemas.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom sqlalchemy import Column, String, JSON, Boolean, Float, Integer\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nStockTagsBase = declarative_base()\n\n\nclass IndustryInfo(StockTagsBase, Mixin):\n __tablename__ = \"industry_info\"\n\n industry_name = Column(String, unique=True)\n description = Column(String)\n # related main tag\n main_tag = Column(String)\n\n\nclass MainTagInfo(StockTagsBase, Mixin):\n __tablename__ = \"main_tag_info\"\n\n tag = Column(String, unique=True)\n tag_reason = Column(String)\n\n\nclass SubTagInfo(StockTagsBase, Mixin):\n __tablename__ = \"sub_tag_info\"\n\n tag = Column(String, unique=True)\n tag_reason = Column(String)\n\n # related main tag\n main_tag = Column(String)\n\n\nclass HiddenTagInfo(StockTagsBase, Mixin):\n __tablename__ = \"hidden_tag_info\"\n\n tag = Column(String, unique=True)\n tag_reason = Column(String)\n\n\nclass StockTags(StockTagsBase, Mixin):\n \"\"\"\n Schema for storing stock tags\n \"\"\"\n\n __tablename__ = \"stock_tags\"\n\n entity_type = Column(String(length=64))\n\n code = Column(String(length=64))\n name = Column(String(length=128))\n\n main_tag = Column(String)\n main_tag_reason = Column(String)\n main_tags = Column(JSON)\n\n sub_tag = Column(String)\n sub_tag_reason = Column(String)\n sub_tags = Column(JSON)\n\n active_hidden_tags = Column(JSON)\n hidden_tags = Column(JSON)\n set_by_user = Column(Boolean, default=False)\n\n\nclass StockSystemTags(StockTagsBase, Mixin):\n __tablename__ = \"stock_system_tags\"\n #: 编码\n code = Column(String(length=64))\n #: 名字\n name = Column(String(length=128))\n #: 减持\n recent_reduction = Column(Boolean)\n #: 增持\n recent_acquisition = Column(Boolean)\n #: 解禁\n recent_unlock = Column(Boolean)\n #: 增发配股\n recent_additional_or_rights_issue = Column(Boolean)\n #: 业绩利好\n recent_positive_earnings_news = Column(Boolean)\n #: 业绩利空\n recent_negative_earnings_news = Column(Boolean)\n #: 上榜次数\n recent_dragon_and_tiger_count = Column(Integer)\n #: 违规行为\n recent_violation_alert = Column(Boolean)\n #: 利好\n recent_positive_news = Column(Boolean)\n #: 利空\n recent_negative_news = Column(Boolean)\n #: 新闻总结\n recent_news_summary = Column(JSON)\n\n\nclass StockPoolInfo(StockTagsBase, Mixin):\n __tablename__ = \"stock_pool_info\"\n stock_pool_type = Column(String)\n stock_pool_name = Column(String, unique=True)\n\n\nclass StockPools(StockTagsBase, Mixin):\n __tablename__ = \"stock_pools\"\n\n entity_type = Column(String(length=64))\n\n stock_pool_name = Column(String)\n entity_ids = Column(JSON)\n\n\nclass TagStats(StockTagsBase, Mixin):\n __tablename__ = \"tag_stats\"\n\n entity_type = Column(String(length=64))\n\n stock_pool_name = Column(String)\n main_tag = Column(String)\n turnover = Column(Float)\n entity_count = Column(Integer)\n position = Column(Integer)\n is_main_line = Column(Boolean)\n main_line_continuous_days = Column(Integer)\n entity_ids = Column(JSON)\n\n\nregister_schema(db_name=\"stock_tags\", schema_base=StockTagsBase, internal=True)\n\n\n# the __all__ is generated\n__all__ = [\n \"IndustryInfo\",\n \"MainTagInfo\",\n \"SubTagInfo\",\n \"HiddenTagInfo\",\n \"StockTags\",\n \"StockSystemTags\",\n \"StockPoolInfo\",\n \"StockPools\",\n \"TagStats\",\n]\n"
},
{
"path": "src/zvt/tag/tag_service.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nfrom typing import List\n\nimport pandas as pd\nfrom fastapi import HTTPException\nfrom sqlalchemy import func, text\n\nimport zvt.contract.api as contract_api\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.contract.api import decode_entity_id\nfrom zvt.domain import BlockStock, Block, Stock, Stockus, Stockhk\nfrom zvt.tag.common import TagType, TagStatsQueryType, StockPoolType, InsertMode\nfrom zvt.tag.tag_models import (\n SetStockTagsModel,\n CreateStockPoolInfoModel,\n CreateStockPoolsModel,\n QueryStockTagStatsModel,\n ActivateSubTagsModel,\n BatchSetStockTagsModel,\n TagParameter,\n CreateTagInfoModel,\n StockTagOptions,\n ChangeMainTagModel,\n BuildMainTagIndustryRelationModel,\n)\nfrom zvt.tag.tag_schemas import (\n StockTags,\n StockPools,\n StockPoolInfo,\n TagStats,\n StockSystemTags,\n MainTagInfo,\n SubTagInfo,\n HiddenTagInfo,\n IndustryInfo,\n)\nfrom zvt.tag.tag_utils import (\n get_sub_tags,\n get_stock_pool_names,\n get_main_tag_by_sub_tag,\n get_main_tag_by_industry,\n)\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, current_date, now_pd_timestamp\nfrom zvt.utils.utils import fill_dict, compare_dicts, flatten_list\n\nlogger = logging.getLogger(__name__)\n\n\ndef _stock_tags_need_update(stock_tags: StockTags, set_stock_tags_model: SetStockTagsModel):\n if (\n stock_tags.main_tag != set_stock_tags_model.main_tag\n or stock_tags.main_tag_reason != set_stock_tags_model.main_tag_reason\n or stock_tags.sub_tag != set_stock_tags_model.sub_tag\n or stock_tags.sub_tag_reason != set_stock_tags_model.sub_tag_reason\n or not compare_dicts(stock_tags.active_hidden_tags, set_stock_tags_model.active_hidden_tags)\n ):\n return True\n return False\n\n\ndef get_stock_tag_options(entity_id: str) -> StockTagOptions:\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n datas: List[StockTags] = StockTags.query_data(\n entity_id=entity_id, order=StockTags.timestamp.desc(), limit=1, return_type=\"domain\", session=session\n )\n main_tag_options = []\n sub_tag_options = []\n hidden_tag_options = []\n\n main_tag = None\n sub_tag = None\n active_hidden_tags = None\n stock_tags = None\n if datas:\n stock_tags = datas[0]\n main_tag = stock_tags.main_tag\n sub_tag = stock_tags.sub_tag\n\n if stock_tags.main_tags:\n main_tag_options = [\n CreateTagInfoModel(tag=tag, tag_reason=tag_reason)\n for tag, tag_reason in stock_tags.main_tags.items()\n ]\n\n if stock_tags.sub_tags:\n sub_tag_options = [\n CreateTagInfoModel(tag=tag, tag_reason=tag_reason)\n for tag, tag_reason in stock_tags.sub_tags.items()\n ]\n\n if stock_tags.active_hidden_tags:\n active_hidden_tags = stock_tags.active_hidden_tags\n\n if stock_tags.hidden_tags:\n hidden_tag_options = [\n CreateTagInfoModel(tag=tag, tag_reason=tag_reason)\n for tag, tag_reason in stock_tags.hidden_tags.items()\n ]\n\n main_tags_info: List[MainTagInfo] = MainTagInfo.query_data(session=session, return_type=\"domain\")\n if not main_tag:\n main_tag = main_tags_info[0].tag\n\n main_tag_options = main_tag_options + [\n CreateTagInfoModel(tag=item.tag, tag_reason=item.tag_reason)\n for item in main_tags_info\n if not stock_tags or (not stock_tags.main_tags) or (item.tag not in stock_tags.main_tags)\n ]\n\n sub_tags_info: List[SubTagInfo] = SubTagInfo.query_data(session=session, return_type=\"domain\")\n if not sub_tag:\n sub_tag = sub_tags_info[0].tag\n sub_tag_options = sub_tag_options + [\n CreateTagInfoModel(tag=item.tag, tag_reason=item.tag_reason)\n for item in sub_tags_info\n if not stock_tags or (not stock_tags.sub_tags) or (item.tag not in stock_tags.sub_tags)\n ]\n\n hidden_tags_info: List[HiddenTagInfo] = HiddenTagInfo.query_data(session=session, return_type=\"domain\")\n hidden_tag_options = hidden_tag_options + [\n CreateTagInfoModel(tag=item.tag, tag_reason=item.tag_reason)\n for item in hidden_tags_info\n if not stock_tags or (not stock_tags.hidden_tags) or (item.tag not in stock_tags.hidden_tags)\n ]\n\n return StockTagOptions(\n main_tag=main_tag,\n sub_tag=sub_tag,\n active_hidden_tags=active_hidden_tags,\n main_tag_options=main_tag_options,\n sub_tag_options=sub_tag_options,\n hidden_tag_options=hidden_tag_options,\n )\n\n\ndef build_stock_tags(\n set_stock_tags_model: SetStockTagsModel, timestamp: pd.Timestamp, set_by_user: bool, keep_current=False\n):\n logger.info(set_stock_tags_model)\n\n main_tag_info = CreateTagInfoModel(\n tag=set_stock_tags_model.main_tag, tag_reason=set_stock_tags_model.main_tag_reason\n )\n if not is_tag_info_existed(tag=main_tag_info.tag, tag_type=TagType.main_tag):\n create_tag_info(tag_info=main_tag_info, tag_type=TagType.main_tag)\n\n if set_stock_tags_model.sub_tag:\n sub_tag_info = CreateTagInfoModel(\n tag=set_stock_tags_model.sub_tag, tag_reason=set_stock_tags_model.sub_tag_reason\n )\n if not is_tag_info_existed(tag=sub_tag_info.tag, tag_type=TagType.sub_tag):\n create_tag_info(tag_info=sub_tag_info, tag_type=TagType.sub_tag)\n\n if set_stock_tags_model.active_hidden_tags:\n for tag in set_stock_tags_model.active_hidden_tags:\n hidden_tag_info = CreateTagInfoModel(tag=tag, tag_reason=set_stock_tags_model.active_hidden_tags.get(tag))\n if not is_tag_info_existed(tag=hidden_tag_info.tag, tag_type=TagType.hidden_tag):\n create_tag_info(tag_info=hidden_tag_info, tag_type=TagType.hidden_tag)\n\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n entity_id = set_stock_tags_model.entity_id\n main_tags = {}\n sub_tags = {}\n hidden_tags = {}\n\n entity_type, _, _ = decode_entity_id(entity_id)\n\n datas = StockTags.query_data(\n session=session,\n entity_id=entity_id,\n filters=[StockTags.entity_type == entity_type],\n limit=1,\n return_type=\"domain\",\n )\n\n if datas:\n current_stock_tags: StockTags = datas[0]\n\n # nothing change\n if not _stock_tags_need_update(current_stock_tags, set_stock_tags_model):\n logger.info(f\"Not change stock_tags for {set_stock_tags_model.entity_id}\")\n return current_stock_tags\n\n if current_stock_tags.main_tags:\n main_tags = dict(current_stock_tags.main_tags)\n if current_stock_tags.sub_tags:\n sub_tags = dict(current_stock_tags.sub_tags)\n if current_stock_tags.hidden_tags:\n hidden_tags = dict(current_stock_tags.hidden_tags)\n\n else:\n current_stock_tags = StockTags(\n entity_type=entity_type,\n id=f\"{entity_id}_tags\",\n entity_id=entity_id,\n timestamp=timestamp,\n )\n\n # update tag\n if not keep_current:\n current_stock_tags.main_tag = set_stock_tags_model.main_tag\n current_stock_tags.main_tag_reason = set_stock_tags_model.main_tag_reason\n\n if set_stock_tags_model.sub_tag:\n current_stock_tags.sub_tag = set_stock_tags_model.sub_tag\n if set_stock_tags_model.sub_tag_reason:\n current_stock_tags.sub_tag_reason = set_stock_tags_model.sub_tag_reason\n # could update to None\n current_stock_tags.active_hidden_tags = set_stock_tags_model.active_hidden_tags\n # update tags\n main_tags[set_stock_tags_model.main_tag] = set_stock_tags_model.main_tag_reason\n if set_stock_tags_model.sub_tag:\n sub_tags[set_stock_tags_model.sub_tag] = set_stock_tags_model.sub_tag_reason\n if set_stock_tags_model.active_hidden_tags:\n for k, v in set_stock_tags_model.active_hidden_tags.items():\n hidden_tags[k] = v\n current_stock_tags.main_tags = main_tags\n current_stock_tags.sub_tags = sub_tags\n current_stock_tags.hidden_tags = hidden_tags\n\n current_stock_tags.set_by_user = set_by_user\n\n session.add(current_stock_tags)\n session.commit()\n session.refresh(current_stock_tags)\n return current_stock_tags\n\n\ndef build_tag_parameter(tag_type: TagType, tag, tag_reason, stock_tag: StockTags):\n hidden_tag = None\n hidden_tag_reason = None\n\n if tag_type == TagType.main_tag:\n main_tag = tag\n if main_tag in stock_tag.main_tags:\n main_tag_reason = stock_tag.main_tags.get(main_tag, tag_reason)\n else:\n main_tag_reason = tag_reason\n sub_tag = stock_tag.sub_tag\n sub_tag_reason = stock_tag.sub_tag_reason\n elif tag_type == TagType.sub_tag:\n sub_tag = tag\n if sub_tag in stock_tag.sub_tags:\n sub_tag_reason = stock_tag.sub_tags.get(sub_tag, tag_reason)\n else:\n sub_tag_reason = tag_reason\n main_tag = stock_tag.main_tag\n main_tag_reason = stock_tag.main_tag_reason\n elif tag_type == TagType.hidden_tag:\n hidden_tag = tag\n if stock_tag.hidden_tags and (hidden_tag in stock_tag.hidden_tags):\n hidden_tag_reason = stock_tag.hidden_tags.get(hidden_tag, tag_reason)\n else:\n hidden_tag_reason = tag_reason\n\n sub_tag = stock_tag.sub_tag\n sub_tag_reason = stock_tag.sub_tag_reason\n\n main_tag = stock_tag.main_tag\n main_tag_reason = stock_tag.main_tag_reason\n\n else:\n assert False\n\n return TagParameter(\n main_tag=main_tag,\n main_tag_reason=main_tag_reason,\n sub_tag=sub_tag,\n sub_tag_reason=sub_tag_reason,\n hidden_tag=hidden_tag,\n hidden_tag_reason=hidden_tag_reason,\n )\n\n\ndef batch_set_stock_tags(batch_set_stock_tags_model: BatchSetStockTagsModel):\n if not batch_set_stock_tags_model.entity_ids:\n return []\n\n tag_info = CreateTagInfoModel(tag=batch_set_stock_tags_model.tag, tag_reason=batch_set_stock_tags_model.tag_reason)\n if not is_tag_info_existed(tag=tag_info.tag, tag_type=batch_set_stock_tags_model.tag_type):\n create_tag_info(tag_info=tag_info, tag_type=batch_set_stock_tags_model.tag_type)\n\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n tag_type = batch_set_stock_tags_model.tag_type\n if tag_type == TagType.main_tag:\n main_tag = batch_set_stock_tags_model.tag\n stock_tags: List[StockTags] = StockTags.query_data(\n entity_ids=batch_set_stock_tags_model.entity_ids,\n filters=[StockTags.main_tag != main_tag],\n session=session,\n return_type=\"domain\",\n )\n elif tag_type == TagType.sub_tag:\n sub_tag = batch_set_stock_tags_model.tag\n stock_tags: List[StockTags] = StockTags.query_data(\n entity_ids=batch_set_stock_tags_model.entity_ids,\n filters=[StockTags.sub_tag != sub_tag],\n session=session,\n return_type=\"domain\",\n )\n elif tag_type == TagType.hidden_tag:\n hidden_tag = batch_set_stock_tags_model.tag\n stock_tags: List[StockTags] = StockTags.query_data(\n entity_ids=batch_set_stock_tags_model.entity_ids,\n # 需要sqlite3版本>=3.37.0\n filters=[func.json_extract(StockTags.active_hidden_tags, f'$.\"{hidden_tag}\"') == None],\n session=session,\n return_type=\"domain\",\n )\n\n for stock_tag in stock_tags:\n tag_parameter: TagParameter = build_tag_parameter(\n tag_type=tag_type,\n tag=batch_set_stock_tags_model.tag,\n tag_reason=batch_set_stock_tags_model.tag_reason,\n stock_tag=stock_tag,\n )\n if tag_type == TagType.hidden_tag:\n active_hidden_tags = {batch_set_stock_tags_model.tag: batch_set_stock_tags_model.tag_reason}\n else:\n active_hidden_tags = stock_tag.active_hidden_tags\n\n set_stock_tags_model = SetStockTagsModel(\n entity_id=stock_tag.entity_id,\n main_tag=tag_parameter.main_tag,\n main_tag_reason=tag_parameter.main_tag_reason,\n sub_tag=tag_parameter.sub_tag,\n sub_tag_reason=tag_parameter.sub_tag_reason,\n active_hidden_tags=active_hidden_tags,\n )\n\n build_stock_tags(\n set_stock_tags_model=set_stock_tags_model,\n timestamp=now_pd_timestamp(),\n set_by_user=True,\n keep_current=False,\n )\n session.refresh(stock_tag)\n return stock_tags\n\n\ndef build_default_main_tag(entity_type=\"stock\", entity_ids=None, force_rebuild=False):\n \"\"\"\n build default main tag by industry\n\n :param entity_type:\n :param entity_ids: entity ids\n :param force_rebuild: always rebuild it if True otherwise only build which not existed\n \"\"\"\n if not entity_ids:\n entity_ids = get_entity_ids_by_filter(\n entity_type=entity_type, provider=\"em\", ignore_delist=True, ignore_st=False, ignore_new_stock=False\n )\n\n if entity_type == \"stock\":\n df_block = Block.query_data(provider=\"em\", filters=[Block.category == \"industry\"])\n industry_codes = df_block[\"code\"].tolist()\n block_stocks: List[BlockStock] = BlockStock.query_data(\n provider=\"em\",\n filters=[BlockStock.code.in_(industry_codes), BlockStock.stock_id.in_(entity_ids)],\n return_type=\"domain\",\n )\n entity_industry_mapping = {block_stock.stock_id: block_stock.name for block_stock in block_stocks}\n elif entity_type == \"stockus\":\n datas: List[Stockus] = Stockus.query_data(entity_ids=entity_ids, return_type=\"domain\")\n entity_industry_mapping = {item.entity_id: item.industry for item in datas}\n elif entity_type == \"stockhk\":\n datas: List[Stockhk] = Stockhk.query_data(entity_ids=entity_ids, return_type=\"domain\")\n entity_industry_mapping = {item.entity_id: item.industry for item in datas}\n else:\n raise ValueError(f\"Unsupported entity_type: {entity_type}\")\n\n for entity_id in entity_ids:\n stock_tags: List[StockTags] = StockTags.query_data(entity_id=entity_id, return_type=\"domain\")\n if not force_rebuild and stock_tags:\n logger.info(f\"{entity_id} main tag has been set.\")\n continue\n\n logger.info(f\"build main tag for: {entity_id}\")\n\n industry = entity_industry_mapping.get(entity_id)\n if industry:\n main_tag = get_main_tag_by_industry(industry_name=industry)\n main_tag_reason = f\"来自行业:{industry}\"\n else:\n main_tag = \"其他\"\n main_tag_reason = \"其他\"\n\n build_stock_tags(\n set_stock_tags_model=SetStockTagsModel(\n entity_id=entity_id,\n main_tag=main_tag,\n main_tag_reason=main_tag_reason,\n sub_tag=None,\n sub_tag_reason=None,\n active_hidden_tags=None,\n ),\n timestamp=now_pd_timestamp(),\n set_by_user=False,\n keep_current=False,\n )\n\n\ndef build_default_sub_tags(entity_ids=None):\n if not entity_ids:\n entity_ids = get_entity_ids_by_filter(\n provider=\"em\", ignore_delist=True, ignore_st=False, ignore_new_stock=False\n )\n\n for entity_id in entity_ids:\n logger.info(f\"build sub tag for: {entity_id}\")\n datas = StockTags.query_data(entity_id=entity_id, limit=1, return_type=\"domain\")\n if not datas:\n raise AssertionError(f\"Main tag must be set at first for {entity_id}\")\n\n current_stock_tags: StockTags = datas[0]\n keep_current = False\n if current_stock_tags.set_by_user:\n logger.info(f\"keep current tags set by user for: {entity_id}\")\n keep_current = True\n\n current_sub_tag = current_stock_tags.sub_tag\n filters = [BlockStock.stock_id == entity_id]\n if current_sub_tag:\n logger.info(f\"{entity_id} current_sub_tag: {current_sub_tag}\")\n current_sub_tags = current_stock_tags.sub_tags.keys()\n filters = filters + [BlockStock.name.notin_(current_sub_tags)]\n\n df_block = Block.query_data(provider=\"em\", filters=[Block.category == \"concept\"])\n concept_codes = df_block[\"code\"].tolist()\n filters = filters + [BlockStock.code.in_(concept_codes)]\n\n block_stocks: List[BlockStock] = BlockStock.query_data(\n provider=\"em\",\n filters=filters,\n return_type=\"domain\",\n )\n if not block_stocks:\n logger.info(f\"no block_stocks for: {entity_id}\")\n continue\n\n for block_stock in block_stocks:\n sub_tag = block_stock.name\n if sub_tag in get_sub_tags():\n sub_tag_reason = f\"来自概念:{sub_tag}\"\n\n main_tag = get_main_tag_by_sub_tag(sub_tag)\n main_tag_reason = sub_tag_reason\n if (main_tag == \"其他\" or not main_tag) and current_stock_tags.main_tag:\n main_tag = current_stock_tags.main_tag\n main_tag_reason = current_stock_tags.main_tag_reason\n\n build_stock_tags(\n set_stock_tags_model=SetStockTagsModel(\n entity_id=entity_id,\n main_tag=main_tag,\n main_tag_reason=main_tag_reason,\n sub_tag=sub_tag,\n sub_tag_reason=sub_tag_reason,\n active_hidden_tags=current_stock_tags.active_hidden_tags,\n ),\n timestamp=now_pd_timestamp(),\n set_by_user=False,\n keep_current=keep_current,\n )\n else:\n logger.info(f\"ignore {sub_tag} not in sub_tag_info yet\")\n\n\ndef get_tag_info_schema(tag_type: TagType):\n if tag_type == TagType.main_tag:\n data_schema = MainTagInfo\n elif tag_type == TagType.sub_tag:\n data_schema = SubTagInfo\n elif tag_type == TagType.hidden_tag:\n data_schema = HiddenTagInfo\n else:\n assert False\n\n return data_schema\n\n\ndef is_tag_info_existed(tag: str, tag_type: TagType):\n data_schema = get_tag_info_schema(tag_type=tag_type)\n with contract_api.DBSession(provider=\"zvt\", data_schema=data_schema)() as session:\n current_tags_info = data_schema.query_data(\n session=session, filters=[data_schema.tag == tag], return_type=\"domain\"\n )\n if current_tags_info:\n return True\n return False\n\n\ndef create_tag_info(tag_info: CreateTagInfoModel, tag_type: TagType):\n \"\"\"\n Create tags info\n \"\"\"\n if is_tag_info_existed(tag=tag_info.tag, tag_type=tag_type):\n raise HTTPException(status_code=409, detail=f\"This tag has been registered in {tag_type}\")\n\n data_schema = get_tag_info_schema(tag_type=tag_type)\n with contract_api.DBSession(provider=\"zvt\", data_schema=data_schema)() as session:\n timestamp = current_date()\n entity_id = \"admin\"\n tag_info_db = data_schema(\n id=f\"admin_{tag_info.tag}\",\n entity_id=entity_id,\n timestamp=timestamp,\n tag=tag_info.tag,\n tag_reason=tag_info.tag_reason,\n )\n session.add(tag_info_db)\n session.commit()\n session.refresh(tag_info_db)\n return tag_info_db\n\n\ndef build_stock_pool_info(create_stock_pool_info_model: CreateStockPoolInfoModel, timestamp):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockPoolInfo)() as session:\n stock_pool_info = StockPoolInfo(\n entity_id=\"admin\",\n timestamp=to_pd_timestamp(timestamp),\n id=f\"admin_{create_stock_pool_info_model.stock_pool_name}\",\n stock_pool_type=create_stock_pool_info_model.stock_pool_type.value,\n stock_pool_name=create_stock_pool_info_model.stock_pool_name,\n )\n session.add(stock_pool_info)\n session.commit()\n session.refresh(stock_pool_info)\n return stock_pool_info\n\n\ndef build_stock_pool(create_stock_pools_model: CreateStockPoolsModel, target_date=current_date()):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockPools)() as session:\n entity_type = create_stock_pools_model.entity_type\n stock_pool_name = create_stock_pools_model.stock_pool_name\n\n if stock_pool_name not in get_stock_pool_names():\n build_stock_pool_info(\n CreateStockPoolInfoModel(stock_pool_type=StockPoolType.custom, stock_pool_name=stock_pool_name),\n timestamp=target_date,\n )\n # one instance per day for entity_type\n stock_pool_id = f\"{entity_type}_{stock_pool_name}_{to_date_time_str(target_date)}\"\n datas: List[StockPools] = StockPools.query_data(\n session=session,\n filters=[\n StockPools.id == stock_pool_id,\n ],\n return_type=\"domain\",\n )\n if datas:\n stock_pool = datas[0]\n if create_stock_pools_model.insert_mode == InsertMode.overwrite:\n stock_pool.entity_ids = create_stock_pools_model.entity_ids\n else:\n stock_pool.entity_ids = list(set(stock_pool.entity_ids + create_stock_pools_model.entity_ids))\n else:\n stock_pool = StockPools(\n entity_id=f\"{entity_type}_{stock_pool_name}\",\n timestamp=to_pd_timestamp(target_date),\n id=stock_pool_id,\n entity_type=entity_type,\n stock_pool_name=stock_pool_name,\n entity_ids=create_stock_pools_model.entity_ids,\n )\n session.add(stock_pool)\n session.commit()\n session.refresh(stock_pool)\n return stock_pool\n\n\ndef delete_stock_pool(stock_pool_name: str):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockPoolInfo)() as session:\n stock_pool_info = StockPoolInfo.query_data(\n session=session,\n filters=[StockPoolInfo.stock_pool_name == stock_pool_name],\n return_type=\"domain\",\n )\n\n contract_api.del_data(data_schema=StockPools, filters=[StockPools.stock_pool_name == stock_pool_name])\n\n if stock_pool_info:\n session.delete(stock_pool_info[0])\n session.commit()\n return \"success\"\n return \"not found\"\n\n\ndef get_main_tags_in_stock_pool(stock_pool_name: str) -> List[str]:\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockPools)() as session:\n stock_pool_info = StockPoolInfo.query_data(\n session=session,\n filters=[StockPoolInfo.stock_pool_name == stock_pool_name],\n return_type=\"domain\",\n )\n if not stock_pool_info:\n raise HTTPException(status_code=404, detail=f\"Stock pool info {stock_pool_name} not found\")\n\n entity_ids = None\n if stock_pool_name != \"all\":\n stock_pools: List[StockPools] = StockPools.query_data(\n session=session,\n filters=[StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if not stock_pools:\n raise HTTPException(status_code=404, detail=f\"Stock pool {stock_pool_name} not found\")\n\n entity_ids = stock_pools[0].entity_ids\n if not entity_ids:\n return []\n\n df = StockTags.query_data(\n session=session,\n entity_ids=entity_ids,\n columns=[\"main_tag\", \"entity_id\"],\n return_type=\"df\",\n )\n grouped_df = df.groupby(\"main_tag\").agg(entity_count=(\"entity_id\", \"count\")).reset_index()\n sorted_df = grouped_df.sort_values(by=[\"entity_count\"], ascending=[False])\n return sorted_df[\"main_tag\"].tolist()\n\n\ndef query_stock_tag_stats(query_stock_tag_stats_model: QueryStockTagStatsModel):\n with contract_api.DBSession(provider=\"zvt\", data_schema=TagStats)() as session:\n datas = TagStats.query_data(\n session=session,\n filters=[TagStats.stock_pool_name == query_stock_tag_stats_model.stock_pool_name],\n order=TagStats.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if not datas:\n return []\n\n target_date = datas[0].timestamp\n\n tag_stats_list: List[dict] = TagStats.query_data(\n session=session,\n filters=[\n TagStats.stock_pool_name == query_stock_tag_stats_model.stock_pool_name,\n TagStats.timestamp == target_date,\n ],\n return_type=\"dict\",\n order=TagStats.position.asc(),\n )\n\n if query_stock_tag_stats_model.query_type == TagStatsQueryType.simple:\n return tag_stats_list\n\n entity_ids = flatten_list([tag_stats[\"entity_ids\"] for tag_stats in tag_stats_list])\n\n # get stocks meta\n stocks = Stock.query_data(provider=\"em\", entity_ids=entity_ids, return_type=\"domain\")\n entity_map = {item.entity_id: item for item in stocks}\n\n # get stock tags\n tags_dict = StockTags.query_data(\n session=session,\n filters=[StockTags.entity_id.in_(entity_ids)],\n return_type=\"dict\",\n )\n entity_tags_map = {item[\"entity_id\"]: item for item in tags_dict}\n\n # get stock system tags\n system_tags_dict = StockSystemTags.query_data(\n session=session,\n filters=[StockSystemTags.timestamp == target_date, StockSystemTags.entity_id.in_(entity_ids)],\n return_type=\"dict\",\n )\n entity_system_tags_map = {item[\"entity_id\"]: item for item in system_tags_dict}\n\n for tag_stats in tag_stats_list:\n stock_details = []\n for entity_id in tag_stats[\"entity_ids\"]:\n stock_details_model = {\n \"entity_id\": entity_id,\n \"main_tag\": tag_stats[\"main_tag\"],\n \"code\": entity_map.get(entity_id).code,\n \"name\": entity_map.get(entity_id).name,\n }\n\n stock_tags = entity_tags_map.get(entity_id)\n stock_details_model[\"sub_tag\"] = stock_tags[\"sub_tag\"]\n if stock_tags[\"active_hidden_tags\"] is not None:\n stock_details_model[\"hidden_tags\"] = stock_tags[\"active_hidden_tags\"].keys()\n else:\n stock_details_model[\"hidden_tags\"] = None\n\n stock_system_tags = entity_system_tags_map.get(entity_id)\n stock_details_model = fill_dict(stock_system_tags, stock_details_model)\n\n stock_details.append(stock_details_model)\n tag_stats[\"stock_details\"] = stock_details\n\n return tag_stats_list\n\n\ndef refresh_main_tag_by_sub_tag(stock_tag: StockTags, set_by_user=False) -> StockTags:\n if not stock_tag.sub_tags:\n logger.warning(f\"{stock_tag.entity_id} has no sub_tags yet\")\n return stock_tag\n\n sub_tag = stock_tag.sub_tag\n sub_tag_reason = stock_tag.sub_tags[sub_tag]\n\n main_tag = get_main_tag_by_sub_tag(sub_tag)\n main_tag_reason = sub_tag_reason\n if main_tag == \"其他\":\n main_tag = stock_tag.main_tag\n main_tag_reason = stock_tag.main_tag_reason\n\n set_stock_tags_model = SetStockTagsModel(\n entity_id=stock_tag.entity_id,\n main_tag=main_tag,\n main_tag_reason=main_tag_reason,\n sub_tag=sub_tag,\n sub_tag_reason=sub_tag_reason,\n active_hidden_tags=stock_tag.active_hidden_tags,\n )\n logger.info(f\"set_stock_tags_model:{set_stock_tags_model}\")\n\n return build_stock_tags(\n set_stock_tags_model=set_stock_tags_model,\n timestamp=stock_tag.timestamp,\n set_by_user=set_by_user,\n keep_current=False,\n )\n\n\ndef refresh_all_main_tag_by_sub_tag():\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n stock_tags = StockTags.query_data(\n session=session,\n return_type=\"domain\",\n )\n for stock_tag in stock_tags:\n refresh_main_tag_by_sub_tag(stock_tag)\n\n\ndef reset_to_default_main_tag(current_main_tag: str):\n df = StockTags.query_data(\n filters=[StockTags.main_tag == current_main_tag],\n columns=[StockTags.entity_id],\n return_type=\"df\",\n )\n entity_ids = df[\"entity_id\"].tolist()\n if not entity_ids:\n logger.info(f\"all stocks with main_tag: {current_main_tag} has been reset\")\n return\n build_default_main_tag(entity_ids=entity_ids, force_rebuild=True)\n\n\ndef activate_industry_list(industry_list: List[str]):\n df_block = Block.query_data(provider=\"em\", filters=[Block.category == \"industry\", Block.name.in_(industry_list)])\n industry_codes = df_block[\"code\"].tolist()\n block_stocks: List[BlockStock] = BlockStock.query_data(\n provider=\"em\",\n filters=[BlockStock.code.in_(industry_codes)],\n return_type=\"domain\",\n )\n entity_ids = [block_stock.stock_id for block_stock in block_stocks]\n\n if not entity_ids:\n logger.info(f\"No stocks in {industry_list}\")\n return\n\n build_default_main_tag(entity_ids=entity_ids, force_rebuild=True)\n\n\ndef activate_sub_tags(activate_sub_tags_model: ActivateSubTagsModel):\n sub_tags = activate_sub_tags_model.sub_tags\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n result = {}\n for sub_tag in sub_tags:\n # df = StockTags.query_data(\n # session=session,\n # filters=[StockTags.sub_tag != sub_tag],\n # columns=[StockTags.entity_id],\n # return_type=\"df\",\n # )\n # entity_ids = df[\"entity_id\"].tolist()\n entity_ids = None\n\n # stock_tag with sub_tag but not set to related main_tag yet\n stock_tags = StockTags.query_data(\n session=session,\n entity_ids=entity_ids,\n # 需要sqlite3版本>=3.37.0\n filters=[func.json_extract(StockTags.sub_tags, f'$.\"{sub_tag}\"') != None],\n return_type=\"domain\",\n )\n if not stock_tags:\n logger.info(f\"all stocks with sub_tag: {sub_tag} has been activated\")\n continue\n for stock_tag in stock_tags:\n stock_tag.sub_tag = sub_tag\n session.commit()\n session.refresh(stock_tag)\n result[stock_tag.entity_id] = refresh_main_tag_by_sub_tag(stock_tag, set_by_user=True)\n return result\n\n\ndef delete_main_tag(main_tag: str):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n stock_tags = StockTags.query_data(\n session=session,\n # 需要sqlite3版本>=3.37.0\n filters=[func.json_extract(StockTags.main_tags, f'$.\"{main_tag}\"') != None],\n return_type=\"domain\",\n )\n\n sql = text(f'update industry_info set main_tag = \"其他\" where main_tag = \"{main_tag}\"')\n session.execute(sql)\n session.commit()\n\n sql = text(f'update sub_tag_info set main_tag = \"其他\" where main_tag = \"{main_tag}\"')\n session.execute(sql)\n session.commit()\n\n for stock_tag in stock_tags:\n logger.info(f\"remove main_tag: {main_tag} for {stock_tag.entity_id}\")\n\n main_tags = dict(stock_tag.main_tags)\n\n logger.info(f\"main_tags before deleted: {main_tags}\")\n main_tags.pop(main_tag)\n logger.info(f\"main_tags after deleted: {main_tags}\")\n\n stock_tag.main_tags = main_tags\n\n if main_tag == stock_tag.main_tag:\n logger.info(f\"main_tag before deleted: {stock_tag.main_tag}\")\n\n if main_tags:\n # set to first main tag\n stock_tag.main_tag = list(main_tags.keys())[0]\n stock_tag.main_tag_reason = main_tags[stock_tag.main_tag]\n else:\n stock_tag.main_tag = \"其他\"\n stock_tag.main_tag_reason = \"其他\"\n\n logger.info(f\"main_tag after deleted: {stock_tag.main_tag}\")\n session.commit()\n\n\ndef delete_sub_tag(sub_tag: str):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n stock_tags = StockTags.query_data(\n session=session,\n # 需要sqlite3版本>=3.37.0\n filters=[func.json_extract(StockTags.sub_tags, f'$.\"{sub_tag}\"') != None],\n return_type=\"domain\",\n )\n\n for stock_tag in stock_tags:\n logger.info(f\"remove sub_tag: {sub_tag} for {stock_tag.entity_id}\")\n\n sub_tags = dict(stock_tag.sub_tags)\n\n logger.info(f\"sub_tags before deleted: {sub_tags}\")\n sub_tags.pop(sub_tag)\n logger.info(f\"sub_tags after deleted: {sub_tags}\")\n\n stock_tag.sub_tags = sub_tags\n\n if sub_tag == stock_tag.sub_tag:\n logger.info(f\"sub_tag before deleted: {stock_tag.sub_tag}\")\n\n if sub_tags:\n # set to first main tag\n stock_tag.sub_tag = list(sub_tags.keys())[0]\n stock_tag.sub_tag_reason = sub_tags[stock_tag.sub_tag]\n else:\n stock_tag.sub_tag = \"其他\"\n stock_tag.sub_tag_reason = \"其他\"\n\n logger.info(f\"sub_tag after deleted: {stock_tag.sub_tag}\")\n session.commit()\n\n\ndef delete_hidden_tag(hidden_tag: str):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n stock_tags = StockTags.query_data(\n session=session,\n # 需要sqlite3版本>=3.37.0\n filters=[func.json_extract(StockTags.hidden_tags, f'$.\"{hidden_tag}\"') != None],\n return_type=\"domain\",\n )\n\n for stock_tag in stock_tags:\n logger.info(f\"delete hidden_tag: {hidden_tag} for {stock_tag.entity_id}\")\n\n hidden_tags = dict(stock_tag.hidden_tags)\n\n logger.info(f\"hidden_tags before deleted: {hidden_tags}\")\n hidden_tags.pop(hidden_tag)\n stock_tag.hidden_tags = hidden_tags\n logger.info(f\"hidden_tags after deleted: {hidden_tags}\")\n\n if stock_tag.active_hidden_tags and (hidden_tag in stock_tag.active_hidden_tags):\n active_hidden_tags = dict(stock_tag.active_hidden_tags)\n\n logger.info(f\"active_hidden_tags before deleted: {active_hidden_tags}\")\n active_hidden_tags.pop(hidden_tag)\n stock_tag.active_hidden_tags = active_hidden_tags\n logger.info(f\"active_hidden_tags after deleted: {active_hidden_tags}\")\n\n session.commit()\n\n\ndef delete_tag(tag: str, tag_type: TagType):\n data_schema = get_tag_info_schema(tag_type=tag_type)\n with contract_api.DBSession(provider=\"zvt\", data_schema=data_schema)() as session:\n current_tags_info = data_schema.query_data(\n session=session, filters=[data_schema.tag == tag], return_type=\"domain\"\n )\n\n if not current_tags_info:\n logger.info(f\"tag_type: {tag_type}, tag: {tag} not exists, ignore delete tag info\")\n else:\n logger.info(f\"delete tag info, tag_type: {tag_type}, tag: {tag} \")\n session.delete(current_tags_info[0])\n session.commit()\n\n if tag_type == TagType.main_tag:\n delete_main_tag(main_tag=tag)\n elif tag_type == TagType.sub_tag:\n delete_sub_tag(sub_tag=tag)\n elif tag_type == TagType.hidden_tag:\n delete_hidden_tag(hidden_tag=tag)\n else:\n raise HTTPException(status_code=400, detail=f\"Unsupported tag type: {tag_type}\")\n\n\ndef _create_main_tag_if_not_existed(main_tag, main_tag_reason):\n main_tag_info = CreateTagInfoModel(tag=main_tag, tag_reason=main_tag_reason)\n if not is_tag_info_existed(tag=main_tag_info.tag, tag_type=TagType.main_tag):\n create_tag_info(tag_info=main_tag_info, tag_type=TagType.main_tag)\n\n\ndef get_main_tag_industry_relation(main_tag):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n df = IndustryInfo.query_data(\n session=session,\n columns=[IndustryInfo.industry_name],\n filters=[IndustryInfo.main_tag == main_tag],\n return_type=\"df\",\n )\n return {\"main_tag\": main_tag, \"industry_list\": df[\"industry_name\"].tolist()}\n\n\ndef get_main_tag_sub_tag_relation(main_tag):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n df = SubTagInfo.query_data(\n session=session,\n columns=[SubTagInfo.tag],\n filters=[SubTagInfo.main_tag == main_tag],\n return_type=\"df\",\n )\n return {\"main_tag\": main_tag, \"sub_tag_list\": df[\"tag\"].tolist()}\n\n\ndef build_main_tag_industry_relation(build_relation_model: BuildMainTagIndustryRelationModel):\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n main_tag = build_relation_model.main_tag\n _create_main_tag_if_not_existed(main_tag=main_tag, main_tag_reason=main_tag)\n\n industry_list = build_relation_model.industry_list\n\n datas: List[IndustryInfo] = IndustryInfo.query_data(\n session=session,\n filters=[IndustryInfo.main_tag == main_tag, IndustryInfo.industry_name.notin_(industry_list)],\n return_type=\"domain\",\n )\n for data in datas:\n data.main_tag = \"其他\"\n session.commit()\n\n industry_info_list: List[IndustryInfo] = IndustryInfo.query_data(\n session=session,\n filters=[IndustryInfo.industry_name.in_(industry_list)],\n return_type=\"domain\",\n )\n for industry_info in industry_info_list:\n industry_info.main_tag = main_tag\n session.commit()\n\n if build_relation_model.activate:\n # activate industry\n activate_industry_list(industry_list=industry_list)\n\n\ndef build_main_tag_sub_tag_relation(build_relation_model: BuildMainTagIndustryRelationModel):\n with contract_api.DBSession(provider=\"zvt\", data_schema=SubTagInfo)() as session:\n main_tag = build_relation_model.main_tag\n _create_main_tag_if_not_existed(main_tag=main_tag, main_tag_reason=main_tag)\n\n sub_tag_list = build_relation_model.sub_tag_list\n\n # update others to \"其他\"\n datas: List[SubTagInfo] = SubTagInfo.query_data(\n session=session,\n filters=[SubTagInfo.main_tag == main_tag, SubTagInfo.tag.notin_(sub_tag_list)],\n return_type=\"domain\",\n )\n for data in datas:\n data.main_tag = \"其他\"\n session.commit()\n\n # update sub tag info\n sub_tag_info_list: List[SubTagInfo] = SubTagInfo.query_data(\n session=session,\n filters=[SubTagInfo.tag.in_(sub_tag_list)],\n return_type=\"domain\",\n )\n for sub_tag_info in sub_tag_info_list:\n sub_tag_info.main_tag = main_tag\n session.commit()\n\n if build_relation_model.activate:\n # activate sub tags\n activate_sub_tags(ActivateSubTagsModel(sub_tags=sub_tag_list))\n\n\ndef change_main_tag(change_main_tag_model: ChangeMainTagModel):\n current_main_tag = change_main_tag_model.current_main_tag\n new_main_tag = change_main_tag_model.new_main_tag\n\n _create_main_tag_if_not_existed(main_tag=new_main_tag, main_tag_reason=new_main_tag)\n with contract_api.DBSession(provider=\"zvt\", data_schema=StockTags)() as session:\n stock_tags: List[StockTags] = StockTags.query_data(\n filters=[StockTags.main_tag == current_main_tag],\n session=session,\n return_type=\"domain\",\n )\n\n for stock_tag in stock_tags:\n tag_parameter: TagParameter = build_tag_parameter(\n tag_type=TagType.main_tag,\n tag=new_main_tag,\n tag_reason=new_main_tag,\n stock_tag=stock_tag,\n )\n set_stock_tags_model = SetStockTagsModel(\n entity_id=stock_tag.entity_id,\n main_tag=tag_parameter.main_tag,\n main_tag_reason=tag_parameter.main_tag_reason,\n sub_tag=tag_parameter.sub_tag,\n sub_tag_reason=tag_parameter.sub_tag_reason,\n active_hidden_tags=stock_tag.active_hidden_tags,\n )\n\n build_stock_tags(\n set_stock_tags_model=set_stock_tags_model,\n timestamp=now_pd_timestamp(),\n set_by_user=True,\n keep_current=False,\n )\n session.refresh(stock_tag)\n return stock_tags\n\n\nif __name__ == \"__main__\":\n # print(get_main_tags_in_stock_pool(\"涨停梯队\"))\n # print(delete_tag(tag=\"赛马概念\", tag_type=TagType.sub_tag))\n activate_industry_list(industry_list=[\"航天航空\"])\n # activate_sub_tags(ActivateSubTagsModel(sub_tags=[\"航天概念\", \"天基互联\", \"北斗导航\", \"通用航空\"]))\n # build_default_main_tag(entity_type=\"stockhk\")\n\n# the __all__ is generated\n__all__ = [\n \"get_stock_tag_options\",\n \"build_stock_tags\",\n \"build_tag_parameter\",\n \"batch_set_stock_tags\",\n \"build_default_main_tag\",\n \"build_default_sub_tags\",\n \"get_tag_info_schema\",\n \"is_tag_info_existed\",\n \"create_tag_info\",\n \"build_stock_pool_info\",\n \"build_stock_pool\",\n \"query_stock_tag_stats\",\n \"refresh_main_tag_by_sub_tag\",\n \"refresh_all_main_tag_by_sub_tag\",\n \"reset_to_default_main_tag\",\n \"activate_industry_list\",\n \"activate_sub_tags\",\n]\n"
},
{
"path": "src/zvt/tag/tag_stats.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nfrom typing import List\n\nimport pandas as pd\nimport sqlalchemy\n\nfrom zvt.api.kdata import get_kdata_schema\nfrom zvt.contract import AdjustType, IntervalLevel\nfrom zvt.contract.api import df_to_db, get_db_session\nfrom zvt.domain.quotes import KdataCommon\nfrom zvt.factors.top_stocks import TopStocks, get_top_stocks\nfrom zvt.tag.common import InsertMode\nfrom zvt.tag.tag_models import CreateStockPoolsModel\nfrom zvt.tag.tag_schemas import TagStats, StockTags, StockPools\nfrom zvt.tag.tag_service import build_stock_pool\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp, current_date, next_date\n\nlogger = logging.getLogger(__name__)\n\n\ndef build_system_stock_pools(start_date, entity_type, force_update=False):\n if entity_type == \"stock\":\n stock_pool_names = [\"主线\", \"年线\", \"大局\"]\n elif entity_type == \"stockus\":\n stock_pool_names = [\"美股主线\"]\n elif entity_type == \"stockhk\":\n stock_pool_names = [\"港股主线\"]\n else:\n raise ValueError(f\"entity_type {entity_type} not supported\")\n\n for stock_pool_name in stock_pool_names:\n if not force_update:\n datas = StockPools.query_data(\n limit=1,\n filters=[StockPools.entity_type == entity_type, StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.desc(),\n return_type=\"domain\",\n )\n if datas:\n start_date = max(next_date(the_time=datas[0].timestamp), start_date)\n\n df = TopStocks.query_data(\n start_timestamp=start_date,\n filters=[TopStocks.entity_type == entity_type],\n columns=[TopStocks.timestamp],\n order=TopStocks.timestamp.asc(),\n )\n if not pd_is_not_null(df):\n logger.info(f\"no data for top_stocks {entity_type} {start_date}\")\n continue\n\n dates = df[\"timestamp\"].tolist()\n for target_date in dates:\n logger.info(f\"build_system_stock_pools {entity_type} {stock_pool_name} to {target_date}\")\n if stock_pool_name == \"主线\" or stock_pool_name == \"美股主线\" or stock_pool_name == \"港股主线\":\n short_stocks = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type=\"short\")\n long_stocks = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type=\"long\")\n entity_ids = list(set(short_stocks + long_stocks))\n elif stock_pool_name == \"年线\":\n small_stocks = get_top_stocks(\n entity_type=entity_type, target_date=target_date, return_type=\"small_vol_up\"\n )\n big_stocks = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type=\"big_vol_up\")\n entity_ids = list(set(small_stocks + big_stocks))\n elif stock_pool_name == \"大局\":\n entity_ids = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type=\"all\")\n else:\n assert False\n\n if not entity_ids:\n logger.info(f\"no data for {entity_type} {stock_pool_name} {target_date}\")\n break\n\n create_stock_pools_model: CreateStockPoolsModel = CreateStockPoolsModel(\n entity_type=entity_type,\n stock_pool_name=stock_pool_name,\n entity_ids=entity_ids,\n insert_mode=InsertMode.overwrite,\n )\n build_stock_pool(create_stock_pools_model, target_date=target_date)\n\n\ndef build_stock_pool_tag_stats(\n stock_pool_name,\n entity_type=\"stock\",\n force_rebuild_latest=False,\n target_date=None,\n adjust_type=AdjustType.qfq,\n provider=\"em\",\n):\n datas = TagStats.query_data(\n limit=1,\n filters=[TagStats.entity_type == entity_type, TagStats.stock_pool_name == stock_pool_name],\n order=TagStats.timestamp.desc(),\n return_type=\"domain\",\n )\n start = target_date\n current_df = None\n if datas:\n if force_rebuild_latest:\n session = get_db_session(\"zvt\", data_schema=TagStats)\n session.query(TagStats).filter(TagStats.entity_type == entity_type).filter(\n TagStats.stock_pool_name == stock_pool_name\n ).filter(TagStats.timestamp == datas[0].timestamp).delete()\n session.commit()\n return build_stock_pool_tag_stats(stock_pool_name=stock_pool_name, force_rebuild_latest=False)\n\n latest_tag_stats_timestamp = datas[0].timestamp\n current_df = TagStats.query_data(\n filters=[\n TagStats.entity_type == entity_type,\n TagStats.stock_pool_name == stock_pool_name,\n TagStats.timestamp == latest_tag_stats_timestamp,\n ]\n )\n start = next_date(the_time=latest_tag_stats_timestamp)\n\n stock_pools: List[StockPools] = StockPools.query_data(\n start_timestamp=start,\n filters=[StockPools.entity_type == entity_type, StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.asc(),\n return_type=\"domain\",\n )\n if not stock_pools:\n logger.info(f\"no data to build tag stats: {entity_type} {stock_pool_name} {start}\")\n return None\n\n for stock_pool in stock_pools:\n target_date = stock_pool.timestamp\n logger.info(f\"build_stock_pool_tag_stats for {entity_type} {stock_pool_name} {target_date}\")\n\n entity_ids = stock_pool.entity_ids\n tags_df = StockTags.query_data(\n entity_ids=entity_ids, filters=[StockTags.entity_type == entity_type], return_type=\"df\", index=\"entity_id\"\n )\n kdata_schema: KdataCommon = get_kdata_schema(\n entity_type=entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type\n )\n kdata_df = kdata_schema.query_data(\n provider=provider,\n entity_ids=entity_ids,\n filters=[kdata_schema.timestamp == to_pd_timestamp(target_date)],\n columns=[kdata_schema.entity_id, kdata_schema.name, kdata_schema.turnover],\n index=\"entity_id\",\n )\n\n df = pd.concat([tags_df, kdata_df[[\"turnover\", \"name\"]]], axis=1)\n\n grouped_df = (\n df.groupby(\"main_tag\")\n .agg(\n turnover=(\"turnover\", \"sum\"),\n entity_count=(\"entity_id\", \"count\"),\n entity_ids=(\"entity_id\", lambda entity_id: list(entity_id)),\n )\n .reset_index()\n )\n sorted_df = grouped_df.sort_values(by=[\"turnover\", \"entity_count\"], ascending=[False, False])\n # sorted_df = grouped_df.sort_values(by=[\"entity_count\"], ascending=[False])\n sorted_df = sorted_df.reset_index(drop=True)\n sorted_df[\"position\"] = sorted_df.index\n sorted_df[\"is_main_line\"] = sorted_df.index < 5\n sorted_df[\"main_line_continuous_days\"] = sorted_df[\"is_main_line\"].apply(lambda x: 1 if x else 0)\n # logger.info(f\"current_df\\n: {current_df}\")\n if pd_is_not_null(current_df):\n sorted_df.set_index(\"main_tag\", inplace=True, drop=False)\n current_df.set_index(\"main_tag\", inplace=True, drop=False)\n common_index = sorted_df[sorted_df[\"is_main_line\"]].index.intersection(\n current_df[current_df[\"is_main_line\"]].index\n )\n pre_selected = current_df.loc[common_index]\n if pd_is_not_null(pre_selected):\n pre_selected = pre_selected.reindex(sorted_df.index, fill_value=0)\n sorted_df[\"main_line_continuous_days\"] = (\n sorted_df[\"main_line_continuous_days\"] + pre_selected[\"main_line_continuous_days\"]\n )\n sorted_df[\"entity_id\"] = f\"{entity_type}_{stock_pool_name}\"\n sorted_df[\"timestamp\"] = target_date\n sorted_df[\"entity_type\"] = entity_type\n sorted_df[\"stock_pool_name\"] = stock_pool_name\n sorted_df[\"id\"] = sorted_df[[\"entity_id\", \"timestamp\", \"main_tag\"]].apply(\n lambda x: \"_\".join(x.astype(str)), axis=1\n )\n df_to_db(\n provider=\"zvt\",\n df=sorted_df,\n data_schema=TagStats,\n force_update=True,\n dtype={\"entity_ids\": sqlalchemy.JSON},\n )\n current_df = sorted_df\n\n\ndef refresh_stock_pool(stock_pool_name, entity_ids, insert_mode=InsertMode.append, target_date=current_date()):\n create_stock_pools_model: CreateStockPoolsModel = CreateStockPoolsModel(\n stock_pool_name=stock_pool_name, entity_ids=entity_ids, insert_mode=insert_mode\n )\n\n build_stock_pool(create_stock_pools_model, target_date=target_date)\n\n\ndef get_tags_order_by_position(stock_pool_name, entity_type=\"stock\"):\n latest_data = TagStats.query_data(\n filters=[TagStats.entity_type == entity_type, TagStats.stock_pool_name == stock_pool_name],\n order=TagStats.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n target_date = to_pd_timestamp(latest_data[0].timestamp)\n\n tag_stats_list: List[TagStats] = TagStats.query_data(\n filters=[\n TagStats.timestamp == to_pd_timestamp(target_date),\n TagStats.entity_type == entity_type,\n TagStats.stock_pool_name == stock_pool_name,\n ],\n order=TagStats.position.asc(),\n return_type=\"domain\",\n )\n\n all_tags = [item.main_tag for item in tag_stats_list]\n main_line_tag1 = all_tags[0:1]\n main_line_tag2 = all_tags[1:2]\n sub_line_tags1 = all_tags[2:4]\n sub_line_tags2 = all_tags[4:6]\n following_tags = all_tags[6:8]\n other_tags = all_tags[8:]\n return main_line_tag1, main_line_tag2, sub_line_tags1, sub_line_tags2, following_tags, other_tags\n\n\nif __name__ == \"__main__\":\n # build_system_stock_pools()\n # build_stock_pool_tag_stats(stock_pool_name=\"主线\", force_rebuild_latest=True)\n # build_stock_pool_tag_stats(stock_pool_name=\"年线\")\n print(get_tags_order_by_position(stock_pool_name=\"主线\", entity_type=\"stockus\"))\n\n\n# the __all__ is generated\n__all__ = [\"build_system_stock_pools\", \"build_stock_pool_tag_stats\", \"refresh_stock_pool\"]\n"
},
{
"path": "src/zvt/tag/tag_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport json\nimport os\nfrom typing import List, Dict\n\nimport pandas as pd\n\nfrom zvt import zvt_env\nfrom zvt.contract.api import df_to_db\nfrom zvt.domain import Block\nfrom zvt.tag.common import StockPoolType\nfrom zvt.tag.tag_schemas import MainTagInfo, SubTagInfo, HiddenTagInfo, StockPoolInfo, IndustryInfo\nfrom zvt.utils.time_utils import now_pd_timestamp\n\n\ndef _get_default_industry_main_tag_mapping() -> Dict[str, str]:\n result = {}\n with open(os.path.join(zvt_env[\"resource_path\"], \"industry_main_tag_mapping.json\"), encoding=\"utf-8\") as f:\n result = json.load(f)\n with open(os.path.join(zvt_env[\"resource_path\"], \"us_industry_main_tag_mapping.json\"), encoding=\"utf-8\") as f:\n result.update(json.load(f))\n with open(os.path.join(zvt_env[\"resource_path\"], \"hk_industry_main_tag_mapping.json\"), encoding=\"utf-8\") as f:\n result.update(json.load(f))\n\n return result\n\n\ndef _get_default_main_tag_industry_mapping() -> Dict[str, List[str]]:\n mapping = _get_default_industry_main_tag_mapping()\n result = {}\n for industry, main_tag in mapping.items():\n result.setdefault(main_tag, [])\n result.get(main_tag).append(industry)\n return result\n\n\ndef _get_default_concept_main_tag_mapping() -> Dict[str, str]:\n with open(os.path.join(zvt_env[\"resource_path\"], \"concept_main_tag_mapping.json\"), encoding=\"utf-8\") as f:\n return json.load(f)\n\n\ndef _get_default_main_tag_concept_mapping() -> Dict[str, List[str]]:\n mapping = _get_default_concept_main_tag_mapping()\n result = {}\n for concept, main_tag in mapping.items():\n result.setdefault(main_tag, [])\n result.get(main_tag).append(concept)\n return result\n\n\ndef _get_initial_sub_tags() -> List[str]:\n return list(_get_default_concept_main_tag_mapping().keys())\n\n\ndef _get_industry_list():\n df = Block.query_data(\n filters=[Block.category == \"industry\"], columns=[Block.name], return_type=\"df\", order=Block.timestamp.desc()\n )\n return df[\"name\"].tolist()\n\n\ndef _get_concept_list():\n df = Block.query_data(\n filters=[Block.category == \"concept\"], columns=[Block.name], return_type=\"df\", order=Block.timestamp.desc()\n )\n\n return df[\"name\"].tolist()\n\n\ndef _check_missed_industry():\n current_industry_list = _get_default_industry_main_tag_mapping().keys()\n return list(set(_get_industry_list()) - set(current_industry_list))\n\n\ndef _check_missed_concept():\n current_concept_list = _get_default_concept_main_tag_mapping().keys()\n return list(set(_get_concept_list()) - set(current_concept_list))\n\n\ndef _get_initial_main_tag_info():\n timestamp = now_pd_timestamp()\n entity_id = \"admin\"\n\n from_industry = [\n {\n \"id\": f\"{entity_id}_{main_tag}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"tag\": main_tag,\n \"tag_reason\": f\"来自这些行业:{industry}\",\n }\n for main_tag, industry in _get_default_main_tag_industry_mapping().items()\n ]\n\n from_concept = []\n for tag, concepts in _get_default_main_tag_concept_mapping().items():\n if tag not in _get_default_main_tag_industry_mapping():\n from_concept.append(\n {\n \"id\": f\"{entity_id}_{tag}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"tag\": tag,\n \"tag_reason\": f\"来自这些概念:{','.join(concepts)}\",\n }\n )\n\n return from_industry + from_concept\n\n\ndef _get_initial_industry_info():\n timestamp = now_pd_timestamp()\n entity_id = \"admin\"\n industry_info = [\n {\n \"id\": f\"{entity_id}_{industry}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"industry_name\": industry,\n \"description\": industry,\n \"main_tag\": main_tag,\n }\n for industry, main_tag in _get_default_industry_main_tag_mapping().items()\n ]\n return industry_info\n\n\ndef _get_initial_sub_tag_info():\n timestamp = now_pd_timestamp()\n entity_id = \"admin\"\n\n return [\n {\n \"id\": f\"{entity_id}_{sub_tag}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"tag\": sub_tag,\n \"tag_reason\": sub_tag,\n \"main_tag\": main_tag,\n }\n for sub_tag, main_tag in _get_default_concept_main_tag_mapping().items()\n ]\n\n\ndef _get_initial_stock_pool_info():\n timestamp = now_pd_timestamp()\n entity_id = \"admin\"\n return [\n {\n \"id\": f\"{entity_id}_{stock_pool_name}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"stock_pool_type\": StockPoolType.system.value,\n \"stock_pool_name\": stock_pool_name,\n }\n for stock_pool_name in [\"主线\", \"年线\", \"大局\", \"A股\", \"美股主线\", \"港股主线\"]\n ]\n\n\n_hidden_tags = {\n \"中字头\": \"央企，国资委控股\",\n \"核心资产\": \"高ROE 高现金流 高股息 低应收 低资本开支 低财务杠杆 有增长\",\n \"高股息\": \"高股息\",\n \"微盘股\": \"市值50亿以下\",\n \"次新股\": \"上市未满两年\",\n}\n\n\ndef _get_initial_hidden_tag_info():\n timestamp = now_pd_timestamp()\n entity_id = \"admin\"\n return [\n {\n \"id\": f\"{entity_id}_{tag}\",\n \"entity_id\": entity_id,\n \"timestamp\": timestamp,\n \"tag\": tag,\n \"tag_reason\": tag_reason,\n }\n for tag, tag_reason in _hidden_tags.items()\n ]\n\n\ndef build_initial_main_tag_info(force_update=False):\n main_tag_info_list = _get_initial_main_tag_info()\n df = pd.DataFrame.from_records(main_tag_info_list)\n df_to_db(df=df, data_schema=MainTagInfo, provider=\"zvt\", force_update=force_update)\n\n\ndef build_initial_industry_info(force_update=False):\n initial_industry_info = _get_initial_industry_info()\n df = pd.DataFrame.from_records(initial_industry_info)\n df_to_db(df=df, data_schema=IndustryInfo, provider=\"zvt\", force_update=force_update)\n\n\ndef build_initial_sub_tag_info(force_update=False):\n sub_tag_info_list = _get_initial_sub_tag_info()\n df = pd.DataFrame.from_records(sub_tag_info_list)\n df_to_db(df=df, data_schema=SubTagInfo, provider=\"zvt\", force_update=force_update)\n\n\ndef build_initial_stock_pool_info(force_update=False):\n stock_pool_info_list = _get_initial_stock_pool_info()\n df = pd.DataFrame.from_records(stock_pool_info_list)\n df_to_db(df=df, data_schema=StockPoolInfo, provider=\"zvt\", force_update=force_update)\n\n\ndef build_initial_hidden_tag_info(force_update=False):\n hidden_tag_info_list = _get_initial_hidden_tag_info()\n df = pd.DataFrame.from_records(hidden_tag_info_list)\n df_to_db(df=df, data_schema=HiddenTagInfo, provider=\"zvt\", force_update=force_update)\n\n\ndef get_main_tags():\n df = MainTagInfo.query_data(columns=[MainTagInfo.tag])\n return df[\"tag\"].tolist()\n\n\ndef get_main_tag_by_sub_tag(sub_tag):\n datas: List[SubTagInfo] = SubTagInfo.query_data(filters=[SubTagInfo.tag == sub_tag], return_type=\"domain\")\n if datas:\n return datas[0].main_tag\n else:\n return _get_default_concept_main_tag_mapping().get(sub_tag, \"其他\")\n\n\ndef get_main_tag_by_industry(industry_name):\n datas: List[IndustryInfo] = IndustryInfo.query_data(\n filters=[IndustryInfo.industry_name == industry_name], return_type=\"domain\"\n )\n if datas:\n return datas[0].main_tag\n else:\n _get_default_industry_main_tag_mapping().get(industry_name, \"其他\")\n\n\ndef get_sub_tags():\n df = SubTagInfo.query_data(columns=[SubTagInfo.tag])\n return df[\"tag\"].tolist()\n\n\ndef get_hidden_tags():\n df = HiddenTagInfo.query_data(columns=[HiddenTagInfo.tag])\n return df[\"tag\"].tolist()\n\n\ndef get_stock_pool_names():\n df = StockPoolInfo.query_data(columns=[StockPoolInfo.stock_pool_name])\n return df[\"stock_pool_name\"].tolist()\n\n\ndef match_tag_by_type(alias, tag_type=\"main_tag\"):\n if tag_type == \"main_tag\":\n tags = get_main_tags()\n elif tag_type == \"sub_tag\":\n tags = get_sub_tags()\n elif tag_type == \"industry\":\n tags = _get_industry_list()\n else:\n assert False\n\n max_intersection_length = 0\n max_tag = None\n\n for tag in tags:\n intersection_length = len(set(alias) & set(tag))\n # at least 2 same chars\n if intersection_length < 2:\n continue\n\n if intersection_length > max_intersection_length:\n max_intersection_length = intersection_length\n max_tag = tag\n\n return max_tag\n\n\ndef match_tag(alias):\n tag = match_tag_by_type(alias, tag_type=\"main_tag\")\n if tag:\n return \"main_tag\", tag\n\n tag = match_tag_by_type(alias, tag_type=\"sub_tag\")\n if tag:\n return \"sub_tag\", tag\n\n tag = match_tag_by_type(alias, tag_type=\"industry\")\n if tag:\n return \"main_tag\", get_main_tag_by_industry(tag)\n\n return \"new_tag\", alias\n\n\nif __name__ == \"__main__\":\n print(build_initial_industry_info())\n print(build_initial_main_tag_info())\n\n\n# the __all__ is generated\n__all__ = [\n \"build_initial_main_tag_info\",\n \"build_initial_industry_info\",\n \"build_initial_sub_tag_info\",\n \"build_initial_stock_pool_info\",\n \"build_initial_hidden_tag_info\",\n \"get_main_tags\",\n \"get_main_tag_by_sub_tag\",\n \"get_main_tag_by_industry\",\n \"get_sub_tags\",\n \"get_hidden_tags\",\n \"get_stock_pool_names\",\n \"match_tag_by_type\",\n \"match_tag\",\n]\n"
},
{
"path": "src/zvt/tag/tagger.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nfrom typing import Type\n\nfrom zvt.contract import Mixin\nfrom zvt.contract import TradableEntity\nfrom zvt.contract.api import get_db_session\nfrom zvt.contract.base_service import OneStateService\nfrom zvt.contract.zvt_info import TaggerState\nfrom zvt.domain import Stock\nfrom zvt.tag.tag_schemas import StockTags\n\nlogger = logging.getLogger(__name__)\n\n\nclass Tagger(OneStateService):\n state_schema = TaggerState\n\n entity_schema: Type[TradableEntity] = None\n\n data_schema: Type[Mixin] = None\n\n start_timestamp = \"2018-01-01\"\n\n def __init__(self, force=False) -> None:\n super().__init__()\n assert self.entity_schema is not None\n assert self.data_schema is not None\n self.force = force\n self.session = get_db_session(provider=\"zvt\", data_schema=self.data_schema)\n if self.state and not self.force:\n logger.info(f\"get start_timestamp from state\")\n self.start_timestamp = self.state[\"current_timestamp\"]\n logger.info(f\"tag start_timestamp: {self.start_timestamp}\")\n\n def tag(self):\n raise NotImplementedError\n\n\nclass StockTagger(Tagger):\n data_schema = StockTags\n entity_schema = Stock\n\n def tag(self):\n raise NotImplementedError\n\n\n# the __all__ is generated\n__all__ = [\"Tagger\", \"StockTagger\"]\n"
},
{
"path": "src/zvt/tasks/init_tag_system.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import Block, BlockStock, Stock\nfrom zvt.tag.tag_service import build_default_main_tag, build_default_sub_tags\nfrom zvt.tag.tag_utils import (\n build_initial_stock_pool_info,\n build_initial_main_tag_info,\n build_initial_sub_tag_info,\n build_initial_industry_info,\n)\nfrom zvt.trading.trading_service import build_default_query_stock_quote_setting\n\nif __name__ == \"__main__\":\n # init industry info\n build_initial_industry_info()\n\n # init tag info\n build_initial_main_tag_info()\n build_initial_sub_tag_info()\n build_initial_stock_pool_info()\n build_default_query_stock_quote_setting()\n\n Stock.record_data(provider=\"em\")\n Block.record_data(provider=\"em\", sleeping_time=0)\n BlockStock.record_data(provider=\"em\", sleeping_time=0)\n # init default main tag\n build_default_main_tag()\n\n # init default sub tags\n build_default_sub_tags()\n"
},
{
"path": "src/zvt/tasks/qmt_data_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\n\nimport pandas as pd\nfrom xtquant import xtdata\n\nfrom zvt import init_log\nfrom zvt.broker.qmt.qmt_quote import get_qmt_stocks\nfrom zvt.contract import AdjustType\nfrom zvt.recorders.qmt.meta import QMTStockRecorder\nfrom zvt.recorders.qmt.quotes import QMTStockKdataRecorder\n\nlogger = logging.getLogger(__name__)\n\n\ndef download_data(download_tick=False):\n period = \"1d\"\n xtdata.download_sector_data()\n stock_codes = get_qmt_stocks()\n stock_codes = sorted(stock_codes)\n count = len(stock_codes)\n download_status = {\"ok\": False}\n\n def update_progress(data, download_status: dict = download_status):\n logger.info(data)\n finished = data[\"finished\"]\n total = data[\"total\"]\n download_status[\"finished\"] = finished\n download_status[\"total\"] = total\n if finished == total:\n download_status[\"ok\"] = True\n\n start_time = time.time()\n\n xtdata.download_history_data2(stock_list=stock_codes, period=period, callback=update_progress)\n\n while True:\n logger.info(f\"current download_status:{download_status}\")\n if download_status[\"ok\"]:\n logger.info(f\"finish download 1d kdata\")\n break\n cost_time = time.time() - start_time\n if cost_time >= 60 * 30:\n logger.info(f\"timeout download 1d kdata\")\n break\n time.sleep(10)\n\n QMTStockRecorder().run()\n QMTStockKdataRecorder(adjust_type=AdjustType.qfq, sleeping_time=0).run()\n\n xtdata.download_financial_data2(\n stock_list=stock_codes, table_list=[\"Capital\"], start_time=\"\", end_time=\"\", callback=lambda x: print(x)\n )\n logger.info(\"download capital data ok\")\n\n if download_tick:\n for index, stock_code in enumerate(stock_codes):\n logger.info(f\"run to {index + 1}/{count}\")\n\n records = xtdata.get_market_data(\n stock_list=[stock_code],\n period=period,\n count=5,\n dividend_type=\"front\",\n fill_data=False,\n )\n dfs = []\n for col in records:\n df = records[col].T\n df.columns = [col]\n dfs.append(df)\n kdatas = pd.concat(dfs, axis=1)\n start_time = kdatas.index.to_list()[0]\n xtdata.download_history_data(stock_code, period=\"tick\", start_time=start_time)\n logger.info(f\"download {stock_code} tick from {start_time} ok\")\n\n\nif __name__ == \"__main__\":\n init_log(\"qmt_data_runner.log\")\n from apscheduler.schedulers.background import BackgroundScheduler\n\n sched = BackgroundScheduler()\n download_data()\n sched.add_job(func=download_data, trigger=\"cron\", hour=15, minute=30, day_of_week=\"mon-fri\")\n sched.start()\n sched._thread.join()\n"
},
{
"path": "src/zvt/tasks/qmt_tick_runner.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt import init_log\nfrom zvt.broker.qmt.qmt_quote import record_stock_quote\n\nif __name__ == \"__main__\":\n init_log(\"qmt_tick_runner.log\")\n from apscheduler.schedulers.background import BackgroundScheduler\n\n sched = BackgroundScheduler()\n record_stock_quote()\n sched.add_job(func=record_stock_quote, trigger=\"cron\", hour=9, minute=18, day_of_week=\"mon-fri\")\n sched.start()\n sched._thread.join()\n"
},
{
"path": "src/zvt/tasks/stock_pool_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import zvt_config, init_log\nfrom zvt.api.kdata import get_latest_kdata_date, get_kdata_schema\nfrom zvt.api.selector import get_entity_ids_by_filter\nfrom zvt.contract import AdjustType\nfrom zvt.contract.api import get_entity_ids, get_data_count\nfrom zvt.domain import (\n Stock,\n Block,\n Block1dKdata,\n BlockCategory,\n Index,\n Index1dKdata,\n LimitUpInfo,\n)\nfrom zvt.factors import compute_top_stocks\nfrom zvt.informer import EmailInformer\nfrom zvt.informer.inform_utils import inform_email\nfrom zvt.informer.informer import QiyeWechatBot\nfrom zvt.tag.tag_stats import build_system_stock_pools, build_stock_pool_tag_stats\nfrom zvt.utils.recorder_utils import run_data_recorder\nfrom zvt.utils.time_utils import current_date\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\nemail_informer = EmailInformer()\nbot = QiyeWechatBot()\n\n\ndef report_limit_up():\n latest_data = LimitUpInfo.query_data(order=LimitUpInfo.timestamp.desc(), limit=1, return_type=\"domain\")\n timestamp = latest_data[0].timestamp\n df = LimitUpInfo.query_data(start_timestamp=timestamp, end_timestamp=timestamp, columns=[\"code\", \"name\", \"reason\"])\n df[\"reason\"] = df[\"reason\"].str.split(\"+\")\n print(df)\n email_informer.send_message(zvt_config[\"email_username\"], f\"{timestamp} 热门报告\", f\"{df}\")\n\n\ndef record_stock_data(data_provider=\"em\", entity_provider=\"em\", sleeping_time=0, adjust_type=AdjustType.qfq):\n # 涨停数据\n run_data_recorder(domain=LimitUpInfo, data_provider=None, force_update=False)\n report_limit_up()\n\n # A股指数\n run_data_recorder(domain=Index, data_provider=data_provider, force_update=False)\n # A股指数行情\n run_data_recorder(\n domain=Index1dKdata,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=0,\n )\n\n # 板块(概念，行业)\n run_data_recorder(domain=Block, entity_provider=entity_provider, data_provider=entity_provider, force_update=False)\n\n entity_ids = get_entity_ids(entity_schema=Block, filters=[Block.timestamp == None], provider=entity_provider)\n if entity_ids:\n # 板块行情(概念，行业)\n run_data_recorder(\n entity_ids=entity_ids,\n split_entity_ids_size=0,\n domain=Block1dKdata,\n entity_provider=entity_provider,\n data_provider=entity_provider,\n day_data=True,\n sleeping_time=0,\n )\n\n # 报告新概念和行业\n df = Block.query_data(\n filters=[Block.category == BlockCategory.concept.value],\n order=Block.list_date.desc(),\n index=\"entity_id\",\n limit=7,\n )\n\n inform_email(\n entity_ids=df.index.tolist(),\n entity_type=\"block\",\n target_date=current_date(),\n title=\"report 新概念\",\n provider=\"em\",\n )\n\n target_date = get_latest_kdata_date(entity_type=\"index\", provider=data_provider)\n # A股标的\n run_data_recorder(domain=Stock, data_provider=data_provider, force_update=False)\n # A股行情\n normal_stock_ids = get_entity_ids_by_filter(entity_type=\"stock\", provider=\"em\", ignore_delist=True)\n\n kdata_schema = get_kdata_schema(entity_type=\"stock\", level=\"1d\", adjust_type=adjust_type)\n\n run_data_recorder(\n entity_ids=normal_stock_ids,\n split_entity_ids_size=0,\n domain=kdata_schema,\n data_provider=data_provider,\n entity_provider=entity_provider,\n day_data=True,\n sleeping_time=5,\n return_unfinished=True,\n end_timestamp=target_date,\n )\n\n\ndef record_stock_data_and_build_stock_pools(provider=\"em\", adjust_type=AdjustType.qfq):\n # 获取 涨停 指数 板块(概念) 个股行情数据\n record_stock_data(data_provider=provider, adjust_type=adjust_type)\n\n kdata_date = get_latest_kdata_date(provider=provider, entity_type=\"stock\", adjust_type=adjust_type)\n\n kdata_schema = get_kdata_schema(entity_type=\"stock\", level=\"1d\", adjust_type=adjust_type)\n kdata_size = get_data_count(kdata_schema, filters=[kdata_schema.timestamp == kdata_date], provider=provider)\n\n if kdata_size < 5000:\n logger.warning(f\"当前行情数据量过少: {kdata_size}, 不进行股票池构建\")\n email_informer.send_message(zvt_config[\"email_username\"], f\"{kdata_date} k线数据不完整\", \"k线数据不完整\")\n bot.send_message(f\"{kdata_date} k线数据不完整\")\n return\n\n # 计算短期/中期最强 放量突破年线半年线个股\n compute_top_stocks(start_date=kdata_date, entity_type=\"stock\", force_update=False, adjust_type=adjust_type)\n # 放入股票池\n build_system_stock_pools(start_date=kdata_date, entity_type=\"stock\", force_update=False)\n for stock_pool_name in [\"主线\", \"年线\", \"大局\"]:\n build_stock_pool_tag_stats(stock_pool_name=stock_pool_name, entity_type=\"stock\", force_rebuild_latest=True)\n\n bot.send_message(f\"{kdata_date} 股票池构建完成\")\n\n\nif __name__ == \"__main__\":\n init_log(\"stock_pool_runner.log\")\n record_stock_data_and_build_stock_pools()\n sched.add_job(\n func=record_stock_data_and_build_stock_pools, trigger=\"cron\", hour=16, minute=00, day_of_week=\"mon-fri\"\n )\n sched.start()\n sched._thread.join()\n"
},
{
"path": "src/zvt/tasks/today_shoot_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import List\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.api.selector import get_shoot_today\nfrom zvt.domain import Stock\nfrom zvt.informer.inform_utils import add_to_eastmoney\nfrom zvt.tag.common import InsertMode\nfrom zvt.tag.tag_schemas import StockPools\nfrom zvt.tag.tag_stats import refresh_stock_pool\nfrom zvt.utils.time_utils import (\n now_pd_timestamp,\n current_date,\n)\n\nlogger = logging.getLogger(__name__)\n\nsched = BackgroundScheduler()\n\n\ndef calculate_shoot():\n first_time = True\n stock_pool_name = \"今日异动\"\n while True:\n if not first_time and Stock.in_trading_time() and not Stock.in_real_trading_time():\n logger.info(f\"Sleeping time......\")\n time.sleep(30 * 1)\n continue\n\n try:\n shoot_up, shoot_down = get_shoot_today()\n\n if len(shoot_up) > 80:\n logger.warning(f\"shoot_up count size: {len(shoot_up)}\")\n shoot_up = shoot_up[:80]\n\n if shoot_up:\n over_write = False\n stock_pools: List[StockPools] = StockPools.query_data(\n filters=[StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if stock_pools and (len(stock_pools[0].entity_ids) > 80):\n over_write = True\n\n refresh_stock_pool(\n entity_ids=shoot_up,\n stock_pool_name=stock_pool_name,\n insert_mode=InsertMode.overwrite if over_write else InsertMode.append,\n target_date=current_date(),\n )\n add_to_eastmoney(\n codes=[entity_id.split(\"_\")[2] for entity_id in shoot_up],\n group=stock_pool_name,\n over_write=over_write,\n )\n except Exception as e:\n logger.error(\"Failed to handle shoot today\", exc_info=True)\n\n first_time = False\n\n if not Stock.in_trading_time():\n current_timestamp = now_pd_timestamp()\n logger.info(f\"calculate shoot finished at: {current_timestamp}\")\n break\n\n logger.info(f\"Sleep 10 seconds\")\n time.sleep(10)\n\n\nif __name__ == \"__main__\":\n init_log(\"today_shoot_runner.log\")\n calculate_shoot()\n sched.add_job(func=calculate_shoot, trigger=\"cron\", hour=9, minute=50, day_of_week=\"mon-fri\")\n sched.start()\n sched._thread.join()\n"
},
{
"path": "src/zvt/tasks/today_top_runner.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import List\n\nimport eastmoneypy\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom zvt import init_log\nfrom zvt.api.selector import get_top_up_today, get_top_down_today, get_top_vol\nfrom zvt.domain import Stock\nfrom zvt.informer.inform_utils import add_to_eastmoney\nfrom zvt.recorders.em.em_api import record_hot_topic\nfrom zvt.tag.common import InsertMode\nfrom zvt.tag.tag_schemas import StockPools\nfrom zvt.tag.tag_stats import refresh_stock_pool\nfrom zvt.utils.time_utils import now_pd_timestamp, current_date\n\nlogger = logging.getLogger(__name__)\n\n\nsched = BackgroundScheduler()\n\n\ndef calculate_top(clear_em=True):\n if clear_em:\n try:\n eastmoneypy.del_group(\"今日强势\")\n except:\n pass\n\n seed = 0\n add_all_to_em = False\n while True:\n current_timestamp = now_pd_timestamp()\n\n if not Stock.in_trading_time():\n logger.info(f\"calculate top finished at: {current_timestamp}\")\n break\n\n if Stock.in_trading_time() and not Stock.in_real_trading_time():\n logger.info(f\"Sleeping time......\")\n time.sleep(60 * 1)\n continue\n\n if seed == 0:\n record_hot_topic()\n seed = seed + 1\n if seed == 5:\n seed = 0\n target_date = current_date()\n top_up_entity_ids = get_top_up_today()\n if top_up_entity_ids:\n refresh_stock_pool(\n entity_ids=top_up_entity_ids,\n stock_pool_name=\"今日强势\",\n insert_mode=InsertMode.append,\n target_date=target_date,\n )\n try:\n to_added = top_up_entity_ids\n if add_all_to_em:\n stock_pools: List[StockPools] = StockPools.query_data(\n filters=[StockPools.stock_pool_name == \"今日强势\"],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if stock_pools:\n to_added = stock_pools[0].entity_ids\n if len(to_added) > 500:\n to_added = get_top_vol(entity_ids=to_added, limit=500)\n\n add_to_eastmoney(\n codes=[entity_id.split(\"_\")[2] for entity_id in to_added], group=\"今日强势\", over_write=False\n )\n add_all_to_em = False\n except Exception as e:\n logger.error(e)\n add_all_to_em = True\n\n top_down_entity_ids = get_top_down_today()\n if top_down_entity_ids:\n refresh_stock_pool(\n entity_ids=top_down_entity_ids,\n stock_pool_name=\"今日弱势\",\n insert_mode=InsertMode.append,\n target_date=target_date,\n )\n\n logger.info(f\"Sleep 2 minutes to compute {target_date} top stock tag stats\")\n time.sleep(60 * 2)\n\n\nif __name__ == \"__main__\":\n init_log(\"today_top_runner.log\")\n calculate_top(clear_em=False)\n sched.add_job(func=calculate_top, trigger=\"cron\", hour=9, minute=26, day_of_week=\"mon-fri\")\n sched.start()\n sched._thread.join()\n"
},
{
"path": "src/zvt/trader/__init__.py",
"content": "# -*- coding: utf-8 -*-\nfrom enum import Enum\nfrom typing import Union, List\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.utils.decorator import to_string\n\n\nclass TradingSignalType(Enum):\n open_long = \"open_long\"\n open_short = \"open_short\"\n keep_long = \"keep_long\"\n keep_short = \"keep_short\"\n close_long = \"close_long\"\n close_short = \"close_short\"\n\n\nclass OrderType(Enum):\n order_long = \"order_long\"\n order_short = \"order_short\"\n order_close_long = \"order_close_long\"\n order_close_short = \"order_close_short\"\n\n\ndef trading_signal_type_to_order_type(trading_signal_type):\n if trading_signal_type == TradingSignalType.open_long:\n return OrderType.order_long\n elif trading_signal_type == TradingSignalType.open_short:\n return OrderType.order_short\n elif trading_signal_type == TradingSignalType.close_long:\n return OrderType.order_close_long\n elif trading_signal_type == TradingSignalType.close_short:\n return OrderType.order_close_short\n\n\n@to_string\nclass TradingSignal:\n def __init__(\n self,\n entity_id: str,\n due_timestamp: Union[str, pd.Timestamp],\n happen_timestamp: Union[str, pd.Timestamp],\n trading_level: IntervalLevel,\n trading_signal_type: TradingSignalType,\n position_pct: float = None,\n order_money: float = None,\n order_amount: int = None,\n ):\n \"\"\"\n\n :param entity_id: the entity id\n :param due_timestamp: the signal due time\n :param happen_timestamp: the time when generating the signal\n :param trading_level: the level\n :param trading_signal_type:\n :param position_pct: percentage of account to order\n :param order_money: money to order\n :param order_amount: amount to order\n \"\"\"\n self.entity_id = entity_id\n self.due_timestamp = due_timestamp\n self.happen_timestamp = happen_timestamp\n self.trading_level = trading_level\n self.trading_signal_type = trading_signal_type\n\n if len([x for x in (position_pct, order_money, order_amount) if x is not None]) != 1:\n assert False\n # use position_pct or order_money or order_amount\n self.position_pct = position_pct\n # when close the position,just use position_pct\n self.order_money = order_money\n self.order_amount = order_amount\n\n\nclass TradingListener(object):\n def on_trading_open(self, timestamp):\n raise NotImplementedError\n\n def on_trading_signals(self, trading_signals: List[TradingSignal]):\n raise NotImplementedError\n\n def on_trading_close(self, timestamp):\n raise NotImplementedError\n\n def on_trading_finish(self, timestamp):\n raise NotImplementedError\n\n def on_trading_error(self, timestamp, error):\n raise NotImplementedError\n\n\nclass AccountService(TradingListener):\n def get_positions(self):\n pass\n\n def get_current_position(self, entity_id, create_if_not_exist=False):\n \"\"\"\n overwrite it to provide your real position\n\n :param entity_id:\n \"\"\"\n pass\n\n def get_current_account(self):\n pass\n\n def order_by_position_pct(\n self,\n entity_id,\n order_price,\n order_timestamp,\n order_type,\n order_position_pct: float,\n ):\n pass\n\n def order_by_money(\n self,\n entity_id,\n order_price,\n order_timestamp,\n order_type,\n order_money,\n ):\n pass\n\n def order_by_amount(\n self,\n entity_id,\n order_price,\n order_timestamp,\n order_type,\n order_amount,\n ):\n pass\n\n\n# the __all__ is generated\n__all__ = [\"TradingSignalType\", \"TradingListener\", \"OrderType\", \"AccountService\", \"trading_signal_type_to_order_type\"]\n\n# __init__.py structure:\n# common code of the package\n# export interface in __all__ which contains __all__ of its sub modules\n\n# import all from submodule trader\nfrom .trader import *\nfrom .trader import __all__ as _trader_all\n\n__all__ += _trader_all\n\n# import all from submodule errors\nfrom .errors import *\nfrom .errors import __all__ as _errors_all\n\n__all__ += _errors_all\n\n# import all from submodule account\nfrom .sim_account import *\nfrom .sim_account import __all__ as _account_all\n\n__all__ += _account_all\n"
},
{
"path": "src/zvt/trader/errors.py",
"content": "# -*- coding: utf-8 -*-\nclass TraderError(Exception):\n \"\"\"Base class for exceptions in this module.\"\"\"\n\n pass\n\n\nclass InvalidOrderParamError(TraderError):\n def __init__(self, message=\"invalid order param\"):\n self.message = message\n\n\nclass NotEnoughMoneyError(TraderError):\n def __init__(self, message=\"not enough money\"):\n self.message = message\n\n\nclass NotEnoughPositionError(TraderError):\n def __init__(self, message=\"not enough position\"):\n self.message = message\n\n\nclass InvalidOrderError(TraderError):\n def __init__(self, message=\"invalid order\"):\n self.message = message\n\n\nclass WrongKdataError(TraderError):\n def __init__(self, message=\"wrong kdata\"):\n self.message = message\n\n\n# the __all__ is generated\n__all__ = [\n \"TraderError\",\n \"InvalidOrderParamError\",\n \"NotEnoughMoneyError\",\n \"NotEnoughPositionError\",\n \"InvalidOrderError\",\n \"WrongKdataError\",\n]\n"
},
{
"path": "src/zvt/trader/sim_account.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport math\nfrom typing import List, Optional\n\nfrom zvt.api.kdata import get_kdata, get_kdata_schema\nfrom zvt.contract import IntervalLevel, TradableEntity, AdjustType\nfrom zvt.contract.api import get_db_session, decode_entity_id\nfrom zvt.trader import TradingSignal, AccountService, OrderType, trading_signal_type_to_order_type\nfrom zvt.trader.errors import (\n NotEnoughMoneyError,\n InvalidOrderError,\n NotEnoughPositionError,\n InvalidOrderParamError,\n WrongKdataError,\n)\nfrom zvt.trader.trader_info_api import get_trader_info, clear_trader\nfrom zvt.trader.trader_models import AccountStatsModel, PositionModel\nfrom zvt.trader.trader_schemas import AccountStats, Position, Order, TraderInfo\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, TIME_FORMAT_ISO8601, is_same_date\nfrom zvt.utils.utils import fill_domain_from_dict\n\n\nclass SimAccountService(AccountService):\n def __init__(\n self,\n entity_schema: TradableEntity,\n trader_name,\n timestamp,\n provider=None,\n level=IntervalLevel.LEVEL_1DAY,\n base_capital=1000000,\n buy_cost=0.001,\n sell_cost=0.001,\n slippage=0.001,\n rich_mode=True,\n adjust_type: AdjustType = None,\n keep_history=False,\n real_time=False,\n kdata_use_begin_time=False,\n ):\n self.logger = logging.getLogger(self.__class__.__name__)\n\n self.entity_schema = entity_schema\n self.base_capital = base_capital\n self.buy_cost = buy_cost\n self.sell_cost = sell_cost\n self.slippage = slippage\n self.rich_mode = rich_mode\n self.adjust_type = adjust_type\n self.trader_name = trader_name\n\n self.session = get_db_session(\"zvt\", data_schema=TraderInfo)\n self.provider = provider\n self.level = level\n self.start_timestamp = timestamp\n self.keep_history = keep_history\n self.real_time = real_time\n self.kdata_use_begin_time = kdata_use_begin_time\n\n self.account = self.init_account()\n\n account_info = (\n f\"init_account,holding size:{len(self.account.positions)} profit:{self.account.profit} input_money:{self.account.input_money} \"\n f\"cash:{self.account.cash} value:{self.account.value} all_value:{self.account.all_value}\"\n )\n self.logger.info(account_info)\n\n def input_money(self, money=1000000):\n self.account.input_money += money\n self.account.cash += money\n\n def clear_account(self):\n trader_info = get_trader_info(session=self.session, trader_name=self.trader_name, return_type=\"domain\", limit=1)\n\n if trader_info:\n self.logger.warning(\"trader:{} has run before,old result would be deleted\".format(self.trader_name))\n clear_trader(session=self.session, trader_name=self.trader_name)\n\n def init_account(self) -> AccountStats:\n # 清除历史数据\n if not self.keep_history:\n self.clear_account()\n\n # 读取之前保存的账户\n if self.keep_history:\n self.account = self.load_account()\n if self.account:\n return self.account\n\n # init trader info\n entity_type = self.entity_schema.__name__.lower()\n sim_account = TraderInfo(\n id=self.trader_name,\n entity_id=f\"trader_zvt_{self.trader_name}\",\n timestamp=self.start_timestamp,\n trader_name=self.trader_name,\n entity_type=entity_type,\n start_timestamp=self.start_timestamp,\n provider=self.provider,\n level=self.level.value,\n real_time=self.real_time,\n kdata_use_begin_time=self.kdata_use_begin_time,\n kdata_adjust_type=self.adjust_type.value,\n )\n self.session.add(sim_account)\n self.session.commit()\n\n return AccountStats(\n entity_id=f\"trader_zvt_{self.trader_name}\",\n timestamp=self.start_timestamp,\n trader_name=self.trader_name,\n cash=self.base_capital,\n input_money=self.base_capital,\n all_value=self.base_capital,\n value=0,\n closing=False,\n )\n\n def load_account(self) -> AccountStats:\n records = AccountStats.query_data(\n filters=[AccountStats.trader_name == self.trader_name],\n order=AccountStats.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if not records:\n return self.account\n latest_record: AccountStats = records[0]\n\n # create new orm object from latest record\n account_stats_model = AccountStatsModel.from_orm(latest_record)\n account = AccountStats()\n fill_domain_from_dict(account, account_stats_model.model_dump(exclude={\"id\", \"positions\"}))\n\n positions: List[Position] = []\n for position_domain in latest_record.positions:\n position_model = PositionModel.from_orm(position_domain)\n self.logger.debug(\"current position:{}\".format(position_model))\n position = Position()\n fill_domain_from_dict(position, position_model.model_dump())\n positions.append(position)\n\n account.positions = positions\n\n return account\n\n def on_trading_open(self, timestamp):\n self.logger.info(\"on_trading_open:{}\".format(timestamp))\n if is_same_date(timestamp, self.start_timestamp):\n return\n self.account = self.load_account()\n\n def on_trading_error(self, timestamp, error):\n pass\n\n def on_trading_finish(self, timestamp):\n pass\n\n def on_trading_signals(self, trading_signals: List[TradingSignal]):\n for trading_signal in trading_signals:\n try:\n self.handle_trading_signal(trading_signal)\n except Exception as e:\n self.logger.exception(e)\n self.on_trading_error(timestamp=trading_signal.happen_timestamp, error=e)\n\n def handle_trading_signal(self, trading_signal: TradingSignal):\n entity_id = trading_signal.entity_id\n happen_timestamp = trading_signal.happen_timestamp\n order_type = trading_signal_type_to_order_type(trading_signal.trading_signal_type)\n trading_level = trading_signal.trading_level.value\n if order_type:\n try:\n kdata = get_kdata(\n provider=self.provider,\n entity_id=entity_id,\n level=trading_level,\n start_timestamp=happen_timestamp,\n end_timestamp=happen_timestamp,\n limit=1,\n adjust_type=self.adjust_type,\n )\n except Exception as e:\n self.logger.error(e)\n raise WrongKdataError(\"could not get kdata\")\n\n if pd_is_not_null(kdata):\n entity_type, _, _ = decode_entity_id(kdata[\"entity_id\"][0])\n\n the_price = kdata[\"close\"][0]\n\n if the_price:\n if trading_signal.position_pct:\n self.order_by_position_pct(\n entity_id=entity_id,\n order_price=the_price,\n order_timestamp=happen_timestamp,\n order_position_pct=trading_signal.position_pct,\n order_type=order_type,\n )\n elif trading_signal.order_money:\n self.order_by_money(\n entity_id=entity_id,\n order_price=the_price,\n order_timestamp=happen_timestamp,\n order_money=trading_signal.order_money,\n order_type=order_type,\n )\n elif trading_signal.order_amount:\n self.order_by_amount(\n entity_id=entity_id,\n order_price=the_price,\n order_timestamp=happen_timestamp,\n order_amount=trading_signal.order_amount,\n order_type=order_type,\n )\n else:\n assert False\n else:\n self.logger.warning(\n \"ignore trading signal,wrong kdata,entity_id:{},timestamp:{},kdata:{}\".format(\n entity_id, happen_timestamp, kdata.to_dict(orient=\"records\")\n )\n )\n\n else:\n self.logger.warning(\n \"ignore trading signal,could not get kdata,entity_id:{},timestamp:{}\".format(\n entity_id, happen_timestamp\n )\n )\n\n def on_trading_close(self, timestamp):\n self.logger.info(\"on_trading_close:{}\".format(timestamp))\n # remove the empty position\n self.account.positions = [\n position for position in self.account.positions if position.long_amount > 0 or position.short_amount > 0\n ]\n\n # clear the data which need recomputing\n the_id = \"{}_{}\".format(self.trader_name, to_date_time_str(timestamp, TIME_FORMAT_ISO8601))\n\n self.account.value = 0\n self.account.all_value = 0\n for position in self.account.positions:\n entity_type, _, _ = decode_entity_id(position.entity_id)\n data_schema = get_kdata_schema(entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=self.adjust_type)\n\n kdata = get_kdata(\n provider=self.provider,\n level=IntervalLevel.LEVEL_1DAY,\n entity_id=position.entity_id,\n order=data_schema.timestamp.desc(),\n end_timestamp=timestamp,\n limit=1,\n adjust_type=self.adjust_type,\n )\n\n closing_price = kdata[\"close\"][0]\n\n position.available_long = position.long_amount\n position.available_short = position.short_amount\n\n if closing_price:\n if (position.long_amount is not None) and position.long_amount > 0:\n position.value = position.long_amount * closing_price\n self.account.value += position.value\n elif (position.short_amount is not None) and position.short_amount > 0:\n position.value = 2 * (position.short_amount * position.average_short_price)\n position.value -= position.short_amount * closing_price\n self.account.value += position.value\n\n # refresh profit\n position.profit = (closing_price - position.average_long_price) * position.long_amount\n position.profit_rate = position.profit / (position.average_long_price * position.long_amount)\n\n else:\n self.logger.warning(\n \"could not refresh close value for position:{},timestamp:{}\".format(position.entity_id, timestamp)\n )\n\n position.id = \"{}_{}_{}\".format(\n self.trader_name, position.entity_id, to_date_time_str(timestamp, TIME_FORMAT_ISO8601)\n )\n position.timestamp = to_pd_timestamp(timestamp)\n position.account_stats_id = the_id\n\n self.account.id = the_id\n self.account.all_value = self.account.value + self.account.cash\n self.account.closing = True\n self.account.timestamp = to_pd_timestamp(timestamp)\n self.account.profit = self.account.all_value - self.account.input_money\n self.account.profit_rate = self.account.profit / self.account.input_money\n\n self.session.add(self.account)\n self.session.commit()\n account_info = (\n f\"on_trading_close,holding size:{len(self.account.positions)} profit:{self.account.profit} input_money:{self.account.input_money} \"\n f\"cash:{self.account.cash} value:{self.account.value} all_value:{self.account.all_value}\"\n )\n self.logger.info(account_info)\n\n def get_current_position(self, entity_id, create_if_not_exist=False) -> Optional[Position]:\n \"\"\"\n get position for entity_id\n\n :param entity_id: the entity id\n :param create_if_not_exist: create an empty position if not exist in current account\n :return:\n \"\"\"\n for position in self.account.positions:\n if position.entity_id == entity_id:\n return position\n if create_if_not_exist:\n trading_t = self.entity_schema.get_trading_t()\n current_position = Position(\n trader_name=self.trader_name,\n entity_id=entity_id,\n long_amount=0,\n available_long=0,\n average_long_price=0,\n short_amount=0,\n available_short=0,\n average_short_price=0,\n profit=0,\n value=0,\n trading_t=trading_t,\n )\n # add it to account\n self.account.positions.append(current_position)\n return current_position\n return None\n\n def get_current_account(self):\n return self.account\n\n def update_position(self, current_position, order_amount, current_price, order_type, timestamp):\n \"\"\"\n\n :param timestamp:\n :type timestamp:\n :param current_position:\n :type current_position: Position\n :param order_amount:\n :type order_amount:\n :param current_price:\n :type current_price:\n :param order_type:\n :type order_type:\n \"\"\"\n if order_type == OrderType.order_long:\n need_money = (order_amount * current_price) * (1 + self.slippage + self.buy_cost)\n if self.account.cash < need_money:\n if self.rich_mode:\n self.input_money()\n else:\n raise NotEnoughMoneyError()\n\n self.account.cash -= need_money\n\n # 计算平均价\n long_amount = current_position.long_amount + order_amount\n if long_amount == 0:\n current_position.average_long_price = 0\n current_position.average_long_price = (\n current_position.average_long_price * current_position.long_amount + current_price * order_amount\n ) / long_amount\n\n current_position.long_amount = long_amount\n\n if current_position.trading_t == 0:\n current_position.available_long += order_amount\n\n elif order_type == OrderType.order_short:\n need_money = (order_amount * current_price) * (1 + self.slippage + self.buy_cost)\n if self.account.cash < need_money:\n if self.rich_mode:\n self.input_money()\n else:\n raise NotEnoughMoneyError()\n\n self.account.cash -= need_money\n\n short_amount = current_position.short_amount + order_amount\n current_position.average_short_price = (\n current_position.average_short_price * current_position.short_amount + current_price * order_amount\n ) / short_amount\n\n current_position.short_amount = short_amount\n\n if current_position.trading_t == 0:\n current_position.available_short += order_amount\n\n elif order_type == OrderType.order_close_long:\n self.account.cash += order_amount * current_price * (1 - self.slippage - self.sell_cost)\n # FIXME:如果没卖完，重新计算计算平均价\n\n current_position.available_long -= order_amount\n current_position.long_amount -= order_amount\n\n elif order_type == OrderType.order_close_short:\n self.account.cash += 2 * (order_amount * current_position.average_short_price)\n self.account.cash -= order_amount * current_price * (1 + self.slippage + self.sell_cost)\n\n current_position.available_short -= order_amount\n current_position.short_amount -= order_amount\n else:\n assert False\n\n # save the order info to db\n order_id = \"{}_{}_{}_{}\".format(\n self.trader_name, order_type, current_position.entity_id, to_date_time_str(timestamp, TIME_FORMAT_ISO8601)\n )\n order = Order(\n id=order_id,\n timestamp=to_pd_timestamp(timestamp),\n trader_name=self.trader_name,\n entity_id=current_position.entity_id,\n order_price=current_price,\n order_amount=order_amount,\n order_type=order_type.value,\n level=self.level.value,\n status=\"success\",\n )\n self.session.add(order)\n self.session.commit()\n\n def cal_amount_by_money(\n self,\n order_price: float,\n order_money: float,\n ):\n if order_money > self.account.cash:\n if self.rich_mode:\n self.input_money()\n else:\n raise NotEnoughMoneyError()\n\n cost = order_price * (1 + self.slippage + self.buy_cost)\n order_amount = order_money // cost\n\n return order_amount\n\n def cal_amount_by_position_pct(self, entity_id, order_price: float, order_position_pct: float, order_type):\n if order_type == OrderType.order_long or order_type == OrderType.order_short:\n cost = order_price * (1 + self.slippage + self.buy_cost)\n want_pay = self.account.cash * order_position_pct\n order_amount = want_pay // cost\n\n if order_amount < 1:\n if self.rich_mode:\n self.input_money()\n order_amount = max((self.account.cash * order_position_pct) // cost, 1)\n else:\n raise NotEnoughMoneyError()\n return order_amount\n elif order_type == OrderType.order_close_long or order_type == OrderType.order_close_short:\n current_position = self.get_current_position(entity_id=entity_id, create_if_not_exist=True)\n if order_type == OrderType.order_close_long:\n available = current_position.available_long\n else:\n available = current_position.available_short\n if available > 0:\n if order_position_pct == 1.0:\n order_amount = available\n else:\n order_amount = math.floor(available * order_position_pct)\n return order_amount\n else:\n raise NotEnoughPositionError()\n\n def order_by_position_pct(\n self,\n entity_id,\n order_timestamp,\n order_price: float,\n order_type: OrderType,\n order_position_pct: float = 0.2,\n ):\n order_amount = self.cal_amount_by_position_pct(\n entity_id=entity_id, order_price=order_price, order_position_pct=order_position_pct, order_type=order_type\n )\n\n self.order_by_amount(\n entity_id=entity_id,\n order_price=order_price,\n order_amount=order_amount,\n order_timestamp=order_timestamp,\n order_type=order_type,\n )\n\n def order_by_money(\n self,\n entity_id,\n order_timestamp,\n order_price: float,\n order_type: OrderType,\n order_money: float,\n ):\n if order_type not in (OrderType.order_long, OrderType.order_short):\n raise InvalidOrderParamError(f\"order type: {order_type.value} not support order_by_money\")\n\n order_amount = self.cal_amount_by_money(order_price=order_price, order_money=order_money)\n self.order_by_amount(\n entity_id=entity_id,\n order_price=order_price,\n order_amount=order_amount,\n order_timestamp=order_timestamp,\n order_type=order_type,\n )\n\n def order_by_amount(\n self,\n entity_id,\n order_price,\n order_timestamp,\n order_type,\n order_amount,\n ):\n current_position = self.get_current_position(entity_id=entity_id, create_if_not_exist=True)\n\n # 开多\n if order_type == OrderType.order_long:\n if current_position.short_amount > 0:\n raise InvalidOrderError(\"close the short position before open long\")\n\n self.update_position(current_position, order_amount, order_price, order_type, order_timestamp)\n # 开空\n elif order_type == OrderType.order_short:\n if current_position.long_amount > 0:\n raise InvalidOrderError(\"close the long position before open short\")\n\n self.update_position(current_position, order_amount, order_price, order_type, order_timestamp)\n # 平多\n elif order_type == OrderType.order_close_long:\n if current_position.available_long >= order_amount:\n self.update_position(current_position, order_amount, order_price, order_type, order_timestamp)\n else:\n raise NotEnoughPositionError()\n # 平空\n elif order_type == OrderType.order_close_short:\n if current_position.available_short >= order_amount:\n self.update_position(current_position, order_amount, order_price, order_type, order_timestamp)\n else:\n raise Exception(\"not enough position\")\n\n\n# the __all__ is generated\n__all__ = [\"AccountService\", \"SimAccountService\"]\n"
},
{
"path": "src/zvt/trader/trader.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import List, Union, Type, Tuple\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel, TradableEntity, AdjustType\nfrom zvt.contract.drawer import Drawer\nfrom zvt.contract.factor import Factor, TargetType\nfrom zvt.contract.normal_data import NormalData\nfrom zvt.domain import Stock\nfrom zvt.trader import TradingSignal, TradingSignalType, TradingListener\nfrom zvt.trader.sim_account import SimAccountService\nfrom zvt.trader.trader_info_api import AccountStatsReader\nfrom zvt.trader.trader_schemas import AccountStats, Position\nfrom zvt.utils.time_utils import (\n to_pd_timestamp,\n now_pd_timestamp,\n to_date_time_str,\n is_same_date,\n date_time_by_interval,\n)\n\n\nclass Trader(object):\n entity_schema: Type[TradableEntity] = None\n\n def __init__(\n self,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n provider: str = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n trader_name: str = None,\n real_time: bool = False,\n kdata_use_begin_time: bool = False,\n draw_result: bool = True,\n rich_mode: bool = False,\n adjust_type: AdjustType = None,\n profit_threshold=(3, -0.3),\n keep_history=False,\n pre_load_days=365,\n ) -> None:\n assert self.entity_schema is not None\n assert start_timestamp is not None\n assert end_timestamp is not None\n\n self.logger = logging.getLogger(__name__)\n\n if trader_name:\n self.trader_name = trader_name\n else:\n self.trader_name = type(self).__name__.lower()\n\n self.entity_ids = entity_ids\n self.exchanges = exchanges\n self.codes = codes\n self.provider = provider\n # make sure the min level factor correspond to the provider and level\n self.level = IntervalLevel(level)\n self.real_time = real_time\n self.start_timestamp = to_pd_timestamp(start_timestamp)\n self.end_timestamp = to_pd_timestamp(end_timestamp)\n self.pre_load_days = pre_load_days\n\n self.trading_dates = self.entity_schema.get_trading_dates(\n start_date=self.start_timestamp, end_date=self.end_timestamp\n )\n\n if real_time:\n self.logger.info(\n \"real_time mode, end_timestamp should be future,you could set it big enough for running forever\"\n )\n assert self.end_timestamp >= now_pd_timestamp()\n\n # false: 收到k线时，该k线已完成\n # true: 收到k线时，该k线可能未完成\n self.kdata_use_begin_time = kdata_use_begin_time\n self.draw_result = draw_result\n self.rich_mode = rich_mode\n\n self.adjust_type = AdjustType(adjust_type)\n self.profit_threshold = profit_threshold\n self.keep_history = keep_history\n\n self.level_map_long_targets = {}\n self.level_map_short_targets = {}\n self.trading_signals: List[TradingSignal] = []\n self.trading_signal_listeners: List[TradingListener] = []\n\n self.account_service = SimAccountService(\n entity_schema=self.entity_schema,\n trader_name=self.trader_name,\n timestamp=self.start_timestamp,\n provider=self.provider,\n level=self.level,\n rich_mode=self.rich_mode,\n adjust_type=self.adjust_type,\n keep_history=self.keep_history,\n )\n\n self.register_trading_signal_listener(self.account_service)\n\n self.factors = self.init_factors(\n entity_ids=self.entity_ids,\n entity_schema=self.entity_schema,\n exchanges=self.exchanges,\n codes=self.codes,\n start_timestamp=date_time_by_interval(self.start_timestamp, -self.pre_load_days),\n end_timestamp=self.end_timestamp,\n adjust_type=self.adjust_type,\n )\n\n if self.factors:\n self.trading_level_asc = list(set([IntervalLevel(factor.level) for factor in self.factors]))\n self.trading_level_asc.sort()\n\n self.logger.info(f\"trader level:{self.level},factors level:{self.trading_level_asc}\")\n\n if self.level != self.trading_level_asc[0]:\n raise Exception(\"trader level should be the min of the factors\")\n\n self.trading_level_desc = list(self.trading_level_asc)\n self.trading_level_desc.reverse()\n else:\n self.trading_level_asc = [self.level]\n self.trading_level_desc = [self.level]\n self.on_init()\n\n def on_init(self):\n self.logger.info(f\"trader:{self.trader_name} on_start\")\n\n def init_entities(self, timestamp):\n \"\"\"\n init the entities for timestamp\n\n :param timestamp:\n :return:\n \"\"\"\n self.logger.info(f\"timestamp: {timestamp} init_entities\")\n return self.entity_ids\n\n def init_factors(\n self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None\n ):\n \"\"\"\n overwrite it to init factors if you want to use factor computing model\n :param adjust_type:\n\n \"\"\"\n return []\n\n def update_targets_by_level(\n self,\n level: IntervalLevel,\n long_targets: List[str],\n short_targets: List[str],\n ) -> None:\n \"\"\"\n the trading signals is generated in min level,before that,we should cache targets of all levels\n\n :param level:\n :param long_targets:\n :param short_targets:\n \"\"\"\n self.logger.debug(\n f\"level:{level},old long targets:{self.level_map_long_targets.get(level)},new long targets:{long_targets}\"\n )\n self.level_map_long_targets[level] = long_targets\n\n self.logger.debug(\n f\"level:{level},old short targets:{self.level_map_short_targets.get(level)},new short targets:{short_targets}\"\n )\n self.level_map_short_targets[level] = short_targets\n\n def get_long_targets_by_level(self, level: IntervalLevel) -> List[str]:\n return self.level_map_long_targets.get(level)\n\n def get_short_targets_by_level(self, level: IntervalLevel) -> List[str]:\n return self.level_map_short_targets.get(level)\n\n def on_targets_selected_from_levels(self, timestamp) -> Tuple[List[str], List[str]]:\n \"\"\"\n this method's called in every min level cycle to select targets in all levels generated by the previous cycle\n the default implementation is selecting the targets in all levels\n overwrite it for your custom logic\n\n :param timestamp: current event time\n :return: long targets, short targets\n \"\"\"\n\n long_selected = None\n\n short_selected = None\n\n for level in self.trading_level_desc:\n long_targets = self.level_map_long_targets.get(level)\n # long must in all\n if long_targets:\n long_targets = set(long_targets)\n if long_selected is None:\n long_selected = long_targets\n else:\n long_selected = long_selected & long_targets\n else:\n long_selected = set()\n\n short_targets = self.level_map_short_targets.get(level)\n # short any\n if short_targets:\n short_targets = set(short_targets)\n if short_selected is None:\n short_selected = short_targets\n else:\n short_selected = short_selected | short_targets\n\n return long_selected, short_selected\n\n def get_current_account(self) -> AccountStats:\n return self.account_service.get_current_account()\n\n def get_current_positions(self) -> List[Position]:\n return self.get_current_account().positions\n\n def long_position_control(self):\n positions = self.get_current_positions()\n\n position_pct = 1.0\n if not positions:\n # 没有仓位，买2成\n position_pct = 0.2\n elif len(positions) <= 10:\n # 小于10个持仓，买5成\n position_pct = 0.5\n\n # 买完\n return position_pct\n\n def short_position_control(self):\n # 卖完\n return 1.0\n\n def on_profit_control(self):\n if self.profit_threshold and self.get_current_positions():\n positive = self.profit_threshold[0]\n negative = self.profit_threshold[1]\n close_long_entity_ids = []\n for position in self.get_current_positions():\n if position.available_long > 1:\n # 止盈\n if position.profit_rate >= positive:\n close_long_entity_ids.append(position.entity_id)\n self.logger.info(f\"close profit {position.profit_rate} for {position.entity_id}\")\n # 止损\n if position.profit_rate <= negative:\n close_long_entity_ids.append(position.entity_id)\n self.logger.info(f\"cut lost {position.profit_rate} for {position.entity_id}\")\n\n return close_long_entity_ids, None\n return None, None\n\n def buy(self, timestamp, entity_ids, ignore_in_position=True):\n if ignore_in_position:\n account = self.get_current_account()\n current_holdings = []\n if account.positions:\n current_holdings = [\n position.entity_id\n for position in account.positions\n if position != None and position.available_long > 0\n ]\n\n entity_ids = set(entity_ids) - set(current_holdings)\n\n if entity_ids:\n position_pct = self.long_position_control()\n position_pct = (1.0 / len(entity_ids)) * position_pct\n\n due_timestamp = to_pd_timestamp(timestamp) + pd.Timedelta(seconds=self.level.to_second())\n for entity_id in entity_ids:\n trading_signal = TradingSignal(\n entity_id=entity_id,\n due_timestamp=due_timestamp,\n happen_timestamp=timestamp,\n trading_signal_type=TradingSignalType.open_long,\n trading_level=self.level,\n position_pct=position_pct,\n )\n self.trading_signals.append(trading_signal)\n\n def sell(self, timestamp, entity_ids):\n # current position\n account = self.get_current_account()\n current_holdings = []\n if account.positions:\n current_holdings = [\n position.entity_id for position in account.positions if position != None and position.available_long > 0\n ]\n\n shorted = set(current_holdings) & set(entity_ids)\n\n if shorted:\n position_pct = self.short_position_control()\n\n due_timestamp = to_pd_timestamp(timestamp) + pd.Timedelta(seconds=self.level.to_second())\n for entity_id in shorted:\n trading_signal = TradingSignal(\n entity_id=entity_id,\n due_timestamp=due_timestamp,\n happen_timestamp=timestamp,\n trading_signal_type=TradingSignalType.close_long,\n trading_level=self.level,\n position_pct=position_pct,\n )\n self.trading_signals.append(trading_signal)\n\n def on_finish(self, timestamp):\n self.on_trading_finish(timestamp)\n # show the result\n if self.draw_result:\n reader = AccountStatsReader(trader_names=[self.trader_name])\n df = reader.data_df\n drawer = Drawer(\n main_data=NormalData(df.copy()[[\"trader_name\", \"timestamp\", \"all_value\"]], category_field=\"trader_name\")\n )\n drawer.draw_line(show=True)\n\n def on_factor_targets_filtered(\n self, timestamp, level, factor: Factor, long_targets: List[str], short_targets: List[str]\n ) -> Tuple[List[str], List[str]]:\n \"\"\"\n overwrite it to filter the targets from factor\n\n :param timestamp: the event time\n :param level: the level\n :param factor: the factor\n :param long_targets: the long targets from the factor\n :param short_targets: the short targets from the factor\n :return: filtered long targets, filtered short targets\n \"\"\"\n self.logger.info(f\"on_targets_filtered {level} long:{long_targets}\")\n\n if len(long_targets) > 10:\n long_targets = long_targets[0:10]\n self.logger.info(f\"on_targets_filtered {level} filtered long:{long_targets}\")\n\n return long_targets, short_targets\n\n def in_trading_date(self, timestamp):\n return to_date_time_str(timestamp) in self.trading_dates\n\n def on_time(self, timestamp: pd.Timestamp):\n \"\"\"\n called in every min level cycle\n\n :param timestamp: event time\n \"\"\"\n self.logger.debug(f\"current timestamp:{timestamp}\")\n\n def on_trading_signals(self, trading_signals: List[TradingSignal]):\n for l in self.trading_signal_listeners:\n l.on_trading_signals(trading_signals)\n # clear after all listener handling\n self.trading_signals = []\n\n def on_trading_open(self, timestamp):\n for l in self.trading_signal_listeners:\n l.on_trading_open(timestamp)\n\n def on_trading_close(self, timestamp):\n for l in self.trading_signal_listeners:\n l.on_trading_close(timestamp)\n\n def on_trading_finish(self, timestamp):\n for l in self.trading_signal_listeners:\n l.on_trading_finish(timestamp)\n\n def on_trading_error(self, timestamp, error):\n for l in self.trading_signal_listeners:\n l.on_trading_error(timestamp, error)\n\n def on_non_trading_day(self, timestamp):\n self.logger.info(f\"on_non_trading_day: {timestamp}\")\n\n def get_factors_by_level(self, level):\n return [factor for factor in self.factors if factor.level == level]\n\n def handle_factor_targets(self, timestamp: pd.Timestamp):\n \"\"\"\n select targets from factors\n :param timestamp: the timestamp for next kdata coming\n \"\"\"\n # 一般来说factor计算 多标的 历史数据比较快，多级别的计算也比较方便，常用于全市场标的粗过滤\n # 更细节的控制可以在on_targets_filtered里进一步处理\n # 也可以在on_time里面设计一些自己的逻辑配合过滤\n # 多级别的遍历算法要点:\n # 1)计算各级别的 标的，通过 on_factor_targets_filtered 过滤，缓存在level_map_long_targets，level_map_short_targets\n # 2)在最小的level通过 on_targets_selected_from_levels 根据多级别的缓存标的，生成最终的选中标的\n # 这里需要注意的是，小级别拿到上一个周期的大级别的标的，这是合理的\n for level in self.trading_level_asc:\n self.logger.info(f\"level: {level}\")\n # in every cycle, all level factor do its job in its time\n if self.entity_schema.is_finished_kdata_timestamp(timestamp=timestamp, level=level):\n all_long_targets = []\n all_short_targets = []\n\n # 从该level的factor中过滤targets\n current_level_factors = self.get_factors_by_level(level=level)\n for factor in current_level_factors:\n long_targets = factor.get_targets(timestamp=timestamp, target_type=TargetType.positive)\n short_targets = factor.get_targets(timestamp=timestamp, target_type=TargetType.negative)\n\n if long_targets or short_targets:\n long_targets, short_targets = self.on_factor_targets_filtered(\n timestamp=timestamp,\n level=level,\n factor=factor,\n long_targets=long_targets,\n short_targets=short_targets,\n )\n\n if long_targets:\n all_long_targets += long_targets\n if short_targets:\n all_short_targets += short_targets\n\n # 将各级别的targets缓存在level_map_long_targets，level_map_short_targets\n self.update_targets_by_level(level, all_long_targets, all_short_targets)\n\n def run(self):\n # iterate timestamp of the min level,e.g,9:30,9:35,9.40...for 5min level\n # timestamp represents the timestamp in kdata\n for timestamp in self.entity_schema.get_interval_timestamps(\n start_date=self.start_timestamp, end_date=self.end_timestamp, level=self.level\n ):\n self.logger.info(f\">>>>>>>>>>\")\n\n self.entity_ids = self.init_entities(timestamp=timestamp)\n self.logger.info(f\"current entities: {self.entity_ids}\")\n\n if not self.in_trading_date(timestamp=timestamp):\n self.on_non_trading_day(timestamp=timestamp)\n continue\n\n # on_trading_open to set the account\n if self.level >= IntervalLevel.LEVEL_1DAY or (\n self.level != IntervalLevel.LEVEL_1DAY and self.entity_schema.is_open_timestamp(timestamp)\n ):\n self.on_trading_open(timestamp=timestamp)\n\n # the signals were generated by previous timestamp kdata\n if self.trading_signals:\n self.logger.info(\"current signals:\")\n for signal in self.trading_signals:\n self.logger.info(str(signal))\n self.on_trading_signals(self.trading_signals)\n\n for factor in self.factors:\n factor.add_entities(entity_ids=self.entity_ids)\n\n waiting_seconds = 0\n\n if self.level == IntervalLevel.LEVEL_1DAY:\n if is_same_date(timestamp, now_pd_timestamp()):\n while True:\n self.logger.info(f\"time is:{now_pd_timestamp()},just smoke for minutes\")\n time.sleep(600)\n current = now_pd_timestamp()\n if current.hour >= 19:\n waiting_seconds = 20\n break\n\n elif self.real_time:\n # all factor move on to handle the coming data\n if self.kdata_use_begin_time:\n real_end_timestamp = timestamp + pd.Timedelta(seconds=self.level.to_second())\n else:\n real_end_timestamp = timestamp\n\n seconds = (now_pd_timestamp() - real_end_timestamp).total_seconds()\n waiting_seconds = self.level.to_second() - seconds\n\n # meaning the future kdata not ready yet,we could move on to check\n if waiting_seconds > 0:\n # iterate the factor from min to max which in finished timestamp kdata\n for level in self.trading_level_asc:\n if self.entity_schema.is_finished_kdata_timestamp(timestamp=timestamp, level=level):\n factors = self.get_factors_by_level(level=level)\n for factor in factors:\n factor.move_on(to_timestamp=timestamp, timeout=waiting_seconds + 20)\n\n if self.factors:\n self.handle_factor_targets(timestamp=timestamp)\n\n self.on_time(timestamp=timestamp)\n\n long_selected, short_selected = self.on_targets_selected_from_levels(timestamp)\n\n # 处理 止赢 止损\n passive_short, _ = self.on_profit_control()\n if passive_short:\n if not short_selected:\n short_selected = passive_short\n else:\n short_selected = list(set(short_selected) | set(passive_short))\n\n if short_selected:\n self.sell(timestamp=timestamp, entity_ids=short_selected)\n if long_selected:\n self.buy(timestamp=timestamp, entity_ids=long_selected)\n\n # on_trading_close to calculate date account\n if self.level >= IntervalLevel.LEVEL_1DAY or (\n self.level != IntervalLevel.LEVEL_1DAY and self.entity_schema.is_close_timestamp(timestamp)\n ):\n self.on_trading_close(timestamp)\n\n self.logger.info(f\"<<<<<<<<<<\\n\")\n\n self.on_finish(timestamp)\n\n def register_trading_signal_listener(self, listener):\n if listener not in self.trading_signal_listeners:\n self.trading_signal_listeners.append(listener)\n\n def deregister_trading_signal_listener(self, listener):\n if listener in self.trading_signal_listeners:\n self.trading_signal_listeners.remove(listener)\n\n\nclass StockTrader(Trader):\n entity_schema = Stock\n\n def __init__(\n self,\n entity_ids: List[str] = None,\n exchanges: List[str] = None,\n codes: List[str] = None,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n provider: str = None,\n level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,\n trader_name: str = None,\n real_time: bool = False,\n kdata_use_begin_time: bool = False,\n draw_result: bool = True,\n rich_mode: bool = False,\n adjust_type: AdjustType = AdjustType.hfq,\n profit_threshold=(3, -0.3),\n keep_history=False,\n ) -> None:\n super().__init__(\n entity_ids,\n exchanges,\n codes,\n start_timestamp,\n end_timestamp,\n provider,\n level,\n trader_name,\n real_time,\n kdata_use_begin_time,\n draw_result,\n rich_mode,\n adjust_type,\n profit_threshold,\n keep_history,\n )\n\n\n# the __all__ is generated\n__all__ = [\"Trader\", \"StockTrader\"]\n"
},
{
"path": "src/zvt/trader/trader_info_api.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Union\n\nimport pandas as pd\n\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import get_data, get_db_session\nfrom zvt.contract.drawer import Drawer\nfrom zvt.contract.normal_data import NormalData\nfrom zvt.contract.reader import DataReader\nfrom zvt.trader.trader_schemas import AccountStats, Order, TraderInfo, Position\n\n\ndef clear_trader(trader_name, session=None):\n if not session:\n session = get_db_session(\"zvt\", data_schema=TraderInfo)\n session.query(TraderInfo).filter(TraderInfo.trader_name == trader_name).delete()\n session.query(AccountStats).filter(AccountStats.trader_name == trader_name).delete()\n session.query(Position).filter(Position.trader_name == trader_name).delete()\n session.query(Order).filter(Order.trader_name == trader_name).delete()\n session.commit()\n\n\ndef get_trader_info(\n trader_name=None,\n return_type=\"df\",\n start_timestamp=None,\n end_timestamp=None,\n filters=None,\n session=None,\n order=None,\n limit=None,\n) -> List[TraderInfo]:\n if trader_name:\n if filters:\n filters = filters + [TraderInfo.trader_name == trader_name]\n else:\n filters = [TraderInfo.trader_name == trader_name]\n\n return get_data(\n data_schema=TraderInfo,\n entity_id=None,\n codes=None,\n level=None,\n provider=\"zvt\",\n columns=None,\n return_type=return_type,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=filters,\n session=session,\n order=order,\n limit=limit,\n )\n\n\ndef get_order_securities(trader_name):\n items = (\n get_db_session(provider=\"zvt\", data_schema=Order)\n .query(Order.entity_id)\n .filter(Order.trader_name == trader_name)\n .group_by(Order.entity_id)\n .all()\n )\n\n return [item[0] for item in items]\n\n\nclass AccountStatsReader(DataReader):\n def __init__(\n self,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n level: IntervalLevel = IntervalLevel.LEVEL_1DAY,\n trader_names: List[str] = None,\n ) -> None:\n self.trader_names = trader_names\n\n self.filters = filters\n\n if self.trader_names:\n filter = [AccountStats.trader_name == name for name in self.trader_names]\n if self.filters:\n self.filters += filter\n else:\n self.filters = filter\n super().__init__(\n AccountStats,\n None,\n None,\n None,\n None,\n None,\n None,\n start_timestamp,\n end_timestamp,\n columns,\n self.filters,\n order,\n None,\n level,\n category_field=\"trader_name\",\n time_field=\"timestamp\",\n keep_window=None,\n )\n\n def draw_line(self, show=True):\n drawer = Drawer(\n main_data=NormalData(\n self.data_df.copy()[[\"trader_name\", \"timestamp\", \"all_value\"]], category_field=\"trader_name\"\n )\n )\n return drawer.draw_line(show=show)\n\n\nclass OrderReader(DataReader):\n def __init__(\n self,\n start_timestamp: Union[str, pd.Timestamp] = None,\n end_timestamp: Union[str, pd.Timestamp] = None,\n columns: List = None,\n filters: List = None,\n order: object = None,\n level: IntervalLevel = None,\n trader_names: List[str] = None,\n ) -> None:\n self.trader_names = trader_names\n\n self.filters = filters\n\n if self.trader_names:\n filter = [Order.trader_name == name for name in self.trader_names]\n if self.filters:\n self.filters += filter\n else:\n self.filters = filter\n\n super().__init__(\n Order,\n None,\n None,\n None,\n None,\n None,\n None,\n start_timestamp,\n end_timestamp,\n columns,\n self.filters,\n order,\n None,\n level,\n category_field=\"trader_name\",\n time_field=\"timestamp\",\n keep_window=None,\n )\n\n\nif __name__ == \"__main__\":\n reader = AccountStatsReader(trader_names=[\"000338_ma_trader\"])\n drawer = Drawer(\n main_data=NormalData(\n reader.data_df.copy()[[\"trader_name\", \"timestamp\", \"all_value\"]], category_field=\"trader_name\"\n )\n )\n drawer.draw_line()\n# the __all__ is generated\n__all__ = [\"clear_trader\", \"get_trader_info\", \"get_order_securities\", \"AccountStatsReader\", \"OrderReader\"]\n"
},
{
"path": "src/zvt/trader/trader_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nfrom zvt.contract.model import MixinModel\n\n\nclass PositionModel(MixinModel):\n #: 机器人名字\n trader_name: str\n #: 做多数量\n long_amount: float\n #: 可平多数量\n available_long: float\n #: 平均做多价格\n average_long_price: float\n #: 做空数量\n short_amount: float\n #: 可平空数量\n available_short: float\n #: 平均做空价格\n average_short_price: float\n #: 盈亏\n profit: float\n #: 盈亏比例\n profit_rate: float\n #: 市值 或者 占用的保证金(方便起见，总是100%)\n value: float\n #: 交易类型(0代表T+0,1代表T+1)\n trading_t: int\n\n\nclass AccountStatsModel(MixinModel):\n #: 投入金额\n input_money: float\n #: 机器人名字\n trader_name: str\n #: 具体仓位\n positions: List[PositionModel]\n #: 市值\n value: float\n #: 可用现金\n cash: float\n #: value + cash\n all_value: float\n\n #: 盈亏\n profit: float\n #: 盈亏比例\n profit_rate: float\n\n #: 收盘计算\n closing: bool\n"
},
{
"path": "src/zvt/trader/trader_schemas.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, String, DateTime, Boolean, Float, Integer, ForeignKey\nfrom sqlalchemy.orm import declarative_base\nfrom sqlalchemy.orm import relationship\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\nfrom zvt.utils.decorator import to_string\n\nTraderBase = declarative_base()\n\n\nclass TraderInfo(TraderBase, Mixin):\n \"\"\"\n trader info\n \"\"\"\n\n __tablename__ = \"trader_info\"\n #: 机器人名字\n trader_name = Column(String(length=128))\n\n entity_type = Column(String(length=128))\n start_timestamp = Column(DateTime)\n end_timestamp = Column(DateTime)\n provider = Column(String(length=32))\n level = Column(String(length=32))\n real_time = Column(Boolean)\n kdata_use_begin_time = Column(Boolean)\n kdata_adjust_type = Column(String(length=32))\n\n\n@to_string\nclass AccountStats(TraderBase, Mixin):\n \"\"\"\n account stats of every day\n \"\"\"\n\n __tablename__ = \"account_stats\"\n\n input_money = Column(Float)\n\n #: 机器人名字\n trader_name = Column(String(length=128))\n #: 可用现金\n cash = Column(Float)\n #: 具体仓位\n positions = relationship(\"Position\", back_populates=\"account_stats\")\n #: 市值\n value = Column(Float)\n #: 市值+cash\n all_value = Column(Float)\n\n #: 盈亏\n profit = Column(Float)\n #: 盈亏比例\n profit_rate = Column(Float)\n\n #: 收盘计算\n closing = Column(Boolean)\n\n\n#: the position for specific entity of every day\nclass Position(TraderBase, Mixin):\n __tablename__ = \"position\"\n\n #: 机器人名字\n trader_name = Column(String(length=128))\n #: 账户id\n account_stats_id = Column(Integer, ForeignKey(\"account_stats.id\"))\n account_stats = relationship(\"AccountStats\", back_populates=\"positions\")\n\n #: 做多数量\n long_amount = Column(Float)\n #: 可平多数量\n available_long = Column(Float)\n #: 平均做多价格\n average_long_price = Column(Float)\n\n #: 做空数量\n short_amount = Column(Float)\n #: 可平空数量\n available_short = Column(Float)\n #: 平均做空价格\n average_short_price = Column(Float)\n\n #: 盈亏\n profit = Column(Float)\n #: 盈亏比例\n profit_rate = Column(Float)\n #: 市值 或者 占用的保证金(方便起见，总是100%)\n value = Column(Float)\n #: 交易类型(0代表T+0,1代表T+1)\n trading_t = Column(Integer)\n\n\n#: 委托单\nclass Order(TraderBase, Mixin):\n __tablename__ = \"order\"\n\n #: 机器人名字\n trader_name = Column(String(length=128))\n #: 订单价格\n order_price = Column(Float)\n #: 订单数量\n order_amount = Column(Float)\n #: 订单类型\n order_type = Column(String(length=64))\n #: 订单状态\n status = Column(String(length=64))\n\n #: 产生订单的selector/factor level\n level = Column(String(length=32))\n\n\nregister_schema(db_name=\"trader_info\", schema_base=TraderBase, internal=True)\n\n# the __all__ is generated\n__all__ = [\"TraderInfo\", \"AccountStats\", \"Position\", \"Order\"]\n"
},
{
"path": "src/zvt/trading/__init__.py",
"content": "# -*- coding: utf-8 -*-\n\n\n# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/trading/common.py",
"content": "# -*- coding: utf-8 -*-\nfrom enum import Enum\n\n\nclass ExecutionStatus(Enum):\n init = \"init\"\n success = \"success\"\n failed = \"failed\"\n\n\n# the __all__ is generated\n__all__ = [\"ExecutionStatus\"]\n"
},
{
"path": "src/zvt/trading/trading_models.py",
"content": "# -*- coding: utf-8 -*-\nfrom datetime import datetime\nfrom typing import List, Optional\nfrom typing import Union\n\nfrom pydantic import BaseModel, Field\nfrom pydantic import field_validator\n\nfrom zvt.common.query_models import TimeRange, OrderByType\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.contract.model import MixinModel, CustomModel\nfrom zvt.tag.tag_utils import get_stock_pool_names\nfrom zvt.trader import TradingSignalType\nfrom zvt.trading.common import ExecutionStatus\nfrom zvt.utils.time_utils import date_time_by_interval, current_date\nfrom zvt.utils.time_utils import tomorrow_date, to_pd_timestamp\n\n\nclass KdataRequestModel(BaseModel):\n entity_ids: List[str]\n data_provider: str = Field(default=\"em\")\n start_timestamp: datetime = Field(default=date_time_by_interval(current_date(), -500))\n end_timestamp: Optional[datetime] = Field(default=None)\n level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)\n adjust_type: AdjustType = Field(default=AdjustType.qfq)\n\n\nclass KdataModel(BaseModel):\n entity_id: str\n code: str\n name: str\n level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)\n datas: List\n\n\nclass TSRequestModel(BaseModel):\n entity_ids: List[str]\n data_provider: str = Field(default=\"qmt\")\n days_count: int = Field(default=5)\n\n\nclass TSModel(BaseModel):\n entity_id: str\n code: str\n name: str\n datas: List\n\n\nclass QuoteStatsModel(BaseModel):\n #: UNIX时间戳\n time: int\n #: 涨停数\n limit_up_count: int\n #: 跌停数\n limit_down_count: int\n #: 上涨数\n up_count: int\n #: 下跌数\n down_count: int\n #: 涨幅\n change_pct: float\n #: 成交额\n turnover: float\n #: 昨日成交额\n pre_turnover: Optional[float] = Field(default=None)\n #: 同比\n turnover_change: Optional[float] = Field(default=None)\n\n\nclass QueryStockQuoteSettingModel(CustomModel):\n stock_pool_name: Optional[str] = Field(default=None)\n main_tags: Optional[List[str]] = Field(default=None)\n\n\nclass BuildQueryStockQuoteSettingModel(CustomModel):\n stock_pool_name: str\n main_tags: Optional[List[str]] = Field(default=None)\n\n @field_validator(\"stock_pool_name\")\n @classmethod\n def stock_pool_name_existed(cls, v: str) -> str:\n if v not in get_stock_pool_names():\n raise ValueError(f\"Invalid stock_pool_name: {v}\")\n return v\n\n\nclass QueryTagQuoteModel(CustomModel):\n stock_pool_name: str\n main_tags: List[str]\n\n\nclass QueryStockQuoteModel(CustomModel):\n main_tag: Optional[str] = Field(default=None)\n entity_ids: Optional[List[str]] = Field(default=None)\n stock_pool_name: Optional[str] = Field(default=None)\n # the amount is not huge, just ignore now\n limit: int = Field(default=100)\n order_by_type: Optional[OrderByType] = Field(default=OrderByType.desc)\n order_by_field: Optional[str] = Field(default=\"change_pct\")\n\n\nclass StockQuoteModel(MixinModel):\n #: 代码\n code: str\n #: 名字\n name: str\n\n #: UNIX时间戳\n time: int\n #: 最新价\n price: float\n # 涨跌幅\n change_pct: float\n # 成交金额\n turnover: float\n # 换手率\n turnover_rate: float\n #: 是否涨停\n is_limit_up: bool\n #: 封涨停金额\n # limit_up_amount: Optional[float] = Field(default=None)\n #: 是否跌停\n is_limit_down: bool\n #: 封跌停金额\n # limit_down_amount: Optional[float] = Field(default=None)\n #: 5挡卖单金额\n # ask_amount: float\n #: 5挡买单金额\n # bid_amount: float\n #: 流通市值\n float_cap: float\n #: 总市值\n total_cap: float\n\n main_tag: Optional[str] = Field(default=None)\n sub_tag: Union[str, None] = Field(default=None)\n hidden_tags: Union[List[str], None] = Field(default=None)\n\n\nclass TagQuoteStatsModel(CustomModel):\n main_tag: str\n #: 涨停数\n limit_up_count: int\n #: 跌停数\n limit_down_count: int\n #: 上涨数\n up_count: int\n #: 下跌数\n down_count: int\n #: 涨幅\n change_pct: float\n #: 成交额\n turnover: float\n\n\nclass StockQuoteStatsModel(CustomModel):\n #: 涨停数\n limit_up_count: int\n #: 跌停数\n limit_down_count: int\n #: 上涨数\n up_count: int\n #: 下跌数\n down_count: int\n #: 涨幅\n change_pct: float\n #: 成交额\n turnover: float\n\n quotes: List[StockQuoteModel]\n\n\nclass TradingPlanModel(MixinModel):\n stock_id: str\n stock_code: str\n stock_name: str\n # 执行交易日\n trading_date: datetime\n # 预期开盘涨跌幅\n expected_open_pct: float\n buy_price: Optional[float]\n sell_price: Optional[float]\n # 操作理由\n trading_reason: str\n # 交易信号\n trading_signal_type: TradingSignalType\n # 执行状态\n status: ExecutionStatus = Field(default=ExecutionStatus.init)\n # 复盘\n review: Optional[str]\n\n\nclass BuildTradingPlanModel(BaseModel):\n stock_id: str\n # 执行交易日\n trading_date: datetime\n # 预期开盘涨跌幅\n expected_open_pct: float\n buy_price: Optional[float]\n sell_price: Optional[float]\n # 操作理由\n trading_reason: str\n # 交易信号\n trading_signal_type: TradingSignalType\n\n @field_validator(\"trading_date\")\n @classmethod\n def trading_date_must_be_future(cls, v: str) -> str:\n if to_pd_timestamp(v) < tomorrow_date():\n raise ValueError(f\"trading_date: {v} must set to future trading date\")\n return v\n\n\nclass QueryTradingPlanModel(BaseModel):\n time_range: TimeRange\n\n\n# the __all__ is generated\n__all__ = [\n \"QueryTagQuoteModel\",\n \"QueryStockQuoteSettingModel\",\n \"BuildQueryStockQuoteSettingModel\",\n \"QueryStockQuoteModel\",\n \"StockQuoteModel\",\n \"StockQuoteStatsModel\",\n \"TradingPlanModel\",\n \"BuildTradingPlanModel\",\n \"QueryTradingPlanModel\",\n]\n"
},
{
"path": "src/zvt/trading/trading_schemas.py",
"content": "# -*- coding: utf-8 -*-\nfrom sqlalchemy import Column, Float, DateTime, Integer\nfrom sqlalchemy import String, JSON\nfrom sqlalchemy.orm import declarative_base\n\nfrom zvt.contract import Mixin\nfrom zvt.contract.register import register_schema\n\nTradingBase = declarative_base()\n\n\nclass TagQuoteStats(Mixin, TradingBase):\n __tablename__ = \"tag_quote_stats\"\n stock_pool_name = Column(String)\n main_tag = Column(String)\n limit_up_count = Column(Integer)\n limit_down_count = Column(Integer)\n up_count = Column(Integer)\n down_count = Column(Integer)\n change_pct = Column(Float)\n turnover = Column(Float)\n\n\nclass TradingPlan(TradingBase, Mixin):\n __tablename__ = \"trading_plan\"\n stock_id = Column(String)\n stock_code = Column(String)\n stock_name = Column(String)\n trading_date = Column(DateTime)\n # 预期开盘涨跌幅\n expected_open_pct = Column(Float, nullable=False)\n buy_price = Column(Float)\n sell_price = Column(Float)\n # 操作理由\n trading_reason = Column(String)\n # 交易信号\n trading_signal_type = Column(String)\n # 执行状态\n status = Column(String)\n # 复盘\n review = Column(String)\n\n\nclass QueryStockQuoteSetting(TradingBase, Mixin):\n __tablename__ = \"query_stock_quote_setting\"\n stock_pool_name = Column(String)\n main_tags = Column(JSON)\n\n\nregister_schema(db_name=\"stock_trading\", schema_base=TradingBase, internal=True)\n\n\n# the __all__ is generated\n__all__ = [\"TradingPlan\", \"QueryStockQuoteSetting\"]\n"
},
{
"path": "src/zvt/trading/trading_service.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nfrom typing import List\n\nimport pandas as pd\nfrom fastapi import HTTPException\nfrom fastapi_pagination.ext.sqlalchemy import paginate\n\nimport zvt.api.kdata as kdata_api\nimport zvt.contract.api as contract_api\nfrom zvt.common.query_models import TimeUnit\nfrom zvt.domain import Stock, StockQuote, Stock1mQuote\nfrom zvt.domain.quotes.stockhk.stockhk_quote import StockhkQuote\nfrom zvt.domain.quotes.stockus.stockus_quote import StockusQuote\nfrom zvt.tag.tag_schemas import StockTags, StockPools, StockPoolInfo\nfrom zvt.trading.common import ExecutionStatus\nfrom zvt.trading.trading_models import (\n BuildTradingPlanModel,\n QueryTradingPlanModel,\n QueryTagQuoteModel,\n QueryStockQuoteModel,\n BuildQueryStockQuoteSettingModel,\n KdataRequestModel,\n TSRequestModel,\n)\nfrom zvt.trading.trading_schemas import TradingPlan, QueryStockQuoteSetting, TagQuoteStats\nfrom zvt.utils.pd_utils import pd_is_not_null\nfrom zvt.utils.time_utils import (\n to_date_time_str,\n to_pd_timestamp,\n now_pd_timestamp,\n date_time_by_interval,\n current_date,\n date_and_time,\n)\n\nlogger = logging.getLogger(__name__)\n\n\ndef query_kdata(kdata_request_model: KdataRequestModel):\n kdata_df = kdata_api.get_kdata(\n entity_ids=kdata_request_model.entity_ids,\n provider=kdata_request_model.data_provider,\n start_timestamp=kdata_request_model.start_timestamp,\n end_timestamp=kdata_request_model.end_timestamp,\n adjust_type=kdata_request_model.adjust_type,\n )\n if pd_is_not_null(kdata_df):\n kdata_df[\"timestamp\"] = kdata_df[\"timestamp\"].apply(lambda x: int(x.timestamp()))\n kdata_df[\"data\"] = kdata_df.apply(\n lambda x: x[\n [\"timestamp\", \"open\", \"high\", \"low\", \"close\", \"volume\", \"turnover\", \"change_pct\", \"turnover_rate\"]\n ].values.tolist(),\n axis=1,\n )\n df = kdata_df.groupby(\"entity_id\").agg(\n code=(\"code\", \"first\"),\n name=(\"name\", \"first\"),\n level=(\"level\", \"first\"),\n datas=(\"data\", lambda data: list(data)),\n )\n df = df.reset_index(drop=False)\n return df.to_dict(orient=\"records\")\n\n\ndef query_ts(ts_request_model: TSRequestModel):\n trading_dates = kdata_api.get_recent_trade_dates(entity_type=\"stock\", days_count=ts_request_model.days_count)\n ts_df = Stock1mQuote.query_data(\n entity_ids=ts_request_model.entity_ids,\n provider=ts_request_model.data_provider,\n start_timestamp=trading_dates[0],\n )\n if pd_is_not_null(ts_df):\n ts_df[\"data\"] = ts_df.apply(\n lambda x: x[\n [\"time\", \"price\", \"avg_price\", \"change_pct\", \"volume\", \"turnover\", \"turnover_rate\"]\n ].values.tolist(),\n axis=1,\n )\n df = ts_df.groupby(\"entity_id\").agg(\n code=(\"code\", \"first\"),\n name=(\"name\", \"first\"),\n datas=(\"data\", lambda data: list(data)),\n )\n df = df.reset_index(drop=False)\n return df.to_dict(orient=\"records\")\n\n\ndef build_trading_plan(build_trading_plan_model: BuildTradingPlanModel):\n with contract_api.DBSession(provider=\"zvt\", data_schema=TradingPlan)() as session:\n stock_id = build_trading_plan_model.stock_id\n trading_date_str = to_date_time_str(build_trading_plan_model.trading_date)\n trading_date = to_pd_timestamp(trading_date_str)\n signal = build_trading_plan_model.trading_signal_type.value\n plan_id = f\"{stock_id}_{trading_date_str}_{signal}\"\n\n datas = TradingPlan.query_data(\n session=session, filters=[TradingPlan.id == plan_id], limit=1, return_type=\"domain\"\n )\n if datas:\n assert len(datas) == 1\n plan = datas[0]\n else:\n datas = Stock.query_data(provider=\"em\", entity_id=stock_id, return_type=\"domain\")\n stock = datas[0]\n plan = TradingPlan(\n id=plan_id,\n entity_id=stock_id,\n stock_id=stock_id,\n stock_code=stock.code,\n stock_name=stock.name,\n trading_date=trading_date,\n expected_open_pct=build_trading_plan_model.expected_open_pct,\n buy_price=build_trading_plan_model.buy_price,\n sell_price=build_trading_plan_model.sell_price,\n trading_reason=build_trading_plan_model.trading_reason,\n trading_signal_type=signal,\n status=ExecutionStatus.init.value,\n )\n plan.timestamp = now_pd_timestamp()\n session.add(plan)\n session.commit()\n session.refresh(plan)\n return plan\n\n\ndef query_trading_plan(query_trading_plan_model: QueryTradingPlanModel):\n with contract_api.DBSession(provider=\"zvt\", data_schema=TradingPlan)() as session:\n time_range = query_trading_plan_model.time_range\n if time_range.relative_time_range:\n start_timestamp = date_time_by_interval(\n current_date(), time_range.relative_time_range.interval, time_range.relative_time_range.time_unit\n )\n end_timestamp = None\n else:\n start_timestamp = time_range.absolute_time_range.start_timestamp\n end_timestamp = time_range.absolute_time_range.end_timestamp\n selectable = TradingPlan.query_data(\n session=session, start_timestamp=start_timestamp, end_timestamp=end_timestamp, return_type=\"select\"\n )\n return paginate(session, selectable)\n\n\ndef get_current_trading_plan():\n with contract_api.DBSession(provider=\"zvt\", data_schema=TradingPlan)() as session:\n return TradingPlan.query_data(\n session=session,\n filters=[TradingPlan.status == ExecutionStatus.pending.value],\n order=TradingPlan.trading_date.asc(),\n return_type=\"domain\",\n )\n\n\ndef get_future_trading_plan():\n with contract_api.DBSession(provider=\"zvt\", data_schema=TradingPlan)() as session:\n return TradingPlan.query_data(\n session=session,\n filters=[TradingPlan.status == ExecutionStatus.init.value],\n order=TradingPlan.trading_date.asc(),\n return_type=\"domain\",\n )\n\n\ndef check_trading_plan():\n with contract_api.DBSession(provider=\"zvt\", data_schema=TradingPlan)() as session:\n plans = TradingPlan.query_data(\n session=session,\n filters=[TradingPlan.status == ExecutionStatus.init.value, TradingPlan.trading_date == current_date()],\n order=TradingPlan.trading_date.asc(),\n return_type=\"domain\",\n )\n\n logger.debug(f\"current plans:{plans}\")\n\n\ndef query_quote_stats():\n quote_df = StockQuote.query_data(\n return_type=\"df\",\n filters=[StockQuote.change_pct >= -0.31, StockQuote.change_pct <= 0.31],\n columns=[\"timestamp\", \"entity_id\", \"time\", \"change_pct\", \"turnover\", \"is_limit_up\", \"is_limit_down\"],\n )\n current_stats = cal_quote_stats(quote_df)\n start_timestamp = current_stats[\"timestamp\"]\n\n pre_date_df = Stock1mQuote.query_data(\n filters=[Stock1mQuote.timestamp < to_date_time_str(start_timestamp)],\n order=Stock1mQuote.timestamp.desc(),\n limit=1,\n columns=[\"timestamp\"],\n )\n\n if pd_is_not_null(pre_date_df):\n pre_date = pre_date_df[\"timestamp\"].tolist()[0]\n\n if start_timestamp.hour >= 15:\n start_timestamp = date_and_time(pre_date, \"15:00\")\n else:\n start_timestamp = date_and_time(pre_date, f\"{start_timestamp.hour}:{start_timestamp.minute}\")\n end_timestamp = date_time_by_interval(start_timestamp, 1, TimeUnit.minute)\n\n pre_df = Stock1mQuote.query_data(\n return_type=\"df\",\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n filters=[Stock1mQuote.change_pct >= -0.31, Stock1mQuote.change_pct <= 0.31],\n columns=[\"timestamp\", \"entity_id\", \"time\", \"change_pct\", \"turnover\", \"is_limit_up\", \"is_limit_down\"],\n )\n\n if pd_is_not_null(pre_df):\n pre_df = pre_df.drop_duplicates(subset=[\"entity_id\"], keep=\"first\")\n pre_stats = cal_quote_stats(pre_df)\n current_stats[\"pre_turnover\"] = pre_stats[\"turnover\"]\n current_stats[\"turnover_change\"] = current_stats[\"turnover\"] - current_stats[\"pre_turnover\"]\n else:\n current_stats[\"pre_turnover\"] = current_stats[\"turnover\"]\n current_stats[\"turnover_change\"] = 0\n\n return current_stats\n\n\ndef cal_quote_stats(quote_df):\n quote_df[\"ss\"] = 1\n\n df = (\n quote_df.groupby(\"ss\")\n .agg(\n timestamp=(\"timestamp\", \"last\"),\n time=(\"time\", \"last\"),\n up_count=(\"change_pct\", lambda x: (x > 0).sum()),\n down_count=(\"change_pct\", lambda x: (x <= 0).sum()),\n turnover=(\"turnover\", \"sum\"),\n change_pct=(\"change_pct\", \"mean\"),\n limit_up_count=(\"is_limit_up\", \"sum\"),\n limit_down_count=(\"is_limit_down\", lambda x: (x == True).sum()),\n )\n .reset_index(drop=True)\n )\n\n return df.to_dict(orient=\"records\")[0]\n\n\ndef cal_tag_quote_stats(stock_pool_name):\n stock_pools: List[StockPools] = StockPools.query_data(\n filters=[StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if stock_pools:\n entity_ids = stock_pools[0].entity_ids\n else:\n entity_ids = None\n\n tag_df = StockTags.query_data(\n entity_ids=entity_ids,\n filters=[StockTags.main_tag.isnot(None)],\n columns=[StockTags.entity_id, StockTags.main_tag],\n return_type=\"df\",\n index=\"entity_id\",\n )\n\n entity_ids = tag_df[\"entity_id\"].tolist()\n\n quote_df = StockQuote.query_data(entity_ids=entity_ids, return_type=\"df\", index=\"entity_id\")\n timestamp = quote_df[\"timestamp\"].tolist()[0]\n\n df = pd.concat([tag_df, quote_df], axis=1)\n grouped_df = (\n df.groupby(\"main_tag\")\n .agg(\n up_count=(\"change_pct\", lambda x: (x > 0).sum()),\n down_count=(\"change_pct\", lambda x: (x <= 0).sum()),\n turnover=(\"turnover\", \"sum\"),\n change_pct=(\"change_pct\", \"mean\"),\n limit_up_count=(\"is_limit_up\", \"sum\"),\n limit_down_count=(\"is_limit_down\", lambda x: (x == True).sum()),\n total_count=(\"main_tag\", \"size\"), # 添加计数，计算每个分组的总行数\n )\n .reset_index(drop=False)\n )\n grouped_df[\"stock_pool_name\"] = stock_pool_name\n\n grouped_df[\"entity_id\"] = grouped_df[[\"stock_pool_name\", \"main_tag\"]].apply(\n lambda se: \"{}_{}\".format(se[\"stock_pool_name\"], se[\"main_tag\"]), axis=1\n )\n grouped_df[\"timestamp\"] = timestamp\n grouped_df[\"id\"] = grouped_df[[\"entity_id\", \"timestamp\"]].apply(\n lambda se: \"{}_{}\".format(se[\"entity_id\"], to_date_time_str(se[\"timestamp\"])), axis=1\n )\n\n print(grouped_df)\n\n contract_api.df_to_db(\n df=grouped_df, data_schema=TagQuoteStats, provider=\"zvt\", force_update=True, drop_duplicates=False\n )\n\n\ndef query_tag_quotes(query_tag_quote_model: QueryTagQuoteModel):\n stock_pools: List[StockPools] = StockPools.query_data(\n filters=[StockPools.stock_pool_name == query_tag_quote_model.stock_pool_name],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if stock_pools:\n entity_ids = stock_pools[0].entity_ids\n else:\n entity_ids = None\n\n entity_type = \"stock\"\n if entity_ids:\n entity_id = entity_ids[0]\n entity_type, _, _ = contract_api.decode_entity_id(entity_id)\n\n tag_df = StockTags.query_data(\n entity_ids=entity_ids,\n filters=[StockTags.entity_type == entity_type],\n columns=[StockTags.entity_id, StockTags.main_tag],\n return_type=\"df\",\n index=\"entity_id\",\n )\n\n entity_ids = tag_df[\"entity_id\"].tolist()\n\n if entity_type == \"stock\":\n quote_df = StockQuote.query_data(entity_ids=entity_ids, return_type=\"df\", index=\"entity_id\")\n elif entity_type == \"stockus\":\n quote_df = StockusQuote.query_data(entity_ids=entity_ids, return_type=\"df\", index=\"entity_id\")\n elif entity_type == \"stockhk\":\n quote_df = StockhkQuote.query_data(entity_ids=entity_ids, return_type=\"df\", index=\"entity_id\")\n else:\n raise HTTPException(status_code=400, detail=f\"Unsupported entity type: {entity_type}\")\n\n df = pd.concat([tag_df, quote_df], axis=1)\n grouped_df = (\n df.groupby(\"main_tag\")\n .agg(\n up_count=(\"change_pct\", lambda x: (x > 0).sum()),\n down_count=(\"change_pct\", lambda x: (x <= 0).sum()),\n turnover=(\"turnover\", \"sum\"),\n change_pct=(\"change_pct\", \"mean\"),\n limit_up_count=(\"is_limit_up\", \"sum\"),\n limit_down_count=(\"is_limit_down\", lambda x: (x == True).sum()),\n total_count=(\"main_tag\", \"size\"), # 添加计数，计算每个分组的总行数\n )\n .reset_index(drop=False)\n )\n sorted_df = grouped_df.sort_values(by=[\"turnover\", \"total_count\"], ascending=[False, False])\n\n return sorted_df.to_dict(orient=\"records\")\n\n\ndef query_stock_quotes(query_stock_quote_model: QueryStockQuoteModel):\n entity_ids = None\n if query_stock_quote_model.stock_pool_name:\n stock_pool_name = query_stock_quote_model.stock_pool_name\n stock_pool_info = StockPoolInfo.query_data(\n filters=[StockPoolInfo.stock_pool_name == stock_pool_name],\n return_type=\"domain\",\n )\n if not stock_pool_info:\n raise HTTPException(status_code=404, detail=f\"Stock pool info {stock_pool_name} not found\")\n\n if stock_pool_name != \"A股\":\n stock_pools: List[StockPools] = StockPools.query_data(\n filters=[StockPools.stock_pool_name == stock_pool_name],\n order=StockPools.timestamp.desc(),\n limit=1,\n return_type=\"domain\",\n )\n if not stock_pools:\n raise HTTPException(status_code=404, detail=f\"Stock pool {stock_pool_name} not found\")\n if stock_pools:\n entity_ids = stock_pools[0].entity_ids\n else:\n entity_ids = query_stock_quote_model.entity_ids\n\n entity_type = \"stock\"\n if entity_ids:\n entity_id = entity_ids[0]\n entity_type, _, _ = contract_api.decode_entity_id(entity_id)\n\n if query_stock_quote_model.main_tag:\n tags_dict = StockTags.query_data(\n entity_ids=entity_ids,\n filters=[StockTags.entity_type == entity_type, StockTags.main_tag == query_stock_quote_model.main_tag],\n return_type=\"dict\",\n )\n if not tags_dict:\n return None\n entity_ids = [item[\"entity_id\"] for item in tags_dict]\n else:\n tags_dict = StockTags.query_data(\n return_type=\"dict\",\n )\n\n entity_tags_map = {item[\"entity_id\"]: item for item in tags_dict}\n\n if entity_type == \"stock\":\n order = eval(\n f\"StockQuote.{query_stock_quote_model.order_by_field}.{query_stock_quote_model.order_by_type.value}()\"\n )\n df = StockQuote.query_data(order=order, entity_ids=entity_ids, return_type=\"df\")\n elif entity_type == \"stockus\":\n order = eval(\n f\"StockusQuote.{query_stock_quote_model.order_by_field}.{query_stock_quote_model.order_by_type.value}()\"\n )\n df = StockusQuote.query_data(order=order, entity_ids=entity_ids, return_type=\"df\")\n elif entity_type == \"stockhk\":\n order = eval(\n f\"StockhkQuote.{query_stock_quote_model.order_by_field}.{query_stock_quote_model.order_by_type.value}()\"\n )\n df = StockhkQuote.query_data(order=order, entity_ids=entity_ids, return_type=\"df\")\n else:\n raise HTTPException(status_code=400, detail=f\"Unsupported entity type: {entity_type}\")\n\n if not pd_is_not_null(df):\n return None\n\n def set_tags(quote):\n entity_id = quote[\"entity_id\"]\n main_tag = entity_tags_map.get(entity_id, {}).get(\"main_tag\", None)\n sub_tag = entity_tags_map.get(entity_id, {}).get(\"sub_tag\", None)\n active_hidden_tags = entity_tags_map.get(entity_id, {}).get(\"active_hidden_tags\", None)\n if active_hidden_tags:\n hidden_tags = list(active_hidden_tags.keys())\n else:\n hidden_tags = None\n return pd.Series({\"main_tag\": main_tag, \"sub_tag\": sub_tag, \"hidden_tags\": hidden_tags})\n\n df[[\"main_tag\", \"sub_tag\", \"hidden_tags\"]] = df.apply(set_tags, axis=1)\n\n up_count = (df[\"change_pct\"] > 0).sum()\n down_count = (df[\"change_pct\"] < 0).sum()\n turnover = df[\"turnover\"].sum()\n change_pct = df[\"change_pct\"].mean()\n limit_up_count = df[\"is_limit_up\"].sum()\n limit_down_count = df[\"is_limit_down\"].sum()\n\n quotes = df.to_dict(orient=\"records\")\n\n result = {\n \"up_count\": up_count,\n \"down_count\": down_count,\n \"turnover\": turnover,\n \"change_pct\": change_pct,\n \"limit_up_count\": limit_up_count,\n \"limit_down_count\": limit_down_count,\n \"quotes\": quotes[: query_stock_quote_model.limit],\n }\n return result\n\n\ndef buy_stocks():\n pass\n\n\ndef sell_stocks():\n pass\n\n\ndef build_query_stock_quote_setting(build_query_stock_quote_setting_model: BuildQueryStockQuoteSettingModel):\n with contract_api.DBSession(provider=\"zvt\", data_schema=QueryStockQuoteSetting)() as session:\n the_id = \"admin_setting\"\n datas = QueryStockQuoteSetting.query_data(ids=[the_id], session=session, return_type=\"domain\")\n if datas:\n query_setting = datas[0]\n else:\n query_setting = QueryStockQuoteSetting(entity_id=\"admin\", id=the_id)\n query_setting.timestamp = current_date()\n query_setting.stock_pool_name = build_query_stock_quote_setting_model.stock_pool_name\n query_setting.main_tags = build_query_stock_quote_setting_model.main_tags\n session.add(query_setting)\n session.commit()\n session.refresh(query_setting)\n return query_setting\n\n\ndef build_default_query_stock_quote_setting():\n datas = QueryStockQuoteSetting.query_data(ids=[\"admin_setting\"], return_type=\"domain\")\n if datas:\n return\n build_query_stock_quote_setting(BuildQueryStockQuoteSettingModel(stock_pool_name=\"all\", main_tags=[\"消费电子\"]))\n\n\nif __name__ == \"__main__\":\n # print(query_tag_quotes(QueryTagQuoteModel(stock_pool_name=\"all\", main_tags=[\"低空经济\", \"半导体\", \"化工\", \"消费电子\"])))\n # print(query_stock_quotes(QueryStockQuoteModel(stock_pool_name=\"all\", main_tag=\"半导体\")))\n print(query_quote_stats())\n# the __all__ is generated\n__all__ = [\n \"build_trading_plan\",\n \"query_trading_plan\",\n \"get_current_trading_plan\",\n \"get_future_trading_plan\",\n \"check_trading_plan\",\n \"query_stock_quotes\",\n \"buy_stocks\",\n \"sell_stocks\",\n \"build_query_stock_quote_setting\",\n]\n"
},
{
"path": "src/zvt/ui/__init__.py",
"content": "# -*- coding: utf-8 -*-\nimport os\n\nimport dash\nimport dash_bootstrap_components as dbc\n\nassets_path = os.path.abspath(os.path.join(os.path.dirname(__file__), \"assets\"))\n\nzvt_app = dash.Dash(\n __name__,\n meta_tags=[{\"name\": \"viewport\", \"content\": \"width=device-width\"}],\n assets_folder=assets_path,\n external_stylesheets=[dbc.themes.BOOTSTRAP],\n)\n\nzvt_app.config.suppress_callback_exceptions = True\n\nserver = zvt_app.server\n"
},
{
"path": "src/zvt/ui/apps/factor_app.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List\n\nimport dash_daq as daq\nfrom dash import dash\nfrom dash import dcc\nfrom dash import html\nfrom dash.dependencies import Input, Output, State\n\nfrom zvt.contract import Mixin\nfrom zvt.contract import zvt_context, IntervalLevel\nfrom zvt.contract.api import get_entities, get_schema_by_name, get_schema_columns\nfrom zvt.contract.drawer import StackedDrawer\nfrom zvt.trader.trader_info_api import AccountStatsReader, OrderReader, get_order_securities\nfrom zvt.trader.trader_info_api import get_trader_info\nfrom zvt.trader.trader_schemas import TraderInfo\nfrom zvt.ui import zvt_app\nfrom zvt.ui.components.dcc_components import get_account_stats_figure\nfrom zvt.utils.pd_utils import pd_is_not_null\n\naccount_readers = []\norder_readers = []\n\n# init the data\ntraders: List[TraderInfo] = []\n\ntrader_names: List[str] = []\n\n\ndef order_type_flag(order_type):\n if order_type == \"order_long\" or order_type == \"order_close_short\":\n return \"B\"\n else:\n return \"S\"\n\n\ndef order_type_color(order_type):\n if order_type == \"order_long\" or order_type == \"order_close_short\":\n return \"#ec0000\"\n else:\n return \"#00da3c\"\n\n\ndef load_traders():\n global traders\n global trader_names\n\n traders = get_trader_info(return_type=\"domain\")\n account_readers.clear()\n order_readers.clear()\n for trader in traders:\n account_readers.append(AccountStatsReader(level=trader.level, trader_names=[trader.trader_name]))\n order_readers.append(\n OrderReader(start_timestamp=trader.start_timestamp, level=trader.level, trader_names=[trader.trader_name])\n )\n\n trader_names = [item.trader_name for item in traders]\n\n\nload_traders()\n\n\ndef factor_layout():\n layout = html.Div(\n [\n # controls\n html.Div(\n className=\"three columns card\",\n children=[\n html.Div(\n className=\"bg-white user-control\",\n children=[\n html.Div(\n className=\"padding-top-bot\",\n children=[\n html.H6(\"select trader:\"),\n dcc.Dropdown(\n id=\"trader-selector\",\n placeholder=\"select the trader\",\n options=[{\"label\": item, \"value\": i} for i, item in enumerate(trader_names)],\n ),\n ],\n ),\n # select entity type\n html.Div(\n className=\"padding-top-bot\",\n children=[\n html.H6(\"select entity type:\"),\n dcc.Dropdown(\n id=\"entity-type-selector\",\n placeholder=\"select entity type\",\n options=[\n {\"label\": name, \"value\": name}\n for name in zvt_context.tradable_schema_map.keys()\n ],\n value=\"stock\",\n clearable=False,\n ),\n ],\n ),\n # select entity provider\n html.Div(\n className=\"padding-top-bot\",\n children=[\n html.H6(\"select entity provider:\"),\n dcc.Dropdown(id=\"entity-provider-selector\", placeholder=\"select entity provider\"),\n ],\n ),\n # select entity\n html.Div(\n className=\"padding-top-bot\",\n children=[\n html.H6(\"select entity:\"),\n dcc.Dropdown(id=\"entity-selector\", placeholder=\"select entity\"),\n ],\n ),\n # select levels\n html.Div(\n className=\"padding-top-bot\",\n children=[\n html.H6(\"select levels:\"),\n dcc.Dropdown(\n id=\"levels-selector\",\n options=[\n {\"label\": level.name, \"value\": level.value}\n for level in (IntervalLevel.LEVEL_1WEEK, IntervalLevel.LEVEL_1DAY)\n ],\n value=\"1d\",\n multi=True,\n ),\n ],\n ),\n # select factor\n html.Div(\n className=\"padding-top-bot\",\n children=[\n html.H6(\"select factor:\"),\n dcc.Dropdown(\n id=\"factor-selector\",\n placeholder=\"select factor\",\n options=[\n {\"label\": name, \"value\": name}\n for name in zvt_context.factor_cls_registry.keys()\n ],\n value=\"TechnicalFactor\",\n ),\n ],\n ),\n # select data\n html.Div(\n children=[\n html.Div(\n [\n html.H6(\n \"related/all data to show in sub graph\",\n style={\"display\": \"inline-block\"},\n ),\n daq.BooleanSwitch(\n id=\"data-switch\",\n on=True,\n style={\n \"display\": \"inline-block\",\n \"float\": \"right\",\n \"vertical-align\": \"middle\",\n \"padding\": \"8px\",\n },\n ),\n ],\n ),\n dcc.Dropdown(id=\"data-selector\", placeholder=\"schema\"),\n ],\n style={\"padding-top\": \"12px\"},\n ),\n # select properties\n html.Div(\n children=[dcc.Dropdown(id=\"schema-column-selector\", placeholder=\"properties\")],\n style={\"padding-top\": \"6px\"},\n ),\n ],\n )\n ],\n ),\n # Graph\n html.Div(\n className=\"nine columns card-left\",\n children=[\n html.Div(\n id=\"trader-details\",\n className=\"bg-white\",\n ),\n html.Div(id=\"factor-details\"),\n ],\n ),\n ]\n )\n\n return layout\n\n\n@zvt_app.callback(\n [\n Output(\"trader-details\", \"children\"),\n Output(\"entity-type-selector\", \"options\"),\n Output(\"entity-provider-selector\", \"options\"),\n Output(\"entity-selector\", \"options\"),\n ],\n [\n Input(\"trader-selector\", \"value\"),\n Input(\"entity-type-selector\", \"value\"),\n Input(\"entity-provider-selector\", \"value\"),\n ],\n)\ndef update_trader_details(trader_index, entity_type, entity_provider):\n if trader_index is not None:\n # change entity_type options\n entity_type = traders[trader_index].entity_type\n if not entity_type:\n entity_type = \"stock\"\n entity_type_options = [{\"label\": entity_type, \"value\": entity_type}]\n\n # account stats\n account_stats = get_account_stats_figure(account_stats_reader=account_readers[trader_index])\n\n providers = zvt_context.tradable_schema_map.get(entity_type).get_providers()\n entity_provider_options = [{\"label\": name, \"value\": name} for name in providers]\n\n # entities\n entity_ids = get_order_securities(trader_name=trader_names[trader_index])\n df = get_entities(\n provider=entity_provider,\n entity_type=entity_type,\n entity_ids=entity_ids,\n columns=[\"entity_id\", \"code\", \"name\"],\n index=\"entity_id\",\n )\n entity_options = [\n {\"label\": f'{entity_id}({entity[\"name\"]})', \"value\": entity_id} for entity_id, entity in df.iterrows()\n ]\n\n return account_stats, entity_type_options, entity_provider_options, entity_options\n else:\n entity_type_options = [{\"label\": name, \"value\": name} for name in zvt_context.tradable_schema_map.keys()]\n account_stats = None\n providers = zvt_context.tradable_schema_map.get(entity_type).get_providers()\n entity_provider_options = [{\"label\": name, \"value\": name} for name in providers]\n df = get_entities(\n provider=entity_provider, entity_type=entity_type, columns=[\"entity_id\", \"code\", \"name\"], index=\"entity_id\"\n )\n entity_options = [\n {\"label\": f'{entity_id}({entity[\"name\"]})', \"value\": entity_id} for entity_id, entity in df.iterrows()\n ]\n return account_stats, entity_type_options, entity_provider_options, entity_options\n\n\n@zvt_app.callback(\n Output(\"data-selector\", \"options\"), [Input(\"entity-type-selector\", \"value\"), Input(\"data-switch\", \"on\")]\n)\ndef update_entity_selector(entity_type, related):\n if entity_type is not None:\n if related:\n schemas = zvt_context.entity_map_schemas.get(entity_type)\n else:\n schemas = zvt_context.schemas\n return [{\"label\": schema.__name__, \"value\": schema.__name__} for schema in schemas]\n raise dash.PreventUpdate()\n\n\n@zvt_app.callback(Output(\"schema-column-selector\", \"options\"), [Input(\"data-selector\", \"value\")])\ndef update_column_selector(schema_name):\n if schema_name:\n schema = get_schema_by_name(name=schema_name)\n cols = get_schema_columns(schema=schema)\n\n return [{\"label\": col, \"value\": col} for col in cols]\n raise dash.PreventUpdate()\n\n\n@zvt_app.callback(\n Output(\"factor-details\", \"children\"),\n [\n Input(\"factor-selector\", \"value\"),\n Input(\"entity-type-selector\", \"value\"),\n Input(\"entity-selector\", \"value\"),\n Input(\"levels-selector\", \"value\"),\n Input(\"schema-column-selector\", \"value\"),\n ],\n [State(\"trader-selector\", \"value\"), State(\"data-selector\", \"value\")],\n)\ndef update_factor_details(factor, entity_type, entity, levels, columns, trader_index, schema_name):\n if factor and entity_type and entity and levels:\n sub_df = None\n # add sub graph\n if columns:\n if type(columns) == str:\n columns = [columns]\n columns = columns + [\"entity_id\", \"timestamp\"]\n schema: Mixin = get_schema_by_name(name=schema_name)\n sub_df = schema.query_data(entity_id=entity, columns=columns)\n\n # add trading signals as annotation\n annotation_df = None\n if trader_index is not None:\n order_reader = order_readers[trader_index]\n annotation_df = order_reader.data_df.copy()\n annotation_df = annotation_df[annotation_df.entity_id == entity].copy()\n if pd_is_not_null(annotation_df):\n annotation_df[\"value\"] = annotation_df[\"order_price\"]\n annotation_df[\"flag\"] = annotation_df[\"order_type\"].apply(lambda x: order_type_flag(x))\n annotation_df[\"color\"] = annotation_df[\"order_type\"].apply(lambda x: order_type_color(x))\n print(annotation_df.tail())\n\n if type(levels) is list and len(levels) >= 2:\n levels.sort()\n drawers = []\n for level in levels:\n drawers.append(\n zvt_context.factor_cls_registry[factor](\n entity_schema=zvt_context.tradable_schema_map[entity_type], level=level, entity_ids=[entity]\n ).drawer()\n )\n stacked = StackedDrawer(*drawers)\n\n return dcc.Graph(id=f\"{factor}-{entity_type}-{entity}\", figure=stacked.draw_kline(show=False, height=900))\n else:\n if type(levels) is list:\n level = levels[0]\n else:\n level = levels\n drawer = zvt_context.factor_cls_registry[factor](\n entity_schema=zvt_context.tradable_schema_map[entity_type],\n level=level,\n entity_ids=[entity],\n need_persist=False,\n ).drawer()\n if pd_is_not_null(sub_df):\n drawer.add_sub_df(sub_df)\n if pd_is_not_null(annotation_df):\n drawer.annotation_df = annotation_df\n\n return dcc.Graph(id=f\"{factor}-{entity_type}-{entity}\", figure=drawer.draw_kline(show=False, height=800))\n raise dash.PreventUpdate()\n"
},
{
"path": "src/zvt/ui/assets/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "src/zvt/ui/assets/base.css",
"content": "/* Table of contents\n––––––––––––––––––––––––––––––––––––––––––––––––––\n- Plotly.js\n- Grid\n- Base Styles\n- Typography\n- Links\n- Buttons\n- Forms\n- Lists\n- Code\n- Tables\n- Spacing\n- Utilities\n- Clearing\n- Media Queries\n*/\n\n/* PLotly.js\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n/* plotly.js's modebar's z-index is 1001 by default\n * https://github.com/plotly/plotly.js/blob/7e4d8ab164258f6bd48be56589dacd9bdd7fded2/src/css/_modebar.scss#L5\n * In case a dropdown is above the graph, the dropdown's options\n * will be rendered below the modebar\n * Increase the select option's z-index\n */\n\n/* This was actually not quite right -\n dropdowns were overlapping each other (edited October 26)\n\n.Select {\n z-index: 1002;\n}*/\n\n/* Grid\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n.container {\n position: relative;\n width: 100%;\n max-width: 960px;\n margin: 0 auto;\n padding: 0 20px;\n box-sizing: border-box;\n}\n\n.column,\n.columns {\n width: 100%;\n float: left;\n box-sizing: border-box;\n}\n\n/* For devices larger than 400px */\n@media (min-width: 400px) {\n .container {\n width: 85%;\n padding: 0;\n }\n}\n\n/* For devices larger than 550px */\n@media (min-width: 550px) {\n .container {\n width: 80%;\n }\n\n .column,\n .columns {\n margin-left: 4%;\n }\n\n .column:first-child,\n .columns:first-child {\n margin-left: 0;\n }\n\n .one.column,\n .one.columns {\n width: 4.66666666667%;\n }\n\n .two.columns {\n width: 13.3333333333%;\n }\n\n .three.columns {\n width: 22%;\n }\n\n .four.columns {\n width: 30.6666666667%;\n }\n\n .five.columns {\n width: 39.3333333333%;\n }\n\n .six.columns {\n width: 48%;\n }\n\n .seven.columns {\n width: 56.6666666667%;\n }\n\n .eight.columns {\n width: 65.3333333333%;\n }\n\n .nine.columns {\n width: 74.0%;\n }\n\n .ten.columns {\n width: 82.6666666667%;\n }\n\n .eleven.columns {\n width: 91.3333333333%;\n }\n\n .twelve.columns {\n width: 100%;\n margin-left: 0;\n }\n\n .one-third.column {\n width: 30.6666666667%;\n }\n\n .two-thirds.column {\n width: 65.3333333333%;\n }\n\n .one-half.column {\n width: 48%;\n }\n\n /* Offsets */\n .offset-by-one.column,\n .offset-by-one.columns {\n margin-left: 8.66666666667%;\n }\n\n .offset-by-two.column,\n .offset-by-two.columns {\n margin-left: 17.3333333333%;\n }\n\n .offset-by-three.column,\n .offset-by-three.columns {\n margin-left: 26%;\n }\n\n .offset-by-four.column,\n .offset-by-four.columns {\n margin-left: 34.6666666667%;\n }\n\n .offset-by-five.column,\n .offset-by-five.columns {\n margin-left: 43.3333333333%;\n }\n\n .offset-by-six.column,\n .offset-by-six.columns {\n margin-left: 52%;\n }\n\n .offset-by-seven.column,\n .offset-by-seven.columns {\n margin-left: 60.6666666667%;\n }\n\n .offset-by-eight.column,\n .offset-by-eight.columns {\n margin-left: 69.3333333333%;\n }\n\n .offset-by-nine.column,\n .offset-by-nine.columns {\n margin-left: 78.0%;\n }\n\n .offset-by-ten.column,\n .offset-by-ten.columns {\n margin-left: 86.6666666667%;\n }\n\n .offset-by-eleven.column,\n .offset-by-eleven.columns {\n margin-left: 95.3333333333%;\n }\n\n .offset-by-one-third.column,\n .offset-by-one-third.columns {\n margin-left: 34.6666666667%;\n }\n\n .offset-by-two-thirds.column,\n .offset-by-two-thirds.columns {\n margin-left: 69.3333333333%;\n }\n\n .offset-by-one-half.column,\n .offset-by-one-half.columns {\n margin-left: 52%;\n }\n\n}\n\n\n/* Base Styles\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n/* NOTE\nhtml is set to 62.5% so that all the REM measurements throughout Skeleton\nare based on 10px sizing. So basically 1.5rem = 15px :) */\nhtml {\n font-size: 62.5%;\n}\n\nbody {\n font-size: 1.5em; /* currently ems cause chrome bug misinterpreting rems on body element */\n line-height: 1.6;\n font-weight: 400;\n font-family: \"Open Sans\", \"HelveticaNeue\", \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n color: rgb(50, 50, 50);\n}\n\n\n/* Typography\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\nh1, h2, h3, h4, h5, h6 {\n margin-top: 0;\n margin-bottom: 0;\n font-weight: 300;\n}\n\nh1 {\n font-size: 4.5rem;\n line-height: 1.2;\n letter-spacing: -.1rem;\n margin-bottom: 2rem;\n}\n\nh2 {\n font-size: 3.6rem;\n line-height: 1.25;\n letter-spacing: -.1rem;\n margin-bottom: 1.8rem;\n margin-top: 1.8rem;\n}\n\nh3 {\n font-size: 3.0rem;\n line-height: 1.3;\n letter-spacing: -.1rem;\n margin-bottom: 1.5rem;\n margin-top: 1.5rem;\n}\n\nh4 {\n font-size: 2.6rem;\n line-height: 1.35;\n letter-spacing: -.08rem;\n margin-bottom: 1.2rem;\n margin-top: 1.2rem;\n}\n\nh5 {\n font-size: 2.2rem;\n line-height: 1.5;\n letter-spacing: -.05rem;\n margin-bottom: 0.6rem;\n margin-top: 0.6rem;\n}\n\nh6 {\n font-size: 2.0rem;\n line-height: 1.6;\n letter-spacing: 0;\n margin-bottom: 0.75rem;\n margin-top: 0.75rem;\n}\n\np {\n margin-top: 0;\n}\n\n\n/* Blockquotes\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\nblockquote {\n border-left: 4px lightgrey solid;\n padding-left: 1rem;\n margin-top: 2rem;\n margin-bottom: 2rem;\n margin-left: 0rem;\n}\n\n\n/* Links\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\na {\n color: #1EAEDB;\n text-decoration: underline;\n cursor: pointer;\n}\n\na:hover {\n color: #0FA0CE;\n}\n\n\n/* Buttons\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n.button,\nbutton,\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n display: inline-block;\n height: 38px;\n padding: 0 30px;\n color: #555;\n text-align: center;\n font-size: 11px;\n font-weight: 600;\n line-height: 38px;\n letter-spacing: .1rem;\n text-transform: uppercase;\n text-decoration: none;\n white-space: nowrap;\n background-color: transparent;\n border-radius: 4px;\n border: 1px solid #bbb;\n cursor: pointer;\n box-sizing: border-box;\n}\n\n.button:hover,\nbutton:hover,\ninput[type=\"submit\"]:hover,\ninput[type=\"reset\"]:hover,\ninput[type=\"button\"]:hover,\n.button:focus,\nbutton:focus,\ninput[type=\"submit\"]:focus,\ninput[type=\"reset\"]:focus,\ninput[type=\"button\"]:focus {\n color: #333;\n border-color: #888;\n outline: 0;\n}\n\n.button.button-primary,\nbutton.button-primary,\ninput[type=\"submit\"].button-primary,\ninput[type=\"reset\"].button-primary,\ninput[type=\"button\"].button-primary {\n color: #FFF;\n background-color: #33C3F0;\n border-color: #33C3F0;\n}\n\n.button.button-primary:hover,\nbutton.button-primary:hover,\ninput[type=\"submit\"].button-primary:hover,\ninput[type=\"reset\"].button-primary:hover,\ninput[type=\"button\"].button-primary:hover,\n.button.button-primary:focus,\nbutton.button-primary:focus,\ninput[type=\"submit\"].button-primary:focus,\ninput[type=\"reset\"].button-primary:focus,\ninput[type=\"button\"].button-primary:focus {\n color: #FFF;\n background-color: #1EAEDB;\n border-color: #1EAEDB;\n}\n\n\n/* Forms\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\ninput[type=\"email\"],\ninput[type=\"number\"],\ninput[type=\"search\"],\ninput[type=\"text\"],\ninput[type=\"tel\"],\ninput[type=\"url\"],\ninput[type=\"password\"],\ntextarea,\nselect {\n height: 38px;\n padding: 6px 10px; /* The 6px vertically centers text on FF, ignored by Webkit */\n background-color: #fff;\n border: 1px solid #D1D1D1;\n border-radius: 4px;\n box-shadow: none;\n box-sizing: border-box;\n font-family: inherit;\n font-size: inherit; /*https://stackoverflow.com/questions/6080413/why-doesnt-input-inherit-the-font-from-body*/\n}\n\n/* Removes awkward default styles on some inputs for iOS */\ninput[type=\"email\"],\ninput[type=\"number\"],\ninput[type=\"search\"],\ninput[type=\"text\"],\ninput[type=\"tel\"],\ninput[type=\"url\"],\ninput[type=\"password\"],\ntextarea {\n -webkit-appearance: none;\n -moz-appearance: none;\n appearance: none;\n}\n\ntextarea {\n min-height: 65px;\n padding-top: 6px;\n padding-bottom: 6px;\n}\n\ninput[type=\"email\"]:focus,\ninput[type=\"number\"]:focus,\ninput[type=\"search\"]:focus,\ninput[type=\"text\"]:focus,\ninput[type=\"tel\"]:focus,\ninput[type=\"url\"]:focus,\ninput[type=\"password\"]:focus,\ntextarea:focus,\nselect:focus {\n border: 1px solid #33C3F0;\n outline: 0;\n}\n\nlabel,\nlegend {\n display: block;\n margin-bottom: 0px;\n}\n\nfieldset {\n padding: 0;\n border-width: 0;\n}\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n display: inline;\n}\n\nlabel > .label-body {\n display: inline-block;\n margin-left: .5rem;\n font-weight: normal;\n}\n\n\n/* Lists\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\nul {\n list-style: circle inside;\n}\n\nol {\n list-style: decimal inside;\n}\n\nol, ul {\n padding-left: 0;\n margin-top: 0;\n}\n\nul ul,\nul ol,\nol ol,\nol ul {\n margin: 1.5rem 0 1.5rem 3rem;\n font-size: 90%;\n}\n\nli {\n margin-bottom: 1rem;\n}\n\n\n/* Tables\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\ntable {\n border-collapse: collapse;\n}\n\nth,\ntd {\n padding: 12px 15px;\n text-align: left;\n border-bottom: 1px solid #E1E1E1;\n}\n\nth:first-child,\ntd:first-child {\n padding-left: 0;\n}\n\nth:last-child,\ntd:last-child {\n padding-right: 0;\n}\n\n\n/* Spacing\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\nbutton,\n.button {\n margin-bottom: 0rem;\n}\n\ninput,\ntextarea,\nselect,\nfieldset {\n margin-bottom: 0rem;\n}\n\npre,\ndl,\nfigure,\ntable,\nform {\n margin-bottom: 0rem;\n}\n\np,\nul,\nol {\n margin-bottom: 0.75rem;\n}\n\n/* Utilities\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n.u-full-width {\n width: 100%;\n box-sizing: border-box;\n}\n\n.u-max-full-width {\n max-width: 100%;\n box-sizing: border-box;\n}\n\n.u-pull-right {\n float: right;\n}\n\n.u-pull-left {\n float: left;\n}\n\n\n/* Misc\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\nhr {\n margin-top: 3rem;\n margin-bottom: 3.5rem;\n border-width: 0;\n border-top: 1px solid #E1E1E1;\n}\n\n\n/* Clearing\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n\n/* Self Clearing Goodness */\n.container:after,\n.row:after,\n.u-cf {\n content: \"\";\n display: table;\n clear: both;\n}\n\n\n/* Media Queries\n–––––––––––––––––––––––––––––––––––––––––––––––––– */\n/*\nNote: The best way to structure the use of media queries is to create the queries\nnear the relevant code. For example, if you wanted to change the styles for buttons\non small devices, paste the mobile query code up in the buttons section and style it\nthere.\n*/\n\n\n/* Larger than mobile */\n@media (min-width: 400px) {\n}\n\n/* Larger than phablet (also point when grid becomes active) */\n@media (min-width: 550px) {\n}\n\n/* Larger than tablet */\n@media (min-width: 750px) {\n}\n\n/* Larger than desktop */\n@media (min-width: 1000px) {\n}\n\n/* Larger than Desktop HD */\n@media (min-width: 1200px) {\n}"
},
{
"path": "src/zvt/ui/assets/custom.css",
"content": "/*Fonts ––––––––––––––––––––––––––––––––––––––––––––––––––*/\n@import url('https://fonts.googleapis.com/css?family=Roboto&display=swap');\n\nbody {\n margin: 0px;\n padding: 0px;\n background-color: #F3F4F9;\n font-family: 'Roboto';\n color: #203cb3;\n}\n\n.zvt-banner {\n color: #7f7f7f;\n font-weight: 600;\n font-size: 20px;\n background: #fafbfc;\n padding: 12px;\n padding-left: 24px;\n border-bottom: 2px solid lightgray;\n}\n\n.zvt-nav {\n color: #7f7f7f;\n background: #fafbfc;\n padding: 12px;\n padding-left: 36px;\n}\n\n.div-logo {\n display: inline-block;\n float: right;\n}\n\n.logo {\n height: 35px;\n padding: 6px;\n margin-top: 3px;\n}\n\n.h2-title, .h2-title-mobile {\n font-family: 'Roboto';\n display: inline-block;\n letter-spacing: 3.8px;\n font-weight: 800;\n font-size: 20px;\n}\n\n.h2-title-mobile {\n display: none;\n}\n\nh5, h6 {\n font-family: 'Roboto';\n font-weight: 600;\n font-size: 16px;\n}\n\nh5 {\n padding-left: 42px;\n}\n\n.alert {\n padding: 20px;\n background-color: #f44336;\n color: white;\n}\n\n.bg-white {\n background-color: white;\n padding: 24px 32px;\n}\n\n\n.card {\n padding: 24px 12px 24px 12px;\n margin-left: 4%;\n}\n\n.card-left {\n padding: 24px 12px 24px 12px;\n margin-left: 0px;\n}\n\n.padding-top-bot {\n padding-top: 12px;\n padding-bottom: 18px;\n}\n\n.upload {\n width: 100%;\n line-height: 60px;\n border-width: 1px;\n border-style: dashed;\n border-radius: 5px;\n text-align: center;\n}\n\n.upload p, .upload a {\n display: inline;\n}\n\n.Select-control {\n border: 1px solid #203cb3;\n}\n\n@media only screen and (max-width: 320px) {\n .Select-menu-outer, .Select-value {\n font-size: 10.5px;\n }\n\n .upload {\n padding: 5px;\n }\n}\n\n/* mobile */\n@media only screen and (max-width: 768px) {\n .upload {\n line-height: 60px;\n border-width: 1px;\n border-style: dashed;\n border-radius: 5px;\n text-align: center;\n font-size: small;\n }\n\n .columns {\n width: 100%;\n }\n\n .card, .card-left {\n padding: 12px;\n margin: 0px;\n }\n\n .bg-white {\n height: auto;\n }\n\n .logo {\n height: 28px;\n padding-left: 0px;\n padding-bottom: 0px;\n }\n\n .div-logo {\n float: left;\n display: block;\n width: 100%;\n }\n\n .h2-title {\n display: none;\n }\n\n .h2-title-mobile {\n display: block;\n float: left;\n }\n\n .app-body {\n margin-left: 0px;\n }\n\n .columns {\n text-align: center;\n }\n\n .user-control {\n padding-top: 24px;\n padding-bottom: 24px;\n }\n}\n"
},
{
"path": "src/zvt/ui/components/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "src/zvt/ui/components/dcc_components.py",
"content": "# -*- coding: utf-8 -*-\n\nfrom dash import dcc\n\nfrom zvt.api.kdata import get_kdata_schema\nfrom zvt.contract import zvt_context\nfrom zvt.contract.api import decode_entity_id\nfrom zvt.contract.drawer import Drawer\nfrom zvt.contract.reader import DataReader\nfrom zvt.trader.trader_info_api import OrderReader, AccountStatsReader\nfrom zvt.utils.pd_utils import pd_is_not_null\n\n\ndef order_type_color(order_type):\n if order_type == \"order_long\" or order_type == \"order_close_short\":\n return \"#ec0000\"\n else:\n return \"#00da3c\"\n\n\ndef order_type_flag(order_type):\n if order_type == \"order_long\" or order_type == \"order_close_short\":\n return \"B\"\n else:\n return \"S\"\n\n\ndef get_trading_signals_figure(\n order_reader: OrderReader, entity_id: str, start_timestamp=None, end_timestamp=None, adjust_type=None\n):\n entity_type, _, _ = decode_entity_id(entity_id)\n\n data_schema = get_kdata_schema(entity_type=entity_type, level=order_reader.level, adjust_type=adjust_type)\n if not start_timestamp:\n start_timestamp = order_reader.start_timestamp\n if not end_timestamp:\n end_timestamp = order_reader.end_timestamp\n kdata_reader = DataReader(\n data_schema=data_schema,\n entity_schema=zvt_context.tradable_schema_map.get(entity_type),\n entity_ids=[entity_id],\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n level=order_reader.level,\n )\n\n # generate the annotation df\n order_reader.move_on(timeout=0)\n df = order_reader.data_df.copy()\n df = df[df.entity_id == entity_id].copy()\n if pd_is_not_null(df):\n df[\"value\"] = df[\"order_price\"]\n df[\"flag\"] = df[\"order_type\"].apply(lambda x: order_type_flag(x))\n df[\"color\"] = df[\"order_type\"].apply(lambda x: order_type_color(x))\n print(df.tail())\n\n drawer = Drawer(main_df=kdata_reader.data_df, annotation_df=df)\n return drawer.draw_kline(show=False, height=800)\n\n\ndef get_account_stats_figure(account_stats_reader: AccountStatsReader):\n graph_list = []\n\n # 账户统计曲线\n if account_stats_reader:\n fig = account_stats_reader.draw_line(show=False)\n\n for trader_name in account_stats_reader.trader_names:\n graph_list.append(dcc.Graph(id=\"{}-account\".format(trader_name), figure=fig))\n\n return graph_list\n"
},
{
"path": "src/zvt/utils/__init__.py",
"content": "# the __all__ is generated\n__all__ = []\n"
},
{
"path": "src/zvt/utils/decorator.py",
"content": "# -*- coding: utf-8 -*-\ndef to_string(cls):\n def __str__(self):\n return \"%s(%s)\" % (type(self).__name__, \", \".join(\"%s=%s\" % item for item in vars(self).items()))\n\n cls.__str__ = __str__\n return cls\n\n\n# the __all__ is generated\n__all__ = [\"to_string\"]\n"
},
{
"path": "src/zvt/utils/file_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport os\nfrom typing import List, Optional\n\n\ndef list_all_files(\n dir_path: str = \"./domain\", ext: Optional[str] = \".py\", excludes=None, includes=None, return_base_name=False\n) -> List[str]:\n \"\"\"\n list all files with extension in specific directory recursively\n\n :param includes: including files, None means all\n :param dir_path: the directory path\n :param ext: file extension\n :param excludes: excluding files\n :param return_base_name: return file name if True otherwise abs path\n :return:\n \"\"\"\n files = []\n for entry in os.scandir(dir_path):\n if entry.is_dir():\n files += list_all_files(entry.path, ext=ext, excludes=excludes, return_base_name=return_base_name)\n elif entry.is_file():\n if not ext or (ext and entry.path.endswith(ext)):\n if excludes and entry.path.endswith(excludes):\n continue\n if includes and not entry.path.endswith(includes):\n continue\n if return_base_name:\n files.append(os.path.basename(entry.path))\n else:\n files.append(entry.path)\n else:\n pass\n return files\n\n\n# the __all__ is generated\n__all__ = [\"list_all_files\"]\n"
},
{
"path": "src/zvt/utils/git_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport subprocess\n\n\ndef get_git_user_name():\n try:\n return subprocess.check_output([\"git\", \"config\", \"--get\", \"user.name\"]).decode(\"utf8\").strip()\n except:\n return \"foolcage\"\n\n\ndef get_git_user_email():\n try:\n return subprocess.check_output([\"git\", \"config\", \"--get\", \"user.email\"]).decode(\"utf8\").strip()\n except:\n return \"\"\n\n\n# the __all__ is generated\n__all__ = [\"get_git_user_name\", \"get_git_user_email\"]\n"
},
{
"path": "src/zvt/utils/model_utils.py",
"content": "# -*- coding: utf-8 -*-\ndef update_model(db_model, schema):\n for key, value in schema.dict().items():\n if value is not None:\n setattr(db_model, key, value)\n\n\n# the __all__ is generated\n__all__ = [\"update_model\"]\n"
},
{
"path": "src/zvt/utils/pd_utils.py",
"content": "# -*- coding: utf-8 -*-\nfrom typing import List, Union\n\nimport pandas as pd\n\n\ndef drop_continue_duplicate(s: Union[pd.Series, pd.DataFrame], col=None):\n if type(s) == pd.Series:\n return s[s.shift() != s]\n if type(s) == pd.DataFrame:\n ss = s[col]\n selected = ss[ss.shift() != ss]\n return s.loc[selected.index, :]\n\n\ndef is_filter_result_df(df: pd.DataFrame):\n return pd_is_not_null(df) and \"filter_result\" in df.columns\n\n\ndef is_score_result_df(df: pd.DataFrame):\n return pd_is_not_null(df) and \"score_result\" in df.columns\n\n\ndef pd_is_not_null(df: Union[pd.DataFrame, pd.Series]):\n return df is not None and not df.empty\n\n\ndef group_by_entity_id(input_df: pd.DataFrame):\n return input_df.groupby(level=0)\n\n\ndef normalize_group_compute_result(group_result):\n if group_result.index.nlevels == 3:\n return group_result.reset_index(level=0, drop=True)\n return group_result\n\n\ndef merge_filter_result(input_df: pd.DataFrame, filter_result: pd.Series):\n if is_filter_result_df(input_df):\n input_df[\"filter_result\"] = input_df[\"filter_result\"] & filter_result\n else:\n input_df[\"filter_result\"] = filter_result\n\n return input_df\n\n\ndef index_df(df, index=\"timestamp\", inplace=True, drop=False, time_field=\"timestamp\"):\n if time_field:\n df[time_field] = pd.to_datetime(df[time_field])\n\n if inplace:\n df.set_index(index, drop=drop, inplace=inplace)\n else:\n df = df.set_index(index, drop=drop, inplace=inplace)\n\n if type(index) == str:\n df = df.sort_index()\n elif type(index) == list:\n df.index.names = index\n level = list(range(len(index)))\n df = df.sort_index(level=level)\n return df\n\n\ndef normal_index_df(df, category_field=\"entity_id\", time_filed=\"timestamp\", drop=True, default_entity=\"entity\"):\n if type(df) == pd.Series:\n df = df.to_frame(name=\"value\")\n\n index = [category_field, time_filed]\n if is_normal_df(df):\n return df\n\n if df.index.nlevels == 1:\n if (time_filed != df.index.name) and (time_filed not in df.columns):\n assert False\n if category_field not in df.columns:\n df[category_field] = default_entity\n if time_filed not in df.columns:\n df = df.reset_index()\n\n return index_df(df=df, index=index, drop=drop, time_field=\"timestamp\")\n\n\ndef is_normal_df(df, category_field=\"entity_id\", time_filed=\"timestamp\"):\n if pd_is_not_null(df) and df.index.nlevels == 2:\n names = df.index.names\n\n if len(names) == 2 and names[0] == category_field and names[1] == time_filed:\n return True\n\n return False\n\n\ndef df_subset(df, columns=None):\n if columns:\n return df.loc[:, columns]\n return df\n\n\ndef fill_with_same_index(df_list: List[pd.DataFrame]):\n idx = None\n for df in df_list:\n if idx is None:\n idx = df.index\n else:\n idx = idx.append(df.index).drop_duplicates()\n idx = idx.sort_values()\n\n result = []\n for df in df_list:\n # print(df[df.index.duplicated()])\n added_index = idx.difference(df.index.drop_duplicates())\n added_df = pd.DataFrame(index=added_index, columns=df.columns)\n\n # df1 = df.reindex(idx)\n # df1 = df.append(added_df)\n df1 = pd.concat([df, added_df])\n df1 = df1.sort_index()\n result.append(df1)\n return result\n\n\n# the __all__ is generated\n__all__ = [\n \"drop_continue_duplicate\",\n \"is_filter_result_df\",\n \"is_score_result_df\",\n \"pd_is_not_null\",\n \"group_by_entity_id\",\n \"normalize_group_compute_result\",\n \"merge_filter_result\",\n \"index_df\",\n \"normal_index_df\",\n \"is_normal_df\",\n \"df_subset\",\n \"fill_with_same_index\",\n]\n"
},
{
"path": "src/zvt/utils/recorder_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport time\nfrom typing import Type\n\nimport zvt as zvt\nfrom zvt import zvt_config\nfrom zvt.informer import EmailInformer\n\nlogger = logging.getLogger(\"__name__\")\n\n\ndef run_data_recorder(\n domain: Type[\"zvt.contract.Mixin\"],\n entity_provider=None,\n data_provider=None,\n entity_ids=None,\n split_entity_ids_size=0,\n retry_times=10,\n sleeping_time=10,\n return_unfinished=False,\n **recorder_kv,\n):\n if (entity_ids is not None) and split_entity_ids_size > 0:\n import numpy as np\n\n size = len(entity_ids)\n if size >= split_entity_ids_size:\n step_size = int(size / split_entity_ids_size)\n if size % split_entity_ids_size:\n step_size = step_size + 1\n else:\n step_size = 1\n\n entity_ids_list = np.array_split(entity_ids, step_size)\n\n for split_entity_ids in entity_ids_list:\n run_data_recorder(\n domain=domain,\n entity_provider=entity_provider,\n data_provider=data_provider,\n entity_ids=list(split_entity_ids),\n split_entity_ids_size=0,\n retry_times=retry_times,\n sleeping_time=sleeping_time,\n return_unfinished=return_unfinished,\n **recorder_kv,\n )\n time.sleep(90)\n return\n\n logger.info(f\" record data: {domain.__name__}, entity_provider: {entity_provider}, data_provider: {data_provider}\")\n\n unfinished_entity_ids = entity_ids\n email_action = EmailInformer()\n\n while retry_times > 0:\n try:\n entity_size = len(entity_ids) if entity_ids else \"all\"\n\n if return_unfinished:\n unfinished_entity_ids = domain.record_data(\n entity_ids=unfinished_entity_ids,\n provider=data_provider,\n sleeping_time=sleeping_time,\n return_unfinished=return_unfinished,\n **recorder_kv,\n )\n if unfinished_entity_ids:\n logger.info(f\"unfinished_entity_ids({len(unfinished_entity_ids)}): {unfinished_entity_ids}\")\n raise Exception(\"Would retry with unfinished latter!\")\n else:\n domain.record_data(\n entity_ids=entity_ids,\n provider=data_provider,\n sleeping_time=sleeping_time,\n return_unfinished=return_unfinished,\n **recorder_kv,\n )\n\n msg = f\"record {domain.__name__} {entity_size} success\"\n logger.info(msg)\n email_action.send_message(zvt_config[\"email_username\"], msg, msg)\n break\n except Exception as e:\n logger.exception(\"report error:{}\".format(e))\n time.sleep(60 * 2)\n retry_times = retry_times - 1\n if retry_times == 0:\n email_action.send_message(\n zvt_config[\"email_username\"],\n f\"record {domain.__name__} error\",\n f\"record {domain.__name__} error: {e}\",\n )\n\n\nif __name__ == \"__main__\":\n run_data_recorder()\n# the __all__ is generated\n__all__ = [\"run_data_recorder\"]\n"
},
{
"path": "src/zvt/utils/str_utils.py",
"content": "# -*- coding: utf-8 -*-\n\n\ndef to_snake_str(input: str) -> str:\n parts = []\n part = \"\"\n for c in input:\n if c.isupper() or c.isdigit():\n if part:\n parts.append(part)\n part = c.lower()\n else:\n part = part + c\n\n parts.append(part)\n\n if len(parts) > 1:\n return \"_\".join(parts)\n elif parts:\n return parts[0]\n\n\ndef to_camel_str(input: str) -> str:\n parts = input.split(\"_\")\n domain_name = \"\"\n for part in parts:\n domain_name = domain_name + part.capitalize()\n return domain_name\n\n\n# the __all__ is generated\n__all__ = [\"to_snake_str\", \"to_camel_str\"]\n"
},
{
"path": "src/zvt/utils/time_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport calendar\nimport datetime\n\nimport arrow\nimport pandas as pd\n\nfrom zvt.common.query_models import TimeUnit\n\nCHINA_TZ = \"Asia/Shanghai\"\nUS_TZ = \"America/New_York\"\n\nTIME_FORMAT_ISO8601 = \"YYYY-MM-DDTHH:mm:ss.SSS\"\n\nTIME_FORMAT_MON = \"YYYY-MM\"\n\nTIME_FORMAT_DAY = \"YYYY-MM-DD\"\n\nTIME_FORMAT_DAY1 = \"YYYYMMDD\"\n\nTIME_FORMAT_MINUTE = \"YYYYMMDDHHmm\"\n\nTIME_FORMAT_SECOND = \"YYYYMMDDHHmmss\"\n\nTIME_FORMAT_MINUTE1 = \"HH:mm\"\n\nTIME_FORMAT_MINUTE2 = \"YYYY-MM-DD HH:mm:ss\"\n\n\n# ms(int) or second(float) or str\ndef to_pd_timestamp(the_time, tz=None) -> pd.Timestamp | None:\n if the_time is None:\n return None\n # treat int as milliseconds, e.g. return from js\n if type(the_time) == int:\n return pd.Timestamp.fromtimestamp(the_time / 1000)\n\n # treat float as seconds, e.g. return from\n # timestamp_seconds = time.time()\n if type(the_time) == float:\n return pd.Timestamp.fromtimestamp(the_time)\n\n return pd.Timestamp(the_time, tz=tz)\n\n\ndef get_local_timezone():\n now = datetime.datetime.now()\n local_now = now.astimezone()\n local_tz = local_now.tzinfo\n return local_tz\n\n\ndef to_timestamp_ms(the_time, tz=None) -> int:\n \"\"\"\n Convert a time to a timestamp in milliseconds.\n\n :param the_time:\n :param tz:\n :return:\n \"\"\"\n if not tz:\n tz = get_local_timezone()\n return int(to_pd_timestamp(the_time).tz_localize(tz).timestamp() * 1000)\n\n\ndef now_timestamp_ms():\n return int(pd.Timestamp.utcnow().timestamp() * 1000)\n\n\ndef now_pd_timestamp(tz=None) -> pd.Timestamp:\n if tz:\n return pd.Timestamp(arrow.now(tz=tz).datetime)\n else:\n return pd.Timestamp.now()\n\n\ndef current_date(tz=None) -> pd.Timestamp:\n return to_pd_timestamp(now_pd_timestamp(tz=tz).date())\n\n\ndef tomorrow_date(tz=None):\n return to_pd_timestamp(date_time_by_interval(now_pd_timestamp(tz=tz), 1).date())\n\n\ndef to_date_time_str(date_time, fmt=TIME_FORMAT_DAY, tz=None):\n try:\n return arrow.get(to_pd_timestamp(date_time, tz=tz)).format(fmt)\n except Exception as e:\n return date_time\n\n\ndef now_date_time_str(fmt=TIME_FORMAT_DAY, tz=None):\n return to_date_time_str(date_time=now_pd_timestamp(), tz=tz, fmt=fmt)\n\n\ndef recent_year_date(tz=None):\n return date_time_by_interval(the_time=current_date(tz=tz), interval=-365)\n\n\ndef next_date(the_time, tz=None):\n \"\"\"\n Get the next date from the given time.\n :param the_time: The time to get the next date from.\n :param tz: The timezone to use.\n :return: The next date as a pd.Timestamp.\n \"\"\"\n return date_time_by_interval(the_time=the_time, interval=1, unit=TimeUnit.day, tz=tz)\n\n\ndef date_time_by_interval(the_time, interval=1, unit: TimeUnit = TimeUnit.day, tz=None):\n time_delta = None\n if unit == TimeUnit.year:\n time_delta = datetime.timedelta(days=interval * 365)\n elif unit == TimeUnit.month:\n time_delta = datetime.timedelta(days=interval * 30)\n elif unit == TimeUnit.day:\n time_delta = datetime.timedelta(days=interval)\n elif unit == TimeUnit.minute:\n time_delta = datetime.timedelta(minutes=interval)\n elif unit == TimeUnit.second:\n time_delta = datetime.timedelta(seconds=interval)\n\n return to_pd_timestamp(the_time, tz=tz) + time_delta\n\n\ndef pre_month(t=None, tz=None):\n if not t:\n t = current_date()\n else:\n t = to_pd_timestamp(t, tz=tz)\n t = t.replace(day=1)\n if t.month > 1:\n year = t.year\n month = t.month - 1\n else:\n year = t.year - 1\n month = 12\n last_valid_date = t.replace(year=year, month=month)\n return last_valid_date\n\n\ndef pre_month_start_date(t=current_date()):\n return month_start_date(pre_month(t))\n\n\ndef pre_month_end_date(t=current_date()):\n return month_end_date(pre_month(t))\n\n\ndef month_start_date(the_date):\n the_date = to_pd_timestamp(the_date)\n return the_date.replace(day=1)\n\n\ndef month_end_date(the_date):\n the_date = to_pd_timestamp(the_date)\n\n _, day = calendar.monthrange(the_date.year, the_date.month)\n return the_date.replace(day=day)\n\n\ndef month_start_end_ranges(start_date, end_date):\n days = pd.date_range(start=start_date, end=end_date, freq=\"M\")\n return [(month_start_date(d), month_end_date(d)) for d in days]\n\n\ndef is_same_date(one, two):\n return to_pd_timestamp(one).date() == to_pd_timestamp(two).date()\n\n\ndef is_same_date_time(one, two):\n return to_timestamp_ms(one) == to_timestamp_ms(two)\n\n\ndef get_year_quarter(time):\n time = to_pd_timestamp(time)\n return time.year, ((time.month - 1) // 3) + 1\n\n\ndef day_offset_today(offset=0):\n return now_pd_timestamp() + datetime.timedelta(days=offset)\n\n\ndef get_year_quarters(start, end=pd.Timestamp.now()):\n start_year_quarter = get_year_quarter(start)\n current_year_quarter = get_year_quarter(end)\n if current_year_quarter[0] == start_year_quarter[0]:\n return [(current_year_quarter[0], x) for x in range(start_year_quarter[1], current_year_quarter[1] + 1)]\n elif current_year_quarter[0] - start_year_quarter[0] == 1:\n return [(start_year_quarter[0], x) for x in range(start_year_quarter[1], 5)] + [\n (current_year_quarter[0], x) for x in range(1, current_year_quarter[1] + 1)\n ]\n elif current_year_quarter[0] - start_year_quarter[0] > 1:\n return (\n [(start_year_quarter[0], x) for x in range(start_year_quarter[1], 5)]\n + [(x, y) for x in range(start_year_quarter[0] + 1, current_year_quarter[0]) for y in range(1, 5)]\n + [(current_year_quarter[0], x) for x in range(1, current_year_quarter[1] + 1)]\n )\n else:\n raise Exception(\"wrong start time:{}\".format(start))\n\n\ndef date_and_time(the_date, the_time, tz=None):\n time_str = \"{}T{}:00.000\".format(to_date_time_str(the_date), the_time)\n\n return to_pd_timestamp(time_str, tz=tz)\n\n\ndef split_time_interval(start, end, method=None, interval=30, freq=\"D\"):\n start = to_pd_timestamp(start)\n end = to_pd_timestamp(end)\n if not method:\n while start < end:\n interval_end = min(date_time_by_interval(the_time=start, interval=interval), end)\n yield pd.date_range(start=start, end=interval_end, freq=freq)\n start = date_time_by_interval(interval_end, 1)\n\n if method == \"month\":\n while start <= end:\n _, day = calendar.monthrange(start.year, start.month)\n\n interval_end = min(to_pd_timestamp(f\"{start.year}-{start.month}-{day}\"), end)\n yield pd.date_range(start=start, end=interval_end, freq=freq)\n start = date_time_by_interval(interval_end, 1)\n\n\ndef count_interval(start_date, end_date):\n start_date = to_pd_timestamp(start_date)\n end_date = to_pd_timestamp(end_date)\n delta = end_date - start_date\n return delta.days\n\n\nif __name__ == \"__main__\":\n print(recent_year_date(tz=CHINA_TZ))\n print(recent_year_date(tz=US_TZ))\n# the __all__ is generated\n__all__ = [\n \"CHINA_TZ\",\n \"TIME_FORMAT_ISO8601\",\n \"TIME_FORMAT_MON\",\n \"TIME_FORMAT_DAY\",\n \"TIME_FORMAT_DAY1\",\n \"TIME_FORMAT_MINUTE\",\n \"TIME_FORMAT_SECOND\",\n \"TIME_FORMAT_MINUTE1\",\n \"TIME_FORMAT_MINUTE2\",\n \"to_pd_timestamp\",\n \"get_local_timezone\",\n \"to_timestamp_ms\",\n \"now_timestamp_ms\",\n \"now_pd_timestamp\",\n \"current_date\",\n \"tomorrow_date\",\n \"to_date_time_str\",\n \"now_date_time_str\",\n \"recent_year_date\",\n \"date_time_by_interval\",\n \"pre_month\",\n \"pre_month_start_date\",\n \"pre_month_end_date\",\n \"month_start_date\",\n \"month_end_date\",\n \"month_start_end_ranges\",\n \"is_same_date\",\n \"is_same_date_time\",\n \"get_year_quarter\",\n \"day_offset_today\",\n \"get_year_quarters\",\n \"date_and_time\",\n \"split_time_interval\",\n \"count_interval\",\n]\n"
},
{
"path": "src/zvt/utils/utils.py",
"content": "# -*- coding: utf-8 -*-\nimport logging\nimport numbers\nfrom decimal import *\nfrom urllib import parse\n\nimport pandas as pd\n\ngetcontext().prec = 16\n\nlogger = logging.getLogger(__name__)\n\nnone_values = [\"不变\", \"--\", \"-\", \"新进\"]\nzero_values = [\"不变\", \"--\", \"-\", \"新进\"]\n\n\ndef first_item_to_float(the_list):\n return to_float(the_list[0])\n\n\ndef second_item_to_float(the_list):\n return to_float(the_list[1])\n\n\ndef add_func_to_value(the_map, the_func):\n for k, v in the_map.items():\n the_map[k] = (v, the_func)\n return the_map\n\n\ndef to_float(the_str, default=None):\n if not the_str:\n return default\n if the_str in none_values:\n return None\n\n if \"%\" in the_str:\n return pct_to_float(the_str)\n try:\n scale = 1.0\n if the_str[-2:] == \"万亿\":\n the_str = the_str[0:-2]\n scale = 1000000000000\n elif the_str[-1] == \"亿\":\n the_str = the_str[0:-1]\n scale = 100000000\n elif the_str[-1] == \"万\":\n the_str = the_str[0:-1]\n scale = 10000\n if not the_str:\n return default\n return float(Decimal(the_str.replace(\",\", \"\")) * Decimal(scale))\n except Exception as e:\n logger.error(\"the_str:{}\".format(the_str))\n logger.exception(e)\n return default\n\n\ndef pct_to_float(the_str, default=None):\n if the_str in none_values:\n return None\n\n try:\n return float(Decimal(the_str.replace(\"%\", \"\")) / Decimal(100))\n except Exception as e:\n logger.exception(e)\n return default\n\n\ndef float_to_pct(input_float: float) -> str:\n # Convert the float to a percentage and format it to two decimal places\n return f\"{input_float * 100:.2f}%\"\n\n\ndef format_number_to_yi(number):\n return f\"{number / 1e8:.2f}亿\"\n\n\ndef json_callback_param(the_str):\n json_str = the_str[the_str.index(\"(\") + 1 : the_str.rindex(\")\")].replace(\"null\", \"None\")\n return eval(json_str)\n\n\ndef fill_domain_from_dict(the_domain, the_dict: dict, the_map: dict = None, default_func=lambda x: x):\n \"\"\"\n use field map and related func to fill properties from the dict to the domain\n\n\n :param the_domain:\n :type the_domain: DeclarativeMeta\n :param the_dict:\n :type the_dict: dict\n :param the_map:\n :type the_map: dict\n :param default_func:\n :type default_func: function\n \"\"\"\n if not the_map:\n the_map = {}\n for k in the_dict:\n the_map[k] = (k, default_func)\n\n for k, v in the_map.items():\n if isinstance(v, tuple):\n field_in_dict = v[0]\n the_func = v[1]\n else:\n field_in_dict = v\n the_func = default_func\n\n the_value = the_dict.get(field_in_dict)\n if the_value is not None:\n to_value = the_value\n if to_value in none_values:\n setattr(the_domain, k, None)\n else:\n result_value = the_func(to_value)\n setattr(the_domain, k, result_value)\n exec(\"the_domain.{}=result_value\".format(k))\n\n\nSUPPORT_ENCODINGS = [\"GB2312\", \"GBK\", \"GB18030\", \"UTF-8\"]\n\n\ndef read_csv(f, encoding, sep=None, na_values=None):\n encodings = [encoding] + SUPPORT_ENCODINGS\n for encoding in encodings:\n try:\n if sep:\n return pd.read_csv(f, sep=sep, encoding=encoding, na_values=na_values)\n else:\n return pd.read_csv(f, encoding=encoding, na_values=na_values)\n except UnicodeDecodeError as e:\n logger.warning(\"read_csv failed by using encoding:{}\".format(encoding), e)\n f.seek(0)\n continue\n return None\n\n\ndef chrome_copy_header_to_dict(src):\n lines = src.split(\"\\n\")\n header = {}\n if lines:\n for line in lines:\n try:\n index = line.index(\":\")\n key = line[:index]\n value = line[index + 1 :]\n if key and value:\n header.setdefault(key.strip(), value.strip())\n except Exception:\n pass\n return header\n\n\ndef to_positive_number(number):\n if isinstance(number, numbers.Number):\n return abs(number)\n\n return 0\n\n\ndef multiple_number(number, factor):\n try:\n return number * factor\n except:\n return number\n\n\ndef add_to_map_list(the_map, key, value):\n result = []\n if key in the_map:\n result = the_map[key]\n else:\n the_map[key] = result\n\n if value not in result:\n result.append(value)\n\n\ndef iterate_with_step(data, sub_size=100):\n size = len(data)\n if size >= sub_size:\n step_count = int(size / sub_size)\n if size % sub_size:\n step_count = step_count + 1\n else:\n step_count = 1\n\n for step in range(step_count):\n if type(data) == pd.DataFrame or type(data) == pd.Series:\n yield data.iloc[sub_size * step : sub_size * (step + 1)]\n else:\n yield data[sub_size * step : sub_size * (step + 1)]\n\n\ndef url_unquote(url):\n return parse.unquote(url)\n\n\ndef parse_url_params(url):\n url = url_unquote(url)\n return parse.parse_qs(parse.urlsplit(url).query)\n\n\ndef set_one_and_only_one(**kwargs):\n all_none = all(kwargs[v] is None for v in kwargs)\n if all_none:\n raise ValueError(f\"{kwargs} must be set one at least\")\n\n set_size = len([v for v in kwargs if kwargs[v] is not None])\n if set_size != 1:\n raise ValueError(f\"{kwargs} could only set one\")\n\n return True\n\n\ndef flatten_list(input_list):\n if not input_list:\n return input_list\n result = []\n for item in input_list:\n if isinstance(item, list):\n result.extend(item)\n elif isinstance(item, dict):\n result.append(item)\n else:\n result.append(item)\n return result\n\n\ndef to_str(str_or_list):\n if not str_or_list:\n return None\n if isinstance(str_or_list, str):\n return str_or_list\n if isinstance(str_or_list, list):\n str_list = [str(item) for item in str_or_list]\n return \";\".join(str_list)\n\n\ndef compare_dicts(dict1, dict2):\n # Check if both dictionaries are None\n if dict1 is None and dict2 is None:\n return True\n\n # Check if only one dictionary is None\n if dict1 is None or dict2 is None:\n return False\n\n # Check if the keys are the same\n if set(dict1.keys()) != set(dict2.keys()):\n return False\n\n # Check if the values are the same for each key\n for key in dict1:\n if dict1[key] != dict2[key]:\n return False\n\n # If all keys and values match, return True\n return True\n\n\ndef fill_dict(src, dst):\n \"\"\"\n Fills items from the source dictionary (src) into the destination dictionary (dst)\n if the keys are not already present in dst.\n\n Args:\n src (dict): The source dictionary to copy items from.\n dst (dict): The destination dictionary to copy items into.\n\n Returns:\n dict: The updated destination dictionary with new items from the source dictionary.\n \"\"\"\n if not src:\n return dst\n for key, value in src.items():\n if key not in dst:\n dst[key] = value\n return dst\n\n\nif __name__ == \"__main__\":\n url = url_unquote(\n \"https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_DAILYBILLBOARD_DETAILS&sty=ALL&source=DataCenter&client=WAP&p=1&ps=20&sr=-1,1&st=TRADE_DATE,SECURITY_CODE&filter=(TRADE_DATE%3E=%272022-04-01%27)(TRADE_DATE%3C=%272022-04-29%27)(MARKET=%22SH%22)&?v=05160638952989893\"\n )\n print(url)\n\n# the __all__ is generated\n__all__ = [\n \"none_values\",\n \"zero_values\",\n \"first_item_to_float\",\n \"second_item_to_float\",\n \"add_func_to_value\",\n \"to_float\",\n \"pct_to_float\",\n \"float_to_pct\",\n \"json_callback_param\",\n \"fill_domain_from_dict\",\n \"SUPPORT_ENCODINGS\",\n \"read_csv\",\n \"chrome_copy_header_to_dict\",\n \"to_positive_number\",\n \"multiple_number\",\n \"add_to_map_list\",\n \"iterate_with_step\",\n \"url_unquote\",\n \"parse_url_params\",\n \"set_one_and_only_one\",\n \"flatten_list\",\n \"to_str\",\n \"compare_dicts\",\n \"fill_dict\",\n]\n"
},
{
"path": "src/zvt/utils/zip_utils.py",
"content": "# -*- coding: utf-8 -*-\n\nimport datetime\nimport os\nimport pathlib\nimport zipfile\n\n\ndef zip_dir(src_dir, dst_dir=None, zip_file_name=None):\n if not zip_file_name:\n zip_file_name = \"data-{}.zip\".format(datetime.datetime.today())\n\n if dst_dir:\n dst_path = os.path.join(dst_dir, zip_file_name)\n else:\n dst_path = zip_file_name\n\n # os.remove(dst_path)\n\n the_zip_file = zipfile.ZipFile(dst_path, \"w\")\n\n for folder, subfolders, files in os.walk(src_dir):\n for file in files:\n the_path = os.path.join(folder, file)\n # if 'zvt_business.db' in the_path:\n # continue\n print(\"zip {}\".format(the_path))\n the_zip_file.write(the_path, os.path.relpath(the_path, src_dir), compress_type=zipfile.ZIP_DEFLATED)\n\n the_zip_file.close()\n\n\ndef unzip(zip_file, dst_dir):\n the_zip_file = zipfile.ZipFile(zip_file)\n print(\"start unzip {} to {}\".format(zip_file, dst_dir))\n\n for name in the_zip_file.namelist():\n extracted_path = pathlib.Path(the_zip_file.extract(name, path=dst_dir))\n extracted_path.rename(f\"{extracted_path}\".encode(\"cp437\").decode(\"gbk\"))\n print(\"finish unzip {} to {}\".format(zip_file, dst_dir))\n the_zip_file.close()\n\n\n# the __all__ is generated\n__all__ = [\"zip_dir\", \"unzip\"]\n"
},
{
"path": "src/zvt/zvt_server.py",
"content": "# -*- coding: utf-8 -*-\nimport os\n\nimport uvicorn\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.responses import ORJSONResponse\nfrom fastapi_pagination import add_pagination\n\nfrom zvt import zvt_env\nfrom zvt.rest.data import data_router\nfrom zvt.rest.factor import factor_router\nfrom zvt.rest.misc import misc_router\nfrom zvt.rest.trading import trading_router\nfrom zvt.rest.work import work_router\n\napp = FastAPI(default_response_class=ORJSONResponse)\n\norigins = [\"*\"]\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n@app.get(\"/\")\nasync def root():\n return {\"message\": \"Hello World\"}\n\n\napp.include_router(data_router)\napp.include_router(factor_router)\napp.include_router(work_router)\napp.include_router(trading_router)\napp.include_router(misc_router)\n\nadd_pagination(app)\n\n\ndef main():\n log_config = os.path.join(zvt_env[\"resource_path\"], \"log_conf.yaml\")\n uvicorn.run(\"zvt_server:app\", host=\"0.0.0.0\", reload=True, port=8090, log_config=log_config)\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "tests/__init__.py",
"content": "# -*- coding: utf-8 -*-\nimport os\nimport sys\n\nos.environ.setdefault(\"TESTING_ZVT\", \"True\")\nos.environ.setdefault(\"SQLALCHEMY_WARN_20\", \"1\")\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"../src\")))\n"
},
{
"path": "tests/api/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/api/test_common.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.utils import get_recent_report_date\nfrom zvt.contract import IntervalLevel\nfrom zvt.api.kdata import get_kdata, to_high_level_kdata\nfrom ..context import init_test_context\n\ninit_test_context()\n\n\ndef test_to_high_level_kdata():\n day_df = get_kdata(provider=\"joinquant\", level=IntervalLevel.LEVEL_1DAY, entity_id=\"stock_sz_000338\")\n print(day_df)\n\n df = to_high_level_kdata(kdata_df=day_df.loc[:\"2019-09-01\", :], to_level=IntervalLevel.LEVEL_1WEEK)\n\n print(df)\n\n\ndef test_get_recent_report_date():\n assert \"2018-12-31\" == get_recent_report_date(\"2019-01-01\", 0)\n assert \"2018-09-30\" == get_recent_report_date(\"2019-01-01\", 1)\n assert \"2018-06-30\" == get_recent_report_date(\"2019-01-01\", 2)\n assert \"2018-03-31\" == get_recent_report_date(\"2019-01-01\", 3)\n"
},
{
"path": "tests/api/test_dividend_financing.py",
"content": "from ..context import init_test_context\n\ninit_test_context()\n\nfrom zvt.domain import SpoDetail, RightsIssueDetail, DividendFinancing\nfrom zvt.contract.api import get_db_session\nfrom zvt.utils.time_utils import to_pd_timestamp\n\nsession = get_db_session(provider=\"eastmoney\", db_name=\"dividend_financing\") # type: sqlalchemy.orm.Session\n\n\n# 增发详情\ndef test_000778_spo_detial():\n result = SpoDetail.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n order=SpoDetail.timestamp.desc(),\n )\n assert len(result) == 4\n latest: SpoDetail = result[0]\n assert latest.timestamp == to_pd_timestamp(\"2017-04-01\")\n assert latest.spo_issues == 347600000\n assert latest.spo_price == 5.15\n assert latest.spo_raising_fund == 1766000000\n\n\n# 配股详情\ndef test_000778_rights_issue_detail():\n result = RightsIssueDetail.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n order=RightsIssueDetail.timestamp.desc(),\n )\n assert len(result) == 2\n latest: RightsIssueDetail = result[0]\n assert latest.timestamp == to_pd_timestamp(\"2001-09-10\")\n assert latest.rights_issues == 43570000\n assert latest.rights_raising_fund == 492300000\n assert latest.rights_issue_price == 11.3\n\n\n# 分红融资\ndef test_000778_dividend_financing():\n result = DividendFinancing.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n order=DividendFinancing.timestamp.desc(),\n )\n assert len(result) == 22\n latest: DividendFinancing = result[1]\n assert latest.timestamp == to_pd_timestamp(\"2017\")\n assert latest.dividend_money == 598632026.4\n assert latest.spo_issues == 347572815.0\n assert latest.rights_issues == 0\n assert latest.ipo_issues == 0\n"
},
{
"path": "tests/api/test_finance.py",
"content": "from ..context import init_test_context\n\ninit_test_context()\n\nfrom zvt.domain import FinanceFactor, BalanceSheet, IncomeStatement, CashFlowStatement\nfrom zvt.contract.api import get_db_session\nfrom zvt.utils.time_utils import to_date_time_str\n\nsession = get_db_session(provider=\"eastmoney\", db_name=\"finance\") # type: sqlalchemy.orm.Session\n\n\n# 银行指标\ndef test_000001_finance_factor():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-09-30\",\n \"2002-06-30\",\n \"2002-03-31\",\n \"2001-12-31\",\n \"2001-09-30\",\n \"2001-06-30\",\n \"2001-03-31\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n \"1997-12-31\",\n \"1997-06-30\",\n \"1996-12-31\",\n \"1996-06-30\",\n \"1995-12-31\",\n \"1995-06-30\",\n \"1994-12-31\",\n \"1994-06-30\",\n \"1993-12-31\",\n \"1993-06-30\",\n \"1992-12-31\",\n \"1991-12-31\",\n \"1990-12-31\",\n \"1989-12-31\",\n ]\n result = FinanceFactor.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000001\"],\n end_timestamp=\"2018-12-30\",\n order=FinanceFactor.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: FinanceFactor = result[0]\n assert latest.basic_eps == 1.14\n assert latest.deducted_eps == 1.13\n assert latest.diluted_eps == 1.14\n assert latest.bps == 12.538\n assert latest.capital_reserve_ps == 3.2886\n assert latest.undistributed_profit_ps == 5.3566\n assert latest.op_cash_flow_ps == -0.6587\n\n assert latest.total_op_income == 86660000000\n assert latest.net_profit == 20460000000\n assert latest.deducted_net_profit == 20350000000\n assert latest.op_income_growth_yoy == 0.0856\n assert latest.net_profit_growth_yoy == 0.068\n assert latest.deducted_net_profit_growth_yoy == 0.0636\n assert latest.op_income_growth_qoq == 0.0336\n assert latest.net_profit_growth_qoq == 0.0202\n assert latest.deducted_net_profit_growth_qoq == 0.0168\n\n assert latest.roe == 0.0948\n assert latest.deducted_roe == 0.0943\n assert latest.rota == 0.0062\n assert latest.net_margin == 0.2360\n\n assert latest.debt_asset_ratio == 0.9298\n assert latest.em == 14.25\n assert latest.equity_ratio == 13.25\n\n assert latest.fi_total_deposit == 2130000000000\n assert latest.fi_total_loan == 1920000000000\n assert latest.fi_loan_deposit_ratio == 0.9004\n assert latest.fi_npl_ratio == 0.0168\n assert latest.fi_npl_provision_coverage == 1.6914\n\n\n# 银行资产负债表\ndef test_000001_balance_sheet():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-09-30\",\n \"2002-06-30\",\n \"2002-03-31\",\n \"2001-12-31\",\n \"2001-06-30\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n \"1997-12-31\",\n \"1997-06-30\",\n \"1996-12-31\",\n \"1996-06-30\",\n \"1995-12-31\",\n \"1995-06-30\",\n \"1994-12-31\",\n \"1994-06-30\",\n \"1993-12-31\",\n \"1992-12-31\",\n \"1991-12-31\",\n \"1990-12-31\",\n \"1989-12-31\",\n ]\n result = BalanceSheet.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000001\"],\n end_timestamp=\"2018-12-30\",\n order=BalanceSheet.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: BalanceSheet = result[0]\n assert latest.fi_cash_and_deposit_in_central_bank == 287600000000\n assert latest.fi_deposit_in_other_fi == 95310000000\n assert latest.fi_expensive_metals == 72020000000\n assert latest.fi_lending_to_other_fi == 88100000000\n assert latest.fi_financial_assets_effect_current_income == 103500000000\n assert latest.fi_financial_derivative_asset == 25650000000\n\n assert latest.fi_buying_sell_back_fi__asset == 32760000000\n assert latest.accounts_receivable == 22830000000\n assert latest.fi_interest_receivable == 18310000000\n assert latest.fi_disbursing_loans_and_advances == 1870000000000\n assert latest.real_estate_investment == 194000000\n assert latest.fixed_assets == 9374000000\n assert latest.intangible_assets == 4722000000\n assert latest.goodwill == 7568000000\n assert latest.deferred_tax_assets == 28880000000\n assert latest.fi_other_asset == 15630000000\n assert latest.total_assets == 3350000000000\n\n assert latest.fi_borrowings_from_central_bank == 149900000000\n assert latest.fi_deposit_from_other_fi == 402300000000\n assert latest.fi_borrowings_from_fi == 17830000000\n assert latest.fi_financial_liability_effect_current_income == 9599000000\n assert latest.fi_financial_derivative_liability == 20730000000\n assert latest.fi_sell_buy_back_fi_asset == 2000000000\n assert latest.fi_savings_absorption == 2130000000000\n assert latest.fi_notes_payable == 0\n\n assert latest.employee_benefits_payable == 10550000000\n assert latest.taxes_payable == 7595000000\n assert latest.interest_payable == 27670000000\n assert latest.fi_estimated_liabilities == 24000000\n assert latest.fi_bond_payable == 321500000000\n assert latest.fi_other_liability == 12570000000\n assert latest.total_liabilities == 3120000000000\n\n assert latest.fi_capital == 17170000000\n assert latest.fi_other_equity_instruments == 19950000000\n assert latest.fi_preferred_stock == 19950000000\n assert latest.capital_reserve == 56470000000\n assert latest.surplus_reserve == 10780000000\n assert latest.fi_generic_risk_reserve == 38550000000\n assert latest.undistributed_profits == 91970000000\n\n assert latest.equity == 235200000000\n assert latest.total_liabilities_and_equity == 3350000000000\n\n\n# 银行利润表\ndef test_000001_income_statement():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-09-30\",\n \"2002-06-30\",\n \"2002-03-31\",\n \"2001-12-31\",\n \"2001-09-30\",\n \"2001-06-30\",\n \"2001-03-31\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n \"1997-12-31\",\n \"1997-06-30\",\n \"1996-12-31\",\n \"1996-06-30\",\n \"1995-12-31\",\n \"1995-06-30\",\n \"1994-12-31\",\n \"1994-06-30\",\n \"1993-12-31\",\n \"1993-06-30\",\n \"1992-12-31\",\n \"1991-12-31\",\n \"1990-12-31\",\n \"1989-12-31\",\n ]\n result = IncomeStatement.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000001\"],\n end_timestamp=\"2018-12-30\",\n order=IncomeStatement.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: IncomeStatement = result[0]\n\n assert latest.operating_income == 86660000000\n assert latest.fi_net_interest_income == 54530000000\n assert latest.fi_interest_income == 121700000000\n assert latest.fi_interest_expenses == 67130000000\n assert latest.fi_net_incomes_from_fees_and_commissions == 23710000000\n assert latest.fi_incomes_from_fees_and_commissions == 28920000000\n assert latest.fi_expenses_for_fees_and_commissions == 5218000000\n assert latest.investment_income == 7099000000\n assert latest.fi_income_from_fair_value_change == 1047000000\n assert latest.fi_income_from_exchange == -40000000\n assert latest.fi_other_income == 144000000\n\n assert latest.operating_costs == 26430000000\n assert latest.business_taxes_and_surcharges == 847000000\n assert latest.fi_operate_and_manage_expenses == 25580000000\n\n assert latest.operating_profit == 26610000000\n assert latest.non_operating_income == 14000000\n assert latest.non_operating_costs == 62000000\n\n assert latest.total_profits == 26570000000\n\n\n# 银行现金流量表\ndef test_000001_cash_flow_statement():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-06-30\",\n \"2001-12-31\",\n \"2001-06-30\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n ]\n result = CashFlowStatement.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000001\"],\n end_timestamp=\"2018-12-30\",\n order=CashFlowStatement.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: CashFlowStatement = result[0]\n\n # 00000000\n assert latest.fi_deposit_increase == 104700000000\n assert latest.fi_borrow_from_central_bank_increase == 18960000000\n assert latest.fi_deposit_in_others_decrease == 60880000000\n assert latest.fi_lending_and_buy_repurchase_decrease == 12330000000\n assert latest.fi_lending_decrease == 12270000000\n assert latest.fi_buy_repurchase_decrease == 56000000\n assert latest.fi_cash_from_interest_commission == 133700000000\n assert latest.cash_from_other_op == 20540000000\n assert latest.total_op_cash_inflows == 381200000000\n assert latest.fi_loan_advance_increase == 250300000000\n assert latest.fi_borrowing_and_sell_repurchase_decrease == 14350000000\n assert latest.fi_borrowing_decrease == 10190000000\n assert latest.fi_sell_repurchase_decrease == 4155000000\n assert latest.fi_cash_to_interest_commission == 56760000000\n\n assert latest.cash_to_employees == 12930000000\n assert latest.taxes_and_surcharges == 20010000000\n assert latest.cash_to_other_related_op == 38150000000\n assert latest.total_op_cash_outflows == 392500000000\n assert latest.net_op_cash_flows == -11310000000\n\n assert latest.cash_from_disposal_of_investments == 348900000000\n assert latest.cash_from_returns_on_investments == 21080000000\n assert latest.cash_from_disposal_fixed_intangible_assets == 108000000\n assert latest.total_investing_cash_inflows == 370100000000\n assert latest.cash_to_investments == 294600000000\n assert latest.cash_to_acquire_fixed_intangible_assets == 1518000000\n assert latest.total_investing_cash_outflows == 296100000000\n assert latest.net_investing_cash_flows == 73960000000\n assert latest.cash_from_issuing_bonds == 581200000000\n assert latest.total_financing_cash_inflows == 581200000000\n assert latest.cash_to_repay_borrowings == 612500000000\n assert latest.fi_cash_to_pay_interest == 2511000000\n assert latest.cash_to_pay_interest_dividend == 3209000000\n assert latest.total_financing_cash_outflows == 618300000000\n assert latest.net_financing_cash_flows == -37080000000\n assert latest.foreign_exchange_rate_effect == 2018000000\n assert latest.net_cash_increase == 27590000000\n assert latest.cash_at_beginning == 137000000000\n assert latest.cash == 164600000000\n\n\n# 企业指标\ndef test_000778_finance_factor():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-09-30\",\n \"2002-06-30\",\n \"2002-03-31\",\n \"2001-12-31\",\n \"2001-06-30\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n \"1997-12-31\",\n \"1997-06-30\",\n \"1996-12-31\",\n \"1995-12-31\",\n \"1994-12-31\",\n ]\n result = FinanceFactor.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-12-30\",\n order=FinanceFactor.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: FinanceFactor = result[0]\n\n assert latest.basic_eps == 0.4537\n assert latest.diluted_eps == 0.4537\n assert latest.bps == 5.0919\n assert latest.capital_reserve_ps == 2.1769\n assert latest.undistributed_profit_ps == 1.8132\n assert latest.op_cash_flow_ps == 1.0148\n assert latest.total_op_income == 31710000000\n assert latest.gross_profit == 5491000000\n assert latest.net_profit == 1811000000\n assert latest.deducted_net_profit == 1897000000\n assert latest.op_income_growth_yoy == -0.1024\n assert latest.net_profit_growth_yoy == 1.2404\n assert latest.deducted_net_profit_growth_yoy == 1.4813\n assert latest.op_income_growth_qoq == 0.0408\n assert latest.net_profit_growth_qoq == 0.2143\n assert latest.deducted_net_profit_growth_qoq == 0.2955\n\n assert latest.roe == 0.0882\n assert latest.rota == 0.0376\n assert latest.gross_profit_margin == 0.1731\n assert latest.net_margin == 0.0591\n\n assert latest.advance_receipts_per_op_income == 0.1\n assert latest.sales_net_cash_flow_per_op_income == 0.88\n assert latest.op_net_cash_flow_per_op_income == 0.13\n assert latest.actual_tax_rate == 0.2362\n\n assert latest.current_ratio == 1\n assert latest.quick_ratio == 0.84\n assert latest.cash_flow_ratio == 0.17\n assert latest.debt_asset_ratio == 0.5766\n assert latest.em == 2.36\n assert latest.equity_ratio == 1.43\n\n assert latest.total_assets_turnover_days == 423.91\n assert latest.inventory_turnover_days == 32.88\n assert latest.receivables_turnover_days == 52.23\n assert latest.total_assets_turnover == 0.64\n assert latest.inventory_turnover == 8.21\n assert latest.receivables_turnover == 5.17\n\n\n# 企业资产负债表\ndef test_000778_balance_sheet():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-09-30\",\n \"2002-06-30\",\n \"2002-03-31\",\n \"2001-12-31\",\n \"2001-06-30\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n \"1997-12-31\",\n \"1997-06-30\",\n \"1996-12-31\",\n \"1995-12-31\",\n \"1994-12-31\",\n ]\n result = BalanceSheet.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-12-30\",\n order=BalanceSheet.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: BalanceSheet = result[0]\n\n assert latest.cash_and_cash_equivalents == 6381000000\n assert latest.note_receivable == 4655000000\n assert latest.accounts_receivable == 1920000000\n assert latest.advances_to_suppliers == 2112000000\n assert latest.other_receivables == 4671000000\n assert latest.inventories == 3891000000\n assert latest.current_portion_of_non_current_assets == 270000000\n assert latest.other_current_assets == 220100000\n assert latest.total_current_assets == 24120000000\n\n assert latest.fi_assets_saleable == 914900000\n assert latest.long_term_receivables == 1078000000\n assert latest.long_term_equity_investment == 6071000000\n assert latest.real_estate_investment == 24130000\n assert latest.fixed_assets == 15440000000\n assert latest.construction_in_process == 1196000000\n assert latest.intangible_assets == 853800000\n assert latest.goodwill == 32990000\n assert latest.long_term_prepaid_expenses == 2527000\n assert latest.deferred_tax_assets == 292300000\n assert latest.other_non_current_assets == 332800000\n assert latest.total_assets == 50350000000\n\n assert latest.short_term_borrowing == 9104000000\n assert latest.accept_money_deposits == 0\n assert latest.advances_from_customers == 3199000000\n assert latest.employee_benefits_payable == 207800000\n assert latest.taxes_payable == 735200000\n assert latest.other_payable == 2022000000\n assert latest.current_portion_of_non_current_liabilities == 1099000000\n assert latest.other_current_liabilities == 1000000000\n assert latest.total_current_liabilities == 24050000000\n\n assert latest.long_term_borrowing == 914400000\n assert latest.fi_bond_payable == 2992000000\n assert latest.long_term_payable == 636800000\n assert latest.deferred_tax_liabilities == 314800000\n assert latest.other_non_current_liabilities == 127800000\n assert latest.total_non_current_liabilities == 4986000000\n assert latest.total_liabilities == 29040000000\n\n assert latest.capital == 3991000000\n assert latest.capital_reserve == 8688000000\n assert latest.special_reserve == 40730000\n assert latest.surplus_reserve == 1286000000\n assert latest.undistributed_profits == 7236000000\n\n assert latest.equity == 20320000000\n assert latest.equity_as_minority_interest == 997400000\n assert latest.total_equity == 21320000000\n assert latest.total_liabilities_and_equity == 50350000000\n\n\n# 企业利润表\ndef test_000778_income_statement():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-09-30\",\n \"2002-06-30\",\n \"2002-03-31\",\n \"2001-12-31\",\n \"2001-06-30\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1999-06-30\",\n \"1998-12-31\",\n \"1998-06-30\",\n \"1997-12-31\",\n \"1997-06-30\",\n \"1996-12-31\",\n \"1995-12-31\",\n \"1994-12-31\",\n ]\n result = IncomeStatement.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-12-30\",\n order=IncomeStatement.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: IncomeStatement = result[0]\n\n assert latest.operating_income == 31710000000\n assert latest.total_operating_costs == 29230000000\n assert latest.operating_costs == 26220000000\n assert latest.rd_costs == 185500000\n assert latest.net_change_in_insurance_contract_reserves == 0\n assert latest.business_taxes_and_surcharges == 359700000\n assert latest.sales_costs == 771400000\n assert latest.managing_costs == 472900000\n assert latest.financing_costs == 397500000\n assert latest.assets_devaluation == 824400000\n assert latest.investment_income == 104100000\n assert latest.investment_income_from_related_enterprise == 61290000\n\n assert latest.operating_profit == 2637000000\n assert latest.non_operating_income == 38340000\n assert latest.non_operating_costs == 221700000\n\n assert latest.total_profits == 2454000000\n assert latest.tax_expense == 579600000\n assert latest.net_profit == 1874000000\n assert latest.net_profit_as_parent == 1811000000\n assert latest.net_profit_as_minority_interest == 63570000\n assert latest.deducted_net_profit == 1897000000\n\n assert latest.eps == 0.4537\n assert latest.diluted_eps == 0.4537\n\n assert latest.other_comprehensive_income == -521000000\n assert latest.other_comprehensive_income_as_parent == -522400000\n assert latest.other_comprehensive_income_as_minority_interest == 1403000\n assert latest.total_comprehensive_income == 1353000000\n assert latest.total_comprehensive_income_as_parent == 1288000000\n assert latest.total_comprehensive_income_as_minority_interest == 64980000\n\n\n# 银行现金流量表\ndef test_000778_cash_flow_statement():\n correct_timestamps = [\n \"2018-09-30\",\n \"2018-06-30\",\n \"2018-03-31\",\n \"2017-12-31\",\n \"2017-09-30\",\n \"2017-06-30\",\n \"2017-03-31\",\n \"2016-12-31\",\n \"2016-09-30\",\n \"2016-06-30\",\n \"2016-03-31\",\n \"2015-12-31\",\n \"2015-09-30\",\n \"2015-06-30\",\n \"2015-03-31\",\n \"2014-12-31\",\n \"2014-09-30\",\n \"2014-06-30\",\n \"2014-03-31\",\n \"2013-12-31\",\n \"2013-09-30\",\n \"2013-06-30\",\n \"2013-03-31\",\n \"2012-12-31\",\n \"2012-09-30\",\n \"2012-06-30\",\n \"2012-03-31\",\n \"2011-12-31\",\n \"2011-09-30\",\n \"2011-06-30\",\n \"2011-03-31\",\n \"2010-12-31\",\n \"2010-09-30\",\n \"2010-06-30\",\n \"2010-03-31\",\n \"2009-12-31\",\n \"2009-09-30\",\n \"2009-06-30\",\n \"2009-03-31\",\n \"2008-12-31\",\n \"2008-09-30\",\n \"2008-06-30\",\n \"2008-03-31\",\n \"2007-12-31\",\n \"2007-09-30\",\n \"2007-06-30\",\n \"2007-03-31\",\n \"2006-12-31\",\n \"2006-09-30\",\n \"2006-06-30\",\n \"2006-03-31\",\n \"2005-12-31\",\n \"2005-09-30\",\n \"2005-06-30\",\n \"2005-03-31\",\n \"2004-12-31\",\n \"2004-09-30\",\n \"2004-06-30\",\n \"2004-03-31\",\n \"2003-12-31\",\n \"2003-09-30\",\n \"2003-06-30\",\n \"2003-03-31\",\n \"2002-12-31\",\n \"2002-06-30\",\n \"2001-12-31\",\n \"2001-06-30\",\n \"2000-12-31\",\n \"2000-06-30\",\n \"1999-12-31\",\n \"1998-12-31\",\n \"1998-06-30\",\n ]\n result = CashFlowStatement.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-12-30\",\n order=CashFlowStatement.report_date.desc(),\n time_field=\"report_date\",\n )\n assert len(correct_timestamps) == len(result)\n timestamps = [to_date_time_str(item.report_date) for item in result]\n assert set(correct_timestamps) == set(timestamps)\n latest: CashFlowStatement = result[0]\n\n assert latest.cash_from_selling == 27784000000\n assert latest.tax_refund == 60700000\n assert latest.cash_from_other_op == 1463000000\n assert latest.total_op_cash_inflows == 29310000000\n assert latest.cash_to_goods_services == 21210000000\n assert latest.cash_to_employees == 1460000000\n assert latest.taxes_and_surcharges == 2016000000\n assert latest.cash_to_other_related_op == 573700000\n assert latest.total_op_cash_outflows == 25260000000\n assert latest.net_op_cash_flows == 4050000000\n\n assert latest.cash_from_disposal_of_investments == 556500000\n assert latest.cash_from_returns_on_investments == 44180000\n assert latest.cash_from_disposal_fixed_intangible_assets == 457200\n assert latest.cash_from_disposal_subsidiaries == 1046000000\n assert latest.cash_from_other_investing == 553000000\n assert latest.total_investing_cash_inflows == 2201000000\n assert latest.cash_to_acquire_fixed_intangible_assets == 2521000000\n assert latest.cash_to_investments == 1808000000\n assert latest.total_investing_cash_outflows == 4329000000\n assert latest.net_investing_cash_flows == -2128000000\n\n assert latest.cash_from_accepting_investment == 24500000\n assert latest.cash_from_subsidiaries_accepting_minority_interest == 24500000\n assert latest.cash_from_borrowings == 10080000000\n assert latest.cash_from_issuing_bonds == 997000000\n assert latest.cash_from_other_financing == 200000000\n assert latest.total_financing_cash_inflows == 11300000000\n assert latest.cash_to_repay_borrowings == 11940000000\n assert latest.cash_to_pay_interest_dividend == 892100000\n assert latest.cash_to_other_financing == 328500000\n assert latest.total_financing_cash_outflows == 13160000000\n assert latest.net_financing_cash_flows == -1862000000\n\n assert latest.foreign_exchange_rate_effect == 21350000\n assert latest.net_cash_increase == 81240000\n assert latest.cash_at_beginning == 5078000000\n assert latest.cash == 5159000000\n"
},
{
"path": "tests/api/test_holder.py",
"content": "from zvt.contract.api import get_db_session\nfrom ..context import init_test_context\n\ninit_test_context()\n\nfrom typing import List\n\nfrom zvt.domain import TopTenHolder, TopTenTradableHolder\n\nsession = get_db_session(provider=\"eastmoney\", db_name=\"holder\") # type: sqlalchemy.orm.Session\n\n\n# 十大股东\ndef test_000778_top_ten_holder():\n result: List[TopTenHolder] = TopTenHolder.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n start_timestamp=\"2018-09-30\",\n order=TopTenHolder.shareholding_ratio.desc(),\n )\n assert len(result) == 10\n assert result[0].holder_name == \"新兴际华集团有限公司\"\n assert result[0].shareholding_numbers == 1595000000\n assert result[0].shareholding_ratio == 0.3996\n assert result[0].change == 32080000\n assert result[0].change_ratio == 0.0205\n\n\ndef test_000778_top_ten_tradable_holder():\n result: List[TopTenHolder] = TopTenTradableHolder.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n start_timestamp=\"2018-09-30\",\n order=TopTenTradableHolder.shareholding_ratio.desc(),\n )\n assert len(result) == 10\n assert result[0].holder_name == \"新兴际华集团有限公司\"\n assert result[0].shareholding_numbers == 1525000000\n assert result[0].shareholding_ratio == 0.389\n assert result[0].change == 38560000\n assert result[0].change_ratio == 0.0259\n"
},
{
"path": "tests/api/test_intent.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.intent import compare, distribute, composite, composite_all\nfrom zvt.contract.drawer import ChartType\nfrom zvt.domain import FinanceFactor, CashFlowStatement, BalanceSheet, Stock1dKdata\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\ndef test_compare_kdata():\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n compare(entity_ids=entity_ids, scale_value=10)\n compare(entity_ids=entity_ids, start_timestamp=\"2010-01-01\")\n\n\ndef test_compare_line():\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n compare(entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]})\n\n\ndef test_compare_scatter():\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n compare(\n entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}, chart_type=ChartType.scatter\n )\n\n\ndef test_compare_area():\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n compare(entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}, chart_type=ChartType.area)\n\n\ndef test_compare_bar():\n entity_ids = [\"stock_sz_000338\", \"stock_sh_601318\"]\n compare(entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}, chart_type=ChartType.bar)\n\n\ndef test_distribute():\n distribute(entity_ids=None, data_schema=FinanceFactor, columns=[\"roe\"])\n\n\ndef test_composite():\n composite(\n entity_id=\"stock_sz_000338\",\n data_schema=CashFlowStatement,\n columns=[\n CashFlowStatement.net_op_cash_flows,\n CashFlowStatement.net_investing_cash_flows,\n CashFlowStatement.net_financing_cash_flows,\n ],\n filters=[\n CashFlowStatement.report_period == \"year\",\n CashFlowStatement.report_date == to_pd_timestamp(\"2016-12-31\"),\n ],\n )\n composite(\n entity_id=\"stock_sz_000338\",\n data_schema=BalanceSheet,\n columns=[\n BalanceSheet.total_current_assets,\n BalanceSheet.total_non_current_assets,\n BalanceSheet.total_current_liabilities,\n BalanceSheet.total_non_current_liabilities,\n ],\n filters=[BalanceSheet.report_period == \"year\", BalanceSheet.report_date == to_pd_timestamp(\"2016-12-31\")],\n )\n\n\ndef test_composite_all():\n composite_all(\n provider=\"joinquant\",\n entity_ids=None,\n data_schema=Stock1dKdata,\n column=Stock1dKdata.turnover,\n timestamp=to_pd_timestamp(\"2016-12-02\"),\n )\n"
},
{
"path": "tests/api/test_joinquant_quotes.py",
"content": "from zvt.api.kdata import get_kdata\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.api import get_db_session\nfrom ..context import init_test_context\n\ninit_test_context()\n\nday_k_session = get_db_session(provider=\"joinquant\", db_name=\"stock_1d_kdata\") # type: sqlalchemy.orm.Session\n\nday_1h_session = get_db_session(provider=\"joinquant\", db_name=\"stock_1h_kdata\") # type: sqlalchemy.orm.Session\n\n\ndef test_jq_603220_kdata():\n df = get_kdata(\n entity_id=\"stock_sh_603220\", session=day_k_session, level=IntervalLevel.LEVEL_1DAY, provider=\"joinquant\"\n )\n print(df)\n df = get_kdata(\n entity_id=\"stock_sh_603220\", session=day_1h_session, level=IntervalLevel.LEVEL_1HOUR, provider=\"joinquant\"\n )\n print(df)\n"
},
{
"path": "tests/api/test_kdata.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.kdata import get_kdata\nfrom zvt.api.kdata import get_latest_kdata_date\nfrom zvt.contract import IntervalLevel, AdjustType\n\n\ndef test_jq_1mon_kdata():\n df = get_kdata(entity_id=\"stock_sz_000338\", provider=\"joinquant\", level=IntervalLevel.LEVEL_1MON)\n se = df.loc[\"2010-01-29\"]\n # make sure our fq is ok\n assert round(se[\"open\"], 2) <= 5.44\n assert round(se[\"high\"], 2) <= 6.43\n assert round(se[\"low\"], 2) <= 5.2\n assert round(se[\"close\"], 2) <= 5.45\n\n\ndef test_jq_1wk_kdata():\n df = get_kdata(entity_id=\"stock_sz_000338\", provider=\"joinquant\", level=IntervalLevel.LEVEL_1WEEK)\n print(df)\n\n\ndef test_jq_1d_kdata():\n df = get_kdata(entity_id=\"stock_sz_000338\", provider=\"joinquant\", level=IntervalLevel.LEVEL_1DAY)\n print(df)\n\n se = df.loc[\"2019-04-08\"]\n # make sure our fq is ok\n assert round(se[\"open\"], 2) <= 12.86\n assert round(se[\"high\"], 2) <= 14.16\n assert round(se[\"low\"], 2) <= 12.86\n assert round(se[\"close\"], 2) <= 14.08\n\n\ndef test_jq_1d_hfq_kdata():\n df = get_kdata(entity_id=\"stock_sz_000338\", provider=\"joinquant\", level=IntervalLevel.LEVEL_1DAY, adjust_type=\"hfq\")\n se = df.loc[\"2019-04-08\"]\n print(se)\n assert round(se[\"open\"], 2) == 249.29\n assert round(se[\"high\"], 2) == 273.68\n assert round(se[\"low\"], 2) == 249.29\n assert round(se[\"close\"], 2) == 272.18\n\n\ndef test_get_latest_kdata_date():\n date = get_latest_kdata_date(provider=\"joinquant\", entity_type=\"stock\", adjust_type=AdjustType.hfq)\n assert date is not None\n"
},
{
"path": "tests/api/test_technical.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract.api import get_entities\nfrom ..context import init_test_context\n\ninit_test_context()\n\n\ndef test_basic_get_securities():\n items = get_entities(entity_type=\"stock\", provider=\"eastmoney\")\n print(items)\n items = get_entities(entity_type=\"index\", provider=\"exchange\")\n print(items)\n"
},
{
"path": "tests/api/test_trading.py",
"content": "from zvt.contract.api import get_db_session\nfrom ..context import init_test_context\n\ninit_test_context()\n\nfrom typing import List\n\nfrom zvt.domain import HolderTrading, ManagerTrading\n\nsession = get_db_session(provider=\"eastmoney\", db_name=\"trading\") # type: sqlalchemy.orm.Session\n\n\n# 股东交易\ndef test_000778_holder_trading():\n result: List[HolderTrading] = HolderTrading.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n start_timestamp=\"2018-09-30\",\n order=HolderTrading.holding_pct.desc(),\n )\n assert len(result) == 6\n assert result[0].holder_name == \"新兴际华集团有限公司\"\n assert result[0].change_pct == 0.0205\n assert result[0].volume == 32080000\n assert result[0].holding_pct == 0.3996\n\n\n# 高管交易\ndef test_000778_manager_trading():\n result: List[ManagerTrading] = ManagerTrading.query_data(\n session=session,\n provider=\"eastmoney\",\n return_type=\"domain\",\n codes=[\"000778\"],\n end_timestamp=\"2018-09-30\",\n start_timestamp=\"2017-09-30\",\n order=ManagerTrading.holding.desc(),\n )\n assert len(result) == 1\n assert result[0].trading_person == \"巩国平\"\n assert result[0].volume == 8400\n assert result[0].price == None\n assert result[0].holding == 18700\n assert result[0].trading_way == \"增持\"\n assert result[0].manager_position == \"职工监事\"\n assert result[0].manager == \"巩国平\"\n assert result[0].relationship_with_manager == \"本人\"\n"
},
{
"path": "tests/context.py",
"content": "# -*- coding: utf-8 -*-\n\n\ndef init_test_context():\n import os\n import sys\n\n sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"../src\")))\n"
},
{
"path": "tests/contract/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/contract/test_add_provider_plugin.py",
"content": "# -*- coding: utf-8 -*-\n\n\ndef test_add_tushare_provider():\n pass\n # register_schema(providers=[\"tushare\"], db_name=\"stock_meta\", schema_base=StockMetaBase)\n #\n # from zvt.domain import Stock\n #\n # Stock.query_data(provider=\"tushare\")\n # try:\n # Stock.record_data(provider=\"tushare\")\n # assert False\n # except Exception as e:\n # print(e)\n"
},
{
"path": "tests/contract/test_entity.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import TradableEntity, IntervalLevel\nfrom zvt.utils.time_utils import to_pd_timestamp\n\n\ndef test_get_1min_timestamps():\n timestamps = []\n for timestamp in TradableEntity.get_interval_timestamps(\n start_date=\"2020-06-17\", end_date=\"2020-06-18\", level=IntervalLevel.LEVEL_1MIN\n ):\n timestamps.append(timestamp)\n\n assert to_pd_timestamp(\"2020-06-17 09:31:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 11:30:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 13:01:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 15:00:00\") in timestamps\n\n assert to_pd_timestamp(\"2020-06-17 09:31:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 11:30:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 13:01:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-18 15:00:00\") in timestamps\n\n\ndef test_get_1h_timestamps():\n timestamps = []\n for timestamp in TradableEntity.get_interval_timestamps(\n start_date=\"2020-06-17\", end_date=\"2020-06-18\", level=IntervalLevel.LEVEL_1HOUR\n ):\n timestamps.append(timestamp)\n\n assert to_pd_timestamp(\"2020-06-17 10:30:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 11:30:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 14:00:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 15:00:00\") in timestamps\n\n assert to_pd_timestamp(\"2020-06-17 10:30:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 11:30:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-17 14:00:00\") in timestamps\n assert to_pd_timestamp(\"2020-06-18 15:00:00\") in timestamps\n\n\ndef test_is_finished_kdata_timestamp():\n assert TradableEntity.is_finished_kdata_timestamp(\"2020-06-17 10:30\", IntervalLevel.LEVEL_30MIN)\n assert not TradableEntity.is_finished_kdata_timestamp(\"2020-06-17 10:30\", IntervalLevel.LEVEL_1DAY)\n\n assert TradableEntity.is_finished_kdata_timestamp(\"2020-06-17 11:30\", IntervalLevel.LEVEL_30MIN)\n assert not TradableEntity.is_finished_kdata_timestamp(\"2020-06-17 11:30\", IntervalLevel.LEVEL_1DAY)\n\n assert TradableEntity.is_finished_kdata_timestamp(\"2020-06-17 13:30\", IntervalLevel.LEVEL_30MIN)\n assert not TradableEntity.is_finished_kdata_timestamp(\"2020-06-17 13:30\", IntervalLevel.LEVEL_1DAY)\n\n\ndef test_open_close():\n assert TradableEntity.is_open_timestamp(\"2020-06-17 09:30\")\n assert TradableEntity.is_close_timestamp(\"2020-06-17 15:00\")\n\n timestamps = []\n for timestamp in TradableEntity.get_interval_timestamps(\n start_date=\"2020-06-17\", end_date=\"2020-06-18\", level=IntervalLevel.LEVEL_1HOUR\n ):\n timestamps.append(timestamp)\n\n assert TradableEntity.is_open_timestamp(timestamps[0])\n assert TradableEntity.is_close_timestamp(timestamps[-1])\n"
},
{
"path": "tests/contract/test_reader.py",
"content": "# -*- coding: utf-8 -*-\nfrom ..context import init_test_context\n\ninit_test_context()\n\nimport time\n\nfrom zvt.domain import Stock1dKdata, Stock\n\nfrom zvt.utils.time_utils import to_date_time_str\n\nfrom zvt.contract.reader import DataReader\nfrom zvt.contract import IntervalLevel\n\n\ndef test_china_stock_reader():\n data_reader = DataReader(\n provider=\"joinquant\",\n data_schema=Stock1dKdata,\n entity_schema=Stock,\n entity_provider=\"eastmoney\",\n codes=[\"002572\", \"000338\"],\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-06-10\",\n )\n\n categories = data_reader.data_df.index.levels[0].to_list()\n\n df = data_reader.data_df\n\n assert \"stock_sz_002572\" in categories\n assert \"stock_sz_000338\" in categories\n\n assert (\"stock_sz_002572\", \"2019-01-02\") in df.index\n assert (\"stock_sz_000338\", \"2019-01-02\") in df.index\n assert (\"stock_sz_002572\", \"2019-06-10\") in df.index\n assert (\"stock_sz_000338\", \"2019-06-10\") in df.index\n\n for timestamp in Stock.get_interval_timestamps(\n start_date=\"2019-06-11\", end_date=\"2019-06-14\", level=IntervalLevel.LEVEL_1DAY\n ):\n data_reader.move_on(to_timestamp=timestamp)\n\n df = data_reader.data_df\n\n assert (\"stock_sz_002572\", timestamp) in df.index\n assert (\"stock_sz_000338\", to_date_time_str(timestamp)) in df.index\n\n\ndef test_reader_move_on():\n data_reader = DataReader(\n data_schema=Stock1dKdata,\n entity_schema=Stock,\n entity_provider=\"eastmoney\",\n codes=[\"002572\", \"000338\"],\n start_timestamp=\"2019-06-13\",\n end_timestamp=\"2019-06-14\",\n )\n\n data_reader.move_on(to_timestamp=\"2019-06-15\")\n assert (\"stock_sz_002572\", \"2019-06-15\") not in data_reader.data_df.index\n assert (\"stock_sz_000338\", \"2019-06-15\") not in data_reader.data_df.index\n\n start_time = time.time()\n data_reader.move_on(to_timestamp=\"2019-06-20\", timeout=5)\n assert time.time() - start_time < 5\n"
},
{
"path": "tests/contract/test_schema.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import Stock, Stockhk, Stockus\n\n\ndef test_stock_trading_time():\n assert Stock.before_trading_time(timestamp=\"2024-09-02 08:00\") is True\n assert Stock.before_trading_time(timestamp=\"2024-09-02 12:00\") is False\n\n assert Stock.after_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stock.after_trading_time(timestamp=\"2024-09-02 12:00\") is False\n assert Stock.after_trading_time(timestamp=\"2024-09-02 14:00\") is False\n assert Stock.after_trading_time(timestamp=\"2024-09-02 16:00\") is True\n\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 09:20\") is True\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 09:30\") is True\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 11:00\") is True\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 11:30\") is True\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 11:40\") is False\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 13:00\") is True\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 15:00\") is True\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 15:10\") is False\n assert Stock.in_real_trading_time(timestamp=\"2024-09-02 16:10\") is False\n\n assert Stock.in_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stock.in_trading_time(timestamp=\"2024-09-02 09:20\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 09:30\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 11:00\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 11:30\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 11:40\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 13:00\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 15:00\") is True\n assert Stock.in_trading_time(timestamp=\"2024-09-02 15:10\") is False\n assert Stock.in_trading_time(timestamp=\"2024-09-02 16:10\") is False\n\n\ndef test_stock_hk_trading_time():\n assert Stockhk.before_trading_time(timestamp=\"2024-09-02 08:00\") is True\n assert Stockhk.before_trading_time(timestamp=\"2024-09-02 12:00\") is False\n\n assert Stockhk.after_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stockhk.after_trading_time(timestamp=\"2024-09-02 12:00\") is False\n assert Stockhk.after_trading_time(timestamp=\"2024-09-02 14:00\") is False\n assert Stockhk.after_trading_time(timestamp=\"2024-09-02 17:00\") is True\n\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 09:15\") is True\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 09:30\") is True\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 11:00\") is True\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 12:00\") is True\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 12:40\") is False\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 13:00\") is True\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 15:00\") is True\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 16:10\") is False\n assert Stockhk.in_real_trading_time(timestamp=\"2024-09-02 17:10\") is False\n\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 09:20\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 09:30\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 11:00\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 11:30\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 11:40\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 12:00\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 13:00\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 15:00\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 16:00\") is True\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 16:10\") is False\n assert Stockhk.in_trading_time(timestamp=\"2024-09-02 17:10\") is False\n\n\ndef test_stock_us_trading_time():\n assert Stockus.before_trading_time(timestamp=\"2024-09-02 08:00\") is True\n assert Stockus.before_trading_time(timestamp=\"2024-09-02 11:00\") is False\n\n assert Stockus.after_trading_time(timestamp=\"2024-09-02 17:00\") is True\n assert Stockus.after_trading_time(timestamp=\"2024-09-02 15:00\") is False\n\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 09:30\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 11:00\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 12:00\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 12:40\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 13:00\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 15:00\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 16:00\") is True\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 16:10\") is False\n assert Stockus.in_real_trading_time(timestamp=\"2024-09-02 17:10\") is False\n\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 08:00\") is False\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 09:20\") is False\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 09:30\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 11:00\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 11:30\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 11:40\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 12:00\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 13:00\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 15:00\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 16:00\") is True\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 16:10\") is False\n assert Stockus.in_trading_time(timestamp=\"2024-09-02 17:10\") is False\n"
},
{
"path": "tests/factors/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/factors/test_algorithm.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.factors.algorithm import point_in_range, intersect, intersect_ranges, combine, distance\n\n\ndef test_point_in_range():\n assert point_in_range(1, (1, 2))\n assert point_in_range(2, (1, 2))\n assert point_in_range(1.5, (1, 2))\n\n assert not point_in_range(0.5, (1, 2))\n assert not point_in_range(2.4, (1, 2))\n\n\ndef test_intersect():\n a = (1, 2)\n b = (1.5, 3)\n assert intersect(a, b) == (1.5, 2)\n assert intersect(b, a) == (1.5, 2)\n\n a = (1, 2)\n b = (3, 4)\n assert intersect(a, b) == None\n assert intersect(b, a) == None\n\n a = (1, 4)\n b = (2, 3)\n assert intersect(a, b) == (2, 3)\n assert intersect(b, a) == (2, 3)\n\n\ndef test_intersect_ranges():\n a = (1, 2)\n b = (1.5, 3)\n c = (1.5, 5)\n assert intersect_ranges([a, b, c]) == (1.5, 2)\n assert intersect_ranges([b, a, c]) == (1.5, 2)\n assert intersect_ranges([a, c, b]) == (1.5, 2)\n\n a = (1, 2)\n b = (1.5, 3)\n c = (4, 5)\n assert intersect_ranges([a, b, c]) == None\n assert intersect_ranges([b, a, c]) == None\n assert intersect_ranges([a, c, b]) == None\n\n a = (1, 10)\n b = (1.5, 3)\n c = (2, 5)\n assert intersect_ranges([a, b, c]) == (2, 3)\n assert intersect_ranges([b, a, c]) == (2, 3)\n assert intersect_ranges([a, c, b]) == (2, 3)\n\n\ndef test_combine():\n a = (1, 2)\n b = (1.5, 3)\n assert combine(a, b) == (1, 3)\n assert combine(b, a) == (1, 3)\n\n a = (1, 2)\n b = (3, 4)\n assert combine(a, b) == None\n assert combine(b, a) == None\n\n a = (1, 4)\n b = (2, 3)\n assert combine(a, b) == (1, 4)\n assert combine(b, a) == (1, 4)\n\n\ndef test_distance():\n a = (1, 2)\n b = (1.5, 3)\n assert distance(a, b) == (4.5 / 2 - 1.5) / 1.5\n assert distance(b, a) == (1.5 - 4.5 / 2) / 2.25\n\n a = (1, 2)\n b = (3, 4)\n assert distance(a, b) == (3.5 - 1.5) / 1.5\n assert distance(b, a) == (1.5 - 3.5) / 3.5\n\n assert distance(a, b, use_max=True) == 3\n assert distance(b, a, use_max=True) == -3 / 4\n\n a = (1, 4)\n b = (2, 3)\n assert distance(a, b) == 0\n assert distance(b, a) == 0\n"
},
{
"path": "tests/factors/test_factor_select_targets.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel\nfrom zvt.factors.ma.ma_factor import CrossMaFactor\n\nfrom zvt.contract.factor import TargetType\nfrom zvt.factors.macd.macd_factor import BullFactor\nfrom ..context import init_test_context\n\ninit_test_context()\n\n\ndef test_cross_ma_select_targets():\n entity_ids = [\"stock_sz_000338\"]\n start_timestamp = \"2018-01-01\"\n end_timestamp = \"2019-06-30\"\n factor = CrossMaFactor(\n provider=\"joinquant\",\n entity_ids=entity_ids,\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n keep_window=10,\n windows=[5, 10],\n need_persist=False,\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=\"hfq\",\n )\n assert \"stock_sz_000338\" in factor.get_targets(timestamp=\"2018-01-19\")\n\n\ndef test_bull_select_targets():\n factor = BullFactor(\n start_timestamp=\"2019-01-01\", end_timestamp=\"2019-06-10\", level=IntervalLevel.LEVEL_1DAY, provider=\"joinquant\"\n )\n\n targets = factor.get_targets(timestamp=\"2019-05-08\", target_type=TargetType.positive)\n\n assert \"stock_sz_000338\" not in targets\n assert \"stock_sz_002572\" not in targets\n\n targets = factor.get_targets(\"2019-05-08\", target_type=TargetType.negative)\n assert \"stock_sz_000338\" in targets\n assert \"stock_sz_002572\" not in targets\n\n factor.move_on(timeout=0)\n\n targets = factor.get_targets(timestamp=\"2019-06-19\", target_type=TargetType.positive)\n\n assert \"stock_sz_000338\" in targets\n\n assert \"stock_sz_002572\" not in targets\n"
},
{
"path": "tests/factors/test_factors.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.factors.zen.zen_factor import ZenFactor\n\n\ndef test_zen_factor():\n z = ZenFactor(\n codes=[\"000338\"],\n need_persist=False,\n provider=\"joinquant\",\n )\n z.draw(show=True)\n\n z = ZenFactor(\n codes=[\"000338\", \"601318\"],\n need_persist=True,\n provider=\"joinquant\",\n )\n z.draw(show=True)\n"
},
{
"path": "tests/factors/test_technical_factor.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel\nfrom zvt.factors.algorithm import MaTransformer, MacdTransformer\nfrom zvt.factors.ma.ma_factor import CrossMaFactor\nfrom ..context import init_test_context\n\ninit_test_context()\n\nfrom zvt.factors.technical_factor import TechnicalFactor\n\n\ndef test_ma():\n factor = TechnicalFactor(\n provider=\"joinquant\",\n codes=[\"000338\"],\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-06-10\",\n level=IntervalLevel.LEVEL_1DAY,\n keep_window=30,\n transformer=MaTransformer(windows=[5, 10, 30]),\n adjust_type=\"qfq\",\n )\n\n print(factor.factor_df.tail())\n\n # compare with east money manually\n ma5 = factor.factor_df[\"ma5\"]\n ma10 = factor.factor_df[\"ma10\"]\n ma30 = factor.factor_df[\"ma30\"]\n\n assert round(ma5.loc[(\"stock_sz_000338\", \"2019-06-10\")], 2) <= 11.23\n assert round(ma10.loc[(\"stock_sz_000338\", \"2019-06-10\")], 2) <= 11.43\n assert round(ma30.loc[(\"stock_sz_000338\", \"2019-06-10\")], 2) <= 11.52\n\n factor.move_on(to_timestamp=\"2019-06-17\")\n ma5 = factor.factor_df[\"ma5\"]\n ma10 = factor.factor_df[\"ma10\"]\n ma30 = factor.factor_df[\"ma30\"]\n\n assert round(ma5.loc[(\"stock_sz_000338\", \"2019-06-17\")], 2) <= 12.06\n assert round(ma10.loc[(\"stock_sz_000338\", \"2019-06-17\")], 2) <= 11.64\n assert round(ma30.loc[(\"stock_sz_000338\", \"2019-06-17\")], 2) <= 11.50\n\n\ndef test_macd():\n factor = TechnicalFactor(\n provider=\"joinquant\",\n codes=[\"000338\"],\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-06-10\",\n level=IntervalLevel.LEVEL_1DAY,\n keep_window=None,\n transformer=MacdTransformer(),\n adjust_type=\"qfq\",\n )\n\n print(factor.factor_df.tail())\n\n # compare with east money manually\n diff = factor.factor_df[\"diff\"]\n dea = factor.factor_df[\"dea\"]\n macd = factor.factor_df[\"macd\"]\n\n assert round(diff.loc[(\"stock_sz_000338\", \"2019-06-10\")], 2) == -0.14\n assert round(dea.loc[(\"stock_sz_000338\", \"2019-06-10\")], 2) == -0.15\n assert round(macd.loc[(\"stock_sz_000338\", \"2019-06-10\")], 2) == 0.02\n\n factor.move_on(to_timestamp=\"2019-06-17\")\n diff = factor.factor_df[\"diff\"]\n dea = factor.factor_df[\"dea\"]\n macd = factor.factor_df[\"macd\"]\n\n assert round(diff.loc[(\"stock_sz_000338\", \"2019-06-17\")], 2) == 0.06\n assert round(dea.loc[(\"stock_sz_000338\", \"2019-06-17\")], 2) == -0.03\n assert round(macd.loc[(\"stock_sz_000338\", \"2019-06-17\")], 2) <= 0.19\n\n\ndef test_cross_ma():\n factor = CrossMaFactor(\n codes=[\"000338\"],\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-06-10\",\n level=IntervalLevel.LEVEL_1DAY,\n provider=\"joinquant\",\n windows=[5, 10],\n adjust_type=\"qfq\",\n )\n print(factor.factor_df.tail())\n print(factor.result_df.tail())\n\n score = factor.result_df[\"filter_result\"]\n\n assert score[(\"stock_sz_000338\", \"2019-06-03\")] == True\n assert score[(\"stock_sz_000338\", \"2019-06-04\")] == True\n assert (\"stock_sz_000338\", \"2019-06-05\") not in score or score[(\"stock_sz_000338\", \"2019-06-05\")] == False\n assert (\"stock_sz_000338\", \"2019-06-06\") not in score or score[(\"stock_sz_000338\", \"2019-06-06\")] == False\n assert (\"stock_sz_000338\", \"2019-06-10\") not in score or score[(\"stock_sz_000338\", \"2019-06-10\")] == False\n\n factor.move_on()\n score = factor.result_df[\"filter_result\"]\n assert score[(\"stock_sz_000338\", \"2019-06-17\")] == True\n"
},
{
"path": "tests/factors/test_transformers.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.kdata import get_kdata\nfrom zvt.factors.algorithm import MaTransformer, MacdTransformer\n\n\ndef test_ma_transformer():\n df = get_kdata(\n entity_id=\"stock_sz_000338\",\n start_timestamp=\"2019-01-01\",\n provider=\"joinquant\",\n index=[\"entity_id\", \"timestamp\"],\n )\n\n t = MaTransformer(windows=[5, 10])\n\n result_df = t.transform(df)\n\n print(result_df)\n\n\ndef test_MacdTransformer():\n df = get_kdata(\n entity_id=\"stock_sz_000338\",\n start_timestamp=\"2019-01-01\",\n provider=\"joinquant\",\n index=[\"entity_id\", \"timestamp\"],\n )\n\n t = MacdTransformer()\n\n result_df = t.transform(df)\n\n print(result_df)\n"
},
{
"path": "tests/ml/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/ml/test_sgd.py",
"content": "# -*- coding: utf-8 -*-\nfrom sklearn.linear_model import SGDClassifier, SGDRegressor\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nfrom zvt.contract import AdjustType\nfrom zvt.ml import MaStockMLMachine\n\nstart_timestamp = \"2015-01-01\"\nend_timestamp = \"2019-01-01\"\npredict_start_timestamp = \"2018-06-01\"\n\n\ndef test_sgd_classification():\n machine = MaStockMLMachine(\n data_provider=\"joinquant\",\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n predict_start_timestamp=predict_start_timestamp,\n entity_ids=[\"stock_sz_000001\"],\n label_method=\"behavior_cls\",\n adjust_type=AdjustType.qfq,\n )\n clf = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000, tol=1e-3))\n machine.train(model=clf)\n machine.predict()\n machine.draw_result(entity_id=\"stock_sz_000001\")\n\n\ndef test_sgd_regressor():\n machine = MaStockMLMachine(\n data_provider=\"joinquant\",\n start_timestamp=start_timestamp,\n end_timestamp=end_timestamp,\n predict_start_timestamp=predict_start_timestamp,\n entity_ids=[\"stock_sz_000001\"],\n label_method=\"raw\",\n adjust_type=AdjustType.qfq,\n )\n reg = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3))\n machine.train(model=reg)\n machine.predict()\n machine.draw_result(entity_id=\"stock_sz_000001\")\n"
},
{
"path": "tests/recorders/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/common/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/common/test_china_stock_list_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.recorders.eastmoney import EastmoneyStockRecorder\n\n\ndef test_china_stock_recorder():\n recorder = EastmoneyStockRecorder()\n\n try:\n recorder.run()\n except:\n assert False\n"
},
{
"path": "tests/recorders/eastmoney/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/eastmoney/test_dividend_financing_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import DividendDetail, RightsIssueDetail, SpoDetail, DividendFinancing\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.consts import SAMPLE_STOCK_CODES\n\n\ndef test_dividend_detail():\n try:\n DividendDetail.record_data(provider=\"eastmoney\", codes=SAMPLE_STOCK_CODES)\n except:\n assert False\n\n\ndef test_rights_issue_detail():\n try:\n RightsIssueDetail.record_data(provider=\"eastmoney\", codes=SAMPLE_STOCK_CODES)\n except:\n assert False\n\n\ndef test_spo_detail():\n try:\n SpoDetail.record_data(provider=\"eastmoney\", codes=SAMPLE_STOCK_CODES)\n except:\n assert False\n\n\ndef test_dividend_financing():\n try:\n DividendFinancing.record_data(provider=\"eastmoney\", codes=SAMPLE_STOCK_CODES)\n except:\n assert False\n"
},
{
"path": "tests/recorders/eastmoney/test_finance.py",
"content": "# -*- coding: utf-8 -*-\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.consts import SAMPLE_STOCK_CODES\n\nfrom zvt.recorders.eastmoney.finance.eastmoney_finance_factor_recorder import ChinaStockFinanceFactorRecorder\nfrom zvt.recorders.eastmoney.finance.eastmoney_cash_flow_recorder import ChinaStockCashFlowRecorder\nfrom zvt.recorders.eastmoney.finance.eastmoney_balance_sheet_recorder import ChinaStockBalanceSheetRecorder\nfrom zvt.recorders.eastmoney.finance.eastmoney_income_statement_recorder import ChinaStockIncomeStatementRecorder\n\n\ndef test_finance_factor_recorder():\n recorder = ChinaStockFinanceFactorRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_cash_flow_recorder():\n recorder = ChinaStockCashFlowRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_balance_sheet_recorder():\n recorder = ChinaStockBalanceSheetRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_income_statement_recorder():\n recorder = ChinaStockIncomeStatementRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n"
},
{
"path": "tests/recorders/eastmoney/test_holder_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.consts import SAMPLE_STOCK_CODES\n\nfrom zvt.recorders.eastmoney.holder.eastmoney_top_ten_holder_recorder import TopTenHolderRecorder\nfrom zvt.recorders.eastmoney.holder.eastmoney_top_ten_tradable_holder_recorder import TopTenTradableHolderRecorder\n\n\ndef test_top_ten_holder_recorder():\n recorder = TopTenHolderRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_top_ten_tradable_holder_recorder():\n recorder = TopTenTradableHolderRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n"
},
{
"path": "tests/recorders/eastmoney/test_meta_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder import EastmoneyStockDetailRecorder\n\nfrom zvt.consts import SAMPLE_STOCK_CODES\n\n\ndef test_meta_recorder():\n recorder = EastmoneyStockDetailRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n"
},
{
"path": "tests/recorders/eastmoney/test_trading_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.consts import SAMPLE_STOCK_CODES\n\nfrom zvt.recorders.eastmoney.trading.eastmoney_manager_trading_recorder import ManagerTradingRecorder\nfrom zvt.recorders.eastmoney.trading.eastmoney_holder_trading_recorder import HolderTradingRecorder\n\n\ndef test_manager_trading_recorder():\n recorder = ManagerTradingRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_holder_trading_recorder():\n recorder = HolderTradingRecorder(codes=SAMPLE_STOCK_CODES)\n try:\n recorder.run()\n except:\n assert False\n"
},
{
"path": "tests/recorders/em/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/em/test_em_api.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.recorders.em import em_api\n\nimport requests\n\n\ndef test_get_kdata():\n # 上证A股\n session = requests.Session()\n df = em_api.get_kdata(\n session=session,\n entity_id=\"stock_sh_601318\",\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.qfq,\n limit=5,\n )\n print(df)\n df = em_api.get_kdata(\n session=session,\n entity_id=\"stock_sh_601318\",\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.hfq,\n limit=5,\n )\n print(df)\n df = em_api.get_kdata(\n session=session,\n entity_id=\"stock_sh_601318\",\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.bfq,\n limit=5,\n )\n print(df)\n # 深圳A股\n df = em_api.get_kdata(\n session=session,\n entity_id=\"stock_sz_000338\",\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.qfq,\n limit=5,\n )\n print(df)\n df = em_api.get_kdata(\n session=session,\n entity_id=\"stock_sz_000338\",\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.hfq,\n limit=5,\n )\n print(df)\n df = em_api.get_kdata(\n session=session,\n entity_id=\"stock_sz_000338\",\n level=IntervalLevel.LEVEL_1DAY,\n adjust_type=AdjustType.bfq,\n limit=5,\n )\n print(df)\n"
},
{
"path": "tests/recorders/em/test_kdata_recorder.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/joinquant/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/joinquant/test_quote_recorder.py",
"content": "# -*- coding: utf-8 -*-\nfrom ...context import init_test_context\n\ninit_test_context()\n\nfrom zvt.contract import IntervalLevel\n\nfrom zvt.consts import SAMPLE_STOCK_CODES\nfrom zvt.recorders.joinquant.quotes.jq_stock_kdata_recorder import JqChinaStockKdataRecorder\n\n\ndef test_1wk_kdata_recorder():\n recorder = JqChinaStockKdataRecorder(\n codes=SAMPLE_STOCK_CODES, sleeping_time=0, level=IntervalLevel.LEVEL_1WEEK, real_time=False\n )\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_1mon_kdata_recorder():\n recorder = JqChinaStockKdataRecorder(\n codes=SAMPLE_STOCK_CODES, sleeping_time=0, level=IntervalLevel.LEVEL_1MON, real_time=False\n )\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_1d_kdata_recorder():\n recorder = JqChinaStockKdataRecorder(\n codes=SAMPLE_STOCK_CODES, sleeping_time=0, level=IntervalLevel.LEVEL_1DAY, real_time=False\n )\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_1d_hfq_kdata_recorder():\n recorder = JqChinaStockKdataRecorder(\n codes=[\"000338\"], sleeping_time=0, level=IntervalLevel.LEVEL_1DAY, real_time=False, adjust_type=\"hfq\"\n )\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_1h_kdata_recorder():\n recorder = JqChinaStockKdataRecorder(\n codes=[\"000338\"],\n sleeping_time=0,\n level=IntervalLevel.LEVEL_1HOUR,\n real_time=False,\n start_timestamp=\"2019-01-01\",\n )\n try:\n recorder.run()\n except:\n assert False\n\n\ndef test_5m_kdata_recorder():\n recorder = JqChinaStockKdataRecorder(\n codes=[\"000338\"], sleeping_time=0, level=IntervalLevel.LEVEL_5MIN, real_time=False, start_timestamp=\"2019-01-01\"\n )\n try:\n recorder.run()\n except:\n assert False\n"
},
{
"path": "tests/recorders/joinquant/test_stock_money_flow.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.domain import StockMoneyFlow, Stock\n\n\ndef test_stock_money_flow():\n provider = \"joinquant\"\n # Stock.record_data(provider=provider)\n StockMoneyFlow.record_data(\n codes=[\"300999\", \"688981\"], provider=provider, start_timestamp=\"2020-12-14\", compute_index_money_flow=False\n )\n\n data_samples = [\n {\n \"id\": \"stock_sz_300999_2020-12-14\",\n \"timestamp\": \"2020-12-14\",\n \"code\": \"300999\",\n \"net_main_inflows\": 46378.96 * 10000,\n \"net_main_inflow_rate\": 9.3 / 100,\n \"net_huge_inflows\": 50111.54 * 10000,\n \"net_huge_inflow_rate\": 10.04 / 100,\n \"net_big_inflows\": -3732.58 * 10000,\n \"net_big_inflow_rate\": -0.75 / 100,\n \"net_medium_inflows\": -23493.71 * 10000,\n \"net_medium_inflow_rate\": -4.71 / 100,\n \"net_small_inflows\": -22885.25 * 10000,\n \"net_small_inflow_rate\": -4.59 / 100,\n },\n {\n \"id\": \"stock_sh_688981_2020-12-14\",\n \"timestamp\": \"2020-12-14\",\n \"code\": \"688981\",\n \"net_main_inflows\": -14523.55 * 10000,\n \"net_main_inflow_rate\": -10.77 / 100,\n \"net_huge_inflows\": -17053.72 * 10000,\n \"net_huge_inflow_rate\": -12.65 / 100,\n \"net_big_inflows\": 2530.17 * 10000,\n \"net_big_inflow_rate\": 1.88 / 100,\n \"net_medium_inflows\": 6945.23 * 10000,\n \"net_medium_inflow_rate\": 5.15 / 100,\n \"net_small_inflows\": 7578.32 * 10000,\n \"net_small_inflow_rate\": 5.62 / 100,\n },\n ]\n StockMoneyFlow.test_data_correctness(provider=provider, data_samples=data_samples)\n"
},
{
"path": "tests/recorders/sina/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/recorders/sina/test_money_flow_recorder.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/test_generator.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.autocode.generator import _remove_start_end\n\n\ndef test_remove_start_end():\n cls = _remove_start_end(\"class A(object)\", \"class \", \"(\")\n assert cls == \"A\"\n\n func = _remove_start_end(\"def aaa(arg1, arg2)\", \"def \", \"(\")\n assert func == \"aaa\"\n\n var = _remove_start_end(\"zvt_env = \", \"\", \" =\")\n assert var == \"zvt_env\"\n"
},
{
"path": "tests/trader/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/trader/test_trader.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.api.kdata import get_kdata\nfrom zvt.contract import IntervalLevel, AdjustType\nfrom zvt.samples import MyBullTrader, StockTrader\nfrom zvt.utils.time_utils import is_same_date\n\nbuy_timestamp = \"2019-05-29\"\nsell_timestamp = \"2020-01-06\"\n\n\nclass SingleTrader(StockTrader):\n def on_time(self, timestamp):\n if is_same_date(buy_timestamp, timestamp):\n self.buy(timestamp=buy_timestamp, entity_ids=[\"stock_sz_000338\"])\n if is_same_date(sell_timestamp, timestamp):\n self.sell(timestamp=sell_timestamp, entity_ids=[\"stock_sz_000338\"])\n\n def long_position_control(self):\n return 1\n\n\ndef test_single_trader():\n trader = SingleTrader(\n provider=\"joinquant\",\n codes=[\"000338\"],\n level=IntervalLevel.LEVEL_1DAY,\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2020-01-10\",\n trader_name=\"000338_single_trader\",\n draw_result=True,\n )\n trader.run()\n\n positions = trader.get_current_account().positions\n print(positions)\n\n account = trader.get_current_account()\n\n print(account)\n\n buy_price = get_kdata(\n provider=\"joinquant\",\n entity_id=\"stock_sz_000338\",\n start_timestamp=buy_timestamp,\n end_timestamp=buy_timestamp,\n return_type=\"domain\",\n )[0]\n sell_price = get_kdata(\n provider=\"joinquant\",\n entity_id=\"stock_sz_000338\",\n start_timestamp=sell_timestamp,\n end_timestamp=sell_timestamp,\n return_type=\"domain\",\n )[0]\n\n sell_lost = trader.account_service.slippage + trader.account_service.sell_cost\n buy_lost = trader.account_service.slippage + trader.account_service.buy_cost\n pct = (sell_price.close * (1 - sell_lost) - buy_price.close * (1 + buy_lost)) / buy_price.close * (1 + buy_lost)\n\n profit_rate = (account.all_value - account.input_money) / account.input_money\n\n assert round(profit_rate, 2) == round(pct, 2)\n\n\nclass MultipleTrader(StockTrader):\n has_buy = False\n\n def on_time(self, timestamp):\n if is_same_date(buy_timestamp, timestamp):\n self.buy(timestamp=timestamp, entity_ids=[\"stock_sz_000338\"])\n self.has_buy = True\n self.buy(timestamp=timestamp, entity_ids=[\"stock_sh_601318\"])\n if is_same_date(sell_timestamp, timestamp):\n self.sell(\n timestamp=timestamp,\n entity_ids=[\"stock_sz_000338\", \"stock_sh_601318\"],\n )\n\n def long_position_control(self):\n if self.has_buy:\n position_pct = 1.0\n else:\n position_pct = 0.5\n\n return position_pct\n\n\ndef test_multiple_trader():\n trader = MultipleTrader(\n provider=\"joinquant\",\n codes=[\"000338\", \"601318\"],\n level=IntervalLevel.LEVEL_1DAY,\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2020-01-10\",\n trader_name=\"multiple_trader\",\n draw_result=False,\n adjust_type=AdjustType.qfq,\n )\n trader.run()\n\n positions = trader.get_current_account().positions\n print(positions)\n\n account = trader.get_current_account()\n\n print(account)\n\n # 000338\n buy_price = get_kdata(\n provider=\"joinquant\",\n entity_id=\"stock_sz_000338\",\n start_timestamp=buy_timestamp,\n end_timestamp=buy_timestamp,\n return_type=\"domain\",\n )[0]\n sell_price = get_kdata(\n provider=\"joinquant\",\n entity_id=\"stock_sz_000338\",\n start_timestamp=sell_timestamp,\n end_timestamp=sell_timestamp,\n return_type=\"domain\",\n )[0]\n\n sell_lost = trader.account_service.slippage + trader.account_service.sell_cost\n buy_lost = trader.account_service.slippage + trader.account_service.buy_cost\n pct1 = (sell_price.close * (1 - sell_lost) - buy_price.close * (1 + buy_lost)) / buy_price.close * (1 + buy_lost)\n\n # 601318\n buy_price = get_kdata(\n provider=\"joinquant\",\n entity_id=\"stock_sh_601318\",\n start_timestamp=buy_timestamp,\n end_timestamp=buy_timestamp,\n return_type=\"domain\",\n )[0]\n sell_price = get_kdata(\n provider=\"joinquant\",\n entity_id=\"stock_sh_601318\",\n start_timestamp=sell_timestamp,\n end_timestamp=sell_timestamp,\n return_type=\"domain\",\n )[0]\n\n pct2 = (sell_price.close * (1 - sell_lost) - buy_price.close * (1 + buy_lost)) / buy_price.close * (1 + buy_lost)\n\n profit_rate = (account.all_value - account.input_money) / account.input_money\n\n assert profit_rate - (pct1 + pct2) / 2 <= 0.2\n\n\ndef test_basic_trader():\n try:\n MyBullTrader(\n provider=\"joinquant\",\n codes=[\"000338\"],\n level=IntervalLevel.LEVEL_1DAY,\n start_timestamp=\"2018-01-01\",\n end_timestamp=\"2019-06-30\",\n trader_name=\"000338_bull_trader\",\n draw_result=False,\n ).run()\n except:\n assert False\n"
},
{
"path": "tests/utils/__init__.py",
"content": "# -*- coding: utf-8 -*-\n"
},
{
"path": "tests/utils/test_pd_utils.py",
"content": "# -*- coding: utf-8 -*-\nimport pandas as pd\n\nfrom zvt.utils.pd_utils import drop_continue_duplicate\n\n\ndef test_drop_continue_duplicate():\n data1 = [1, 2, 2, 3, 4, 4, 5]\n s = pd.Series(data=data1)\n s1 = drop_continue_duplicate(s=s)\n assert s1.tolist() == [1, 2, 3, 4, 5]\n\n data2 = [1, 2, 2, 2, 4, 4, 5]\n\n df = pd.DataFrame(data={\"A\": data1, \"B\": data2})\n print(df)\n df1 = drop_continue_duplicate(s=df, col=\"A\")\n assert df1[\"A\"].tolist() == [1, 2, 3, 4, 5]\n\n df2 = drop_continue_duplicate(s=df, col=\"B\")\n assert df2[\"A\"].tolist() == [1, 2, 4, 5]\n"
},
{
"path": "tests/utils/test_time_utils.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract import IntervalLevel\nfrom zvt.contract.utils import evaluate_size_from_timestamp, next_timestamp_on_level, is_finished_kdata_timestamp\nfrom zvt.utils.time_utils import (\n to_pd_timestamp,\n split_time_interval,\n is_same_date,\n month_start_end_ranges,\n count_interval,\n)\n\n\ndef test_evaluate_size_from_timestamp():\n size = evaluate_size_from_timestamp(\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-01-02\",\n level=IntervalLevel.LEVEL_1MON,\n one_day_trading_minutes=4 * 60,\n )\n\n assert size == 2\n\n size = evaluate_size_from_timestamp(\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-01-02\",\n level=IntervalLevel.LEVEL_1WEEK,\n one_day_trading_minutes=4 * 60,\n )\n\n assert size == 2\n\n size = evaluate_size_from_timestamp(\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-01-02\",\n level=IntervalLevel.LEVEL_1DAY,\n one_day_trading_minutes=4 * 60,\n )\n\n assert size == 2\n\n size = evaluate_size_from_timestamp(\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-01-02\",\n level=IntervalLevel.LEVEL_1HOUR,\n one_day_trading_minutes=4 * 60,\n )\n\n assert size == 9\n\n size = evaluate_size_from_timestamp(\n start_timestamp=\"2019-01-01\",\n end_timestamp=\"2019-01-02\",\n level=IntervalLevel.LEVEL_1MIN,\n one_day_trading_minutes=4 * 60,\n )\n\n assert size == 481\n\n\ndef test_next_timestamp():\n current = \"2019-01-10 13:15\"\n assert next_timestamp_on_level(current, level=IntervalLevel.LEVEL_1MIN) == to_pd_timestamp(\"2019-01-10 13:16\")\n assert next_timestamp_on_level(current, level=IntervalLevel.LEVEL_5MIN) == to_pd_timestamp(\"2019-01-10 13:20\")\n assert next_timestamp_on_level(current, level=IntervalLevel.LEVEL_15MIN) == to_pd_timestamp(\"2019-01-10 13:30\")\n\n\ndef test_is_finished_kdata_timestamp():\n timestamp = \"2019-01-10 13:05\"\n assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1DAY)\n assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1HOUR)\n assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_30MIN)\n assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_15MIN)\n assert is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_5MIN)\n assert is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1MIN)\n\n timestamp = \"2019-01-10\"\n assert is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1DAY)\n\n\ndef test_split_time_interval():\n first = None\n last = None\n start = \"2020-01-01\"\n end = \"2021-01-01\"\n for interval in split_time_interval(start, end, interval=30):\n if first is None:\n first = interval\n last = interval\n\n print(first)\n print(last)\n\n assert is_same_date(first[0], start)\n assert is_same_date(first[-1], \"2020-01-31\")\n\n assert is_same_date(last[-1], end)\n\n\ndef test_split_time_interval_month():\n first = None\n last = None\n start = \"2020-01-01\"\n end = \"2021-01-01\"\n for interval in split_time_interval(start, end, method=\"month\"):\n if first is None:\n first = interval\n last = interval\n\n print(first)\n print(last)\n\n assert is_same_date(first[0], start)\n assert is_same_date(first[-1], \"2020-01-31\")\n\n assert is_same_date(last[0], \"2021-01-01\")\n assert is_same_date(last[-1], \"2021-01-01\")\n\n\ndef test_month_start_end_range():\n start = \"2020-01-01\"\n end = \"2021-01-01\"\n ranges = month_start_end_ranges(start_date=start, end_date=end)\n print(ranges)\n assert is_same_date(ranges[0][0], \"2020-01-01\")\n assert is_same_date(ranges[0][1], \"2020-01-31\")\n\n assert is_same_date(ranges[-1][0], \"2020-12-01\")\n assert is_same_date(ranges[-1][1], \"2020-12-31\")\n\n start = \"2020-01-01\"\n end = \"2021-01-31\"\n ranges = month_start_end_ranges(start_date=start, end_date=end)\n print(ranges)\n assert is_same_date(ranges[0][0], \"2020-01-01\")\n assert is_same_date(ranges[0][1], \"2020-01-31\")\n\n assert is_same_date(ranges[-1][0], \"2021-01-01\")\n assert is_same_date(ranges[-1][1], \"2021-01-31\")\n\n\ndef test_count_interval():\n start = \"2020-01-01\"\n end = \"2021-01-01\"\n print(count_interval(start_date=start, end_date=end))\n"
},
{
"path": "tests/utils/test_utils.py",
"content": "# -*- coding: utf-8 -*-\nfrom zvt.contract.api import get_entities\nfrom zvt.utils.utils import iterate_with_step, to_str, float_to_pct\n\n\ndef test_iterate_with_step():\n data = range(1000)\n first = None\n last = None\n for sub_data in iterate_with_step(data):\n if not first:\n first = sub_data\n last = sub_data\n\n assert first[0] == 0\n assert first[-1] == 99\n\n assert last[0] == 900\n assert last[-1] == 999\n\n\ndef test_iterate_entities():\n data = get_entities(entity_type=\"stock\")\n first = None\n last = None\n for sub_data in iterate_with_step(data):\n if first is None:\n first = sub_data\n last = sub_data\n\n assert len(first) == 100\n assert len(last) <= 100\n\n\ndef test_to_str():\n assert to_str(None) is None\n assert to_str(\"\") is None\n assert to_str(\"a\") == \"a\"\n assert to_str([\"a\", \"b\"]) == \"a;b\"\n assert to_str([1, 2]) == \"1;2\"\n\n\ndef test_float_to_pct():\n assert float_to_pct(0.1) == \"10.00%\"\n assert float_to_pct(0.111) == \"11.10%\"\n assert float_to_pct(0.8) == \"80.00%\"\n assert float_to_pct(0.555) == \"55.50%\"\n assert float_to_pct(0.33333) == \"33.33%\"\n"
}
]