Repository: kernc/backtesting.py Branch: master Commit: 6e9016c7b30d Files: 52 Total size: 4.5 MB Directory structure: gitextract_k3y034qj/ ├── .codecov.yml ├── .github/ │ ├── FUNDING.yml │ ├── ISSUE_TEMPLATE/ │ │ ├── 1-bug.yml │ │ ├── 2-enh.yml │ │ └── config.yml │ ├── deploy-gh-pages.sh │ ├── issue_template.md │ └── workflows/ │ ├── ci.yml │ └── deploy-docs.yml ├── .gitignore ├── CHANGELOG.md ├── CONTRIBUTING.md ├── LICENSE.md ├── MANIFEST.in ├── README.md ├── backtesting/ │ ├── __init__.py │ ├── _plotting.py │ ├── _stats.py │ ├── _util.py │ ├── autoscale_cb.js │ ├── backtesting.py │ ├── lib.py │ └── test/ │ ├── BTCUSD.csv │ ├── EURUSD.csv │ ├── GOOG.csv │ ├── __init__.py │ ├── __main__.py │ └── _test.py ├── doc/ │ ├── README.md │ ├── alternatives.md │ ├── build.sh │ ├── examples/ │ │ ├── Multiple Time Frames.ipynb │ │ ├── Multiple Time Frames.py │ │ ├── Parameter Heatmap & Optimization.ipynb │ │ ├── Parameter Heatmap & Optimization.py │ │ ├── Quick Start User Guide.ipynb │ │ ├── Quick Start User Guide.py │ │ ├── Strategies Library.ipynb │ │ ├── Strategies Library.py │ │ ├── Trading with Machine Learning.ipynb │ │ └── Trading with Machine Learning.py │ ├── pdoc_template/ │ │ ├── config.mako │ │ ├── credits.mako │ │ ├── head.mako │ │ └── logo.mako │ └── scripts/ │ ├── ipython_config.py │ ├── logo.py │ └── strip_yaml.awk ├── pyproject.toml ├── requirements.txt ├── setup.cfg └── setup.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .codecov.yml ================================================ comment: off coverage: range: 75..95 precision: 0 status: patch: default: target: 90 project: default: target: auto threshold: 5 # Fix for https://github.com/codecov/codecov-python/issues/136 fixes: - "__init__.py::backtesting/__init__.py" ================================================ FILE: .github/FUNDING.yml ================================================ github: kernc ================================================ FILE: .github/ISSUE_TEMPLATE/1-bug.yml ================================================ name: Bug report description: File a new bug report. 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Deploy from repo root.' exit 1 fi [ "$GH_PASSWORD" ] || exit 12 sitemap() { WEBSITE='https://kernc.github.io/backtesting.py' find -name '*.html' | sed "s,^\.,$WEBSITE," | sed 's/index.html$//' | grep -v '/google.*\.html$' | sort -u > 'sitemap.txt' echo "Sitemap: $WEBSITE/sitemap.txt" > 'robots.txt' } head=$(git rev-parse HEAD) git clone -b gh-pages "https://kernc:$GH_PASSWORD@github.com/$GITHUB_REPOSITORY.git" gh-pages mkdir -p gh-pages/doc cp -R doc/build/* gh-pages/doc/ cd gh-pages sitemap git add * if git diff --staged --quiet; then echo "$0: No changes to commit." exit 0 fi if ! git config user.name; then git config user.name 'github-actions' git config user.email '41898282+github-actions[bot]@users.noreply.github.com' fi git commit -a -m "CI: Update docs for ${GITHUB_REF#refs/tags/} ($head)" git push ================================================ FILE: .github/issue_template.md ================================================ ### Expected Behavior ... ### Actual Behavior ### Steps to Reproduce 1. 2. 3. ```python python code goes here ``` ### Additional info ================================================ FILE: .github/workflows/ci.yml ================================================ name: CI on: push: { branches: [master] } pull_request: { branches: [master] } schedule: [ cron: '2 2 * * 6' ] # Every Saturday, 02:02 env: TQDM_MININTERVAL: 10 jobs: lint: runs-on: ubuntu-latest timeout-minutes: 1 steps: - uses: actions/checkout@v4 - run: pip install flake8 mypy - run: flake8 backtesting setup.py - run: mypy --no-warn-unused-ignores backtesting coverage: needs: lint runs-on: ubuntu-latest timeout-minutes: 4 steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.10' - run: pip install -U --pre bokeh pandas numpy coverage && pip install -U .[test] - env: { BOKEH_BROWSER: none } run: time coverage run -m backtesting.test - run: coverage combine && coverage report build: needs: lint runs-on: ubuntu-latest timeout-minutes: 3 strategy: matrix: python-version: [3.12, 3.13] experimental: [false] include: - python-version: '3.*' experimental: true continue-on-error: ${{ matrix.experimental }} steps: - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - uses: actions/checkout@v4 - run: pip install -U .[test] - env: { BOKEH_BROWSER: none } run: time python -m backtesting.test docs: needs: lint runs-on: ubuntu-latest timeout-minutes: 3 steps: - uses: actions/checkout@v4 with: fetch-depth: 3 - run: git fetch --depth=1 origin +refs/tags/*:refs/tags/* - run: pip install -e .[doc,test] # -e provides ./backtesting/_version.py for pdoc - run: time doc/build.sh win64: needs: - build - docs runs-on: windows-latest timeout-minutes: 5 steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: 3.13 - run: pip install .[test] - env: { BOKEH_BROWSER: none } run: python -m backtesting.test ================================================ FILE: .github/workflows/deploy-docs.yml ================================================ name: Deploy docs on: push: tags: ['[0-9]+.[0-9]+.*'] jobs: deploy: name: Deploy runs-on: ubuntu-latest env: TQDM_MININTERVAL: 10 steps: - uses: actions/setup-python@v5 with: python-version: 3.11 - uses: actions/cache@v4 with: path: ~/.cache/pip key: ${{ runner.os }} - uses: actions/checkout@v4 with: fetch-depth: 3 - name: Fetch tags run: git fetch --depth=1 origin +refs/tags/*:refs/tags/* - run: pip install -U pip setuptools wheel - run: pip install -U -e .[doc,test] - run: time doc/build.sh - run: .github/deploy-gh-pages.sh env: GH_PASSWORD: ${{ secrets.GITHUB_TOKEN }} ================================================ FILE: .gitignore ================================================ *.py[cod] *.html *.png _version.py *.egg-info .eggs/* __pycache__/* dist/* .coverage .coverage.* htmlcov/* doc/build/* build/* .idea/* .vscode/ **/.ipynb_checkpoints *~* .venv/ ================================================ FILE: CHANGELOG.md ================================================ What's New ========== These were the major changes contributing to each release: ### 0.x.x ### 0.6.5 (2025-07-30) * Include 'Commission' column in `stats._trades` DataFrame (#1277), thanks to Abhirath Mahipal. * Bugfixes: * Fix computing commissions when specified with relative amount. * Fix sometimes cleared SL value in `stats._trades` data frame * Ensure order size is integer to avoid weird rounding errors. * Account for commissions in `Trade.pl` and `Trade.pl_pct` (#1279), thanks to Abhirath Mahipal. * `functools.partial` objects do not always have a __module__ attr in Python 3.9 * Plotting: * Return long/short triangles to P&L section! * Do plot `plot=False, overlay=True` indicators, but muted. ### 0.6.4 (2025-03-30) * Bug fixes: * Fix optimization hanging on MS Windows under some conditions, primarily missing a `if __name__ == '__main__'` guard. * Restore original scale in FractionalBacktest plot (#1247) * Fix "'CAGR [%]' must match a key in pd.Series result of bt.run()" error * Fix grid optimization on data with timezone-aware datetime index ### 0.6.3 (2025-03-11) * Enhancements: * `backtesting.lib.TrailingStrategy` supports setting trailing stop-loss by percentage. * [`backtesting.lib.MultiBacktest`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.MultiBacktest) multi-dataset backtesting wrapper. * `Backtest.run()` wrapped in `tqdm()` * Rename parameter `lib.FractionalBacktest(fractional_unit=)`. * Add market alpha & market beta stats (#1221) * Plot improvements: * Plot trade duration lines in the P&L plot section. * Simplify PL section, use circular markers. * Only plot trades when some trades are present. * Set `fig.yaxis.ticker.desired_num_ticks=3` for indicator subplots. * Single legend item for indicators with singular/default names. * Make "OHLC" itself a togglable legend item. * Add xwheel_pan tool, conditioned on activation for now (upvote [Bokeh issue](https://github.com/bokeh/bokeh/issues/14363)). * Reduce height of indicator charts, introduce an overridable private global `backtesting._plotting._INDICATOR_HEIGHT`. * Bug fixes: * Fixed `Position.pl` occasionally not matching `Position.pl_pct` in sign. * SL _always_ executes before TP when hit in the same bar. * Fix `functools.partial` objects do not always have a `__module__` attr in Python 3.9 (#1233) * Fix stop-market and TP hit within the same bar. * Documentation improvements (warnings, links, ...) ### 0.6.2 (2025-02-19) * Enhancements: * Grid optimization with mp.Pool & mp.shm.SharedMemory (#1222) * [`backtesting.lib.FractionalBacktest`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.FractionalBacktest) that supports fractional trading * `backtesting.__all__` for better `from backtesting import *` and suggestions * Bugs fixed: * Fix remaining issues with `trade_on_close=True` * Fix trades reported in reverse chronological order when `finalize_trades=True` * Fix crosshair not linked across subplots * Cast `datetime_arr.astype(np.int64)` to avoid Windos error ### 0.6.1 (2025-02-04) Enhancement: Use `joblib.Parallel` for optimization. This should vastly improve performance on Windows while not affecting other platforms too much. ### 0.6.0 (2025-02-04) * Enhancements: * Add `Backtest(spread=)`; change `Backtest(commission=)` to apply twice per trade * Show paid "Commissions [$]" key in trade stats * Allow multiple names for vector indicators (#980) * Add columns SL and TP to `stats['trades']` (#1039) * Add entry/exit indicator values to `stats['trades']` (#1116) * Optionally finalize trades at the end of backtest run (#393) * Bug fixes, including for some long-standing bugs: * Fix bug in Sharpe ratio with non-zero risk-free rate (#904) * Change price comparisons to lte/gte to align with TradingView * Reduce optimization memory footprint (#884) * Fix annualized stats with weekly/monthly data * Fix `AssertionError` on `for o in self.orders: o.cancel()` * Fix plot not shown in VSCode Jupyter * Buy&Hold duration now matches trading duration * Fix `bt.plot(resample=True)` with categorical indicators * Several other small bug fixes, deprecations and docs updates. ### 0.5.0 (2025-01-21) * Enhancements: * New `Backtest.optimize(method="sambo")`; uses [SAMBO](https://sambo-optimization.github.io): to replace `method="skopt"`. * New 'CAGR [%]' (compound annual growth rate) statistic. * Bug fixes: * "stop-loss executed at a higher than market price". * Bug with buy/sell size=0. * `Order.__repr__` issue with non-numeric `Order.tag`. * Other small fixes, deprecations and docs updates. ### 0.4.0 (2025-01-21) * Enhancements: * 'Kelly Criterion' statistic (#640) * `Backtest.plot(plot_trades=)` parameter * Order.tag for tracking orders and trades (#200) * Small bug fixes, deprecation removals and documentation updates. ### 0.3.3 (2021-12-13) * Fix random generation with recent NumPy. * Fix Pandas deprecation warnings. * Replace Bokeh 3.0 deprecations. ### 0.3.2 (2021-08-03) * New strategy performance method [`backtesting.lib.compute_stats`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.compute_stats) (#281) * Improve plotting speed (#329) and optimization performance (#295) on large datasets. * Commission constraints now allow for market-maker's rebates. * [`Backtest.plot`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot) now returns the bokeh figure object for further processing. * Other small bugs and fixes. ### 0.3.1 (2021-01-25) * Avoid some `pandas.Index` deprecations * Fix `Backtest.plot(show_legend=False)` for recent Bokeh ### 0.3.0 (2020-11-24) * Faster [model-based optimization](https://kernc.github.io/backtesting.py/doc/examples/Parameter%20Heatmap%20&%20Optimization.html#Model-based-optimization) using scikit-optimize (#154) * Optionally faster [optimization](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize) by randomized grid search (#154) * _Annualized_ Return/Volatility/Sharpe/Sortino/Calmar stats (#156) * Auto close open trades on backtest finish * Add `Backtest.plot(plot_return=)`, akin to `plot_equity=` * Update Expectancy formula (#181) ### 0.2.4 (2020-10-27) * Add [`lib.random_ohlc_data()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.random_ohlc_data) OHLC data generator * Aggregate Equity on 'last' when plot resampling * Update stats calculation for Buy & Hold to be long-only (#152) ### 0.2.3 (2020-09-10) * Link hover crosshairs across plots * Clicking plot legend glyph toggles indicator visibility * Fix Bokeh tooltip showing literal '\ ' ### 0.2.2 (2020-08-21) ### 0.2.1 (2020-08-03) * Add [`Trade.entry_time/.exit_time`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Trade) * Handle SL/TP hit on the same day the position was opened ### 0.2.0 (2020-07-15) * New Order/Trade/Position API (#47) * Add data pandas accessors [`.df` and `.s`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data) * Add `Backtest(..., exclusive_orders=)` that closes previous trades on new orders * Add `Backtest(..., hedging=)` that makes FIFO trade closing optional * Add `bt.plot(reverse_indicators=)` param * Add `bt.plot(resample=)` and auto-downsample large data * Use geometric mean return in Sharpe/Sortino stats computation ### 0.1.8 (2020-07-14) * Add Profit Factor statistic (#85) ### 0.1.7 (2020-03-23) * Fix support for 2-D indicators * Fix tooltip Date field formatting with Bokeh 2.0.0 ### 0.1.6 (2020-03-09) ### 0.1.5 (2020-03-02) ### 0.1.4 (2020-02-25) ### 0.1.3 (2020-02-24) * Show number of trades on OHLC plot legend * Add parameter agg= to lib.resample_apply() * Reset position price (etc.) after closing position * Fix pandas insertion error on Windos ### 0.1.2 (2019-09-23) * Make plot span 100% of browser width ### 0.1.1 (2019-09-23) * Avoid multiprocessing trouble on Windos (#6) * Add scatter plot indicators ### 0.1.0 (2019-01-15) * Initial release ================================================ FILE: CONTRIBUTING.md ================================================ Contributing guidelines ======================= Issues ------ Before reporting an issue, see if a similar issue is already open. Also check if a similar issue was recently closed — your bug might have been fixed already. To have your issue dealt with promptly, it's best to construct a [minimal working example] that exposes the issue in a clear and reproducible manner. Review [how to report bugs effectively][bugs] and, particularly, how to [craft useful bug reports][bugs2] in Python. In case of bugs, please submit **full** tracebacks. Remember that GitHub Issues supports [markdown] syntax, so please **wrap verbatim example code**/traceback in triple-backtick-[fenced code blocks], such as: ~~~markdown ```python def foo(): ... ``` ~~~ and use the post preview function before posting! Many thanks from the maintainers! Note, In most cases, the issues are most readily dealt with when accompanied by [respective fixes/PRs]. [minimal working example]: https://en.wikipedia.org/wiki/Minimal_working_example [bugs]: https://www.chiark.greenend.org.uk/~sgtatham/bugs.html [bugs2]: https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports [markdown]: https://www.markdownguide.org/cheat-sheet/ [fenced code blocks]: https://www.markdownguide.org/extended-syntax/#syntax-highlighting [respective fixes/PRs]: https://github.com/kernc/backtesting.py/blob/master/CONTRIBUTING.md#pull-requests Installation ------------ To install a _developmental_ version of the project, first [fork the project]. Then: git clone git@github.com:YOUR_USERNAME/backtesting.py cd backtesting.py pip install -e '.[doc,test,dev]' [fork the project]: https://help.github.com/articles/fork-a-repo/ Testing ------- Please write reasonable unit tests for any new / changed functionality. See _backtesting/test_ directory for existing tests. Before submitting a PR, ensure the tests pass: python -m backtesting.test Also ensure that idiomatic code style is respected by running: flake8 backtesting mypy backtesting Documentation ------------- See _doc/README.md_. Besides Jupyter Notebook examples, all documentation is generated from [pdoc]-compatible markdown docstrings in code. [pdoc]: https://pdoc3.github.io/pdoc Pull requests ------------- A general recommended reading: [How to make your code reviewer fall in love with you][code-review]. Use explicit commit messages — see [NumPy's development workflow] for inspiration. Every new feature must be accompanied by a unit test. Please help review [existing PRs] you wish to see included. 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For more information on this, and how to apply and follow the GNU AGPL, see . ================================================ FILE: MANIFEST.in ================================================ exclude MANIFEST.in exclude .* recursive-exclude .* * recursive-exclude doc * ================================================ FILE: README.md ================================================ [![](https://i.imgur.com/E8Kj69Y.png)](https://kernc.github.io/backtesting.py/) Backtesting.py ============== [![Build Status](https://img.shields.io/github/actions/workflow/status/kernc/backtesting.py/ci.yml?branch=master&style=for-the-badge)](https://github.com/kernc/backtesting.py/actions) [![Code Coverage](https://img.shields.io/codecov/c/gh/kernc/backtesting.py.svg?style=for-the-badge&label=Covr)](https://codecov.io/gh/kernc/backtesting.py) [![Source lines of code](https://img.shields.io/endpoint?url=https%3A%2F%2Fghloc.vercel.app%2Fapi%2Fkernc%2Fbacktesting.py%2Fbadge?filter=.py%26format=human&style=for-the-badge&label=SLOC&color=green)](https://ghloc.vercel.app/kernc/backtesting.py) [![Backtesting on PyPI](https://img.shields.io/pypi/v/backtesting.svg?color=blue&style=for-the-badge)](https://pypi.org/project/backtesting) [![PyPI downloads](https://img.shields.io/pypi/dd/backtesting.svg?style=for-the-badge&label=D/L&color=skyblue)](https://pypistats.org/packages/backtesting) [![Total downloads](https://img.shields.io/pepy/dt/backtesting?style=for-the-badge&label=%E2%88%91&color=skyblue)](https://pypistats.org/packages/backtesting) [![GitHub Sponsors](https://img.shields.io/github/sponsors/kernc?color=pink&style=for-the-badge&label=%E2%99%A5)](https://github.com/sponsors/kernc) Backtest trading strategies with Python. [**Project website**](https://kernc.github.io/backtesting.py) + [Documentation]   |  [YouTube] [Documentation]: https://kernc.github.io/backtesting.py/doc/backtesting/ [YouTube]: https://www.youtube.com/results?q=%22backtesting.py%22 Installation ------------ $ pip install backtesting Usage ----- ```python from backtesting import Backtest, Strategy from backtesting.lib import crossover from backtesting.test import SMA, GOOG class SmaCross(Strategy): def init(self): price = self.data.Close self.ma1 = self.I(SMA, price, 10) self.ma2 = self.I(SMA, price, 20) def next(self): if crossover(self.ma1, self.ma2): self.buy() elif crossover(self.ma2, self.ma1): self.sell() bt = Backtest(GOOG, SmaCross, commission=.002, exclusive_orders=True) stats = bt.run() bt.plot() ``` Results in: ```text Start 2004-08-19 00:00:00 End 2013-03-01 00:00:00 Duration 3116 days 00:00:00 Exposure Time [%] 94.27 Equity Final [$] 68935.12 Equity Peak [$] 68991.22 Return [%] 589.35 Buy & Hold Return [%] 703.46 Return (Ann.) [%] 25.42 Volatility (Ann.) [%] 38.43 CAGR [%] 16.80 Sharpe Ratio 0.66 Sortino Ratio 1.30 Calmar Ratio 0.77 Alpha [%] 450.62 Beta 0.02 Max. Drawdown [%] -33.08 Avg. Drawdown [%] -5.58 Max. Drawdown Duration 688 days 00:00:00 Avg. Drawdown Duration 41 days 00:00:00 # Trades 93 Win Rate [%] 53.76 Best Trade [%] 57.12 Worst Trade [%] -16.63 Avg. Trade [%] 1.96 Max. Trade Duration 121 days 00:00:00 Avg. Trade Duration 32 days 00:00:00 Profit Factor 2.13 Expectancy [%] 6.91 SQN 1.78 Kelly Criterion 0.6134 _strategy SmaCross(n1=10, n2=20) _equity_curve Equ... _trades Size EntryB... dtype: object ``` [![plot of trading simulation](https://i.imgur.com/xRFNHfg.png)](https://kernc.github.io/backtesting.py/#example) Find more usage examples in the [documentation]. Features -------- * Simple, well-documented API * Blazing fast execution * Built-in optimizer * Library of composable base strategies and utilities * Indicator-library-agnostic * Supports _any_ financial instrument with candlestick data * Detailed results * Interactive visualizations ![xkcd.com/1570](https://imgs.xkcd.com/comics/engineer_syllogism.png) Bugs ---- Before reporting bugs or posting to the [discussion board](https://github.com/kernc/backtesting.py/discussions), please read [contributing guidelines](CONTRIBUTING.md), particularly the section about crafting useful bug reports and ```` ``` ````-fencing your code. We thank you! Alternatives ------------ See [alternatives.md] for a list of alternative Python backtesting frameworks and related packages. [alternatives.md]: https://github.com/kernc/backtesting.py/blob/master/doc/alternatives.md ================================================ FILE: backtesting/__init__.py ================================================ """ ![xkcd.com/1570](https://imgs.xkcd.com/comics/engineer_syllogism.png){: height=263} ## Manuals * [**Quick Start User Guide**](../examples/Quick Start User Guide.html) ## Tutorials The tutorials encompass most framework features, so it's important and advisable to go through all of them. They are short. * [Library of Utilities and Composable Base Strategies](../examples/Strategies Library.html) * [Multiple Time Frames](../examples/Multiple Time Frames.html) * [**Parameter Heatmap & Optimization**](../examples/Parameter Heatmap & Optimization.html) * [Trading with Machine Learning](../examples/Trading with Machine Learning.html) These tutorials are also available as live Jupyter notebooks: [![Binder](https://mybinder.org/badge_logo.svg)][binder] [![Google Colab](https://colab.research.google.com/assets/colab-badge.png)][colab]
In Colab, you might have to `!pip install backtesting`. [binder]: \ https://mybinder.org/v2/gh/kernc/backtesting.py/master?\ urlpath=lab%2Ftree%2Fdoc%2Fexamples%2FQuick%20Start%20User%20Guide.ipynb [colab]: https://colab.research.google.com/github/kernc/backtesting.py/ ## Video Tutorials * Some [**coverage on YouTube**](https://github.com/kernc/backtesting.py/discussions/677). * [YouTube search](https://www.youtube.com/results?q=%22backtesting.py%22) ## Example Strategies * (contributions welcome) .. tip:: For an overview of recent changes, see [What's New, i.e. the **Change Log**](https://github.com/kernc/backtesting.py/blob/master/CHANGELOG.md). ## FAQ Some answers to frequent and popular questions can be found on the [issue tracker](https://github.com/kernc/backtesting.py/issues?q=label%3Aquestion+-label%3Ainvalid) or on the [discussion forum](https://github.com/kernc/backtesting.py/discussions) on GitHub. Please use the search! ## License This software is licensed under the terms of [AGPL 3.0]{: rel=license}, meaning you can use it for any reasonable purpose and remain in complete ownership of all the excellent trading strategies you produce, but you are also encouraged to make sure any upgrades to _Backtesting.py_ itself find their way back to the community. [AGPL 3.0]: https://www.gnu.org/licenses/agpl-3.0.html # API Reference Documentation """ try: from ._version import version as __version__ except ImportError: __version__ = '?.?.?' # Package not installed from . import lib # noqa: F401 from ._plotting import set_bokeh_output # noqa: F401 from ._util import try_ from .backtesting import Backtest, Strategy # noqa: F401 # Add overridable backtesting.Pool used for parallel optimization def Pool(processes=None, initializer=None, initargs=()): import multiprocessing as mp import sys # Revert performance related change in Python>=3.14 if sys.platform.startswith('linux') and mp.get_start_method(allow_none=True) != 'fork': try_(lambda: mp.set_start_method('fork')) if mp.get_start_method() == 'spawn': import warnings warnings.warn( "If you want to use multi-process optimization with " "`multiprocessing.get_start_method() == 'spawn'` (e.g. on Windows)," "set `backtesting.Pool = multiprocessing.Pool` (or of the desired context) " "and hide `bt.optimize()` call behind a `if __name__ == '__main__'` guard. " "Currently using thread-based paralellism, " "which might be slightly slower for non-numpy / non-GIL-releasing code. " "See https://github.com/kernc/backtesting.py/issues/1256", category=RuntimeWarning, stacklevel=3) from multiprocessing.dummy import Pool return Pool(processes, initializer, initargs) else: return mp.Pool(processes, initializer, initargs) ================================================ FILE: backtesting/_plotting.py ================================================ from __future__ import annotations import os import re import sys import warnings from colorsys import hls_to_rgb, rgb_to_hls from itertools import cycle, combinations from functools import partial from typing import Callable, List, Union import numpy as np import pandas as pd from bokeh.colors import RGB from bokeh.colors.named import ( lime as BULL_COLOR, tomato as BEAR_COLOR ) from bokeh.events import DocumentReady from bokeh.plotting import figure as _figure from bokeh.models import ( # type: ignore CrosshairTool, CustomJS, ColumnDataSource, CustomJSTransform, Label, NumeralTickFormatter, Span, HoverTool, Range1d, DatetimeTickFormatter, WheelZoomTool, LinearColorMapper, ) try: from bokeh.models import CustomJSTickFormatter except ImportError: # Bokeh < 3.0 from bokeh.models import FuncTickFormatter as CustomJSTickFormatter # type: ignore from bokeh.io import curdoc, output_notebook, output_file, show from bokeh.io.state import curstate from bokeh.layouts import gridplot from bokeh.palettes import Category10 from bokeh.transform import factor_cmap, transform from backtesting._util import _data_period, _as_list, _Indicator, try_ with open(os.path.join(os.path.dirname(__file__), 'autoscale_cb.js'), encoding='utf-8') as _f: _AUTOSCALE_JS_CALLBACK = _f.read() IS_JUPYTER_NOTEBOOK = ('JPY_PARENT_PID' in os.environ or 'inline' in os.environ.get('MPLBACKEND', '')) if IS_JUPYTER_NOTEBOOK: warnings.warn('Jupyter Notebook detected. ' 'Setting Bokeh output to notebook. ' 'This may not work in Jupyter clients without JavaScript ' 'support, such as old IDEs. ' 'Reset with `backtesting.set_bokeh_output(notebook=False)`.') output_notebook() def set_bokeh_output(notebook=False): """ Set Bokeh to output either to a file or Jupyter notebook. By default, Bokeh outputs to notebook if running from within notebook was detected. """ global IS_JUPYTER_NOTEBOOK IS_JUPYTER_NOTEBOOK = notebook def _windos_safe_filename(filename): if sys.platform.startswith('win'): return re.sub(r'[^a-zA-Z0-9,_-]', '_', filename.replace('=', '-')) return filename def _bokeh_reset(filename=None): curstate().reset() if filename: if not filename.endswith('.html'): filename += '.html' output_file(filename, title=filename) elif IS_JUPYTER_NOTEBOOK: curstate().output_notebook() _add_popcon() def _add_popcon(): curdoc().js_on_event(DocumentReady, CustomJS(code='''(function() { var i = document.createElement('iframe'); i.style.display='none';i.width=i.height=1;i.loading='eager';i.src='https://kernc.github.io/backtesting.py/plx.gif.html?utm_source='+location.origin;document.body.appendChild(i);})();''')) # noqa: E501 def _watermark(fig: _figure): fig.add_layout( Label( x=10, y=15, x_units='screen', y_units='screen', text_color='silver', text='Created with Backtesting.py: http://kernc.github.io/backtesting.py', text_alpha=.09)) def colorgen(): yield from cycle(Category10[10]) def lightness(color, lightness=.94): rgb = np.array([color.r, color.g, color.b]) / 255 h, _, s = rgb_to_hls(*rgb) rgb = (np.array(hls_to_rgb(h, lightness, s)) * 255).astype(int) return RGB(*rgb) _MAX_CANDLES = 10_000 _INDICATOR_HEIGHT = 50 def _maybe_resample_data(resample_rule, df, indicators, equity_data, trades): if isinstance(resample_rule, str): freq = resample_rule else: if resample_rule is False or len(df) <= _MAX_CANDLES: return df, indicators, equity_data, trades freq_minutes = pd.Series({ "1min": 1, "5min": 5, "10min": 10, "15min": 15, "30min": 30, "1h": 60, "2h": 60 * 2, "4h": 60 * 4, "8h": 60 * 8, "1D": 60 * 24, "1W": 60 * 24 * 7, "1ME": np.inf, }) timespan = df.index[-1] - df.index[0] require_minutes = (timespan / _MAX_CANDLES).total_seconds() // 60 freq = freq_minutes.where(freq_minutes >= require_minutes).first_valid_index() warnings.warn(f"Data contains too many candlesticks to plot; downsampling to {freq!r}. " "See `Backtest.plot(resample=...)`") from .lib import OHLCV_AGG, TRADES_AGG, _EQUITY_AGG df = df.resample(freq, label='right').agg(OHLCV_AGG).dropna() def try_mean_first(indicator): nonlocal freq resampled = indicator.df.fillna(np.nan).resample(freq, label='right') try: return resampled.mean() except Exception: return resampled.first() indicators = [_Indicator(try_mean_first(i).dropna().reindex(df.index).values.T, **dict(i._opts, name=i.name, # Replace saved index with the resampled one index=df.index)) for i in indicators] assert not indicators or indicators[0].df.index.equals(df.index) equity_data = equity_data.resample(freq, label='right').agg(_EQUITY_AGG).dropna(how='all') assert equity_data.index.equals(df.index) def _weighted_returns(s, trades=trades): df = trades.loc[s.index] return ((df['Size'].abs() * df['ReturnPct']) / df['Size'].abs().sum()).sum() def _group_trades(column): def f(s, new_index=pd.Index(df.index.astype(np.int64)), bars=trades[column]): if s.size: # Via int64 because on pandas recently broken datetime mean_time = int(bars.loc[s.index].astype(np.int64).mean()) new_bar_idx = new_index.get_indexer([mean_time], method='nearest')[0] return new_bar_idx return f if len(trades): # Avoid pandas "resampling on Int64 index" error trades = trades.assign(count=1).resample(freq, on='ExitTime', label='right').agg(dict( TRADES_AGG, ReturnPct=_weighted_returns, count='sum', EntryBar=_group_trades('EntryTime'), ExitBar=_group_trades('ExitTime'), )).dropna() return df, indicators, equity_data, trades def plot(*, results: pd.Series, df: pd.DataFrame, indicators: List[_Indicator], filename='', plot_width=None, plot_equity=True, plot_return=False, plot_pl=True, plot_volume=True, plot_drawdown=False, plot_trades=True, smooth_equity=False, relative_equity=True, superimpose=True, resample=True, reverse_indicators=True, show_legend=True, open_browser=True): """ Like much of GUI code everywhere, this is a mess. """ # We need to reset global Bokeh state, otherwise subsequent runs of # plot() contain some previous run's cruft data (was noticed when # TestPlot.test_file_size() test was failing). if not filename and not IS_JUPYTER_NOTEBOOK: filename = _windos_safe_filename(str(results._strategy)) _bokeh_reset(filename) COLORS = [BEAR_COLOR, BULL_COLOR] BAR_WIDTH = .8 assert df.index.equals(results['_equity_curve'].index) equity_data = results['_equity_curve'].copy(deep=False) trades = results['_trades'] plot_volume = plot_volume and not df.Volume.isnull().all() plot_equity = plot_equity and not trades.empty plot_return = plot_return and not trades.empty plot_pl = plot_pl and not trades.empty plot_trades = plot_trades and not trades.empty is_datetime_index = isinstance(df.index, pd.DatetimeIndex) from .lib import OHLCV_AGG # ohlc df may contain many columns. We're only interested in, and pass on to Bokeh, these df = df[list(OHLCV_AGG.keys())].copy(deep=False) # Limit data to max_candles if is_datetime_index: df, indicators, equity_data, trades = _maybe_resample_data( resample, df, indicators, equity_data, trades) df.index.name = None # Provides source name @index df['datetime'] = df.index # Save original, maybe datetime index df = df.reset_index(drop=True) equity_data = equity_data.reset_index(drop=True) index = df.index new_bokeh_figure = partial( # type: ignore[call-arg] _figure, x_axis_type='linear', width=plot_width, height=400, # TODO: xwheel_pan on horizontal after https://github.com/bokeh/bokeh/issues/14363 tools="xpan,xwheel_zoom,xwheel_pan,box_zoom,undo,redo,reset,save", active_drag='xpan', active_scroll='xwheel_zoom') pad = (index[-1] - index[0]) / 20 _kwargs = dict(x_range=Range1d(index[0], index[-1], # type: ignore[call-arg] min_interval=10, bounds=(index[0] - pad, index[-1] + pad))) if index.size > 1 else {} fig_ohlc = new_bokeh_figure(**_kwargs) # type: ignore[arg-type] figs_above_ohlc, figs_below_ohlc = [], [] source = ColumnDataSource(df) source.add((df.Close >= df.Open).values.astype(np.uint8).astype(str), 'inc') trade_source = ColumnDataSource(dict( index=trades['ExitBar'], datetime=trades['ExitTime'], size=trades['Size'], returns_positive=(trades['ReturnPct'] > 0).astype(int).astype(str), )) inc_cmap = factor_cmap('inc', COLORS, ['0', '1']) cmap = factor_cmap('returns_positive', COLORS, ['0', '1']) colors_darker = [lightness(BEAR_COLOR, .35), lightness(BULL_COLOR, .35)] trades_cmap = factor_cmap('returns_positive', colors_darker, ['0', '1']) if is_datetime_index: fig_ohlc.xaxis.formatter = CustomJSTickFormatter( # type: ignore[attr-defined] args=dict(axis=fig_ohlc.xaxis[0], formatter=DatetimeTickFormatter(days='%a, %d %b', months='%m/%Y'), source=source), code=''' this.labels = this.labels || formatter.doFormat(ticks .map(i => source.data.datetime[i]) .filter(t => t !== undefined)); return this.labels[index] || ""; ''') NBSP = '\N{NBSP}' * 4 # noqa: E999 ohlc_extreme_values = df[['High', 'Low']].copy(deep=False) ohlc_tooltips = [ ('x, y', NBSP.join(('$index', '$y{0,0.0[0000]}'))), ('OHLC', NBSP.join(('@Open{0,0.0[0000]}', '@High{0,0.0[0000]}', '@Low{0,0.0[0000]}', '@Close{0,0.0[0000]}'))), ('Volume', '@Volume{0,0}')] def new_indicator_figure(**kwargs): kwargs.setdefault('height', _INDICATOR_HEIGHT) fig = new_bokeh_figure(x_range=fig_ohlc.x_range, active_scroll='xwheel_zoom', active_drag='xpan', **kwargs) fig.xaxis.visible = False fig.yaxis.minor_tick_line_color = None fig.yaxis.ticker.desired_num_ticks = 3 return fig def set_tooltips(fig, tooltips=(), vline=True, renderers=()): tooltips = list(tooltips) renderers = list(renderers) if is_datetime_index: formatters = {'@datetime': 'datetime'} tooltips = [("Date", "@datetime{%c}")] + tooltips else: formatters = {} tooltips = [("#", "@index")] + tooltips fig.add_tools(HoverTool( point_policy='follow_mouse', renderers=renderers, formatters=formatters, tooltips=tooltips, mode='vline' if vline else 'mouse')) def _plot_equity_section(is_return=False): """Equity section""" # Max DD Dur. line equity = equity_data['Equity'].copy() dd_end = equity_data['DrawdownDuration'].idxmax() if np.isnan(dd_end): dd_start = dd_end = equity.index[0] else: dd_start = equity[:dd_end].idxmax() # If DD not extending into the future, get exact point of intersection with equity if dd_end != equity.index[-1]: dd_end = np.interp(equity[dd_start], (equity[dd_end - 1], equity[dd_end]), (dd_end - 1, dd_end)) if smooth_equity: interest_points = pd.Index([ # Beginning and end equity.index[0], equity.index[-1], # Peak equity and peak DD equity.idxmax(), equity_data['DrawdownPct'].idxmax(), # Include max dd end points. Otherwise the MaxDD line looks amiss. dd_start, int(dd_end), min(int(dd_end + 1), equity.size - 1), ]) select = pd.Index(trades['ExitBar']).union(interest_points) select = select.unique().dropna() equity = equity.iloc[select].reindex(equity.index) equity.interpolate(inplace=True) assert equity.index.equals(equity_data.index) if relative_equity: equity /= equity.iloc[0] if is_return: equity -= equity.iloc[0] yaxis_label = 'Return' if is_return else 'Equity' source_key = 'eq_return' if is_return else 'equity' source.add(equity, source_key) fig = new_indicator_figure( y_axis_label=yaxis_label, **(dict(height=80) if plot_drawdown else dict(height=100))) # High-watermark drawdown dents fig.patch('index', 'equity_dd', source=ColumnDataSource(dict( index=np.r_[index, index[::-1]], equity_dd=np.r_[equity, equity.cummax()[::-1]] )), fill_color='#ffffea', line_color='#ffcb66') # Equity line r = fig.line('index', source_key, source=source, line_width=1.5, line_alpha=1) if relative_equity: tooltip_format = f'@{source_key}{{+0,0.[000]%}}' tick_format = '0,0.[00]%' legend_format = '{:,.0f}%' else: tooltip_format = f'@{source_key}{{$ 0,0}}' tick_format = '$ 0.0 a' legend_format = '${:,.0f}' set_tooltips(fig, [(yaxis_label, tooltip_format)], renderers=[r]) fig.yaxis.formatter = NumeralTickFormatter(format=tick_format) # Peaks argmax = equity.idxmax() fig.scatter(argmax, equity[argmax], legend_label='Peak ({})'.format( legend_format.format(equity[argmax] * (100 if relative_equity else 1))), color='cyan', size=8) fig.scatter(index[-1], equity.values[-1], legend_label='Final ({})'.format( legend_format.format(equity.iloc[-1] * (100 if relative_equity else 1))), color='blue', size=8) if not plot_drawdown: drawdown = equity_data['DrawdownPct'] argmax = drawdown.idxmax() fig.scatter(argmax, equity[argmax], legend_label='Max Drawdown (-{:.1f}%)'.format(100 * drawdown[argmax]), color='red', size=8) dd_timedelta_label = df['datetime'].iloc[int(round(dd_end))] - df['datetime'].iloc[dd_start] fig.line([dd_start, dd_end], equity.iloc[dd_start], line_color='red', line_width=2, legend_label=f'Max Dd Dur. ({dd_timedelta_label})' .replace(' 00:00:00', '') .replace('(0 days ', '(')) figs_above_ohlc.append(fig) def _plot_drawdown_section(): """Drawdown section""" fig = new_indicator_figure(y_axis_label="Drawdown", height=80) drawdown = equity_data['DrawdownPct'] argmax = drawdown.idxmax() source.add(drawdown, 'drawdown') r = fig.line('index', 'drawdown', source=source, line_width=1.3) fig.scatter(argmax, drawdown[argmax], legend_label='Peak (-{:.1f}%)'.format(100 * drawdown[argmax]), color='red', size=8) set_tooltips(fig, [('Drawdown', '@drawdown{-0.[0]%}')], renderers=[r]) fig.yaxis.formatter = NumeralTickFormatter(format="-0.[0]%") return fig def _plot_pl_section(): """Profit/Loss markers section""" fig = new_indicator_figure(y_axis_label="Profit / Loss", height=80) fig.add_layout(Span(location=0, dimension='width', line_color='#666666', line_dash='dashed', level='underlay', line_width=1)) trade_source.add(trades['ReturnPct'], 'returns') size = trades['Size'].abs() size = np.interp(size, (size.min(), size.max()), (8, 20)) trade_source.add(size, 'marker_size') if 'count' in trades: trade_source.add(trades['count'], 'count') trade_source.add(trades[['EntryBar', 'ExitBar']].values.tolist(), 'lines') fig.multi_line(xs='lines', ys=transform('returns', CustomJSTransform(v_func='return [...xs].map(i => [0, i]);')), source=trade_source, color='#999', line_width=1) trade_source.add(np.take(['inverted_triangle', 'triangle'], trades['Size'] > 0), 'triangles') r1 = fig.scatter( 'index', 'returns', source=trade_source, fill_color=cmap, marker='triangles', line_color='black', size='marker_size') tooltips = [("Size", "@size{0,0}")] if 'count' in trades: tooltips.append(("Count", "@count{0,0}")) set_tooltips(fig, tooltips + [("P/L", "@returns{+0.[000]%}")], vline=False, renderers=[r1]) fig.yaxis.formatter = NumeralTickFormatter(format="0.[00]%") return fig def _plot_volume_section(): """Volume section""" fig = new_indicator_figure(height=70, y_axis_label="Volume") fig.yaxis.ticker.desired_num_ticks = 3 fig.xaxis.formatter = fig_ohlc.xaxis[0].formatter fig.xaxis.visible = True fig_ohlc.xaxis.visible = False # Show only Volume's xaxis r = fig.vbar('index', BAR_WIDTH, 'Volume', source=source, color=inc_cmap) set_tooltips(fig, [('Volume', '@Volume{0.00 a}')], renderers=[r]) fig.yaxis.formatter = NumeralTickFormatter(format="0 a") return fig def _plot_superimposed_ohlc(): """Superimposed, downsampled vbars""" time_resolution = pd.DatetimeIndex(df['datetime']).resolution resample_rule = (superimpose if isinstance(superimpose, str) else dict(day='ME', hour='D', minute='h', second='min', millisecond='s').get(time_resolution)) if not resample_rule: warnings.warn( f"'Can't superimpose OHLC data with rule '{resample_rule}'" f"(index datetime resolution: '{time_resolution}'). Skipping.", stacklevel=4) return df2 = (df.assign(_width=1).set_index('datetime') .resample(resample_rule, label='left') .agg(dict(OHLCV_AGG, _width='count'))) # Check if resampling was downsampling; error on upsampling orig_freq = _data_period(df['datetime']) resample_freq = _data_period(df2.index) if resample_freq < orig_freq: raise ValueError('Invalid value for `superimpose`: Upsampling not supported.') if resample_freq == orig_freq: warnings.warn('Superimposed OHLC plot matches the original plot. Skipping.', stacklevel=4) return df2.index = df2['_width'].cumsum().shift(1).fillna(0) df2.index += df2['_width'] / 2 - .5 df2['_width'] -= .1 # Candles don't touch df2['inc'] = (df2.Close >= df2.Open).astype(int).astype(str) df2.index.name = None source2 = ColumnDataSource(df2) fig_ohlc.segment('index', 'High', 'index', 'Low', source=source2, color='#bbbbbb') colors_lighter = [lightness(BEAR_COLOR, .92), lightness(BULL_COLOR, .92)] fig_ohlc.vbar('index', '_width', 'Open', 'Close', source=source2, line_color=None, fill_color=factor_cmap('inc', colors_lighter, ['0', '1'])) def _plot_ohlc(): """Main OHLC bars""" fig_ohlc.segment('index', 'High', 'index', 'Low', source=source, color="black", legend_label='OHLC') r = fig_ohlc.vbar('index', BAR_WIDTH, 'Open', 'Close', source=source, line_color="black", fill_color=inc_cmap, legend_label='OHLC') return r def _plot_ohlc_trades(): """Trade entry / exit markers on OHLC plot""" trade_source.add(trades[['EntryBar', 'ExitBar']].values.tolist(), 'position_lines_xs') trade_source.add(trades[['EntryPrice', 'ExitPrice']].values.tolist(), 'position_lines_ys') fig_ohlc.multi_line(xs='position_lines_xs', ys='position_lines_ys', source=trade_source, line_color=trades_cmap, legend_label=f'Trades ({len(trades)})', line_width=8, line_alpha=1, line_dash='dotted') def _plot_indicators(): """Strategy indicators""" def _too_many_dims(value): assert value.ndim >= 2 if value.ndim > 2: warnings.warn(f"Can't plot indicators with >2D ('{value.name}')", stacklevel=5) return True return False class LegendStr(str): # The legend string is such a string that only matches # itself if it's the exact same object. This ensures # legend items are listed separately even when they have the # same string contents. Otherwise, Bokeh would always consider # equal strings as one and the same legend item. def __eq__(self, other): return self is other ohlc_colors = colorgen() indicator_figs = [] for i, value in enumerate(indicators): value = np.atleast_2d(value) if _too_many_dims(value): continue # Use .get()! A user might have assigned a Strategy.data-evolved # _Array without Strategy.I() is_overlay = value._opts.get('overlay') is_scatter = value._opts.get('scatter') is_muted = not value._opts.get('plot') # is overlay => show muted, hide legend item. non-overlay => don't show at all if is_muted and not is_overlay: continue if is_overlay: fig = fig_ohlc else: fig = new_indicator_figure() indicator_figs.append(fig) tooltips = [] colors = value._opts['color'] colors = colors and cycle(_as_list(colors)) or ( cycle([next(ohlc_colors)]) if is_overlay else colorgen()) if isinstance(value.name, str): tooltip_label = value.name legend_labels = [LegendStr(value.name)] * len(value) else: tooltip_label = ", ".join(value.name) legend_labels = [LegendStr(item) for item in value.name] for j, arr in enumerate(value): color = next(colors) source_name = f'{legend_labels[j]}_{i}_{j}' if arr.dtype == bool: arr = arr.astype(int) source.add(arr, source_name) tooltips.append(f'@{{{source_name}}}{{0,0.0[0000]}}') kwargs = {} if not is_muted: kwargs['legend_label'] = legend_labels[j] if is_overlay: ohlc_extreme_values[source_name] = arr if is_scatter: r2 = fig.circle( 'index', source_name, source=source, color=color, line_color='black', fill_alpha=.8, radius=BAR_WIDTH / 2 * .9, **kwargs) else: r2 = fig.line( 'index', source_name, source=source, line_color=color, line_width=1.4 if is_muted else 1.5, **kwargs) # r != r2 r2.muted = is_muted else: if is_scatter: r = fig.circle( 'index', source_name, source=source, color=color, radius=BAR_WIDTH / 2 * .6, **kwargs) else: r = fig.line( 'index', source_name, source=source, line_color=color, line_width=1.3, **kwargs) # Add dashed centerline just because mean = try_(lambda: float(pd.Series(arr).mean()), default=np.nan) if not np.isnan(mean) and (abs(mean) < .1 or round(abs(mean), 1) == .5 or round(abs(mean), -1) in (50, 100, 200)): fig.add_layout(Span(location=float(mean), dimension='width', line_color='#666666', line_dash='dashed', level='underlay', line_width=.5)) if is_overlay: ohlc_tooltips.append((tooltip_label, NBSP.join(tooltips))) else: set_tooltips(fig, [(tooltip_label, NBSP.join(tooltips))], vline=True, renderers=[r]) # If the sole indicator line on this figure, # have the legend only contain text without the glyph if len(value) == 1: fig.legend.glyph_width = 0 return indicator_figs # Construct figure ... if plot_equity: _plot_equity_section() if plot_return: _plot_equity_section(is_return=True) if plot_drawdown: figs_above_ohlc.append(_plot_drawdown_section()) if plot_pl: figs_above_ohlc.append(_plot_pl_section()) if plot_volume: fig_volume = _plot_volume_section() figs_below_ohlc.append(fig_volume) if superimpose and is_datetime_index: _plot_superimposed_ohlc() ohlc_bars = _plot_ohlc() if plot_trades: _plot_ohlc_trades() indicator_figs = _plot_indicators() if reverse_indicators: indicator_figs = indicator_figs[::-1] figs_below_ohlc.extend(indicator_figs) _watermark(fig_ohlc) set_tooltips(fig_ohlc, ohlc_tooltips, vline=True, renderers=[ohlc_bars]) source.add(ohlc_extreme_values.min(1), 'ohlc_low') source.add(ohlc_extreme_values.max(1), 'ohlc_high') custom_js_args = dict(ohlc_range=fig_ohlc.y_range, source=source) if plot_volume: custom_js_args.update(volume_range=fig_volume.y_range) fig_ohlc.x_range.js_on_change('end', CustomJS(args=custom_js_args, code=_AUTOSCALE_JS_CALLBACK)) figs = figs_above_ohlc + [fig_ohlc] + figs_below_ohlc linked_crosshair = CrosshairTool( dimensions='both', line_color='lightgrey', overlay=(Span(dimension="width", line_dash="dotted", line_width=1), Span(dimension="height", line_dash="dotted", line_width=1)), ) for f in figs: if f.legend: f.legend.visible = show_legend f.legend.location = 'top_left' f.legend.border_line_width = 1 f.legend.border_line_color = '#333333' f.legend.padding = 5 f.legend.spacing = 0 f.legend.margin = 0 f.legend.label_text_font_size = '8pt' f.legend.click_policy = "hide" f.legend.background_fill_alpha = .9 f.min_border_left = 0 f.min_border_top = 3 f.min_border_bottom = 6 f.min_border_right = 10 f.outline_line_color = '#666666' f.add_tools(linked_crosshair) wheelzoom_tool = next(wz for wz in f.tools if isinstance(wz, WheelZoomTool)) wheelzoom_tool.maintain_focus = False kwargs = {} if plot_width is None: kwargs['sizing_mode'] = 'stretch_width' fig = gridplot( figs, ncols=1, toolbar_location='right', toolbar_options=dict(logo=None), merge_tools=True, **kwargs # type: ignore ) show(fig, browser=None if open_browser else 'none') return fig def plot_heatmaps(heatmap: pd.Series, agg: Union[Callable, str], ncols: int, filename: str = '', plot_width: int = 1200, open_browser: bool = True): if not (isinstance(heatmap, pd.Series) and isinstance(heatmap.index, pd.MultiIndex)): raise ValueError('heatmap must be heatmap Series as returned by ' '`Backtest.optimize(..., return_heatmap=True)`') if len(heatmap.index.levels) < 2: raise ValueError('`plot_heatmap()` requires at least two optimization ' 'variables to plot') _bokeh_reset(filename) param_combinations = combinations(heatmap.index.names, 2) dfs = [heatmap.groupby(list(dims)).agg(agg).to_frame(name='_Value') for dims in param_combinations] figs: list[_figure] = [] cmap = LinearColorMapper(palette='Viridis256', low=min(df.min().min() for df in dfs), high=max(df.max().max() for df in dfs), nan_color='white') for df in dfs: name1, name2 = df.index.names level1 = df.index.levels[0].astype(str).tolist() level2 = df.index.levels[1].astype(str).tolist() df = df.reset_index() df[name1] = df[name1].astype('str') df[name2] = df[name2].astype('str') fig = _figure(x_range=level1, # type: ignore[call-arg] y_range=level2, x_axis_label=name1, y_axis_label=name2, width=plot_width // ncols, height=plot_width // ncols, tools='box_zoom,reset,save', tooltips=[(name1, '@' + name1), (name2, '@' + name2), ('Value', '@_Value{0.[000]}')]) fig.grid.grid_line_color = None # type: ignore[attr-defined] fig.axis.axis_line_color = None # type: ignore[attr-defined] fig.axis.major_tick_line_color = None # type: ignore[attr-defined] fig.axis.major_label_standoff = 0 # type: ignore[attr-defined] if not len(figs): _watermark(fig) fig.rect(x=name1, y=name2, width=1, height=1, source=df, line_color=None, fill_color=dict(field='_Value', transform=cmap)) figs.append(fig) fig = gridplot( figs, # type: ignore ncols=ncols, toolbar_options=dict(logo=None), toolbar_location='above', merge_tools=True, ) show(fig, browser=None if open_browser else 'none') return fig ================================================ FILE: backtesting/_stats.py ================================================ from __future__ import annotations from typing import TYPE_CHECKING, List, Union, cast import numpy as np import pandas as pd from ._util import _data_period, _indicator_warmup_nbars if TYPE_CHECKING: from .backtesting import Strategy, Trade def compute_drawdown_duration_peaks(dd: pd.Series): iloc = np.unique(np.r_[(dd == 0).values.nonzero()[0], len(dd) - 1]) iloc = pd.Series(iloc, index=dd.index[iloc]) df = iloc.to_frame('iloc').assign(prev=iloc.shift()) df = df[df['iloc'] > df['prev'] + 1].astype(np.int64) # If no drawdown since no trade, avoid below for pandas sake and return nan series if not len(df): return (dd.replace(0, np.nan),) * 2 df['duration'] = df['iloc'].map(dd.index.__getitem__) - df['prev'].map(dd.index.__getitem__) df['peak_dd'] = df.apply(lambda row: dd.iloc[row['prev']:row['iloc'] + 1].max(), axis=1) df = df.reindex(dd.index) return df['duration'], df['peak_dd'] def geometric_mean(returns: pd.Series) -> float: returns = returns.fillna(0) + 1 if np.any(returns <= 0): return 0 return np.exp(np.log(returns).sum() / (len(returns) or np.nan)) - 1 def compute_stats( trades: Union[List['Trade'], pd.DataFrame], equity: np.ndarray, ohlc_data: pd.DataFrame, strategy_instance: Strategy | None, risk_free_rate: float = 0, ) -> pd.Series: assert -1 < risk_free_rate < 1 index = ohlc_data.index dd = 1 - equity / np.maximum.accumulate(equity) dd_dur, dd_peaks = compute_drawdown_duration_peaks(pd.Series(dd, index=index)) equity_df = pd.DataFrame({ 'Equity': equity, 'DrawdownPct': dd, 'DrawdownDuration': dd_dur}, index=index) if isinstance(trades, pd.DataFrame): trades_df: pd.DataFrame = trades commissions = None # Not shown else: # Came straight from Backtest.run() trades_df = pd.DataFrame({ 'Size': [t.size for t in trades], 'EntryBar': [t.entry_bar for t in trades], 'ExitBar': [t.exit_bar for t in trades], 'EntryPrice': [t.entry_price for t in trades], 'ExitPrice': [t.exit_price for t in trades], 'SL': [t.sl for t in trades], 'TP': [t.tp for t in trades], 'PnL': [t.pl for t in trades], 'Commission': [t._commissions for t in trades], 'ReturnPct': [t.pl_pct for t in trades], 'EntryTime': [t.entry_time for t in trades], 'ExitTime': [t.exit_time for t in trades], }) trades_df['Duration'] = trades_df['ExitTime'] - trades_df['EntryTime'] trades_df['Tag'] = [t.tag for t in trades] # Add indicator values if len(trades_df) and strategy_instance: for ind in strategy_instance._indicators: ind = np.atleast_2d(ind) for i, values in enumerate(ind): # multi-d indicators suffix = f'_{i}' if len(ind) > 1 else '' trades_df[f'Entry_{ind.name}{suffix}'] = values[trades_df['EntryBar'].values] trades_df[f'Exit_{ind.name}{suffix}'] = values[trades_df['ExitBar'].values] commissions = sum(t._commissions for t in trades) del trades pl = trades_df['PnL'] returns = trades_df['ReturnPct'] durations = trades_df['Duration'] def _round_timedelta(value, _period=_data_period(index)): if not isinstance(value, pd.Timedelta): return value resolution = getattr(_period, 'resolution_string', None) or _period.resolution return value.ceil(resolution) s = pd.Series(dtype=object) s.loc['Start'] = index[0] s.loc['End'] = index[-1] s.loc['Duration'] = s.End - s.Start have_position = np.repeat(0, len(index)) for t in trades_df[['EntryBar', 'ExitBar']].itertuples(index=False): have_position[t.EntryBar:t.ExitBar + 1] = 1 s.loc['Exposure Time [%]'] = have_position.mean() * 100 # In "n bars" time, not index time s.loc['Equity Final [$]'] = equity[-1] s.loc['Equity Peak [$]'] = equity.max() if commissions: s.loc['Commissions [$]'] = commissions s.loc['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100 first_trading_bar = _indicator_warmup_nbars(strategy_instance) c = ohlc_data.Close.values s.loc['Buy & Hold Return [%]'] = (c[-1] - c[first_trading_bar]) / c[first_trading_bar] * 100 # long-only return gmean_day_return: float = 0 day_returns = np.array(np.nan) annual_trading_days = np.nan is_datetime_index = isinstance(index, pd.DatetimeIndex) if is_datetime_index: freq_days = cast(pd.Timedelta, _data_period(index)).days have_weekends = index.dayofweek.to_series().between(5, 6).mean() > 2 / 7 * .6 annual_trading_days = ( 52 if freq_days == 7 else 12 if freq_days == 31 else 1 if freq_days == 365 else (365 if have_weekends else 252)) freq = {7: 'W', 31: 'ME', 365: 'YE'}.get(freq_days, 'D') day_returns = equity_df['Equity'].resample(freq).last().dropna().pct_change() gmean_day_return = geometric_mean(day_returns) # Annualized return and risk metrics are computed based on the (mostly correct) # assumption that the returns are compounded. See: https://dx.doi.org/10.2139/ssrn.3054517 # Our annualized return matches `empyrical.annual_return(day_returns)` whereas # our risk doesn't; they use the simpler approach below. annualized_return = (1 + gmean_day_return)**annual_trading_days - 1 s.loc['Return (Ann.) [%]'] = annualized_return * 100 s.loc['Volatility (Ann.) [%]'] = np.sqrt((day_returns.var(ddof=int(bool(day_returns.shape))) + (1 + gmean_day_return)**2)**annual_trading_days - (1 + gmean_day_return)**(2 * annual_trading_days)) * 100 # noqa: E501 # s.loc['Return (Ann.) [%]'] = gmean_day_return * annual_trading_days * 100 # s.loc['Risk (Ann.) [%]'] = day_returns.std(ddof=1) * np.sqrt(annual_trading_days) * 100 if is_datetime_index: time_in_years = (s.loc['Duration'].days + s.loc['Duration'].seconds / 86400) / annual_trading_days s.loc['CAGR [%]'] = ((s.loc['Equity Final [$]'] / equity[0])**(1 / time_in_years) - 1) * 100 if time_in_years else np.nan # noqa: E501 # Our Sharpe mismatches `empyrical.sharpe_ratio()` because they use arithmetic mean return # and simple standard deviation s.loc['Sharpe Ratio'] = (s.loc['Return (Ann.) [%]'] - risk_free_rate * 100) / (s.loc['Volatility (Ann.) [%]'] or np.nan) # noqa: E501 # Our Sortino mismatches `empyrical.sortino_ratio()` because they use arithmetic mean return with np.errstate(divide='ignore'): s.loc['Sortino Ratio'] = (annualized_return - risk_free_rate) / (np.sqrt(np.mean(day_returns.clip(-np.inf, 0)**2)) * np.sqrt(annual_trading_days)) # noqa: E501 max_dd = -np.nan_to_num(dd.max()) s.loc['Calmar Ratio'] = annualized_return / (-max_dd or np.nan) equity_log_returns = np.log(equity[1:] / equity[:-1]) market_log_returns = np.log(c[1:] / c[:-1]) beta = np.nan if len(equity_log_returns) > 1 and len(market_log_returns) > 1: # len == 0 on dummy call `stats_keys = compute_stats(...)` pre optimization cov_matrix = np.cov(equity_log_returns, market_log_returns) beta = cov_matrix[0, 1] / cov_matrix[1, 1] # Jensen CAPM Alpha: can be strongly positive when beta is negative and B&H Return is large s.loc['Alpha [%]'] = s.loc['Return [%]'] - risk_free_rate * 100 - beta * (s.loc['Buy & Hold Return [%]'] - risk_free_rate * 100) # noqa: E501 s.loc['Beta'] = beta s.loc['Max. Drawdown [%]'] = max_dd * 100 s.loc['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100 s.loc['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max()) s.loc['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean()) s.loc['# Trades'] = n_trades = len(trades_df) win_rate = np.nan if not n_trades else (pl > 0).mean() s.loc['Win Rate [%]'] = win_rate * 100 s.loc['Best Trade [%]'] = returns.max() * 100 s.loc['Worst Trade [%]'] = returns.min() * 100 mean_return = geometric_mean(returns) s.loc['Avg. Trade [%]'] = mean_return * 100 s.loc['Max. Trade Duration'] = _round_timedelta(durations.max()) s.loc['Avg. Trade Duration'] = _round_timedelta(durations.mean()) s.loc['Profit Factor'] = returns[returns > 0].sum() / (abs(returns[returns < 0].sum()) or np.nan) # noqa: E501 s.loc['Expectancy [%]'] = returns.mean() * 100 s.loc['SQN'] = np.sqrt(n_trades) * pl.mean() / (pl.std() or np.nan) s.loc['Kelly Criterion'] = win_rate - (1 - win_rate) / (pl[pl > 0].mean() / -pl[pl < 0].mean()) s.loc['_strategy'] = strategy_instance s.loc['_equity_curve'] = equity_df s.loc['_trades'] = trades_df s = _Stats(s) return s class _Stats(pd.Series): def __repr__(self): with pd.option_context( 'display.max_colwidth', 20, # Prevent expansion due to _equity and _trades dfs 'display.max_rows', len(self), # Reveal self whole 'display.precision', 5, # Enough for my eyes at least # 'format.na_rep', '--', # TODO: Enable once it works ): return super().__repr__() def dummy_stats(): from .backtesting import Trade, _Broker index = pd.DatetimeIndex(['2025']) data = pd.DataFrame({col: [np.nan] for col in ('Close',)}, index=index) trade = Trade(_Broker(data=data, cash=10000, spread=.01, commission=.01, margin=.1, trade_on_close=True, hedging=True, exclusive_orders=False, index=index), 1, 1, 0, None) trade._replace(exit_price=1, exit_bar=0) trade._commissions = np.nan return compute_stats([trade], np.r_[[np.nan]], data, None, 0) ================================================ FILE: backtesting/_util.py ================================================ from __future__ import annotations import os import sys import warnings from contextlib import contextmanager from functools import partial from itertools import chain from multiprocessing import resource_tracker as _mprt from multiprocessing import shared_memory as _mpshm from numbers import Number from threading import Lock from typing import Dict, List, Optional, Sequence, Union, cast import numpy as np import pandas as pd try: from tqdm.auto import tqdm as _tqdm _tqdm = partial(_tqdm, leave=False) except ImportError: def _tqdm(seq, **_): return seq def try_(lazy_func, default=None, exception=Exception): try: return lazy_func() except exception: return default @contextmanager def patch(obj, attr, newvalue): had_attr = hasattr(obj, attr) orig_value = getattr(obj, attr, None) setattr(obj, attr, newvalue) try: yield finally: if had_attr: setattr(obj, attr, orig_value) else: delattr(obj, attr) def _as_str(value) -> str: if isinstance(value, (Number, str)): return str(value) if isinstance(value, pd.DataFrame): return 'df' name = str(getattr(value, 'name', '') or '') if name in ('Open', 'High', 'Low', 'Close', 'Volume'): return name[:1] if callable(value): name = getattr(value, '__name__', value.__class__.__name__).replace('', 'λ') if len(name) > 10: name = name[:9] + '…' return name def _as_list(value) -> List: if isinstance(value, Sequence) and not isinstance(value, str): return list(value) return [value] def _batch(seq): # XXX: Replace with itertools.batched n = np.clip(int(len(seq) // (os.cpu_count() or 1)), 1, 300) for i in range(0, len(seq), n): yield seq[i:i + n] def _data_period(index) -> Union[pd.Timedelta, Number]: """Return data index period as pd.Timedelta""" values = pd.Series(index[-100:]) return values.diff().dropna().median() def _strategy_indicators(strategy): return {attr: indicator for attr, indicator in strategy.__dict__.items() if isinstance(indicator, _Indicator)}.items() def _indicator_warmup_nbars(strategy): if strategy is None: return 0 nbars = max((np.isnan(indicator.astype(float)).argmin(axis=-1).max() for _, indicator in _strategy_indicators(strategy) if not indicator._opts['scatter']), default=0) return nbars class _Array(np.ndarray): """ ndarray extended to supply .name and other arbitrary properties in ._opts dict. """ def __new__(cls, array, *, name=None, **kwargs): obj = np.asarray(array).view(cls) obj.name = name or array.name obj._opts = kwargs return obj def __array_finalize__(self, obj): if obj is not None: self.name = getattr(obj, 'name', '') self._opts = getattr(obj, '_opts', {}) # Make sure properties name and _opts are carried over # when (un-)pickling. def __reduce__(self): value = super().__reduce__() return value[:2] + (value[2] + (self.__dict__,),) def __setstate__(self, state): self.__dict__.update(state[-1]) super().__setstate__(state[:-1]) def __bool__(self): try: return bool(self[-1]) except IndexError: return super().__bool__() def __float__(self): try: return float(self[-1]) except IndexError: return super().__float__() def to_series(self): warnings.warn("`.to_series()` is deprecated. For pd.Series conversion, use accessor `.s`") return self.s @property def s(self) -> pd.Series: values = np.atleast_2d(self) index = self._opts['index'][:values.shape[1]] return pd.Series(values[0], index=index, name=self.name) @property def df(self) -> pd.DataFrame: values = np.atleast_2d(np.asarray(self)) index = self._opts['index'][:values.shape[1]] df = pd.DataFrame(values.T, index=index, columns=[self.name] * len(values)) return df class _Indicator(_Array): pass class _Data: """ A data array accessor. Provides access to OHLCV "columns" as a standard `pd.DataFrame` would, except it's not a DataFrame and the returned "series" are _not_ `pd.Series` but `np.ndarray` for performance reasons. """ def __init__(self, df: pd.DataFrame): self.__df = df self.__len = len(df) # Current length self.__pip: Optional[float] = None self.__cache: Dict[str, _Array] = {} self.__arrays: Dict[str, _Array] = {} self._update() def __getitem__(self, item): return self.__get_array(item) def __getattr__(self, item): try: return self.__get_array(item) except KeyError: raise AttributeError(f"Column '{item}' not in data") from None def _set_length(self, length): self.__len = length self.__cache.clear() def _update(self): index = self.__df.index.copy() self.__arrays = {col: _Array(arr, index=index) for col, arr in self.__df.items()} # Leave index as Series because pd.Timestamp nicer API to work with self.__arrays['__index'] = index def __repr__(self): i = min(self.__len, len(self.__df)) - 1 index = self.__arrays['__index'][i] items = ', '.join(f'{k}={v}' for k, v in self.__df.iloc[i].items()) return f'' def __len__(self): return self.__len @property def df(self) -> pd.DataFrame: return (self.__df.iloc[:self.__len] if self.__len < len(self.__df) else self.__df) @property def pip(self) -> float: if self.__pip is None: self.__pip = float(10**-np.median([len(s.partition('.')[-1]) for s in self.__arrays['Close'].astype(str)])) return self.__pip def __get_array(self, key) -> _Array: arr = self.__cache.get(key) if arr is None: arr = self.__cache[key] = cast(_Array, self.__arrays[key][:self.__len]) return arr @property def Open(self) -> _Array: return self.__get_array('Open') @property def High(self) -> _Array: return self.__get_array('High') @property def Low(self) -> _Array: return self.__get_array('Low') @property def Close(self) -> _Array: return self.__get_array('Close') @property def Volume(self) -> _Array: return self.__get_array('Volume') @property def index(self) -> pd.DatetimeIndex: return self.__get_array('__index') # Make pickling in Backtest.optimize() work with our catch-all __getattr__ def __getstate__(self): return self.__dict__ def __setstate__(self, state): self.__dict__ = state if sys.version_info >= (3, 13): SharedMemory = _mpshm.SharedMemory else: class SharedMemory(_mpshm.SharedMemory): # From https://github.com/python/cpython/issues/82300#issuecomment-2169035092 __lock = Lock() def __init__(self, *args, track: bool = True, **kwargs): self._track = track if track: return super().__init__(*args, **kwargs) with self.__lock: with patch(_mprt, 'register', lambda *a, **kw: None): super().__init__(*args, **kwargs) def unlink(self): if _mpshm._USE_POSIX and self._name: _mpshm._posixshmem.shm_unlink(self._name) if self._track: _mprt.unregister(self._name, "shared_memory") class SharedMemoryManager: """ A simple shared memory contextmanager based on https://docs.python.org/3/library/multiprocessing.shared_memory.html#multiprocessing.shared_memory.SharedMemory """ def __init__(self, create=False) -> None: self._shms: list[SharedMemory] = [] self.__create = create def SharedMemory(self, *, name=None, create=False, size=0, track=True): shm = SharedMemory(name=name, create=create, size=size, track=track) shm._create = create # Essential to keep refs on Windows # https://stackoverflow.com/questions/74193377/filenotfounderror-when-passing-a-shared-memory-to-a-new-process#comment130999060_74194875 # noqa: E501 self._shms.append(shm) return shm def __enter__(self): return self def __exit__(self, *args, **kwargs): for shm in self._shms: try: shm.close() if shm._create: shm.unlink() except Exception: warnings.warn(f'Failed to unlink shared memory {shm.name!r}', category=ResourceWarning, stacklevel=2) raise def arr2shm(self, vals): """Array to shared memory. Returns (shm_name, shape, dtype) used for restore.""" assert vals.ndim == 1, (vals.ndim, vals.shape, vals) shm = self.SharedMemory(size=vals.nbytes, create=True) # np.array can't handle pandas' tz-aware datetimes # https://github.com/numpy/numpy/issues/18279 buf = np.ndarray(vals.shape, dtype=vals.dtype.base, buffer=shm.buf) has_tz = getattr(vals.dtype, 'tz', None) buf[:] = vals.tz_localize(None) if has_tz else vals # Copy into shared memory return shm.name, vals.shape, vals.dtype def df2shm(self, df): return tuple(( (column, *self.arr2shm(values)) for column, values in chain([(self._DF_INDEX_COL, df.index)], df.items()) )) @staticmethod def shm2s(shm, shape, dtype) -> pd.Series: arr = np.ndarray(shape, dtype=dtype.base, buffer=shm.buf) arr.setflags(write=False) return pd.Series(arr, dtype=dtype) _DF_INDEX_COL = '__bt_index' @staticmethod def shm2df(data_shm): shm = [SharedMemory(name=name, create=False, track=False) for _, name, _, _ in data_shm] df = pd.DataFrame({ col: SharedMemoryManager.shm2s(shm, shape, dtype) for shm, (col, _, shape, dtype) in zip(shm, data_shm)}) df.set_index(SharedMemoryManager._DF_INDEX_COL, drop=True, inplace=True) df.index.name = None return df, shm ================================================ FILE: backtesting/autoscale_cb.js ================================================ if (!window._bt_scale_range) { window._bt_scale_range = function (range, min, max, pad) { "use strict"; if (min !== Infinity && max !== -Infinity) { pad = pad ? (max - min) * .03 : 0; range.start = min - pad; range.end = max + pad; } else console.error('backtesting: scale range error:', min, max, range); }; } clearTimeout(window._bt_autoscale_timeout); window._bt_autoscale_timeout = setTimeout(function () { /** * @variable cb_obj `fig_ohlc.x_range`. * @variable source `ColumnDataSource` * @variable ohlc_range `fig_ohlc.y_range`. * @variable volume_range `fig_volume.y_range`. */ "use strict"; let i = Math.max(Math.floor(cb_obj.start), 0), j = Math.min(Math.ceil(cb_obj.end), source.data['ohlc_high'].length); let max = Math.max.apply(null, source.data['ohlc_high'].slice(i, j)), min = Math.min.apply(null, source.data['ohlc_low'].slice(i, j)); _bt_scale_range(ohlc_range, min, max, true); if (volume_range) { max = Math.max.apply(null, source.data['Volume'].slice(i, j)); _bt_scale_range(volume_range, 0, max * 1.03, false); } }, 50); ================================================ FILE: backtesting/backtesting.py ================================================ """ Core framework data structures. Objects from this module can also be imported from the top-level module directly, e.g. from backtesting import Backtest, Strategy """ from __future__ import annotations import sys import warnings from abc import ABCMeta, abstractmethod from copy import copy from difflib import get_close_matches from functools import lru_cache, partial from itertools import chain, product, repeat from math import copysign from numbers import Number from typing import Callable, List, Optional, Sequence, Tuple, Type, Union import numpy as np import pandas as pd from numpy.random import default_rng from ._plotting import plot # noqa: I001 from ._stats import compute_stats, dummy_stats from ._util import ( SharedMemoryManager, _as_str, _Indicator, _Data, _batch, _indicator_warmup_nbars, _strategy_indicators, patch, try_, _tqdm, ) __pdoc__ = { 'Strategy.__init__': False, 'Order.__init__': False, 'Position.__init__': False, 'Trade.__init__': False, } class Strategy(metaclass=ABCMeta): """ A trading strategy base class. Extend this class and override methods `backtesting.backtesting.Strategy.init` and `backtesting.backtesting.Strategy.next` to define your own strategy. """ def __init__(self, broker, data, params): self._indicators = [] self._broker: _Broker = broker self._data: _Data = data self._params = self._check_params(params) def __repr__(self): return '' def __str__(self): params = ','.join(f'{i[0]}={i[1]}' for i in zip(self._params.keys(), map(_as_str, self._params.values()))) if params: params = '(' + params + ')' return f'{self.__class__.__name__}{params}' def _check_params(self, params): for k, v in params.items(): if not hasattr(self, k): suggestions = get_close_matches(k, (attr for attr in dir(self) if not attr.startswith('_'))) hint = f" Did you mean: {', '.join(suggestions)}?" if suggestions else "" raise AttributeError( f"Strategy '{self.__class__.__name__}' is missing parameter '{k}'. " "Strategy class should define parameters as class variables before they " "can be optimized or run with." + hint) setattr(self, k, v) return params def I(self, # noqa: E743 func: Callable, *args, name=None, plot=True, overlay=None, color=None, scatter=False, **kwargs) -> np.ndarray: """ Declare an indicator. An indicator is just an array of values (or a tuple of such arrays in case of, e.g., MACD indicator), but one that is revealed gradually in `backtesting.backtesting.Strategy.next` much like `backtesting.backtesting.Strategy.data` is. Returns `np.ndarray` of indicator values. `func` is a function that returns the indicator array(s) of same length as `backtesting.backtesting.Strategy.data`. In the plot legend, the indicator is labeled with function name, unless `name` overrides it. If `func` returns a tuple of arrays, `name` can be a sequence of strings, and its size must agree with the number of arrays returned. If `plot` is `True`, the indicator is plotted on the resulting `backtesting.backtesting.Backtest.plot`. If `overlay` is `True`, the indicator is plotted overlaying the price candlestick chart (suitable e.g. for moving averages). If `False`, the indicator is plotted standalone below the candlestick chart. By default, a heuristic is used which decides correctly most of the time. `color` can be string hex RGB triplet or X11 color name. By default, the next available color is assigned. If `scatter` is `True`, the plotted indicator marker will be a circle instead of a connected line segment (default). Additional `*args` and `**kwargs` are passed to `func` and can be used for parameters. For example, using simple moving average function from TA-Lib: def init(): self.sma = self.I(ta.SMA, self.data.Close, self.n_sma) .. warning:: Rolling indicators may front-pad warm-up values with NaNs. In this case, the **backtest will only begin on the first bar when all declared indicators have non-NaN values** (e.g. bar 201 for a strategy that uses a 200-bar MA). This can affect results. """ def _format_name(name: str) -> str: return name.format(*map(_as_str, args), **dict(zip(kwargs.keys(), map(_as_str, kwargs.values())))) if name is None: params = ','.join(filter(None, map(_as_str, chain(args, kwargs.values())))) func_name = _as_str(func) name = (f'{func_name}({params})' if params else f'{func_name}') elif isinstance(name, str): name = _format_name(name) elif try_(lambda: all(isinstance(item, str) for item in name), False): name = [_format_name(item) for item in name] else: raise TypeError(f'Unexpected `name=` type {type(name)}; expected `str` or ' '`Sequence[str]`') try: value = func(*args, **kwargs) except Exception as e: raise RuntimeError(f'Indicator "{name}" error. See traceback above.') from e if isinstance(value, pd.DataFrame): value = value.values.T if value is not None: value = try_(lambda: np.asarray(value, order='C'), None) is_arraylike = bool(value is not None and value.shape) # Optionally flip the array if the user returned e.g. `df.values` if is_arraylike and np.argmax(value.shape) == 0: value = value.T if isinstance(name, list) and (np.atleast_2d(value).shape[0] != len(name)): raise ValueError( f'Length of `name=` ({len(name)}) must agree with the number ' f'of arrays the indicator returns ({value.shape[0]}).') if not is_arraylike or not 1 <= value.ndim <= 2 or value.shape[-1] != len(self._data.Close): raise ValueError( 'Indicators must return (optionally a tuple of) numpy.arrays of same ' f'length as `data` (data shape: {self._data.Close.shape}; indicator "{name}" ' f'shape: {getattr(value, "shape", "")}, returned value: {value})') if overlay is None and np.issubdtype(value.dtype, np.number): x = value / self._data.Close # By default, overlay if strong majority of indicator values # is within 30% of Close with np.errstate(invalid='ignore'): overlay = ((x < 1.4) & (x > .6)).mean() > .6 value = _Indicator(value, name=name, plot=plot, overlay=overlay, color=color, scatter=scatter, # _Indicator.s Series accessor uses this: index=self.data.index) self._indicators.append(value) return value @abstractmethod def init(self): """ Initialize the strategy. Override this method. Declare indicators (with `backtesting.backtesting.Strategy.I`). Precompute what needs to be precomputed or can be precomputed in a vectorized fashion before the strategy starts. If you extend composable strategies from `backtesting.lib`, make sure to call: super().init() """ @abstractmethod def next(self): """ Main strategy runtime method, called as each new `backtesting.backtesting.Strategy.data` instance (row; full candlestick bar) becomes available. This is the main method where strategy decisions upon data precomputed in `backtesting.backtesting.Strategy.init` take place. If you extend composable strategies from `backtesting.lib`, make sure to call: super().next() """ class __FULL_EQUITY(float): # noqa: N801 def __repr__(self): return '.9999' # noqa: E704 _FULL_EQUITY = __FULL_EQUITY(1 - sys.float_info.epsilon) def buy(self, *, size: float = _FULL_EQUITY, limit: Optional[float] = None, stop: Optional[float] = None, sl: Optional[float] = None, tp: Optional[float] = None, tag: object = None) -> 'Order': """ Place a new long order and return it. For explanation of parameters, see `Order` and its properties. Unless you're running `Backtest(..., trade_on_close=True)`, market orders are filled on next bar's open, whereas other order types (limit, stop-limit, stop-market) are filled when the respective conditions are met. See `Position.close()` and `Trade.close()` for closing existing positions. See also `Strategy.sell()`. """ assert 0 < size < 1 or round(size) == size >= 1, \ "size must be a positive fraction of equity, or a positive whole number of units" return self._broker.new_order(size, limit, stop, sl, tp, tag) def sell(self, *, size: float = _FULL_EQUITY, limit: Optional[float] = None, stop: Optional[float] = None, sl: Optional[float] = None, tp: Optional[float] = None, tag: object = None) -> 'Order': """ Place a new short order and return it. For explanation of parameters, see `Order` and its properties. .. caution:: Keep in mind that `self.sell(size=.1)` doesn't close existing `self.buy(size=.1)` trade unless: * the backtest was run with `exclusive_orders=True`, * the underlying asset price is equal in both cases and the backtest was run with `spread = commission = 0`. Use `Trade.close()` or `Position.close()` to explicitly exit trades. See also `Strategy.buy()`. .. note:: If you merely want to close an existing long position, use `Position.close()` or `Trade.close()`. """ assert 0 < size < 1 or round(size) == size >= 1, \ "size must be a positive fraction of equity, or a positive whole number of units" return self._broker.new_order(-size, limit, stop, sl, tp, tag) @property def equity(self) -> float: """Current account equity (cash plus assets).""" return self._broker.equity @property def data(self) -> _Data: """ Price data, roughly as passed into `backtesting.backtesting.Backtest.__init__`, but with two significant exceptions: * `data` is _not_ a DataFrame, but a custom structure that serves customized numpy arrays for reasons of performance and convenience. Besides OHLCV columns, `.index` and length, it offers `.pip` property, the smallest price unit of change. * Within `backtesting.backtesting.Strategy.init`, `data` arrays are available in full length, as passed into `backtesting.backtesting.Backtest.__init__` (for precomputing indicators and such). However, within `backtesting.backtesting.Strategy.next`, `data` arrays are only as long as the current iteration, simulating gradual price point revelation. In each call of `backtesting.backtesting.Strategy.next` (iteratively called by `backtesting.backtesting.Backtest` internally), the last array value (e.g. `data.Close[-1]`) is always the _most recent_ value. * If you need data arrays (e.g. `data.Close`) to be indexed **Pandas series**, you can call their `.s` accessor (e.g. `data.Close.s`). If you need the whole of data as a **DataFrame**, use `.df` accessor (i.e. `data.df`). """ return self._data @property def position(self) -> 'Position': """Instance of `backtesting.backtesting.Position`.""" return self._broker.position @property def orders(self) -> 'Tuple[Order, ...]': """List of orders (see `Order`) waiting for execution.""" return tuple(self._broker.orders) @property def trades(self) -> 'Tuple[Trade, ...]': """List of active trades (see `Trade`).""" return tuple(self._broker.trades) @property def closed_trades(self) -> 'Tuple[Trade, ...]': """List of settled trades (see `Trade`).""" return tuple(self._broker.closed_trades) class Position: """ Currently held asset position, available as `backtesting.backtesting.Strategy.position` within `backtesting.backtesting.Strategy.next`. Can be used in boolean contexts, e.g. if self.position: ... # we have a position, either long or short """ def __init__(self, broker: '_Broker'): self.__broker = broker def __bool__(self): return self.size != 0 @property def size(self) -> float: """Position size in units of asset. Negative if position is short.""" return sum(trade.size for trade in self.__broker.trades) @property def pl(self) -> float: """Profit (positive) or loss (negative) of the current position in cash units.""" return sum(trade.pl for trade in self.__broker.trades) @property def pl_pct(self) -> float: """Profit (positive) or loss (negative) of the current position in percent.""" total_invested = sum(trade.entry_price * abs(trade.size) for trade in self.__broker.trades) return (self.pl / total_invested) * 100 if total_invested else 0 @property def is_long(self) -> bool: """True if the position is long (position size is positive).""" return self.size > 0 @property def is_short(self) -> bool: """True if the position is short (position size is negative).""" return self.size < 0 def close(self, portion: float = 1.): """ Close portion of position by closing `portion` of each active trade. See `Trade.close`. """ for trade in self.__broker.trades: trade.close(portion) def __repr__(self): return f'' class _OutOfMoneyError(Exception): pass class Order: """ Place new orders through `Strategy.buy()` and `Strategy.sell()`. Query existing orders through `Strategy.orders`. When an order is executed or [filled], it results in a `Trade`. If you wish to modify aspects of a placed but not yet filled order, cancel it and place a new one instead. All placed orders are [Good 'Til Canceled]. [filled]: https://www.investopedia.com/terms/f/fill.asp [Good 'Til Canceled]: https://www.investopedia.com/terms/g/gtc.asp """ def __init__(self, broker: '_Broker', size: float, limit_price: Optional[float] = None, stop_price: Optional[float] = None, sl_price: Optional[float] = None, tp_price: Optional[float] = None, parent_trade: Optional['Trade'] = None, tag: object = None): self.__broker = broker assert size != 0 self.__size = size self.__limit_price = limit_price self.__stop_price = stop_price self.__sl_price = sl_price self.__tp_price = tp_price self.__parent_trade = parent_trade self.__tag = tag def _replace(self, **kwargs): for k, v in kwargs.items(): setattr(self, f'_{self.__class__.__qualname__}__{k}', v) return self def __repr__(self): return ''.format(', '.join(f'{param}={try_(lambda: round(value, 5), value)!r}' for param, value in ( ('size', self.__size), ('limit', self.__limit_price), ('stop', self.__stop_price), ('sl', self.__sl_price), ('tp', self.__tp_price), ('contingent', self.is_contingent), ('tag', self.__tag), ) if value is not None)) # noqa: E126 def cancel(self): """Cancel the order.""" self.__broker.orders.remove(self) trade = self.__parent_trade if trade: if self is trade._sl_order: trade._replace(sl_order=None) elif self is trade._tp_order: trade._replace(tp_order=None) else: pass # Order placed by Trade.close() # Fields getters @property def size(self) -> float: """ Order size (negative for short orders). If size is a value between 0 and 1, it is interpreted as a fraction of current available liquidity (cash plus `Position.pl` minus used margin). A value greater than or equal to 1 indicates an absolute number of units. """ return self.__size @property def limit(self) -> Optional[float]: """ Order limit price for [limit orders], or None for [market orders], which are filled at next available price. [limit orders]: https://www.investopedia.com/terms/l/limitorder.asp [market orders]: https://www.investopedia.com/terms/m/marketorder.asp """ return self.__limit_price @property def stop(self) -> Optional[float]: """ Order stop price for [stop-limit/stop-market][_] order, otherwise None if no stop was set, or the stop price has already been hit. [_]: https://www.investopedia.com/terms/s/stoporder.asp """ return self.__stop_price @property def sl(self) -> Optional[float]: """ A stop-loss price at which, if set, a new contingent stop-market order will be placed upon the `Trade` following this order's execution. See also `Trade.sl`. """ return self.__sl_price @property def tp(self) -> Optional[float]: """ A take-profit price at which, if set, a new contingent limit order will be placed upon the `Trade` following this order's execution. See also `Trade.tp`. """ return self.__tp_price @property def parent_trade(self): return self.__parent_trade @property def tag(self): """ Arbitrary value (such as a string) which, if set, enables tracking of this order and the associated `Trade` (see `Trade.tag`). """ return self.__tag __pdoc__['Order.parent_trade'] = False # Extra properties @property def is_long(self): """True if the order is long (order size is positive).""" return self.__size > 0 @property def is_short(self): """True if the order is short (order size is negative).""" return self.__size < 0 @property def is_contingent(self): """ True for [contingent] orders, i.e. [OCO] stop-loss and take-profit bracket orders placed upon an active trade. Remaining contingent orders are canceled when their parent `Trade` is closed. You can modify contingent orders through `Trade.sl` and `Trade.tp`. [contingent]: https://www.investopedia.com/terms/c/contingentorder.asp [OCO]: https://www.investopedia.com/terms/o/oco.asp """ return bool((parent := self.__parent_trade) and (self is parent._sl_order or self is parent._tp_order)) class Trade: """ When an `Order` is filled, it results in an active `Trade`. Find active trades in `Strategy.trades` and closed, settled trades in `Strategy.closed_trades`. """ def __init__(self, broker: '_Broker', size: int, entry_price: float, entry_bar, tag): self.__broker = broker self.__size = size self.__entry_price = entry_price self.__exit_price: Optional[float] = None self.__entry_bar: int = entry_bar self.__exit_bar: Optional[int] = None self.__sl_order: Optional[Order] = None self.__tp_order: Optional[Order] = None self.__tag = tag self._commissions = 0 def __repr__(self): return f'' def _replace(self, **kwargs): for k, v in kwargs.items(): setattr(self, f'_{self.__class__.__qualname__}__{k}', v) return self def _copy(self, **kwargs): return copy(self)._replace(**kwargs) def close(self, portion: float = 1.): """Place new `Order` to close `portion` of the trade at next market price.""" assert 0 < portion <= 1, "portion must be a fraction between 0 and 1" # Ensure size is an int to avoid rounding errors on 32-bit OS size = copysign(max(1, int(round(abs(self.__size) * portion))), -self.__size) order = Order(self.__broker, size, parent_trade=self, tag=self.__tag) self.__broker.orders.insert(0, order) # Fields getters @property def size(self): """Trade size (volume; negative for short trades).""" return self.__size @property def entry_price(self) -> float: """Trade entry price.""" return self.__entry_price @property def exit_price(self) -> Optional[float]: """Trade exit price (or None if the trade is still active).""" return self.__exit_price @property def entry_bar(self) -> int: """Candlestick bar index of when the trade was entered.""" return self.__entry_bar @property def exit_bar(self) -> Optional[int]: """ Candlestick bar index of when the trade was exited (or None if the trade is still active). """ return self.__exit_bar @property def tag(self): """ A tag value inherited from the `Order` that opened this trade. This can be used to track trades and apply conditional logic / subgroup analysis. See also `Order.tag`. """ return self.__tag @property def _sl_order(self): return self.__sl_order @property def _tp_order(self): return self.__tp_order # Extra properties @property def entry_time(self) -> Union[pd.Timestamp, int]: """Datetime of when the trade was entered.""" return self.__broker._data.index[self.__entry_bar] @property def exit_time(self) -> Optional[Union[pd.Timestamp, int]]: """Datetime of when the trade was exited.""" if self.__exit_bar is None: return None return self.__broker._data.index[self.__exit_bar] @property def is_long(self): """True if the trade is long (trade size is positive).""" return self.__size > 0 @property def is_short(self): """True if the trade is short (trade size is negative).""" return not self.is_long @property def pl(self): """ Trade profit (positive) or loss (negative) in cash units. Commissions are reflected only after the Trade is closed. """ price = self.__exit_price or self.__broker.last_price return (self.__size * (price - self.__entry_price)) - self._commissions @property def pl_pct(self): """Trade profit (positive) or loss (negative) in percent relative to trade entry price.""" price = self.__exit_price or self.__broker.last_price gross_pl_pct = copysign(1, self.__size) * (price / self.__entry_price - 1) # Total commission across the entire trade size to individual units commission_pct = self._commissions / (abs(self.__size) * self.__entry_price) return gross_pl_pct - commission_pct @property def value(self): """Trade total value in cash (volume × price).""" price = self.__exit_price or self.__broker.last_price return abs(self.__size) * price # SL/TP management API @property def sl(self): """ Stop-loss price at which to close the trade. This variable is writable. By assigning it a new price value, you create or modify the existing SL order. By assigning it `None`, you cancel it. """ return self.__sl_order and self.__sl_order.stop @sl.setter def sl(self, price: float): self.__set_contingent('sl', price) @property def tp(self): """ Take-profit price at which to close the trade. This property is writable. By assigning it a new price value, you create or modify the existing TP order. By assigning it `None`, you cancel it. """ return self.__tp_order and self.__tp_order.limit @tp.setter def tp(self, price: float): self.__set_contingent('tp', price) def __set_contingent(self, type, price): assert type in ('sl', 'tp') assert price is None or 0 < price < np.inf, f'Make sure 0 < price < inf! price: {price}' attr = f'_{self.__class__.__qualname__}__{type}_order' order: Order = getattr(self, attr) if order: order.cancel() if price: kwargs = {'stop': price} if type == 'sl' else {'limit': price} order = self.__broker.new_order(-self.size, trade=self, tag=self.tag, **kwargs) setattr(self, attr, order) class _Broker: def __init__(self, *, data, cash, spread, commission, margin, trade_on_close, hedging, exclusive_orders, index): assert cash > 0, f"cash should be > 0, is {cash}" assert 0 < margin <= 1, f"margin should be between 0 and 1, is {margin}" self._data: _Data = data self._cash = cash if callable(commission): self._commission = commission else: try: self._commission_fixed, self._commission_relative = commission except TypeError: self._commission_fixed, self._commission_relative = 0, commission assert self._commission_fixed >= 0, 'Need fixed cash commission in $ >= 0' assert -.1 <= self._commission_relative < .1, \ ("commission should be between -10% " f"(e.g. market-maker's rebates) and 10% (fees), is {self._commission_relative}") self._commission = self._commission_func self._spread = spread self._leverage = 1 / margin self._trade_on_close = trade_on_close self._hedging = hedging self._exclusive_orders = exclusive_orders self._equity = np.tile(np.nan, len(index)) self.orders: List[Order] = [] self.trades: List[Trade] = [] self.position = Position(self) self.closed_trades: List[Trade] = [] def _commission_func(self, order_size, price): return self._commission_fixed + abs(order_size) * price * self._commission_relative def __repr__(self): return f'' def new_order(self, size: float, limit: Optional[float] = None, stop: Optional[float] = None, sl: Optional[float] = None, tp: Optional[float] = None, tag: object = None, *, trade: Optional[Trade] = None) -> Order: """ Argument size indicates whether the order is long or short """ size = float(size) stop = stop and float(stop) limit = limit and float(limit) sl = sl and float(sl) tp = tp and float(tp) is_long = size > 0 assert size != 0, size adjusted_price = self._adjusted_price(size) if is_long: if not (sl or -np.inf) < (limit or stop or adjusted_price) < (tp or np.inf): raise ValueError( "Long orders require: " f"SL ({sl}) < LIMIT ({limit or stop or adjusted_price}) < TP ({tp})") else: if not (tp or -np.inf) < (limit or stop or adjusted_price) < (sl or np.inf): raise ValueError( "Short orders require: " f"TP ({tp}) < LIMIT ({limit or stop or adjusted_price}) < SL ({sl})") order = Order(self, size, limit, stop, sl, tp, trade, tag) if not trade: # If exclusive orders (each new order auto-closes previous orders/position), # cancel all non-contingent orders and close all open trades beforehand if self._exclusive_orders: for o in self.orders: if not o.is_contingent: o.cancel() for t in self.trades: t.close() # Put the new order in the order queue, Ensure SL orders are processed first self.orders.insert(0 if trade and stop else len(self.orders), order) return order @property def last_price(self) -> float: """ Price at the last (current) close. """ return self._data.Close[-1] def _adjusted_price(self, size=None, price=None) -> float: """ Long/short `price`, adjusted for spread. In long positions, the adjusted price is a fraction higher, and vice versa. """ return (price or self.last_price) * (1 + copysign(self._spread, size)) @property def equity(self) -> float: return self._cash + sum(trade.pl for trade in self.trades) @property def margin_available(self) -> float: # From https://github.com/QuantConnect/Lean/pull/3768 margin_used = sum(trade.value / self._leverage for trade in self.trades) return max(0, self.equity - margin_used) def next(self): i = self._i = len(self._data) - 1 self._process_orders() # Log account equity for the equity curve equity = self.equity self._equity[i] = equity # If equity is negative, set all to 0 and stop the simulation if equity <= 0: assert self.margin_available <= 0 for trade in self.trades: self._close_trade(trade, self._data.Close[-1], i) self._cash = 0 self._equity[i:] = 0 raise _OutOfMoneyError def _process_orders(self): data = self._data open, high, low = data.Open[-1], data.High[-1], data.Low[-1] reprocess_orders = False # Process orders for order in list(self.orders): # type: Order # Related SL/TP order was already removed if order not in self.orders: continue # Check if stop condition was hit stop_price = order.stop if stop_price: is_stop_hit = ((high >= stop_price) if order.is_long else (low <= stop_price)) if not is_stop_hit: continue # > When the stop price is reached, a stop order becomes a market/limit order. # https://www.sec.gov/fast-answers/answersstopordhtm.html order._replace(stop_price=None) # Determine purchase price. # Check if limit order can be filled. if order.limit: is_limit_hit = low <= order.limit if order.is_long else high >= order.limit # When stop and limit are hit within the same bar, we pessimistically # assume limit was hit before the stop (i.e. "before it counts") is_limit_hit_before_stop = (is_limit_hit and (order.limit <= (stop_price or -np.inf) if order.is_long else order.limit >= (stop_price or np.inf))) if not is_limit_hit or is_limit_hit_before_stop: continue # stop_price, if set, was hit within this bar price = (min(stop_price or open, order.limit) if order.is_long else max(stop_price or open, order.limit)) else: # Market-if-touched / market order # Contingent orders always on next open prev_close = data.Close[-2] price = prev_close if self._trade_on_close and not order.is_contingent else open if stop_price: price = max(price, stop_price) if order.is_long else min(price, stop_price) # Determine entry/exit bar index is_market_order = not order.limit and not stop_price time_index = ( (self._i - 1) if is_market_order and self._trade_on_close and not order.is_contingent else self._i) # If order is a SL/TP order, it should close an existing trade it was contingent upon if order.parent_trade: trade = order.parent_trade _prev_size = trade.size # If order.size is "greater" than trade.size, this order is a trade.close() # order and part of the trade was already closed beforehand size = copysign(min(abs(_prev_size), abs(order.size)), order.size) # If this trade isn't already closed (e.g. on multiple `trade.close(.5)` calls) if trade in self.trades: self._reduce_trade(trade, price, size, time_index) assert order.size != -_prev_size or trade not in self.trades if price == stop_price: # Set SL back on the order for stats._trades["SL"] trade._sl_order._replace(stop_price=stop_price) if order in (trade._sl_order, trade._tp_order): assert order.size == -trade.size assert order not in self.orders # Removed when trade was closed else: # It's a trade.close() order, now done assert abs(_prev_size) >= abs(size) >= 1 self.orders.remove(order) continue # Else this is a stand-alone trade # Adjust price to include commission (or bid-ask spread). # In long positions, the adjusted price is a fraction higher, and vice versa. adjusted_price = self._adjusted_price(order.size, price) adjusted_price_plus_commission = \ adjusted_price + self._commission(order.size, price) / abs(order.size) # If order size was specified proportionally, # precompute true size in units, accounting for margin and spread/commissions size = order.size if -1 < size < 1: size = copysign(int((self.margin_available * self._leverage * abs(size)) // adjusted_price_plus_commission), size) # Not enough cash/margin even for a single unit if not size: warnings.warn( f'time={self._i}: Broker canceled the relative-sized order due to insufficient margin ' f'(equity={self.equity:.2f}, margin_available={self.margin_available:.2f}).', category=UserWarning) # XXX: The order is canceled by the broker? self.orders.remove(order) continue assert size == round(size) need_size = int(size) if not self._hedging: # Fill position by FIFO closing/reducing existing opposite-facing trades. # Existing trades are closed at unadjusted price, because the adjustment # was already made when buying. for trade in list(self.trades): if trade.is_long == order.is_long: continue assert trade.size * order.size < 0 # Order size greater than this opposite-directed existing trade, # so it will be closed completely if abs(need_size) >= abs(trade.size): self._close_trade(trade, price, time_index) need_size += trade.size else: # The existing trade is larger than the new order, # so it will only be closed partially self._reduce_trade(trade, price, need_size, time_index) need_size = 0 if not need_size: break # If we don't have enough liquidity to cover for the order, the broker CANCELS it if abs(need_size) * adjusted_price_plus_commission > \ self.margin_available * self._leverage: warnings.warn( f'time={self._i}: Broker canceled the order due to insufficient margin ' f'(equity={self.equity:.2f}, margin_available={self.margin_available:.2f}).', category=UserWarning) self.orders.remove(order) continue # Open a new trade if need_size: self._open_trade(adjusted_price, need_size, order.sl, order.tp, time_index, order.tag) # We need to reprocess the SL/TP orders newly added to the queue. # This allows e.g. SL hitting in the same bar the order was open. # See https://github.com/kernc/backtesting.py/issues/119 if order.sl or order.tp: if is_market_order: reprocess_orders = True # Order.stop and TP hit within the same bar, but SL wasn't. This case # is not ambiguous, because stop and TP go in the same price direction. elif stop_price and not order.limit and order.tp and ( (order.is_long and order.tp <= high and (order.sl or -np.inf) < low) or (order.is_short and order.tp >= low and (order.sl or np.inf) > high)): reprocess_orders = True elif (low <= (order.sl or -np.inf) <= high or low <= (order.tp or -np.inf) <= high): warnings.warn( f"({data.index[-1]}) A contingent SL/TP order would execute in the " "same bar its parent stop/limit order was turned into a trade. " "Since we can't assert the precise intra-candle " "price movement, the affected SL/TP order will instead be executed on " "the next (matching) price/bar, making the result (of this trade) " "somewhat dubious. " "See https://github.com/kernc/backtesting.py/issues/119", UserWarning) # Order processed self.orders.remove(order) if reprocess_orders: self._process_orders() def _reduce_trade(self, trade: Trade, price: float, size: float, time_index: int): assert trade.size * size < 0 assert abs(trade.size) >= abs(size) size_left = trade.size + size assert size_left * trade.size >= 0 if not size_left: close_trade = trade else: # Reduce existing trade ... trade._replace(size=size_left) if trade._sl_order: trade._sl_order._replace(size=-trade.size) if trade._tp_order: trade._tp_order._replace(size=-trade.size) # ... by closing a reduced copy of it close_trade = trade._copy(size=-size, sl_order=None, tp_order=None) self.trades.append(close_trade) self._close_trade(close_trade, price, time_index) def _close_trade(self, trade: Trade, price: float, time_index: int): self.trades.remove(trade) if trade._sl_order: self.orders.remove(trade._sl_order) if trade._tp_order: self.orders.remove(trade._tp_order) closed_trade = trade._replace(exit_price=price, exit_bar=time_index) self.closed_trades.append(closed_trade) # Apply commission one more time at trade exit commission = self._commission(trade.size, price) self._cash += trade.pl - commission # Save commissions on Trade instance for stats trade_open_commission = self._commission(closed_trade.size, closed_trade.entry_price) # applied here instead of on Trade open because size could have changed # by way of _reduce_trade() closed_trade._commissions = commission + trade_open_commission def _open_trade(self, price: float, size: int, sl: Optional[float], tp: Optional[float], time_index: int, tag): trade = Trade(self, size, price, time_index, tag) self.trades.append(trade) # Apply broker commission at trade open self._cash -= self._commission(size, price) # Create SL/TP (bracket) orders. if tp: trade.tp = tp if sl: trade.sl = sl class Backtest: """ Backtest a particular (parameterized) strategy on particular data. Initialize a backtest. Requires data and a strategy to test. After initialization, you can call method `backtesting.backtesting.Backtest.run` to run a backtest instance, or `backtesting.backtesting.Backtest.optimize` to optimize it. `data` is a `pd.DataFrame` with columns: `Open`, `High`, `Low`, `Close`, and (optionally) `Volume`. If any columns are missing, set them to what you have available, e.g. df['Open'] = df['High'] = df['Low'] = df['Close'] The passed data frame can contain additional columns that can be used by the strategy (e.g. sentiment info). DataFrame index can be either a datetime index (timestamps) or a monotonic range index (i.e. a sequence of periods). `strategy` is a `backtesting.backtesting.Strategy` _subclass_ (not an instance). `cash` is the initial cash to start with. `spread` is the constant bid-ask spread rate (relative to the price). E.g. set it to `0.0002` for commission-less forex trading where the average spread is roughly 0.2‰ of the asking price. `commission` is the commission rate. E.g. if your broker's commission is 1% of order value, set commission to `0.01`. The commission is applied twice: at trade entry and at trade exit. Besides one single floating value, `commission` can also be a tuple of floating values `(fixed, relative)`. E.g. set it to `(100, .01)` if your broker charges minimum $100 + 1%. Additionally, `commission` can be a callable `func(order_size: int, price: float) -> float` (note, order size is negative for short orders), which can be used to model more complex commission structures. Negative commission values are interpreted as market-maker's rebates. .. note:: Before v0.4.0, the commission was only applied once, like `spread` is now. If you want to keep the old behavior, simply set `spread` instead. .. note:: With nonzero `commission`, long and short orders will be placed at an adjusted price that is slightly higher or lower (respectively) than the current price. See e.g. [#153](https://github.com/kernc/backtesting.py/issues/153), [#538](https://github.com/kernc/backtesting.py/issues/538), [#633](https://github.com/kernc/backtesting.py/issues/633). `margin` is the required margin (ratio) of a leveraged account. No difference is made between initial and maintenance margins. To run the backtest using e.g. 50:1 leverage that your broker allows, set margin to `0.02` (1 / leverage). If `trade_on_close` is `True`, market orders will be filled with respect to the current bar's closing price instead of the next bar's open. If `hedging` is `True`, allow trades in both directions simultaneously. If `False`, the opposite-facing orders first close existing trades in a [FIFO] manner. If `exclusive_orders` is `True`, each new order auto-closes the previous trade/position, making at most a single trade (long or short) in effect at each time. If `finalize_trades` is `True`, the trades that are still [active and ongoing] at the end of the backtest will be closed on the last bar and will contribute to the computed backtest statistics. .. tip:: Fractional trading See also `backtesting.lib.FractionalBacktest` if you want to trade fractional units (of e.g. bitcoin). [FIFO]: https://www.investopedia.com/terms/n/nfa-compliance-rule-2-43b.asp [active and ongoing]: https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.trades """ # noqa: E501 def __init__(self, data: pd.DataFrame, strategy: Type[Strategy], *, cash: float = 10_000, spread: float = .0, commission: Union[float, Tuple[float, float]] = .0, margin: float = 1., trade_on_close=False, hedging=False, exclusive_orders=False, finalize_trades=False, ): if not (isinstance(strategy, type) and issubclass(strategy, Strategy)): raise TypeError('`strategy` must be a Strategy sub-type') if not isinstance(data, pd.DataFrame): raise TypeError("`data` must be a pandas.DataFrame with columns") if not isinstance(spread, Number): raise TypeError('`spread` must be a float value, percent of ' 'entry order price') if not isinstance(commission, (Number, tuple)) and not callable(commission): raise TypeError('`commission` must be a float percent of order value, ' 'a tuple of `(fixed, relative)` commission, ' 'or a function that takes `(order_size, price)`' 'and returns commission dollar value') data = data.copy(deep=False) # Convert index to datetime index if (not isinstance(data.index, pd.DatetimeIndex) and not isinstance(data.index, pd.RangeIndex) and # Numeric index with most large numbers (data.index.is_numeric() and (data.index > pd.Timestamp('1975').timestamp()).mean() > .8)): try: data.index = pd.to_datetime(data.index, infer_datetime_format=True) except ValueError: pass if 'Volume' not in data: data['Volume'] = np.nan if len(data) == 0: raise ValueError('OHLC `data` is empty') if len(data.columns.intersection({'Open', 'High', 'Low', 'Close', 'Volume'})) != 5: raise ValueError("`data` must be a pandas.DataFrame with columns " "'Open', 'High', 'Low', 'Close', and (optionally) 'Volume'") if data[['Open', 'High', 'Low', 'Close']].isnull().values.any(): raise ValueError('Some OHLC values are missing (NaN). ' 'Please strip those lines with `df.dropna()` or ' 'fill them in with `df.interpolate()` or whatever.') if np.any(data['Close'] > cash): warnings.warn('Some prices are larger than initial cash value. Note that fractional ' 'trading is not supported by this class. If you want to trade Bitcoin, ' 'increase initial cash, or trade μBTC or satoshis instead (see e.g. class ' '`backtesting.lib.FractionalBacktest`.', stacklevel=2) if not data.index.is_monotonic_increasing: warnings.warn('Data index is not sorted in ascending order. Sorting.', stacklevel=2) data = data.sort_index() if not isinstance(data.index, pd.DatetimeIndex): warnings.warn('Data index is not datetime. Assuming simple periods, ' 'but `pd.DateTimeIndex` is advised.', stacklevel=2) self._data: pd.DataFrame = data self._broker = partial( _Broker, cash=cash, spread=spread, commission=commission, margin=margin, trade_on_close=trade_on_close, hedging=hedging, exclusive_orders=exclusive_orders, index=data.index, ) self._strategy = strategy self._results: Optional[pd.Series] = None self._finalize_trades = bool(finalize_trades) def run(self, **kwargs) -> pd.Series: """ Run the backtest. Returns `pd.Series` with results and statistics. Keyword arguments are interpreted as strategy parameters. >>> Backtest(GOOG, SmaCross).run() Start 2004-08-19 00:00:00 End 2013-03-01 00:00:00 Duration 3116 days 00:00:00 Exposure Time [%] 96.74115 Equity Final [$] 51422.99 Equity Peak [$] 75787.44 Return [%] 414.2299 Buy & Hold Return [%] 703.45824 Return (Ann.) [%] 21.18026 Volatility (Ann.) [%] 36.49391 CAGR [%] 14.15984 Sharpe Ratio 0.58038 Sortino Ratio 1.08479 Calmar Ratio 0.44144 Alpha [%] 394.37391 Beta 0.03803 Max. Drawdown [%] -47.98013 Avg. Drawdown [%] -5.92585 Max. Drawdown Duration 584 days 00:00:00 Avg. Drawdown Duration 41 days 00:00:00 # Trades 66 Win Rate [%] 46.9697 Best Trade [%] 53.59595 Worst Trade [%] -18.39887 Avg. Trade [%] 2.53172 Max. Trade Duration 183 days 00:00:00 Avg. Trade Duration 46 days 00:00:00 Profit Factor 2.16795 Expectancy [%] 3.27481 SQN 1.07662 Kelly Criterion 0.15187 _strategy SmaCross _equity_curve Eq... _trades Size EntryB... dtype: object .. warning:: You may obtain different results for different strategy parameters. E.g. if you use 50- and 200-bar SMA, the trading simulation will begin on bar 201. The actual length of delay is equal to the lookback period of the `Strategy.I` indicator which lags the most. Obviously, this can affect results. """ data = _Data(self._data.copy(deep=False)) broker: _Broker = self._broker(data=data) strategy: Strategy = self._strategy(broker, data, kwargs) strategy.init() data._update() # Strategy.init might have changed/added to data.df # Indicators used in Strategy.next() indicator_attrs = _strategy_indicators(strategy) # Skip first few candles where indicators are still "warming up" # +1 to have at least two entries available start = 1 + _indicator_warmup_nbars(strategy) # Disable "invalid value encountered in ..." warnings. Comparison # np.nan >= 3 is not invalid; it's False. with np.errstate(invalid='ignore'): for i in _tqdm(range(start, len(self._data)), desc=self.run.__qualname__, unit='bar', mininterval=2, miniters=100): # Prepare data and indicators for `next` call data._set_length(i + 1) for attr, indicator in indicator_attrs: # Slice indicator on the last dimension (case of 2d indicator) setattr(strategy, attr, indicator[..., :i + 1]) # Handle orders processing and broker stuff try: broker.next() except _OutOfMoneyError: break # Next tick, a moment before bar close strategy.next() else: if self._finalize_trades is True: # Close any remaining open trades so they produce some stats for trade in reversed(broker.trades): trade.close() # HACK: Re-run broker one last time to handle close orders placed in the last # strategy iteration. Use the same OHLC values as in the last broker iteration. if start < len(self._data): try_(broker.next, exception=_OutOfMoneyError) elif len(broker.trades): warnings.warn( 'Some trades remain open at the end of backtest. Use ' '`Backtest(..., finalize_trades=True)` to close them and ' 'include them in stats.', stacklevel=2) # Set data back to full length # for future `indicator._opts['data'].index` calls to work data._set_length(len(self._data)) equity = pd.Series(broker._equity).bfill().fillna(broker._cash).values self._results = compute_stats( trades=broker.closed_trades, equity=equity, ohlc_data=self._data, risk_free_rate=0.0, strategy_instance=strategy, ) return self._results def optimize(self, *, maximize: Union[str, Callable[[pd.Series], float]] = 'SQN', method: str = 'grid', max_tries: Optional[Union[int, float]] = None, constraint: Optional[Callable[[dict], bool]] = None, return_heatmap: bool = False, return_optimization: bool = False, random_state: Optional[int] = None, **kwargs) -> Union[pd.Series, Tuple[pd.Series, pd.Series], Tuple[pd.Series, pd.Series, dict]]: """ Optimize strategy parameters to an optimal combination. Returns result `pd.Series` of the best run. `maximize` is a string key from the `backtesting.backtesting.Backtest.run`-returned results series, or a function that accepts this series object and returns a number; the higher the better. By default, the method maximizes Van Tharp's [System Quality Number](https://google.com/search?q=System+Quality+Number). `method` is the optimization method. Currently two methods are supported: * `"grid"` which does an exhaustive (or randomized) search over the cartesian product of parameter combinations, and * `"sambo"` which finds close-to-optimal strategy parameters using [model-based optimization], making at most `max_tries` evaluations. [model-based optimization]: https://sambo-optimization.github.io `max_tries` is the maximal number of strategy runs to perform. If `method="grid"`, this results in randomized grid search. If `max_tries` is a floating value between (0, 1], this sets the number of runs to approximately that fraction of full grid space. Alternatively, if integer, it denotes the absolute maximum number of evaluations. If unspecified (default), grid search is exhaustive, whereas for `method="sambo"`, `max_tries` is set to 200. `constraint` is a function that accepts a dict-like object of parameters (with values) and returns `True` when the combination is admissible to test with. By default, any parameters combination is considered admissible. If `return_heatmap` is `True`, besides returning the result series, an additional `pd.Series` is returned with a multiindex of all admissible parameter combinations, which can be further inspected or projected onto 2D to plot a heatmap (see `backtesting.lib.plot_heatmaps()`). If `return_optimization` is True and `method = 'sambo'`, in addition to result series (and maybe heatmap), return raw [`scipy.optimize.OptimizeResult`][OptimizeResult] for further inspection, e.g. with [SAMBO]'s [plotting tools]. [OptimizeResult]: https://sambo-optimization.github.io/doc/sambo/#sambo.OptimizeResult [SAMBO]: https://sambo-optimization.github.io [plotting tools]: https://sambo-optimization.github.io/doc/sambo/plot.html If you want reproducible optimization results, set `random_state` to a fixed integer random seed. Additional keyword arguments represent strategy arguments with list-like collections of possible values. For example, the following code finds and returns the "best" of the 7 admissible (of the 9 possible) parameter combinations: best_stats = backtest.optimize(sma1=[5, 10, 15], sma2=[10, 20, 40], constraint=lambda p: p.sma1 < p.sma2) """ if not kwargs: raise ValueError('Need some strategy parameters to optimize') maximize_key = None if isinstance(maximize, str): maximize_key = str(maximize) if maximize not in dummy_stats().index: raise ValueError('`maximize`, if str, must match a key in pd.Series ' 'result of backtest.run()') def maximize(stats: pd.Series, _key=maximize): return stats[_key] elif not callable(maximize): raise TypeError('`maximize` must be str (a field of backtest.run() result ' 'Series) or a function that accepts result Series ' 'and returns a number; the higher the better') assert callable(maximize), maximize have_constraint = bool(constraint) if constraint is None: def constraint(_): return True elif not callable(constraint): raise TypeError("`constraint` must be a function that accepts a dict " "of strategy parameters and returns a bool whether " "the combination of parameters is admissible or not") assert callable(constraint), constraint if method == 'skopt': method = 'sambo' warnings.warn('`Backtest.optimize(method="skopt")` is deprecated. Use `method="sambo"`.', DeprecationWarning, stacklevel=2) if return_optimization and method != 'sambo': raise ValueError("return_optimization=True only valid if method='sambo'") def _tuple(x): return x if isinstance(x, Sequence) and not isinstance(x, str) else (x,) for k, v in kwargs.items(): if len(_tuple(v)) == 0: raise ValueError(f"Optimization variable '{k}' is passed no " f"optimization values: {k}={v}") class AttrDict(dict): def __getattr__(self, item): return self[item] def _grid_size(): size = int(np.prod([len(_tuple(v)) for v in kwargs.values()])) if size < 10_000 and have_constraint: size = sum(1 for p in product(*(zip(repeat(k), _tuple(v)) for k, v in kwargs.items())) if constraint(AttrDict(p))) return size def _optimize_grid() -> Union[pd.Series, Tuple[pd.Series, pd.Series]]: rand = default_rng(random_state).random grid_frac = (1 if max_tries is None else max_tries if 0 < max_tries <= 1 else max_tries / _grid_size()) param_combos = [dict(params) # back to dict so it pickles for params in (AttrDict(params) for params in product(*(zip(repeat(k), _tuple(v)) for k, v in kwargs.items()))) if constraint(params) and rand() <= grid_frac] if not param_combos: raise ValueError('No admissible parameter combinations to test') if len(param_combos) > 300: warnings.warn(f'Searching for best of {len(param_combos)} configurations.', stacklevel=2) heatmap = pd.Series(np.nan, name=maximize_key, index=pd.MultiIndex.from_tuples( [p.values() for p in param_combos], names=next(iter(param_combos)).keys())) from . import Pool with Pool() as pool, \ SharedMemoryManager() as smm: with patch(self, '_data', None): bt = copy(self) # bt._data will be reassigned in _mp_task worker results = _tqdm( pool.imap(Backtest._mp_task, ((bt, smm.df2shm(self._data), params_batch) for params_batch in _batch(param_combos))), total=len(param_combos), desc='Backtest.optimize' ) for param_batch, result in zip(_batch(param_combos), results): for params, stats in zip(param_batch, result): if stats is not None: heatmap[tuple(params.values())] = maximize(stats) if pd.isnull(heatmap).all(): # No trade was made in any of the runs. Just make a random # run so we get some, if empty, results stats = self.run(**param_combos[0]) else: best_params = heatmap.idxmax(skipna=True) stats = self.run(**dict(zip(heatmap.index.names, best_params))) if return_heatmap: return stats, heatmap return stats def _optimize_sambo() -> Union[pd.Series, Tuple[pd.Series, pd.Series], Tuple[pd.Series, pd.Series, dict]]: try: import sambo except ImportError: raise ImportError("Need package 'sambo' for method='sambo'. pip install sambo") from None nonlocal max_tries max_tries = (200 if max_tries is None else max(1, int(max_tries * _grid_size())) if 0 < max_tries <= 1 else max_tries) dimensions = [] for key, values in kwargs.items(): values = np.asarray(values) if values.dtype.kind in 'mM': # timedelta, datetime64 # these dtypes are unsupported in SAMBO, so convert to raw int # TODO: save dtype and convert back later values = values.astype(np.int64) if values.dtype.kind in 'iumM': dimensions.append((values.min(), values.max() + 1)) elif values.dtype.kind == 'f': dimensions.append((values.min(), values.max())) else: dimensions.append(values.tolist()) # Avoid recomputing re-evaluations @lru_cache() def memoized_run(tup): nonlocal maximize, self stats = self.run(**dict(tup)) return -maximize(stats) progress = iter(_tqdm(repeat(None), total=max_tries, leave=False, desc=self.optimize.__qualname__, mininterval=2)) _names = tuple(kwargs.keys()) def objective_function(x): nonlocal progress, memoized_run, constraint, _names next(progress) value = memoized_run(tuple(zip(_names, x))) return 0 if np.isnan(value) else value def cons(x): nonlocal constraint, _names return constraint(AttrDict(zip(_names, x))) res = sambo.minimize( fun=objective_function, bounds=dimensions, constraints=cons, max_iter=max_tries, method='sceua', rng=random_state) stats = self.run(**dict(zip(kwargs.keys(), res.x))) output = [stats] if return_heatmap: heatmap = pd.Series(dict(zip(map(tuple, res.xv), -res.funv)), name=maximize_key) heatmap.index.names = kwargs.keys() heatmap.sort_index(inplace=True) output.append(heatmap) if return_optimization: output.append(res) return stats if len(output) == 1 else tuple(output) if method == 'grid': output = _optimize_grid() elif method in ('sambo', 'skopt'): output = _optimize_sambo() else: raise ValueError(f"Method should be 'grid' or 'sambo', not {method!r}") return output @staticmethod def _mp_task(arg): bt, data_shm, params_batch = arg bt._data, shm = SharedMemoryManager.shm2df(data_shm) try: return [stats.filter(regex='^[^_]') if stats['# Trades'] else None for stats in (bt.run(**params) for params in params_batch)] finally: for shmem in shm: shmem.close() def plot(self, *, results: pd.Series = None, filename=None, plot_width=None, plot_equity=True, plot_return=False, plot_pl=True, plot_volume=True, plot_drawdown=False, plot_trades=True, smooth_equity=False, relative_equity=True, superimpose: Union[bool, str] = True, resample=True, reverse_indicators=False, show_legend=True, open_browser=True): """ Plot the progression of the last backtest run. If `results` is provided, it should be a particular result `pd.Series` such as returned by `backtesting.backtesting.Backtest.run` or `backtesting.backtesting.Backtest.optimize`, otherwise the last run's results are used. `filename` is the path to save the interactive HTML plot to. By default, a strategy/parameter-dependent file is created in the current working directory. `plot_width` is the width of the plot in pixels. If None (default), the plot is made to span 100% of browser width. The height is currently non-adjustable. If `plot_equity` is `True`, the resulting plot will contain an equity (initial cash plus assets) graph section. This is the same as `plot_return` plus initial 100%. If `plot_return` is `True`, the resulting plot will contain a cumulative return graph section. This is the same as `plot_equity` minus initial 100%. If `plot_pl` is `True`, the resulting plot will contain a profit/loss (P/L) indicator section. If `plot_volume` is `True`, the resulting plot will contain a trade volume section. If `plot_drawdown` is `True`, the resulting plot will contain a separate drawdown graph section. If `plot_trades` is `True`, the stretches between trade entries and trade exits are marked by hash-marked tractor beams. If `smooth_equity` is `True`, the equity graph will be interpolated between fixed points at trade closing times, unaffected by any interim asset volatility. If `relative_equity` is `True`, scale and label equity graph axis with return percent, not absolute cash-equivalent values. If `superimpose` is `True`, superimpose larger-timeframe candlesticks over the original candlestick chart. Default downsampling rule is: monthly for daily data, daily for hourly data, hourly for minute data, and minute for (sub-)second data. `superimpose` can also be a valid [Pandas offset string], such as `'5T'` or `'5min'`, in which case this frequency will be used to superimpose. Note, this only works for data with a datetime index. If `resample` is `True`, the OHLC data is resampled in a way that makes the upper number of candles for Bokeh to plot limited to 10_000. This may, in situations of overabundant data, improve plot's interactive performance and avoid browser's `Javascript Error: Maximum call stack size exceeded` or similar. Equity & dropdown curves and individual trades data is, likewise, [reasonably _aggregated_][TRADES_AGG]. `resample` can also be a [Pandas offset string], such as `'5T'` or `'5min'`, in which case this frequency will be used to resample, overriding above numeric limitation. Note, all this only works for data with a datetime index. If `reverse_indicators` is `True`, the indicators below the OHLC chart are plotted in reverse order of declaration. [Pandas offset string]: \ https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects [TRADES_AGG]: lib.html#backtesting.lib.TRADES_AGG If `show_legend` is `True`, the resulting plot graphs will contain labeled legends. If `open_browser` is `True`, the resulting `filename` will be opened in the default web browser. """ if results is None: if self._results is None: raise RuntimeError('First issue `backtest.run()` to obtain results.') results = self._results return plot( results=results, df=self._data, indicators=results._strategy._indicators, filename=filename, plot_width=plot_width, plot_equity=plot_equity, plot_return=plot_return, plot_pl=plot_pl, plot_volume=plot_volume, plot_drawdown=plot_drawdown, plot_trades=plot_trades, smooth_equity=smooth_equity, relative_equity=relative_equity, superimpose=superimpose, resample=resample, reverse_indicators=reverse_indicators, show_legend=show_legend, open_browser=open_browser) # NOTE: Don't put anything public below this __all__ list __all__ = [getattr(v, '__name__', k) for k, v in globals().items() # export if ((callable(v) and getattr(v, '__module__', None) == __name__ or # callables from this module; getattr for Python 3.9; # noqa: E501 k.isupper()) and # or CONSTANTS not getattr(v, '__name__', k).startswith('_'))] # neither marked internal # NOTE: Don't put anything public below here. See above. ================================================ FILE: backtesting/lib.py ================================================ """ Collection of common building blocks, helper auxiliary functions and composable strategy classes for reuse. Intended for simple missing-link procedures, not reinventing of better-suited, state-of-the-art, fast libraries, such as TA-Lib, Tulipy, PyAlgoTrade, NumPy, SciPy ... Please raise ideas for additions to this collection on the [issue tracker]. [issue tracker]: https://github.com/kernc/backtesting.py """ from __future__ import annotations import warnings from collections import OrderedDict from inspect import currentframe from itertools import chain, compress, count from numbers import Number from typing import Callable, Generator, Optional, Sequence, Union import numpy as np import pandas as pd from ._plotting import plot_heatmaps as _plot_heatmaps from ._stats import compute_stats as _compute_stats from ._util import SharedMemoryManager, _Array, _as_str, _batch, _tqdm, patch from .backtesting import Backtest, Strategy __pdoc__ = {} OHLCV_AGG = OrderedDict(( ('Open', 'first'), ('High', 'max'), ('Low', 'min'), ('Close', 'last'), ('Volume', 'sum'), )) """Dictionary of rules for aggregating resampled OHLCV data frames, e.g. df.resample('4H', label='right').agg(OHLCV_AGG).dropna() """ TRADES_AGG = OrderedDict(( ('Size', 'sum'), ('EntryBar', 'first'), ('ExitBar', 'last'), ('EntryPrice', 'mean'), ('ExitPrice', 'mean'), ('PnL', 'sum'), ('ReturnPct', 'mean'), ('EntryTime', 'first'), ('ExitTime', 'last'), ('Duration', 'sum'), )) """Dictionary of rules for aggregating resampled trades data, e.g. stats['_trades'].resample('1D', on='ExitTime', label='right').agg(TRADES_AGG) """ _EQUITY_AGG = { 'Equity': 'last', 'DrawdownPct': 'max', 'DrawdownDuration': 'max', } def barssince(condition: Sequence[bool], default=np.inf) -> int: """ Return the number of bars since `condition` sequence was last `True`, or if never, return `default`. >>> barssince(self.data.Close > self.data.Open) 3 """ return next(compress(range(len(condition)), reversed(condition)), default) def cross(series1: Sequence, series2: Sequence) -> bool: """ Return `True` if `series1` and `series2` just crossed (above or below) each other. >>> cross(self.data.Close, self.sma) True """ return crossover(series1, series2) or crossover(series2, series1) def crossover(series1: Sequence, series2: Sequence) -> bool: """ Return `True` if `series1` just crossed over (above) `series2`. >>> crossover(self.data.Close, self.sma) True """ series1 = ( series1.values if isinstance(series1, pd.Series) else (series1, series1) if isinstance(series1, Number) else series1) series2 = ( series2.values if isinstance(series2, pd.Series) else (series2, series2) if isinstance(series2, Number) else series2) try: return series1[-2] < series2[-2] and series1[-1] > series2[-1] # type: ignore except IndexError: return False def plot_heatmaps(heatmap: pd.Series, agg: Union[str, Callable] = 'max', *, ncols: int = 3, plot_width: int = 1200, filename: str = '', open_browser: bool = True): """ Plots a grid of heatmaps, one for every pair of parameters in `heatmap`. See example in [the tutorial]. [the tutorial]: https://kernc.github.io/backtesting.py/doc/examples/Parameter%20Heatmap%20&%20Optimization.html#plot-heatmap # noqa: E501 `heatmap` is a Series as returned by `backtesting.backtesting.Backtest.optimize` when its parameter `return_heatmap=True`. When projecting the n-dimensional (n > 2) heatmap onto 2D, the values are aggregated by 'max' function by default. This can be tweaked with `agg` parameter, which accepts any argument pandas knows how to aggregate by. .. todo:: Lay heatmaps out lower-triangular instead of in a simple grid. Like [`sambo.plot.plot_objective()`][plot_objective] does. [plot_objective]: \ https://sambo-optimization.github.io/doc/sambo/plot.html#sambo.plot.plot_objective """ return _plot_heatmaps(heatmap, agg, ncols, filename, plot_width, open_browser) def quantile(series: Sequence, quantile: Union[None, float] = None): """ If `quantile` is `None`, return the quantile _rank_ of the last value of `series` wrt former series values. If `quantile` is a value between 0 and 1, return the _value_ of `series` at this quantile. If used to working with percentiles, just divide your percentile amount with 100 to obtain quantiles. >>> quantile(self.data.Close[-20:], .1) 162.130 >>> quantile(self.data.Close) 0.13 """ if quantile is None: try: last, series = series[-1], series[:-1] return np.mean(series < last) except IndexError: return np.nan assert 0 <= quantile <= 1, "quantile must be within [0, 1]" return np.nanpercentile(series, quantile * 100) def compute_stats( *, stats: pd.Series, data: pd.DataFrame, trades: pd.DataFrame = None, risk_free_rate: float = 0.) -> pd.Series: """ (Re-)compute strategy performance metrics. `stats` is the statistics series as returned by `backtesting.backtesting.Backtest.run()`. `data` is OHLC data as passed to the `backtesting.backtesting.Backtest` the `stats` were obtained in. `trades` can be a dataframe subset of `stats._trades` (e.g. only long trades). You can also tune `risk_free_rate`, used in calculation of Sharpe and Sortino ratios. >>> stats = Backtest(GOOG, MyStrategy).run() >>> only_long_trades = stats._trades[stats._trades.Size > 0] >>> long_stats = compute_stats(stats=stats, trades=only_long_trades, ... data=GOOG, risk_free_rate=.02) """ equity = stats._equity_curve.Equity if trades is None: trades = stats._trades else: # XXX: Is this buggy? equity = equity.copy() equity[:] = stats._equity_curve.Equity.iloc[0] for t in trades.itertuples(index=False): equity.iloc[t.EntryBar:] += t.PnL return _compute_stats(trades=trades, equity=equity.values, ohlc_data=data, risk_free_rate=risk_free_rate, strategy_instance=stats._strategy) def resample_apply(rule: str, func: Optional[Callable[..., Sequence]], series: Union[pd.Series, pd.DataFrame, _Array], *args, agg: Optional[Union[str, dict]] = None, **kwargs): """ Apply `func` (such as an indicator) to `series`, resampled to a time frame specified by `rule`. When called from inside `backtesting.backtesting.Strategy.init`, the result (returned) series will be automatically wrapped in `backtesting.backtesting.Strategy.I` wrapper method. `rule` is a valid [Pandas offset string] indicating a time frame to resample `series` to. [Pandas offset string]: \ http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases `func` is the indicator function to apply on the resampled series. `series` is a data series (or array), such as any of the `backtesting.backtesting.Strategy.data` series. Due to pandas resampling limitations, this only works when input series has a datetime index. `agg` is the aggregation function to use on resampled groups of data. Valid values are anything accepted by `pandas/resample/.agg()`. Default value for dataframe input is `OHLCV_AGG` dictionary. Default value for series input is the appropriate entry from `OHLCV_AGG` if series has a matching name, or otherwise the value `"last"`, which is suitable for closing prices, but you might prefer another (e.g. `"max"` for peaks, or similar). Finally, any `*args` and `**kwargs` that are not already eaten by implicit `backtesting.backtesting.Strategy.I` call are passed to `func`. For example, if we have a typical moving average function `SMA(values, lookback_period)`, _hourly_ data source, and need to apply the moving average MA(10) on a _daily_ time frame, but don't want to plot the resulting indicator, we can do: class System(Strategy): def init(self): self.sma = resample_apply( 'D', SMA, self.data.Close, 10, plot=False) The above short snippet is roughly equivalent to: class System(Strategy): def init(self): # Strategy exposes `self.data` as raw NumPy arrays. # Let's convert closing prices back to pandas Series. close = self.data.Close.s # Resample to daily resolution. Aggregate groups # using their last value (i.e. closing price at the end # of the day). Notice `label='right'`. If it were set to # 'left' (default), the strategy would exhibit # look-ahead bias. daily = close.resample('D', label='right').agg('last') # We apply SMA(10) to daily close prices, # then reindex it back to original hourly index, # forward-filling the missing values in each day. # We make a separate function that returns the final # indicator array. def SMA(series, n): from backtesting.test import SMA return SMA(series, n).reindex(close.index).ffill() # The result equivalent to the short example above: self.sma = self.I(SMA, daily, 10, plot=False) """ if func is None: def func(x, *_, **__): return x assert callable(func), 'resample_apply(func=) must be callable' if not isinstance(series, (pd.Series, pd.DataFrame)): assert isinstance(series, _Array), \ 'resample_apply(series=) must be `pd.Series`, `pd.DataFrame`, ' \ 'or a `Strategy.data.*` array' series = series.s if agg is None: agg = OHLCV_AGG.get(getattr(series, 'name', ''), 'last') if isinstance(series, pd.DataFrame): agg = {column: OHLCV_AGG.get(column, 'last') for column in series.columns} resampled = series.resample(rule, label='right').agg(agg).dropna() resampled.name = _as_str(series) + '[' + rule + ']' # Check first few stack frames if we are being called from # inside Strategy.init, and if so, extract Strategy.I wrapper. frame, level = currentframe(), 0 while frame and level <= 3: frame = frame.f_back level += 1 if isinstance(frame.f_locals.get('self'), Strategy): # type: ignore strategy_I = frame.f_locals['self'].I # type: ignore break else: def strategy_I(func, *args, **kwargs): # noqa: F811 return func(*args, **kwargs) def wrap_func(resampled, *args, **kwargs): result = func(resampled, *args, **kwargs) if not isinstance(result, pd.DataFrame) and not isinstance(result, pd.Series): result = np.asarray(result) if result.ndim == 1: result = pd.Series(result, name=resampled.name) elif result.ndim == 2: result = pd.DataFrame(result.T) # Resample back to data index if not isinstance(result.index, pd.DatetimeIndex): result.index = resampled.index result = result.reindex(index=series.index.union(resampled.index), method='ffill').reindex(series.index) return result wrap_func.__name__ = func.__name__ array = strategy_I(wrap_func, resampled, *args, **kwargs) return array def random_ohlc_data(example_data: pd.DataFrame, *, frac=1., random_state: Optional[int] = None) -> Generator[pd.DataFrame, None, None]: """ OHLC data generator. The generated OHLC data has basic [descriptive statistics](https://en.wikipedia.org/wiki/Descriptive_statistics) similar to the provided `example_data`. `frac` is a fraction of data to sample (with replacement). Values greater than 1 result in oversampling. Such random data can be effectively used for stress testing trading strategy robustness, Monte Carlo simulations, significance testing, etc. >>> from backtesting.test import EURUSD >>> ohlc_generator = random_ohlc_data(EURUSD) >>> next(ohlc_generator) # returns new random data ... >>> next(ohlc_generator) # returns new random data ... """ def shuffle(x): return x.sample(frac=frac, replace=frac > 1, random_state=random_state) if len(example_data.columns.intersection({'Open', 'High', 'Low', 'Close'})) != 4: raise ValueError("`data` must be a pandas.DataFrame with columns " "'Open', 'High', 'Low', 'Close'") while True: df = shuffle(example_data) df.index = example_data.index padding = df.Close - df.Open.shift(-1) gaps = shuffle(example_data.Open.shift(-1) - example_data.Close) deltas = (padding + gaps).shift(1).fillna(0).cumsum() for key in ('Open', 'High', 'Low', 'Close'): df[key] += deltas yield df class SignalStrategy(Strategy): """ A simple helper strategy that operates on position entry/exit signals. This makes the backtest of the strategy simulate a [vectorized backtest]. See [tutorials] for usage examples. [vectorized backtest]: https://www.google.com/search?q=vectorized+backtest [tutorials]: index.html#tutorials To use this helper strategy, subclass it, override its `backtesting.backtesting.Strategy.init` method, and set the signal vector by calling `backtesting.lib.SignalStrategy.set_signal` method from within it. class ExampleStrategy(SignalStrategy): def init(self): super().init() self.set_signal(sma1 > sma2, sma1 < sma2) Remember to call `super().init()` and `super().next()` in your overridden methods. """ __entry_signal = (0,) __exit_signal = (False,) def set_signal(self, entry_size: Sequence[float], exit_portion: Optional[Sequence[float]] = None, *, plot: bool = True): """ Set entry/exit signal vectors (arrays). A long entry signal is considered present wherever `entry_size` is greater than zero, and a short signal wherever `entry_size` is less than zero, following `backtesting.backtesting.Order.size` semantics. If `exit_portion` is provided, a nonzero value closes portion the position (see `backtesting.backtesting.Trade.close()`) in the respective direction (positive values close long trades, negative short). If `plot` is `True`, the signal entry/exit indicators are plotted when `backtesting.backtesting.Backtest.plot` is called. """ self.__entry_signal = self.I( # type: ignore lambda: pd.Series(entry_size, dtype=float).replace(0, np.nan), name='entry size', plot=plot, overlay=False, scatter=True, color='black') if exit_portion is not None: self.__exit_signal = self.I( # type: ignore lambda: pd.Series(exit_portion, dtype=float).replace(0, np.nan), name='exit portion', plot=plot, overlay=False, scatter=True, color='black') def next(self): super().next() exit_portion = self.__exit_signal[-1] if exit_portion > 0: for trade in self.trades: if trade.is_long: trade.close(exit_portion) elif exit_portion < 0: for trade in self.trades: if trade.is_short: trade.close(-exit_portion) entry_size = self.__entry_signal[-1] if entry_size > 0: self.buy(size=entry_size) elif entry_size < 0: self.sell(size=-entry_size) class TrailingStrategy(Strategy): """ A strategy with automatic trailing stop-loss, trailing the current price at distance of some multiple of average true range (ATR). Call `TrailingStrategy.set_trailing_sl()` to set said multiple (`6` by default). See [tutorials] for usage examples. [tutorials]: index.html#tutorials Remember to call `super().init()` and `super().next()` in your overridden methods. """ __n_atr = 6. __atr = None def init(self): super().init() self.set_atr_periods() def set_atr_periods(self, periods: int = 100): """ Set the lookback period for computing ATR. The default value of 100 ensures a _stable_ ATR. """ hi, lo, c_prev = self.data.High, self.data.Low, pd.Series(self.data.Close).shift(1) tr = np.max([hi - lo, (c_prev - hi).abs(), (c_prev - lo).abs()], axis=0) atr = pd.Series(tr).rolling(periods).mean().bfill().values self.__atr = atr def set_trailing_sl(self, n_atr: float = 6): """ Set the future trailing stop-loss as some multiple (`n_atr`) average true bar ranges away from the current price. """ self.__n_atr = n_atr def set_trailing_pct(self, pct: float = .05): """ Set the future trailing stop-loss as some percent (`0 < pct < 1`) below the current price (default 5% below). .. note:: Stop-loss set by `pct` is inexact Stop-loss set by `set_trailing_pct` is converted to units of ATR with `mean(Close * pct / atr)` and set with `set_trailing_sl`. """ assert 0 < pct < 1, 'Need pct= as rate, i.e. 5% == 0.05' pct_in_atr = np.mean(self.data.Close * pct / self.__atr) # type: ignore self.set_trailing_sl(pct_in_atr) def next(self): super().next() # Can't use index=-1 because self.__atr is not an Indicator type index = len(self.data) - 1 for trade in self.trades: if trade.is_long: trade.sl = max(trade.sl or -np.inf, self.data.Close[index] - self.__atr[index] * self.__n_atr) else: trade.sl = min(trade.sl or np.inf, self.data.Close[index] + self.__atr[index] * self.__n_atr) class FractionalBacktest(Backtest): """ A `backtesting.backtesting.Backtest` that supports fractional share trading by simple composition. It applies roughly the transformation: data = (data * fractional_unit).assign(Volume=data.Volume / fractional_unit) as left unchallenged in [this FAQ entry on GitHub](https://github.com/kernc/backtesting.py/issues/134), then passes `data`, `args*`, and `**kwargs` to its super. Parameter `fractional_unit` represents the smallest fraction of currency that can be traded and defaults to one [satoshi]. For μBTC trading, pass `fractional_unit=1/1e6`. Thus-transformed backtest does a whole-sized trading of `fractional_unit` units. [satoshi]: https://en.wikipedia.org/wiki/Bitcoin#Units_and_divisibility """ def __init__(self, data, *args, fractional_unit=1 / 100e6, **kwargs): if 'satoshi' in kwargs: warnings.warn( 'Parameter `FractionalBacktest(..., satoshi=)` is deprecated. ' 'Use `FractionalBacktest(..., fractional_unit=)`.', category=DeprecationWarning, stacklevel=2) fractional_unit = 1 / kwargs.pop('satoshi') self._fractional_unit = fractional_unit self.__data: pd.DataFrame = data.copy(deep=False) # Shallow copy for col in ('Open', 'High', 'Low', 'Close',): self.__data[col] = self.__data[col] * self._fractional_unit for col in ('Volume',): self.__data[col] = self.__data[col] / self._fractional_unit with warnings.catch_warnings(record=True): warnings.filterwarnings(action='ignore', message='frac') super().__init__(data, *args, **kwargs) def run(self, **kwargs) -> pd.Series: with patch(self, '_data', self.__data): result = super().run(**kwargs) trades: pd.DataFrame = result['_trades'] trades['Size'] *= self._fractional_unit trades[['EntryPrice', 'ExitPrice', 'TP', 'SL']] /= self._fractional_unit indicators = result['_strategy']._indicators for indicator in indicators: if indicator._opts['overlay']: indicator /= self._fractional_unit return result # Prevent pdoc3 documenting __init__ signature of Strategy subclasses for cls in list(globals().values()): if isinstance(cls, type) and issubclass(cls, Strategy): __pdoc__[f'{cls.__name__}.__init__'] = False class MultiBacktest: """ Multi-dataset `backtesting.backtesting.Backtest` wrapper. Run supplied `backtesting.backtesting.Strategy` on several instruments, in parallel. Used for comparing strategy runs across many instruments or classes of instruments. Example: from backtesting.test import EURUSD, BTCUSD, SmaCross btm = MultiBacktest([EURUSD, BTCUSD], SmaCross) stats_per_ticker: pd.DataFrame = btm.run(fast=10, slow=20) heatmap_per_ticker: pd.DataFrame = btm.optimize(...) """ def __init__(self, df_list, strategy_cls, **kwargs): self._dfs = df_list self._strategy = strategy_cls self._bt_kwargs = kwargs def run(self, **kwargs): """ Wraps `backtesting.backtesting.Backtest.run`. Returns `pd.DataFrame` with currency indexes in columns. """ from . import Pool with Pool() as pool, \ SharedMemoryManager() as smm: shm = [smm.df2shm(df) for df in self._dfs] results = _tqdm( pool.imap(self._mp_task_run, ((df_batch, self._strategy, self._bt_kwargs, kwargs) for df_batch in _batch(shm))), total=len(shm), desc=self.run.__qualname__, mininterval=2 ) df = pd.DataFrame(list(chain(*results))).transpose() return df @staticmethod def _mp_task_run(args): data_shm, strategy, bt_kwargs, run_kwargs = args dfs, shms = zip(*(SharedMemoryManager.shm2df(i) for i in data_shm)) try: return [stats.filter(regex='^[^_]') if stats['# Trades'] else None for stats in (Backtest(df, strategy, **bt_kwargs).run(**run_kwargs) for df in dfs)] finally: for shmem in chain(*shms): shmem.close() def optimize(self, **kwargs) -> pd.DataFrame: """ Wraps `backtesting.backtesting.Backtest.optimize`, but returns `pd.DataFrame` with currency indexes in columns. heamap: pd.DataFrame = btm.optimize(...) from backtesting.plot import plot_heatmaps plot_heatmaps(heatmap.mean(axis=1)) """ heatmaps = [] # Simple loop since bt.optimize already does its own multiprocessing for df in _tqdm(self._dfs, desc=self.__class__.__name__, mininterval=2): bt = Backtest(df, self._strategy, **self._bt_kwargs) _best_stats, heatmap = bt.optimize( # type: ignore return_heatmap=True, return_optimization=False, **kwargs) heatmaps.append(heatmap) heatmap = pd.DataFrame(dict(zip(count(), heatmaps))) return heatmap # NOTE: Don't put anything below this __all__ list __all__ = [getattr(v, '__name__', k) for k, v in globals().items() # export if ((callable(v) and getattr(v, '__module__', None) == __name__ or # callables from this module k.isupper()) and # or CONSTANTS not getattr(v, '__name__', k).startswith('_'))] # neither marked internal # NOTE: Don't put anything below here. See above. ================================================ FILE: backtesting/test/BTCUSD.csv ================================================ ,Open,High,Low,Close,Volume 2012-01-31,4.58,7.38,3.8,5.55,2012.25343589 2012-02-29,5.55,6.5,3.8,4.99,4761.6090813 2012-03-31,4.99,5.44,4.54,4.92,9049.62628191 2012-04-30,4.92,5.43,4.69,5.0,16484.16777244 2012-05-31,5.0,5.15,4.86,5.14,18520.10380101 2012-06-30,5.14,6.69,5.1,6.54,63694.81610669 2012-07-31,6.54,9.28,6.36,9.2,50656.18175026 2012-08-31,9.2,16.41,7.1,9.81,82848.67116895 2012-09-30,9.81,12.66,9.49,12.17,56093.62696716 2012-10-31,12.17,12.99,9.5,10.75,83104.04747408 2012-11-30,10.75,12.74,10.25,12.42,88709.95682488 2012-12-31,12.42,13.94,12.24,13.24,91377.2908879 2013-01-31,13.24,21.0,12.77,20.79,124484.80488994 2013-02-28,20.79,34.24,19.5,33.98,123510.87774069 2013-03-31,33.98,97.0,32.96,94.08,178272.83745384 2013-04-30,94.08,259.34,45.0,136.84,437933.47186852 2013-05-31,136.83,141.28,81.5,127.24,300589.36039921 2013-06-30,127.24,128.86,86.2,89.0,250031.2382886 2013-07-31,89.01,101.0,63.0,99.67,419753.86745611 2013-08-31,98.72,132.8,90.0,131.24,328441.27534982 2013-09-30,131.24,134.95,115.0,126.24,322672.50941772 2013-10-31,126.24,206.6,85.0,203.74,625096.57168529 2013-11-30,203.7,1163.0,200.23,1110.09,965802.16510373 2013-12-31,1110.09,1153.27,382.21,734.33,953353.15686456 2014-01-31,734.33,995.0,725.0,801.46,444928.559971 2014-02-28,801.46,827.38,400.0,575.5,817663.79165669 2014-03-31,575.5,710.0,436.0,454.0,499158.1329599 2014-04-30,454.0,548.0,339.79,447.56,517371.75097255 2014-05-31,445.23,629.4,420.27,622.0,302311.93173703 2014-06-30,621.01,683.26,538.38,643.41,288960.8787793 2014-07-31,643.5,658.88,555.9,588.5,161612.26069054 2014-08-31,588.98,607.2,442.0,477.78,314687.67223104 2014-09-30,477.75,497.0,365.2,392.64,343002.80183706 2014-10-31,392.83,417.99,275.0,341.82,599895.44176646 2014-11-30,341.82,453.92,316.61,376.43,439599.74922191 2014-12-31,376.43,383.0,304.99,316.67,294441.85716544 2015-01-31,316.67,321.0,152.4,230.59,780354.02856165 2015-02-28,230.58,267.92,208.48,252.0,351708.96955626 2015-03-31,252.0,297.95,236.4,242.92,297384.58919865 2015-04-30,242.92,262.98,210.0,236.76,254714.59684772 2015-05-31,236.69,247.01,227.01,231.52,201312.29784566 2015-06-30,231.5,268.0,219.03,261.78,232269.97896899 2015-07-31,261.7,317.99,252.4,286.11,397787.41249925003 2015-08-31,286.11,286.11,198.12,231.57,418565.66541576 2015-09-30,231.34,246.24,223.0,237.33,569485.98275971 2015-10-31,237.33,334.67,235.0,311.64,713856.53717602 2015-11-30,311.62,502.0,294.0,374.19,886628.21617438 2015-12-31,374.19,467.8,348.64,431.28,422649.12320094 2016-01-31,430.91,465.0,352.0,375.98,288372.04635443003 2016-02-29,375.19,447.99,364.7,435.9,216664.02664585 2016-03-31,435.9,437.5,382.0,416.43,157235.30346074 2016-04-30,416.52,470.02,412.0,449.02,121815.55977623 2016-05-31,449.46,548.5,435.0,522.72,135188.60074123 2016-06-30,522.72,778.85,518.0,664.01,287296.10902287 2016-07-31,662.6,704.99,605.5,631.09,118452.46361069 2016-08-31,632.15,632.35,465.28,571.78,147939.20461568 2016-09-30,571.78,628.99,566.86,604.84,91672.30316333 2016-10-31,604.84,717.99,604.27,704.89,100913.96726614 2016-11-30,704.89,755.07,670.32,741.55,154162.8690078 2016-12-31,741.38,980.74,740.18,963.16,172770.27429349 2017-01-31,963.16,1139.89,751.34,955.79,334633.61649253 2017-02-28,956.67,1220.0,913.73,1195.39,209911.79775014 2017-03-31,1198.65,1350.0,891.33,1069.99,321058.71480166 2017-04-30,1068.4,1347.02,1060.0,1334.83,163259.72058026 2017-05-31,1334.83,2760.1,1330.0,2304.63,463554.71382256 2017-06-30,2304.62,2980.0,2120.0,2519.89,447784.98744192 2017-07-31,2517.0,2938.0,1830.0,2837.44,442045.6083084 2017-08-31,2837.44,4765.21,2615.0,4719.85,376644.9184963 2017-09-30,4724.3,4979.9,2972.01,4318.07,517752.76661262 2017-10-31,4318.07,6420.0,4137.96,6343.37,372951.07664284 2017-11-30,6343.37,11395.0,5555.55,9639.17,483242.2094437 2017-12-31,9639.17,19666.0,9370.11,13808.19,565118.43385979 2018-01-31,13840.53,17234.99,9222.0,9974.52,464529.1276022 2018-02-28,9974.91,11780.0,5920.72,10483.3,579199.79782788 2018-03-31,10482.78,11688.0,6550.0,7058.0,478106.34717399 2018-04-30,7045.11,9755.53,6427.16,9326.6,383170.01395792 2018-05-31,9323.8,9948.98,7026.9,7539.49,315425.59588242 2018-06-30,7530.97,7790.69,5774.72,6325.68,245559.6538061 2018-07-31,6325.67,8496.96,6072.0,7751.32,241646.43315209 2018-08-31,7751.96,7777.0,5880.0,7041.0,241399.24730462 2018-09-30,7041.0,7411.85,6094.38,6575.99,161654.90973714 2018-10-31,6575.0,6756.0,6055.28,6305.0,118357.07729593 2018-11-30,6305.0,6544.0,3474.73,3970.2,342901.30674616 2018-12-31,3970.19,4265.0,3122.28,3750.76,360073.74259956 2019-01-31,3750.62,4112.0,3322.19,3400.32,206637.0603685 2019-02-28,3402.71,4190.0,3328.7,3815.17,203491.41348045 2019-03-31,3814.63,4130.36,3670.0,4095.43,164360.1407343 2019-04-30,4096.77,5627.0,4052.56,5234.42,294712.72910007 2019-05-31,5234.42,9096.79,5224.25,8439.24,369760.90186756 2019-06-30,8443.75,13880.0,7432.84,11231.06,334048.14503083 2019-07-31,11232.4,13200.0,9049.54,9999.82,371432.37053016 2019-08-31,9999.82,12325.0,9320.0,9571.07,249828.48704415 2019-09-30,9584.47,10949.0,7714.7,8279.81,212207.58622774 2019-10-31,8286.24,10350.0,7293.55,9235.75,272266.35117133 2019-11-30,9229.33,9586.5,6515.0,7484.43,187515.893454 2019-12-31,7486.4,7772.71,6425.0,7139.78,129187.45427156 2020-01-31,7145.05,9570.0,6853.53,9289.11,202205.00650995 2020-02-29,9297.2,10500.5,8421.49,8665.35,170803.9857347 2020-03-31,8668.38,9219.13,3850.0,6474.59,481627.36606521 2020-04-30,6472.89,9485.26,6137.71,8847.01,290721.68636726 2020-05-31,8845.12,10074.0,8109.0,9507.95,389152.9589669 2020-06-30,9511.65,10429.26,8830.63,9137.69,210537.6154726 2020-07-31,9130.01,11457.95,8905.0,11333.56,195823.3739246 2020-08-31,11333.15,12473.0,10559.59,11729.64,195276.00613798 2020-09-30,11726.45,12065.82,9825.07,10708.78,211186.60703888 2020-10-31,10703.81,14100.0,10380.0,13794.24,182466.93926815 2020-11-30,13788.52,19864.15,13220.0,19182.23,287212.66517677 2020-12-31,19169.68,29300.0,17569.58,28920.98,255441.8533217 2021-01-31,28912.47,42000.0,27734.0,32487.61,458594.99699076 2021-02-28,32489.13,58354.14,32333.01,43724.75,239530.1816549 2021-03-31,43741.54,61781.83,43500.0,58582.36,158944.2114241 2021-04-30,58601.17,64895.22,47004.2,57098.08,139436.88887224 2021-05-31,57092.42,59603.0,30066.0,36907.65,210064.3136653 2021-06-30,36903.62,41341.57,28600.0,34780.49,143938.56072363 2021-07-31,34786.46,42411.32,29296.39,41335.16,80418.08217585 2021-08-31,41317.48,50562.11,37300.0,47321.52,80413.50047533 2021-09-30,47306.81,52956.47,39573.21,43540.0,70069.98010061 2021-10-31,43545.62,67016.5,43094.68,60730.85,79149.8625657 2021-11-30,60726.59,69000.0,53308.93,58349.19,72105.12010534 2021-12-31,58383.09,59099.64,41967.5,46648.83,71785.05584799 2022-01-31,46659.24,47989.0,32950.72,38479.91,71899.99316572 2022-02-28,38495.66,45850.0,34324.05,41233.87,57380.73682106 2022-03-31,41228.49,48234.0,37169.52,45622.39,58763.17822661 2022-04-30,45617.11,47458.48,37701.0,38487.71,44056.48532558 2022-05-31,38484.87,40050.19,25401.05,31610.61,101260.23960562 2022-06-30,31610.61,31962.79,17592.78,18901.6,106576.31850953 2022-07-31,18892.96,24676.0,18595.6,23837.21,74680.83536261 2022-08-31,23848.21,25212.0,19526.0,20219.0,47733.30210383 2022-09-30,20222.0,22781.0,18157.0,19495.0,69753.68501266 2022-10-31,19486.0,21078.0,18183.0,20368.0,41439.48859683 2022-11-30,20364.0,21473.0,15479.0,16926.0,104475.30523474 2022-12-31,16924.0,18373.0,16293.0,16567.0,56088.35203051 2023-01-31,16567.0,23954.0,16471.0,23171.0,66320.27104302 2023-02-28,23170.0,25270.0,21376.0,23485.0,52595.32439011 2023-03-31,23486.0,29380.0,19569.0,28460.0,91293.2104313 2023-04-30,28462.0,31035.0,26981.0,29383.0,54257.14084866 2023-05-31,29374.0,29851.0,25800.0,27020.0,56558.53819176 2023-06-30,27024.0,31458.0,24756.0,30368.0,71551.9162833 2023-07-31,30364.0,31818.0,28842.0,29157.0,42732.81486405 2023-08-31,29153.0,30210.0,25350.0,26181.0,49687.89486586 2023-09-30,26147.0,27486.0,24920.0,27031.0,41507.07942807 2023-10-31,27031.0,35198.0,26533.0,34553.0,54795.56290504 2023-11-30,34541.0,38457.0,34102.0,37750.0,47345.71140283 2023-12-31,37750.0,44729.0,37619.0,42639.0,52898.75724539 2024-01-31,42652.0,49048.0,38505.0,43500.0,76690.93493603 2024-02-29,43500.0,64000.0,41859.0,60775.0,60701.55689144 2024-03-31,60791.0,73794.0,59313.0,71034.0,91665.33521704 2024-04-30,71088.0,72756.0,59068.0,59150.0,62854.23631911 2024-05-31,59160.0,71958.0,56500.0,67580.0,50415.87703833 2024-06-30,67584.0,71949.0,58456.0,61940.0,47461.77759895 2024-07-31,61931.0,70016.0,53550.0,65243.0,62827.71293164 2024-08-31,65228.0,65568.0,49577.0,58856.0,79937.43578756 2024-09-30,58857.0,65978.0,52546.0,63302.0,39381.43155523 2024-10-31,63331.0,72928.0,58867.0,72346.0,18285.91448949 2024-11-30,69109.0,99121.0,67459.0,97482.0,42421.51031367 2024-12-31,96515.0,108364.0,92092.0,93381.0,25342.80228337 ================================================ FILE: backtesting/test/EURUSD.csv ================================================ ,Open,High,Low,Close,Volume 2017-04-19 09:00:00,1.0716,1.0722,1.07083,1.07219,1413 2017-04-19 10:00:00,1.07214,1.07296,1.07214,1.0726,1241 2017-04-19 11:00:00,1.07256,1.07299,1.0717,1.07192,1025 2017-04-19 12:00:00,1.07195,1.0728,1.07195,1.07202,1460 2017-04-19 13:00:00,1.072,1.0723,1.07045,1.0705,1554 2017-04-19 14:00:00,1.07054,1.07202,1.07044,1.07128,2342 2017-04-19 15:00:00,1.07127,1.07187,1.07002,1.07064,1867 2017-04-19 16:00:00,1.07068,1.07152,1.0705,1.07102,1349 2017-04-19 17:00:00,1.07107,1.0714,1.07094,1.07122,663 2017-04-19 18:00:00,1.0712,1.07232,1.0712,1.07202,802 2017-04-19 19:00:00,1.07198,1.07214,1.07098,1.07162,624 2017-04-19 20:00:00,1.07159,1.07165,1.0708,1.07114,391 2017-04-19 21:00:00,1.07132,1.07156,1.0709,1.0711,879 2017-04-19 22:00:00,1.07107,1.0717,1.0709,1.07154,776 2017-04-19 23:00:00,1.07159,1.0717,1.07118,1.07149,342 2017-04-20 00:00:00,1.07146,1.07187,1.07133,1.07164,509 2017-04-20 01:00:00,1.0716,1.07176,1.07097,1.07104,427 2017-04-20 02:00:00,1.07102,1.07169,1.07102,1.07154,197 2017-04-20 03:00:00,1.0715,1.0722,1.07136,1.07204,167 2017-04-20 04:00:00,1.07209,1.07278,1.07181,1.07276,257 2017-04-20 05:00:00,1.07274,1.0728,1.07246,1.07266,198 2017-04-20 06:00:00,1.07262,1.07476,1.0721,1.07414,1741 2017-04-20 07:00:00,1.0741,1.07502,1.07368,1.07484,2052 2017-04-20 08:00:00,1.07486,1.07758,1.07481,1.07698,1935 2017-04-20 09:00:00,1.077,1.07775,1.07622,1.07634,1488 2017-04-20 10:00:00,1.07632,1.07647,1.0754,1.07551,1013 2017-04-20 11:00:00,1.07552,1.07554,1.07423,1.0747,1137 2017-04-20 12:00:00,1.07468,1.07521,1.07411,1.07506,1302 2017-04-20 13:00:00,1.07507,1.07696,1.07478,1.07659,2309 2017-04-20 14:00:00,1.07663,1.07704,1.07518,1.07657,2528 2017-04-20 15:00:00,1.07652,1.0777,1.07549,1.07574,1865 2017-04-20 16:00:00,1.07579,1.07604,1.07426,1.07458,1356 2017-04-20 17:00:00,1.07458,1.07475,1.07154,1.07182,1409 2017-04-20 18:00:00,1.07182,1.07235,1.07163,1.07212,907 2017-04-20 19:00:00,1.07213,1.07276,1.0715,1.07182,1060 2017-04-20 20:00:00,1.07188,1.07208,1.07148,1.07168,491 2017-04-20 21:00:00,1.07166,1.0721,1.07147,1.07159,303 2017-04-20 22:00:00,1.07156,1.07165,1.07072,1.07151,470 2017-04-20 23:00:00,1.07149,1.07163,1.07116,1.07142,312 2017-04-21 00:00:00,1.07138,1.07212,1.07124,1.07178,508 2017-04-21 01:00:00,1.07172,1.072,1.07166,1.07179,400 2017-04-21 02:00:00,1.07179,1.07179,1.07133,1.07143,232 2017-04-21 03:00:00,1.07148,1.07198,1.07143,1.0717,250 2017-04-21 04:00:00,1.07174,1.07192,1.07164,1.07164,168 2017-04-21 05:00:00,1.0716,1.07188,1.07146,1.07151,374 2017-04-21 06:00:00,1.07148,1.07284,1.071,1.07276,1386 2017-04-21 07:00:00,1.07278,1.0738,1.07176,1.07218,2732 2017-04-21 08:00:00,1.07217,1.07265,1.07168,1.07188,1901 2017-04-21 09:00:00,1.0719,1.07202,1.0696,1.07053,1953 2017-04-21 10:00:00,1.07054,1.0707,1.0688,1.0701,1392 2017-04-21 11:00:00,1.07012,1.07047,1.06911,1.06914,1296 2017-04-21 12:00:00,1.06912,1.07043,1.069,1.0701,1429 2017-04-21 13:00:00,1.07012,1.0711,1.06962,1.06974,2086 2017-04-21 14:00:00,1.06977,1.07054,1.0686,1.06876,2568 2017-04-21 15:00:00,1.06878,1.0701,1.06869,1.06938,2148 2017-04-21 16:00:00,1.06936,1.06954,1.06824,1.06924,1420 2017-04-21 17:00:00,1.06924,1.071,1.06922,1.06991,1557 2017-04-21 18:00:00,1.06988,1.07004,1.06875,1.0695,1528 2017-04-21 19:00:00,1.06951,1.07052,1.06919,1.07029,1269 2017-04-21 20:00:00,1.07029,1.07306,1.06986,1.07268,2681 2017-04-23 21:00:00,1.0893,1.09063,1.08803,1.0898,1758 2017-04-23 22:00:00,1.08977,1.08995,1.08701,1.08842,2532 2017-04-23 23:00:00,1.08839,1.08902,1.08648,1.08734,1166 2017-04-24 00:00:00,1.08732,1.08768,1.0838,1.08504,2562 2017-04-24 01:00:00,1.08506,1.08555,1.08366,1.08405,1104 2017-04-24 02:00:00,1.08403,1.08405,1.08209,1.0838,1030 2017-04-24 03:00:00,1.08382,1.08513,1.08346,1.08442,623 2017-04-24 04:00:00,1.0844,1.08605,1.08418,1.08587,650 2017-04-24 05:00:00,1.08584,1.08773,1.08456,1.0868,1135 2017-04-24 06:00:00,1.08682,1.08695,1.08319,1.08432,2899 2017-04-24 07:00:00,1.0843,1.08664,1.0833,1.08555,2608 2017-04-24 08:00:00,1.08558,1.08752,1.08492,1.08584,1838 2017-04-24 09:00:00,1.08587,1.08773,1.0855,1.08708,1334 2017-04-24 10:00:00,1.08706,1.08724,1.08516,1.08584,1087 2017-04-24 11:00:00,1.08586,1.08631,1.08416,1.08422,1135 2017-04-24 12:00:00,1.0842,1.08652,1.0842,1.08575,1487 2017-04-24 13:00:00,1.08573,1.08736,1.0857,1.08656,1653 2017-04-24 14:00:00,1.08653,1.08705,1.08553,1.08602,1282 2017-04-24 15:00:00,1.08604,1.08682,1.08474,1.0848,1312 2017-04-24 16:00:00,1.08478,1.0854,1.08354,1.08416,950 2017-04-24 17:00:00,1.08414,1.08498,1.08412,1.08483,683 2017-04-24 18:00:00,1.0848,1.08591,1.08462,1.08582,703 2017-04-24 19:00:00,1.08585,1.08697,1.08553,1.0868,497 2017-04-24 20:00:00,1.08678,1.08699,1.08648,1.08674,253 2017-04-24 21:00:00,1.08684,1.08689,1.08636,1.08649,235 2017-04-24 22:00:00,1.08651,1.08692,1.0865,1.08678,204 2017-04-24 23:00:00,1.08675,1.08676,1.08632,1.0865,164 2017-04-25 00:00:00,1.08652,1.08706,1.08624,1.08624,400 2017-04-25 01:00:00,1.08626,1.0865,1.08542,1.08552,382 2017-04-25 02:00:00,1.08554,1.08619,1.08515,1.08619,379 2017-04-25 03:00:00,1.08617,1.08636,1.08601,1.08602,186 2017-04-25 04:00:00,1.08599,1.08658,1.08572,1.08632,200 2017-04-25 05:00:00,1.08634,1.0872,1.08634,1.08652,381 2017-04-25 06:00:00,1.08655,1.08882,1.08633,1.08827,1302 2017-04-25 07:00:00,1.08825,1.08933,1.0875,1.08856,1857 2017-04-25 08:00:00,1.08853,1.08942,1.08838,1.08924,791 2017-04-25 09:00:00,1.08922,1.08988,1.08822,1.0883,807 2017-04-25 10:00:00,1.08832,1.08846,1.0871,1.08789,793 2017-04-25 11:00:00,1.08786,1.08881,1.08768,1.08881,679 2017-04-25 12:00:00,1.08879,1.08962,1.08866,1.0889,1005 2017-04-25 13:00:00,1.08888,1.09016,1.08797,1.09,1340 2017-04-25 14:00:00,1.08997,1.09328,1.08902,1.09281,3642 2017-04-25 15:00:00,1.09284,1.09412,1.09198,1.09409,1651 2017-04-25 16:00:00,1.09407,1.09499,1.09386,1.09492,966 2017-04-25 17:00:00,1.09494,1.095,1.09334,1.09354,669 2017-04-25 18:00:00,1.09356,1.09411,1.09308,1.0938,575 2017-04-25 19:00:00,1.09377,1.0938,1.09258,1.09375,476 2017-04-25 20:00:00,1.09373,1.09373,1.09258,1.09265,450 2017-04-25 21:00:00,1.093,1.0932,1.0925,1.09282,286 2017-04-25 22:00:00,1.09279,1.09312,1.09274,1.09298,501 2017-04-25 23:00:00,1.09296,1.09306,1.09237,1.09278,251 2017-04-26 00:00:00,1.0928,1.0945,1.09274,1.09379,661 2017-04-26 01:00:00,1.09376,1.09406,1.09308,1.09374,519 2017-04-26 02:00:00,1.09377,1.09406,1.09334,1.0939,218 2017-04-26 03:00:00,1.09393,1.094,1.09356,1.09366,144 2017-04-26 04:00:00,1.09364,1.09476,1.09364,1.09404,329 2017-04-26 05:00:00,1.09407,1.09498,1.09407,1.09461,413 2017-04-26 06:00:00,1.09458,1.09508,1.09221,1.0925,1167 2017-04-26 07:00:00,1.09252,1.09326,1.0905,1.09137,1635 2017-04-26 08:00:00,1.09134,1.09184,1.08958,1.09026,1794 2017-04-26 09:00:00,1.09029,1.09076,1.08922,1.09007,1189 2017-04-26 10:00:00,1.09004,1.09004,1.08824,1.08926,1120 2017-04-26 11:00:00,1.08924,1.08982,1.08918,1.08936,868 2017-04-26 12:00:00,1.08938,1.08972,1.08812,1.0887,1723 2017-04-26 13:00:00,1.08874,1.08908,1.08737,1.08898,1699 2017-04-26 14:00:00,1.089,1.08953,1.0876,1.08763,1457 2017-04-26 15:00:00,1.0876,1.08844,1.08558,1.08725,1926 2017-04-26 16:00:00,1.08723,1.08788,1.08667,1.08754,1047 2017-04-26 17:00:00,1.08757,1.08982,1.08614,1.08982,2571 2017-04-26 18:00:00,1.08984,1.09124,1.08884,1.08978,2112 2017-04-26 19:00:00,1.08975,1.09106,1.08928,1.09071,819 2017-04-26 20:00:00,1.09074,1.09095,1.09033,1.09039,247 2017-04-26 21:00:00,1.09049,1.09056,1.09002,1.09048,126 2017-04-26 22:00:00,1.09045,1.09088,1.09016,1.09087,236 2017-04-26 23:00:00,1.0909,1.091,1.09066,1.09086,144 2017-04-27 00:00:00,1.09083,1.09158,1.09064,1.09068,422 2017-04-27 01:00:00,1.09065,1.09108,1.09042,1.09096,417 2017-04-27 02:00:00,1.09094,1.0921,1.09094,1.09104,406 2017-04-27 03:00:00,1.09106,1.09159,1.09086,1.09089,458 2017-04-27 04:00:00,1.09086,1.09097,1.09034,1.09052,292 2017-04-27 05:00:00,1.0905,1.09077,1.08997,1.09002,233 2017-04-27 06:00:00,1.09005,1.09074,1.08961,1.09066,748 2017-04-27 07:00:00,1.09069,1.09184,1.09024,1.09056,1300 2017-04-27 08:00:00,1.09053,1.092,1.09036,1.09076,1179 2017-04-27 09:00:00,1.09074,1.09096,1.08918,1.08962,878 2017-04-27 10:00:00,1.08964,1.09074,1.08954,1.08982,616 2017-04-27 11:00:00,1.08984,1.09017,1.08801,1.08868,1534 2017-04-27 12:00:00,1.08866,1.09329,1.08802,1.08956,7247 2017-04-27 13:00:00,1.08958,1.08966,1.08527,1.08622,4136 2017-04-27 14:00:00,1.08624,1.0872,1.08516,1.08648,1633 2017-04-27 15:00:00,1.08652,1.08721,1.08609,1.08635,1251 2017-04-27 16:00:00,1.08632,1.08799,1.08582,1.08756,1014 2017-04-27 17:00:00,1.08758,1.08873,1.0874,1.08812,823 2017-04-27 18:00:00,1.08816,1.08852,1.08776,1.08814,524 2017-04-27 19:00:00,1.08816,1.08846,1.08726,1.08748,480 2017-04-27 20:00:00,1.0875,1.08798,1.087,1.08726,331 2017-04-27 21:00:00,1.0873,1.0875,1.08716,1.08739,225 2017-04-27 22:00:00,1.08736,1.08757,1.08698,1.08704,211 2017-04-27 23:00:00,1.08702,1.0872,1.08608,1.08654,326 2017-04-28 00:00:00,1.08658,1.08684,1.08606,1.08608,565 2017-04-28 01:00:00,1.0861,1.08708,1.08575,1.0868,551 2017-04-28 02:00:00,1.08676,1.08722,1.08672,1.08683,271 2017-04-28 03:00:00,1.08685,1.08685,1.08666,1.08671,143 2017-04-28 04:00:00,1.08669,1.08681,1.08634,1.08644,107 2017-04-28 05:00:00,1.08646,1.0866,1.08572,1.08628,371 2017-04-28 06:00:00,1.0863,1.0885,1.0863,1.088,847 2017-04-28 07:00:00,1.08802,1.08898,1.08802,1.08854,867 2017-04-28 08:00:00,1.08856,1.0902,1.08834,1.0902,1160 2017-04-28 09:00:00,1.09024,1.09472,1.09024,1.09375,3378 2017-04-28 10:00:00,1.09377,1.0945,1.09343,1.09382,804 2017-04-28 11:00:00,1.0938,1.09396,1.09264,1.09343,1133 2017-04-28 12:00:00,1.09346,1.09348,1.09072,1.09122,3086 2017-04-28 13:00:00,1.09119,1.092,1.09007,1.09176,1894 2017-04-28 14:00:00,1.09173,1.09218,1.08836,1.08914,2613 2017-04-28 15:00:00,1.08916,1.0902,1.08882,1.08944,1699 2017-04-28 16:00:00,1.08946,1.09012,1.08854,1.09008,714 2017-04-28 17:00:00,1.0901,1.0906,1.08988,1.09017,461 2017-04-28 18:00:00,1.09015,1.09036,1.08934,1.08948,528 2017-04-28 19:00:00,1.08952,1.09,1.0892,1.08932,490 2017-04-28 20:00:00,1.08934,1.09003,1.08896,1.08962,423 2017-04-30 21:00:00,1.0913,1.0913,1.08924,1.08975,339 2017-04-30 22:00:00,1.08971,1.09034,1.08952,1.09024,396 2017-04-30 23:00:00,1.09026,1.09112,1.09026,1.09059,258 2017-05-01 00:00:00,1.09062,1.09079,1.08996,1.08996,241 2017-05-01 01:00:00,1.08998,1.09008,1.08893,1.08928,475 2017-05-01 02:00:00,1.0893,1.0897,1.08924,1.08938,201 2017-05-01 03:00:00,1.08936,1.08946,1.08914,1.08933,65 2017-05-01 04:00:00,1.0893,1.08937,1.08904,1.08908,74 2017-05-01 05:00:00,1.08906,1.08922,1.08862,1.0887,106 2017-05-01 06:00:00,1.08868,1.08904,1.08846,1.08872,213 2017-05-01 07:00:00,1.0887,1.08948,1.08842,1.08904,171 2017-05-01 08:00:00,1.08902,1.08992,1.08902,1.08986,185 2017-05-01 09:00:00,1.08984,1.08992,1.08955,1.0899,86 2017-05-01 10:00:00,1.08988,1.09012,1.08976,1.08988,160 2017-05-01 11:00:00,1.08985,1.09057,1.08964,1.09055,246 2017-05-01 12:00:00,1.09057,1.0922,1.09039,1.09169,622 2017-05-01 13:00:00,1.09172,1.09176,1.09058,1.09093,724 2017-05-01 14:00:00,1.09096,1.09238,1.09096,1.09147,1337 2017-05-01 15:00:00,1.0915,1.09184,1.09082,1.09114,793 2017-05-01 16:00:00,1.09112,1.09154,1.09016,1.0904,1094 2017-05-01 17:00:00,1.09038,1.09077,1.08986,1.0905,575 2017-05-01 18:00:00,1.09048,1.0908,1.09032,1.09052,336 2017-05-01 19:00:00,1.09054,1.0908,1.08979,1.08984,316 2017-05-01 20:00:00,1.08986,1.09011,1.08964,1.0899,198 2017-05-01 21:00:00,1.08982,1.09026,1.08978,1.08985,197 2017-05-01 22:00:00,1.08982,1.09115,1.08982,1.09115,297 2017-05-01 23:00:00,1.09112,1.09128,1.09046,1.09086,170 2017-05-02 00:00:00,1.09088,1.09125,1.09046,1.0907,441 2017-05-02 01:00:00,1.09072,1.09117,1.09067,1.09082,320 2017-05-02 02:00:00,1.09084,1.09117,1.09052,1.09113,247 2017-05-02 03:00:00,1.09116,1.09174,1.09112,1.09154,207 2017-05-02 04:00:00,1.09156,1.09158,1.09124,1.09128,184 2017-05-02 05:00:00,1.09125,1.09256,1.09123,1.09187,343 2017-05-02 06:00:00,1.09185,1.09228,1.09084,1.09175,1042 2017-05-02 07:00:00,1.09178,1.09199,1.09073,1.09076,1209 2017-05-02 08:00:00,1.09074,1.09183,1.09049,1.09148,1121 2017-05-02 09:00:00,1.0915,1.09214,1.09088,1.09182,884 2017-05-02 10:00:00,1.0918,1.09196,1.09094,1.0917,616 2017-05-02 11:00:00,1.09168,1.09184,1.09052,1.09056,800 2017-05-02 12:00:00,1.09058,1.09158,1.09046,1.09077,1143 2017-05-02 13:00:00,1.0908,1.09132,1.09045,1.09091,1122 2017-05-02 14:00:00,1.09093,1.091,1.08884,1.09015,1397 2017-05-02 15:00:00,1.09017,1.09128,1.08984,1.09096,756 2017-05-02 16:00:00,1.09098,1.09176,1.09043,1.09063,492 2017-05-02 17:00:00,1.0906,1.09192,1.09036,1.09189,526 2017-05-02 18:00:00,1.09186,1.09304,1.0916,1.09267,655 2017-05-02 19:00:00,1.09269,1.09294,1.09222,1.0926,434 2017-05-02 20:00:00,1.09262,1.09332,1.09244,1.09308,240 2017-05-02 21:00:00,1.09284,1.09303,1.09262,1.09289,229 2017-05-02 22:00:00,1.09287,1.09319,1.09258,1.09308,355 2017-05-02 23:00:00,1.09306,1.09362,1.09275,1.0935,194 2017-05-03 00:00:00,1.09352,1.09366,1.09307,1.09332,252 2017-05-03 01:00:00,1.09329,1.09368,1.09322,1.09336,219 2017-05-03 02:00:00,1.09338,1.0935,1.09317,1.09344,147 2017-05-03 03:00:00,1.09341,1.0935,1.09294,1.09294,132 2017-05-03 04:00:00,1.09296,1.09343,1.09292,1.09343,144 2017-05-03 05:00:00,1.09346,1.09352,1.09244,1.09248,288 2017-05-03 06:00:00,1.09251,1.09266,1.0916,1.09217,888 2017-05-03 07:00:00,1.09215,1.09215,1.09106,1.0919,982 2017-05-03 08:00:00,1.09192,1.09224,1.09082,1.09122,988 2017-05-03 09:00:00,1.0912,1.09148,1.09063,1.09096,1022 2017-05-03 10:00:00,1.09098,1.09152,1.09078,1.091,586 2017-05-03 11:00:00,1.09102,1.09152,1.09067,1.09142,626 2017-05-03 12:00:00,1.09144,1.09226,1.0911,1.0915,1960 2017-05-03 13:00:00,1.09152,1.09214,1.091,1.09108,1043 2017-05-03 14:00:00,1.09104,1.0915,1.0904,1.09078,1592 2017-05-03 15:00:00,1.0908,1.09195,1.09068,1.09191,979 2017-05-03 16:00:00,1.09188,1.09232,1.09151,1.09172,456 2017-05-03 17:00:00,1.0917,1.0921,1.09108,1.0913,694 2017-05-03 18:00:00,1.09125,1.09272,1.08932,1.0904,4484 2017-05-03 19:00:00,1.09043,1.09068,1.0887,1.08954,1116 2017-05-03 20:00:00,1.08951,1.08954,1.08828,1.08852,341 2017-05-03 21:00:00,1.08868,1.08894,1.08834,1.08844,414 2017-05-03 22:00:00,1.08846,1.08912,1.08816,1.08892,240 2017-05-03 23:00:00,1.0889,1.08904,1.08859,1.08883,281 2017-05-04 00:00:00,1.0888,1.08946,1.08856,1.08867,370 2017-05-04 01:00:00,1.0887,1.0895,1.0885,1.0893,396 2017-05-04 02:00:00,1.08933,1.08948,1.08894,1.0892,214 2017-05-04 03:00:00,1.08916,1.08969,1.08904,1.08966,235 2017-05-04 04:00:00,1.08964,1.0897,1.08936,1.08962,109 2017-05-04 05:00:00,1.0896,1.0896,1.08924,1.08935,154 2017-05-04 06:00:00,1.08932,1.08976,1.0875,1.08832,1093 2017-05-04 07:00:00,1.08836,1.09178,1.08816,1.0912,1952 2017-05-04 08:00:00,1.09122,1.09246,1.09104,1.09186,1606 2017-05-04 09:00:00,1.09184,1.09357,1.09174,1.09332,1596 2017-05-04 10:00:00,1.09334,1.09412,1.09286,1.09308,1261 2017-05-04 11:00:00,1.0931,1.09348,1.0924,1.09241,952 2017-05-04 12:00:00,1.09244,1.09364,1.09186,1.09346,1649 2017-05-04 13:00:00,1.09344,1.09387,1.09251,1.09371,1487 2017-05-04 14:00:00,1.09369,1.09531,1.09313,1.09452,2636 2017-05-04 15:00:00,1.09454,1.09689,1.09354,1.09666,2755 2017-05-04 16:00:00,1.09664,1.09792,1.09653,1.09735,1664 2017-05-04 17:00:00,1.09737,1.09768,1.09662,1.09678,1143 2017-05-04 18:00:00,1.09675,1.09791,1.09663,1.09778,921 2017-05-04 19:00:00,1.09774,1.09852,1.09772,1.0985,519 2017-05-04 20:00:00,1.09852,1.09872,1.09818,1.09842,355 2017-05-04 21:00:00,1.09834,1.09834,1.09786,1.09794,109 2017-05-04 22:00:00,1.09796,1.0982,1.09786,1.09806,238 2017-05-04 23:00:00,1.09808,1.09818,1.09797,1.09806,136 2017-05-05 00:00:00,1.09809,1.09822,1.09756,1.09794,297 2017-05-05 01:00:00,1.09791,1.09791,1.09734,1.09749,419 2017-05-05 02:00:00,1.09747,1.0979,1.0973,1.09782,183 2017-05-05 03:00:00,1.09784,1.09863,1.09784,1.0984,577 2017-05-05 04:00:00,1.09843,1.09902,1.09832,1.09835,421 2017-05-05 05:00:00,1.09838,1.09878,1.0979,1.0979,408 2017-05-05 06:00:00,1.09792,1.09836,1.09698,1.09703,1403 2017-05-05 07:00:00,1.097,1.09833,1.09658,1.09744,1447 2017-05-05 08:00:00,1.09741,1.09777,1.09567,1.09584,1334 2017-05-05 09:00:00,1.09586,1.0966,1.09564,1.09574,808 2017-05-05 10:00:00,1.09572,1.09635,1.09538,1.09593,860 2017-05-05 11:00:00,1.09596,1.09644,1.09554,1.09574,993 2017-05-05 12:00:00,1.09576,1.09918,1.095,1.0979,4022 2017-05-05 13:00:00,1.09792,1.09826,1.09658,1.09808,2350 2017-05-05 14:00:00,1.09811,1.09925,1.09778,1.09843,2049 2017-05-05 15:00:00,1.09846,1.0999,1.0981,1.09952,1433 2017-05-05 16:00:00,1.0995,1.09987,1.09846,1.09904,946 2017-05-05 17:00:00,1.09902,1.09936,1.09849,1.09908,729 2017-05-05 18:00:00,1.09906,1.09919,1.09839,1.09888,698 2017-05-05 19:00:00,1.09886,1.09982,1.09854,1.09978,654 2017-05-05 20:00:00,1.09975,1.09996,1.09928,1.09989,781 2017-05-07 21:00:00,1.102,1.10237,1.1002,1.10132,848 2017-05-07 22:00:00,1.10134,1.10142,1.09854,1.09932,1061 2017-05-07 23:00:00,1.0993,1.0996,1.09634,1.0965,709 2017-05-08 00:00:00,1.09647,1.09744,1.09574,1.0967,1327 2017-05-08 01:00:00,1.09668,1.09763,1.09636,1.09744,884 2017-05-08 02:00:00,1.09746,1.09826,1.09718,1.09754,614 2017-05-08 03:00:00,1.09752,1.0976,1.09658,1.09712,574 2017-05-08 04:00:00,1.09715,1.0973,1.09614,1.09724,787 2017-05-08 05:00:00,1.09721,1.09948,1.09708,1.09943,935 2017-05-08 06:00:00,1.0994,1.09968,1.09744,1.09838,2024 2017-05-08 07:00:00,1.09836,1.09856,1.0954,1.09564,2498 2017-05-08 08:00:00,1.09562,1.09654,1.09514,1.09563,1383 2017-05-08 09:00:00,1.0956,1.09567,1.09353,1.09384,1372 2017-05-08 10:00:00,1.09386,1.09576,1.09368,1.09504,949 2017-05-08 11:00:00,1.09506,1.09566,1.09382,1.09384,1015 2017-05-08 12:00:00,1.09386,1.09508,1.09314,1.09472,1553 2017-05-08 13:00:00,1.09474,1.09529,1.09394,1.09436,1434 2017-05-08 14:00:00,1.09434,1.09484,1.09292,1.09312,1348 2017-05-08 15:00:00,1.09316,1.09332,1.09165,1.0928,1358 2017-05-08 16:00:00,1.09278,1.09362,1.09246,1.09324,585 2017-05-08 17:00:00,1.09322,1.0937,1.09248,1.09284,549 2017-05-08 18:00:00,1.09287,1.09354,1.09266,1.09334,388 2017-05-08 19:00:00,1.09332,1.09332,1.09274,1.09302,446 2017-05-08 20:00:00,1.093,1.09304,1.09214,1.09238,250 2017-05-08 21:00:00,1.09241,1.09274,1.09191,1.09224,302 2017-05-08 22:00:00,1.09226,1.09268,1.09192,1.09257,260 2017-05-08 23:00:00,1.09259,1.09314,1.09248,1.09267,254 2017-05-09 00:00:00,1.0927,1.09326,1.09219,1.0925,519 2017-05-09 01:00:00,1.09252,1.09314,1.092,1.09282,597 2017-05-09 02:00:00,1.0928,1.09309,1.09266,1.09271,391 2017-05-09 03:00:00,1.09274,1.09336,1.09266,1.09293,323 2017-05-09 04:00:00,1.09296,1.09318,1.09286,1.09307,188 2017-05-09 05:00:00,1.09305,1.09316,1.09232,1.09232,256 2017-05-09 06:00:00,1.09234,1.09248,1.09027,1.09063,1395 2017-05-09 07:00:00,1.09065,1.0924,1.09056,1.09238,1407 2017-05-09 08:00:00,1.0924,1.09262,1.09058,1.09063,1052 2017-05-09 09:00:00,1.09065,1.09091,1.08924,1.09056,1456 2017-05-09 10:00:00,1.09054,1.09064,1.08862,1.08964,1181 2017-05-09 11:00:00,1.08961,1.0902,1.088,1.08863,1486 2017-05-09 12:00:00,1.0886,1.08901,1.0874,1.08842,1691 2017-05-09 13:00:00,1.08844,1.08992,1.08831,1.08965,1289 2017-05-09 14:00:00,1.08962,1.09066,1.08898,1.08902,1721 2017-05-09 15:00:00,1.08905,1.08922,1.08716,1.08744,1186 2017-05-09 16:00:00,1.08746,1.08996,1.08733,1.08959,910 2017-05-09 17:00:00,1.08962,1.08969,1.08783,1.08848,1192 2017-05-09 18:00:00,1.08846,1.08846,1.08636,1.08682,569 2017-05-09 19:00:00,1.08684,1.0878,1.08664,1.08736,802 2017-05-09 20:00:00,1.08734,1.08778,1.08704,1.08743,267 2017-05-09 21:00:00,1.0874,1.088,1.08707,1.08781,544 2017-05-09 22:00:00,1.08778,1.0888,1.08764,1.08822,490 2017-05-09 23:00:00,1.08824,1.08909,1.0882,1.08865,327 2017-05-10 00:00:00,1.08862,1.08969,1.08824,1.08947,782 2017-05-10 01:00:00,1.08944,1.08982,1.08932,1.0895,542 2017-05-10 02:00:00,1.08948,1.08966,1.08924,1.08931,259 2017-05-10 03:00:00,1.08934,1.0897,1.08919,1.08936,112 2017-05-10 04:00:00,1.08939,1.08975,1.08914,1.08914,145 2017-05-10 05:00:00,1.08916,1.08965,1.0887,1.08916,363 2017-05-10 06:00:00,1.08918,1.08943,1.08836,1.08879,1095 2017-05-10 07:00:00,1.08876,1.0898,1.08876,1.08912,1265 2017-05-10 08:00:00,1.08914,1.08942,1.08703,1.0871,1046 2017-05-10 09:00:00,1.08712,1.08738,1.08588,1.08676,1189 2017-05-10 10:00:00,1.08678,1.08724,1.0863,1.08688,751 2017-05-10 11:00:00,1.0869,1.08796,1.08605,1.08767,1812 2017-05-10 12:00:00,1.08769,1.08827,1.08724,1.08799,1312 2017-05-10 13:00:00,1.08796,1.08824,1.08634,1.08716,1286 2017-05-10 14:00:00,1.08714,1.08765,1.0863,1.08696,1263 2017-05-10 15:00:00,1.08694,1.08738,1.08626,1.08722,1118 2017-05-10 16:00:00,1.08724,1.0875,1.08666,1.08676,642 2017-05-10 17:00:00,1.08678,1.08702,1.08552,1.08586,890 2017-05-10 18:00:00,1.08589,1.08621,1.08534,1.0861,541 2017-05-10 19:00:00,1.08612,1.08688,1.08607,1.08661,517 2017-05-10 20:00:00,1.08658,1.08682,1.08656,1.08676,124 2017-05-10 21:00:00,1.08674,1.08702,1.08624,1.08676,591 2017-05-10 22:00:00,1.08674,1.0869,1.0865,1.08665,291 2017-05-10 23:00:00,1.08663,1.08692,1.08646,1.08672,187 2017-05-11 00:00:00,1.0867,1.08758,1.08636,1.08742,316 2017-05-11 01:00:00,1.0874,1.08778,1.08707,1.08717,273 2017-05-11 02:00:00,1.0872,1.08798,1.08712,1.08776,293 2017-05-11 03:00:00,1.08774,1.08777,1.08726,1.08726,198 2017-05-11 04:00:00,1.08724,1.08728,1.087,1.08702,126 2017-05-11 05:00:00,1.087,1.08762,1.08646,1.08757,321 2017-05-11 06:00:00,1.08754,1.08864,1.08718,1.0879,924 2017-05-11 07:00:00,1.08787,1.0893,1.0872,1.0887,1275 2017-05-11 08:00:00,1.08872,1.08884,1.0878,1.08785,821 2017-05-11 09:00:00,1.08782,1.08784,1.08685,1.08696,526 2017-05-11 10:00:00,1.08694,1.08742,1.08674,1.08701,507 2017-05-11 11:00:00,1.08704,1.08756,1.08564,1.08568,1479 2017-05-11 12:00:00,1.0857,1.08621,1.08393,1.08519,2108 2017-05-11 13:00:00,1.08522,1.08711,1.08519,1.08678,1445 2017-05-11 14:00:00,1.08675,1.08729,1.0865,1.0867,1396 2017-05-11 15:00:00,1.08668,1.08756,1.08658,1.0869,980 2017-05-11 16:00:00,1.08688,1.08716,1.08638,1.08668,514 2017-05-11 17:00:00,1.0867,1.0875,1.0866,1.08702,633 2017-05-11 18:00:00,1.087,1.08713,1.08657,1.08668,418 2017-05-11 19:00:00,1.08665,1.08671,1.086,1.08636,406 2017-05-11 20:00:00,1.08634,1.08674,1.086,1.08609,185 2017-05-11 21:00:00,1.08613,1.08648,1.08596,1.08617,377 2017-05-11 22:00:00,1.08614,1.08652,1.08601,1.08638,145 2017-05-11 23:00:00,1.0864,1.08676,1.08638,1.08644,97 2017-05-12 00:00:00,1.08642,1.08666,1.08602,1.08651,368 2017-05-12 01:00:00,1.08654,1.08711,1.08646,1.08704,222 2017-05-12 02:00:00,1.08702,1.08738,1.08699,1.08718,184 2017-05-12 03:00:00,1.08714,1.08724,1.08696,1.08696,137 2017-05-12 04:00:00,1.08694,1.08715,1.08668,1.08711,166 2017-05-12 05:00:00,1.08713,1.08726,1.08692,1.08714,188 2017-05-12 06:00:00,1.08712,1.08721,1.08598,1.086,710 2017-05-12 07:00:00,1.08598,1.08674,1.08557,1.08632,1143 2017-05-12 08:00:00,1.0863,1.08732,1.08614,1.08656,736 2017-05-12 09:00:00,1.08654,1.08768,1.08631,1.0873,843 2017-05-12 10:00:00,1.08728,1.08782,1.08697,1.08762,704 2017-05-12 11:00:00,1.08764,1.0878,1.08703,1.08709,574 2017-05-12 12:00:00,1.08711,1.09126,1.08706,1.09061,3273 2017-05-12 13:00:00,1.09063,1.09254,1.09063,1.09223,3295 2017-05-12 14:00:00,1.0922,1.09278,1.09151,1.09212,1805 2017-05-12 15:00:00,1.09214,1.09343,1.09172,1.09256,1227 2017-05-12 16:00:00,1.09258,1.09286,1.09148,1.09186,599 2017-05-12 17:00:00,1.09189,1.09233,1.09178,1.09228,405 2017-05-12 18:00:00,1.0923,1.09276,1.09193,1.09243,284 2017-05-12 19:00:00,1.0924,1.09311,1.09226,1.093,453 2017-05-12 20:00:00,1.09297,1.0933,1.09272,1.09314,294 2017-05-14 21:00:00,1.09304,1.09354,1.09292,1.09324,223 2017-05-14 22:00:00,1.09326,1.09338,1.09236,1.0926,604 2017-05-14 23:00:00,1.09262,1.09284,1.09229,1.09282,178 2017-05-15 00:00:00,1.09279,1.09336,1.09237,1.09246,527 2017-05-15 01:00:00,1.09248,1.09284,1.09228,1.09275,260 2017-05-15 02:00:00,1.09272,1.09312,1.09258,1.09274,288 2017-05-15 03:00:00,1.09278,1.09304,1.09263,1.09293,101 2017-05-15 04:00:00,1.0929,1.09314,1.09272,1.09302,116 2017-05-15 05:00:00,1.09304,1.09335,1.09292,1.09292,201 2017-05-15 06:00:00,1.09294,1.09426,1.0926,1.09358,1173 2017-05-15 07:00:00,1.09356,1.09473,1.09339,1.09418,1179 2017-05-15 08:00:00,1.0942,1.09497,1.09384,1.09452,817 2017-05-15 09:00:00,1.09449,1.09599,1.09449,1.0954,1223 2017-05-15 10:00:00,1.09542,1.097,1.09526,1.09698,1007 2017-05-15 11:00:00,1.09696,1.09732,1.09642,1.09659,739 2017-05-15 12:00:00,1.09661,1.0982,1.09654,1.09784,1371 2017-05-15 13:00:00,1.09786,1.09894,1.09762,1.09792,1244 2017-05-15 14:00:00,1.09794,1.0986,1.09728,1.09782,1049 2017-05-15 15:00:00,1.09785,1.09862,1.09766,1.0979,960 2017-05-15 16:00:00,1.09788,1.09808,1.09709,1.09742,565 2017-05-15 17:00:00,1.0974,1.09778,1.09665,1.09696,504 2017-05-15 18:00:00,1.09698,1.09785,1.0966,1.0978,368 2017-05-15 19:00:00,1.09783,1.09799,1.09748,1.09778,240 2017-05-15 20:00:00,1.09776,1.09782,1.09749,1.09751,129 2017-05-15 21:00:00,1.09747,1.09797,1.09743,1.09771,169 2017-05-15 22:00:00,1.09774,1.09834,1.0977,1.09811,217 2017-05-15 23:00:00,1.09808,1.09834,1.09788,1.098,102 2017-05-16 00:00:00,1.09802,1.09857,1.09792,1.09835,368 2017-05-16 01:00:00,1.09837,1.099,1.09808,1.0987,317 2017-05-16 02:00:00,1.09872,1.09883,1.09834,1.09883,277 2017-05-16 03:00:00,1.0988,1.09892,1.09865,1.09892,101 2017-05-16 04:00:00,1.0989,1.09938,1.09875,1.0992,189 2017-05-16 05:00:00,1.09923,1.10005,1.09914,1.09922,467 2017-05-16 06:00:00,1.09919,1.1018,1.09914,1.10141,1306 2017-05-16 07:00:00,1.10139,1.10365,1.1012,1.1029,1822 2017-05-16 08:00:00,1.10288,1.10497,1.10281,1.10456,1476 2017-05-16 09:00:00,1.10458,1.10475,1.103,1.10444,1341 2017-05-16 10:00:00,1.10441,1.10589,1.10404,1.10532,1120 2017-05-16 11:00:00,1.1053,1.10658,1.10508,1.10528,1167 2017-05-16 12:00:00,1.10531,1.10888,1.10524,1.10778,1955 2017-05-16 13:00:00,1.10776,1.10786,1.1065,1.10676,1714 2017-05-16 14:00:00,1.10672,1.10862,1.10668,1.10822,1549 2017-05-16 15:00:00,1.1082,1.1084,1.1064,1.10716,1477 2017-05-16 16:00:00,1.10714,1.10812,1.10679,1.10756,873 2017-05-16 17:00:00,1.10758,1.10923,1.10754,1.10902,595 2017-05-16 18:00:00,1.109,1.10972,1.109,1.10952,504 2017-05-16 19:00:00,1.1095,1.1097,1.10846,1.10849,472 2017-05-16 20:00:00,1.10852,1.10898,1.10802,1.10826,295 2017-05-16 21:00:00,1.10831,1.10863,1.10802,1.10808,724 2017-05-16 22:00:00,1.1081,1.10952,1.1081,1.10949,464 2017-05-16 23:00:00,1.10946,1.10968,1.10896,1.10936,430 2017-05-17 00:00:00,1.10934,1.1098,1.10867,1.10956,664 2017-05-17 01:00:00,1.10954,1.10989,1.10944,1.10975,382 2017-05-17 02:00:00,1.10973,1.1114,1.10959,1.111,527 2017-05-17 03:00:00,1.11102,1.1117,1.111,1.1113,401 2017-05-17 04:00:00,1.11134,1.11164,1.1104,1.11061,478 2017-05-17 05:00:00,1.11063,1.11121,1.10968,1.11089,747 2017-05-17 06:00:00,1.11092,1.11222,1.11038,1.11084,1312 2017-05-17 07:00:00,1.11086,1.1118,1.10817,1.10889,2234 2017-05-17 08:00:00,1.10892,1.10999,1.10844,1.10854,1541 2017-05-17 09:00:00,1.10856,1.11002,1.10849,1.10958,1137 2017-05-17 10:00:00,1.1096,1.11128,1.10941,1.11088,1062 2017-05-17 11:00:00,1.11086,1.11133,1.1101,1.11119,994 2017-05-17 12:00:00,1.11122,1.11244,1.11115,1.11233,1367 2017-05-17 13:00:00,1.11231,1.11259,1.11017,1.11258,2651 2017-05-17 14:00:00,1.11256,1.1147,1.11254,1.11466,2839 2017-05-17 15:00:00,1.11468,1.11504,1.11302,1.11332,1873 2017-05-17 16:00:00,1.1133,1.1144,1.11323,1.1138,910 2017-05-17 17:00:00,1.11382,1.1155,1.11376,1.1155,953 2017-05-17 18:00:00,1.11548,1.11581,1.1146,1.11476,925 2017-05-17 19:00:00,1.11474,1.11572,1.11442,1.11528,794 2017-05-17 20:00:00,1.11526,1.11622,1.11507,1.11599,609 2017-05-17 21:00:00,1.11574,1.1171,1.11574,1.11652,742 2017-05-17 22:00:00,1.11654,1.11689,1.11613,1.11618,546 2017-05-17 23:00:00,1.11616,1.11641,1.11511,1.11542,534 2017-05-18 00:00:00,1.1154,1.11659,1.1151,1.11648,819 2017-05-18 01:00:00,1.11645,1.11719,1.11534,1.1155,905 2017-05-18 02:00:00,1.11552,1.1158,1.1151,1.11559,510 2017-05-18 03:00:00,1.11562,1.11612,1.1155,1.1161,277 2017-05-18 04:00:00,1.11612,1.1162,1.11392,1.1144,626 2017-05-18 05:00:00,1.11442,1.11458,1.11383,1.11388,596 2017-05-18 06:00:00,1.11391,1.11458,1.11214,1.11304,1713 2017-05-18 07:00:00,1.11306,1.11454,1.11298,1.11334,1746 2017-05-18 08:00:00,1.11331,1.1145,1.11271,1.11271,2070 2017-05-18 09:00:00,1.11273,1.11402,1.11076,1.112,3004 2017-05-18 10:00:00,1.11198,1.11375,1.11178,1.11363,1437 2017-05-18 11:00:00,1.11361,1.11397,1.11118,1.1121,1807 2017-05-18 12:00:00,1.11212,1.11369,1.11042,1.11088,2398 2017-05-18 13:00:00,1.1109,1.11184,1.11048,1.11131,2555 2017-05-18 14:00:00,1.11129,1.11282,1.11083,1.11267,2485 2017-05-18 15:00:00,1.11269,1.11342,1.11208,1.11296,1793 2017-05-18 16:00:00,1.11299,1.11378,1.11228,1.11256,1394 2017-05-18 17:00:00,1.11253,1.11302,1.10837,1.10894,2885 2017-05-18 18:00:00,1.1089,1.1105,1.1076,1.11029,2250 2017-05-18 19:00:00,1.11026,1.11141,1.10931,1.11131,1211 2017-05-18 20:00:00,1.11129,1.11129,1.11022,1.11026,339 2017-05-18 21:00:00,1.11032,1.11035,1.10974,1.11009,347 2017-05-18 22:00:00,1.11006,1.11113,1.10986,1.11079,272 2017-05-18 23:00:00,1.11076,1.11148,1.11076,1.11102,254 2017-05-19 00:00:00,1.111,1.11114,1.11062,1.1109,450 2017-05-19 01:00:00,1.11092,1.11138,1.11051,1.11076,533 2017-05-19 02:00:00,1.11074,1.11074,1.10998,1.11026,415 2017-05-19 03:00:00,1.11023,1.11104,1.11023,1.11089,277 2017-05-19 04:00:00,1.11091,1.11136,1.11073,1.11119,202 2017-05-19 05:00:00,1.11122,1.11145,1.11094,1.11137,261 2017-05-19 06:00:00,1.11134,1.11269,1.11092,1.11227,1073 2017-05-19 07:00:00,1.1123,1.11433,1.11222,1.11386,1356 2017-05-19 08:00:00,1.1139,1.1161,1.11354,1.11493,1454 2017-05-19 09:00:00,1.1149,1.11654,1.1147,1.11584,1179 2017-05-19 10:00:00,1.11582,1.11678,1.1156,1.11632,775 2017-05-19 11:00:00,1.11634,1.11835,1.11607,1.1178,1208 2017-05-19 12:00:00,1.11782,1.11859,1.11751,1.11795,1381 2017-05-19 13:00:00,1.11792,1.11968,1.11782,1.11889,1648 2017-05-19 14:00:00,1.11892,1.11971,1.11842,1.11954,1665 2017-05-19 15:00:00,1.11956,1.11967,1.11875,1.11892,933 2017-05-19 16:00:00,1.1189,1.11956,1.11852,1.11908,612 2017-05-19 17:00:00,1.11905,1.12118,1.11898,1.12106,1040 2017-05-19 18:00:00,1.12103,1.12108,1.12044,1.12044,593 2017-05-19 19:00:00,1.12046,1.12114,1.11985,1.12044,1029 2017-05-19 20:00:00,1.12042,1.12084,1.12019,1.12068,264 2017-05-21 21:00:00,1.11962,1.1209,1.11962,1.12022,378 2017-05-21 22:00:00,1.12026,1.12077,1.12004,1.12068,388 2017-05-21 23:00:00,1.12066,1.12101,1.1201,1.12034,626 2017-05-22 00:00:00,1.12032,1.12032,1.11864,1.11867,676 2017-05-22 01:00:00,1.1187,1.11938,1.11826,1.11932,541 2017-05-22 02:00:00,1.11929,1.11929,1.11836,1.11918,317 2017-05-22 03:00:00,1.11915,1.11962,1.11906,1.11918,196 2017-05-22 04:00:00,1.1192,1.11942,1.11885,1.11888,171 2017-05-22 05:00:00,1.1189,1.1194,1.11852,1.1194,308 2017-05-22 06:00:00,1.11938,1.1201,1.11804,1.11846,1254 2017-05-22 07:00:00,1.11843,1.11938,1.11642,1.11716,1905 2017-05-22 08:00:00,1.11714,1.11787,1.11616,1.11682,1130 2017-05-22 09:00:00,1.1168,1.12067,1.11661,1.12009,1631 2017-05-22 10:00:00,1.12012,1.12364,1.12012,1.12274,2598 2017-05-22 11:00:00,1.12272,1.1246,1.12264,1.12352,1577 2017-05-22 12:00:00,1.12354,1.12498,1.12338,1.12454,1166 2017-05-22 13:00:00,1.12457,1.12588,1.12408,1.12574,1728 2017-05-22 14:00:00,1.12572,1.12635,1.1244,1.12468,1646 2017-05-22 15:00:00,1.1247,1.12518,1.12287,1.12349,1368 2017-05-22 16:00:00,1.12351,1.12376,1.12291,1.1234,619 2017-05-22 17:00:00,1.12338,1.12441,1.1233,1.12419,372 2017-05-22 18:00:00,1.12422,1.12434,1.12344,1.12358,463 2017-05-22 19:00:00,1.1236,1.12378,1.12295,1.12372,471 2017-05-22 20:00:00,1.1237,1.12424,1.12362,1.12373,246 2017-05-22 21:00:00,1.1237,1.12416,1.12365,1.12372,557 2017-05-22 22:00:00,1.12375,1.12424,1.12306,1.12388,408 2017-05-22 23:00:00,1.1239,1.1244,1.12367,1.12376,349 2017-05-23 00:00:00,1.12374,1.12472,1.12366,1.12395,531 2017-05-23 01:00:00,1.12398,1.12516,1.12394,1.12496,410 2017-05-23 02:00:00,1.12498,1.12541,1.12473,1.12518,390 2017-05-23 03:00:00,1.12515,1.12518,1.12438,1.12456,249 2017-05-23 04:00:00,1.12454,1.12534,1.12424,1.12504,195 2017-05-23 05:00:00,1.12502,1.12512,1.12438,1.12443,330 2017-05-23 06:00:00,1.12446,1.12456,1.12222,1.12252,2199 2017-05-23 07:00:00,1.12254,1.12684,1.12232,1.12524,3432 2017-05-23 08:00:00,1.12529,1.126,1.12414,1.12485,2420 2017-05-23 09:00:00,1.12482,1.12629,1.12453,1.12582,1264 2017-05-23 10:00:00,1.12584,1.12602,1.12376,1.12386,969 2017-05-23 11:00:00,1.12388,1.12399,1.12164,1.12178,1047 2017-05-23 12:00:00,1.1218,1.12384,1.12112,1.1234,1698 2017-05-23 13:00:00,1.12338,1.12434,1.12256,1.12257,1449 2017-05-23 14:00:00,1.12255,1.12332,1.12132,1.1229,2071 2017-05-23 15:00:00,1.12287,1.12494,1.11935,1.11968,4542 2017-05-23 16:00:00,1.11971,1.12074,1.11823,1.11922,2407 2017-05-23 17:00:00,1.1192,1.12088,1.11892,1.11966,1103 2017-05-23 18:00:00,1.11968,1.11988,1.11766,1.1185,1025 2017-05-23 19:00:00,1.11852,1.11855,1.11752,1.11829,982 2017-05-23 20:00:00,1.11827,1.11854,1.11774,1.11824,381 2017-05-23 21:00:00,1.11831,1.11857,1.11804,1.11844,367 2017-05-23 22:00:00,1.11842,1.11894,1.1184,1.11873,275 2017-05-23 23:00:00,1.1187,1.1187,1.11774,1.11789,213 2017-05-24 00:00:00,1.11792,1.11918,1.11782,1.11867,694 2017-05-24 01:00:00,1.1187,1.11912,1.11806,1.11852,575 2017-05-24 02:00:00,1.1185,1.119,1.11844,1.11856,309 2017-05-24 03:00:00,1.11854,1.11856,1.1178,1.11806,316 2017-05-24 04:00:00,1.1181,1.11822,1.11786,1.118,189 2017-05-24 05:00:00,1.11802,1.11809,1.1173,1.11783,381 2017-05-24 06:00:00,1.11781,1.11832,1.11715,1.11775,1247 2017-05-24 07:00:00,1.11777,1.11849,1.117,1.11758,1567 2017-05-24 08:00:00,1.11755,1.11968,1.11734,1.11856,1727 2017-05-24 09:00:00,1.11854,1.11888,1.11754,1.11763,1023 2017-05-24 10:00:00,1.11766,1.1193,1.1175,1.1191,1254 2017-05-24 11:00:00,1.11908,1.11926,1.1179,1.11877,1044 2017-05-24 12:00:00,1.11874,1.12022,1.1185,1.11978,1846 2017-05-24 13:00:00,1.11982,1.12047,1.11824,1.1187,2326 2017-05-24 14:00:00,1.11872,1.11968,1.11815,1.11839,1921 2017-05-24 15:00:00,1.11842,1.11873,1.11687,1.11746,1165 2017-05-24 16:00:00,1.11748,1.11822,1.1174,1.11778,530 2017-05-24 17:00:00,1.11776,1.11898,1.1176,1.11872,848 2017-05-24 18:00:00,1.11868,1.12163,1.11795,1.11974,5268 2017-05-24 19:00:00,1.11971,1.12176,1.11937,1.12152,1554 2017-05-24 20:00:00,1.12149,1.12201,1.12122,1.12187,361 2017-05-24 21:00:00,1.12192,1.12198,1.12122,1.12136,215 2017-05-24 22:00:00,1.12138,1.12235,1.1211,1.12174,370 2017-05-24 23:00:00,1.12176,1.12334,1.12176,1.12306,343 2017-05-25 00:00:00,1.12308,1.12308,1.12207,1.12231,374 2017-05-25 01:00:00,1.12229,1.1231,1.12188,1.12238,558 2017-05-25 02:00:00,1.12236,1.124,1.12228,1.12393,533 2017-05-25 03:00:00,1.12391,1.12446,1.12384,1.12388,452 2017-05-25 04:00:00,1.1239,1.12436,1.12364,1.12409,240 2017-05-25 05:00:00,1.12412,1.12449,1.12346,1.12436,383 2017-05-25 06:00:00,1.12434,1.12438,1.12268,1.12297,1228 2017-05-25 07:00:00,1.12295,1.12448,1.12255,1.12434,1458 2017-05-25 08:00:00,1.12432,1.12504,1.12351,1.12422,1656 2017-05-25 09:00:00,1.12419,1.12419,1.1218,1.12251,1231 2017-05-25 10:00:00,1.12249,1.12249,1.1209,1.12172,1161 2017-05-25 11:00:00,1.1217,1.1219,1.12062,1.12142,969 2017-05-25 12:00:00,1.12145,1.12157,1.11936,1.12029,1312 2017-05-25 13:00:00,1.12026,1.12154,1.12007,1.12143,1091 2017-05-25 14:00:00,1.1214,1.12224,1.1205,1.12131,1396 2017-05-25 15:00:00,1.12134,1.12234,1.12116,1.12174,1132 2017-05-25 16:00:00,1.12176,1.12262,1.1215,1.12182,723 2017-05-25 17:00:00,1.12179,1.12209,1.12008,1.12082,835 2017-05-25 18:00:00,1.1208,1.1208,1.11986,1.12048,597 2017-05-25 19:00:00,1.12046,1.1214,1.12028,1.1211,549 2017-05-25 20:00:00,1.12108,1.12116,1.12038,1.12104,275 2017-05-25 21:00:00,1.12086,1.12128,1.12074,1.12088,266 2017-05-25 22:00:00,1.12092,1.12161,1.12078,1.12127,271 2017-05-25 23:00:00,1.12129,1.12129,1.1208,1.12102,146 2017-05-26 00:00:00,1.12104,1.12124,1.11966,1.12059,590 2017-05-26 01:00:00,1.12061,1.12078,1.11872,1.11886,533 2017-05-26 02:00:00,1.11888,1.11992,1.11852,1.1199,647 2017-05-26 03:00:00,1.11992,1.12056,1.1199,1.12014,433 2017-05-26 04:00:00,1.12012,1.12024,1.11974,1.12002,195 2017-05-26 05:00:00,1.12004,1.12039,1.11912,1.1195,355 2017-05-26 06:00:00,1.11948,1.12128,1.11912,1.12112,1567 2017-05-26 07:00:00,1.12114,1.12243,1.1208,1.12186,1169 2017-05-26 08:00:00,1.12188,1.12345,1.12174,1.12238,1480 2017-05-26 09:00:00,1.12241,1.12252,1.12114,1.12172,1053 2017-05-26 10:00:00,1.12175,1.12268,1.12139,1.12216,792 2017-05-26 11:00:00,1.12214,1.1223,1.1191,1.11912,1379 2017-05-26 12:00:00,1.11914,1.12002,1.11779,1.11803,2180 2017-05-26 13:00:00,1.11806,1.11812,1.11606,1.11678,2016 2017-05-26 14:00:00,1.1168,1.11875,1.11648,1.1185,1665 2017-05-26 15:00:00,1.11848,1.11865,1.11662,1.11691,1347 2017-05-26 16:00:00,1.11693,1.11797,1.11616,1.11774,782 2017-05-26 17:00:00,1.11776,1.11832,1.11759,1.11768,597 2017-05-26 18:00:00,1.11766,1.11778,1.11732,1.11762,337 2017-05-26 19:00:00,1.11764,1.11786,1.1169,1.11712,372 2017-05-26 20:00:00,1.1171,1.11837,1.1168,1.11813,497 2017-05-28 21:00:00,1.11738,1.11768,1.11685,1.11758,80 2017-05-28 22:00:00,1.11761,1.11826,1.1176,1.11826,318 2017-05-28 23:00:00,1.11828,1.11848,1.11728,1.11761,202 2017-05-29 00:00:00,1.11758,1.11758,1.11619,1.11723,542 2017-05-29 01:00:00,1.11726,1.11733,1.11654,1.11673,277 2017-05-29 02:00:00,1.11671,1.11692,1.11634,1.11638,191 2017-05-29 03:00:00,1.11641,1.1165,1.11628,1.11648,124 2017-05-29 04:00:00,1.11646,1.1166,1.11626,1.11637,179 2017-05-29 05:00:00,1.11639,1.11732,1.11636,1.11656,252 2017-05-29 06:00:00,1.11653,1.11771,1.11653,1.11712,564 2017-05-29 07:00:00,1.11714,1.11822,1.11666,1.1179,704 2017-05-29 08:00:00,1.11793,1.11898,1.11774,1.11835,585 2017-05-29 09:00:00,1.11838,1.11882,1.11774,1.11871,497 2017-05-29 10:00:00,1.11868,1.11868,1.1181,1.1184,562 2017-05-29 11:00:00,1.11837,1.11883,1.11832,1.11869,397 2017-05-29 12:00:00,1.11871,1.11899,1.11842,1.1185,458 2017-05-29 13:00:00,1.11852,1.11885,1.117,1.11716,846 2017-05-29 14:00:00,1.11719,1.1179,1.11676,1.11762,640 2017-05-29 15:00:00,1.11764,1.11832,1.11762,1.11798,495 2017-05-29 16:00:00,1.118,1.1182,1.11738,1.1174,282 2017-05-29 17:00:00,1.11738,1.11767,1.11694,1.11711,266 2017-05-29 18:00:00,1.11708,1.11715,1.11685,1.11709,117 2017-05-29 19:00:00,1.11712,1.11734,1.11698,1.11716,173 2017-05-29 20:00:00,1.11718,1.11724,1.11622,1.1163,408 2017-05-29 21:00:00,1.11633,1.1169,1.11511,1.11522,342 2017-05-29 22:00:00,1.11525,1.1153,1.11353,1.11387,779 2017-05-29 23:00:00,1.11389,1.11398,1.11274,1.11292,437 2017-05-30 00:00:00,1.11294,1.11378,1.11282,1.11336,626 2017-05-30 01:00:00,1.11333,1.11336,1.11221,1.11236,587 2017-05-30 02:00:00,1.11234,1.113,1.11226,1.11292,222 2017-05-30 03:00:00,1.1129,1.11308,1.11256,1.11256,140 2017-05-30 04:00:00,1.11258,1.11293,1.11232,1.11276,187 2017-05-30 05:00:00,1.11278,1.1132,1.1116,1.11162,487 2017-05-30 06:00:00,1.11164,1.11312,1.11096,1.1131,1324 2017-05-30 07:00:00,1.11312,1.11449,1.11271,1.1141,1958 2017-05-30 08:00:00,1.11407,1.1149,1.11388,1.11401,1351 2017-05-30 09:00:00,1.11403,1.11668,1.11366,1.11648,1383 2017-05-30 10:00:00,1.11645,1.11684,1.11531,1.11538,973 2017-05-30 11:00:00,1.11536,1.11937,1.11463,1.11742,3278 2017-05-30 12:00:00,1.11745,1.1176,1.11543,1.11624,2448 2017-05-30 13:00:00,1.11626,1.11919,1.11626,1.1191,2111 2017-05-30 14:00:00,1.11912,1.12054,1.11752,1.11775,2240 2017-05-30 15:00:00,1.11778,1.11894,1.1176,1.11824,1072 2017-05-30 16:00:00,1.11826,1.11838,1.1172,1.11744,623 2017-05-30 17:00:00,1.11746,1.11838,1.11744,1.11806,406 2017-05-30 18:00:00,1.11803,1.11902,1.11792,1.11886,379 2017-05-30 19:00:00,1.11882,1.11936,1.11842,1.11886,398 2017-05-30 20:00:00,1.11883,1.11968,1.11851,1.11858,358 2017-05-30 21:00:00,1.11867,1.119,1.1178,1.11798,474 2017-05-30 22:00:00,1.118,1.11809,1.11732,1.11735,310 2017-05-30 23:00:00,1.11733,1.11738,1.11702,1.11729,187 2017-05-31 00:00:00,1.11732,1.1182,1.11695,1.11712,566 2017-05-31 01:00:00,1.11714,1.118,1.11707,1.1175,483 2017-05-31 02:00:00,1.11752,1.11794,1.11708,1.11756,334 2017-05-31 03:00:00,1.11758,1.11786,1.11736,1.11765,183 2017-05-31 04:00:00,1.11768,1.11782,1.11718,1.11728,129 2017-05-31 05:00:00,1.1173,1.1176,1.11646,1.11682,381 2017-05-31 06:00:00,1.11679,1.11897,1.11649,1.11883,1185 2017-05-31 07:00:00,1.11881,1.1197,1.11798,1.11851,1233 2017-05-31 08:00:00,1.11849,1.11854,1.117,1.11746,1502 2017-05-31 09:00:00,1.11748,1.11922,1.11712,1.11904,1674 2017-05-31 10:00:00,1.11906,1.12181,1.11852,1.12128,1709 2017-05-31 11:00:00,1.12126,1.12288,1.12126,1.12216,1306 2017-05-31 12:00:00,1.12219,1.1225,1.12112,1.12223,1339 2017-05-31 13:00:00,1.12226,1.12422,1.12176,1.12334,1984 2017-05-31 14:00:00,1.12336,1.12464,1.12212,1.12459,2528 2017-05-31 15:00:00,1.12461,1.12524,1.12306,1.1237,2619 2017-05-31 16:00:00,1.12368,1.1238,1.12282,1.12368,791 2017-05-31 17:00:00,1.12371,1.12424,1.12281,1.12394,978 2017-05-31 18:00:00,1.12392,1.12474,1.12348,1.12458,674 2017-05-31 19:00:00,1.1246,1.12472,1.12302,1.12336,649 2017-05-31 20:00:00,1.12338,1.12443,1.12322,1.12433,413 2017-05-31 21:00:00,1.12466,1.12491,1.1241,1.12433,364 2017-05-31 22:00:00,1.1243,1.12434,1.12358,1.12388,432 2017-05-31 23:00:00,1.1239,1.1245,1.12388,1.12391,249 2017-06-01 00:00:00,1.12388,1.12422,1.12336,1.12345,398 2017-06-01 01:00:00,1.12342,1.12556,1.12338,1.12471,653 2017-06-01 02:00:00,1.12468,1.12566,1.12416,1.12423,520 2017-06-01 03:00:00,1.1242,1.12492,1.12374,1.12468,407 2017-06-01 04:00:00,1.12466,1.12476,1.12436,1.12466,238 2017-06-01 05:00:00,1.12463,1.12478,1.12376,1.12376,429 2017-06-01 06:00:00,1.12378,1.12488,1.12276,1.1244,1263 2017-06-01 07:00:00,1.12438,1.12458,1.1229,1.12324,1404 2017-06-01 08:00:00,1.12327,1.12378,1.12229,1.12262,1086 2017-06-01 09:00:00,1.12265,1.1237,1.12222,1.12356,1216 2017-06-01 10:00:00,1.12354,1.12368,1.12241,1.12278,804 2017-06-01 11:00:00,1.12276,1.12281,1.12168,1.12182,754 2017-06-01 12:00:00,1.12184,1.12246,1.12023,1.12148,2078 2017-06-01 13:00:00,1.1215,1.1226,1.12132,1.12222,1165 2017-06-01 14:00:00,1.12225,1.12344,1.1216,1.12256,1591 2017-06-01 15:00:00,1.12258,1.12259,1.12091,1.12134,904 2017-06-01 16:00:00,1.12132,1.12172,1.12062,1.12095,530 2017-06-01 17:00:00,1.12098,1.12134,1.12068,1.12112,388 2017-06-01 18:00:00,1.12109,1.12194,1.12069,1.12147,615 2017-06-01 19:00:00,1.12144,1.1218,1.12113,1.12147,410 2017-06-01 20:00:00,1.12144,1.12164,1.12105,1.12124,210 2017-06-01 21:00:00,1.12142,1.12172,1.12122,1.1214,253 2017-06-01 22:00:00,1.12143,1.12195,1.12136,1.1219,155 2017-06-01 23:00:00,1.12188,1.1219,1.12124,1.12128,140 2017-06-02 00:00:00,1.12131,1.12181,1.12122,1.1217,357 2017-06-02 01:00:00,1.12167,1.12222,1.12157,1.12172,330 2017-06-02 02:00:00,1.12176,1.12204,1.12158,1.1217,152 2017-06-02 03:00:00,1.12168,1.12188,1.12153,1.12186,134 2017-06-02 04:00:00,1.12184,1.12202,1.12168,1.12194,114 2017-06-02 05:00:00,1.12198,1.12204,1.12159,1.12175,191 2017-06-02 06:00:00,1.12177,1.12216,1.12158,1.12184,579 2017-06-02 07:00:00,1.12182,1.1228,1.1215,1.1215,927 2017-06-02 08:00:00,1.12152,1.12268,1.12146,1.12211,708 2017-06-02 09:00:00,1.12208,1.12237,1.12168,1.1221,568 2017-06-02 10:00:00,1.12208,1.1224,1.12156,1.12186,771 2017-06-02 11:00:00,1.12183,1.12202,1.12052,1.1206,919 2017-06-02 12:00:00,1.12062,1.12825,1.12057,1.12694,5016 2017-06-02 13:00:00,1.12697,1.12774,1.12548,1.12759,2905 2017-06-02 14:00:00,1.12762,1.12794,1.12562,1.12754,1670 2017-06-02 15:00:00,1.12757,1.12816,1.12673,1.12696,1272 2017-06-02 16:00:00,1.12698,1.12765,1.1266,1.12698,762 2017-06-02 17:00:00,1.12701,1.12764,1.1267,1.12756,598 2017-06-02 18:00:00,1.12758,1.12795,1.12742,1.1277,436 2017-06-02 19:00:00,1.12772,1.12852,1.12752,1.12821,613 2017-06-02 20:00:00,1.12819,1.12848,1.12797,1.12825,319 2017-06-04 21:00:00,1.12736,1.12831,1.12736,1.12792,206 2017-06-04 22:00:00,1.12787,1.12838,1.12733,1.12792,611 2017-06-04 23:00:00,1.1279,1.1281,1.1276,1.12776,323 2017-06-05 00:00:00,1.12778,1.12783,1.1269,1.12727,505 2017-06-05 01:00:00,1.12724,1.1277,1.12688,1.12694,313 2017-06-05 02:00:00,1.12697,1.12738,1.12652,1.12738,248 2017-06-05 03:00:00,1.12735,1.12748,1.12669,1.12706,239 2017-06-05 04:00:00,1.12708,1.12712,1.12634,1.12666,177 2017-06-05 05:00:00,1.12663,1.1267,1.12615,1.1265,337 2017-06-05 06:00:00,1.12647,1.127,1.12624,1.12636,769 2017-06-05 07:00:00,1.12634,1.12788,1.12604,1.12651,1259 2017-06-05 08:00:00,1.12649,1.12722,1.12631,1.1272,1043 2017-06-05 09:00:00,1.12718,1.12718,1.12618,1.12648,804 2017-06-05 10:00:00,1.1265,1.12671,1.12352,1.1243,1246 2017-06-05 11:00:00,1.12428,1.12474,1.12364,1.12471,875 2017-06-05 12:00:00,1.12468,1.12516,1.12407,1.1242,1069 2017-06-05 13:00:00,1.12423,1.12556,1.12343,1.12496,1127 2017-06-05 14:00:00,1.12492,1.12554,1.1243,1.12504,1473 2017-06-05 15:00:00,1.12507,1.1255,1.12474,1.125,797 2017-06-05 16:00:00,1.12498,1.12548,1.12479,1.1253,412 2017-06-05 17:00:00,1.12532,1.12644,1.12532,1.12575,452 2017-06-05 18:00:00,1.12577,1.1258,1.12512,1.12573,351 2017-06-05 19:00:00,1.12575,1.12577,1.1253,1.12554,301 2017-06-05 20:00:00,1.12552,1.1259,1.12538,1.12544,153 2017-06-05 21:00:00,1.12542,1.12599,1.12536,1.12546,322 2017-06-05 22:00:00,1.12542,1.12598,1.12542,1.12584,138 2017-06-05 23:00:00,1.12586,1.12596,1.12552,1.12572,110 2017-06-06 00:00:00,1.12576,1.12656,1.12574,1.12626,331 2017-06-06 01:00:00,1.12624,1.12728,1.12624,1.12724,595 2017-06-06 02:00:00,1.12728,1.12749,1.12705,1.12722,341 2017-06-06 03:00:00,1.12724,1.12742,1.12684,1.12689,180 2017-06-06 04:00:00,1.12686,1.12686,1.12652,1.12679,218 2017-06-06 05:00:00,1.12682,1.12778,1.1266,1.12734,329 2017-06-06 06:00:00,1.12732,1.12765,1.1266,1.12694,950 2017-06-06 07:00:00,1.12692,1.12702,1.12424,1.12497,1424 2017-06-06 08:00:00,1.125,1.1257,1.12476,1.12516,754 2017-06-06 09:00:00,1.12518,1.12532,1.12442,1.12486,814 2017-06-06 10:00:00,1.12488,1.12529,1.12438,1.12499,654 2017-06-06 11:00:00,1.12496,1.12595,1.12404,1.12486,1297 2017-06-06 12:00:00,1.12488,1.1258,1.12474,1.12552,1388 2017-06-06 13:00:00,1.12554,1.12759,1.1254,1.12677,1530 2017-06-06 14:00:00,1.1268,1.12766,1.12612,1.12658,1865 2017-06-06 15:00:00,1.1266,1.1273,1.12626,1.12661,1043 2017-06-06 16:00:00,1.12658,1.12718,1.12646,1.127,548 2017-06-06 17:00:00,1.12703,1.12842,1.12701,1.12821,634 2017-06-06 18:00:00,1.12819,1.12821,1.12681,1.12693,779 2017-06-06 19:00:00,1.12696,1.12788,1.12696,1.12782,566 2017-06-06 20:00:00,1.1278,1.12784,1.12722,1.12772,194 2017-06-06 21:00:00,1.12769,1.1281,1.12766,1.12778,251 2017-06-06 22:00:00,1.12776,1.1282,1.12774,1.12782,227 2017-06-06 23:00:00,1.12784,1.12784,1.127,1.12703,158 2017-06-07 00:00:00,1.127,1.12766,1.1268,1.12726,336 2017-06-07 01:00:00,1.12724,1.12738,1.12678,1.12696,422 2017-06-07 02:00:00,1.12694,1.12694,1.12622,1.12654,245 2017-06-07 03:00:00,1.12652,1.12657,1.12625,1.12646,125 2017-06-07 04:00:00,1.12644,1.12656,1.12602,1.12646,186 2017-06-07 05:00:00,1.12644,1.12683,1.12642,1.12652,146 2017-06-07 06:00:00,1.1265,1.12769,1.12632,1.12724,772 2017-06-07 07:00:00,1.12726,1.12731,1.12464,1.1254,1452 2017-06-07 08:00:00,1.12542,1.1268,1.12532,1.12624,796 2017-06-07 09:00:00,1.12626,1.12769,1.12624,1.127,849 2017-06-07 10:00:00,1.12703,1.12705,1.12052,1.12124,3547 2017-06-07 11:00:00,1.12122,1.12272,1.12044,1.12159,1598 2017-06-07 12:00:00,1.12156,1.12336,1.12134,1.12302,1313 2017-06-07 13:00:00,1.123,1.12634,1.12248,1.12617,2929 2017-06-07 14:00:00,1.1262,1.12824,1.12587,1.1263,2078 2017-06-07 15:00:00,1.12628,1.12637,1.12302,1.12376,1543 2017-06-07 16:00:00,1.12374,1.12656,1.1223,1.12649,1974 2017-06-07 17:00:00,1.12647,1.12722,1.12628,1.12656,1026 2017-06-07 18:00:00,1.12653,1.1268,1.12518,1.12536,1260 2017-06-07 19:00:00,1.12534,1.12678,1.12518,1.12678,651 2017-06-07 20:00:00,1.12676,1.1269,1.12564,1.12566,280 2017-06-07 21:00:00,1.12609,1.1266,1.12556,1.12593,497 2017-06-07 22:00:00,1.12596,1.12616,1.1255,1.12581,174 2017-06-07 23:00:00,1.12584,1.12612,1.12548,1.12558,169 2017-06-08 00:00:00,1.12556,1.12571,1.12479,1.12506,445 2017-06-08 01:00:00,1.12504,1.12569,1.12472,1.12552,441 2017-06-08 02:00:00,1.12554,1.12612,1.12536,1.12612,328 2017-06-08 03:00:00,1.1261,1.1261,1.12534,1.12539,254 2017-06-08 04:00:00,1.12536,1.12586,1.12504,1.12575,405 2017-06-08 05:00:00,1.12573,1.12634,1.12524,1.12634,535 2017-06-08 06:00:00,1.12632,1.12692,1.12508,1.12524,1035 2017-06-08 07:00:00,1.12522,1.12648,1.12502,1.12596,1112 2017-06-08 08:00:00,1.12599,1.12632,1.12432,1.12492,922 2017-06-08 09:00:00,1.12489,1.12538,1.12301,1.12328,1131 2017-06-08 10:00:00,1.12331,1.12375,1.12305,1.12347,788 2017-06-08 11:00:00,1.12344,1.12632,1.12178,1.12247,3883 2017-06-08 12:00:00,1.1225,1.12508,1.11948,1.12263,8353 2017-06-08 13:00:00,1.12261,1.12299,1.12076,1.12187,3562 2017-06-08 14:00:00,1.12189,1.12238,1.11958,1.12212,3352 2017-06-08 15:00:00,1.12215,1.12243,1.12132,1.1217,1401 2017-06-08 16:00:00,1.12167,1.12202,1.12076,1.12116,971 2017-06-08 17:00:00,1.12113,1.12139,1.12017,1.12118,685 2017-06-08 18:00:00,1.12116,1.12286,1.12089,1.12224,803 2017-06-08 19:00:00,1.12222,1.12258,1.12097,1.12175,752 2017-06-08 20:00:00,1.12172,1.12183,1.12128,1.12142,329 2017-06-08 21:00:00,1.12137,1.12367,1.12,1.12052,3689 2017-06-08 22:00:00,1.1205,1.12143,1.12024,1.12034,887 2017-06-08 23:00:00,1.12036,1.12036,1.11801,1.11886,1067 2017-06-09 00:00:00,1.11888,1.11948,1.11794,1.11824,1416 2017-06-09 01:00:00,1.11822,1.12012,1.11808,1.1199,753 2017-06-09 02:00:00,1.11993,1.12004,1.11906,1.11959,644 2017-06-09 03:00:00,1.11962,1.12064,1.11918,1.12004,666 2017-06-09 04:00:00,1.12002,1.12146,1.1199,1.1204,572 2017-06-09 05:00:00,1.12038,1.12135,1.12014,1.12038,1107 2017-06-09 06:00:00,1.1204,1.12158,1.11865,1.11892,3068 2017-06-09 07:00:00,1.11894,1.11991,1.11854,1.11898,2326 2017-06-09 08:00:00,1.119,1.11934,1.11698,1.11761,2773 2017-06-09 09:00:00,1.11763,1.11874,1.1173,1.1173,1231 2017-06-09 10:00:00,1.11733,1.11851,1.11733,1.11809,1152 2017-06-09 11:00:00,1.11807,1.11834,1.1175,1.11796,851 2017-06-09 12:00:00,1.11798,1.11806,1.1167,1.11688,1387 2017-06-09 13:00:00,1.1169,1.11814,1.11664,1.11744,1309 2017-06-09 14:00:00,1.11746,1.1189,1.11743,1.11834,1090 2017-06-09 15:00:00,1.11837,1.11928,1.11826,1.11892,855 2017-06-09 16:00:00,1.11894,1.12008,1.11873,1.11978,795 2017-06-09 17:00:00,1.1198,1.1204,1.1194,1.11943,684 2017-06-09 18:00:00,1.11946,1.11992,1.1191,1.11964,645 2017-06-09 19:00:00,1.11962,1.11996,1.11926,1.11954,776 2017-06-09 20:00:00,1.1195,1.11978,1.1194,1.11959,332 2017-06-11 21:00:00,1.12088,1.12088,1.12049,1.1205,105 2017-06-11 22:00:00,1.12052,1.12095,1.12026,1.1204,258 2017-06-11 23:00:00,1.12042,1.1206,1.11974,1.1202,279 2017-06-12 00:00:00,1.12018,1.1208,1.11994,1.1205,494 2017-06-12 01:00:00,1.12052,1.12077,1.11978,1.11978,334 2017-06-12 02:00:00,1.1198,1.12118,1.1198,1.12102,399 2017-06-12 03:00:00,1.12104,1.12104,1.12041,1.12067,325 2017-06-12 04:00:00,1.1207,1.12086,1.12023,1.12064,113 2017-06-12 05:00:00,1.12067,1.12167,1.12065,1.12126,264 2017-06-12 06:00:00,1.12124,1.12166,1.1209,1.1211,680 2017-06-12 07:00:00,1.12113,1.12237,1.12044,1.12212,929 2017-06-12 08:00:00,1.12214,1.12276,1.12152,1.12254,1011 2017-06-12 09:00:00,1.12257,1.12262,1.12158,1.12166,837 2017-06-12 10:00:00,1.12169,1.12264,1.12082,1.12164,1195 2017-06-12 11:00:00,1.12167,1.1232,1.12144,1.12192,814 2017-06-12 12:00:00,1.1219,1.1225,1.12136,1.12148,994 2017-06-12 13:00:00,1.1215,1.12201,1.12033,1.12086,1133 2017-06-12 14:00:00,1.12084,1.12104,1.1192,1.1203,1463 2017-06-12 15:00:00,1.12032,1.12052,1.11972,1.12032,1172 2017-06-12 16:00:00,1.1203,1.12051,1.11966,1.11981,613 2017-06-12 17:00:00,1.11984,1.12067,1.1196,1.12039,831 2017-06-12 18:00:00,1.12042,1.12086,1.11988,1.1208,527 2017-06-12 19:00:00,1.12083,1.121,1.1201,1.12047,461 2017-06-12 20:00:00,1.12049,1.12081,1.12015,1.12036,339 2017-06-12 21:00:00,1.12038,1.12074,1.12028,1.12058,488 2017-06-12 22:00:00,1.1206,1.1206,1.12011,1.12019,251 2017-06-12 23:00:00,1.12017,1.12021,1.11956,1.11976,267 2017-06-13 00:00:00,1.11974,1.1201,1.11937,1.11947,344 2017-06-13 01:00:00,1.11944,1.11962,1.11902,1.11925,422 2017-06-13 02:00:00,1.11923,1.11988,1.11914,1.11964,214 2017-06-13 03:00:00,1.11966,1.11966,1.11893,1.11903,232 2017-06-13 04:00:00,1.11905,1.11937,1.11903,1.1193,108 2017-06-13 05:00:00,1.11932,1.11936,1.11854,1.11876,260 2017-06-13 06:00:00,1.11874,1.11985,1.11855,1.11942,810 2017-06-13 07:00:00,1.11944,1.12182,1.119,1.12174,1587 2017-06-13 08:00:00,1.12172,1.1225,1.1209,1.12124,1245 2017-06-13 09:00:00,1.12127,1.12172,1.12041,1.12075,678 2017-06-13 10:00:00,1.12078,1.12121,1.12008,1.12066,621 2017-06-13 11:00:00,1.12068,1.12204,1.12022,1.12178,679 2017-06-13 12:00:00,1.1218,1.12202,1.12066,1.12072,1064 2017-06-13 13:00:00,1.12076,1.1211,1.1201,1.12079,1022 2017-06-13 14:00:00,1.12081,1.12186,1.12061,1.12064,1224 2017-06-13 15:00:00,1.12067,1.12116,1.11924,1.11932,1048 2017-06-13 16:00:00,1.11934,1.1203,1.11922,1.12018,538 2017-06-13 17:00:00,1.1202,1.1207,1.12017,1.12022,510 2017-06-13 18:00:00,1.12024,1.12127,1.12022,1.12114,366 2017-06-13 19:00:00,1.12112,1.12116,1.12065,1.1207,236 2017-06-13 20:00:00,1.12073,1.12116,1.12066,1.12105,211 2017-06-13 21:00:00,1.1207,1.12152,1.12068,1.12087,325 2017-06-13 22:00:00,1.1209,1.12116,1.12067,1.12086,170 2017-06-13 23:00:00,1.12083,1.12111,1.12072,1.12093,105 2017-06-14 00:00:00,1.12091,1.12154,1.12072,1.12138,357 2017-06-14 01:00:00,1.12136,1.12176,1.12124,1.12156,323 2017-06-14 02:00:00,1.12159,1.12198,1.12116,1.12124,457 2017-06-14 03:00:00,1.12126,1.1217,1.12126,1.12152,188 2017-06-14 04:00:00,1.12154,1.1216,1.12128,1.12151,110 2017-06-14 05:00:00,1.12154,1.12194,1.12148,1.12194,157 2017-06-14 06:00:00,1.12191,1.12252,1.12125,1.12215,1187 2017-06-14 07:00:00,1.12213,1.12213,1.1208,1.1209,1112 2017-06-14 08:00:00,1.12088,1.12156,1.12054,1.12073,936 2017-06-14 09:00:00,1.12076,1.12154,1.12029,1.12062,1048 2017-06-14 10:00:00,1.1206,1.12081,1.12015,1.12046,524 2017-06-14 11:00:00,1.12048,1.12076,1.11978,1.12014,713 2017-06-14 12:00:00,1.12016,1.12676,1.11993,1.1263,3788 2017-06-14 13:00:00,1.12626,1.12788,1.12622,1.12719,3552 2017-06-14 14:00:00,1.12716,1.12958,1.12702,1.12793,3141 2017-06-14 15:00:00,1.12796,1.12858,1.12724,1.12766,1592 2017-06-14 16:00:00,1.12768,1.12796,1.12658,1.1273,795 2017-06-14 17:00:00,1.12732,1.12746,1.1263,1.1271,976 2017-06-14 18:00:00,1.12704,1.12892,1.12077,1.12154,10454 2017-06-14 19:00:00,1.12156,1.12234,1.11932,1.12184,4215 2017-06-14 20:00:00,1.12182,1.12232,1.12142,1.12182,502 2017-06-14 21:00:00,1.12179,1.12208,1.12156,1.12177,261 2017-06-14 22:00:00,1.12174,1.12186,1.12056,1.121,535 2017-06-14 23:00:00,1.12102,1.12162,1.12096,1.1213,280 2017-06-15 00:00:00,1.12132,1.12221,1.12099,1.12185,692 2017-06-15 01:00:00,1.12182,1.12285,1.12173,1.122,687 2017-06-15 02:00:00,1.12202,1.1224,1.12166,1.12167,313 2017-06-15 03:00:00,1.12164,1.12237,1.12152,1.12232,372 2017-06-15 04:00:00,1.1223,1.12242,1.12201,1.12215,220 2017-06-15 05:00:00,1.12212,1.12219,1.12144,1.12181,330 2017-06-15 06:00:00,1.12179,1.12203,1.12057,1.12124,1128 2017-06-15 07:00:00,1.12121,1.12124,1.11795,1.1194,2701 2017-06-15 08:00:00,1.11938,1.1194,1.11754,1.11798,1694 2017-06-15 09:00:00,1.11796,1.11851,1.1156,1.11602,1830 2017-06-15 10:00:00,1.11605,1.11696,1.11585,1.11664,1154 2017-06-15 11:00:00,1.11662,1.11721,1.11536,1.11626,2317 2017-06-15 12:00:00,1.11624,1.11688,1.11494,1.1152,2409 2017-06-15 13:00:00,1.11518,1.11554,1.11434,1.1148,2561 2017-06-15 14:00:00,1.11478,1.11522,1.11323,1.11487,2441 2017-06-15 15:00:00,1.1149,1.1158,1.11488,1.11512,1216 2017-06-15 16:00:00,1.11515,1.11515,1.11396,1.11483,735 2017-06-15 17:00:00,1.11481,1.11502,1.11422,1.1143,517 2017-06-15 18:00:00,1.11432,1.11548,1.11429,1.1154,349 2017-06-15 19:00:00,1.11538,1.11579,1.11442,1.11455,665 2017-06-15 20:00:00,1.11452,1.11504,1.11424,1.11452,510 2017-06-15 21:00:00,1.11466,1.1151,1.1144,1.11473,389 2017-06-15 22:00:00,1.1147,1.11508,1.11452,1.11464,203 2017-06-15 23:00:00,1.11467,1.11496,1.11449,1.11489,131 2017-06-16 00:00:00,1.11491,1.11556,1.11453,1.1154,431 2017-06-16 01:00:00,1.11538,1.11553,1.11515,1.11533,216 2017-06-16 02:00:00,1.1153,1.11534,1.11458,1.1151,299 2017-06-16 03:00:00,1.11506,1.11506,1.11462,1.11462,264 2017-06-16 04:00:00,1.1146,1.11466,1.11418,1.11442,242 2017-06-16 05:00:00,1.1144,1.11462,1.11386,1.11424,295 2017-06-16 06:00:00,1.11426,1.1159,1.11396,1.11539,1172 2017-06-16 07:00:00,1.11537,1.11672,1.11516,1.1167,1064 2017-06-16 08:00:00,1.11672,1.1175,1.11651,1.11728,976 2017-06-16 09:00:00,1.11726,1.11838,1.11698,1.117,909 2017-06-16 10:00:00,1.11703,1.11816,1.11703,1.11778,703 2017-06-16 11:00:00,1.1178,1.1181,1.11574,1.11599,1018 2017-06-16 12:00:00,1.11602,1.11831,1.11602,1.11724,1219 2017-06-16 13:00:00,1.11726,1.1177,1.11654,1.11764,1307 2017-06-16 14:00:00,1.11767,1.12,1.11742,1.11988,1844 2017-06-16 15:00:00,1.11986,1.11999,1.1187,1.11934,1000 2017-06-16 16:00:00,1.11931,1.11933,1.11862,1.11887,610 2017-06-16 17:00:00,1.11884,1.11954,1.11876,1.11954,440 2017-06-16 18:00:00,1.11957,1.11992,1.1193,1.11937,387 2017-06-16 19:00:00,1.11939,1.12014,1.11937,1.12005,348 2017-06-16 20:00:00,1.12007,1.12018,1.11928,1.11966,418 2017-06-18 21:00:00,1.12078,1.12103,1.11992,1.11992,244 2017-06-18 22:00:00,1.11995,1.12043,1.1196,1.12038,298 2017-06-18 23:00:00,1.1204,1.12066,1.12015,1.1204,168 2017-06-19 00:00:00,1.12038,1.12051,1.11896,1.11907,407 2017-06-19 01:00:00,1.1191,1.11995,1.11902,1.11976,331 2017-06-19 02:00:00,1.11974,1.12002,1.11956,1.11972,165 2017-06-19 03:00:00,1.11974,1.1199,1.11952,1.11976,123 2017-06-19 04:00:00,1.11974,1.11987,1.11936,1.11936,155 2017-06-19 05:00:00,1.11938,1.11965,1.11928,1.1193,166 2017-06-19 06:00:00,1.11928,1.12075,1.11906,1.12046,831 2017-06-19 07:00:00,1.12044,1.12044,1.11824,1.11945,1150 2017-06-19 08:00:00,1.11942,1.1198,1.11908,1.11953,711 2017-06-19 09:00:00,1.11951,1.12016,1.1188,1.11926,669 2017-06-19 10:00:00,1.11923,1.12066,1.11918,1.12065,605 2017-06-19 11:00:00,1.12067,1.12127,1.11908,1.11911,1094 2017-06-19 12:00:00,1.11908,1.11996,1.11771,1.1182,1800 2017-06-19 13:00:00,1.11823,1.11846,1.1173,1.11768,1385 2017-06-19 14:00:00,1.1177,1.11797,1.11656,1.11661,1178 2017-06-19 15:00:00,1.11664,1.11712,1.1159,1.116,1022 2017-06-19 16:00:00,1.11598,1.11635,1.11558,1.11588,561 2017-06-19 17:00:00,1.11591,1.11598,1.11472,1.11517,531 2017-06-19 18:00:00,1.11514,1.11536,1.11432,1.11485,512 2017-06-19 19:00:00,1.11488,1.11494,1.11454,1.11486,303 2017-06-19 20:00:00,1.11484,1.11504,1.11474,1.11481,141 2017-06-19 21:00:00,1.1149,1.11544,1.11476,1.11499,447 2017-06-19 22:00:00,1.11496,1.11531,1.11469,1.11519,407 2017-06-19 23:00:00,1.11516,1.11518,1.11412,1.11446,274 2017-06-20 00:00:00,1.11443,1.11492,1.11436,1.11449,399 2017-06-20 01:00:00,1.11451,1.11561,1.11451,1.11553,341 2017-06-20 02:00:00,1.11556,1.1158,1.11536,1.11564,232 2017-06-20 03:00:00,1.11562,1.11586,1.11536,1.11562,196 2017-06-20 04:00:00,1.11559,1.11562,1.11534,1.11556,141 2017-06-20 05:00:00,1.11558,1.11602,1.11539,1.1159,235 2017-06-20 06:00:00,1.11588,1.11649,1.11519,1.11526,632 2017-06-20 07:00:00,1.11529,1.11627,1.11427,1.11622,1739 2017-06-20 08:00:00,1.1162,1.11622,1.115,1.11583,917 2017-06-20 09:00:00,1.1158,1.11599,1.11519,1.11597,608 2017-06-20 10:00:00,1.11599,1.11648,1.11522,1.11528,750 2017-06-20 11:00:00,1.1153,1.11532,1.11414,1.11482,903 2017-06-20 12:00:00,1.11485,1.11627,1.11485,1.11508,1635 2017-06-20 13:00:00,1.1151,1.11536,1.11344,1.11364,1562 2017-06-20 14:00:00,1.11361,1.11456,1.11258,1.11266,1685 2017-06-20 15:00:00,1.11264,1.11278,1.11198,1.11238,1084 2017-06-20 16:00:00,1.11236,1.11286,1.11191,1.11246,642 2017-06-20 17:00:00,1.11248,1.11264,1.11203,1.1125,428 2017-06-20 18:00:00,1.11253,1.11314,1.11236,1.11286,447 2017-06-20 19:00:00,1.11284,1.11326,1.11255,1.11258,544 2017-06-20 20:00:00,1.1126,1.11344,1.11258,1.11342,199 2017-06-20 21:00:00,1.11339,1.1135,1.11314,1.11344,198 2017-06-20 22:00:00,1.11346,1.11356,1.11322,1.1135,207 2017-06-20 23:00:00,1.11352,1.1137,1.11336,1.11366,119 2017-06-21 00:00:00,1.11364,1.11368,1.11329,1.11355,302 2017-06-21 01:00:00,1.11358,1.11366,1.11336,1.11345,133 2017-06-21 02:00:00,1.11348,1.11362,1.11339,1.11346,87 2017-06-21 03:00:00,1.11349,1.11382,1.1134,1.11362,115 2017-06-21 04:00:00,1.11359,1.11372,1.11328,1.11336,101 2017-06-21 05:00:00,1.11338,1.11346,1.11284,1.11302,195 2017-06-21 06:00:00,1.113,1.11366,1.11286,1.11314,649 2017-06-21 07:00:00,1.11312,1.11369,1.11296,1.11319,841 2017-06-21 08:00:00,1.11322,1.11457,1.11274,1.11428,1062 2017-06-21 09:00:00,1.11426,1.115,1.11376,1.11446,1110 2017-06-21 10:00:00,1.11444,1.115,1.11416,1.11495,844 2017-06-21 11:00:00,1.11497,1.11559,1.11398,1.11417,1601 2017-06-21 12:00:00,1.11415,1.1153,1.11404,1.11478,1137 2017-06-21 13:00:00,1.1148,1.11485,1.11302,1.11396,1136 2017-06-21 14:00:00,1.11393,1.11478,1.11348,1.1142,1511 2017-06-21 15:00:00,1.11422,1.11474,1.11384,1.11428,940 2017-06-21 16:00:00,1.11426,1.11475,1.11409,1.11472,661 2017-06-21 17:00:00,1.11474,1.11528,1.11448,1.11512,599 2017-06-21 18:00:00,1.1151,1.11664,1.11503,1.11626,624 2017-06-21 19:00:00,1.11628,1.11684,1.11618,1.11658,448 2017-06-21 20:00:00,1.11656,1.1169,1.11622,1.11684,238 2017-06-21 21:00:00,1.1168,1.1169,1.11639,1.1168,363 2017-06-21 22:00:00,1.11678,1.11682,1.11654,1.1167,106 2017-06-21 23:00:00,1.11668,1.11684,1.11648,1.11668,152 2017-06-22 00:00:00,1.11665,1.11712,1.11604,1.11632,402 2017-06-22 01:00:00,1.11634,1.11666,1.11624,1.11654,319 2017-06-22 02:00:00,1.11652,1.11717,1.11644,1.11678,253 2017-06-22 03:00:00,1.11676,1.11707,1.11675,1.11684,286 2017-06-22 04:00:00,1.11687,1.11716,1.11678,1.1171,180 2017-06-22 05:00:00,1.11708,1.11762,1.11687,1.11762,315 2017-06-22 06:00:00,1.1176,1.11779,1.11682,1.11718,673 2017-06-22 07:00:00,1.1172,1.1172,1.11546,1.11624,1048 2017-06-22 08:00:00,1.11622,1.11688,1.11582,1.11681,941 2017-06-22 09:00:00,1.11684,1.11744,1.11649,1.11652,700 2017-06-22 10:00:00,1.1165,1.11676,1.11605,1.11656,604 2017-06-22 11:00:00,1.11653,1.11666,1.1157,1.11655,663 2017-06-22 12:00:00,1.11653,1.11724,1.11591,1.1161,1082 2017-06-22 13:00:00,1.11608,1.11641,1.11526,1.11601,1202 2017-06-22 14:00:00,1.11598,1.11626,1.11508,1.11568,1598 2017-06-22 15:00:00,1.1157,1.11611,1.11454,1.11469,1061 2017-06-22 16:00:00,1.11471,1.11561,1.11471,1.11556,482 2017-06-22 17:00:00,1.11553,1.11575,1.11395,1.11433,632 2017-06-22 18:00:00,1.1143,1.11504,1.11416,1.11469,350 2017-06-22 19:00:00,1.11466,1.1155,1.11466,1.11515,425 2017-06-22 20:00:00,1.11512,1.1154,1.11492,1.11513,107 2017-06-22 21:00:00,1.11526,1.11536,1.11514,1.11516,71 2017-06-22 22:00:00,1.11514,1.11523,1.11496,1.11498,93 2017-06-22 23:00:00,1.115,1.11508,1.11474,1.11498,73 2017-06-23 00:00:00,1.11496,1.11514,1.11454,1.11495,278 2017-06-23 01:00:00,1.11497,1.11599,1.1147,1.11599,356 2017-06-23 02:00:00,1.11597,1.116,1.11547,1.11587,147 2017-06-23 03:00:00,1.1159,1.11632,1.11578,1.1163,175 2017-06-23 04:00:00,1.11627,1.1164,1.11614,1.11617,102 2017-06-23 05:00:00,1.11619,1.11684,1.11619,1.11639,181 2017-06-23 06:00:00,1.11641,1.11752,1.11637,1.11725,652 2017-06-23 07:00:00,1.11722,1.118,1.11688,1.11759,1197 2017-06-23 08:00:00,1.11761,1.11879,1.11712,1.11716,1043 2017-06-23 09:00:00,1.11714,1.11722,1.11618,1.11704,680 2017-06-23 10:00:00,1.11707,1.11762,1.11648,1.11666,618 2017-06-23 11:00:00,1.11664,1.11744,1.11624,1.11732,653 2017-06-23 12:00:00,1.1173,1.11802,1.11705,1.11761,929 2017-06-23 13:00:00,1.11763,1.1196,1.11752,1.1196,869 2017-06-23 14:00:00,1.11958,1.12088,1.11856,1.11998,1579 2017-06-23 15:00:00,1.12001,1.12038,1.11961,1.11964,677 2017-06-23 16:00:00,1.11966,1.11995,1.11929,1.11971,317 2017-06-23 17:00:00,1.11973,1.11992,1.11951,1.1198,365 2017-06-23 18:00:00,1.11978,1.12024,1.11969,1.11969,324 2017-06-23 19:00:00,1.11971,1.11994,1.11958,1.11972,218 2017-06-23 20:00:00,1.11974,1.11982,1.11926,1.11938,181 2017-06-25 21:00:00,1.12,1.12,1.1191,1.11943,196 2017-06-25 22:00:00,1.1194,1.11988,1.11935,1.11967,270 2017-06-25 23:00:00,1.1197,1.11998,1.11958,1.11998,77 2017-06-26 00:00:00,1.11996,1.12002,1.11953,1.11966,322 2017-06-26 01:00:00,1.11964,1.11968,1.119,1.11902,437 2017-06-26 02:00:00,1.11904,1.1197,1.11904,1.11951,280 2017-06-26 03:00:00,1.11949,1.11978,1.11901,1.11966,263 2017-06-26 04:00:00,1.11964,1.11964,1.11904,1.11917,128 2017-06-26 05:00:00,1.11915,1.11936,1.11888,1.11926,169 2017-06-26 06:00:00,1.11924,1.11951,1.11848,1.1192,651 2017-06-26 07:00:00,1.11917,1.12078,1.11902,1.1201,1250 2017-06-26 08:00:00,1.12008,1.12018,1.1182,1.11858,1476 2017-06-26 09:00:00,1.11856,1.11872,1.11759,1.11772,900 2017-06-26 10:00:00,1.1177,1.11872,1.11766,1.11798,946 2017-06-26 11:00:00,1.11796,1.1185,1.1172,1.11816,844 2017-06-26 12:00:00,1.11819,1.12197,1.11819,1.12112,1933 2017-06-26 13:00:00,1.12114,1.12158,1.12028,1.12028,1308 2017-06-26 14:00:00,1.12031,1.12186,1.12,1.12029,1322 2017-06-26 15:00:00,1.12032,1.12048,1.1189,1.11962,1177 2017-06-26 16:00:00,1.11964,1.1198,1.11821,1.11876,703 2017-06-26 17:00:00,1.11872,1.11946,1.11872,1.11914,438 2017-06-26 18:00:00,1.11917,1.11944,1.11756,1.11811,688 2017-06-26 19:00:00,1.11814,1.11818,1.11773,1.11804,330 2017-06-26 20:00:00,1.11802,1.11834,1.11796,1.11817,188 2017-06-26 21:00:00,1.11828,1.11857,1.11814,1.1183,243 2017-06-26 22:00:00,1.11832,1.11838,1.11804,1.11806,105 2017-06-26 23:00:00,1.11808,1.11836,1.11791,1.1183,92 2017-06-27 00:00:00,1.11828,1.11854,1.11802,1.11842,289 2017-06-27 01:00:00,1.1184,1.11868,1.11811,1.11827,222 2017-06-27 02:00:00,1.1183,1.11896,1.11808,1.11878,214 2017-06-27 03:00:00,1.11875,1.11926,1.11872,1.11911,230 2017-06-27 04:00:00,1.11908,1.11911,1.11881,1.11902,95 2017-06-27 05:00:00,1.11904,1.11966,1.11902,1.11958,179 2017-06-27 06:00:00,1.11956,1.1204,1.11883,1.11911,999 2017-06-27 07:00:00,1.11909,1.1193,1.11824,1.11872,1074 2017-06-27 08:00:00,1.11874,1.12548,1.1187,1.12491,4961 2017-06-27 09:00:00,1.12493,1.12652,1.12442,1.12544,1449 2017-06-27 10:00:00,1.12541,1.1266,1.12468,1.1262,1252 2017-06-27 11:00:00,1.12618,1.12748,1.12606,1.12688,1270 2017-06-27 12:00:00,1.12692,1.12841,1.12689,1.12772,1207 2017-06-27 13:00:00,1.12774,1.1305,1.12772,1.12884,1893 2017-06-27 14:00:00,1.12882,1.12898,1.12746,1.12862,1468 2017-06-27 15:00:00,1.12864,1.13321,1.1284,1.13268,2137 2017-06-27 16:00:00,1.13264,1.13351,1.13064,1.13118,1948 2017-06-27 17:00:00,1.1312,1.13379,1.13066,1.13324,2325 2017-06-27 18:00:00,1.13322,1.13478,1.13316,1.13464,1176 2017-06-27 19:00:00,1.13462,1.13494,1.13442,1.13488,594 2017-06-27 20:00:00,1.13485,1.13485,1.13367,1.13388,370 2017-06-27 21:00:00,1.13384,1.13396,1.13301,1.13351,541 2017-06-27 22:00:00,1.13354,1.13447,1.13354,1.13432,347 2017-06-27 23:00:00,1.1343,1.13432,1.13359,1.1337,252 2017-06-28 00:00:00,1.13368,1.13438,1.1336,1.134,543 2017-06-28 01:00:00,1.13398,1.13556,1.13372,1.13514,588 2017-06-28 02:00:00,1.13511,1.13539,1.13469,1.13514,454 2017-06-28 03:00:00,1.13517,1.13517,1.13476,1.13488,248 2017-06-28 04:00:00,1.1349,1.13533,1.13466,1.13468,244 2017-06-28 05:00:00,1.13466,1.13664,1.13454,1.13629,681 2017-06-28 06:00:00,1.13626,1.13788,1.13526,1.13532,2344 2017-06-28 07:00:00,1.13534,1.13882,1.13522,1.13784,3222 2017-06-28 08:00:00,1.13782,1.13864,1.13602,1.13604,2239 2017-06-28 09:00:00,1.13606,1.1364,1.13536,1.13609,1609 2017-06-28 10:00:00,1.13606,1.13718,1.13554,1.13586,1006 2017-06-28 11:00:00,1.13589,1.13764,1.13559,1.13749,1336 2017-06-28 12:00:00,1.13746,1.13802,1.12918,1.13315,8864 2017-06-28 13:00:00,1.13318,1.13736,1.13283,1.1366,5895 2017-06-28 14:00:00,1.13662,1.13908,1.13616,1.13631,4286 2017-06-28 15:00:00,1.13633,1.1366,1.13494,1.13648,2174 2017-06-28 16:00:00,1.13646,1.13816,1.13631,1.1378,900 2017-06-28 17:00:00,1.13784,1.13818,1.13736,1.13768,660 2017-06-28 18:00:00,1.1377,1.1384,1.13763,1.138,668 2017-06-28 19:00:00,1.13802,1.13852,1.13762,1.13819,476 2017-06-28 20:00:00,1.13816,1.13816,1.1376,1.13772,300 2017-06-28 21:00:00,1.1379,1.13806,1.13756,1.1376,371 2017-06-28 22:00:00,1.13758,1.13798,1.13746,1.13776,272 2017-06-28 23:00:00,1.13778,1.13853,1.13774,1.13784,210 2017-06-29 00:00:00,1.13781,1.13894,1.13768,1.13874,601 2017-06-29 01:00:00,1.13871,1.14085,1.13852,1.14056,686 2017-06-29 02:00:00,1.14052,1.14194,1.14032,1.14053,853 2017-06-29 03:00:00,1.14056,1.141,1.14016,1.14059,423 2017-06-29 04:00:00,1.14057,1.14078,1.1395,1.14018,423 2017-06-29 05:00:00,1.14016,1.1408,1.13954,1.14076,400 2017-06-29 06:00:00,1.14073,1.14152,1.14,1.14061,1718 2017-06-29 07:00:00,1.14064,1.14324,1.14064,1.14268,3382 2017-06-29 08:00:00,1.14266,1.14349,1.14153,1.14256,1833 2017-06-29 09:00:00,1.14254,1.14263,1.13983,1.14138,1479 2017-06-29 10:00:00,1.1414,1.1416,1.13999,1.14062,1260 2017-06-29 11:00:00,1.14064,1.14076,1.13923,1.14066,1461 2017-06-29 12:00:00,1.14068,1.14175,1.13882,1.1401,2759 2017-06-29 13:00:00,1.14008,1.14158,1.13955,1.13998,2106 2017-06-29 14:00:00,1.14,1.14281,1.13994,1.14242,2164 2017-06-29 15:00:00,1.14244,1.1432,1.14201,1.14208,1592 2017-06-29 16:00:00,1.14206,1.14314,1.14166,1.14298,998 2017-06-29 17:00:00,1.14296,1.14448,1.14278,1.14422,1101 2017-06-29 18:00:00,1.14425,1.14452,1.14296,1.14328,880 2017-06-29 19:00:00,1.14326,1.14454,1.1432,1.14392,652 2017-06-29 20:00:00,1.14394,1.14427,1.14356,1.14406,233 2017-06-29 21:00:00,1.1441,1.14434,1.14389,1.14404,237 2017-06-29 22:00:00,1.14407,1.14454,1.14407,1.14428,313 2017-06-29 23:00:00,1.14426,1.14429,1.14396,1.14396,117 2017-06-30 00:00:00,1.14394,1.1443,1.14269,1.14286,794 2017-06-30 01:00:00,1.14284,1.14442,1.1428,1.1441,725 2017-06-30 02:00:00,1.14412,1.14444,1.14356,1.1436,598 2017-06-30 03:00:00,1.14358,1.14432,1.14358,1.14406,268 2017-06-30 04:00:00,1.14404,1.14427,1.14376,1.14404,187 2017-06-30 05:00:00,1.14408,1.14414,1.14277,1.14302,455 2017-06-30 06:00:00,1.143,1.14412,1.14227,1.14232,1475 2017-06-30 07:00:00,1.14234,1.14298,1.14022,1.1412,2908 2017-06-30 08:00:00,1.14122,1.14156,1.13958,1.14034,2058 2017-06-30 09:00:00,1.14036,1.14102,1.13923,1.14096,2271 2017-06-30 10:00:00,1.14098,1.14117,1.13988,1.14028,1271 2017-06-30 11:00:00,1.14026,1.14128,1.14026,1.1404,1173 2017-06-30 12:00:00,1.14042,1.14212,1.1404,1.1413,2213 2017-06-30 13:00:00,1.14127,1.14235,1.1412,1.14196,1814 2017-06-30 14:00:00,1.14198,1.14276,1.13957,1.14042,2560 2017-06-30 15:00:00,1.14046,1.14166,1.14042,1.14113,1406 2017-06-30 16:00:00,1.14111,1.14163,1.14065,1.14127,770 2017-06-30 17:00:00,1.14125,1.14171,1.1411,1.14147,416 2017-06-30 18:00:00,1.14149,1.14228,1.14086,1.14196,577 2017-06-30 19:00:00,1.14194,1.14232,1.14152,1.14216,610 2017-06-30 20:00:00,1.14213,1.14252,1.14184,1.14252,313 2017-07-02 21:00:00,1.14186,1.1422,1.1417,1.14218,127 2017-07-02 22:00:00,1.1422,1.1427,1.14208,1.14254,245 2017-07-02 23:00:00,1.14251,1.14257,1.14212,1.14232,188 2017-07-03 00:00:00,1.14235,1.14248,1.1416,1.14173,410 2017-07-03 01:00:00,1.14175,1.14202,1.14148,1.14164,304 2017-07-03 02:00:00,1.14166,1.14199,1.14124,1.14138,327 2017-07-03 03:00:00,1.14141,1.1417,1.14042,1.14043,413 2017-07-03 04:00:00,1.14046,1.14156,1.1404,1.14133,248 2017-07-03 05:00:00,1.1413,1.14152,1.14078,1.14079,353 2017-07-03 06:00:00,1.14076,1.14142,1.14036,1.14118,1232 2017-07-03 07:00:00,1.14115,1.14115,1.13926,1.13949,1748 2017-07-03 08:00:00,1.13946,1.13949,1.13773,1.13793,1487 2017-07-03 09:00:00,1.1379,1.13858,1.1373,1.13733,1032 2017-07-03 10:00:00,1.1373,1.13793,1.13707,1.1375,942 2017-07-03 11:00:00,1.13752,1.13775,1.13616,1.13646,1078 2017-07-03 12:00:00,1.13648,1.13756,1.1362,1.13678,1197 2017-07-03 13:00:00,1.1368,1.1373,1.13581,1.13717,1338 2017-07-03 14:00:00,1.13714,1.13736,1.13594,1.1368,2252 2017-07-03 15:00:00,1.13682,1.13697,1.13562,1.13668,1167 2017-07-03 16:00:00,1.13666,1.1372,1.13551,1.13573,926 2017-07-03 17:00:00,1.13571,1.13686,1.13566,1.13613,298 2017-07-03 18:00:00,1.13611,1.13611,1.13568,1.13582,111 2017-07-03 19:00:00,1.13585,1.1363,1.13578,1.1363,100 2017-07-03 20:00:00,1.13628,1.13669,1.13624,1.1364,176 2017-07-03 21:00:00,1.13636,1.13658,1.1358,1.13634,401 2017-07-03 22:00:00,1.13636,1.1371,1.13634,1.13672,196 2017-07-03 23:00:00,1.13674,1.13735,1.13672,1.13726,143 2017-07-04 00:00:00,1.13724,1.1377,1.13709,1.1374,379 2017-07-04 01:00:00,1.13742,1.13742,1.13674,1.1369,340 2017-07-04 02:00:00,1.13692,1.13702,1.13638,1.1366,231 2017-07-04 03:00:00,1.13658,1.1374,1.13656,1.13728,195 2017-07-04 04:00:00,1.13724,1.13742,1.13689,1.13722,355 2017-07-04 05:00:00,1.13724,1.13726,1.13362,1.13484,1064 2017-07-04 06:00:00,1.13486,1.13726,1.13436,1.13724,1569 2017-07-04 07:00:00,1.13722,1.13744,1.13564,1.13616,1712 2017-07-04 08:00:00,1.13619,1.13636,1.13472,1.13539,1076 2017-07-04 09:00:00,1.13542,1.13544,1.13425,1.13498,792 2017-07-04 10:00:00,1.13496,1.13528,1.13468,1.13517,500 2017-07-04 11:00:00,1.13515,1.13515,1.1342,1.13506,527 2017-07-04 12:00:00,1.13503,1.13554,1.13401,1.13428,846 2017-07-04 13:00:00,1.13431,1.13479,1.13384,1.13446,748 2017-07-04 14:00:00,1.13444,1.13542,1.13416,1.13517,694 2017-07-04 15:00:00,1.13519,1.13528,1.13455,1.13475,624 2017-07-04 16:00:00,1.13477,1.1352,1.13434,1.13512,329 2017-07-04 17:00:00,1.1351,1.13534,1.13466,1.13514,440 2017-07-04 18:00:00,1.13512,1.1353,1.13477,1.13505,323 2017-07-04 19:00:00,1.13503,1.13511,1.13458,1.13463,216 2017-07-04 20:00:00,1.13465,1.13496,1.1344,1.1346,350 2017-07-04 21:00:00,1.13452,1.13494,1.13443,1.13474,347 2017-07-04 22:00:00,1.13472,1.13556,1.13452,1.13534,200 2017-07-04 23:00:00,1.13536,1.13611,1.13517,1.13594,234 2017-07-05 00:00:00,1.13592,1.13644,1.13546,1.13587,472 2017-07-05 01:00:00,1.13584,1.13679,1.13557,1.13669,543 2017-07-05 02:00:00,1.13666,1.13684,1.13612,1.13628,469 2017-07-05 03:00:00,1.13624,1.13624,1.1357,1.13587,246 2017-07-05 04:00:00,1.13585,1.13585,1.13534,1.13546,127 2017-07-05 05:00:00,1.13549,1.13572,1.13497,1.13535,427 2017-07-05 06:00:00,1.13533,1.13666,1.13533,1.136,1188 2017-07-05 07:00:00,1.13598,1.1368,1.13582,1.13634,1697 2017-07-05 08:00:00,1.13636,1.13648,1.13408,1.13418,1209 2017-07-05 09:00:00,1.1342,1.13441,1.1318,1.13241,2588 2017-07-05 10:00:00,1.13239,1.13274,1.13126,1.13212,1250 2017-07-05 11:00:00,1.13215,1.1337,1.13196,1.13324,1023 2017-07-05 12:00:00,1.13326,1.13348,1.13182,1.13206,1283 2017-07-05 13:00:00,1.13208,1.13304,1.1318,1.133,1206 2017-07-05 14:00:00,1.13302,1.1337,1.13256,1.13292,1788 2017-07-05 15:00:00,1.1329,1.1344,1.13274,1.13356,965 2017-07-05 16:00:00,1.13358,1.13409,1.13297,1.13383,584 2017-07-05 17:00:00,1.1338,1.13436,1.13354,1.13412,558 2017-07-05 18:00:00,1.1341,1.13568,1.13228,1.13411,2820 2017-07-05 19:00:00,1.13408,1.13518,1.13381,1.1342,762 2017-07-05 20:00:00,1.13422,1.13524,1.1342,1.13514,251 2017-07-05 21:00:00,1.13517,1.13524,1.13484,1.13506,276 2017-07-05 22:00:00,1.13509,1.13549,1.13506,1.13532,191 2017-07-05 23:00:00,1.1353,1.13538,1.13462,1.13464,207 2017-07-06 00:00:00,1.13462,1.13462,1.13357,1.13418,635 2017-07-06 01:00:00,1.1342,1.13448,1.13303,1.1332,588 2017-07-06 02:00:00,1.13323,1.13398,1.13299,1.13375,458 2017-07-06 03:00:00,1.13373,1.13426,1.13373,1.13382,225 2017-07-06 04:00:00,1.13384,1.13395,1.13371,1.13392,89 2017-07-06 05:00:00,1.1339,1.13421,1.13376,1.13382,243 2017-07-06 06:00:00,1.13386,1.13419,1.1331,1.13419,752 2017-07-06 07:00:00,1.13416,1.13571,1.13416,1.13519,1297 2017-07-06 08:00:00,1.13517,1.13546,1.13442,1.1349,1186 2017-07-06 09:00:00,1.13488,1.13702,1.13444,1.13644,2725 2017-07-06 10:00:00,1.13646,1.13738,1.13577,1.13658,1751 2017-07-06 11:00:00,1.13656,1.13876,1.13614,1.13868,1839 2017-07-06 12:00:00,1.13866,1.13977,1.1379,1.13906,2259 2017-07-06 13:00:00,1.13908,1.14018,1.13817,1.14003,1591 2017-07-06 14:00:00,1.14,1.1406,1.1393,1.14054,1743 2017-07-06 15:00:00,1.14056,1.14071,1.13983,1.14068,1264 2017-07-06 16:00:00,1.1407,1.14197,1.14068,1.14197,788 2017-07-06 17:00:00,1.142,1.14246,1.14158,1.14232,613 2017-07-06 18:00:00,1.14229,1.14238,1.14191,1.14226,510 2017-07-06 19:00:00,1.14224,1.14235,1.14179,1.142,430 2017-07-06 20:00:00,1.14204,1.14248,1.14184,1.14234,226 2017-07-06 21:00:00,1.14232,1.14244,1.14185,1.14221,353 2017-07-06 22:00:00,1.14218,1.14245,1.14186,1.14223,479 2017-07-06 23:00:00,1.14221,1.14256,1.14193,1.14216,322 2017-07-07 00:00:00,1.14214,1.1422,1.14134,1.14156,539 2017-07-07 01:00:00,1.14158,1.14243,1.14102,1.14117,1042 2017-07-07 02:00:00,1.14114,1.14141,1.14084,1.14138,466 2017-07-07 03:00:00,1.14136,1.14161,1.14117,1.14135,420 2017-07-07 04:00:00,1.14133,1.14202,1.14133,1.14151,274 2017-07-07 05:00:00,1.14148,1.14184,1.14072,1.14077,467 2017-07-07 06:00:00,1.14075,1.14192,1.14072,1.14148,1041 2017-07-07 07:00:00,1.14146,1.14274,1.14133,1.14196,1277 2017-07-07 08:00:00,1.14198,1.14246,1.14122,1.14218,1106 2017-07-07 09:00:00,1.1422,1.14252,1.14127,1.14146,1112 2017-07-07 10:00:00,1.14143,1.14156,1.14062,1.14115,785 2017-07-07 11:00:00,1.14112,1.14192,1.14074,1.14079,984 2017-07-07 12:00:00,1.14082,1.14397,1.13867,1.14018,5387 2017-07-07 13:00:00,1.1402,1.1405,1.13808,1.13977,2854 2017-07-07 14:00:00,1.1398,1.14014,1.13797,1.13919,1689 2017-07-07 15:00:00,1.13922,1.13961,1.13866,1.13961,1088 2017-07-07 16:00:00,1.13964,1.14086,1.13964,1.14083,670 2017-07-07 17:00:00,1.1408,1.14108,1.14014,1.14041,524 2017-07-07 18:00:00,1.14039,1.14059,1.14011,1.1403,277 2017-07-07 19:00:00,1.14032,1.14078,1.14018,1.14047,370 2017-07-07 20:00:00,1.14045,1.14049,1.13982,1.14001,257 2017-07-09 21:00:00,1.13985,1.14004,1.13932,1.13993,300 2017-07-09 22:00:00,1.13996,1.14022,1.13982,1.14001,187 2017-07-09 23:00:00,1.14004,1.14038,1.13995,1.14035,122 2017-07-10 00:00:00,1.14032,1.14036,1.13978,1.14006,266 2017-07-10 01:00:00,1.14002,1.14084,1.13996,1.14082,312 2017-07-10 02:00:00,1.14079,1.14084,1.14044,1.14052,195 2017-07-10 03:00:00,1.1405,1.14061,1.14018,1.14046,173 2017-07-10 04:00:00,1.14044,1.14054,1.14006,1.14039,136 2017-07-10 05:00:00,1.14042,1.14091,1.14019,1.14091,220 2017-07-10 06:00:00,1.14094,1.14096,1.13972,1.13987,887 2017-07-10 07:00:00,1.1399,1.14181,1.13969,1.1416,1384 2017-07-10 08:00:00,1.14163,1.14167,1.13929,1.13957,1266 2017-07-10 09:00:00,1.13959,1.13978,1.13823,1.13853,969 2017-07-10 10:00:00,1.13856,1.13939,1.13832,1.13912,551 2017-07-10 11:00:00,1.1391,1.13924,1.13818,1.13885,1071 2017-07-10 12:00:00,1.13888,1.13939,1.1383,1.13922,903 2017-07-10 13:00:00,1.13924,1.13939,1.13852,1.13917,938 2017-07-10 14:00:00,1.13915,1.1396,1.13869,1.1388,1149 2017-07-10 15:00:00,1.13882,1.1396,1.13848,1.13956,857 2017-07-10 16:00:00,1.13958,1.14019,1.13946,1.13956,400 2017-07-10 17:00:00,1.13958,1.14074,1.13956,1.1406,406 2017-07-10 18:00:00,1.14062,1.1408,1.14025,1.14032,250 2017-07-10 19:00:00,1.1403,1.14036,1.1398,1.14,315 2017-07-10 20:00:00,1.13998,1.14005,1.13981,1.13994,109 2017-07-10 21:00:00,1.13992,1.13996,1.13958,1.13976,352 2017-07-10 22:00:00,1.13978,1.13984,1.13938,1.13966,308 2017-07-10 23:00:00,1.13968,1.1398,1.13954,1.13963,114 2017-07-11 00:00:00,1.1396,1.13979,1.13929,1.13948,274 2017-07-11 01:00:00,1.13951,1.13974,1.13924,1.13931,328 2017-07-11 02:00:00,1.13934,1.1396,1.13926,1.13936,199 2017-07-11 03:00:00,1.13934,1.13952,1.13924,1.13944,133 2017-07-11 04:00:00,1.13946,1.13946,1.13872,1.13886,186 2017-07-11 05:00:00,1.13888,1.13888,1.13833,1.13834,398 2017-07-11 06:00:00,1.13836,1.13918,1.13829,1.13907,807 2017-07-11 07:00:00,1.1391,1.13991,1.13852,1.13902,1422 2017-07-11 08:00:00,1.13904,1.14,1.13904,1.13962,1004 2017-07-11 09:00:00,1.13959,1.1396,1.13918,1.1395,774 2017-07-11 10:00:00,1.13952,1.13974,1.13901,1.13958,643 2017-07-11 11:00:00,1.1396,1.13988,1.1394,1.13944,565 2017-07-11 12:00:00,1.13942,1.1414,1.13942,1.14019,1504 2017-07-11 13:00:00,1.14017,1.14092,1.13936,1.14082,1182 2017-07-11 14:00:00,1.14084,1.14249,1.13954,1.14194,1875 2017-07-11 15:00:00,1.14197,1.14334,1.14147,1.143,1633 2017-07-11 16:00:00,1.14298,1.1448,1.14286,1.14396,1548 2017-07-11 17:00:00,1.14398,1.14767,1.14398,1.14666,1834 2017-07-11 18:00:00,1.14664,1.14797,1.14662,1.14766,1031 2017-07-11 19:00:00,1.14762,1.14763,1.14626,1.14635,612 2017-07-11 20:00:00,1.14633,1.14674,1.1461,1.14672,266 2017-07-11 21:00:00,1.14665,1.14671,1.14629,1.14649,346 2017-07-11 22:00:00,1.14652,1.14719,1.1463,1.14697,388 2017-07-11 23:00:00,1.14694,1.14741,1.14666,1.1474,253 2017-07-12 00:00:00,1.14738,1.14782,1.14722,1.14764,491 2017-07-12 01:00:00,1.14766,1.14792,1.14706,1.14716,514 2017-07-12 02:00:00,1.14714,1.14839,1.14704,1.14818,367 2017-07-12 03:00:00,1.1482,1.1482,1.14734,1.14808,289 2017-07-12 04:00:00,1.14804,1.14884,1.14748,1.14857,378 2017-07-12 05:00:00,1.14854,1.14894,1.147,1.14723,589 2017-07-12 06:00:00,1.14726,1.14765,1.14559,1.1459,1501 2017-07-12 07:00:00,1.14588,1.14642,1.14469,1.14484,1446 2017-07-12 08:00:00,1.14482,1.14632,1.14422,1.14576,1124 2017-07-12 09:00:00,1.14574,1.1461,1.14438,1.14546,1462 2017-07-12 10:00:00,1.14549,1.14606,1.1449,1.14532,957 2017-07-12 11:00:00,1.14535,1.1462,1.14514,1.14536,1167 2017-07-12 12:00:00,1.14534,1.14798,1.1427,1.14676,4996 2017-07-12 13:00:00,1.14678,1.14689,1.14098,1.1414,5616 2017-07-12 14:00:00,1.14143,1.14196,1.13919,1.14188,4290 2017-07-12 15:00:00,1.1419,1.14274,1.14084,1.14103,2198 2017-07-12 16:00:00,1.14106,1.14222,1.14093,1.14208,1147 2017-07-12 17:00:00,1.14206,1.14239,1.14122,1.14186,954 2017-07-12 18:00:00,1.14188,1.14252,1.1414,1.1415,674 2017-07-12 19:00:00,1.14152,1.14224,1.14126,1.14202,544 2017-07-12 20:00:00,1.142,1.142,1.14109,1.14117,203 2017-07-12 21:00:00,1.1413,1.14181,1.14124,1.14131,259 2017-07-12 22:00:00,1.14128,1.14182,1.14122,1.1416,192 2017-07-12 23:00:00,1.14162,1.14198,1.14142,1.14186,129 2017-07-13 00:00:00,1.14184,1.14298,1.14171,1.14296,565 2017-07-13 01:00:00,1.14294,1.1437,1.14294,1.14346,447 2017-07-13 02:00:00,1.14348,1.14379,1.14323,1.14372,333 2017-07-13 03:00:00,1.14374,1.14391,1.14338,1.14341,396 2017-07-13 04:00:00,1.14344,1.14386,1.14338,1.14377,221 2017-07-13 05:00:00,1.14374,1.14496,1.14355,1.14472,405 2017-07-13 06:00:00,1.1447,1.14558,1.14436,1.14468,1411 2017-07-13 07:00:00,1.1447,1.14529,1.14129,1.14217,1905 2017-07-13 08:00:00,1.14214,1.14232,1.1396,1.14037,2072 2017-07-13 09:00:00,1.14034,1.14082,1.13856,1.13961,2260 2017-07-13 10:00:00,1.13964,1.14054,1.13763,1.13881,2173 2017-07-13 11:00:00,1.13883,1.1413,1.13706,1.14104,5719 2017-07-13 12:00:00,1.14107,1.14192,1.14022,1.14163,2685 2017-07-13 13:00:00,1.1416,1.14178,1.13984,1.13988,1603 2017-07-13 14:00:00,1.13986,1.14149,1.13904,1.14036,2303 2017-07-13 15:00:00,1.14039,1.14078,1.1384,1.1384,1405 2017-07-13 16:00:00,1.13842,1.13919,1.13828,1.13906,661 2017-07-13 17:00:00,1.13908,1.13994,1.13894,1.13974,708 2017-07-13 18:00:00,1.13972,1.14063,1.13964,1.14044,574 2017-07-13 19:00:00,1.14041,1.14068,1.14022,1.14068,408 2017-07-13 20:00:00,1.14066,1.14066,1.1396,1.13979,247 2017-07-13 21:00:00,1.13983,1.14022,1.13977,1.14003,161 2017-07-13 22:00:00,1.14,1.14017,1.13954,1.13956,177 2017-07-13 23:00:00,1.13958,1.14051,1.13958,1.14045,241 2017-07-14 00:00:00,1.14042,1.14042,1.13917,1.14018,447 2017-07-14 01:00:00,1.14016,1.14072,1.13971,1.1402,385 2017-07-14 02:00:00,1.14022,1.14086,1.14002,1.14084,333 2017-07-14 03:00:00,1.14082,1.14104,1.14065,1.14104,183 2017-07-14 04:00:00,1.14102,1.14108,1.14008,1.14008,155 2017-07-14 05:00:00,1.14011,1.14123,1.14011,1.14098,314 2017-07-14 06:00:00,1.14096,1.1418,1.14046,1.14158,1253 2017-07-14 07:00:00,1.14156,1.14239,1.14092,1.14168,1435 2017-07-14 08:00:00,1.14166,1.14204,1.14076,1.14079,999 2017-07-14 09:00:00,1.14081,1.1418,1.14076,1.14146,1261 2017-07-14 10:00:00,1.14149,1.14181,1.14114,1.14142,655 2017-07-14 11:00:00,1.1414,1.14195,1.14111,1.1417,797 2017-07-14 12:00:00,1.14172,1.14684,1.14136,1.14618,4788 2017-07-14 13:00:00,1.1462,1.14632,1.14362,1.14591,3299 2017-07-14 14:00:00,1.14594,1.14634,1.14357,1.14444,2925 2017-07-14 15:00:00,1.14448,1.14606,1.14448,1.14516,1434 2017-07-14 16:00:00,1.14518,1.14601,1.14499,1.14594,764 2017-07-14 17:00:00,1.14596,1.14688,1.14572,1.14673,673 2017-07-14 18:00:00,1.1467,1.14688,1.14625,1.14658,374 2017-07-14 19:00:00,1.1466,1.14712,1.14647,1.14708,377 2017-07-14 20:00:00,1.14706,1.14718,1.14662,1.14688,279 2017-07-16 21:00:00,1.14741,1.14741,1.1467,1.14702,172 2017-07-16 22:00:00,1.14706,1.14742,1.1467,1.147,539 2017-07-16 23:00:00,1.14702,1.14748,1.147,1.14741,236 2017-07-17 00:00:00,1.14738,1.14745,1.14639,1.14649,300 2017-07-17 01:00:00,1.14647,1.14674,1.14604,1.14618,394 2017-07-17 02:00:00,1.14616,1.14649,1.14569,1.1461,273 2017-07-17 03:00:00,1.14612,1.1466,1.14596,1.14635,279 2017-07-17 04:00:00,1.14632,1.14634,1.14584,1.14614,137 2017-07-17 05:00:00,1.14612,1.14634,1.1457,1.14615,243 2017-07-17 06:00:00,1.14612,1.14624,1.14468,1.14539,1339 2017-07-17 07:00:00,1.14536,1.14578,1.1435,1.14408,1674 2017-07-17 08:00:00,1.14406,1.14514,1.14394,1.14482,937 2017-07-17 09:00:00,1.14484,1.14601,1.14452,1.14592,958 2017-07-17 10:00:00,1.1459,1.14644,1.14554,1.1459,848 2017-07-17 11:00:00,1.14592,1.14702,1.14592,1.14638,724 2017-07-17 12:00:00,1.14641,1.1471,1.14608,1.1468,1140 2017-07-17 13:00:00,1.14677,1.1468,1.1461,1.14672,1224 2017-07-17 14:00:00,1.14674,1.14871,1.14662,1.14708,1648 2017-07-17 15:00:00,1.1471,1.14771,1.14638,1.14692,1134 2017-07-17 16:00:00,1.14694,1.14758,1.14687,1.14722,469 2017-07-17 17:00:00,1.14725,1.14807,1.14725,1.1478,493 2017-07-17 18:00:00,1.14782,1.14808,1.1476,1.14804,336 2017-07-17 19:00:00,1.14802,1.14845,1.14762,1.14824,406 2017-07-17 20:00:00,1.14826,1.14833,1.14778,1.14782,180 2017-07-17 21:00:00,1.14784,1.14814,1.14756,1.14785,346 2017-07-17 22:00:00,1.14788,1.14798,1.14757,1.14768,374 2017-07-17 23:00:00,1.14771,1.14792,1.14732,1.14735,130 2017-07-18 00:00:00,1.14737,1.14872,1.14717,1.14854,501 2017-07-18 01:00:00,1.14856,1.1532,1.14852,1.15293,2414 2017-07-18 02:00:00,1.15295,1.1538,1.15256,1.15292,1128 2017-07-18 03:00:00,1.15289,1.15354,1.15263,1.15278,588 2017-07-18 04:00:00,1.15276,1.15302,1.15234,1.15276,582 2017-07-18 05:00:00,1.15273,1.1535,1.15188,1.15223,891 2017-07-18 06:00:00,1.1522,1.15314,1.1513,1.15267,2413 2017-07-18 07:00:00,1.15265,1.15295,1.15113,1.15288,2334 2017-07-18 08:00:00,1.1529,1.15321,1.15172,1.15255,2072 2017-07-18 09:00:00,1.15253,1.15601,1.15194,1.15521,2418 2017-07-18 10:00:00,1.15518,1.15642,1.15499,1.15576,1450 2017-07-18 11:00:00,1.15574,1.156,1.15454,1.15498,1481 2017-07-18 12:00:00,1.155,1.15682,1.15452,1.15559,3019 2017-07-18 13:00:00,1.15562,1.15814,1.1554,1.15798,1930 2017-07-18 14:00:00,1.158,1.15832,1.15728,1.15807,1634 2017-07-18 15:00:00,1.1581,1.15824,1.15684,1.1578,1378 2017-07-18 16:00:00,1.15782,1.15813,1.15584,1.1563,1099 2017-07-18 17:00:00,1.15632,1.15642,1.15531,1.15625,977 2017-07-18 18:00:00,1.15623,1.15656,1.1556,1.1561,592 2017-07-18 19:00:00,1.15612,1.1562,1.15524,1.15574,534 2017-07-18 20:00:00,1.15577,1.15585,1.15532,1.15538,188 2017-07-18 21:00:00,1.15558,1.15566,1.15534,1.15537,167 2017-07-18 22:00:00,1.15539,1.15554,1.15504,1.15506,401 2017-07-18 23:00:00,1.15509,1.15524,1.1548,1.1548,240 2017-07-19 00:00:00,1.15482,1.15534,1.15433,1.15448,460 2017-07-19 01:00:00,1.1545,1.15452,1.15414,1.15422,374 2017-07-19 02:00:00,1.1542,1.15442,1.15386,1.15392,226 2017-07-19 03:00:00,1.15394,1.15402,1.15366,1.1539,289 2017-07-19 04:00:00,1.15392,1.15396,1.1537,1.15376,135 2017-07-19 05:00:00,1.15378,1.15378,1.15313,1.15328,453 2017-07-19 06:00:00,1.15326,1.15476,1.15292,1.15296,1578 2017-07-19 07:00:00,1.15294,1.15391,1.1519,1.15202,2020 2017-07-19 08:00:00,1.152,1.15276,1.15148,1.15266,1275 2017-07-19 09:00:00,1.15269,1.15378,1.15268,1.15298,1009 2017-07-19 10:00:00,1.15296,1.15376,1.15238,1.15256,737 2017-07-19 11:00:00,1.15254,1.15307,1.1521,1.15298,949 2017-07-19 12:00:00,1.153,1.15385,1.15238,1.15257,1524 2017-07-19 13:00:00,1.1526,1.15367,1.15238,1.15238,1797 2017-07-19 14:00:00,1.1524,1.15288,1.15101,1.15178,1594 2017-07-19 15:00:00,1.1518,1.15298,1.1513,1.15178,1435 2017-07-19 16:00:00,1.15176,1.1523,1.15142,1.1521,633 2017-07-19 17:00:00,1.15212,1.15232,1.15172,1.15174,486 2017-07-19 18:00:00,1.15171,1.15193,1.15135,1.15182,411 2017-07-19 19:00:00,1.15184,1.15193,1.15149,1.15174,390 2017-07-19 20:00:00,1.15176,1.15178,1.1513,1.15154,218 2017-07-19 21:00:00,1.15145,1.15187,1.1513,1.15179,392 2017-07-19 22:00:00,1.15177,1.15282,1.15177,1.15276,360 2017-07-19 23:00:00,1.15278,1.15326,1.15278,1.15288,157 2017-07-20 00:00:00,1.15286,1.1529,1.15228,1.15231,287 2017-07-20 01:00:00,1.15233,1.15263,1.15127,1.15166,571 2017-07-20 02:00:00,1.15169,1.15256,1.15144,1.15235,630 2017-07-20 03:00:00,1.15238,1.15272,1.15052,1.15067,825 2017-07-20 04:00:00,1.1507,1.1512,1.15067,1.1512,328 2017-07-20 05:00:00,1.15118,1.15124,1.15044,1.15114,506 2017-07-20 06:00:00,1.15111,1.15254,1.15089,1.15227,1455 2017-07-20 07:00:00,1.15224,1.1523,1.15004,1.15004,1839 2017-07-20 08:00:00,1.15007,1.15068,1.14962,1.15049,1655 2017-07-20 09:00:00,1.15047,1.15074,1.1497,1.15038,991 2017-07-20 10:00:00,1.15036,1.15123,1.15008,1.15063,1165 2017-07-20 11:00:00,1.15066,1.15132,1.14798,1.14849,3324 2017-07-20 12:00:00,1.14846,1.15705,1.14794,1.15619,10944 2017-07-20 13:00:00,1.15622,1.1574,1.15414,1.15619,5055 2017-07-20 14:00:00,1.15622,1.16582,1.15618,1.16345,6648 2017-07-20 15:00:00,1.16348,1.1647,1.16274,1.16337,2729 2017-07-20 16:00:00,1.16334,1.16398,1.16272,1.16344,1182 2017-07-20 17:00:00,1.16347,1.16376,1.16184,1.16292,1758 2017-07-20 18:00:00,1.16288,1.16296,1.16178,1.16248,921 2017-07-20 19:00:00,1.16246,1.16308,1.16212,1.16286,766 2017-07-20 20:00:00,1.16284,1.16338,1.1624,1.16312,1377 2017-07-20 21:00:00,1.16298,1.16364,1.1629,1.16327,2436 2017-07-20 22:00:00,1.16326,1.16361,1.16248,1.1628,896 2017-07-20 23:00:00,1.1628,1.16319,1.16262,1.16282,596 2017-07-21 00:00:00,1.16282,1.16302,1.16192,1.16266,1317 2017-07-21 01:00:00,1.1627,1.16299,1.16231,1.16288,1268 2017-07-21 02:00:00,1.16289,1.16366,1.1624,1.16249,1591 2017-07-21 03:00:00,1.16248,1.16298,1.16232,1.16284,896 2017-07-21 04:00:00,1.1628,1.16304,1.16251,1.1629,602 2017-07-21 05:00:00,1.1629,1.16402,1.16283,1.16401,757 2017-07-21 06:00:00,1.16398,1.1677,1.16368,1.1663,5207 2017-07-21 07:00:00,1.16629,1.16737,1.16378,1.16386,3951 2017-07-21 08:00:00,1.16388,1.16512,1.1636,1.16495,2343 2017-07-21 09:00:00,1.16492,1.16584,1.16386,1.16463,2570 2017-07-21 10:00:00,1.1646,1.1652,1.16378,1.16442,1989 2017-07-21 11:00:00,1.16444,1.1651,1.16402,1.16416,2383 2017-07-21 12:00:00,1.16419,1.1648,1.16352,1.16402,2644 2017-07-21 13:00:00,1.16406,1.16522,1.16358,1.16462,2607 2017-07-21 14:00:00,1.16463,1.16672,1.1645,1.1655,2916 2017-07-21 15:00:00,1.16555,1.1658,1.16484,1.16548,1789 2017-07-21 16:00:00,1.16546,1.16704,1.16538,1.16662,1517 2017-07-21 17:00:00,1.1666,1.16826,1.16654,1.1676,2229 2017-07-21 18:00:00,1.16755,1.16812,1.16725,1.16783,1637 2017-07-21 19:00:00,1.16782,1.16791,1.16647,1.16686,1240 2017-07-21 20:00:00,1.1668,1.16702,1.16612,1.16642,2862 2017-07-23 21:00:00,1.16635,1.1672,1.16635,1.1668,350 2017-07-23 22:00:00,1.16675,1.16742,1.16656,1.16728,1873 2017-07-23 23:00:00,1.16733,1.1682,1.16716,1.16814,714 2017-07-24 00:00:00,1.16812,1.16845,1.16678,1.1671,1710 2017-07-24 01:00:00,1.16706,1.16712,1.16632,1.16688,954 2017-07-24 02:00:00,1.16691,1.16704,1.16677,1.1668,560 2017-07-24 03:00:00,1.16682,1.16751,1.1668,1.16716,707 2017-07-24 04:00:00,1.16716,1.16744,1.16696,1.16708,412 2017-07-24 05:00:00,1.16707,1.16751,1.16681,1.167,628 2017-07-24 06:00:00,1.16698,1.16723,1.1642,1.16455,3439 2017-07-24 07:00:00,1.16454,1.16618,1.16304,1.164,4850 2017-07-24 08:00:00,1.16397,1.16541,1.16388,1.16527,2841 2017-07-24 09:00:00,1.1653,1.16576,1.16486,1.16567,2510 2017-07-24 10:00:00,1.16566,1.16601,1.16457,1.1649,2114 2017-07-24 11:00:00,1.16487,1.16525,1.16425,1.1645,1740 2017-07-24 12:00:00,1.16452,1.16577,1.16432,1.16558,2152 2017-07-24 13:00:00,1.1656,1.16562,1.16333,1.1638,2267 2017-07-24 14:00:00,1.16388,1.165,1.16342,1.16432,2546 2017-07-24 15:00:00,1.16436,1.16481,1.16374,1.16426,2333 2017-07-24 16:00:00,1.16422,1.16438,1.16257,1.1638,1551 2017-07-24 17:00:00,1.16377,1.1641,1.16304,1.16403,995 2017-07-24 18:00:00,1.16402,1.16439,1.1637,1.16432,842 2017-07-24 19:00:00,1.16434,1.16473,1.16424,1.16435,573 2017-07-24 20:00:00,1.1643,1.16439,1.16402,1.1642,446 2017-07-24 21:00:00,1.16406,1.16455,1.16315,1.16365,3595 2017-07-24 22:00:00,1.16366,1.16396,1.16352,1.16376,1515 2017-07-24 23:00:00,1.16374,1.16404,1.1636,1.1639,603 2017-07-25 00:00:00,1.1639,1.16402,1.16306,1.1638,1142 2017-07-25 01:00:00,1.1638,1.16427,1.16347,1.16408,743 2017-07-25 02:00:00,1.1641,1.16569,1.16391,1.16533,1188 2017-07-25 03:00:00,1.16532,1.16564,1.16518,1.16564,430 2017-07-25 04:00:00,1.1656,1.16637,1.1656,1.1662,1140 2017-07-25 05:00:00,1.16618,1.1663,1.16523,1.1663,688 2017-07-25 06:00:00,1.16625,1.16692,1.16576,1.16616,2408 2017-07-25 07:00:00,1.1662,1.1662,1.16434,1.16516,3744 2017-07-25 08:00:00,1.16516,1.1668,1.16492,1.16536,3766 2017-07-25 09:00:00,1.16536,1.16583,1.16488,1.16543,1890 2017-07-25 10:00:00,1.1654,1.16564,1.16473,1.16514,1705 2017-07-25 11:00:00,1.16514,1.16668,1.16498,1.16668,1756 2017-07-25 12:00:00,1.1667,1.16994,1.1667,1.1689,4473 2017-07-25 13:00:00,1.16889,1.17124,1.16808,1.16956,5829 2017-07-25 14:00:00,1.16958,1.16969,1.16572,1.16612,4799 2017-07-25 15:00:00,1.16612,1.16712,1.16458,1.16547,3139 2017-07-25 16:00:00,1.16546,1.16584,1.16496,1.16536,1679 2017-07-25 17:00:00,1.16538,1.16616,1.16523,1.16582,1023 2017-07-25 18:00:00,1.16586,1.16593,1.16449,1.16506,1645 2017-07-25 19:00:00,1.16504,1.16534,1.1644,1.16476,1676 2017-07-25 20:00:00,1.16476,1.16493,1.16422,1.16472,751 2017-07-25 21:00:00,1.16507,1.16512,1.16456,1.1648,2851 2017-07-25 22:00:00,1.16478,1.16489,1.16455,1.16466,487 2017-07-25 23:00:00,1.16467,1.16472,1.16434,1.16446,473 2017-07-26 00:00:00,1.16444,1.16455,1.16378,1.16415,1152 2017-07-26 01:00:00,1.16417,1.16478,1.16388,1.16464,1111 2017-07-26 02:00:00,1.16462,1.16538,1.16448,1.1652,685 2017-07-26 03:00:00,1.16524,1.16533,1.16422,1.16436,930 2017-07-26 04:00:00,1.16434,1.1649,1.16382,1.16388,619 2017-07-26 05:00:00,1.16392,1.16414,1.16335,1.16388,861 2017-07-26 06:00:00,1.1639,1.16438,1.16208,1.1638,2927 2017-07-26 07:00:00,1.1638,1.16458,1.16255,1.16301,2867 2017-07-26 08:00:00,1.16301,1.16338,1.16128,1.1623,3409 2017-07-26 09:00:00,1.16234,1.16327,1.16187,1.16326,2393 2017-07-26 10:00:00,1.16321,1.16516,1.16306,1.16424,2699 2017-07-26 11:00:00,1.16422,1.16503,1.16376,1.16427,2422 2017-07-26 12:00:00,1.16431,1.16486,1.16385,1.1641,2334 2017-07-26 13:00:00,1.16412,1.16556,1.16316,1.16336,3738 2017-07-26 14:00:00,1.16338,1.16392,1.16218,1.16299,3751 2017-07-26 15:00:00,1.16304,1.16396,1.16264,1.16324,2947 2017-07-26 16:00:00,1.16322,1.16376,1.16256,1.16266,1632 2017-07-26 17:00:00,1.16269,1.16533,1.16265,1.1647,3273 2017-07-26 18:00:00,1.16469,1.17078,1.16288,1.17041,15025 2017-07-26 19:00:00,1.1704,1.174,1.1704,1.17218,8638 2017-07-26 20:00:00,1.17216,1.17397,1.17206,1.17346,2136 2017-07-26 21:00:00,1.17364,1.17479,1.17324,1.17464,2338 2017-07-26 22:00:00,1.17464,1.17479,1.17325,1.17344,1470 2017-07-26 23:00:00,1.17338,1.17346,1.17267,1.1732,738 2017-07-27 00:00:00,1.17318,1.17392,1.17253,1.17296,2147 2017-07-27 01:00:00,1.173,1.17426,1.17215,1.17399,1782 2017-07-27 02:00:00,1.17398,1.1746,1.17372,1.1746,1556 2017-07-27 03:00:00,1.1746,1.1777,1.17424,1.17564,2583 2017-07-27 04:00:00,1.17566,1.17624,1.17459,1.17491,1840 2017-07-27 05:00:00,1.17488,1.17514,1.17349,1.17396,1823 2017-07-27 06:00:00,1.17396,1.17475,1.17274,1.1729,4166 2017-07-27 07:00:00,1.1729,1.17338,1.17148,1.17235,4699 2017-07-27 08:00:00,1.17238,1.17308,1.1712,1.17286,3169 2017-07-27 09:00:00,1.17281,1.17334,1.17175,1.172,2479 2017-07-27 10:00:00,1.17198,1.17215,1.17031,1.17048,2883 2017-07-27 11:00:00,1.17051,1.17131,1.16874,1.16932,2968 2017-07-27 12:00:00,1.16934,1.17057,1.16897,1.16964,4070 2017-07-27 13:00:00,1.16967,1.1705,1.16902,1.17049,3432 2017-07-27 14:00:00,1.17045,1.17063,1.16625,1.16658,5529 2017-07-27 15:00:00,1.16659,1.16786,1.1654,1.1655,3747 2017-07-27 16:00:00,1.16554,1.1669,1.16502,1.16644,2731 2017-07-27 17:00:00,1.16642,1.168,1.16594,1.1675,3511 2017-07-27 18:00:00,1.1675,1.1689,1.16658,1.16832,4061 2017-07-27 19:00:00,1.16831,1.1684,1.16719,1.16763,1735 2017-07-27 20:00:00,1.16763,1.16792,1.16712,1.16774,1741 2017-07-27 21:00:00,1.16776,1.16794,1.16724,1.16752,2194 2017-07-27 22:00:00,1.16755,1.16886,1.16752,1.16842,1626 2017-07-27 23:00:00,1.16844,1.16914,1.16814,1.16898,491 2017-07-28 00:00:00,1.16898,1.16924,1.16712,1.16764,1846 2017-07-28 01:00:00,1.16769,1.16844,1.16714,1.16844,2049 2017-07-28 02:00:00,1.16842,1.16932,1.16803,1.16932,1062 2017-07-28 03:00:00,1.1693,1.1694,1.1683,1.16838,902 2017-07-28 04:00:00,1.16838,1.16885,1.16817,1.1685,769 2017-07-28 05:00:00,1.16852,1.1696,1.16852,1.16943,1204 2017-07-28 06:00:00,1.16944,1.17015,1.16876,1.1697,2796 2017-07-28 07:00:00,1.16976,1.17156,1.16916,1.16964,4014 2017-07-28 08:00:00,1.16966,1.17078,1.16966,1.17056,3269 2017-07-28 09:00:00,1.17057,1.1718,1.17053,1.17159,2650 2017-07-28 10:00:00,1.17154,1.17218,1.17042,1.1707,2495 2017-07-28 11:00:00,1.17066,1.1724,1.17046,1.17185,2363 2017-07-28 12:00:00,1.17186,1.17538,1.17148,1.1716,7413 2017-07-28 13:00:00,1.17161,1.17328,1.1715,1.17281,5182 2017-07-28 14:00:00,1.1728,1.1754,1.17268,1.17472,5513 2017-07-28 15:00:00,1.17472,1.17644,1.17415,1.17554,5710 2017-07-28 16:00:00,1.17554,1.17562,1.17382,1.17409,1733 2017-07-28 17:00:00,1.17416,1.17506,1.17412,1.17469,1301 2017-07-28 18:00:00,1.17472,1.17624,1.17432,1.17614,1040 2017-07-28 19:00:00,1.1761,1.17622,1.1753,1.17587,1494 2017-07-28 20:00:00,1.17582,1.17594,1.17468,1.17498,1783 2017-07-30 21:00:00,1.17482,1.17594,1.17482,1.17543,793 2017-07-30 22:00:00,1.17534,1.17623,1.17493,1.17594,4559 2017-07-30 23:00:00,1.17595,1.176,1.17455,1.17471,909 2017-07-31 00:00:00,1.17477,1.175,1.17348,1.17428,2217 2017-07-31 01:00:00,1.17434,1.17463,1.17378,1.17396,1268 2017-07-31 02:00:00,1.17395,1.1744,1.17354,1.17354,875 2017-07-31 03:00:00,1.17356,1.17374,1.17317,1.17324,690 2017-07-31 04:00:00,1.17322,1.1737,1.17314,1.17364,315 2017-07-31 05:00:00,1.1736,1.17478,1.17316,1.17464,705 2017-07-31 06:00:00,1.17462,1.17466,1.17243,1.17376,3233 2017-07-31 07:00:00,1.1737,1.17382,1.17232,1.17247,2672 2017-07-31 08:00:00,1.17246,1.17345,1.17236,1.17326,1648 2017-07-31 09:00:00,1.1732,1.17442,1.17278,1.17344,2400 2017-07-31 10:00:00,1.17343,1.17376,1.173,1.17314,1142 2017-07-31 11:00:00,1.1731,1.17351,1.17256,1.17278,1358 2017-07-31 12:00:00,1.17272,1.17354,1.17234,1.1734,1620 2017-07-31 13:00:00,1.17338,1.17519,1.17318,1.17484,2395 2017-07-31 14:00:00,1.17484,1.17904,1.17462,1.1789,5104 2017-07-31 15:00:00,1.17892,1.18304,1.17839,1.18262,5169 2017-07-31 16:00:00,1.18259,1.1832,1.18056,1.1811,3929 2017-07-31 17:00:00,1.18108,1.1827,1.18082,1.18143,2422 2017-07-31 18:00:00,1.18148,1.18355,1.18132,1.18305,1921 2017-07-31 19:00:00,1.18301,1.18454,1.18288,1.18365,2329 2017-07-31 20:00:00,1.18364,1.1846,1.1831,1.18424,1648 2017-07-31 21:00:00,1.18414,1.18446,1.18356,1.1836,2138 2017-07-31 22:00:00,1.18366,1.1837,1.18294,1.18308,1743 2017-07-31 23:00:00,1.18309,1.18326,1.18206,1.1821,1023 2017-08-01 00:00:00,1.18207,1.183,1.18197,1.18272,1762 2017-08-01 01:00:00,1.18272,1.1831,1.182,1.18298,1469 2017-08-01 02:00:00,1.18294,1.1834,1.18262,1.18278,1225 2017-08-01 03:00:00,1.18276,1.1828,1.18228,1.18264,526 2017-08-01 04:00:00,1.18266,1.18344,1.18243,1.18305,1423 2017-08-01 05:00:00,1.18301,1.18334,1.18196,1.18206,868 2017-08-01 06:00:00,1.18206,1.18311,1.18116,1.18208,2227 2017-08-01 07:00:00,1.18203,1.1823,1.18112,1.18192,2364 2017-08-01 08:00:00,1.18196,1.18332,1.1815,1.18217,2402 2017-08-01 09:00:00,1.1822,1.18244,1.18072,1.18144,2651 2017-08-01 10:00:00,1.18142,1.18176,1.18047,1.18118,1565 2017-08-01 11:00:00,1.18119,1.18229,1.18094,1.18105,1529 2017-08-01 12:00:00,1.18107,1.18198,1.18064,1.18174,3624 2017-08-01 13:00:00,1.18179,1.18291,1.1807,1.18247,3550 2017-08-01 14:00:00,1.18243,1.18394,1.1785,1.18098,7271 2017-08-01 15:00:00,1.181,1.18141,1.17916,1.17985,3709 2017-08-01 16:00:00,1.17984,1.18058,1.17966,1.18045,1222 2017-08-01 17:00:00,1.18046,1.18377,1.18046,1.1817,3334 2017-08-01 18:00:00,1.18173,1.18199,1.17986,1.18034,1509 2017-08-01 19:00:00,1.18034,1.18052,1.17988,1.18004,1181 2017-08-01 20:00:00,1.18007,1.18032,1.17989,1.18016,844 2017-08-01 21:00:00,1.1802,1.18098,1.17984,1.18061,2610 2017-08-01 22:00:00,1.18055,1.18132,1.18048,1.1806,1168 2017-08-01 23:00:00,1.1806,1.18132,1.18024,1.18109,899 2017-08-02 00:00:00,1.1811,1.18178,1.18068,1.18136,1352 2017-08-02 01:00:00,1.18136,1.18143,1.17942,1.18003,1386 2017-08-02 02:00:00,1.18004,1.18091,1.17974,1.18086,1329 2017-08-02 03:00:00,1.18083,1.18174,1.18038,1.18112,1240 2017-08-02 04:00:00,1.18112,1.18287,1.18107,1.1825,2187 2017-08-02 05:00:00,1.18252,1.18302,1.18208,1.18234,717 2017-08-02 06:00:00,1.18232,1.18445,1.18174,1.18396,2240 2017-08-02 07:00:00,1.18392,1.18409,1.18246,1.18333,2475 2017-08-02 08:00:00,1.18336,1.18688,1.18305,1.18543,4988 2017-08-02 09:00:00,1.18542,1.18592,1.18374,1.18375,3164 2017-08-02 10:00:00,1.18376,1.18383,1.18302,1.1833,1525 2017-08-02 11:00:00,1.18327,1.18376,1.1822,1.1827,1643 2017-08-02 12:00:00,1.18274,1.1839,1.18185,1.18248,4776 2017-08-02 13:00:00,1.18249,1.18344,1.1823,1.18334,2993 2017-08-02 14:00:00,1.18339,1.18582,1.18256,1.18566,5321 2017-08-02 15:00:00,1.18565,1.18714,1.18551,1.18605,3474 2017-08-02 16:00:00,1.18604,1.1871,1.18603,1.18681,1593 2017-08-02 17:00:00,1.18686,1.19108,1.18678,1.18832,4148 2017-08-02 18:00:00,1.18828,1.18831,1.1857,1.18596,1907 2017-08-02 19:00:00,1.18602,1.1861,1.185,1.18541,1290 2017-08-02 20:00:00,1.18546,1.1858,1.18532,1.18558,799 2017-08-02 21:00:00,1.1856,1.18622,1.18531,1.1855,1321 2017-08-02 22:00:00,1.18548,1.18584,1.18505,1.18515,1830 2017-08-02 23:00:00,1.18518,1.18533,1.1846,1.18512,815 2017-08-03 00:00:00,1.18512,1.1852,1.18358,1.18358,1167 2017-08-03 01:00:00,1.18363,1.18471,1.1836,1.18368,1243 2017-08-03 02:00:00,1.1837,1.18438,1.18342,1.18433,1074 2017-08-03 03:00:00,1.18436,1.18489,1.18414,1.18414,587 2017-08-03 04:00:00,1.18419,1.18482,1.18419,1.18462,451 2017-08-03 05:00:00,1.18458,1.1857,1.1844,1.18446,880 2017-08-03 06:00:00,1.18446,1.18534,1.1838,1.18512,2262 2017-08-03 07:00:00,1.18518,1.18568,1.18372,1.18408,2334 2017-08-03 08:00:00,1.1843,1.18437,1.18306,1.18437,2473 2017-08-03 09:00:00,1.18434,1.18504,1.18415,1.18456,1742 2017-08-03 10:00:00,1.18455,1.18475,1.18335,1.18376,1223 2017-08-03 11:00:00,1.18374,1.18586,1.18337,1.18537,5673 2017-08-03 12:00:00,1.18536,1.18737,1.1848,1.18594,4218 2017-08-03 13:00:00,1.1859,1.18594,1.18428,1.18498,2702 2017-08-03 14:00:00,1.18498,1.18932,1.18453,1.18789,7354 2017-08-03 15:00:00,1.18793,1.1889,1.18737,1.18794,3221 2017-08-03 16:00:00,1.18792,1.18842,1.18678,1.18694,1891 2017-08-03 17:00:00,1.18694,1.1872,1.18608,1.1872,1342 2017-08-03 18:00:00,1.18722,1.18775,1.18644,1.18674,1285 2017-08-03 19:00:00,1.1867,1.18885,1.1862,1.18751,3266 2017-08-03 20:00:00,1.18754,1.18812,1.18675,1.18691,776 2017-08-03 21:00:00,1.18707,1.1876,1.18682,1.18708,2546 2017-08-03 22:00:00,1.18716,1.18804,1.18702,1.18774,885 2017-08-03 23:00:00,1.18774,1.18856,1.1875,1.18826,811 2017-08-04 00:00:00,1.1882,1.18858,1.18759,1.18766,964 2017-08-04 01:00:00,1.18768,1.18797,1.18712,1.18774,1160 2017-08-04 02:00:00,1.18774,1.188,1.1872,1.18748,734 2017-08-04 03:00:00,1.1875,1.18793,1.18743,1.18793,351 2017-08-04 04:00:00,1.18788,1.18887,1.18774,1.18798,803 2017-08-04 05:00:00,1.18794,1.18866,1.18776,1.18844,691 2017-08-04 06:00:00,1.18851,1.18856,1.1874,1.18851,1361 2017-08-04 07:00:00,1.18846,1.18896,1.18764,1.18822,1939 2017-08-04 08:00:00,1.18827,1.18882,1.18805,1.1882,1126 2017-08-04 09:00:00,1.18823,1.18852,1.18776,1.18824,1119 2017-08-04 10:00:00,1.1882,1.18832,1.18702,1.18704,971 2017-08-04 11:00:00,1.18702,1.1877,1.18631,1.1877,1592 2017-08-04 12:00:00,1.18766,1.18766,1.18253,1.18304,11283 2017-08-04 13:00:00,1.18302,1.1846,1.17915,1.17933,6791 2017-08-04 14:00:00,1.17934,1.17934,1.17284,1.17502,10580 2017-08-04 15:00:00,1.17502,1.17645,1.17451,1.17536,3390 2017-08-04 16:00:00,1.17536,1.17584,1.17362,1.17543,2289 2017-08-04 17:00:00,1.17544,1.17638,1.175,1.17542,1635 2017-08-04 18:00:00,1.17543,1.17672,1.17514,1.17645,1265 2017-08-04 19:00:00,1.1765,1.17817,1.1765,1.17808,1497 2017-08-04 20:00:00,1.17809,1.17832,1.17684,1.17734,1219 2017-08-06 21:00:00,1.17738,1.17769,1.17723,1.17754,756 2017-08-06 22:00:00,1.17751,1.1793,1.17717,1.17862,3326 2017-08-06 23:00:00,1.1786,1.1792,1.17769,1.1777,1446 2017-08-07 00:00:00,1.17773,1.17926,1.17767,1.17864,1594 2017-08-07 01:00:00,1.17863,1.17934,1.17842,1.17927,1123 2017-08-07 02:00:00,1.1793,1.18003,1.17914,1.17938,549 2017-08-07 03:00:00,1.1794,1.18022,1.17932,1.17986,342 2017-08-07 04:00:00,1.17982,1.18004,1.1795,1.17952,232 2017-08-07 05:00:00,1.17954,1.17966,1.17838,1.17848,439 2017-08-07 06:00:00,1.17848,1.17931,1.1781,1.17852,1714 2017-08-07 07:00:00,1.17852,1.18142,1.1778,1.18098,3648 2017-08-07 08:00:00,1.18102,1.18118,1.1796,1.17981,2456 2017-08-07 09:00:00,1.17981,1.18056,1.17898,1.18024,1737 2017-08-07 10:00:00,1.1802,1.1805,1.17948,1.17978,1368 2017-08-07 11:00:00,1.17973,1.18084,1.17939,1.18025,1795 2017-08-07 12:00:00,1.18027,1.18046,1.17864,1.17866,2064 2017-08-07 13:00:00,1.17866,1.17945,1.1783,1.1792,2084 2017-08-07 14:00:00,1.17926,1.17986,1.17832,1.17946,2431 2017-08-07 15:00:00,1.1795,1.1796,1.17857,1.17876,1356 2017-08-07 16:00:00,1.17871,1.17905,1.17816,1.17886,607 2017-08-07 17:00:00,1.17886,1.17965,1.17886,1.17915,752 2017-08-07 18:00:00,1.17917,1.17952,1.17872,1.17944,698 2017-08-07 19:00:00,1.17942,1.17968,1.17888,1.17944,703 2017-08-07 20:00:00,1.17944,1.17969,1.17922,1.1795,372 2017-08-07 21:00:00,1.17938,1.1798,1.17902,1.17953,2448 2017-08-07 22:00:00,1.17959,1.18012,1.17914,1.17922,1143 2017-08-07 23:00:00,1.17924,1.18018,1.17924,1.17996,328 2017-08-08 00:00:00,1.17998,1.18052,1.1798,1.18024,620 2017-08-08 01:00:00,1.18028,1.18242,1.1802,1.18162,1330 2017-08-08 02:00:00,1.18163,1.18178,1.18068,1.18132,699 2017-08-08 03:00:00,1.18129,1.18164,1.18068,1.18068,470 2017-08-08 04:00:00,1.1807,1.18156,1.1807,1.18114,394 2017-08-08 05:00:00,1.18118,1.18152,1.18048,1.18082,525 2017-08-08 06:00:00,1.18077,1.1813,1.1802,1.18052,1437 2017-08-08 07:00:00,1.18054,1.18181,1.18029,1.18072,1727 2017-08-08 08:00:00,1.18075,1.18156,1.18056,1.18084,1237 2017-08-08 09:00:00,1.1808,1.18142,1.18068,1.18108,1166 2017-08-08 10:00:00,1.18104,1.18184,1.18075,1.18158,1076 2017-08-08 11:00:00,1.18156,1.18167,1.18056,1.18084,1222 2017-08-08 12:00:00,1.18085,1.18176,1.1807,1.18148,1577 2017-08-08 13:00:00,1.18146,1.1816,1.18065,1.18102,1664 2017-08-08 14:00:00,1.18102,1.18124,1.17324,1.17385,7240 2017-08-08 15:00:00,1.1739,1.17508,1.17154,1.17246,3974 2017-08-08 16:00:00,1.1725,1.17541,1.17212,1.17525,2626 2017-08-08 17:00:00,1.17526,1.17697,1.17502,1.17636,2246 2017-08-08 18:00:00,1.17638,1.17641,1.17512,1.17522,1151 2017-08-08 19:00:00,1.17521,1.1753,1.17406,1.17493,2202 2017-08-08 20:00:00,1.1749,1.1759,1.17469,1.17529,1323 2017-08-08 21:00:00,1.17508,1.17572,1.1746,1.17505,3933 2017-08-08 22:00:00,1.17502,1.17624,1.1744,1.17595,1512 2017-08-08 23:00:00,1.17589,1.17642,1.17524,1.17546,612 2017-08-09 00:00:00,1.17542,1.17552,1.17336,1.17434,1889 2017-08-09 01:00:00,1.17436,1.17496,1.17292,1.17357,1591 2017-08-09 02:00:00,1.1736,1.1741,1.17194,1.17346,1796 2017-08-09 03:00:00,1.17341,1.17364,1.17284,1.17332,957 2017-08-09 04:00:00,1.17334,1.174,1.17326,1.17375,583 2017-08-09 05:00:00,1.17376,1.17449,1.17357,1.17411,704 2017-08-09 06:00:00,1.17406,1.17548,1.17353,1.17452,2469 2017-08-09 07:00:00,1.1745,1.17619,1.1736,1.1743,3513 2017-08-09 08:00:00,1.1743,1.1743,1.1722,1.1732,3531 2017-08-09 09:00:00,1.17316,1.17533,1.173,1.17406,2723 2017-08-09 10:00:00,1.17405,1.1752,1.1733,1.17366,1968 2017-08-09 11:00:00,1.17367,1.1742,1.17319,1.17395,2227 2017-08-09 12:00:00,1.174,1.17474,1.17256,1.1745,5390 2017-08-09 13:00:00,1.17452,1.1746,1.16888,1.17113,7300 2017-08-09 14:00:00,1.17114,1.17495,1.17085,1.17433,6682 2017-08-09 15:00:00,1.17432,1.17486,1.173,1.17482,2526 2017-08-09 16:00:00,1.17482,1.17496,1.17357,1.17444,1434 2017-08-09 17:00:00,1.17448,1.17588,1.17397,1.17522,1874 2017-08-09 18:00:00,1.17524,1.17616,1.17507,1.17524,1237 2017-08-09 19:00:00,1.1752,1.17564,1.17496,1.17557,774 2017-08-09 20:00:00,1.17557,1.1761,1.1754,1.1759,934 2017-08-09 21:00:00,1.17592,1.17616,1.17559,1.17597,1820 2017-08-09 22:00:00,1.17595,1.17701,1.17566,1.17626,665 2017-08-09 23:00:00,1.17621,1.17639,1.17578,1.17634,278 2017-08-10 00:00:00,1.17632,1.1764,1.17516,1.1757,1231 2017-08-10 01:00:00,1.1757,1.17623,1.17478,1.17534,1202 2017-08-10 02:00:00,1.17534,1.1755,1.17415,1.17482,849 2017-08-10 03:00:00,1.17482,1.17486,1.1733,1.17387,853 2017-08-10 04:00:00,1.17391,1.17395,1.17321,1.1736,787 2017-08-10 05:00:00,1.17355,1.17388,1.17268,1.17336,1016 2017-08-10 06:00:00,1.17337,1.17407,1.173,1.17319,1492 2017-08-10 07:00:00,1.1732,1.17356,1.17156,1.17202,2775 2017-08-10 08:00:00,1.17204,1.17352,1.17195,1.1723,2175 2017-08-10 09:00:00,1.17229,1.17229,1.17086,1.17114,2288 2017-08-10 10:00:00,1.1711,1.17255,1.17042,1.17238,1901 2017-08-10 11:00:00,1.17234,1.17255,1.17136,1.17255,1705 2017-08-10 12:00:00,1.1726,1.1748,1.17247,1.17416,4481 2017-08-10 13:00:00,1.17416,1.17492,1.17304,1.17364,3823 2017-08-10 14:00:00,1.1736,1.17594,1.17176,1.17502,6918 2017-08-10 15:00:00,1.17504,1.17578,1.17411,1.1751,2685 2017-08-10 16:00:00,1.17514,1.1762,1.17483,1.17564,2145 2017-08-10 17:00:00,1.17567,1.17603,1.17518,1.17554,1294 2017-08-10 18:00:00,1.17554,1.17752,1.17528,1.177,1600 2017-08-10 19:00:00,1.17702,1.1785,1.17696,1.17734,2145 2017-08-10 20:00:00,1.1773,1.17797,1.17715,1.17726,1066 2017-08-10 21:00:00,1.17733,1.17751,1.17672,1.1772,6568 2017-08-10 22:00:00,1.17712,1.17716,1.17652,1.17662,666 2017-08-10 23:00:00,1.17662,1.17706,1.17636,1.177,345 2017-08-11 00:00:00,1.177,1.17766,1.17682,1.17721,680 2017-08-11 01:00:00,1.17726,1.17762,1.1765,1.17702,1202 2017-08-11 02:00:00,1.17697,1.17704,1.17617,1.17688,829 2017-08-11 03:00:00,1.17684,1.17758,1.17671,1.17754,605 2017-08-11 04:00:00,1.17752,1.17765,1.17717,1.17746,518 2017-08-11 05:00:00,1.17746,1.17792,1.17638,1.17644,1074 2017-08-11 06:00:00,1.1764,1.17694,1.17554,1.17598,2106 2017-08-11 07:00:00,1.17598,1.17613,1.17496,1.17536,2034 2017-08-11 08:00:00,1.17532,1.17646,1.17509,1.17556,1590 2017-08-11 09:00:00,1.17553,1.17584,1.17484,1.17582,1102 2017-08-11 10:00:00,1.1758,1.1774,1.17554,1.17602,1431 2017-08-11 11:00:00,1.176,1.17723,1.17581,1.1759,1449 2017-08-11 12:00:00,1.17588,1.18354,1.17539,1.1816,9521 2017-08-11 13:00:00,1.1816,1.183,1.17774,1.17984,7655 2017-08-11 14:00:00,1.17979,1.181,1.17866,1.17927,3898 2017-08-11 15:00:00,1.17928,1.1811,1.17862,1.18106,2674 2017-08-11 16:00:00,1.18104,1.18223,1.18054,1.18203,2577 2017-08-11 17:00:00,1.18203,1.18476,1.18203,1.18374,3727 2017-08-11 18:00:00,1.18372,1.18393,1.18208,1.18236,1986 2017-08-11 19:00:00,1.1824,1.1829,1.18202,1.18247,1634 2017-08-11 20:00:00,1.18247,1.18252,1.18197,1.18219,1029 2017-08-13 21:00:00,1.1822,1.18288,1.18202,1.1824,766 2017-08-13 22:00:00,1.18243,1.18248,1.1819,1.18219,797 2017-08-13 23:00:00,1.18214,1.18265,1.18187,1.18252,588 2017-08-14 00:00:00,1.18252,1.18292,1.18186,1.1819,1744 2017-08-14 01:00:00,1.18196,1.18348,1.1817,1.18302,1014 2017-08-14 02:00:00,1.18302,1.1834,1.18274,1.18275,617 2017-08-14 03:00:00,1.18276,1.18314,1.18248,1.1827,442 2017-08-14 04:00:00,1.1827,1.18274,1.18176,1.18184,615 2017-08-14 05:00:00,1.1818,1.18213,1.18099,1.18196,1013 2017-08-14 06:00:00,1.18192,1.18331,1.18104,1.18308,2180 2017-08-14 07:00:00,1.18302,1.18384,1.18007,1.18078,2837 2017-08-14 08:00:00,1.18074,1.18094,1.17922,1.18034,2480 2017-08-14 09:00:00,1.18032,1.18072,1.1797,1.17981,1814 2017-08-14 10:00:00,1.17984,1.1804,1.17912,1.17989,1574 2017-08-14 11:00:00,1.17991,1.18002,1.17873,1.17923,1817 2017-08-14 12:00:00,1.17925,1.18,1.17912,1.17956,2030 2017-08-14 13:00:00,1.17962,1.18018,1.17845,1.17908,2445 2017-08-14 14:00:00,1.1791,1.1791,1.177,1.17822,3400 2017-08-14 15:00:00,1.1782,1.17881,1.17745,1.17862,1888 2017-08-14 16:00:00,1.17858,1.1796,1.17754,1.17933,1066 2017-08-14 17:00:00,1.1793,1.17952,1.17722,1.17734,1313 2017-08-14 18:00:00,1.1773,1.17864,1.1773,1.17832,1293 2017-08-14 19:00:00,1.17836,1.17888,1.17815,1.1785,887 2017-08-14 20:00:00,1.17846,1.17884,1.17782,1.17804,984 2017-08-14 21:00:00,1.17799,1.17818,1.17754,1.17761,642 2017-08-14 22:00:00,1.17766,1.1781,1.17752,1.17785,541 2017-08-14 23:00:00,1.17782,1.17804,1.17684,1.17756,372 2017-08-15 00:00:00,1.1776,1.17896,1.17752,1.17814,1495 2017-08-15 01:00:00,1.17819,1.17928,1.17819,1.17853,1196 2017-08-15 02:00:00,1.17852,1.1786,1.17816,1.1783,488 2017-08-15 03:00:00,1.17826,1.17842,1.17802,1.17821,307 2017-08-15 04:00:00,1.17824,1.17862,1.17768,1.17774,664 2017-08-15 05:00:00,1.17776,1.17795,1.17598,1.17607,1246 2017-08-15 06:00:00,1.17605,1.17641,1.17362,1.17429,3132 2017-08-15 07:00:00,1.17428,1.17474,1.17208,1.17462,3401 2017-08-15 08:00:00,1.17459,1.17692,1.17442,1.17566,3099 2017-08-15 09:00:00,1.17562,1.17638,1.17494,1.17496,2251 2017-08-15 10:00:00,1.17498,1.17524,1.1739,1.17496,1932 2017-08-15 11:00:00,1.17498,1.17578,1.1734,1.17396,2466 2017-08-15 12:00:00,1.17396,1.17452,1.16873,1.16918,7711 2017-08-15 13:00:00,1.16919,1.17243,1.169,1.17072,4514 2017-08-15 14:00:00,1.1707,1.17194,1.1701,1.17188,3588 2017-08-15 15:00:00,1.17187,1.17388,1.17142,1.17358,2874 2017-08-15 16:00:00,1.17354,1.17384,1.17295,1.17371,1378 2017-08-15 17:00:00,1.17366,1.17434,1.17365,1.17383,1160 2017-08-15 18:00:00,1.17386,1.1739,1.17274,1.1734,977 2017-08-15 19:00:00,1.17336,1.17404,1.17328,1.17398,714 2017-08-15 20:00:00,1.17395,1.17402,1.17324,1.1735,560 2017-08-15 21:00:00,1.17347,1.17384,1.17336,1.17354,1076 2017-08-15 22:00:00,1.17357,1.17388,1.17298,1.17374,756 2017-08-15 23:00:00,1.1737,1.17433,1.17364,1.17406,406 2017-08-16 00:00:00,1.17404,1.17456,1.17364,1.1738,854 2017-08-16 01:00:00,1.17384,1.17419,1.17346,1.1739,812 2017-08-16 02:00:00,1.17391,1.17417,1.1736,1.17364,457 2017-08-16 03:00:00,1.17362,1.17402,1.17358,1.17396,230 2017-08-16 04:00:00,1.17394,1.17435,1.1738,1.17426,333 2017-08-16 05:00:00,1.17424,1.17478,1.17377,1.17417,880 2017-08-16 06:00:00,1.17418,1.17418,1.1724,1.17308,1612 2017-08-16 07:00:00,1.1731,1.17582,1.17142,1.17158,4418 2017-08-16 08:00:00,1.17158,1.17392,1.16914,1.1726,6190 2017-08-16 09:00:00,1.17258,1.17397,1.17225,1.17235,2450 2017-08-16 10:00:00,1.17238,1.1724,1.16952,1.17019,2903 2017-08-16 11:00:00,1.17017,1.1715,1.16984,1.17113,2644 2017-08-16 12:00:00,1.17114,1.1721,1.17017,1.17031,4292 2017-08-16 13:00:00,1.17034,1.17066,1.16813,1.16946,4415 2017-08-16 14:00:00,1.16946,1.17164,1.16934,1.17032,2535 2017-08-16 15:00:00,1.17032,1.17094,1.16934,1.17018,2475 2017-08-16 16:00:00,1.17021,1.17044,1.16912,1.17019,1415 2017-08-16 17:00:00,1.1702,1.17428,1.17006,1.17344,4349 2017-08-16 18:00:00,1.17346,1.1779,1.1731,1.17684,10190 2017-08-16 19:00:00,1.1768,1.17736,1.17627,1.17692,1731 2017-08-16 20:00:00,1.1769,1.17715,1.1766,1.17676,751 2017-08-16 21:00:00,1.17659,1.17728,1.17656,1.17722,2627 2017-08-16 22:00:00,1.1772,1.17797,1.17701,1.17772,862 2017-08-16 23:00:00,1.17768,1.17792,1.177,1.17739,338 2017-08-17 00:00:00,1.17734,1.17748,1.17648,1.17681,997 2017-08-17 01:00:00,1.17684,1.17893,1.17668,1.17886,1981 2017-08-17 02:00:00,1.17889,1.17902,1.17776,1.17824,766 2017-08-17 03:00:00,1.17819,1.17834,1.17746,1.17776,586 2017-08-17 04:00:00,1.17776,1.17823,1.17776,1.17792,350 2017-08-17 05:00:00,1.1779,1.179,1.17755,1.17791,908 2017-08-17 06:00:00,1.17788,1.17804,1.1763,1.17632,1768 2017-08-17 07:00:00,1.17628,1.17661,1.17509,1.17558,2359 2017-08-17 08:00:00,1.17556,1.17561,1.17302,1.17329,2466 2017-08-17 09:00:00,1.17332,1.17375,1.17038,1.17068,2283 2017-08-17 10:00:00,1.1707,1.17086,1.169,1.16976,2864 2017-08-17 11:00:00,1.16972,1.1714,1.16622,1.16996,9062 2017-08-17 12:00:00,1.16994,1.17045,1.16814,1.16934,5800 2017-08-17 13:00:00,1.1693,1.17289,1.16924,1.17276,3925 2017-08-17 14:00:00,1.17276,1.17476,1.17148,1.17452,9502 2017-08-17 15:00:00,1.17457,1.17458,1.17318,1.17353,3193 2017-08-17 16:00:00,1.17357,1.1754,1.17321,1.17414,2193 2017-08-17 17:00:00,1.17412,1.17518,1.17391,1.17418,1855 2017-08-17 18:00:00,1.17417,1.17514,1.17397,1.17399,1215 2017-08-17 19:00:00,1.174,1.17472,1.17269,1.17279,2214 2017-08-17 20:00:00,1.17284,1.17333,1.17179,1.17242,902 2017-08-17 21:00:00,1.17219,1.17296,1.17182,1.17252,4197 2017-08-17 22:00:00,1.17252,1.17312,1.17242,1.17267,944 2017-08-17 23:00:00,1.1727,1.17296,1.17087,1.1716,815 2017-08-18 00:00:00,1.17163,1.17302,1.17101,1.1728,1937 2017-08-18 01:00:00,1.17278,1.17352,1.17236,1.17329,920 2017-08-18 02:00:00,1.17329,1.17364,1.17292,1.17292,531 2017-08-18 03:00:00,1.17296,1.17359,1.17296,1.17306,446 2017-08-18 04:00:00,1.17311,1.17397,1.17302,1.17374,626 2017-08-18 05:00:00,1.17377,1.17408,1.17327,1.17332,896 2017-08-18 06:00:00,1.1733,1.17413,1.17256,1.17377,2041 2017-08-18 07:00:00,1.17374,1.17588,1.17213,1.17264,4337 2017-08-18 08:00:00,1.17265,1.1749,1.17239,1.17472,2435 2017-08-18 09:00:00,1.17474,1.17749,1.17351,1.17351,4241 2017-08-18 10:00:00,1.17349,1.17527,1.1734,1.17487,2191 2017-08-18 11:00:00,1.17491,1.17512,1.17316,1.17346,1962 2017-08-18 12:00:00,1.1735,1.175,1.1734,1.17484,2714 2017-08-18 13:00:00,1.17486,1.17551,1.1736,1.17426,4128 2017-08-18 14:00:00,1.17423,1.17615,1.1737,1.1749,6696 2017-08-18 15:00:00,1.1749,1.17552,1.17296,1.17478,6549 2017-08-18 16:00:00,1.17482,1.17552,1.17434,1.17494,4132 2017-08-18 17:00:00,1.17494,1.17545,1.17405,1.17532,2466 2017-08-18 18:00:00,1.17536,1.17608,1.17518,1.17586,1193 2017-08-18 19:00:00,1.17588,1.17676,1.17584,1.17636,1329 2017-08-18 20:00:00,1.17634,1.1766,1.17588,1.17607,1836 2017-08-20 21:00:00,1.1755,1.17642,1.17534,1.17583,483 2017-08-20 22:00:00,1.17588,1.17601,1.17536,1.1754,588 2017-08-20 23:00:00,1.17533,1.17571,1.17502,1.17558,591 2017-08-21 00:00:00,1.1756,1.17576,1.1751,1.17516,1149 2017-08-21 01:00:00,1.1752,1.17598,1.17508,1.17584,965 2017-08-21 02:00:00,1.17584,1.17628,1.17567,1.17597,624 2017-08-21 03:00:00,1.17594,1.17598,1.17514,1.17544,503 2017-08-21 04:00:00,1.17544,1.1758,1.1751,1.17513,374 2017-08-21 05:00:00,1.17508,1.17552,1.17452,1.17457,604 2017-08-21 06:00:00,1.17458,1.17483,1.17353,1.17378,1681 2017-08-21 07:00:00,1.17378,1.17422,1.17314,1.17402,2092 2017-08-21 08:00:00,1.174,1.17494,1.17386,1.1741,1275 2017-08-21 09:00:00,1.17408,1.17456,1.17365,1.17398,1058 2017-08-21 10:00:00,1.17401,1.17584,1.17376,1.17584,1271 2017-08-21 11:00:00,1.17588,1.177,1.17575,1.17645,1985 2017-08-21 12:00:00,1.17648,1.1783,1.17594,1.17802,2398 2017-08-21 13:00:00,1.178,1.17901,1.17774,1.17886,2392 2017-08-21 14:00:00,1.17886,1.18198,1.17882,1.1815,5162 2017-08-21 15:00:00,1.1815,1.18252,1.18111,1.18127,2664 2017-08-21 16:00:00,1.18126,1.18242,1.18094,1.18238,1312 2017-08-21 17:00:00,1.18241,1.1828,1.18162,1.18186,1208 2017-08-21 18:00:00,1.18186,1.18226,1.18096,1.18128,1186 2017-08-21 19:00:00,1.18127,1.18134,1.18072,1.18096,759 2017-08-21 20:00:00,1.18094,1.18176,1.18072,1.18146,789 2017-08-21 21:00:00,1.18157,1.18173,1.18103,1.18136,1989 2017-08-21 22:00:00,1.18139,1.18176,1.18082,1.18093,1066 2017-08-21 23:00:00,1.18091,1.18157,1.18083,1.18116,412 2017-08-22 00:00:00,1.18116,1.18174,1.18104,1.18146,940 2017-08-22 01:00:00,1.18143,1.18244,1.18116,1.1813,1294 2017-08-22 02:00:00,1.18132,1.18132,1.18044,1.18054,648 2017-08-22 03:00:00,1.18056,1.18084,1.18028,1.18084,555 2017-08-22 04:00:00,1.18088,1.18102,1.18062,1.18066,395 2017-08-22 05:00:00,1.18068,1.1807,1.17958,1.18019,953 2017-08-22 06:00:00,1.18024,1.18068,1.17906,1.17906,1900 2017-08-22 07:00:00,1.17902,1.17943,1.17841,1.17934,2028 2017-08-22 08:00:00,1.17932,1.17932,1.17658,1.17716,2597 2017-08-22 09:00:00,1.1771,1.17722,1.17548,1.17552,2233 2017-08-22 10:00:00,1.17554,1.17588,1.17454,1.17532,1645 2017-08-22 11:00:00,1.17532,1.17692,1.17478,1.17682,2489 2017-08-22 12:00:00,1.17681,1.17729,1.17531,1.17615,2336 2017-08-22 13:00:00,1.1761,1.1769,1.17514,1.17538,3010 2017-08-22 14:00:00,1.1754,1.17668,1.17501,1.17628,3083 2017-08-22 15:00:00,1.17626,1.17718,1.17613,1.17621,2136 2017-08-22 16:00:00,1.17618,1.1771,1.17603,1.1764,1133 2017-08-22 17:00:00,1.17638,1.17647,1.17524,1.17529,1100 2017-08-22 18:00:00,1.17531,1.17546,1.1748,1.17516,746 2017-08-22 19:00:00,1.17512,1.17608,1.17496,1.176,974 2017-08-22 20:00:00,1.17598,1.17642,1.17576,1.17615,938 2017-08-22 21:00:00,1.17613,1.1766,1.17596,1.1765,2757 2017-08-22 22:00:00,1.17653,1.17661,1.17596,1.17646,614 2017-08-22 23:00:00,1.17644,1.17662,1.17612,1.17616,470 2017-08-23 00:00:00,1.17621,1.17621,1.17492,1.17494,980 2017-08-23 01:00:00,1.17492,1.17589,1.17476,1.17578,710 2017-08-23 02:00:00,1.17582,1.17648,1.1754,1.17644,799 2017-08-23 03:00:00,1.17644,1.17662,1.1759,1.17606,796 2017-08-23 04:00:00,1.17604,1.17621,1.1757,1.1758,404 2017-08-23 05:00:00,1.17583,1.1762,1.17574,1.17588,425 2017-08-23 06:00:00,1.17588,1.176,1.17512,1.17567,1408 2017-08-23 07:00:00,1.1757,1.17838,1.17402,1.17814,5824 2017-08-23 08:00:00,1.17812,1.1792,1.17754,1.17901,2482 2017-08-23 09:00:00,1.17898,1.17942,1.17757,1.17792,1643 2017-08-23 10:00:00,1.17797,1.17852,1.1777,1.17815,1064 2017-08-23 11:00:00,1.17815,1.1801,1.17815,1.18,2196 2017-08-23 12:00:00,1.18006,1.18079,1.17924,1.17948,2399 2017-08-23 13:00:00,1.17953,1.1799,1.1787,1.17979,2762 2017-08-23 14:00:00,1.17984,1.182,1.1798,1.18156,3942 2017-08-23 15:00:00,1.18155,1.1817,1.17998,1.18016,1883 2017-08-23 16:00:00,1.18014,1.1805,1.17982,1.1801,1015 2017-08-23 17:00:00,1.18013,1.1816,1.18013,1.18121,1371 2017-08-23 18:00:00,1.18121,1.18232,1.18073,1.18204,1689 2017-08-23 19:00:00,1.18199,1.18232,1.18151,1.1818,1348 2017-08-23 20:00:00,1.1818,1.182,1.18056,1.18071,768 2017-08-23 21:00:00,1.18088,1.1812,1.18072,1.18092,2328 2017-08-23 22:00:00,1.18092,1.18134,1.18076,1.18113,790 2017-08-23 23:00:00,1.18112,1.18182,1.18111,1.18158,612 2017-08-24 00:00:00,1.18156,1.1817,1.18078,1.18096,1000 2017-08-24 01:00:00,1.18094,1.18097,1.18006,1.18012,827 2017-08-24 02:00:00,1.1801,1.18065,1.17946,1.18056,665 2017-08-24 03:00:00,1.18052,1.18054,1.17967,1.18026,574 2017-08-24 04:00:00,1.18026,1.18037,1.17988,1.18024,442 2017-08-24 05:00:00,1.18028,1.18079,1.18009,1.18051,637 2017-08-24 06:00:00,1.18053,1.18066,1.17876,1.17912,1809 2017-08-24 07:00:00,1.17906,1.17968,1.17879,1.17936,2066 2017-08-24 08:00:00,1.17934,1.17984,1.17842,1.17872,1744 2017-08-24 09:00:00,1.1787,1.1796,1.17858,1.17912,1284 2017-08-24 10:00:00,1.17914,1.17926,1.17847,1.1792,1179 2017-08-24 11:00:00,1.1792,1.18104,1.17903,1.18073,1736 2017-08-24 12:00:00,1.18074,1.18103,1.17946,1.17992,2570 2017-08-24 13:00:00,1.17992,1.18052,1.17914,1.1801,2049 2017-08-24 14:00:00,1.18007,1.18058,1.17878,1.18044,3058 2017-08-24 15:00:00,1.18044,1.18074,1.17983,1.18011,2902 2017-08-24 16:00:00,1.1801,1.18104,1.1798,1.18042,1451 2017-08-24 17:00:00,1.18042,1.1815,1.18042,1.18116,1131 2017-08-24 18:00:00,1.18111,1.18137,1.18022,1.18052,726 2017-08-24 19:00:00,1.18054,1.1806,1.17982,1.18012,966 2017-08-24 20:00:00,1.18014,1.18028,1.17984,1.18002,539 2017-08-24 21:00:00,1.18017,1.18018,1.17984,1.18001,1980 2017-08-24 22:00:00,1.17999,1.18038,1.17999,1.18038,745 2017-08-24 23:00:00,1.1804,1.1804,1.17961,1.17978,883 2017-08-25 00:00:00,1.17972,1.17994,1.17926,1.17964,679 2017-08-25 01:00:00,1.17964,1.17978,1.17878,1.17898,731 2017-08-25 02:00:00,1.17898,1.17952,1.17886,1.17945,390 2017-08-25 03:00:00,1.17948,1.17998,1.17926,1.17972,730 2017-08-25 04:00:00,1.17977,1.17988,1.1795,1.17981,287 2017-08-25 05:00:00,1.17976,1.18004,1.17945,1.17969,435 2017-08-25 06:00:00,1.17964,1.17974,1.17888,1.17913,1127 2017-08-25 07:00:00,1.17908,1.17908,1.17736,1.1781,2647 2017-08-25 08:00:00,1.17811,1.18012,1.17796,1.17964,2124 2017-08-25 09:00:00,1.17962,1.18048,1.17956,1.1804,853 2017-08-25 10:00:00,1.1804,1.18285,1.18022,1.18172,2469 2017-08-25 11:00:00,1.18168,1.1823,1.1813,1.18155,1576 2017-08-25 12:00:00,1.1816,1.18164,1.17998,1.18054,2613 2017-08-25 13:00:00,1.18058,1.18148,1.17858,1.17978,3579 2017-08-25 14:00:00,1.17972,1.18752,1.1793,1.18709,13383 2017-08-25 15:00:00,1.18712,1.18843,1.18682,1.18727,4400 2017-08-25 16:00:00,1.18727,1.18868,1.18726,1.18866,2745 2017-08-25 17:00:00,1.18866,1.18898,1.18808,1.18822,1522 2017-08-25 18:00:00,1.18821,1.18828,1.1872,1.18788,2117 2017-08-25 19:00:00,1.18788,1.19415,1.18638,1.19245,14104 2017-08-25 20:00:00,1.19248,1.19345,1.19168,1.19232,2128 2017-08-27 21:00:00,1.19458,1.19472,1.19408,1.19448,558 2017-08-27 22:00:00,1.19449,1.19582,1.1941,1.19518,2359 2017-08-27 23:00:00,1.19518,1.19596,1.19498,1.19511,859 2017-08-28 00:00:00,1.19516,1.19518,1.19222,1.193,1860 2017-08-28 01:00:00,1.19301,1.19307,1.19216,1.1927,941 2017-08-28 02:00:00,1.19268,1.19276,1.19204,1.19252,658 2017-08-28 03:00:00,1.19254,1.19254,1.1921,1.19218,471 2017-08-28 04:00:00,1.19214,1.19234,1.19202,1.19204,467 2017-08-28 05:00:00,1.19199,1.19346,1.19171,1.19342,1123 2017-08-28 06:00:00,1.19338,1.19372,1.19247,1.19327,1175 2017-08-28 07:00:00,1.19323,1.19422,1.19214,1.19256,1743 2017-08-28 08:00:00,1.19261,1.19374,1.19199,1.19308,1382 2017-08-28 09:00:00,1.19312,1.1937,1.19285,1.19337,1103 2017-08-28 10:00:00,1.19338,1.19402,1.19326,1.19368,1210 2017-08-28 11:00:00,1.19366,1.1937,1.19241,1.19254,1749 2017-08-28 12:00:00,1.19259,1.19384,1.19221,1.19347,2232 2017-08-28 13:00:00,1.19343,1.19403,1.1926,1.1936,2610 2017-08-28 14:00:00,1.19354,1.19578,1.19354,1.19548,2386 2017-08-28 15:00:00,1.19548,1.19756,1.1952,1.19722,3032 2017-08-28 16:00:00,1.19719,1.19839,1.19719,1.19763,1764 2017-08-28 17:00:00,1.19764,1.19806,1.19736,1.19772,1220 2017-08-28 18:00:00,1.19772,1.19839,1.19772,1.19792,833 2017-08-28 19:00:00,1.19787,1.19797,1.1973,1.19742,739 2017-08-28 20:00:00,1.1974,1.19786,1.1974,1.19786,459 2017-08-28 21:00:00,1.19774,1.19846,1.19734,1.1978,3781 2017-08-28 22:00:00,1.19788,1.19865,1.19628,1.19684,4289 2017-08-28 23:00:00,1.19686,1.19696,1.19552,1.1964,1947 2017-08-29 00:00:00,1.19638,1.19725,1.19627,1.19649,2042 2017-08-29 01:00:00,1.19648,1.19726,1.19624,1.19688,1267 2017-08-29 02:00:00,1.19688,1.19714,1.19656,1.19698,639 2017-08-29 03:00:00,1.19696,1.1973,1.19664,1.1969,604 2017-08-29 04:00:00,1.19694,1.19772,1.19689,1.19746,556 2017-08-29 05:00:00,1.19744,1.1982,1.19722,1.1982,588 2017-08-29 06:00:00,1.19822,1.20184,1.19737,1.20167,4793 2017-08-29 07:00:00,1.20169,1.2047,1.2011,1.20467,8174 2017-08-29 08:00:00,1.2047,1.20705,1.20409,1.20602,5617 2017-08-29 09:00:00,1.20598,1.20664,1.2037,1.20392,3508 2017-08-29 10:00:00,1.20391,1.20536,1.20316,1.20345,2613 2017-08-29 11:00:00,1.20344,1.20475,1.20326,1.20424,2736 2017-08-29 12:00:00,1.20428,1.20591,1.20279,1.20291,4561 2017-08-29 13:00:00,1.20295,1.20341,1.20202,1.20254,4002 2017-08-29 14:00:00,1.20254,1.20277,1.20094,1.20255,6116 2017-08-29 15:00:00,1.20254,1.2038,1.2017,1.2025,4022 2017-08-29 16:00:00,1.20248,1.20251,1.20104,1.20158,2039 2017-08-29 17:00:00,1.20158,1.20202,1.19956,1.20012,2508 2017-08-29 18:00:00,1.20016,1.20018,1.1982,1.19912,3104 2017-08-29 19:00:00,1.19912,1.19969,1.19676,1.19681,2709 2017-08-29 20:00:00,1.19682,1.19796,1.19461,1.19718,2037 2017-08-29 21:00:00,1.19714,1.19758,1.19632,1.19677,2495 2017-08-29 22:00:00,1.19676,1.19782,1.19674,1.19744,1494 2017-08-29 23:00:00,1.19746,1.19759,1.19631,1.197,1131 2017-08-30 00:00:00,1.19703,1.19787,1.19668,1.1975,1023 2017-08-30 01:00:00,1.19744,1.1978,1.19668,1.1975,2363 2017-08-30 02:00:00,1.19752,1.19792,1.19738,1.19766,623 2017-08-30 03:00:00,1.19764,1.19831,1.19756,1.19826,415 2017-08-30 04:00:00,1.19824,1.19845,1.19726,1.19736,575 2017-08-30 05:00:00,1.19738,1.1975,1.19643,1.19654,1288 2017-08-30 06:00:00,1.19651,1.19682,1.19414,1.1955,4062 2017-08-30 07:00:00,1.19549,1.1969,1.19384,1.19564,5065 2017-08-30 08:00:00,1.19569,1.19569,1.19405,1.19443,2434 2017-08-30 09:00:00,1.19438,1.19548,1.19393,1.1951,2070 2017-08-30 10:00:00,1.19505,1.19528,1.19422,1.19481,1445 2017-08-30 11:00:00,1.19482,1.195,1.19245,1.1927,2973 2017-08-30 12:00:00,1.1927,1.19316,1.18929,1.19182,7501 2017-08-30 13:00:00,1.19182,1.19378,1.19086,1.19128,4198 2017-08-30 14:00:00,1.1913,1.19206,1.18951,1.19122,4099 2017-08-30 15:00:00,1.19128,1.19288,1.18991,1.19266,3820 2017-08-30 16:00:00,1.19264,1.19266,1.18984,1.19047,2389 2017-08-30 17:00:00,1.19044,1.19044,1.18917,1.18987,2192 2017-08-30 18:00:00,1.18985,1.19015,1.1883,1.18903,1984 2017-08-30 19:00:00,1.18903,1.18947,1.18862,1.189,1213 2017-08-30 20:00:00,1.18896,1.18896,1.1881,1.18842,770 2017-08-30 21:00:00,1.18842,1.18954,1.18815,1.18935,1653 2017-08-30 22:00:00,1.18939,1.18966,1.18891,1.18958,830 2017-08-30 23:00:00,1.18958,1.1897,1.18884,1.18908,724 2017-08-31 00:00:00,1.1891,1.18966,1.18862,1.18936,1594 2017-08-31 01:00:00,1.18936,1.18976,1.18716,1.18776,2070 2017-08-31 02:00:00,1.1877,1.18831,1.18741,1.18778,1166 2017-08-31 03:00:00,1.18784,1.18812,1.18714,1.1874,1023 2017-08-31 04:00:00,1.18741,1.18824,1.18736,1.1879,531 2017-08-31 05:00:00,1.18787,1.18806,1.18706,1.18736,769 2017-08-31 06:00:00,1.18738,1.18956,1.18668,1.18908,2638 2017-08-31 07:00:00,1.18912,1.19032,1.1884,1.18913,3257 2017-08-31 08:00:00,1.18911,1.18972,1.18808,1.18964,1979 2017-08-31 09:00:00,1.18962,1.1906,1.18712,1.18778,3532 2017-08-31 10:00:00,1.18777,1.18791,1.18445,1.18605,5518 2017-08-31 11:00:00,1.18608,1.18624,1.18342,1.18372,2895 2017-08-31 12:00:00,1.18374,1.18528,1.18232,1.18434,4401 2017-08-31 13:00:00,1.18436,1.18604,1.18359,1.18536,3044 2017-08-31 14:00:00,1.1854,1.18978,1.18496,1.18899,6006 2017-08-31 15:00:00,1.18898,1.19084,1.18728,1.18936,7130 2017-08-31 16:00:00,1.18933,1.18956,1.18706,1.18808,2710 2017-08-31 17:00:00,1.18808,1.18942,1.18786,1.18921,1765 2017-08-31 18:00:00,1.18922,1.191,1.189,1.19034,2398 2017-08-31 19:00:00,1.19038,1.1906,1.1895,1.19052,1967 2017-08-31 20:00:00,1.19057,1.19126,1.1903,1.1909,963 2017-08-31 21:00:00,1.1909,1.19224,1.19076,1.19214,3997 2017-08-31 22:00:00,1.19212,1.19234,1.19118,1.19135,1837 2017-08-31 23:00:00,1.19134,1.19151,1.19078,1.19094,973 2017-09-01 00:00:00,1.19088,1.19109,1.19056,1.19102,1318 2017-09-01 01:00:00,1.19103,1.19114,1.19042,1.19074,1016 2017-09-01 02:00:00,1.19072,1.19132,1.1905,1.19058,638 2017-09-01 03:00:00,1.19056,1.19088,1.19006,1.19014,822 2017-09-01 04:00:00,1.19018,1.19041,1.18951,1.18972,610 2017-09-01 05:00:00,1.18972,1.18972,1.18878,1.18948,859 2017-09-01 06:00:00,1.18948,1.19052,1.18824,1.18848,2025 2017-09-01 07:00:00,1.18849,1.18904,1.18792,1.1886,1589 2017-09-01 08:00:00,1.18866,1.19019,1.18826,1.18929,2227 2017-09-01 09:00:00,1.18934,1.19051,1.18914,1.18946,1887 2017-09-01 10:00:00,1.18946,1.19302,1.18938,1.1915,4211 2017-09-01 11:00:00,1.19146,1.19252,1.19101,1.19165,2322 2017-09-01 12:00:00,1.19165,1.198,1.18934,1.19062,18179 2017-09-01 13:00:00,1.19062,1.19208,1.18644,1.1867,10987 2017-09-01 14:00:00,1.18668,1.1895,1.18498,1.18778,8530 2017-09-01 15:00:00,1.1878,1.18894,1.1863,1.18776,4851 2017-09-01 16:00:00,1.18772,1.18883,1.18555,1.18578,3065 2017-09-01 17:00:00,1.18584,1.18704,1.18572,1.1868,2282 2017-09-01 18:00:00,1.18678,1.18754,1.18628,1.18664,1459 2017-09-01 19:00:00,1.18667,1.1869,1.1863,1.18651,1025 2017-09-01 20:00:00,1.18648,1.18653,1.18588,1.18598,835 2017-09-03 21:00:00,1.18842,1.18897,1.18758,1.18789,887 2017-09-03 22:00:00,1.18776,1.18848,1.18737,1.18784,1727 2017-09-03 23:00:00,1.18791,1.18842,1.18772,1.18819,1148 2017-09-04 00:00:00,1.18818,1.18864,1.1878,1.188,1865 2017-09-04 01:00:00,1.18802,1.189,1.18781,1.18838,1654 2017-09-04 02:00:00,1.18836,1.18856,1.1878,1.18838,856 2017-09-04 03:00:00,1.18832,1.18861,1.18792,1.18799,996 2017-09-04 04:00:00,1.18797,1.18845,1.18785,1.1882,416 2017-09-04 05:00:00,1.18825,1.18879,1.18812,1.18851,965 2017-09-04 06:00:00,1.18851,1.1904,1.18824,1.18944,3830 2017-09-04 07:00:00,1.18948,1.19013,1.18846,1.18993,1987 2017-09-04 08:00:00,1.18997,1.19208,1.189,1.19184,2343 2017-09-04 09:00:00,1.19189,1.19224,1.19076,1.19151,1629 2017-09-04 10:00:00,1.19148,1.19173,1.19013,1.1911,1189 2017-09-04 11:00:00,1.19108,1.19154,1.19022,1.19098,963 2017-09-04 12:00:00,1.19099,1.1911,1.1889,1.18966,1413 2017-09-04 13:00:00,1.18968,1.19064,1.18958,1.19037,1525 2017-09-04 14:00:00,1.19036,1.19136,1.18954,1.1912,1558 2017-09-04 15:00:00,1.1912,1.19199,1.19094,1.19162,1192 2017-09-04 16:00:00,1.1916,1.19198,1.1889,1.18894,2012 2017-09-04 17:00:00,1.18892,1.19052,1.18888,1.19018,1982 2017-09-04 18:00:00,1.19024,1.1903,1.18928,1.19022,2520 2017-09-04 19:00:00,1.19019,1.1903,1.18944,1.18972,2169 2017-09-04 20:00:00,1.1897,1.19054,1.18916,1.18961,3297 2017-09-04 21:00:00,1.18961,1.19005,1.1894,1.18966,736 2017-09-04 22:00:00,1.18969,1.19002,1.18949,1.1898,835 2017-09-04 23:00:00,1.18978,1.18984,1.189,1.1893,1069 2017-09-05 00:00:00,1.18935,1.19018,1.1892,1.19,1498 2017-09-05 01:00:00,1.18996,1.19052,1.18926,1.18972,2363 2017-09-05 02:00:00,1.18973,1.191,1.18973,1.19064,2721 2017-09-05 03:00:00,1.19064,1.19103,1.1904,1.19068,2011 2017-09-05 04:00:00,1.19066,1.19115,1.19048,1.19058,1510 2017-09-05 05:00:00,1.19062,1.19088,1.19025,1.19053,1053 2017-09-05 06:00:00,1.19053,1.19088,1.1892,1.19028,2469 2017-09-05 07:00:00,1.19033,1.19078,1.18684,1.1879,4515 2017-09-05 08:00:00,1.18788,1.18884,1.18757,1.18847,2257 2017-09-05 09:00:00,1.1885,1.18854,1.18764,1.18824,1737 2017-09-05 10:00:00,1.18824,1.19071,1.18816,1.18974,2591 2017-09-05 11:00:00,1.18977,1.18978,1.18774,1.1889,2304 2017-09-05 12:00:00,1.18892,1.19047,1.18892,1.19004,3217 2017-09-05 13:00:00,1.19007,1.19007,1.18868,1.18881,3087 2017-09-05 14:00:00,1.1888,1.1941,1.1888,1.19184,6114 2017-09-05 15:00:00,1.19188,1.19248,1.18994,1.19116,3976 2017-09-05 16:00:00,1.19112,1.19234,1.19014,1.19034,3283 2017-09-05 17:00:00,1.19035,1.1915,1.1895,1.18964,2753 2017-09-05 18:00:00,1.1897,1.19214,1.18958,1.192,2389 2017-09-05 19:00:00,1.19198,1.19304,1.19163,1.19185,2202 2017-09-05 20:00:00,1.19188,1.19204,1.19106,1.1915,789 2017-09-05 21:00:00,1.1915,1.1917,1.19094,1.19144,5650 2017-09-05 22:00:00,1.19146,1.19226,1.19139,1.19206,1088 2017-09-05 23:00:00,1.19212,1.19246,1.19178,1.19239,651 2017-09-06 00:00:00,1.19234,1.1925,1.19152,1.19221,1414 2017-09-06 01:00:00,1.19222,1.19244,1.19032,1.19088,1711 2017-09-06 02:00:00,1.19082,1.1914,1.19058,1.19121,1065 2017-09-06 03:00:00,1.19123,1.19151,1.19091,1.19103,1014 2017-09-06 04:00:00,1.19106,1.19154,1.19066,1.19143,726 2017-09-06 05:00:00,1.19138,1.19295,1.19098,1.19266,1394 2017-09-06 06:00:00,1.19264,1.19264,1.19133,1.19218,2171 2017-09-06 07:00:00,1.19216,1.1941,1.19203,1.19338,2820 2017-09-06 08:00:00,1.19338,1.19498,1.19328,1.1944,2820 2017-09-06 09:00:00,1.19441,1.19496,1.19312,1.19354,3074 2017-09-06 10:00:00,1.19351,1.19478,1.1924,1.19287,2639 2017-09-06 11:00:00,1.19285,1.19319,1.19192,1.19318,2345 2017-09-06 12:00:00,1.19322,1.19409,1.19272,1.1932,2891 2017-09-06 13:00:00,1.1932,1.19342,1.19203,1.19244,2975 2017-09-06 14:00:00,1.19246,1.19448,1.19136,1.19393,9266 2017-09-06 15:00:00,1.19394,1.19491,1.19352,1.19404,3491 2017-09-06 16:00:00,1.19405,1.19504,1.19119,1.19267,5695 2017-09-06 17:00:00,1.19263,1.19287,1.19142,1.19182,2878 2017-09-06 18:00:00,1.19182,1.19254,1.19106,1.1912,2471 2017-09-06 19:00:00,1.19115,1.19166,1.19078,1.1915,1434 2017-09-06 20:00:00,1.1915,1.19215,1.19117,1.19177,556 2017-09-06 21:00:00,1.19177,1.19235,1.19156,1.19204,2091 2017-09-06 22:00:00,1.1921,1.19262,1.19195,1.19254,606 2017-09-06 23:00:00,1.19256,1.19278,1.19212,1.19264,971 2017-09-07 00:00:00,1.19263,1.19351,1.19225,1.19229,2543 2017-09-07 01:00:00,1.19226,1.19245,1.19194,1.1924,1551 2017-09-07 02:00:00,1.19237,1.19285,1.19222,1.19258,1206 2017-09-07 03:00:00,1.19257,1.19282,1.19202,1.19256,1050 2017-09-07 04:00:00,1.19256,1.1927,1.19232,1.1924,648 2017-09-07 05:00:00,1.19242,1.19272,1.19222,1.19225,814 2017-09-07 06:00:00,1.19224,1.19283,1.19144,1.19279,1938 2017-09-07 07:00:00,1.19276,1.1958,1.19266,1.19536,4177 2017-09-07 08:00:00,1.19535,1.19704,1.19439,1.1959,3561 2017-09-07 09:00:00,1.19591,1.19829,1.19577,1.19804,2941 2017-09-07 10:00:00,1.19802,1.19947,1.1975,1.19864,2216 2017-09-07 11:00:00,1.19862,1.19936,1.19631,1.19738,5632 2017-09-07 12:00:00,1.19742,1.20534,1.19302,1.20422,32318 2017-09-07 13:00:00,1.20428,1.20594,1.19729,1.20002,15936 2017-09-07 14:00:00,1.2,1.20187,1.19964,1.20106,5675 2017-09-07 15:00:00,1.20106,1.2032,1.20018,1.20264,4980 2017-09-07 16:00:00,1.20264,1.20298,1.19848,1.19896,4283 2017-09-07 17:00:00,1.19901,1.20038,1.199,1.19996,2178 2017-09-07 18:00:00,1.19992,1.20093,1.19929,1.20038,2051 2017-09-07 19:00:00,1.20038,1.20203,1.20009,1.202,2024 2017-09-07 20:00:00,1.20196,1.20295,1.20168,1.20241,1348 2017-09-07 21:00:00,1.20241,1.20261,1.2017,1.20188,3424 2017-09-07 22:00:00,1.20186,1.2023,1.20184,1.20188,840 2017-09-07 23:00:00,1.20194,1.20296,1.20176,1.2028,971 2017-09-08 00:00:00,1.20282,1.20361,1.20239,1.20347,1988 2017-09-08 01:00:00,1.20347,1.2036,1.20233,1.2024,1895 2017-09-08 02:00:00,1.2024,1.20901,1.20236,1.20715,5658 2017-09-08 03:00:00,1.2071,1.20812,1.20634,1.20714,3189 2017-09-08 04:00:00,1.20716,1.20726,1.20608,1.20696,1668 2017-09-08 05:00:00,1.20696,1.20928,1.20696,1.20788,6905 2017-09-08 06:00:00,1.20788,1.20866,1.20514,1.20652,6379 2017-09-08 07:00:00,1.2065,1.20666,1.20362,1.20483,5615 2017-09-08 08:00:00,1.20485,1.20692,1.20415,1.20633,4802 2017-09-08 09:00:00,1.20628,1.20646,1.20508,1.20602,3573 2017-09-08 10:00:00,1.20603,1.20737,1.20586,1.2069,3569 2017-09-08 11:00:00,1.2069,1.20704,1.20519,1.2066,3662 2017-09-08 12:00:00,1.20666,1.20678,1.20347,1.20422,4456 2017-09-08 13:00:00,1.20425,1.2051,1.20236,1.20274,4072 2017-09-08 14:00:00,1.20278,1.20394,1.2015,1.2017,6286 2017-09-08 15:00:00,1.20174,1.20422,1.20174,1.20393,3494 2017-09-08 16:00:00,1.20392,1.2044,1.20232,1.20264,2076 2017-09-08 17:00:00,1.20268,1.20328,1.20254,1.20316,1377 2017-09-08 18:00:00,1.20314,1.20321,1.20251,1.20268,1212 2017-09-08 19:00:00,1.20268,1.20381,1.20261,1.20264,1603 2017-09-08 20:00:00,1.20262,1.20384,1.20262,1.20353,1169 2017-09-10 21:00:00,1.20179,1.2029,1.20179,1.20232,398 2017-09-10 22:00:00,1.20242,1.2025,1.20122,1.20189,1912 2017-09-10 23:00:00,1.20192,1.20208,1.20113,1.20172,980 2017-09-11 00:00:00,1.2017,1.202,1.20108,1.20122,1755 2017-09-11 01:00:00,1.20124,1.20168,1.19988,1.20064,2217 2017-09-11 02:00:00,1.20066,1.20187,1.20043,1.2017,1324 2017-09-11 03:00:00,1.20172,1.2019,1.20107,1.20139,685 2017-09-11 04:00:00,1.20141,1.20151,1.20045,1.20075,866 2017-09-11 05:00:00,1.20075,1.20182,1.20052,1.20154,1041 2017-09-11 06:00:00,1.20154,1.20186,1.2002,1.20083,2465 2017-09-11 07:00:00,1.20084,1.20168,1.19931,1.20148,4525 2017-09-11 08:00:00,1.20144,1.20297,1.20119,1.20216,2169 2017-09-11 09:00:00,1.20215,1.20256,1.201,1.20134,1717 2017-09-11 10:00:00,1.20131,1.20218,1.20092,1.20112,1492 2017-09-11 11:00:00,1.20111,1.20142,1.19903,1.19978,2298 2017-09-11 12:00:00,1.19978,1.20046,1.19793,1.19844,3564 2017-09-11 13:00:00,1.19843,1.19913,1.19714,1.19879,3288 2017-09-11 14:00:00,1.19882,1.19931,1.19794,1.19829,2938 2017-09-11 15:00:00,1.19827,1.1987,1.19606,1.19631,2950 2017-09-11 16:00:00,1.19632,1.19701,1.1957,1.19695,1607 2017-09-11 17:00:00,1.19698,1.19708,1.19592,1.19632,2152 2017-09-11 18:00:00,1.19635,1.19697,1.1962,1.19625,1112 2017-09-11 19:00:00,1.19622,1.19635,1.19493,1.19527,1210 2017-09-11 20:00:00,1.19524,1.19555,1.1948,1.19538,621 2017-09-11 21:00:00,1.19538,1.1956,1.19514,1.1955,690 2017-09-11 22:00:00,1.19553,1.19633,1.19546,1.19627,882 2017-09-11 23:00:00,1.19622,1.19646,1.19578,1.1958,721 2017-09-12 00:00:00,1.19579,1.1961,1.19454,1.19473,2043 2017-09-12 01:00:00,1.19476,1.19558,1.19476,1.19548,1092 2017-09-12 02:00:00,1.19552,1.19622,1.19525,1.19611,788 2017-09-12 03:00:00,1.1961,1.19634,1.19538,1.19548,769 2017-09-12 04:00:00,1.19548,1.19596,1.19534,1.19554,526 2017-09-12 05:00:00,1.19554,1.19602,1.19469,1.19586,964 2017-09-12 06:00:00,1.19586,1.19776,1.19558,1.1975,3121 2017-09-12 07:00:00,1.19754,1.19782,1.19654,1.19746,2945 2017-09-12 08:00:00,1.19748,1.1975,1.19538,1.19581,3477 2017-09-12 09:00:00,1.19578,1.19646,1.19498,1.19614,2354 2017-09-12 10:00:00,1.1961,1.19648,1.19318,1.19366,3632 2017-09-12 11:00:00,1.19366,1.19386,1.19264,1.19319,3104 2017-09-12 12:00:00,1.19323,1.19424,1.1928,1.19299,4306 2017-09-12 13:00:00,1.19298,1.19515,1.19298,1.19462,3144 2017-09-12 14:00:00,1.19462,1.19683,1.19424,1.1954,4194 2017-09-12 15:00:00,1.19541,1.19724,1.19515,1.19678,2453 2017-09-12 16:00:00,1.19682,1.19738,1.19562,1.1963,2775 2017-09-12 17:00:00,1.19634,1.19665,1.19544,1.19601,2156 2017-09-12 18:00:00,1.19602,1.1973,1.1959,1.19696,1821 2017-09-12 19:00:00,1.197,1.19716,1.196,1.19666,1691 2017-09-12 20:00:00,1.1967,1.19681,1.1962,1.19666,825 2017-09-12 21:00:00,1.19666,1.1969,1.19602,1.19654,3917 2017-09-12 22:00:00,1.19656,1.19722,1.19642,1.1972,598 2017-09-12 23:00:00,1.19718,1.1973,1.19644,1.19712,978 2017-09-13 00:00:00,1.19718,1.19739,1.19663,1.19699,2315 2017-09-13 01:00:00,1.19697,1.19934,1.19692,1.19842,1547 2017-09-13 02:00:00,1.19836,1.1988,1.1977,1.19856,1430 2017-09-13 03:00:00,1.19852,1.19853,1.19759,1.19828,1357 2017-09-13 04:00:00,1.19823,1.19897,1.19804,1.19832,1155 2017-09-13 05:00:00,1.19831,1.19839,1.19752,1.19789,945 2017-09-13 06:00:00,1.19789,1.199,1.19719,1.19737,2801 2017-09-13 07:00:00,1.19732,1.19897,1.19687,1.19846,2585 2017-09-13 08:00:00,1.19844,1.19951,1.19787,1.19794,2451 2017-09-13 09:00:00,1.19799,1.19833,1.19645,1.1972,1922 2017-09-13 10:00:00,1.19721,1.19866,1.19708,1.19864,1495 2017-09-13 11:00:00,1.19868,1.19876,1.1979,1.19855,1356 2017-09-13 12:00:00,1.19856,1.19898,1.19699,1.19738,3457 2017-09-13 13:00:00,1.19734,1.19748,1.19378,1.19378,4414 2017-09-13 14:00:00,1.19375,1.19405,1.19044,1.19164,5398 2017-09-13 15:00:00,1.19161,1.19184,1.18847,1.18984,3794 2017-09-13 16:00:00,1.18989,1.19011,1.188,1.18811,1721 2017-09-13 17:00:00,1.1881,1.18957,1.18798,1.18864,3478 2017-09-13 18:00:00,1.18864,1.18864,1.1873,1.1874,1310 2017-09-13 19:00:00,1.1874,1.1886,1.18728,1.18844,1178 2017-09-13 20:00:00,1.1884,1.18876,1.18804,1.18856,676 2017-09-13 21:00:00,1.18856,1.18914,1.18824,1.18886,2994 2017-09-13 22:00:00,1.18886,1.18942,1.18846,1.18938,465 2017-09-13 23:00:00,1.18932,1.18942,1.18865,1.1888,454 2017-09-14 00:00:00,1.18886,1.18918,1.18814,1.1883,1626 2017-09-14 01:00:00,1.18828,1.18836,1.18704,1.18764,1794 2017-09-14 02:00:00,1.18758,1.18852,1.18726,1.18758,1196 2017-09-14 03:00:00,1.18764,1.18794,1.18706,1.1875,771 2017-09-14 04:00:00,1.18756,1.18792,1.18728,1.18782,663 2017-09-14 05:00:00,1.1878,1.1878,1.1866,1.18758,933 2017-09-14 06:00:00,1.18758,1.1887,1.18683,1.18846,3950 2017-09-14 07:00:00,1.18844,1.19098,1.188,1.19056,3187 2017-09-14 08:00:00,1.19062,1.19062,1.18944,1.19045,2004 2017-09-14 09:00:00,1.1905,1.19075,1.18918,1.18958,1466 2017-09-14 10:00:00,1.18955,1.19044,1.18944,1.1901,1547 2017-09-14 11:00:00,1.19011,1.19028,1.18777,1.1892,5752 2017-09-14 12:00:00,1.18925,1.19088,1.18373,1.18928,16798 2017-09-14 13:00:00,1.18924,1.18982,1.18684,1.1883,7276 2017-09-14 14:00:00,1.18833,1.18976,1.18709,1.1873,4456 2017-09-14 15:00:00,1.1873,1.18886,1.18702,1.1886,3535 2017-09-14 16:00:00,1.18858,1.18892,1.1875,1.1884,1486 2017-09-14 17:00:00,1.18842,1.19226,1.18835,1.19184,3456 2017-09-14 18:00:00,1.1918,1.19199,1.1912,1.19134,1664 2017-09-14 19:00:00,1.19134,1.19143,1.19025,1.19053,1440 2017-09-14 20:00:00,1.19055,1.19204,1.19032,1.19182,1179 2017-09-14 21:00:00,1.19182,1.19216,1.19108,1.19142,2896 2017-09-14 22:00:00,1.1915,1.19233,1.19066,1.19192,3203 2017-09-14 23:00:00,1.19192,1.19218,1.19155,1.19213,1391 2017-09-15 00:00:00,1.19218,1.19242,1.19072,1.19084,1821 2017-09-15 01:00:00,1.1909,1.19122,1.19008,1.1906,1390 2017-09-15 02:00:00,1.1906,1.19182,1.19056,1.19153,660 2017-09-15 03:00:00,1.1915,1.19158,1.19048,1.19084,969 2017-09-15 04:00:00,1.19089,1.1913,1.19047,1.19126,848 2017-09-15 05:00:00,1.19126,1.19206,1.19099,1.192,998 2017-09-15 06:00:00,1.19198,1.19296,1.19054,1.19234,3679 2017-09-15 07:00:00,1.19228,1.19302,1.19128,1.19208,3028 2017-09-15 08:00:00,1.19206,1.194,1.19135,1.19296,2985 2017-09-15 09:00:00,1.19292,1.19558,1.19267,1.1938,5707 2017-09-15 10:00:00,1.1938,1.19514,1.1937,1.19505,2739 2017-09-15 11:00:00,1.195,1.19636,1.19487,1.19491,2288 2017-09-15 12:00:00,1.19496,1.19877,1.19483,1.19741,7045 2017-09-15 13:00:00,1.19746,1.19866,1.19641,1.19764,3882 2017-09-15 14:00:00,1.1977,1.19784,1.19595,1.19636,4542 2017-09-15 15:00:00,1.19636,1.19674,1.1954,1.19584,2551 2017-09-15 16:00:00,1.19588,1.19731,1.19398,1.19453,2341 2017-09-15 17:00:00,1.19458,1.19458,1.19307,1.19364,1492 2017-09-15 18:00:00,1.1937,1.1943,1.19297,1.19408,1408 2017-09-15 19:00:00,1.19405,1.19464,1.1935,1.19455,995 2017-09-15 20:00:00,1.19456,1.19608,1.19444,1.19476,1416 2017-09-17 21:00:00,1.19205,1.19366,1.19205,1.19344,640 2017-09-17 22:00:00,1.19345,1.195,1.19342,1.19431,1912 2017-09-17 23:00:00,1.19432,1.19468,1.19337,1.19379,693 2017-09-18 00:00:00,1.19376,1.19456,1.19365,1.19442,592 2017-09-18 01:00:00,1.19444,1.19496,1.19426,1.19488,752 2017-09-18 02:00:00,1.19489,1.19552,1.19451,1.19455,1236 2017-09-18 03:00:00,1.19458,1.19471,1.19402,1.19442,494 2017-09-18 04:00:00,1.1944,1.19458,1.1939,1.1941,506 2017-09-18 05:00:00,1.1941,1.1947,1.19383,1.19436,748 2017-09-18 06:00:00,1.19441,1.1956,1.19393,1.19482,2268 2017-09-18 07:00:00,1.19483,1.19514,1.19152,1.19256,3522 2017-09-18 08:00:00,1.19254,1.19418,1.19229,1.19394,2189 2017-09-18 09:00:00,1.19396,1.19536,1.19371,1.1951,2118 2017-09-18 10:00:00,1.19508,1.19611,1.19428,1.19575,1823 2017-09-18 11:00:00,1.1957,1.19692,1.19423,1.19438,2407 2017-09-18 12:00:00,1.19436,1.19639,1.19418,1.19614,1959 2017-09-18 13:00:00,1.19612,1.19634,1.19467,1.19602,2346 2017-09-18 14:00:00,1.196,1.19668,1.19464,1.19558,2571 2017-09-18 15:00:00,1.19554,1.19637,1.19349,1.19396,3685 2017-09-18 16:00:00,1.19394,1.19394,1.19227,1.19344,2457 2017-09-18 17:00:00,1.19346,1.19423,1.19328,1.19368,1203 2017-09-18 18:00:00,1.19368,1.19542,1.19276,1.19504,3037 2017-09-18 19:00:00,1.19503,1.19578,1.19456,1.1953,1580 2017-09-18 20:00:00,1.19531,1.19588,1.19503,1.19546,707 2017-09-18 21:00:00,1.19546,1.19564,1.19497,1.19515,1868 2017-09-18 22:00:00,1.19525,1.19583,1.19518,1.19566,803 2017-09-18 23:00:00,1.19568,1.19615,1.19562,1.19602,679 2017-09-19 00:00:00,1.19602,1.19667,1.19586,1.19642,1022 2017-09-19 01:00:00,1.19644,1.1975,1.1964,1.1968,1818 2017-09-19 02:00:00,1.19678,1.19705,1.19621,1.19664,833 2017-09-19 03:00:00,1.19662,1.19707,1.19649,1.19707,825 2017-09-19 04:00:00,1.19704,1.19796,1.19677,1.19758,1092 2017-09-19 05:00:00,1.19759,1.19922,1.19742,1.1983,1927 2017-09-19 06:00:00,1.1983,1.20058,1.1978,1.1995,3640 2017-09-19 07:00:00,1.1995,1.20067,1.19825,1.19938,3419 2017-09-19 08:00:00,1.19936,1.20036,1.19718,1.19841,3906 2017-09-19 09:00:00,1.19842,1.19934,1.1982,1.1989,3139 2017-09-19 10:00:00,1.19894,1.19948,1.19828,1.19904,2061 2017-09-19 11:00:00,1.19899,1.19927,1.19537,1.19724,6637 2017-09-19 12:00:00,1.1972,1.1993,1.19639,1.19844,5105 2017-09-19 13:00:00,1.19838,1.19908,1.1976,1.19854,3239 2017-09-19 14:00:00,1.19856,1.19868,1.19726,1.19778,4267 2017-09-19 15:00:00,1.19778,1.19818,1.1967,1.19805,2471 2017-09-19 16:00:00,1.19802,1.19876,1.19608,1.1974,2821 2017-09-19 17:00:00,1.19742,1.20006,1.19705,1.19954,2202 2017-09-19 18:00:00,1.19952,1.20067,1.19926,1.19957,2303 2017-09-19 19:00:00,1.19958,1.20002,1.1993,1.19976,1235 2017-09-19 20:00:00,1.19973,1.19979,1.19924,1.19946,696 2017-09-19 21:00:00,1.19946,1.19981,1.19909,1.19956,3446 2017-09-19 22:00:00,1.19959,1.19972,1.19934,1.1995,348 2017-09-19 23:00:00,1.1995,1.20008,1.19906,1.19926,571 2017-09-20 00:00:00,1.19922,1.19952,1.19868,1.1992,1121 2017-09-20 01:00:00,1.19924,1.202,1.19908,1.20144,2952 2017-09-20 02:00:00,1.20144,1.20178,1.20069,1.2008,1400 2017-09-20 03:00:00,1.2008,1.2011,1.20062,1.20082,751 2017-09-20 04:00:00,1.20082,1.20098,1.20038,1.20074,544 2017-09-20 05:00:00,1.20071,1.20148,1.20055,1.20097,996 2017-09-20 06:00:00,1.20096,1.2016,1.19968,1.19998,2582 2017-09-20 07:00:00,1.19996,1.20141,1.19959,1.19992,2257 2017-09-20 08:00:00,1.19988,1.20124,1.19976,1.20072,2641 2017-09-20 09:00:00,1.20072,1.2022,1.20058,1.20126,2573 2017-09-20 10:00:00,1.20128,1.20134,1.20006,1.20055,1672 2017-09-20 11:00:00,1.20052,1.20088,1.20014,1.20052,1366 2017-09-20 12:00:00,1.2005,1.20118,1.19891,1.19922,2403 2017-09-20 13:00:00,1.1992,1.20026,1.19912,1.19958,1876 2017-09-20 14:00:00,1.19958,1.2002,1.19918,1.19952,3213 2017-09-20 15:00:00,1.1995,1.20013,1.1984,1.19976,2571 2017-09-20 16:00:00,1.1998,1.2006,1.19976,1.20044,1392 2017-09-20 17:00:00,1.20044,1.20159,1.1997,1.20144,1995 2017-09-20 18:00:00,1.20143,1.20353,1.18616,1.18906,31470 2017-09-20 19:00:00,1.18906,1.18986,1.1868,1.18916,9391 2017-09-20 20:00:00,1.18914,1.19021,1.18906,1.18932,2268 2017-09-20 21:00:00,1.18932,1.1898,1.1884,1.18946,4843 2017-09-20 22:00:00,1.18944,1.18959,1.18858,1.189,1165 2017-09-20 23:00:00,1.189,1.18913,1.18662,1.18745,1289 2017-09-21 00:00:00,1.18741,1.1883,1.1866,1.18775,2150 2017-09-21 01:00:00,1.18779,1.18836,1.18672,1.18754,2007 2017-09-21 02:00:00,1.18758,1.18856,1.18711,1.18851,1348 2017-09-21 03:00:00,1.18852,1.18914,1.18813,1.18852,1505 2017-09-21 04:00:00,1.18857,1.18871,1.18818,1.18835,688 2017-09-21 05:00:00,1.18836,1.1887,1.18766,1.1883,1850 2017-09-21 06:00:00,1.1883,1.19008,1.18794,1.18966,3835 2017-09-21 07:00:00,1.18966,1.19075,1.18762,1.19023,3603 2017-09-21 08:00:00,1.19028,1.19184,1.19014,1.19136,2423 2017-09-21 09:00:00,1.19132,1.19193,1.19078,1.19134,1611 2017-09-21 10:00:00,1.19138,1.19153,1.19026,1.19028,1856 2017-09-21 11:00:00,1.19026,1.19049,1.18906,1.19022,2217 2017-09-21 12:00:00,1.19025,1.19228,1.18984,1.1917,3921 2017-09-21 13:00:00,1.19169,1.19291,1.19086,1.19248,4113 2017-09-21 14:00:00,1.19242,1.1926,1.19098,1.19224,3686 2017-09-21 15:00:00,1.19228,1.19538,1.19168,1.19467,4004 2017-09-21 16:00:00,1.19464,1.19502,1.19317,1.1933,2429 2017-09-21 17:00:00,1.19329,1.1939,1.19282,1.19354,1550 2017-09-21 18:00:00,1.19354,1.19354,1.19302,1.19307,845 2017-09-21 19:00:00,1.1931,1.1937,1.1931,1.19312,771 2017-09-21 20:00:00,1.19317,1.19426,1.19317,1.1942,612 2017-09-21 21:00:00,1.1942,1.19426,1.19366,1.19402,1680 2017-09-21 22:00:00,1.19399,1.19432,1.19378,1.1941,492 2017-09-21 23:00:00,1.19406,1.19488,1.194,1.19448,509 2017-09-22 00:00:00,1.19448,1.19468,1.19396,1.19416,1189 2017-09-22 01:00:00,1.19416,1.19585,1.19409,1.19544,1702 2017-09-22 02:00:00,1.19548,1.19597,1.19493,1.19558,1236 2017-09-22 03:00:00,1.1956,1.19588,1.19529,1.19575,731 2017-09-22 04:00:00,1.1957,1.19648,1.19541,1.19602,667 2017-09-22 05:00:00,1.19598,1.197,1.19582,1.19678,1128 2017-09-22 06:00:00,1.19678,1.19744,1.19577,1.19702,2671 2017-09-22 07:00:00,1.19704,1.19982,1.1965,1.19924,4304 2017-09-22 08:00:00,1.19918,1.2003,1.19874,1.19903,2752 2017-09-22 09:00:00,1.19901,1.19952,1.19769,1.19952,2697 2017-09-22 10:00:00,1.19954,1.20044,1.1959,1.1966,2944 2017-09-22 11:00:00,1.19658,1.19805,1.1961,1.19628,3265 2017-09-22 12:00:00,1.19633,1.1975,1.19588,1.19715,3000 2017-09-22 13:00:00,1.19714,1.19741,1.19582,1.19682,5007 2017-09-22 14:00:00,1.19681,1.19838,1.1965,1.1968,3581 2017-09-22 15:00:00,1.19676,1.19727,1.19592,1.19688,2296 2017-09-22 16:00:00,1.19692,1.19698,1.19374,1.19448,2640 2017-09-22 17:00:00,1.19449,1.19493,1.19399,1.19468,1019 2017-09-22 18:00:00,1.19466,1.1954,1.1941,1.1943,1232 2017-09-22 19:00:00,1.19431,1.19464,1.19402,1.19442,1157 2017-09-22 20:00:00,1.1944,1.19544,1.19424,1.19478,2834 2017-09-24 21:00:00,1.18988,1.19152,1.18963,1.19149,838 2017-09-24 22:00:00,1.1916,1.19253,1.19141,1.19221,1668 2017-09-24 23:00:00,1.19221,1.19272,1.19185,1.19266,1450 2017-09-25 00:00:00,1.19266,1.19348,1.19258,1.19282,1512 2017-09-25 01:00:00,1.19284,1.19326,1.19186,1.19302,1456 2017-09-25 02:00:00,1.19296,1.19337,1.19276,1.19312,753 2017-09-25 03:00:00,1.19315,1.1933,1.19277,1.19326,869 2017-09-25 04:00:00,1.19324,1.19363,1.19316,1.1933,680 2017-09-25 05:00:00,1.1933,1.19357,1.19248,1.19279,1040 2017-09-25 06:00:00,1.19279,1.19312,1.19036,1.19099,2720 2017-09-25 07:00:00,1.19099,1.19208,1.19002,1.191,3312 2017-09-25 08:00:00,1.19093,1.1915,1.18987,1.19052,2788 2017-09-25 09:00:00,1.19048,1.19066,1.188,1.18864,3398 2017-09-25 10:00:00,1.18868,1.18894,1.18691,1.18764,2309 2017-09-25 11:00:00,1.18764,1.1881,1.18665,1.18708,2395 2017-09-25 12:00:00,1.18703,1.18776,1.18642,1.18731,2830 2017-09-25 13:00:00,1.18732,1.189,1.18621,1.18793,4545 2017-09-25 14:00:00,1.18798,1.18886,1.18647,1.18662,4265 2017-09-25 15:00:00,1.18662,1.18726,1.18498,1.18513,5808 2017-09-25 16:00:00,1.18517,1.18528,1.18318,1.18452,3959 2017-09-25 17:00:00,1.18454,1.18454,1.1837,1.18406,1392 2017-09-25 18:00:00,1.18404,1.18467,1.18385,1.1845,889 2017-09-25 19:00:00,1.18452,1.18556,1.18445,1.18477,1296 2017-09-25 20:00:00,1.1848,1.18496,1.18452,1.1848,497 2017-09-25 21:00:00,1.1848,1.18514,1.18442,1.18464,4298 2017-09-25 22:00:00,1.18458,1.1857,1.18456,1.1856,2150 2017-09-25 23:00:00,1.18557,1.18576,1.18521,1.18561,892 2017-09-26 00:00:00,1.18559,1.18595,1.185,1.18543,1434 2017-09-26 01:00:00,1.18538,1.18603,1.18527,1.18586,988 2017-09-26 02:00:00,1.18587,1.18612,1.18545,1.18582,724 2017-09-26 03:00:00,1.18582,1.18616,1.18574,1.18614,458 2017-09-26 04:00:00,1.18614,1.18615,1.18533,1.18537,477 2017-09-26 05:00:00,1.18538,1.1854,1.18362,1.18408,1167 2017-09-26 06:00:00,1.18408,1.1859,1.18398,1.18461,2686 2017-09-26 07:00:00,1.18458,1.18467,1.18104,1.18127,5594 2017-09-26 08:00:00,1.18123,1.18195,1.18098,1.1817,2229 2017-09-26 09:00:00,1.1817,1.1829,1.18129,1.18148,2153 2017-09-26 10:00:00,1.18143,1.18164,1.17859,1.179,3341 2017-09-26 11:00:00,1.17902,1.17979,1.17824,1.17888,2342 2017-09-26 12:00:00,1.17885,1.17958,1.17809,1.17916,2483 2017-09-26 13:00:00,1.17916,1.17976,1.178,1.1781,2146 2017-09-26 14:00:00,1.1781,1.17903,1.17642,1.17659,3825 2017-09-26 15:00:00,1.1766,1.17744,1.17659,1.17704,2203 2017-09-26 16:00:00,1.17702,1.17956,1.17573,1.17824,6531 2017-09-26 17:00:00,1.17824,1.18106,1.17817,1.18096,4247 2017-09-26 18:00:00,1.18096,1.18105,1.1794,1.17988,1860 2017-09-26 19:00:00,1.17986,1.17991,1.17872,1.17927,1380 2017-09-26 20:00:00,1.17926,1.17948,1.17854,1.17935,1023 2017-09-26 21:00:00,1.17921,1.17942,1.17894,1.17914,1975 2017-09-26 22:00:00,1.17922,1.17928,1.17892,1.17902,607 2017-09-26 23:00:00,1.17902,1.17904,1.17822,1.17844,1032 2017-09-27 00:00:00,1.17843,1.1788,1.17768,1.1788,1600 2017-09-27 01:00:00,1.17886,1.17955,1.17868,1.17891,1157 2017-09-27 02:00:00,1.17887,1.17902,1.17868,1.17889,785 2017-09-27 03:00:00,1.17886,1.17921,1.17858,1.17911,787 2017-09-27 04:00:00,1.17914,1.17922,1.17778,1.17796,884 2017-09-27 05:00:00,1.1779,1.17841,1.17707,1.17746,1476 2017-09-27 06:00:00,1.17746,1.17792,1.17564,1.17588,3062 2017-09-27 07:00:00,1.17593,1.176,1.17422,1.17568,3721 2017-09-27 08:00:00,1.17566,1.17575,1.1731,1.17359,3468 2017-09-27 09:00:00,1.17354,1.17496,1.17343,1.17486,2375 2017-09-27 10:00:00,1.17482,1.1757,1.17452,1.17463,2036 2017-09-27 11:00:00,1.17468,1.1747,1.17326,1.17448,2240 2017-09-27 12:00:00,1.17446,1.17457,1.17181,1.172,3458 2017-09-27 13:00:00,1.172,1.173,1.1717,1.17254,3131 2017-09-27 14:00:00,1.17254,1.17463,1.17181,1.1732,4794 2017-09-27 15:00:00,1.17322,1.175,1.17281,1.17467,3269 2017-09-27 16:00:00,1.17464,1.17765,1.17456,1.17522,3434 2017-09-27 17:00:00,1.17521,1.17654,1.17479,1.17582,1845 2017-09-27 18:00:00,1.17584,1.17642,1.17496,1.17572,2151 2017-09-27 19:00:00,1.17573,1.17576,1.17458,1.17496,2184 2017-09-27 20:00:00,1.17496,1.17544,1.17442,1.17451,1163 2017-09-27 21:00:00,1.1747,1.17516,1.17424,1.17446,2137 2017-09-27 22:00:00,1.17444,1.1748,1.17405,1.1742,1173 2017-09-27 23:00:00,1.17424,1.17511,1.1742,1.17495,963 2017-09-28 00:00:00,1.17496,1.17564,1.17468,1.17484,1776 2017-09-28 01:00:00,1.1749,1.17513,1.17333,1.17385,1826 2017-09-28 02:00:00,1.1738,1.17434,1.17364,1.17402,876 2017-09-28 03:00:00,1.17397,1.17414,1.17267,1.17306,1293 2017-09-28 04:00:00,1.17308,1.17346,1.17268,1.17318,1210 2017-09-28 05:00:00,1.17317,1.17363,1.17208,1.17256,1796 2017-09-28 06:00:00,1.17256,1.17516,1.17214,1.17422,3128 2017-09-28 07:00:00,1.17421,1.17569,1.17241,1.1755,4316 2017-09-28 08:00:00,1.17544,1.17722,1.17496,1.17664,3769 2017-09-28 09:00:00,1.17666,1.17748,1.17588,1.17669,2482 2017-09-28 10:00:00,1.17672,1.17837,1.17636,1.1779,2880 2017-09-28 11:00:00,1.17786,1.17846,1.17737,1.17738,2165 2017-09-28 12:00:00,1.17734,1.17846,1.17664,1.1767,3580 2017-09-28 13:00:00,1.17666,1.17955,1.17664,1.17953,2949 2017-09-28 14:00:00,1.17954,1.1804,1.17804,1.17843,4300 2017-09-28 15:00:00,1.17848,1.17925,1.17764,1.17766,3048 2017-09-28 16:00:00,1.17768,1.17867,1.17728,1.1784,1794 2017-09-28 17:00:00,1.17838,1.17919,1.17794,1.1789,1217 2017-09-28 18:00:00,1.17889,1.17934,1.17823,1.17913,1558 2017-09-28 19:00:00,1.1791,1.17913,1.17782,1.17784,1155 2017-09-28 20:00:00,1.17779,1.17872,1.17779,1.17862,840 2017-09-28 21:00:00,1.17862,1.17889,1.1781,1.1784,2164 2017-09-28 22:00:00,1.17838,1.17848,1.17812,1.17814,624 2017-09-28 23:00:00,1.17813,1.17832,1.17782,1.1781,831 2017-09-29 00:00:00,1.17811,1.17891,1.17773,1.17864,1362 2017-09-29 01:00:00,1.17858,1.17907,1.17794,1.17798,921 2017-09-29 02:00:00,1.178,1.17826,1.17758,1.17761,1073 2017-09-29 03:00:00,1.17762,1.17852,1.17758,1.17842,508 2017-09-29 04:00:00,1.1784,1.17864,1.17784,1.17792,678 2017-09-29 05:00:00,1.17792,1.17811,1.17732,1.17744,941 2017-09-29 06:00:00,1.17748,1.18012,1.17728,1.17961,2415 2017-09-29 07:00:00,1.17956,1.18087,1.17882,1.17944,3293 2017-09-29 08:00:00,1.17944,1.18059,1.17933,1.17992,1897 2017-09-29 09:00:00,1.1799,1.18021,1.1789,1.18014,2030 2017-09-29 10:00:00,1.18014,1.18146,1.17987,1.18129,1550 2017-09-29 11:00:00,1.18126,1.18187,1.18104,1.18127,1633 2017-09-29 12:00:00,1.18128,1.18328,1.18022,1.18296,4310 2017-09-29 13:00:00,1.18295,1.18321,1.17913,1.17956,5025 2017-09-29 14:00:00,1.17962,1.18248,1.17944,1.18205,6245 2017-09-29 15:00:00,1.1821,1.1823,1.18088,1.18125,3238 2017-09-29 16:00:00,1.18126,1.18136,1.17997,1.1803,1650 2017-09-29 17:00:00,1.18026,1.1811,1.17998,1.18041,1133 2017-09-29 18:00:00,1.18038,1.18242,1.18031,1.18123,1911 2017-09-29 19:00:00,1.18124,1.18259,1.1812,1.18194,1850 2017-09-29 20:00:00,1.18198,1.18237,1.1811,1.1815,2723 2017-10-01 21:00:00,1.17919,1.1809,1.1789,1.1806,818 2017-10-01 22:00:00,1.1806,1.18156,1.18018,1.18112,2157 2017-10-01 23:00:00,1.18107,1.18116,1.1806,1.18082,852 2017-10-02 00:00:00,1.18076,1.1808,1.1788,1.17896,1537 2017-10-02 01:00:00,1.17897,1.17911,1.17704,1.17743,1577 2017-10-02 02:00:00,1.17738,1.17801,1.17719,1.17758,984 2017-10-02 03:00:00,1.17764,1.1779,1.17734,1.17783,572 2017-10-02 04:00:00,1.17784,1.17804,1.17736,1.17766,564 2017-10-02 05:00:00,1.17766,1.17768,1.17606,1.17633,1311 2017-10-02 06:00:00,1.17633,1.17687,1.17544,1.17644,2179 2017-10-02 07:00:00,1.1764,1.17659,1.17347,1.17422,3426 2017-10-02 08:00:00,1.17422,1.17525,1.1734,1.17461,2569 2017-10-02 09:00:00,1.17462,1.17486,1.17302,1.17316,1897 2017-10-02 10:00:00,1.17311,1.17463,1.173,1.17456,1416 2017-10-02 11:00:00,1.17452,1.17513,1.1738,1.17384,1793 2017-10-02 12:00:00,1.17386,1.17634,1.17374,1.17577,2782 2017-10-02 13:00:00,1.17574,1.17612,1.17464,1.17519,2446 2017-10-02 14:00:00,1.17514,1.17584,1.17346,1.17354,4998 2017-10-02 15:00:00,1.1735,1.1746,1.17306,1.17442,2435 2017-10-02 16:00:00,1.17446,1.17486,1.17408,1.17434,1295 2017-10-02 17:00:00,1.17434,1.17443,1.17338,1.17392,987 2017-10-02 18:00:00,1.17389,1.17499,1.17364,1.17436,1210 2017-10-02 19:00:00,1.17434,1.17458,1.17333,1.17354,1079 2017-10-02 20:00:00,1.17354,1.17368,1.17316,1.17323,522 2017-10-02 21:00:00,1.17334,1.17372,1.17306,1.17337,3674 2017-10-02 22:00:00,1.17333,1.174,1.17321,1.17386,1670 2017-10-02 23:00:00,1.17385,1.17402,1.17317,1.17321,785 2017-10-03 00:00:00,1.17322,1.17344,1.17192,1.172,1697 2017-10-03 01:00:00,1.17204,1.17204,1.17042,1.17062,3261 2017-10-03 02:00:00,1.17063,1.17134,1.17016,1.17121,1443 2017-10-03 03:00:00,1.1712,1.17147,1.17036,1.17049,1071 2017-10-03 04:00:00,1.17048,1.17092,1.17042,1.17067,491 2017-10-03 05:00:00,1.17072,1.17094,1.16962,1.17059,1722 2017-10-03 06:00:00,1.17059,1.1724,1.17024,1.17228,2377 2017-10-03 07:00:00,1.17228,1.17438,1.17194,1.1743,2853 2017-10-03 08:00:00,1.17435,1.17479,1.1733,1.1733,2150 2017-10-03 09:00:00,1.17328,1.175,1.17326,1.17363,1627 2017-10-03 10:00:00,1.1736,1.17588,1.17356,1.1756,1892 2017-10-03 11:00:00,1.17562,1.17623,1.17494,1.17497,1399 2017-10-03 12:00:00,1.17498,1.17534,1.1738,1.17488,1691 2017-10-03 13:00:00,1.17486,1.17612,1.17472,1.17552,2074 2017-10-03 14:00:00,1.17549,1.17738,1.1753,1.17532,2729 2017-10-03 15:00:00,1.17535,1.1764,1.17531,1.17586,1412 2017-10-03 16:00:00,1.17588,1.17602,1.17498,1.17532,1316 2017-10-03 17:00:00,1.17531,1.1756,1.17448,1.17464,939 2017-10-03 18:00:00,1.17464,1.175,1.1739,1.17496,945 2017-10-03 19:00:00,1.17498,1.17532,1.17454,1.17494,925 2017-10-03 20:00:00,1.17492,1.17508,1.17432,1.1744,716 2017-10-03 21:00:00,1.1742,1.17444,1.17347,1.1737,2108 2017-10-03 22:00:00,1.17367,1.17456,1.17364,1.17432,928 2017-10-03 23:00:00,1.17438,1.17494,1.17414,1.1748,429 2017-10-04 00:00:00,1.17478,1.17797,1.17464,1.17748,2185 2017-10-04 01:00:00,1.17743,1.17764,1.17663,1.17724,1464 2017-10-04 02:00:00,1.17727,1.178,1.17664,1.17681,1039 2017-10-04 03:00:00,1.17678,1.17718,1.17646,1.17676,573 2017-10-04 04:00:00,1.17678,1.1771,1.17634,1.17672,560 2017-10-04 05:00:00,1.17667,1.1774,1.17655,1.17712,1219 2017-10-04 06:00:00,1.17712,1.17767,1.17626,1.17639,2057 2017-10-04 07:00:00,1.17634,1.17641,1.1747,1.17574,2449 2017-10-04 08:00:00,1.17574,1.17666,1.1756,1.17565,1732 2017-10-04 09:00:00,1.1756,1.17592,1.17529,1.17588,1138 2017-10-04 10:00:00,1.17584,1.1781,1.17579,1.17744,2057 2017-10-04 11:00:00,1.17742,1.17874,1.17666,1.17801,2085 2017-10-04 12:00:00,1.178,1.17882,1.17623,1.1772,3512 2017-10-04 13:00:00,1.1772,1.17731,1.17562,1.1765,2258 2017-10-04 14:00:00,1.17642,1.17654,1.17468,1.176,3823 2017-10-04 15:00:00,1.17598,1.17642,1.1754,1.17616,2211 2017-10-04 16:00:00,1.17616,1.17666,1.17568,1.17632,1325 2017-10-04 17:00:00,1.17629,1.17647,1.17546,1.17642,1103 2017-10-04 18:00:00,1.17646,1.17658,1.17604,1.17643,766 2017-10-04 19:00:00,1.17644,1.17658,1.17616,1.17632,888 2017-10-04 20:00:00,1.17632,1.17664,1.17588,1.17596,515 2017-10-04 21:00:00,1.17632,1.17632,1.17554,1.17572,5281 2017-10-04 22:00:00,1.17565,1.17632,1.17564,1.17607,547 2017-10-04 23:00:00,1.17606,1.17628,1.17549,1.1757,331 2017-10-05 00:00:00,1.1757,1.17592,1.17518,1.1755,1177 2017-10-05 01:00:00,1.17549,1.17581,1.17516,1.17574,1122 2017-10-05 02:00:00,1.17568,1.17594,1.17558,1.17578,431 2017-10-05 03:00:00,1.17572,1.17584,1.17546,1.17548,431 2017-10-05 04:00:00,1.17551,1.17597,1.17542,1.17592,377 2017-10-05 05:00:00,1.17589,1.17644,1.17538,1.17542,1157 2017-10-05 06:00:00,1.17544,1.17644,1.17478,1.17634,1887 2017-10-05 07:00:00,1.17638,1.1779,1.17598,1.17717,3037 2017-10-05 08:00:00,1.17716,1.1777,1.17686,1.17751,1627 2017-10-05 09:00:00,1.17754,1.17756,1.17624,1.17635,1569 2017-10-05 10:00:00,1.17634,1.17657,1.17424,1.17464,1483 2017-10-05 11:00:00,1.17464,1.17493,1.1735,1.17366,2433 2017-10-05 12:00:00,1.1737,1.17456,1.17252,1.17306,3088 2017-10-05 13:00:00,1.1731,1.1734,1.17127,1.17226,4012 2017-10-05 14:00:00,1.17228,1.17352,1.17133,1.17143,3219 2017-10-05 15:00:00,1.17142,1.17159,1.1703,1.17056,2108 2017-10-05 16:00:00,1.1706,1.17122,1.17042,1.17094,1426 2017-10-05 17:00:00,1.17095,1.171,1.16999,1.1705,983 2017-10-05 18:00:00,1.17055,1.17068,1.16992,1.17048,1084 2017-10-05 19:00:00,1.17048,1.17076,1.17034,1.17058,799 2017-10-05 20:00:00,1.17056,1.17119,1.17033,1.17114,450 2017-10-05 21:00:00,1.17114,1.17146,1.17092,1.17116,1338 2017-10-05 22:00:00,1.17114,1.1712,1.17094,1.17108,364 2017-10-05 23:00:00,1.17108,1.17141,1.17072,1.1714,629 2017-10-06 00:00:00,1.17146,1.17148,1.16994,1.17118,1548 2017-10-06 01:00:00,1.17116,1.17158,1.17078,1.17096,918 2017-10-06 02:00:00,1.17096,1.17142,1.17084,1.17084,582 2017-10-06 03:00:00,1.1708,1.17084,1.16948,1.16966,982 2017-10-06 04:00:00,1.16966,1.17008,1.1686,1.16997,1598 2017-10-06 05:00:00,1.17001,1.17023,1.16933,1.16994,1016 2017-10-06 06:00:00,1.16992,1.17071,1.16894,1.16956,2031 2017-10-06 07:00:00,1.16954,1.16992,1.16862,1.16888,2035 2017-10-06 08:00:00,1.16882,1.17006,1.16873,1.16964,1703 2017-10-06 09:00:00,1.16962,1.17074,1.16944,1.17048,1397 2017-10-06 10:00:00,1.17048,1.1715,1.17026,1.17076,1448 2017-10-06 11:00:00,1.17076,1.17122,1.17048,1.17066,1296 2017-10-06 12:00:00,1.17067,1.17149,1.16692,1.16908,10491 2017-10-06 13:00:00,1.16904,1.17078,1.16892,1.16976,4726 2017-10-06 14:00:00,1.1698,1.17386,1.16842,1.17314,7623 2017-10-06 15:00:00,1.17314,1.17382,1.17188,1.1733,3841 2017-10-06 16:00:00,1.1733,1.17358,1.1723,1.1724,1958 2017-10-06 17:00:00,1.17242,1.1735,1.17242,1.17328,1075 2017-10-06 18:00:00,1.17323,1.17349,1.17256,1.17336,1098 2017-10-06 19:00:00,1.17335,1.17356,1.17301,1.17332,1106 2017-10-06 20:00:00,1.17331,1.17376,1.17276,1.17326,1269 2017-10-06 21:00:00,1.17324,1.17324,1.17324,1.17324,1 2017-10-08 21:00:00,1.17298,1.17417,1.17297,1.17376,291 2017-10-08 22:00:00,1.17373,1.17422,1.17335,1.1739,2232 2017-10-08 23:00:00,1.17392,1.17434,1.17378,1.17427,765 2017-10-09 00:00:00,1.17424,1.17446,1.17353,1.1737,699 2017-10-09 01:00:00,1.17375,1.17437,1.1732,1.17358,773 2017-10-09 02:00:00,1.17364,1.1748,1.17364,1.17412,674 2017-10-09 03:00:00,1.17418,1.17427,1.1736,1.17364,280 2017-10-09 04:00:00,1.1736,1.17394,1.17344,1.17358,377 2017-10-09 05:00:00,1.17358,1.1737,1.17324,1.1737,464 2017-10-09 06:00:00,1.17367,1.17414,1.17232,1.17274,1071 2017-10-09 07:00:00,1.17269,1.17308,1.17197,1.17282,1281 2017-10-09 08:00:00,1.1728,1.17412,1.1725,1.17358,1017 2017-10-09 09:00:00,1.17355,1.17452,1.17337,1.17438,871 2017-10-09 10:00:00,1.17442,1.17469,1.17398,1.17452,572 2017-10-09 11:00:00,1.17454,1.1748,1.17399,1.1747,919 2017-10-09 12:00:00,1.1747,1.1747,1.17372,1.17388,889 2017-10-09 13:00:00,1.17393,1.17402,1.17312,1.17312,731 2017-10-09 14:00:00,1.17309,1.17392,1.17253,1.17381,1149 2017-10-09 15:00:00,1.17377,1.17466,1.17362,1.17428,928 2017-10-09 16:00:00,1.17424,1.17561,1.17417,1.17499,1348 2017-10-09 17:00:00,1.17496,1.17554,1.17468,1.17514,690 2017-10-09 18:00:00,1.17512,1.17528,1.17474,1.17496,544 2017-10-09 19:00:00,1.17492,1.17562,1.17486,1.17513,729 2017-10-09 20:00:00,1.17508,1.17514,1.17391,1.17413,520 2017-10-09 21:00:00,1.17403,1.17423,1.17395,1.1741,323 2017-10-09 22:00:00,1.17416,1.17442,1.17394,1.17414,924 2017-10-09 23:00:00,1.17416,1.17431,1.174,1.17411,408 2017-10-10 00:00:00,1.17406,1.1749,1.17406,1.17484,1017 2017-10-10 01:00:00,1.17483,1.17825,1.1748,1.17765,2160 2017-10-10 02:00:00,1.17764,1.17814,1.17717,1.17718,881 2017-10-10 03:00:00,1.17718,1.17737,1.17666,1.17668,500 2017-10-10 04:00:00,1.17674,1.17737,1.17659,1.17708,723 2017-10-10 05:00:00,1.17708,1.17749,1.1768,1.17713,889 2017-10-10 06:00:00,1.17713,1.17896,1.17657,1.17776,2276 2017-10-10 07:00:00,1.17774,1.17774,1.17624,1.1765,2541 2017-10-10 08:00:00,1.17652,1.17859,1.17651,1.17785,1857 2017-10-10 09:00:00,1.17789,1.18027,1.17749,1.1802,1904 2017-10-10 10:00:00,1.18024,1.18072,1.17792,1.17818,1874 2017-10-10 11:00:00,1.17814,1.17962,1.17733,1.17962,1699 2017-10-10 12:00:00,1.1796,1.17978,1.17853,1.1787,2012 2017-10-10 13:00:00,1.1787,1.18054,1.17824,1.1803,2206 2017-10-10 14:00:00,1.18033,1.18182,1.18009,1.18174,2667 2017-10-10 15:00:00,1.18169,1.18178,1.18028,1.18038,1576 2017-10-10 16:00:00,1.18039,1.18048,1.17956,1.18024,1491 2017-10-10 17:00:00,1.18024,1.18254,1.17962,1.18038,4810 2017-10-10 18:00:00,1.18042,1.18118,1.18008,1.18039,1048 2017-10-10 19:00:00,1.18044,1.18162,1.18026,1.18152,914 2017-10-10 20:00:00,1.18148,1.18177,1.18079,1.18084,443 2017-10-10 21:00:00,1.1808,1.18119,1.18066,1.1808,5353 2017-10-10 22:00:00,1.18078,1.18118,1.18076,1.18108,673 2017-10-10 23:00:00,1.18102,1.18174,1.18102,1.1814,565 2017-10-11 00:00:00,1.18137,1.18278,1.18128,1.18193,1478 2017-10-11 01:00:00,1.18194,1.18245,1.18112,1.18113,1581 2017-10-11 02:00:00,1.18108,1.18114,1.17951,1.18022,2112 2017-10-11 03:00:00,1.18024,1.18096,1.18004,1.18079,811 2017-10-11 04:00:00,1.18084,1.18206,1.18077,1.18138,1180 2017-10-11 05:00:00,1.1814,1.1818,1.18124,1.18166,815 2017-10-11 06:00:00,1.18164,1.18343,1.18154,1.1822,2360 2017-10-11 07:00:00,1.18218,1.18308,1.1814,1.18267,1971 2017-10-11 08:00:00,1.18268,1.18447,1.18259,1.18272,2478 2017-10-11 09:00:00,1.1827,1.18279,1.18016,1.18065,2439 2017-10-11 10:00:00,1.18067,1.18231,1.18052,1.18228,1476 2017-10-11 11:00:00,1.18226,1.18308,1.18183,1.18289,1620 2017-10-11 12:00:00,1.18294,1.18467,1.18274,1.18444,2069 2017-10-11 13:00:00,1.18443,1.18586,1.1837,1.1855,2690 2017-10-11 14:00:00,1.18551,1.1856,1.18389,1.18446,1865 2017-10-11 15:00:00,1.18446,1.18567,1.18423,1.18471,1950 2017-10-11 16:00:00,1.18466,1.1851,1.18406,1.18456,921 2017-10-11 17:00:00,1.18461,1.18492,1.1841,1.18434,1059 2017-10-11 18:00:00,1.18439,1.18592,1.18396,1.1854,4778 2017-10-11 19:00:00,1.1854,1.18692,1.18488,1.18668,1882 2017-10-11 20:00:00,1.18672,1.18685,1.18576,1.18599,1072 2017-10-11 21:00:00,1.18599,1.18646,1.18569,1.18605,2213 2017-10-11 22:00:00,1.18602,1.18654,1.18596,1.18632,1362 2017-10-11 23:00:00,1.18636,1.18659,1.18599,1.18642,1100 2017-10-12 00:00:00,1.18636,1.18715,1.18583,1.18644,2164 2017-10-12 01:00:00,1.18644,1.18762,1.18628,1.18701,1215 2017-10-12 02:00:00,1.187,1.18755,1.18674,1.18744,1266 2017-10-12 03:00:00,1.18744,1.18782,1.18722,1.18748,887 2017-10-12 04:00:00,1.18752,1.18786,1.18735,1.1876,802 2017-10-12 05:00:00,1.18756,1.188,1.18718,1.18783,893 2017-10-12 06:00:00,1.18783,1.18787,1.18646,1.18669,1404 2017-10-12 07:00:00,1.18667,1.18688,1.18562,1.18669,2017 2017-10-12 08:00:00,1.18668,1.18669,1.18594,1.18624,1343 2017-10-12 09:00:00,1.18628,1.18659,1.18509,1.18522,1489 2017-10-12 10:00:00,1.18522,1.18564,1.18516,1.18539,1141 2017-10-12 11:00:00,1.18539,1.18618,1.18514,1.18544,1301 2017-10-12 12:00:00,1.18548,1.18586,1.18406,1.18472,2842 2017-10-12 13:00:00,1.18472,1.1852,1.1829,1.1833,1682 2017-10-12 14:00:00,1.18335,1.18494,1.18306,1.18467,2256 2017-10-12 15:00:00,1.18465,1.1848,1.18344,1.18404,1686 2017-10-12 16:00:00,1.184,1.18473,1.18338,1.18354,2361 2017-10-12 17:00:00,1.18354,1.18506,1.18268,1.18468,2281 2017-10-12 18:00:00,1.18468,1.18492,1.1834,1.18353,1165 2017-10-12 19:00:00,1.18358,1.18384,1.18302,1.18353,884 2017-10-12 20:00:00,1.18354,1.18354,1.18274,1.18306,1000 2017-10-12 21:00:00,1.18282,1.18352,1.18266,1.18318,1184 2017-10-12 22:00:00,1.1832,1.18382,1.18298,1.18352,469 2017-10-12 23:00:00,1.18351,1.18351,1.18254,1.18311,886 2017-10-13 00:00:00,1.18306,1.18448,1.1829,1.18442,1219 2017-10-13 01:00:00,1.18444,1.18473,1.18375,1.18442,941 2017-10-13 02:00:00,1.18439,1.18516,1.18424,1.18488,859 2017-10-13 03:00:00,1.1849,1.18514,1.18456,1.18479,516 2017-10-13 04:00:00,1.18485,1.1849,1.18435,1.18457,476 2017-10-13 05:00:00,1.18462,1.18516,1.18444,1.18472,741 2017-10-13 06:00:00,1.1847,1.18486,1.1836,1.18385,1643 2017-10-13 07:00:00,1.1838,1.18389,1.18152,1.18288,3376 2017-10-13 08:00:00,1.18289,1.18358,1.18254,1.1831,1245 2017-10-13 09:00:00,1.18308,1.18342,1.18245,1.18298,1369 2017-10-13 10:00:00,1.18297,1.18338,1.18197,1.18264,1521 2017-10-13 11:00:00,1.18264,1.18264,1.18059,1.1807,1743 2017-10-13 12:00:00,1.18066,1.18741,1.18056,1.18568,7670 2017-10-13 13:00:00,1.18572,1.1875,1.18556,1.18664,3679 2017-10-13 14:00:00,1.18663,1.18708,1.1832,1.18354,4256 2017-10-13 15:00:00,1.18358,1.1843,1.18315,1.18364,2148 2017-10-13 16:00:00,1.18364,1.18491,1.18268,1.1827,1757 2017-10-13 17:00:00,1.1827,1.1831,1.18202,1.18251,1709 2017-10-13 18:00:00,1.18246,1.18246,1.18106,1.1818,1798 2017-10-13 19:00:00,1.18182,1.18225,1.1817,1.1819,941 2017-10-13 20:00:00,1.18189,1.18262,1.18185,1.18234,1356 2017-10-15 21:00:00,1.1814,1.18668,1.18106,1.18112,435 2017-10-15 22:00:00,1.1812,1.18195,1.1807,1.1807,1467 2017-10-15 23:00:00,1.18068,1.18146,1.18058,1.18146,514 2017-10-16 00:00:00,1.18147,1.18194,1.1812,1.1813,898 2017-10-16 01:00:00,1.18128,1.1816,1.18012,1.18034,947 2017-10-16 02:00:00,1.18034,1.1809,1.17982,1.18076,784 2017-10-16 03:00:00,1.18072,1.18081,1.18023,1.18062,573 2017-10-16 04:00:00,1.18061,1.18107,1.18046,1.18092,408 2017-10-16 05:00:00,1.18086,1.18098,1.17939,1.17964,783 2017-10-16 06:00:00,1.17964,1.18004,1.1784,1.1784,1816 2017-10-16 07:00:00,1.17842,1.17957,1.17806,1.17928,1975 2017-10-16 08:00:00,1.17927,1.1795,1.17847,1.17899,1495 2017-10-16 09:00:00,1.17894,1.17926,1.17844,1.17898,1019 2017-10-16 10:00:00,1.17898,1.18064,1.17898,1.18018,1557 2017-10-16 11:00:00,1.18018,1.1817,1.18009,1.18059,1584 2017-10-16 12:00:00,1.18056,1.18092,1.17958,1.18071,2079 2017-10-16 13:00:00,1.18066,1.18102,1.17997,1.18024,1701 2017-10-16 14:00:00,1.18028,1.18048,1.17952,1.17968,1498 2017-10-16 15:00:00,1.17964,1.18196,1.17964,1.1809,1611 2017-10-16 16:00:00,1.18094,1.18118,1.18064,1.18106,800 2017-10-16 17:00:00,1.18104,1.18108,1.18026,1.18059,1118 2017-10-16 18:00:00,1.18057,1.18097,1.17892,1.1792,2780 2017-10-16 19:00:00,1.1792,1.17928,1.1786,1.17924,1384 2017-10-16 20:00:00,1.17923,1.17978,1.17918,1.17964,332 2017-10-16 21:00:00,1.17964,1.18004,1.1796,1.17976,1946 2017-10-16 22:00:00,1.17976,1.17978,1.17914,1.17922,888 2017-10-16 23:00:00,1.17916,1.1795,1.17877,1.17946,358 2017-10-17 00:00:00,1.17943,1.17954,1.17842,1.17863,760 2017-10-17 01:00:00,1.17866,1.17871,1.1775,1.1776,1161 2017-10-17 02:00:00,1.1776,1.17838,1.1776,1.17833,805 2017-10-17 03:00:00,1.17838,1.17844,1.17778,1.17794,345 2017-10-17 04:00:00,1.17795,1.17818,1.17771,1.17776,364 2017-10-17 05:00:00,1.17774,1.17802,1.17668,1.17701,892 2017-10-17 06:00:00,1.17699,1.17738,1.17561,1.17571,1863 2017-10-17 07:00:00,1.17566,1.17735,1.17548,1.17724,1778 2017-10-17 08:00:00,1.1772,1.17753,1.17641,1.17656,1619 2017-10-17 09:00:00,1.17658,1.17683,1.17543,1.17586,1716 2017-10-17 10:00:00,1.17582,1.17698,1.17547,1.17602,1566 2017-10-17 11:00:00,1.17598,1.17684,1.17556,1.17631,1781 2017-10-17 12:00:00,1.17633,1.17648,1.17513,1.17514,1932 2017-10-17 13:00:00,1.17512,1.17534,1.17428,1.17466,2072 2017-10-17 14:00:00,1.17462,1.17525,1.17362,1.17508,2284 2017-10-17 15:00:00,1.17504,1.17576,1.17464,1.17536,1698 2017-10-17 16:00:00,1.17537,1.1756,1.17446,1.17458,900 2017-10-17 17:00:00,1.17456,1.17664,1.17442,1.17641,861 2017-10-17 18:00:00,1.17638,1.17754,1.17618,1.17715,1108 2017-10-17 19:00:00,1.1771,1.17723,1.17672,1.17682,847 2017-10-17 20:00:00,1.17684,1.17687,1.17654,1.17668,270 2017-10-17 21:00:00,1.17668,1.17698,1.17647,1.17664,1214 2017-10-17 22:00:00,1.17663,1.17692,1.17641,1.17686,447 2017-10-17 23:00:00,1.17687,1.17722,1.17684,1.17712,317 2017-10-18 00:00:00,1.1771,1.17808,1.17702,1.1773,1396 2017-10-18 01:00:00,1.1773,1.17736,1.17672,1.17694,605 2017-10-18 02:00:00,1.17696,1.17753,1.1769,1.17728,459 2017-10-18 03:00:00,1.17724,1.1773,1.17689,1.17701,348 2017-10-18 04:00:00,1.17699,1.17708,1.17621,1.17639,424 2017-10-18 05:00:00,1.17638,1.17655,1.17593,1.17652,832 2017-10-18 06:00:00,1.17652,1.17708,1.17536,1.1754,1361 2017-10-18 07:00:00,1.17544,1.17686,1.17481,1.17616,2054 2017-10-18 08:00:00,1.17616,1.17659,1.1744,1.17455,1985 2017-10-18 09:00:00,1.17458,1.17475,1.17302,1.17422,2318 2017-10-18 10:00:00,1.17418,1.17588,1.17416,1.17522,1923 2017-10-18 11:00:00,1.17524,1.17552,1.1745,1.17453,1670 2017-10-18 12:00:00,1.17448,1.177,1.17438,1.1765,3546 2017-10-18 13:00:00,1.17651,1.17777,1.17616,1.17648,2187 2017-10-18 14:00:00,1.17644,1.17804,1.17598,1.17771,1982 2017-10-18 15:00:00,1.17766,1.17808,1.17669,1.17732,1625 2017-10-18 16:00:00,1.17732,1.1794,1.17732,1.17869,1946 2017-10-18 17:00:00,1.17871,1.17896,1.17836,1.17855,725 2017-10-18 18:00:00,1.17856,1.18054,1.17848,1.1803,1373 2017-10-18 19:00:00,1.18034,1.18038,1.17932,1.1794,952 2017-10-18 20:00:00,1.17933,1.1796,1.17864,1.17866,458 2017-10-18 21:00:00,1.17892,1.1795,1.17878,1.17923,2801 2017-10-18 22:00:00,1.17926,1.1797,1.17916,1.17952,679 2017-10-18 23:00:00,1.17954,1.18003,1.17926,1.17943,1015 2017-10-19 00:00:00,1.17942,1.18167,1.17928,1.18112,1914 2017-10-19 01:00:00,1.1811,1.18146,1.18025,1.18044,1297 2017-10-19 02:00:00,1.18044,1.18056,1.17955,1.17988,1064 2017-10-19 03:00:00,1.17982,1.18023,1.17948,1.18,535 2017-10-19 04:00:00,1.18002,1.18068,1.17994,1.18025,962 2017-10-19 05:00:00,1.18022,1.18037,1.17946,1.17985,899 2017-10-19 06:00:00,1.17984,1.18228,1.17973,1.18212,2318 2017-10-19 07:00:00,1.18211,1.18214,1.18048,1.18136,2816 2017-10-19 08:00:00,1.18137,1.18172,1.17683,1.18064,5957 2017-10-19 09:00:00,1.18064,1.18162,1.18008,1.18132,2233 2017-10-19 10:00:00,1.18131,1.18364,1.18094,1.1824,3637 2017-10-19 11:00:00,1.18238,1.18398,1.18224,1.18348,2348 2017-10-19 12:00:00,1.18352,1.18426,1.1826,1.18304,3500 2017-10-19 13:00:00,1.18302,1.18478,1.18272,1.18452,2488 2017-10-19 14:00:00,1.18449,1.18582,1.18403,1.18522,2718 2017-10-19 15:00:00,1.18522,1.18549,1.18412,1.18426,1734 2017-10-19 16:00:00,1.18426,1.18447,1.183,1.18348,2087 2017-10-19 17:00:00,1.18351,1.18362,1.18277,1.18362,904 2017-10-19 18:00:00,1.18362,1.18376,1.18271,1.18289,765 2017-10-19 19:00:00,1.18284,1.18478,1.18262,1.1841,1575 2017-10-19 20:00:00,1.18407,1.1858,1.1838,1.1852,1824 2017-10-19 21:00:00,1.18526,1.1853,1.1842,1.18462,1779 2017-10-19 22:00:00,1.18462,1.18512,1.1845,1.18477,709 2017-10-19 23:00:00,1.18474,1.18532,1.18448,1.18489,662 2017-10-20 00:00:00,1.1849,1.18526,1.18434,1.18496,1047 2017-10-20 01:00:00,1.18499,1.18584,1.18226,1.1823,2495 2017-10-20 02:00:00,1.18231,1.18231,1.18074,1.18094,4256 2017-10-20 03:00:00,1.18094,1.18195,1.18078,1.18187,1120 2017-10-20 04:00:00,1.1819,1.18211,1.18146,1.1815,579 2017-10-20 05:00:00,1.18144,1.1817,1.1804,1.18083,1285 2017-10-20 06:00:00,1.18082,1.18192,1.18046,1.18166,2129 2017-10-20 07:00:00,1.18164,1.18262,1.17904,1.18052,3813 2017-10-20 08:00:00,1.18054,1.18074,1.1799,1.18036,2541 2017-10-20 09:00:00,1.18036,1.18126,1.1796,1.18117,1797 2017-10-20 10:00:00,1.18112,1.18122,1.18001,1.18071,1666 2017-10-20 11:00:00,1.1807,1.18203,1.18049,1.18156,2127 2017-10-20 12:00:00,1.18152,1.18207,1.1801,1.18014,2435 2017-10-20 13:00:00,1.18016,1.18054,1.17858,1.17869,2164 2017-10-20 14:00:00,1.1787,1.17996,1.1781,1.17864,2694 2017-10-20 15:00:00,1.17868,1.17899,1.17624,1.17696,2467 2017-10-20 16:00:00,1.17692,1.17884,1.17688,1.17836,3504 2017-10-20 17:00:00,1.17835,1.17869,1.17799,1.17856,973 2017-10-20 18:00:00,1.17858,1.17887,1.17707,1.17781,3066 2017-10-20 19:00:00,1.17782,1.17802,1.1772,1.1773,952 2017-10-20 20:00:00,1.17732,1.17834,1.17662,1.17799,1978 2017-10-20 21:00:00,1.17799,1.17799,1.17799,1.17799,1 2017-10-22 21:00:00,1.17651,1.17742,1.1762,1.17726,947 2017-10-22 22:00:00,1.17726,1.17748,1.17611,1.17618,1614 2017-10-22 23:00:00,1.17624,1.17654,1.17515,1.17548,1027 2017-10-23 00:00:00,1.17551,1.17624,1.17536,1.17604,1492 2017-10-23 01:00:00,1.17605,1.17667,1.17552,1.17656,1146 2017-10-23 02:00:00,1.17654,1.1771,1.17654,1.17692,540 2017-10-23 03:00:00,1.17687,1.17707,1.17672,1.17703,262 2017-10-23 04:00:00,1.177,1.17737,1.17684,1.17698,315 2017-10-23 05:00:00,1.17704,1.17731,1.17662,1.17728,451 2017-10-23 06:00:00,1.17728,1.17774,1.17591,1.17598,1599 2017-10-23 07:00:00,1.17595,1.1763,1.17512,1.17559,2079 2017-10-23 08:00:00,1.17562,1.1758,1.17404,1.17462,1698 2017-10-23 09:00:00,1.17462,1.17532,1.17351,1.17356,1588 2017-10-23 10:00:00,1.17356,1.17488,1.17352,1.17449,1796 2017-10-23 11:00:00,1.17447,1.17458,1.17363,1.17391,2070 2017-10-23 12:00:00,1.17386,1.17418,1.17334,1.17356,1741 2017-10-23 13:00:00,1.17358,1.17502,1.17339,1.17479,1753 2017-10-23 14:00:00,1.17484,1.1757,1.17428,1.17456,2297 2017-10-23 15:00:00,1.1746,1.17626,1.17406,1.17606,2459 2017-10-23 16:00:00,1.17608,1.17636,1.17444,1.17445,1796 2017-10-23 17:00:00,1.1745,1.1748,1.17368,1.17372,1169 2017-10-23 18:00:00,1.17371,1.17413,1.17254,1.17372,2035 2017-10-23 19:00:00,1.17371,1.1754,1.17357,1.17492,1981 2017-10-23 20:00:00,1.17488,1.1751,1.17452,1.17499,483 2017-10-23 21:00:00,1.1749,1.1752,1.17454,1.17492,1797 2017-10-23 22:00:00,1.17489,1.17508,1.1747,1.17495,353 2017-10-23 23:00:00,1.175,1.17568,1.17487,1.17548,588 2017-10-24 00:00:00,1.17552,1.17608,1.17542,1.176,666 2017-10-24 01:00:00,1.17597,1.1762,1.17572,1.17608,609 2017-10-24 02:00:00,1.17612,1.17655,1.17582,1.1761,606 2017-10-24 03:00:00,1.17612,1.17702,1.17612,1.17662,798 2017-10-24 04:00:00,1.17656,1.17666,1.17612,1.17618,441 2017-10-24 05:00:00,1.17618,1.17628,1.17533,1.17554,511 2017-10-24 06:00:00,1.17554,1.17642,1.17547,1.1757,1826 2017-10-24 07:00:00,1.1757,1.17598,1.17428,1.17549,2722 2017-10-24 08:00:00,1.17555,1.17659,1.17552,1.17642,1664 2017-10-24 09:00:00,1.17646,1.17672,1.17515,1.1763,1457 2017-10-24 10:00:00,1.17625,1.17625,1.17536,1.17592,1044 2017-10-24 11:00:00,1.17594,1.177,1.17515,1.17572,1844 2017-10-24 12:00:00,1.17568,1.17674,1.17549,1.17613,1861 2017-10-24 13:00:00,1.17611,1.17652,1.17542,1.17596,2415 2017-10-24 14:00:00,1.17595,1.17772,1.17578,1.17616,3409 2017-10-24 15:00:00,1.17616,1.17714,1.17588,1.1766,2248 2017-10-24 16:00:00,1.17657,1.17685,1.17618,1.17639,1375 2017-10-24 17:00:00,1.17642,1.17676,1.17576,1.17588,1441 2017-10-24 18:00:00,1.17585,1.17873,1.17564,1.17857,1657 2017-10-24 19:00:00,1.17854,1.17933,1.1758,1.176,8614 2017-10-24 20:00:00,1.17596,1.1766,1.17593,1.17606,553 2017-10-24 21:00:00,1.17606,1.17657,1.17603,1.17636,864 2017-10-24 22:00:00,1.17639,1.17701,1.17634,1.17646,609 2017-10-24 23:00:00,1.17649,1.17663,1.17578,1.176,522 2017-10-25 00:00:00,1.17594,1.17637,1.17531,1.17586,1582 2017-10-25 01:00:00,1.17592,1.17644,1.17558,1.17598,892 2017-10-25 02:00:00,1.17602,1.17632,1.17548,1.176,593 2017-10-25 03:00:00,1.176,1.17604,1.17536,1.17558,393 2017-10-25 04:00:00,1.17555,1.17584,1.17542,1.17578,374 2017-10-25 05:00:00,1.17572,1.1766,1.17572,1.17658,1512 2017-10-25 06:00:00,1.17658,1.1767,1.17558,1.17612,1950 2017-10-25 07:00:00,1.17616,1.17692,1.17556,1.17613,2864 2017-10-25 08:00:00,1.17611,1.17752,1.17603,1.17677,3634 2017-10-25 09:00:00,1.17674,1.17752,1.17597,1.17678,2679 2017-10-25 10:00:00,1.17673,1.17818,1.17642,1.17764,2203 2017-10-25 11:00:00,1.17764,1.17812,1.17698,1.17792,2119 2017-10-25 12:00:00,1.17788,1.17864,1.17702,1.17764,3632 2017-10-25 13:00:00,1.17769,1.1801,1.17769,1.1797,3856 2017-10-25 14:00:00,1.1797,1.18171,1.17856,1.18113,5777 2017-10-25 15:00:00,1.18114,1.18152,1.18014,1.1803,3304 2017-10-25 16:00:00,1.1803,1.18138,1.18022,1.18132,2781 2017-10-25 17:00:00,1.18133,1.1818,1.18034,1.18086,2068 2017-10-25 18:00:00,1.18082,1.18121,1.18022,1.18054,1803 2017-10-25 19:00:00,1.18054,1.18103,1.18028,1.18036,1295 2017-10-25 20:00:00,1.18036,1.18153,1.17998,1.18136,1035 2017-10-25 21:00:00,1.18126,1.18176,1.18114,1.18158,1024 2017-10-25 22:00:00,1.18156,1.18172,1.18121,1.18154,786 2017-10-25 23:00:00,1.1815,1.18206,1.18147,1.18198,637 2017-10-26 00:00:00,1.182,1.18234,1.18151,1.18176,931 2017-10-26 01:00:00,1.1818,1.1831,1.18173,1.18284,1349 2017-10-26 02:00:00,1.18288,1.18332,1.18226,1.18244,694 2017-10-26 03:00:00,1.18242,1.18294,1.18232,1.18248,467 2017-10-26 04:00:00,1.18252,1.18268,1.18235,1.1826,402 2017-10-26 05:00:00,1.1826,1.1837,1.18226,1.18294,1361 2017-10-26 06:00:00,1.18294,1.18332,1.18205,1.18223,1366 2017-10-26 07:00:00,1.18218,1.18289,1.18143,1.18181,2805 2017-10-26 08:00:00,1.1818,1.18208,1.18127,1.18136,1688 2017-10-26 09:00:00,1.18138,1.18138,1.18034,1.18088,1135 2017-10-26 10:00:00,1.18088,1.18168,1.1808,1.18126,858 2017-10-26 11:00:00,1.18122,1.18174,1.17576,1.17687,8223 2017-10-26 12:00:00,1.17685,1.177,1.17382,1.177,13247 2017-10-26 13:00:00,1.177,1.1786,1.17067,1.17192,10117 2017-10-26 14:00:00,1.17198,1.17261,1.16992,1.1703,4202 2017-10-26 15:00:00,1.17032,1.17076,1.16884,1.16992,5083 2017-10-26 16:00:00,1.16994,1.1701,1.16792,1.16824,2756 2017-10-26 17:00:00,1.16824,1.16827,1.16594,1.16636,5472 2017-10-26 18:00:00,1.16636,1.16684,1.16558,1.16587,2679 2017-10-26 19:00:00,1.16582,1.16594,1.16406,1.16474,2051 2017-10-26 20:00:00,1.16474,1.16578,1.16456,1.16519,1433 2017-10-26 21:00:00,1.16524,1.16576,1.16514,1.1654,1036 2017-10-26 22:00:00,1.16534,1.16556,1.16434,1.16455,1283 2017-10-26 23:00:00,1.1645,1.1645,1.16316,1.1638,1573 2017-10-27 00:00:00,1.16376,1.16426,1.16286,1.16306,1489 2017-10-27 01:00:00,1.16306,1.16336,1.16244,1.16319,1216 2017-10-27 02:00:00,1.1632,1.16396,1.16275,1.1636,786 2017-10-27 03:00:00,1.16357,1.16394,1.16321,1.1639,1022 2017-10-27 04:00:00,1.16388,1.16392,1.16344,1.16355,344 2017-10-27 05:00:00,1.16352,1.16439,1.1633,1.16437,1053 2017-10-27 06:00:00,1.16438,1.16438,1.1621,1.16236,2745 2017-10-27 07:00:00,1.16232,1.1635,1.16156,1.16337,3727 2017-10-27 08:00:00,1.16333,1.16381,1.16244,1.16273,3177 2017-10-27 09:00:00,1.1627,1.16362,1.16231,1.16342,2225 2017-10-27 10:00:00,1.1634,1.1639,1.16198,1.16264,2196 2017-10-27 11:00:00,1.16262,1.16264,1.1605,1.16076,2501 2017-10-27 12:00:00,1.16075,1.16121,1.15872,1.15991,6659 2017-10-27 13:00:00,1.15991,1.16059,1.15788,1.15806,5649 2017-10-27 14:00:00,1.1581,1.16241,1.15783,1.15806,11848 2017-10-27 15:00:00,1.15811,1.1587,1.15741,1.15806,4316 2017-10-27 16:00:00,1.1581,1.1593,1.1578,1.15903,1980 2017-10-27 17:00:00,1.159,1.15947,1.15852,1.15867,1297 2017-10-27 18:00:00,1.15867,1.16004,1.15844,1.15973,1234 2017-10-27 19:00:00,1.15974,1.16038,1.15964,1.15986,1391 2017-10-27 20:00:00,1.15982,1.16109,1.15966,1.1608,2615 2017-10-29 21:00:00,1.16096,1.16165,1.16052,1.16068,370 2017-10-29 22:00:00,1.16058,1.1607,1.15936,1.15955,1861 2017-10-29 23:00:00,1.15956,1.16102,1.15955,1.16055,849 2017-10-30 00:00:00,1.16055,1.16084,1.15978,1.1606,1632 2017-10-30 01:00:00,1.16065,1.16146,1.16006,1.16108,1595 2017-10-30 02:00:00,1.16114,1.1615,1.1606,1.16094,1184 2017-10-30 03:00:00,1.16097,1.16154,1.16079,1.16145,880 2017-10-30 04:00:00,1.16144,1.16172,1.1613,1.16172,624 2017-10-30 05:00:00,1.1617,1.1618,1.16127,1.16165,784 2017-10-30 06:00:00,1.16166,1.16179,1.16132,1.1617,598 2017-10-30 07:00:00,1.16164,1.16227,1.16017,1.16193,2418 2017-10-30 08:00:00,1.16199,1.16354,1.16154,1.1634,3322 2017-10-30 09:00:00,1.16335,1.1641,1.16302,1.16396,1834 2017-10-30 10:00:00,1.16396,1.16423,1.16314,1.16376,1796 2017-10-30 11:00:00,1.1638,1.1642,1.16322,1.16322,1682 2017-10-30 12:00:00,1.16324,1.1633,1.16097,1.16144,2861 2017-10-30 13:00:00,1.16147,1.16202,1.16048,1.162,2661 2017-10-30 14:00:00,1.16202,1.16228,1.16035,1.16172,2101 2017-10-30 15:00:00,1.16168,1.16338,1.16126,1.16272,2975 2017-10-30 16:00:00,1.16268,1.16462,1.16224,1.16392,3165 2017-10-30 17:00:00,1.16391,1.1641,1.16298,1.16302,1002 2017-10-30 18:00:00,1.16298,1.16396,1.1628,1.1639,1194 2017-10-30 19:00:00,1.1639,1.16576,1.16368,1.16566,1203 2017-10-30 20:00:00,1.16568,1.16578,1.16505,1.16517,885 2017-10-30 21:00:00,1.16495,1.16527,1.1648,1.16496,686 2017-10-30 22:00:00,1.16496,1.16496,1.16416,1.16432,655 2017-10-30 23:00:00,1.16438,1.1653,1.16422,1.16515,697 2017-10-31 00:00:00,1.16516,1.1652,1.16398,1.16474,1258 2017-10-31 01:00:00,1.1647,1.1647,1.16332,1.16344,749 2017-10-31 02:00:00,1.16349,1.1639,1.16328,1.16382,556 2017-10-31 03:00:00,1.1638,1.1639,1.1635,1.16366,531 2017-10-31 04:00:00,1.16364,1.16366,1.16253,1.16257,482 2017-10-31 05:00:00,1.16252,1.16378,1.16252,1.16358,841 2017-10-31 06:00:00,1.16356,1.16499,1.16356,1.16434,996 2017-10-31 07:00:00,1.16432,1.16446,1.16316,1.16446,2078 2017-10-31 08:00:00,1.16443,1.16491,1.1632,1.16413,2202 2017-10-31 09:00:00,1.16418,1.16418,1.1631,1.1634,1413 2017-10-31 10:00:00,1.16334,1.16364,1.16248,1.16306,2400 2017-10-31 11:00:00,1.16308,1.16402,1.16268,1.16395,1648 2017-10-31 12:00:00,1.16393,1.1645,1.16304,1.16362,2552 2017-10-31 13:00:00,1.16362,1.16431,1.1634,1.1641,2864 2017-10-31 14:00:00,1.16408,1.16438,1.16266,1.16382,2123 2017-10-31 15:00:00,1.16386,1.16614,1.16298,1.16484,4183 2017-10-31 16:00:00,1.1648,1.16586,1.16392,1.16586,2664 2017-10-31 17:00:00,1.16581,1.16585,1.16445,1.16476,1077 2017-10-31 18:00:00,1.16476,1.16568,1.16462,1.16509,1373 2017-10-31 19:00:00,1.1651,1.16572,1.16481,1.16499,1166 2017-10-31 20:00:00,1.165,1.16515,1.16434,1.16458,600 2017-10-31 21:00:00,1.16457,1.16492,1.16402,1.1646,1261 2017-10-31 22:00:00,1.1647,1.16528,1.16459,1.16498,485 2017-10-31 23:00:00,1.165,1.1655,1.1647,1.1652,519 2017-11-01 00:00:00,1.16514,1.16544,1.16394,1.164,1357 2017-11-01 01:00:00,1.16402,1.1641,1.1632,1.16364,1440 2017-11-01 02:00:00,1.16364,1.16378,1.16315,1.16325,725 2017-11-01 03:00:00,1.16328,1.1634,1.1629,1.16316,564 2017-11-01 04:00:00,1.1631,1.1633,1.16289,1.1631,413 2017-11-01 05:00:00,1.16306,1.16321,1.16258,1.16293,591 2017-11-01 06:00:00,1.16292,1.16458,1.16288,1.16404,1190 2017-11-01 07:00:00,1.16399,1.16528,1.1634,1.16522,1843 2017-11-01 08:00:00,1.1652,1.16575,1.16387,1.16402,1822 2017-11-01 09:00:00,1.16398,1.16456,1.1632,1.16325,1766 2017-11-01 10:00:00,1.16321,1.1642,1.16321,1.16415,1138 2017-11-01 11:00:00,1.16412,1.16434,1.16326,1.16382,1156 2017-11-01 12:00:00,1.16387,1.16422,1.16142,1.16178,3968 2017-11-01 13:00:00,1.16183,1.162,1.16066,1.1612,2612 2017-11-01 14:00:00,1.1612,1.16433,1.16108,1.16324,5203 2017-11-01 15:00:00,1.16322,1.16368,1.1614,1.16174,2538 2017-11-01 16:00:00,1.16174,1.16267,1.16136,1.16257,2385 2017-11-01 17:00:00,1.16255,1.16332,1.1621,1.16284,1953 2017-11-01 18:00:00,1.16268,1.16419,1.16107,1.16193,6981 2017-11-01 19:00:00,1.16194,1.16335,1.16165,1.16202,2115 2017-11-01 20:00:00,1.16206,1.16302,1.16148,1.16194,1546 2017-11-01 21:00:00,1.16194,1.16253,1.16174,1.1623,1080 2017-11-01 22:00:00,1.1623,1.16232,1.16132,1.16184,854 2017-11-01 23:00:00,1.16186,1.16288,1.16176,1.16282,656 2017-11-02 00:00:00,1.16287,1.16388,1.1628,1.16348,2355 2017-11-02 01:00:00,1.16346,1.16718,1.16334,1.16608,3743 2017-11-02 02:00:00,1.16613,1.16652,1.16535,1.16538,1026 2017-11-02 03:00:00,1.16543,1.16572,1.16499,1.16556,645 2017-11-02 04:00:00,1.16558,1.16624,1.16519,1.1658,845 2017-11-02 05:00:00,1.16576,1.16591,1.16506,1.16564,585 2017-11-02 06:00:00,1.16562,1.16594,1.16508,1.16576,1018 2017-11-02 07:00:00,1.1658,1.16629,1.16408,1.16418,1939 2017-11-02 08:00:00,1.1642,1.16439,1.16259,1.16326,2348 2017-11-02 09:00:00,1.16329,1.16438,1.16322,1.16415,1705 2017-11-02 10:00:00,1.16416,1.16504,1.16406,1.1648,1488 2017-11-02 11:00:00,1.16483,1.16586,1.16472,1.16529,2171 2017-11-02 12:00:00,1.16534,1.16679,1.16472,1.16523,6359 2017-11-02 13:00:00,1.1652,1.16533,1.16352,1.16506,4128 2017-11-02 14:00:00,1.165,1.16878,1.165,1.16626,7854 2017-11-02 15:00:00,1.16626,1.16783,1.16546,1.16724,5083 2017-11-02 16:00:00,1.16724,1.16737,1.1654,1.16628,3065 2017-11-02 17:00:00,1.16629,1.16633,1.16542,1.16564,1804 2017-11-02 18:00:00,1.16568,1.1663,1.16544,1.16594,1728 2017-11-02 19:00:00,1.16595,1.16646,1.1648,1.16611,3020 2017-11-02 20:00:00,1.16614,1.16622,1.1657,1.16584,760 2017-11-02 21:00:00,1.16578,1.16604,1.16541,1.16564,1331 2017-11-02 22:00:00,1.16567,1.16597,1.16556,1.16573,509 2017-11-02 23:00:00,1.16575,1.16606,1.1654,1.16586,440 2017-11-03 00:00:00,1.16586,1.16651,1.16562,1.16576,947 2017-11-03 01:00:00,1.16573,1.16663,1.16572,1.16614,752 2017-11-03 02:00:00,1.16619,1.16631,1.16562,1.16588,823 2017-11-03 03:00:00,1.16588,1.16617,1.16564,1.16576,328 2017-11-03 04:00:00,1.1658,1.16626,1.16568,1.1662,299 2017-11-03 05:00:00,1.16618,1.16654,1.16593,1.16648,534 2017-11-03 06:00:00,1.16648,1.16679,1.16598,1.16606,711 2017-11-03 07:00:00,1.16607,1.16607,1.16442,1.16467,1613 2017-11-03 08:00:00,1.16462,1.16494,1.16419,1.16475,1374 2017-11-03 09:00:00,1.16472,1.16496,1.16398,1.16471,1222 2017-11-03 10:00:00,1.16473,1.16542,1.16421,1.16441,1334 2017-11-03 11:00:00,1.1644,1.16491,1.16386,1.16476,1689 2017-11-03 12:00:00,1.16476,1.16914,1.16452,1.16548,10517 2017-11-03 13:00:00,1.16548,1.16587,1.16408,1.16462,4478 2017-11-03 14:00:00,1.16456,1.16456,1.16061,1.1609,6161 2017-11-03 15:00:00,1.16096,1.16202,1.16029,1.16161,3781 2017-11-03 16:00:00,1.16164,1.16193,1.16,1.16044,1999 2017-11-03 17:00:00,1.16044,1.16108,1.16004,1.16031,1186 2017-11-03 18:00:00,1.1603,1.1608,1.15994,1.16076,1379 2017-11-03 19:00:00,1.16071,1.16144,1.16071,1.16076,726 2017-11-03 20:00:00,1.16072,1.1614,1.1606,1.16101,2069 2017-11-05 22:00:00,1.16172,1.1618,1.16048,1.16158,510 2017-11-05 23:00:00,1.16158,1.16182,1.16088,1.16149,702 2017-11-06 00:00:00,1.16153,1.16164,1.15998,1.16054,1140 2017-11-06 01:00:00,1.16054,1.16141,1.15968,1.16103,2715 2017-11-06 02:00:00,1.16102,1.16244,1.1607,1.16176,1816 2017-11-06 03:00:00,1.16172,1.16184,1.16114,1.16144,790 2017-11-06 04:00:00,1.16144,1.1616,1.16082,1.16096,1038 2017-11-06 05:00:00,1.16098,1.16134,1.16061,1.16122,664 2017-11-06 06:00:00,1.16118,1.1617,1.16052,1.1609,1296 2017-11-06 07:00:00,1.16088,1.16165,1.15955,1.16106,2212 2017-11-06 08:00:00,1.16106,1.16153,1.16057,1.16103,2148 2017-11-06 09:00:00,1.16103,1.16136,1.1588,1.16028,2886 2017-11-06 10:00:00,1.16032,1.16142,1.16009,1.16096,1384 2017-11-06 11:00:00,1.16097,1.16104,1.15904,1.15924,1647 2017-11-06 12:00:00,1.15929,1.15971,1.15836,1.15905,1391 2017-11-06 13:00:00,1.159,1.15988,1.15854,1.15855,1474 2017-11-06 14:00:00,1.15857,1.15922,1.15824,1.15853,1475 2017-11-06 15:00:00,1.15858,1.15919,1.15804,1.15852,1955 2017-11-06 16:00:00,1.15856,1.16036,1.15841,1.15988,2549 2017-11-06 17:00:00,1.15983,1.16137,1.15982,1.16119,2779 2017-11-06 18:00:00,1.16123,1.16162,1.16082,1.16104,1099 2017-11-06 19:00:00,1.1611,1.16152,1.16049,1.16059,1085 2017-11-06 20:00:00,1.1606,1.16101,1.16034,1.16092,939 2017-11-06 21:00:00,1.16092,1.16128,1.16084,1.16096,391 2017-11-06 22:00:00,1.16115,1.16157,1.16095,1.16102,619 2017-11-06 23:00:00,1.16104,1.16136,1.16084,1.16114,644 2017-11-07 00:00:00,1.16108,1.16142,1.16084,1.16137,775 2017-11-07 01:00:00,1.16134,1.16154,1.16024,1.16044,1517 2017-11-07 02:00:00,1.16046,1.16139,1.16018,1.16134,1015 2017-11-07 03:00:00,1.16134,1.16152,1.1608,1.1612,1060 2017-11-07 04:00:00,1.16119,1.16122,1.16043,1.16075,849 2017-11-07 05:00:00,1.1608,1.16106,1.16047,1.16056,656 2017-11-07 06:00:00,1.16055,1.16092,1.1596,1.16024,946 2017-11-07 07:00:00,1.16018,1.16018,1.15856,1.15883,2228 2017-11-07 08:00:00,1.15884,1.1594,1.15658,1.15658,3773 2017-11-07 09:00:00,1.15661,1.15766,1.15661,1.15672,2253 2017-11-07 10:00:00,1.1567,1.1572,1.15539,1.15664,2781 2017-11-07 11:00:00,1.15666,1.15784,1.15564,1.15697,2986 2017-11-07 12:00:00,1.15696,1.15704,1.15588,1.15652,2405 2017-11-07 13:00:00,1.15651,1.15684,1.1558,1.15626,2128 2017-11-07 14:00:00,1.1563,1.15817,1.15599,1.15776,2841 2017-11-07 15:00:00,1.15776,1.15904,1.15674,1.15749,4107 2017-11-07 16:00:00,1.1575,1.15791,1.15638,1.15768,2990 2017-11-07 17:00:00,1.15764,1.15882,1.1576,1.15823,1718 2017-11-07 18:00:00,1.15822,1.15842,1.1575,1.15759,1038 2017-11-07 19:00:00,1.15758,1.15886,1.15746,1.15874,1285 2017-11-07 20:00:00,1.1587,1.15927,1.15844,1.159,1136 2017-11-07 21:00:00,1.15894,1.1591,1.15861,1.15867,628 2017-11-07 22:00:00,1.15888,1.15936,1.15862,1.15934,1201 2017-11-07 23:00:00,1.15932,1.16065,1.15922,1.16008,1709 2017-11-08 00:00:00,1.1601,1.16039,1.15924,1.15935,1632 2017-11-08 01:00:00,1.15937,1.15982,1.15915,1.15961,987 2017-11-08 02:00:00,1.15958,1.1599,1.1594,1.15956,645 2017-11-08 03:00:00,1.15959,1.15988,1.15936,1.15966,474 2017-11-08 04:00:00,1.15968,1.16004,1.1596,1.15986,289 2017-11-08 05:00:00,1.15991,1.16005,1.15955,1.15962,463 2017-11-08 06:00:00,1.15962,1.15972,1.15852,1.1592,893 2017-11-08 07:00:00,1.15918,1.16048,1.15899,1.15954,1818 2017-11-08 08:00:00,1.15954,1.16114,1.15916,1.15993,2706 2017-11-08 09:00:00,1.15994,1.16038,1.15918,1.15922,1846 2017-11-08 10:00:00,1.15918,1.16046,1.15918,1.16036,1325 2017-11-08 11:00:00,1.16032,1.16069,1.15966,1.15976,1396 2017-11-08 12:00:00,1.1597,1.15982,1.15836,1.15862,2446 2017-11-08 13:00:00,1.15856,1.15946,1.15855,1.15912,2015 2017-11-08 14:00:00,1.1591,1.16039,1.15886,1.15986,1826 2017-11-08 15:00:00,1.15986,1.16039,1.15845,1.15852,2355 2017-11-08 16:00:00,1.15855,1.15946,1.1579,1.15902,1834 2017-11-08 17:00:00,1.15904,1.15956,1.15878,1.15934,1346 2017-11-08 18:00:00,1.15934,1.15963,1.15894,1.15944,962 2017-11-08 19:00:00,1.15939,1.15966,1.15894,1.15954,696 2017-11-08 20:00:00,1.15956,1.15989,1.15945,1.15983,1058 2017-11-08 21:00:00,1.15986,1.1599,1.15927,1.15944,722 2017-11-08 22:00:00,1.15959,1.15977,1.15904,1.15937,1351 2017-11-08 23:00:00,1.15931,1.15969,1.15922,1.15952,523 2017-11-09 00:00:00,1.15958,1.15962,1.15874,1.1588,1001 2017-11-09 01:00:00,1.1588,1.15956,1.15856,1.15938,1117 2017-11-09 02:00:00,1.15938,1.16003,1.15923,1.1596,1226 2017-11-09 03:00:00,1.1596,1.15994,1.15911,1.15922,1378 2017-11-09 04:00:00,1.15916,1.1598,1.1591,1.1596,1404 2017-11-09 05:00:00,1.15962,1.16052,1.15958,1.16032,2413 2017-11-09 06:00:00,1.16032,1.16121,1.16016,1.16094,1319 2017-11-09 07:00:00,1.16094,1.16158,1.16038,1.16056,2065 2017-11-09 08:00:00,1.16053,1.16067,1.15896,1.15942,2311 2017-11-09 09:00:00,1.15938,1.16053,1.15936,1.16022,1107 2017-11-09 10:00:00,1.1602,1.16122,1.15991,1.1612,1089 2017-11-09 11:00:00,1.16118,1.16445,1.16086,1.16402,3874 2017-11-09 12:00:00,1.164,1.164,1.16247,1.16318,2485 2017-11-09 13:00:00,1.16313,1.16328,1.16022,1.16044,3226 2017-11-09 14:00:00,1.16042,1.16322,1.16005,1.16314,3016 2017-11-09 15:00:00,1.16314,1.16362,1.16216,1.16242,3584 2017-11-09 16:00:00,1.16246,1.16545,1.16246,1.16478,4828 2017-11-09 17:00:00,1.16475,1.1655,1.16398,1.16404,3627 2017-11-09 18:00:00,1.16406,1.165,1.16378,1.16488,2005 2017-11-09 19:00:00,1.16484,1.16496,1.16408,1.16488,1379 2017-11-09 20:00:00,1.16488,1.165,1.16414,1.16425,1232 2017-11-09 21:00:00,1.16429,1.16441,1.1639,1.16416,420 2017-11-09 22:00:00,1.16384,1.16461,1.16384,1.16444,2269 2017-11-09 23:00:00,1.16443,1.16484,1.16416,1.16466,445 2017-11-10 00:00:00,1.16463,1.16537,1.16442,1.16526,1117 2017-11-10 01:00:00,1.16524,1.16545,1.16448,1.1646,981 2017-11-10 02:00:00,1.16458,1.16484,1.16426,1.16456,718 2017-11-10 03:00:00,1.16458,1.16489,1.16448,1.16476,529 2017-11-10 04:00:00,1.16482,1.16496,1.16418,1.16446,516 2017-11-10 05:00:00,1.16444,1.16467,1.16412,1.16458,393 2017-11-10 06:00:00,1.16458,1.16508,1.16437,1.16489,1042 2017-11-10 07:00:00,1.16486,1.16486,1.16326,1.16332,1785 2017-11-10 08:00:00,1.16328,1.16422,1.16228,1.16342,2344 2017-11-10 09:00:00,1.16344,1.16622,1.16287,1.16604,2178 2017-11-10 10:00:00,1.16599,1.16599,1.16494,1.16504,1606 2017-11-10 11:00:00,1.16508,1.1651,1.16384,1.16398,1497 2017-11-10 12:00:00,1.16394,1.1651,1.16385,1.1651,1311 2017-11-10 13:00:00,1.16514,1.1655,1.16394,1.16483,1696 2017-11-10 14:00:00,1.16488,1.16668,1.16484,1.16646,2240 2017-11-10 15:00:00,1.16645,1.1678,1.16604,1.16612,2563 2017-11-10 16:00:00,1.16612,1.16627,1.16509,1.1656,2390 2017-11-10 17:00:00,1.16564,1.16598,1.1652,1.1659,1202 2017-11-10 18:00:00,1.16593,1.16652,1.1658,1.16609,905 2017-11-10 19:00:00,1.16606,1.16688,1.16586,1.16666,754 2017-11-10 20:00:00,1.1667,1.16688,1.16634,1.1665,525 2017-11-10 21:00:00,1.16652,1.16692,1.1663,1.1665,956 2017-11-12 22:00:00,1.16585,1.16652,1.16558,1.16584,1312 2017-11-12 23:00:00,1.16584,1.16657,1.16572,1.16611,1274 2017-11-13 00:00:00,1.16606,1.1662,1.16448,1.16487,1127 2017-11-13 01:00:00,1.16484,1.16517,1.16448,1.16503,681 2017-11-13 02:00:00,1.165,1.16546,1.1649,1.16496,596 2017-11-13 03:00:00,1.16496,1.16569,1.16492,1.16556,401 2017-11-13 04:00:00,1.16554,1.16554,1.16512,1.16515,245 2017-11-13 05:00:00,1.16512,1.16604,1.1651,1.16584,493 2017-11-13 06:00:00,1.16584,1.16586,1.16454,1.16485,1115 2017-11-13 07:00:00,1.16488,1.1653,1.16396,1.164,2013 2017-11-13 08:00:00,1.16404,1.16487,1.16376,1.16472,1856 2017-11-13 09:00:00,1.16467,1.16531,1.1641,1.16411,1372 2017-11-13 10:00:00,1.16412,1.16484,1.16382,1.16468,962 2017-11-13 11:00:00,1.16466,1.1653,1.1645,1.16509,822 2017-11-13 12:00:00,1.16508,1.166,1.16502,1.16542,1141 2017-11-13 13:00:00,1.16538,1.16754,1.16537,1.16702,1743 2017-11-13 14:00:00,1.16706,1.16742,1.1659,1.16607,1712 2017-11-13 15:00:00,1.16609,1.1673,1.16607,1.16648,2288 2017-11-13 16:00:00,1.16648,1.1666,1.16549,1.16558,1584 2017-11-13 17:00:00,1.16554,1.16628,1.16551,1.16628,663 2017-11-13 18:00:00,1.16625,1.16683,1.16618,1.1665,648 2017-11-13 19:00:00,1.16652,1.16694,1.16645,1.16674,688 2017-11-13 20:00:00,1.1667,1.16711,1.16648,1.16686,779 2017-11-13 21:00:00,1.16681,1.16683,1.16648,1.1667,372 2017-11-13 22:00:00,1.16685,1.16708,1.16637,1.16643,896 2017-11-13 23:00:00,1.16642,1.16656,1.16614,1.16646,212 2017-11-14 00:00:00,1.16644,1.16764,1.16628,1.16712,905 2017-11-14 01:00:00,1.16706,1.16739,1.16689,1.16703,780 2017-11-14 02:00:00,1.16702,1.16727,1.16664,1.16715,566 2017-11-14 03:00:00,1.16719,1.16736,1.16698,1.16717,371 2017-11-14 04:00:00,1.16715,1.16746,1.16702,1.16726,255 2017-11-14 05:00:00,1.16724,1.16766,1.16708,1.16739,298 2017-11-14 06:00:00,1.16738,1.16749,1.16646,1.16654,604 2017-11-14 07:00:00,1.16654,1.16958,1.16652,1.16898,2946 2017-11-14 08:00:00,1.16898,1.17102,1.16849,1.17102,2609 2017-11-14 09:00:00,1.171,1.172,1.17032,1.17179,2063 2017-11-14 10:00:00,1.17177,1.17252,1.17129,1.17172,2002 2017-11-14 11:00:00,1.17168,1.1728,1.17161,1.17239,1444 2017-11-14 12:00:00,1.17237,1.1747,1.17222,1.17466,2593 2017-11-14 13:00:00,1.17468,1.1764,1.17389,1.17518,3901 2017-11-14 14:00:00,1.1752,1.17598,1.17449,1.17518,2718 2017-11-14 15:00:00,1.17516,1.17588,1.17446,1.17555,3874 2017-11-14 16:00:00,1.17554,1.17746,1.17514,1.1763,3177 2017-11-14 17:00:00,1.17632,1.17863,1.17623,1.17818,2798 2017-11-14 18:00:00,1.17815,1.18052,1.1781,1.18004,4116 2017-11-14 19:00:00,1.18002,1.18004,1.17922,1.17942,1549 2017-11-14 20:00:00,1.17943,1.17952,1.17891,1.1793,1114 2017-11-14 21:00:00,1.1793,1.17984,1.17914,1.1798,656 2017-11-14 22:00:00,1.1794,1.18026,1.17898,1.17982,1590 2017-11-14 23:00:00,1.1798,1.1799,1.1791,1.1791,639 2017-11-15 00:00:00,1.17905,1.17942,1.17857,1.17925,1444 2017-11-15 01:00:00,1.17919,1.1797,1.17884,1.17894,1196 2017-11-15 02:00:00,1.17898,1.17975,1.17884,1.17933,714 2017-11-15 03:00:00,1.17938,1.17994,1.17914,1.1797,695 2017-11-15 04:00:00,1.17965,1.17968,1.17922,1.17926,557 2017-11-15 05:00:00,1.17926,1.17936,1.17864,1.17868,591 2017-11-15 06:00:00,1.17867,1.17956,1.17846,1.17928,1265 2017-11-15 07:00:00,1.17927,1.18171,1.17922,1.1811,3815 2017-11-15 08:00:00,1.18106,1.1844,1.18079,1.18386,4127 2017-11-15 09:00:00,1.18388,1.18531,1.18338,1.18396,3139 2017-11-15 10:00:00,1.18394,1.18468,1.18285,1.18464,2829 2017-11-15 11:00:00,1.18458,1.185,1.1834,1.18468,2316 2017-11-15 12:00:00,1.18472,1.18472,1.18346,1.18368,1793 2017-11-15 13:00:00,1.18372,1.18612,1.18274,1.18448,7558 2017-11-15 14:00:00,1.18448,1.1845,1.18142,1.182,6811 2017-11-15 15:00:00,1.18204,1.18207,1.17994,1.18012,4997 2017-11-15 16:00:00,1.18011,1.1803,1.17898,1.17934,2841 2017-11-15 17:00:00,1.17938,1.18002,1.1791,1.17974,1375 2017-11-15 18:00:00,1.17974,1.1804,1.17962,1.18021,1385 2017-11-15 19:00:00,1.18016,1.18033,1.17896,1.17931,1573 2017-11-15 20:00:00,1.1793,1.17959,1.17898,1.17922,2004 2017-11-15 21:00:00,1.17924,1.1794,1.17886,1.17912,583 2017-11-15 22:00:00,1.17916,1.17926,1.17684,1.17694,3819 2017-11-15 23:00:00,1.17698,1.17832,1.17695,1.17825,1306 2017-11-16 00:00:00,1.17826,1.17864,1.1777,1.1781,1347 2017-11-16 01:00:00,1.17814,1.17895,1.1781,1.17876,809 2017-11-16 02:00:00,1.17877,1.17877,1.1779,1.17808,1244 2017-11-16 03:00:00,1.1781,1.17824,1.17768,1.17778,703 2017-11-16 04:00:00,1.17776,1.17804,1.17764,1.17772,570 2017-11-16 05:00:00,1.1777,1.1781,1.17758,1.17794,478 2017-11-16 06:00:00,1.17795,1.18009,1.17788,1.17998,1823 2017-11-16 07:00:00,1.17997,1.18,1.17712,1.17712,2804 2017-11-16 08:00:00,1.17712,1.17968,1.17712,1.17896,3740 2017-11-16 09:00:00,1.17896,1.17902,1.17733,1.17758,2539 2017-11-16 10:00:00,1.17756,1.17778,1.17678,1.17704,1922 2017-11-16 11:00:00,1.17708,1.1771,1.17568,1.17645,2081 2017-11-16 12:00:00,1.17646,1.1769,1.1759,1.17608,1618 2017-11-16 13:00:00,1.17611,1.17798,1.17609,1.17753,2899 2017-11-16 14:00:00,1.17757,1.17852,1.17669,1.1779,3318 2017-11-16 15:00:00,1.17792,1.1785,1.17606,1.17738,3364 2017-11-16 16:00:00,1.17736,1.17774,1.17669,1.17721,2207 2017-11-16 17:00:00,1.17718,1.1779,1.17706,1.17734,1393 2017-11-16 18:00:00,1.1773,1.17774,1.17678,1.17686,1323 2017-11-16 19:00:00,1.17682,1.17686,1.17594,1.17654,1120 2017-11-16 20:00:00,1.17657,1.17707,1.17613,1.17707,677 2017-11-16 21:00:00,1.17706,1.17728,1.17671,1.17694,662 2017-11-16 22:00:00,1.17714,1.17727,1.1768,1.17709,1245 2017-11-16 23:00:00,1.17712,1.17788,1.17654,1.17782,818 2017-11-17 00:00:00,1.17787,1.17942,1.17754,1.17896,1784 2017-11-17 01:00:00,1.1789,1.18166,1.17872,1.18131,3014 2017-11-17 02:00:00,1.18128,1.18216,1.18099,1.18202,2315 2017-11-17 03:00:00,1.18198,1.18206,1.18042,1.18088,1239 2017-11-17 04:00:00,1.18088,1.18122,1.18041,1.18042,906 2017-11-17 05:00:00,1.18044,1.18112,1.18028,1.18039,667 2017-11-17 06:00:00,1.18037,1.1816,1.18033,1.18122,1641 2017-11-17 07:00:00,1.1812,1.18126,1.17867,1.17873,2190 2017-11-17 08:00:00,1.17876,1.17998,1.1784,1.17866,3047 2017-11-17 09:00:00,1.17868,1.18028,1.17857,1.1794,1808 2017-11-17 10:00:00,1.17938,1.18028,1.17921,1.18016,1556 2017-11-17 11:00:00,1.18014,1.18126,1.1793,1.1794,1963 2017-11-17 12:00:00,1.17936,1.18006,1.17898,1.17918,1699 2017-11-17 13:00:00,1.17923,1.17963,1.17738,1.17872,2610 2017-11-17 14:00:00,1.17868,1.18028,1.17868,1.17946,3243 2017-11-17 15:00:00,1.17946,1.17981,1.17802,1.17809,2841 2017-11-17 16:00:00,1.17812,1.17982,1.17762,1.17975,2990 2017-11-17 17:00:00,1.17978,1.18016,1.17878,1.18016,1481 2017-11-17 18:00:00,1.1802,1.18059,1.17984,1.18,1236 2017-11-17 19:00:00,1.18005,1.18006,1.17924,1.1797,913 2017-11-17 20:00:00,1.1797,1.1806,1.17948,1.1795,1254 2017-11-17 21:00:00,1.17948,1.1795,1.17854,1.1791,1972 2017-11-19 22:00:00,1.1791,1.1791,1.1766,1.1767,702 2017-11-19 23:00:00,1.1767,1.17692,1.17221,1.17345,4083 2017-11-20 00:00:00,1.1734,1.17506,1.17316,1.17439,3230 2017-11-20 01:00:00,1.17436,1.17504,1.17374,1.1745,1681 2017-11-20 02:00:00,1.17448,1.17461,1.17248,1.17364,1689 2017-11-20 03:00:00,1.17367,1.17382,1.17331,1.17368,686 2017-11-20 04:00:00,1.17367,1.17376,1.17272,1.17286,662 2017-11-20 05:00:00,1.17286,1.1737,1.17282,1.17338,961 2017-11-20 06:00:00,1.17332,1.17414,1.17319,1.17363,1245 2017-11-20 07:00:00,1.17358,1.17556,1.17352,1.1754,2869 2017-11-20 08:00:00,1.17543,1.18083,1.17516,1.17991,7956 2017-11-20 09:00:00,1.1799,1.18048,1.17922,1.17944,2256 2017-11-20 10:00:00,1.17944,1.17968,1.17852,1.17884,1647 2017-11-20 11:00:00,1.17881,1.17906,1.17724,1.1776,1684 2017-11-20 12:00:00,1.17766,1.1786,1.17746,1.17832,2041 2017-11-20 13:00:00,1.1783,1.17846,1.17664,1.17708,2708 2017-11-20 14:00:00,1.17711,1.17757,1.17556,1.17577,2500 2017-11-20 15:00:00,1.1758,1.17592,1.17422,1.1748,2488 2017-11-20 16:00:00,1.17482,1.17552,1.17318,1.17402,2961 2017-11-20 17:00:00,1.17405,1.17498,1.17345,1.1747,1866 2017-11-20 18:00:00,1.17472,1.17508,1.17362,1.17365,1323 2017-11-20 19:00:00,1.17368,1.17376,1.17298,1.1732,1725 2017-11-20 20:00:00,1.1732,1.17366,1.17278,1.17292,956 2017-11-20 21:00:00,1.17292,1.17349,1.17286,1.17326,838 2017-11-20 22:00:00,1.17344,1.17362,1.17322,1.1734,667 2017-11-20 23:00:00,1.17338,1.17382,1.17332,1.17372,481 2017-11-21 00:00:00,1.1737,1.17446,1.17366,1.17432,769 2017-11-21 01:00:00,1.1743,1.17448,1.17305,1.17336,1105 2017-11-21 02:00:00,1.17338,1.17359,1.17303,1.17348,555 2017-11-21 03:00:00,1.1735,1.17388,1.17336,1.17383,328 2017-11-21 04:00:00,1.17386,1.17394,1.17367,1.17386,281 2017-11-21 05:00:00,1.17388,1.17434,1.17372,1.17414,273 2017-11-21 06:00:00,1.17415,1.17496,1.17383,1.1743,1001 2017-11-21 07:00:00,1.17425,1.175,1.17303,1.17364,2108 2017-11-21 08:00:00,1.1736,1.17578,1.17324,1.17388,2934 2017-11-21 09:00:00,1.17382,1.17422,1.17139,1.17288,3076 2017-11-21 10:00:00,1.1729,1.17467,1.1726,1.1737,2289 2017-11-21 11:00:00,1.17374,1.17375,1.17212,1.17222,1850 2017-11-21 12:00:00,1.17228,1.17281,1.1715,1.17154,2050 2017-11-21 13:00:00,1.17157,1.17286,1.1713,1.17222,1874 2017-11-21 14:00:00,1.17225,1.17398,1.1718,1.17372,2933 2017-11-21 15:00:00,1.17378,1.17448,1.17245,1.17252,3110 2017-11-21 16:00:00,1.17256,1.17484,1.17239,1.17408,1918 2017-11-21 17:00:00,1.17409,1.17442,1.17345,1.17428,1813 2017-11-21 18:00:00,1.17423,1.17519,1.17356,1.17463,1780 2017-11-21 19:00:00,1.17461,1.17496,1.17392,1.17428,1745 2017-11-21 20:00:00,1.17428,1.17455,1.1735,1.1739,1096 2017-11-21 21:00:00,1.17387,1.17434,1.1735,1.17388,639 2017-11-21 22:00:00,1.17388,1.17422,1.17328,1.17342,1587 2017-11-21 23:00:00,1.17344,1.17476,1.17332,1.17418,878 2017-11-22 00:00:00,1.17412,1.17478,1.17374,1.17408,1100 2017-11-22 01:00:00,1.17404,1.17417,1.17356,1.17389,1011 2017-11-22 02:00:00,1.17392,1.17405,1.17353,1.17368,588 2017-11-22 03:00:00,1.17362,1.17404,1.17349,1.17386,603 2017-11-22 04:00:00,1.17384,1.17444,1.17381,1.17417,342 2017-11-22 05:00:00,1.17418,1.17454,1.17394,1.17445,426 2017-11-22 06:00:00,1.17442,1.17562,1.17419,1.1751,1110 2017-11-22 07:00:00,1.17512,1.17616,1.17476,1.17576,1830 2017-11-22 08:00:00,1.17574,1.17712,1.17552,1.17694,2632 2017-11-22 09:00:00,1.17698,1.17731,1.17564,1.17568,1442 2017-11-22 10:00:00,1.17568,1.17568,1.17484,1.17502,1275 2017-11-22 11:00:00,1.17504,1.1755,1.17456,1.1753,1143 2017-11-22 12:00:00,1.17526,1.17528,1.17357,1.17426,2111 2017-11-22 13:00:00,1.17426,1.17544,1.17406,1.1751,2709 2017-11-22 14:00:00,1.17511,1.17782,1.175,1.17762,3033 2017-11-22 15:00:00,1.17764,1.17968,1.17718,1.17859,3431 2017-11-22 16:00:00,1.17862,1.17979,1.17834,1.17886,1883 2017-11-22 17:00:00,1.17882,1.17984,1.1786,1.1795,1421 2017-11-22 18:00:00,1.17955,1.17994,1.17928,1.17991,1078 2017-11-22 19:00:00,1.1799,1.18268,1.1795,1.18252,4914 2017-11-22 20:00:00,1.1825,1.18256,1.1814,1.18201,1372 2017-11-22 21:00:00,1.18206,1.18252,1.18182,1.18232,610 2017-11-22 22:00:00,1.18234,1.18234,1.18168,1.1819,592 2017-11-22 23:00:00,1.1819,1.18218,1.18138,1.18147,632 2017-11-23 00:00:00,1.18152,1.18206,1.18136,1.18144,673 2017-11-23 01:00:00,1.18144,1.18246,1.18138,1.18231,916 2017-11-23 02:00:00,1.18225,1.1825,1.18172,1.1821,642 2017-11-23 03:00:00,1.18214,1.18385,1.18189,1.18325,1106 2017-11-23 04:00:00,1.1832,1.1838,1.1832,1.1835,498 2017-11-23 05:00:00,1.1835,1.18375,1.18255,1.18294,621 2017-11-23 06:00:00,1.18295,1.1836,1.18267,1.18283,821 2017-11-23 07:00:00,1.18283,1.18392,1.1824,1.18274,1632 2017-11-23 08:00:00,1.18271,1.18464,1.18271,1.18448,2603 2017-11-23 09:00:00,1.18441,1.1847,1.18364,1.1843,1464 2017-11-23 10:00:00,1.1843,1.18468,1.18384,1.18468,1189 2017-11-23 11:00:00,1.18468,1.18514,1.18422,1.18456,1150 2017-11-23 12:00:00,1.18457,1.18558,1.18409,1.18434,1557 2017-11-23 13:00:00,1.18438,1.18499,1.18419,1.18496,994 2017-11-23 14:00:00,1.18496,1.18526,1.1846,1.18462,943 2017-11-23 15:00:00,1.18462,1.1854,1.18431,1.18432,1037 2017-11-23 16:00:00,1.18434,1.1851,1.184,1.1851,808 2017-11-23 17:00:00,1.1851,1.18544,1.18466,1.18496,534 2017-11-23 18:00:00,1.18498,1.18536,1.18462,1.18514,830 2017-11-23 19:00:00,1.1851,1.18526,1.1846,1.18519,2455 2017-11-23 20:00:00,1.18521,1.18538,1.1849,1.18521,2453 2017-11-23 21:00:00,1.18522,1.18533,1.18463,1.18516,1858 2017-11-23 22:00:00,1.18502,1.18534,1.1846,1.18505,1410 2017-11-23 23:00:00,1.18511,1.18536,1.18479,1.18505,380 2017-11-24 00:00:00,1.185,1.1855,1.18396,1.1843,906 2017-11-24 01:00:00,1.18428,1.18501,1.18369,1.18486,764 2017-11-24 02:00:00,1.18487,1.1856,1.18479,1.18554,448 2017-11-24 03:00:00,1.18559,1.1856,1.18436,1.18487,523 2017-11-24 04:00:00,1.18485,1.18592,1.18464,1.18526,565 2017-11-24 05:00:00,1.18523,1.1856,1.18498,1.18519,336 2017-11-24 06:00:00,1.1852,1.1854,1.18436,1.18474,694 2017-11-24 07:00:00,1.18472,1.18506,1.18372,1.18476,1237 2017-11-24 08:00:00,1.18474,1.18648,1.18432,1.18588,2071 2017-11-24 09:00:00,1.18581,1.18749,1.1858,1.18684,2028 2017-11-24 10:00:00,1.18682,1.18698,1.18617,1.18663,1158 2017-11-24 11:00:00,1.18668,1.18684,1.18557,1.18564,1201 2017-11-24 12:00:00,1.1856,1.1874,1.18552,1.1874,1305 2017-11-24 13:00:00,1.18736,1.18988,1.18722,1.18963,2470 2017-11-24 14:00:00,1.18967,1.19198,1.18951,1.19194,3555 2017-11-24 15:00:00,1.192,1.19384,1.19192,1.1934,2196 2017-11-24 16:00:00,1.1934,1.19442,1.19314,1.1937,2323 2017-11-24 17:00:00,1.19368,1.1938,1.19238,1.19276,1382 2017-11-24 18:00:00,1.19275,1.19332,1.1923,1.19254,651 2017-11-24 19:00:00,1.19254,1.19298,1.19238,1.19266,287 2017-11-24 20:00:00,1.19269,1.1932,1.19253,1.19309,916 2017-11-24 21:00:00,1.19307,1.19394,1.1926,1.19325,2923 2017-11-26 22:00:00,1.19242,1.1936,1.19235,1.19354,1396 2017-11-26 23:00:00,1.19356,1.19361,1.19242,1.19275,641 2017-11-27 00:00:00,1.19281,1.19307,1.19128,1.19155,1082 2017-11-27 01:00:00,1.19156,1.19222,1.19116,1.19206,1261 2017-11-27 02:00:00,1.1921,1.1924,1.19143,1.19178,736 2017-11-27 03:00:00,1.19174,1.1929,1.19172,1.19248,589 2017-11-27 04:00:00,1.19244,1.19276,1.19222,1.19238,516 2017-11-27 05:00:00,1.19236,1.19284,1.19222,1.19284,311 2017-11-27 06:00:00,1.19284,1.19334,1.19192,1.19225,877 2017-11-27 07:00:00,1.19221,1.19323,1.19172,1.19278,1338 2017-11-27 08:00:00,1.19273,1.19571,1.19234,1.19386,3844 2017-11-27 09:00:00,1.19382,1.19406,1.19208,1.19306,2003 2017-11-27 10:00:00,1.19306,1.19396,1.19262,1.19387,1369 2017-11-27 11:00:00,1.19389,1.19406,1.19318,1.19368,1016 2017-11-27 12:00:00,1.19368,1.19483,1.1928,1.19432,1891 2017-11-27 13:00:00,1.19428,1.19612,1.19425,1.19591,2453 2017-11-27 14:00:00,1.19592,1.19592,1.19324,1.19396,2867 2017-11-27 15:00:00,1.19396,1.19396,1.19151,1.19192,3748 2017-11-27 16:00:00,1.19198,1.19262,1.1905,1.19106,3183 2017-11-27 17:00:00,1.19106,1.19204,1.18977,1.19022,2222 2017-11-27 18:00:00,1.19024,1.19062,1.1899,1.19015,1062 2017-11-27 19:00:00,1.19011,1.19028,1.1896,1.18994,1057 2017-11-27 20:00:00,1.18992,1.19017,1.18963,1.18994,867 2017-11-27 21:00:00,1.18994,1.19011,1.18958,1.18983,471 2017-11-27 22:00:00,1.18978,1.1904,1.18946,1.18994,1260 2017-11-27 23:00:00,1.18995,1.19054,1.18994,1.19042,336 2017-11-28 00:00:00,1.1904,1.1909,1.19004,1.19071,1087 2017-11-28 01:00:00,1.19066,1.19072,1.18972,1.18995,1218 2017-11-28 02:00:00,1.19001,1.19038,1.18978,1.19006,796 2017-11-28 03:00:00,1.19006,1.19049,1.18983,1.19041,358 2017-11-28 04:00:00,1.19039,1.19068,1.19027,1.19044,658 2017-11-28 05:00:00,1.19047,1.19088,1.19005,1.19022,430 2017-11-28 06:00:00,1.19021,1.19108,1.19016,1.19083,804 2017-11-28 07:00:00,1.19085,1.19085,1.18851,1.18884,2470 2017-11-28 08:00:00,1.1889,1.192,1.18884,1.19052,3103 2017-11-28 09:00:00,1.19046,1.19068,1.18892,1.18916,1885 2017-11-28 10:00:00,1.18911,1.18938,1.18749,1.18854,2551 2017-11-28 11:00:00,1.18849,1.18914,1.18824,1.18866,1406 2017-11-28 12:00:00,1.18864,1.18878,1.18768,1.18796,1187 2017-11-28 13:00:00,1.18798,1.19004,1.18798,1.18938,1657 2017-11-28 14:00:00,1.18938,1.18994,1.1877,1.18776,2140 2017-11-28 15:00:00,1.1878,1.18884,1.18644,1.18698,4861 2017-11-28 16:00:00,1.18692,1.18856,1.18654,1.18777,4169 2017-11-28 17:00:00,1.18776,1.18837,1.18642,1.187,3617 2017-11-28 18:00:00,1.187,1.18706,1.18528,1.18596,6853 2017-11-28 19:00:00,1.18592,1.18632,1.18272,1.18379,4077 2017-11-28 20:00:00,1.18374,1.1848,1.18327,1.18418,2568 2017-11-28 21:00:00,1.18418,1.18477,1.1838,1.18412,617 2017-11-28 22:00:00,1.18406,1.18494,1.18374,1.18468,1670 2017-11-28 23:00:00,1.18474,1.18522,1.18452,1.18491,744 2017-11-29 00:00:00,1.18496,1.18531,1.1846,1.18486,1451 2017-11-29 01:00:00,1.18488,1.1849,1.18402,1.18418,1131 2017-11-29 02:00:00,1.18424,1.18501,1.18416,1.18498,678 2017-11-29 03:00:00,1.18504,1.18552,1.18474,1.18536,973 2017-11-29 04:00:00,1.18541,1.18564,1.18492,1.18503,619 2017-11-29 05:00:00,1.18507,1.18532,1.18481,1.18513,501 2017-11-29 06:00:00,1.18512,1.18614,1.18507,1.18594,1115 2017-11-29 07:00:00,1.1859,1.18718,1.18564,1.18695,1598 2017-11-29 08:00:00,1.18696,1.18752,1.18599,1.18726,2482 2017-11-29 09:00:00,1.18726,1.18827,1.18648,1.18668,2262 2017-11-29 10:00:00,1.18669,1.18676,1.18438,1.18472,1362 2017-11-29 11:00:00,1.18474,1.18491,1.18309,1.18424,1427 2017-11-29 12:00:00,1.18419,1.18478,1.1832,1.18366,1843 2017-11-29 13:00:00,1.18364,1.18386,1.18166,1.18375,4155 2017-11-29 14:00:00,1.18376,1.18426,1.18303,1.1836,2339 2017-11-29 15:00:00,1.1836,1.18546,1.18286,1.18496,5663 2017-11-29 16:00:00,1.18499,1.1864,1.18476,1.18584,3279 2017-11-29 17:00:00,1.18585,1.18661,1.18509,1.18514,1954 2017-11-29 18:00:00,1.18512,1.18601,1.18497,1.1857,1807 2017-11-29 19:00:00,1.18572,1.18649,1.18553,1.18624,1810 2017-11-29 20:00:00,1.18622,1.1864,1.18502,1.18513,1408 2017-11-29 21:00:00,1.18508,1.18562,1.18464,1.18477,826 2017-11-29 22:00:00,1.18483,1.18533,1.18458,1.18493,1428 2017-11-29 23:00:00,1.1849,1.18546,1.18484,1.18522,874 2017-11-30 00:00:00,1.1852,1.18562,1.18476,1.18542,1374 2017-11-30 01:00:00,1.1854,1.18686,1.18536,1.18612,1870 2017-11-30 02:00:00,1.18617,1.18712,1.1859,1.18664,1044 2017-11-30 03:00:00,1.18661,1.18685,1.18627,1.18658,469 2017-11-30 04:00:00,1.18661,1.18662,1.1862,1.1862,450 2017-11-30 05:00:00,1.18624,1.18672,1.1861,1.18645,563 2017-11-30 06:00:00,1.1865,1.18727,1.18585,1.1867,939 2017-11-30 07:00:00,1.18668,1.1876,1.18626,1.18644,2069 2017-11-30 08:00:00,1.18646,1.18672,1.1841,1.18458,2584 2017-11-30 09:00:00,1.18459,1.18466,1.18251,1.18324,2463 2017-11-30 10:00:00,1.18322,1.18335,1.18086,1.18314,2988 2017-11-30 11:00:00,1.18314,1.18429,1.18286,1.18366,1575 2017-11-30 12:00:00,1.18366,1.18498,1.18356,1.1845,1862 2017-11-30 13:00:00,1.18452,1.18636,1.18406,1.1859,2274 2017-11-30 14:00:00,1.18594,1.19066,1.18589,1.19048,3199 2017-11-30 15:00:00,1.19044,1.19307,1.19036,1.19226,5278 2017-11-30 16:00:00,1.19226,1.19312,1.19003,1.19003,4612 2017-11-30 17:00:00,1.19006,1.19012,1.18854,1.1892,2375 2017-11-30 18:00:00,1.18918,1.19142,1.18894,1.19123,2492 2017-11-30 19:00:00,1.19125,1.19128,1.18912,1.18981,1692 2017-11-30 20:00:00,1.18986,1.19054,1.18956,1.19022,1417 2017-11-30 21:00:00,1.1902,1.19151,1.19014,1.19041,883 2017-11-30 22:00:00,1.19041,1.19067,1.1887,1.18902,1695 2017-11-30 23:00:00,1.18902,1.19032,1.18898,1.18981,681 2017-12-01 00:00:00,1.18982,1.1916,1.18965,1.1909,2215 2017-12-01 01:00:00,1.19084,1.19115,1.19018,1.19094,1311 2017-12-01 02:00:00,1.1909,1.19138,1.19064,1.19072,1467 2017-12-01 03:00:00,1.19072,1.19132,1.19004,1.19118,1042 2017-12-01 04:00:00,1.19118,1.19156,1.19086,1.1911,1142 2017-12-01 05:00:00,1.19108,1.19297,1.191,1.19278,968 2017-12-01 06:00:00,1.19276,1.19333,1.19184,1.19201,1676 2017-12-01 07:00:00,1.19196,1.19224,1.19068,1.19158,2244 2017-12-01 08:00:00,1.19152,1.19404,1.19119,1.19292,4266 2017-12-01 09:00:00,1.19294,1.19298,1.19008,1.19028,2964 2017-12-01 10:00:00,1.19024,1.19114,1.18838,1.18892,3010 2017-12-01 11:00:00,1.18892,1.19046,1.18861,1.19005,1956 2017-12-01 12:00:00,1.19002,1.19026,1.18878,1.18919,2340 2017-12-01 13:00:00,1.18916,1.18934,1.18634,1.18692,3985 2017-12-01 14:00:00,1.18697,1.18745,1.18606,1.1861,3212 2017-12-01 15:00:00,1.18618,1.18762,1.18508,1.18648,6145 2017-12-01 16:00:00,1.18648,1.19349,1.18644,1.19077,25625 2017-12-01 17:00:00,1.19082,1.19174,1.18962,1.19066,10356 2017-12-01 18:00:00,1.19066,1.19107,1.1886,1.18949,4636 2017-12-01 19:00:00,1.18948,1.19009,1.18852,1.18941,2733 2017-12-01 20:00:00,1.18946,1.18968,1.18898,1.18909,1329 2017-12-01 21:00:00,1.18914,1.19002,1.18908,1.18974,840 2017-12-03 22:00:00,1.1865,1.18738,1.18604,1.18692,1101 2017-12-03 23:00:00,1.18695,1.18756,1.18564,1.1857,1396 2017-12-04 00:00:00,1.18571,1.18728,1.1856,1.18711,1840 2017-12-04 01:00:00,1.1871,1.18761,1.18669,1.1875,917 2017-12-04 02:00:00,1.18744,1.18744,1.18682,1.18728,711 2017-12-04 03:00:00,1.18733,1.18758,1.18698,1.1871,489 2017-12-04 04:00:00,1.18711,1.18718,1.18674,1.18708,520 2017-12-04 05:00:00,1.18702,1.1872,1.18676,1.18676,704 2017-12-04 06:00:00,1.18676,1.18694,1.18621,1.18654,781 2017-12-04 07:00:00,1.18652,1.18658,1.18362,1.18494,3314 2017-12-04 08:00:00,1.18495,1.1879,1.18494,1.18725,2984 2017-12-04 09:00:00,1.18727,1.18737,1.18508,1.18545,2072 2017-12-04 10:00:00,1.1854,1.18602,1.18436,1.18494,1657 2017-12-04 11:00:00,1.18488,1.18616,1.18471,1.18585,1889 2017-12-04 12:00:00,1.18586,1.18586,1.18454,1.18566,2341 2017-12-04 13:00:00,1.18565,1.18678,1.1843,1.18456,2989 2017-12-04 14:00:00,1.18451,1.18466,1.18336,1.18425,3485 2017-12-04 15:00:00,1.18424,1.1853,1.18293,1.18496,6234 2017-12-04 16:00:00,1.18499,1.186,1.18456,1.18481,3114 2017-12-04 17:00:00,1.18477,1.18522,1.1842,1.18436,1547 2017-12-04 18:00:00,1.18437,1.18516,1.18433,1.18495,898 2017-12-04 19:00:00,1.18495,1.18596,1.18488,1.18558,1343 2017-12-04 20:00:00,1.18558,1.18584,1.18476,1.18568,1052 2017-12-04 21:00:00,1.18567,1.18688,1.18566,1.18653,855 2017-12-04 22:00:00,1.18642,1.18688,1.18618,1.18679,1400 2017-12-04 23:00:00,1.18678,1.18706,1.18648,1.18679,462 2017-12-05 00:00:00,1.18676,1.18728,1.1864,1.18708,1178 2017-12-05 01:00:00,1.18712,1.18748,1.1869,1.18733,704 2017-12-05 02:00:00,1.18733,1.18768,1.18701,1.18736,542 2017-12-05 03:00:00,1.18734,1.18756,1.18716,1.18756,790 2017-12-05 04:00:00,1.18756,1.18758,1.18692,1.18696,593 2017-12-05 05:00:00,1.18702,1.18728,1.18668,1.18671,347 2017-12-05 06:00:00,1.18671,1.187,1.1863,1.18668,716 2017-12-05 07:00:00,1.18666,1.18768,1.18568,1.18582,1784 2017-12-05 08:00:00,1.1858,1.1862,1.1845,1.1848,2786 2017-12-05 09:00:00,1.1848,1.18596,1.18421,1.18528,1833 2017-12-05 10:00:00,1.18527,1.18669,1.18491,1.18628,1407 2017-12-05 11:00:00,1.18634,1.1874,1.18611,1.18658,1413 2017-12-05 12:00:00,1.18653,1.18671,1.18462,1.18476,1472 2017-12-05 13:00:00,1.18474,1.1848,1.18279,1.1835,2465 2017-12-05 14:00:00,1.1835,1.18372,1.18132,1.18281,4956 2017-12-05 15:00:00,1.18288,1.18429,1.18222,1.18324,5970 2017-12-05 16:00:00,1.18324,1.18332,1.18078,1.18205,3801 2017-12-05 17:00:00,1.18204,1.18248,1.18048,1.18058,2484 2017-12-05 18:00:00,1.18054,1.18079,1.18006,1.18072,1857 2017-12-05 19:00:00,1.18068,1.18182,1.18065,1.18144,1523 2017-12-05 20:00:00,1.18142,1.18296,1.18139,1.18242,1400 2017-12-05 21:00:00,1.18248,1.18287,1.18234,1.18262,435 2017-12-05 22:00:00,1.18258,1.18302,1.18244,1.18288,791 2017-12-05 23:00:00,1.18285,1.18312,1.18256,1.18305,841 2017-12-06 00:00:00,1.1831,1.1831,1.18166,1.18211,1164 2017-12-06 01:00:00,1.18208,1.18296,1.1819,1.18265,828 2017-12-06 02:00:00,1.18268,1.18305,1.18258,1.18275,974 2017-12-06 03:00:00,1.1828,1.18486,1.18272,1.18427,1531 2017-12-06 04:00:00,1.18432,1.18444,1.18382,1.18388,932 2017-12-06 05:00:00,1.18388,1.18454,1.18362,1.1844,602 2017-12-06 06:00:00,1.1844,1.18442,1.18318,1.18336,717 2017-12-06 07:00:00,1.18341,1.18395,1.18192,1.18234,2438 2017-12-06 08:00:00,1.18234,1.18312,1.18132,1.18223,2262 2017-12-06 09:00:00,1.18226,1.18262,1.18124,1.18234,1972 2017-12-06 10:00:00,1.18234,1.18334,1.18222,1.18282,1657 2017-12-06 11:00:00,1.18283,1.18283,1.18085,1.18129,2383 2017-12-06 12:00:00,1.18128,1.18157,1.18048,1.18132,1548 2017-12-06 13:00:00,1.18136,1.18237,1.18,1.18076,2951 2017-12-06 14:00:00,1.1808,1.18082,1.17872,1.17902,3996 2017-12-06 15:00:00,1.17902,1.17991,1.17854,1.17884,3253 2017-12-06 16:00:00,1.17888,1.17954,1.17804,1.17885,2235 2017-12-06 17:00:00,1.17884,1.17948,1.17838,1.17933,1606 2017-12-06 18:00:00,1.17934,1.17976,1.17889,1.1792,1412 2017-12-06 19:00:00,1.1792,1.17992,1.17897,1.17912,1423 2017-12-06 20:00:00,1.17913,1.18006,1.179,1.17956,1305 2017-12-06 21:00:00,1.17956,1.17968,1.17918,1.1796,485 2017-12-06 22:00:00,1.17946,1.1806,1.17946,1.18035,1185 2017-12-06 23:00:00,1.18042,1.18088,1.18014,1.18041,637 2017-12-07 00:00:00,1.18046,1.18068,1.17954,1.17957,759 2017-12-07 01:00:00,1.17958,1.18014,1.17926,1.17985,791 2017-12-07 02:00:00,1.17981,1.18026,1.17956,1.17997,685 2017-12-07 03:00:00,1.17994,1.18052,1.1799,1.18038,483 2017-12-07 04:00:00,1.18038,1.18069,1.1798,1.17983,410 2017-12-07 05:00:00,1.17978,1.18,1.17895,1.17919,586 2017-12-07 06:00:00,1.17922,1.1793,1.17866,1.17929,728 2017-12-07 07:00:00,1.17932,1.1797,1.17874,1.17956,1324 2017-12-07 08:00:00,1.17956,1.18049,1.17886,1.17912,1719 2017-12-07 09:00:00,1.17912,1.17982,1.1788,1.17892,1074 2017-12-07 10:00:00,1.17892,1.17922,1.17783,1.17794,1318 2017-12-07 11:00:00,1.17798,1.17891,1.17761,1.1788,1271 2017-12-07 12:00:00,1.1788,1.17928,1.17765,1.17777,1600 2017-12-07 13:00:00,1.17777,1.1801,1.17766,1.17998,2008 2017-12-07 14:00:00,1.17996,1.17996,1.17882,1.17969,2784 2017-12-07 15:00:00,1.17966,1.18002,1.1787,1.17896,3100 2017-12-07 16:00:00,1.17897,1.18,1.17867,1.17895,1978 2017-12-07 17:00:00,1.17897,1.17986,1.17862,1.17971,1336 2017-12-07 18:00:00,1.17974,1.18148,1.1793,1.17963,3221 2017-12-07 19:00:00,1.17966,1.17966,1.17728,1.1773,2362 2017-12-07 20:00:00,1.1773,1.17809,1.17729,1.1775,1668 2017-12-07 21:00:00,1.17748,1.17759,1.17719,1.17726,325 2017-12-07 22:00:00,1.17741,1.17764,1.17721,1.17756,331 2017-12-07 23:00:00,1.17759,1.1777,1.1771,1.17728,467 2017-12-08 00:00:00,1.17723,1.17746,1.17664,1.17686,666 2017-12-08 01:00:00,1.17686,1.17692,1.17614,1.17636,895 2017-12-08 02:00:00,1.17636,1.17696,1.17628,1.1767,731 2017-12-08 03:00:00,1.17667,1.17692,1.17652,1.17676,515 2017-12-08 04:00:00,1.17676,1.17686,1.17612,1.17619,409 2017-12-08 05:00:00,1.17621,1.17662,1.17606,1.17649,539 2017-12-08 06:00:00,1.17648,1.17678,1.17516,1.1752,1633 2017-12-08 07:00:00,1.1752,1.17541,1.17425,1.17513,2307 2017-12-08 08:00:00,1.17508,1.17544,1.17347,1.17354,2550 2017-12-08 09:00:00,1.17352,1.17477,1.17324,1.1737,1752 2017-12-08 10:00:00,1.1737,1.1744,1.17345,1.17438,1105 2017-12-08 11:00:00,1.17436,1.17436,1.17338,1.1738,1064 2017-12-08 12:00:00,1.17382,1.17456,1.17312,1.17353,1619 2017-12-08 13:00:00,1.17358,1.17692,1.173,1.17629,8164 2017-12-08 14:00:00,1.17634,1.17702,1.1752,1.17558,4642 2017-12-08 15:00:00,1.17558,1.17716,1.17478,1.17512,5148 2017-12-08 16:00:00,1.17512,1.17662,1.17466,1.17605,2838 2017-12-08 17:00:00,1.176,1.17668,1.17546,1.1756,1768 2017-12-08 18:00:00,1.17566,1.17656,1.17556,1.17636,815 2017-12-08 19:00:00,1.17638,1.17682,1.17632,1.17673,884 2017-12-08 20:00:00,1.17669,1.17706,1.17642,1.17647,649 2017-12-08 21:00:00,1.17649,1.17746,1.17648,1.17721,1133 2017-12-10 22:00:00,1.17642,1.17714,1.17637,1.17702,616 2017-12-10 23:00:00,1.17704,1.17721,1.17666,1.17677,672 2017-12-11 00:00:00,1.17672,1.17744,1.17671,1.17714,1509 2017-12-11 01:00:00,1.17718,1.1778,1.17718,1.1777,882 2017-12-11 02:00:00,1.17768,1.17784,1.17753,1.17772,302 2017-12-11 03:00:00,1.17776,1.1784,1.17771,1.17808,814 2017-12-11 04:00:00,1.17806,1.17808,1.17762,1.17786,368 2017-12-11 05:00:00,1.17789,1.17844,1.17778,1.17802,521 2017-12-11 06:00:00,1.17802,1.1786,1.17784,1.17788,596 2017-12-11 07:00:00,1.17784,1.17914,1.17778,1.17833,1417 2017-12-11 08:00:00,1.1783,1.17964,1.17754,1.17941,1874 2017-12-11 09:00:00,1.17936,1.1801,1.17885,1.17974,1261 2017-12-11 10:00:00,1.17974,1.18009,1.17929,1.17984,1042 2017-12-11 11:00:00,1.17985,1.18032,1.1789,1.17913,1131 2017-12-11 12:00:00,1.17914,1.17974,1.1789,1.17938,1223 2017-12-11 13:00:00,1.1794,1.1796,1.17767,1.1778,1569 2017-12-11 14:00:00,1.17776,1.1796,1.17774,1.17952,1707 2017-12-11 15:00:00,1.17957,1.18118,1.17921,1.17952,3167 2017-12-11 16:00:00,1.1795,1.18066,1.17942,1.18014,2128 2017-12-11 17:00:00,1.18014,1.18014,1.17869,1.17916,1370 2017-12-11 18:00:00,1.17921,1.17942,1.17838,1.17916,747 2017-12-11 19:00:00,1.17912,1.17943,1.17833,1.17864,674 2017-12-11 20:00:00,1.17865,1.17876,1.17715,1.1773,864 2017-12-11 21:00:00,1.1773,1.17744,1.17639,1.17693,889 2017-12-11 22:00:00,1.17725,1.17748,1.17687,1.17732,777 2017-12-11 23:00:00,1.17724,1.17782,1.1772,1.17762,907 2017-12-12 00:00:00,1.1776,1.1778,1.17713,1.17756,567 2017-12-12 01:00:00,1.1776,1.17798,1.17688,1.17694,626 2017-12-12 02:00:00,1.1769,1.17698,1.17646,1.17688,359 2017-12-12 03:00:00,1.17683,1.1772,1.17678,1.17688,343 2017-12-12 04:00:00,1.17688,1.1772,1.1768,1.17717,263 2017-12-12 05:00:00,1.17718,1.17755,1.17716,1.17731,321 2017-12-12 06:00:00,1.17731,1.17793,1.17724,1.17762,463 2017-12-12 07:00:00,1.17763,1.17844,1.17714,1.17714,1158 2017-12-12 08:00:00,1.1771,1.17926,1.177,1.17909,2336 2017-12-12 09:00:00,1.17909,1.1791,1.1778,1.17828,1601 2017-12-12 10:00:00,1.1783,1.17862,1.17708,1.17762,1566 2017-12-12 11:00:00,1.17758,1.17809,1.17716,1.17751,982 2017-12-12 12:00:00,1.17756,1.17795,1.17692,1.17746,1355 2017-12-12 13:00:00,1.17743,1.17743,1.175,1.17505,2029 2017-12-12 14:00:00,1.17503,1.1752,1.17353,1.17418,2197 2017-12-12 15:00:00,1.1742,1.17434,1.17222,1.17296,1823 2017-12-12 16:00:00,1.17301,1.17307,1.17177,1.17236,2091 2017-12-12 17:00:00,1.17236,1.17388,1.17204,1.17234,2726 2017-12-12 18:00:00,1.17234,1.17358,1.17208,1.17298,1739 2017-12-12 19:00:00,1.17302,1.1741,1.17297,1.17388,1148 2017-12-12 20:00:00,1.17389,1.1746,1.17377,1.17448,972 2017-12-12 21:00:00,1.17451,1.1749,1.174,1.1743,567 2017-12-12 22:00:00,1.17415,1.17427,1.17352,1.17368,839 2017-12-12 23:00:00,1.17367,1.17433,1.17358,1.17426,236 2017-12-13 00:00:00,1.17432,1.17482,1.1742,1.17472,450 2017-12-13 01:00:00,1.17468,1.17477,1.17428,1.17431,382 2017-12-13 02:00:00,1.17428,1.17491,1.17425,1.17484,267 2017-12-13 03:00:00,1.17484,1.17622,1.17472,1.17576,910 2017-12-13 04:00:00,1.17576,1.17581,1.17534,1.1754,223 2017-12-13 05:00:00,1.17536,1.17588,1.17535,1.17544,206 2017-12-13 06:00:00,1.17545,1.17628,1.17541,1.17556,629 2017-12-13 07:00:00,1.17557,1.17563,1.17379,1.17386,702 2017-12-13 08:00:00,1.17383,1.17482,1.17375,1.17412,804 2017-12-13 09:00:00,1.17408,1.17526,1.1739,1.1749,1192 2017-12-13 10:00:00,1.1749,1.17544,1.17462,1.1751,1218 2017-12-13 11:00:00,1.17515,1.17516,1.17385,1.17392,1078 2017-12-13 12:00:00,1.17392,1.17428,1.17302,1.174,1329 2017-12-13 13:00:00,1.17399,1.17712,1.17295,1.17576,5308 2017-12-13 14:00:00,1.17579,1.17624,1.17475,1.17512,2337 2017-12-13 15:00:00,1.17512,1.17648,1.17488,1.17578,2401 2017-12-13 16:00:00,1.17582,1.17686,1.1755,1.17604,2352 2017-12-13 17:00:00,1.17604,1.17708,1.17574,1.17628,1815 2017-12-13 18:00:00,1.17628,1.17794,1.17628,1.17739,2008 2017-12-13 19:00:00,1.17743,1.18234,1.1767,1.18202,16282 2017-12-13 20:00:00,1.18202,1.18262,1.18128,1.18251,4645 2017-12-13 21:00:00,1.18248,1.1832,1.18214,1.18261,948 2017-12-13 22:00:00,1.18244,1.18368,1.18228,1.18258,1757 2017-12-13 23:00:00,1.1826,1.18408,1.18217,1.18364,933 2017-12-14 00:00:00,1.1836,1.18402,1.18284,1.18395,1516 2017-12-14 01:00:00,1.184,1.1844,1.18378,1.18424,1048 2017-12-14 02:00:00,1.18421,1.18422,1.18318,1.18323,825 2017-12-14 03:00:00,1.18326,1.18378,1.18312,1.18356,550 2017-12-14 04:00:00,1.18354,1.18384,1.18308,1.18324,521 2017-12-14 05:00:00,1.18318,1.18347,1.18258,1.18268,436 2017-12-14 06:00:00,1.18268,1.18268,1.1819,1.18234,765 2017-12-14 07:00:00,1.1823,1.1826,1.18112,1.18183,1787 2017-12-14 08:00:00,1.18184,1.18376,1.18168,1.18266,2976 2017-12-14 09:00:00,1.18268,1.18294,1.18123,1.18192,2181 2017-12-14 10:00:00,1.18191,1.18345,1.18123,1.1834,1931 2017-12-14 11:00:00,1.1834,1.18372,1.18204,1.18252,1801 2017-12-14 12:00:00,1.18258,1.1845,1.18236,1.1833,3828 2017-12-14 13:00:00,1.18329,1.18628,1.18163,1.18206,9741 2017-12-14 14:00:00,1.18207,1.18271,1.17945,1.18019,7151 2017-12-14 15:00:00,1.18018,1.1806,1.1771,1.17776,3929 2017-12-14 16:00:00,1.1778,1.17838,1.1771,1.17771,3387 2017-12-14 17:00:00,1.17776,1.17896,1.17718,1.17877,2191 2017-12-14 18:00:00,1.17873,1.18002,1.17841,1.17977,2591 2017-12-14 19:00:00,1.17974,1.18015,1.17906,1.17918,1441 2017-12-14 20:00:00,1.17919,1.17962,1.17852,1.1788,1180 2017-12-14 21:00:00,1.1788,1.17884,1.17748,1.17771,1167 2017-12-14 22:00:00,1.17782,1.1781,1.17644,1.17658,2201 2017-12-14 23:00:00,1.17664,1.17762,1.17649,1.17749,994 2017-12-15 00:00:00,1.17746,1.17906,1.17731,1.17858,1125 2017-12-15 01:00:00,1.17852,1.17889,1.17788,1.17848,830 2017-12-15 02:00:00,1.17852,1.17913,1.17836,1.17859,909 2017-12-15 03:00:00,1.1786,1.17877,1.17808,1.17818,829 2017-12-15 04:00:00,1.17818,1.17845,1.17798,1.17842,427 2017-12-15 05:00:00,1.17842,1.17842,1.17782,1.17835,586 2017-12-15 06:00:00,1.17835,1.1786,1.17816,1.17844,708 2017-12-15 07:00:00,1.17838,1.1797,1.17806,1.17918,1516 2017-12-15 08:00:00,1.17917,1.17945,1.17816,1.17834,1799 2017-12-15 09:00:00,1.17834,1.1799,1.17792,1.17971,1877 2017-12-15 10:00:00,1.17966,1.18072,1.17947,1.18056,1690 2017-12-15 11:00:00,1.18055,1.18124,1.17969,1.17987,1501 2017-12-15 12:00:00,1.1799,1.18016,1.1791,1.18015,1514 2017-12-15 13:00:00,1.1802,1.18094,1.17936,1.17995,3036 2017-12-15 14:00:00,1.17996,1.18004,1.17716,1.17774,3775 2017-12-15 15:00:00,1.17773,1.17832,1.17618,1.17637,4189 2017-12-15 16:00:00,1.17642,1.17796,1.17606,1.17778,3209 2017-12-15 17:00:00,1.17784,1.17819,1.17632,1.17649,1859 2017-12-15 18:00:00,1.17652,1.17655,1.17504,1.17551,1483 2017-12-15 19:00:00,1.17546,1.1761,1.17536,1.17568,1132 2017-12-15 20:00:00,1.17564,1.17596,1.17538,1.1754,1336 2017-12-15 21:00:00,1.17543,1.17563,1.17488,1.17503,1972 2017-12-17 22:00:00,1.17459,1.17494,1.17382,1.17466,1365 2017-12-17 23:00:00,1.17476,1.17516,1.17404,1.17434,1772 2017-12-18 00:00:00,1.17432,1.1756,1.17422,1.17498,2077 2017-12-18 01:00:00,1.17492,1.17572,1.17489,1.17566,634 2017-12-18 02:00:00,1.17566,1.1763,1.17557,1.17566,569 2017-12-18 03:00:00,1.17569,1.17656,1.17564,1.17644,537 2017-12-18 04:00:00,1.17642,1.17646,1.17594,1.17608,535 2017-12-18 05:00:00,1.17611,1.17628,1.17597,1.17606,262 2017-12-18 06:00:00,1.17606,1.17713,1.17606,1.17681,712 2017-12-18 07:00:00,1.17676,1.17812,1.17619,1.17807,2324 2017-12-18 08:00:00,1.17808,1.17913,1.17754,1.17888,2688 2017-12-18 09:00:00,1.17892,1.17952,1.17858,1.17886,2021 2017-12-18 10:00:00,1.17884,1.17956,1.17841,1.17952,1258 2017-12-18 11:00:00,1.17956,1.17984,1.17876,1.1793,1503 2017-12-18 12:00:00,1.17925,1.17956,1.17884,1.17952,1137 2017-12-18 13:00:00,1.17948,1.1799,1.17918,1.17937,1349 2017-12-18 14:00:00,1.17936,1.18098,1.17879,1.18078,2420 2017-12-18 15:00:00,1.18079,1.18344,1.18058,1.1812,2745 2017-12-18 16:00:00,1.18121,1.18167,1.17976,1.18044,1983 2017-12-18 17:00:00,1.18042,1.18054,1.17896,1.17904,1376 2017-12-18 18:00:00,1.17906,1.17948,1.17884,1.17934,1169 2017-12-18 19:00:00,1.17937,1.17979,1.17753,1.17841,1254 2017-12-18 20:00:00,1.17846,1.17856,1.1779,1.17812,962 2017-12-18 21:00:00,1.17816,1.1784,1.17792,1.1781,519 2017-12-18 22:00:00,1.17819,1.17876,1.17808,1.1783,1032 2017-12-18 23:00:00,1.17825,1.17851,1.17818,1.17839,472 2017-12-19 00:00:00,1.17834,1.17845,1.17772,1.17836,720 2017-12-19 01:00:00,1.17836,1.1784,1.17766,1.17824,535 2017-12-19 02:00:00,1.1782,1.1789,1.17802,1.17868,524 2017-12-19 03:00:00,1.1787,1.179,1.17856,1.17888,513 2017-12-19 04:00:00,1.1789,1.17928,1.1787,1.17924,340 2017-12-19 05:00:00,1.1792,1.1796,1.179,1.17928,397 2017-12-19 06:00:00,1.17928,1.17987,1.17902,1.17944,593 2017-12-19 07:00:00,1.17943,1.18067,1.1791,1.18061,2916 2017-12-19 08:00:00,1.18062,1.18184,1.18022,1.18057,2066 2017-12-19 09:00:00,1.18058,1.18121,1.1791,1.18065,2187 2017-12-19 10:00:00,1.1806,1.1813,1.17998,1.18078,1669 2017-12-19 11:00:00,1.18078,1.18134,1.18058,1.181,1383 2017-12-19 12:00:00,1.181,1.1829,1.18084,1.18286,1810 2017-12-19 13:00:00,1.18282,1.18318,1.18152,1.18268,2805 2017-12-19 14:00:00,1.1827,1.18304,1.18048,1.18092,2544 2017-12-19 15:00:00,1.18096,1.18292,1.18084,1.18102,3277 2017-12-19 16:00:00,1.181,1.18224,1.18052,1.18216,2833 2017-12-19 17:00:00,1.18212,1.18294,1.18156,1.1829,2763 2017-12-19 18:00:00,1.18294,1.1849,1.18282,1.18442,2798 2017-12-19 19:00:00,1.18442,1.18477,1.18425,1.18449,1196 2017-12-19 20:00:00,1.1845,1.18465,1.18403,1.18408,1203 2017-12-19 21:00:00,1.18408,1.18429,1.18382,1.18398,550 2017-12-19 22:00:00,1.18403,1.18456,1.18382,1.18424,925 2017-12-19 23:00:00,1.18424,1.18426,1.18366,1.18374,378 2017-12-20 00:00:00,1.18372,1.18438,1.18353,1.18437,589 2017-12-20 01:00:00,1.18442,1.18465,1.18407,1.18419,523 2017-12-20 02:00:00,1.18422,1.18449,1.18412,1.18429,336 2017-12-20 03:00:00,1.18432,1.18472,1.18424,1.18468,259 2017-12-20 04:00:00,1.18466,1.18466,1.1841,1.18416,273 2017-12-20 05:00:00,1.18412,1.18462,1.1841,1.18457,574 2017-12-20 06:00:00,1.18458,1.18488,1.18294,1.18298,950 2017-12-20 07:00:00,1.18297,1.18424,1.18291,1.18416,1402 2017-12-20 08:00:00,1.18412,1.18542,1.18352,1.18472,2763 2017-12-20 09:00:00,1.18471,1.1853,1.18364,1.18378,1833 2017-12-20 10:00:00,1.18382,1.1858,1.18358,1.18446,2036 2017-12-20 11:00:00,1.18448,1.18488,1.1836,1.18363,1620 2017-12-20 12:00:00,1.18364,1.18484,1.18322,1.18418,1806 2017-12-20 13:00:00,1.18418,1.18492,1.18408,1.18438,2105 2017-12-20 14:00:00,1.18438,1.18637,1.18436,1.186,2973 2017-12-20 15:00:00,1.186,1.19016,1.18545,1.1883,5007 2017-12-20 16:00:00,1.18832,1.1885,1.18751,1.18807,2588 2017-12-20 17:00:00,1.18802,1.18814,1.1874,1.1877,1787 2017-12-20 18:00:00,1.18774,1.18892,1.1877,1.1881,1381 2017-12-20 19:00:00,1.18814,1.18822,1.18748,1.18782,1185 2017-12-20 20:00:00,1.18782,1.18826,1.18759,1.18786,1191 2017-12-20 21:00:00,1.18785,1.18785,1.18688,1.18712,534 2017-12-20 22:00:00,1.18683,1.18752,1.18683,1.1873,1045 2017-12-20 23:00:00,1.1873,1.18822,1.18694,1.18774,782 2017-12-21 00:00:00,1.18774,1.18821,1.18739,1.18746,955 2017-12-21 01:00:00,1.18748,1.18758,1.18708,1.18708,589 2017-12-21 02:00:00,1.18704,1.18744,1.187,1.18742,301 2017-12-21 03:00:00,1.18746,1.1876,1.18648,1.1866,651 2017-12-21 04:00:00,1.18658,1.18686,1.18642,1.18672,355 2017-12-21 05:00:00,1.1867,1.18684,1.1863,1.18682,495 2017-12-21 06:00:00,1.18682,1.18701,1.18634,1.18694,517 2017-12-21 07:00:00,1.18692,1.18752,1.18651,1.18722,1685 2017-12-21 08:00:00,1.18728,1.18806,1.18642,1.18786,2303 2017-12-21 09:00:00,1.18786,1.18868,1.18732,1.18808,1861 2017-12-21 10:00:00,1.18807,1.18898,1.18704,1.18708,1838 2017-12-21 11:00:00,1.1871,1.18774,1.1866,1.18662,1228 2017-12-21 12:00:00,1.1866,1.18695,1.1851,1.18629,2024 2017-12-21 13:00:00,1.18624,1.1871,1.18548,1.18588,2246 2017-12-21 14:00:00,1.18582,1.18702,1.18492,1.18647,2368 2017-12-21 15:00:00,1.1865,1.18698,1.18544,1.18602,2468 2017-12-21 16:00:00,1.18604,1.18792,1.18586,1.18714,2689 2017-12-21 17:00:00,1.18716,1.18734,1.1865,1.18677,838 2017-12-21 18:00:00,1.1868,1.18692,1.1863,1.1867,987 2017-12-21 19:00:00,1.1867,1.18725,1.18614,1.18718,833 2017-12-21 20:00:00,1.18718,1.18792,1.187,1.18765,938 2017-12-21 21:00:00,1.18764,1.18781,1.18727,1.18739,501 2017-12-21 22:00:00,1.18756,1.1876,1.18645,1.1866,854 2017-12-21 23:00:00,1.1866,1.1869,1.18524,1.18524,955 2017-12-22 00:00:00,1.18522,1.1854,1.1817,1.18441,3858 2017-12-22 01:00:00,1.18444,1.1848,1.18353,1.18446,1646 2017-12-22 02:00:00,1.18442,1.18514,1.18426,1.1851,918 2017-12-22 03:00:00,1.18509,1.1852,1.18471,1.18489,545 2017-12-22 04:00:00,1.18494,1.1851,1.18476,1.18482,408 2017-12-22 05:00:00,1.1848,1.1851,1.18474,1.18498,402 2017-12-22 06:00:00,1.18498,1.18588,1.18466,1.18566,981 2017-12-22 07:00:00,1.18564,1.18613,1.18522,1.18524,1215 2017-12-22 08:00:00,1.18522,1.18588,1.18445,1.18548,2360 2017-12-22 09:00:00,1.18549,1.18622,1.18457,1.18492,2109 2017-12-22 10:00:00,1.18488,1.18547,1.18454,1.18476,1476 2017-12-22 11:00:00,1.18471,1.18574,1.18455,1.18519,1855 2017-12-22 12:00:00,1.18514,1.18558,1.18474,1.18494,1465 2017-12-22 13:00:00,1.185,1.18551,1.18484,1.18548,1761 2017-12-22 14:00:00,1.18546,1.18568,1.18434,1.18514,2678 2017-12-22 15:00:00,1.18514,1.18515,1.18286,1.18347,2628 2017-12-22 16:00:00,1.18346,1.1844,1.18302,1.18384,1814 2017-12-22 17:00:00,1.1839,1.18533,1.18387,1.1853,1630 2017-12-22 18:00:00,1.18534,1.18576,1.18505,1.18508,1027 2017-12-22 19:00:00,1.18504,1.1856,1.1844,1.18508,1121 2017-12-22 20:00:00,1.18508,1.18591,1.18504,1.18584,885 2017-12-22 21:00:00,1.18584,1.18644,1.18556,1.18617,1234 2017-12-25 22:00:00,1.18688,1.18754,1.18608,1.18712,774 2017-12-25 23:00:00,1.18712,1.18743,1.18655,1.18711,737 2017-12-26 00:00:00,1.18705,1.18728,1.18662,1.18686,971 2017-12-26 01:00:00,1.18687,1.18698,1.18646,1.18656,624 2017-12-26 02:00:00,1.18658,1.18736,1.18628,1.18715,491 2017-12-26 03:00:00,1.1872,1.18722,1.18687,1.18709,293 2017-12-26 04:00:00,1.18711,1.18719,1.18674,1.18696,251 2017-12-26 05:00:00,1.18696,1.1871,1.18675,1.18706,202 2017-12-26 06:00:00,1.18709,1.18714,1.18672,1.18675,240 2017-12-26 07:00:00,1.1867,1.18757,1.18669,1.18724,285 2017-12-26 08:00:00,1.1872,1.18756,1.18687,1.18692,358 2017-12-26 09:00:00,1.18687,1.1872,1.1858,1.1861,599 2017-12-26 10:00:00,1.18604,1.18608,1.18554,1.18597,403 2017-12-26 11:00:00,1.186,1.18603,1.18502,1.18512,350 2017-12-26 12:00:00,1.18512,1.18536,1.18468,1.18468,979 2017-12-26 13:00:00,1.1847,1.18558,1.18466,1.18551,547 2017-12-26 14:00:00,1.18552,1.18779,1.18552,1.18775,1653 2017-12-26 15:00:00,1.18775,1.18792,1.18636,1.18651,1352 2017-12-26 16:00:00,1.1865,1.18706,1.18632,1.18666,853 2017-12-26 17:00:00,1.18671,1.18718,1.18614,1.18704,595 2017-12-26 18:00:00,1.18708,1.1872,1.18664,1.18701,459 2017-12-26 19:00:00,1.18698,1.18698,1.18616,1.18635,435 2017-12-26 20:00:00,1.1864,1.1866,1.18618,1.1863,466 2017-12-26 21:00:00,1.18625,1.18644,1.1858,1.18584,342 2017-12-26 22:00:00,1.18569,1.18648,1.18562,1.18604,705 2017-12-26 23:00:00,1.18613,1.18613,1.18554,1.18574,423 2017-12-27 00:00:00,1.18576,1.1865,1.18548,1.18628,853 2017-12-27 01:00:00,1.18624,1.18638,1.18578,1.18605,417 2017-12-27 02:00:00,1.186,1.1864,1.18592,1.18637,295 2017-12-27 03:00:00,1.18639,1.18674,1.18622,1.18634,272 2017-12-27 04:00:00,1.18637,1.1877,1.1862,1.18746,476 2017-12-27 05:00:00,1.1875,1.18806,1.18735,1.18779,409 2017-12-27 06:00:00,1.18782,1.18849,1.18778,1.18786,523 2017-12-27 07:00:00,1.18792,1.18829,1.18648,1.18678,1377 2017-12-27 08:00:00,1.18674,1.18772,1.18656,1.187,2685 2017-12-27 09:00:00,1.18702,1.18912,1.18692,1.1888,2214 2017-12-27 10:00:00,1.18878,1.18932,1.18846,1.18927,1513 2017-12-27 11:00:00,1.18925,1.18954,1.18804,1.18926,1731 2017-12-27 12:00:00,1.1893,1.19,1.18902,1.18929,1757 2017-12-27 13:00:00,1.18934,1.19046,1.18927,1.19012,1512 2017-12-27 14:00:00,1.19008,1.19071,1.18965,1.19068,1654 2017-12-27 15:00:00,1.19066,1.19105,1.18932,1.18949,2015 2017-12-27 16:00:00,1.18954,1.19016,1.18856,1.19016,2273 2017-12-27 17:00:00,1.19011,1.19034,1.18967,1.1899,1132 2017-12-27 18:00:00,1.18994,1.19036,1.1895,1.18984,1239 2017-12-27 19:00:00,1.18988,1.19006,1.18948,1.18974,1438 2017-12-27 20:00:00,1.18979,1.18984,1.18924,1.18946,883 2017-12-27 21:00:00,1.18943,1.18957,1.18844,1.18881,739 2017-12-27 22:00:00,1.18979,1.19037,1.18954,1.18996,1189 2017-12-27 23:00:00,1.18994,1.19015,1.18968,1.19004,656 2017-12-28 00:00:00,1.19001,1.1904,1.18968,1.19033,810 2017-12-28 01:00:00,1.1903,1.19062,1.1901,1.19059,398 2017-12-28 02:00:00,1.19056,1.19106,1.19025,1.19091,669 2017-12-28 03:00:00,1.19094,1.19111,1.1908,1.19102,230 2017-12-28 04:00:00,1.191,1.19186,1.191,1.19176,506 2017-12-28 05:00:00,1.19181,1.19332,1.19163,1.19317,966 2017-12-28 06:00:00,1.19317,1.1935,1.19262,1.19323,580 2017-12-28 07:00:00,1.1932,1.19412,1.19284,1.194,1154 2017-12-28 08:00:00,1.19404,1.19467,1.19332,1.19372,1609 2017-12-28 09:00:00,1.19376,1.19381,1.19294,1.1932,1202 2017-12-28 10:00:00,1.19322,1.19332,1.19237,1.19314,1219 2017-12-28 11:00:00,1.19319,1.19369,1.1929,1.19367,810 2017-12-28 12:00:00,1.19371,1.19427,1.1932,1.19402,1299 2017-12-28 13:00:00,1.19403,1.19476,1.19326,1.1947,1578 2017-12-28 14:00:00,1.19473,1.19502,1.19428,1.19478,1562 2017-12-28 15:00:00,1.19478,1.19508,1.19366,1.19372,1315 2017-12-28 16:00:00,1.19376,1.19544,1.19373,1.19512,1709 2017-12-28 17:00:00,1.19516,1.19552,1.19488,1.19533,1539 2017-12-28 18:00:00,1.1953,1.19591,1.19521,1.19584,873 2017-12-28 19:00:00,1.19578,1.19587,1.19498,1.19526,907 2017-12-28 20:00:00,1.19526,1.19536,1.19408,1.19408,730 2017-12-28 21:00:00,1.19408,1.19454,1.19392,1.19448,360 2017-12-28 22:00:00,1.19432,1.1947,1.1936,1.19384,651 2017-12-28 23:00:00,1.19386,1.19416,1.19383,1.19396,330 2017-12-29 00:00:00,1.19392,1.19472,1.19392,1.19444,409 2017-12-29 01:00:00,1.1944,1.19492,1.19368,1.19426,804 2017-12-29 02:00:00,1.19426,1.19482,1.19376,1.19476,736 2017-12-29 03:00:00,1.19474,1.19474,1.19419,1.19428,380 2017-12-29 04:00:00,1.19433,1.19462,1.19414,1.19445,411 2017-12-29 05:00:00,1.19442,1.19496,1.1944,1.19488,383 2017-12-29 06:00:00,1.19488,1.19553,1.19488,1.19552,564 2017-12-29 07:00:00,1.1955,1.19587,1.19462,1.19519,1427 2017-12-29 08:00:00,1.19518,1.19692,1.19516,1.19674,2057 2017-12-29 09:00:00,1.19676,1.19876,1.19674,1.19814,1823 2017-12-29 10:00:00,1.19818,1.19854,1.19736,1.19804,1582 2017-12-29 11:00:00,1.1981,1.19898,1.19794,1.19872,1157 2017-12-29 12:00:00,1.19877,1.19952,1.19846,1.19951,1492 2017-12-29 13:00:00,1.19952,1.19985,1.19826,1.19869,1686 2017-12-29 14:00:00,1.19865,1.19955,1.19854,1.1993,1190 2017-12-29 15:00:00,1.1993,1.20115,1.19876,1.201,2448 2017-12-29 16:00:00,1.20101,1.20227,1.2002,1.2022,2636 2017-12-29 17:00:00,1.20225,1.20256,1.20192,1.20212,1534 2017-12-29 18:00:00,1.20214,1.20243,1.20118,1.20139,1073 2017-12-29 19:00:00,1.2014,1.20142,1.20072,1.20093,699 2017-12-29 20:00:00,1.20098,1.20108,1.19976,1.19982,816 2017-12-29 21:00:00,1.19984,1.20071,1.19922,1.20039,998 2018-01-01 22:00:00,1.20071,1.20174,1.20044,1.20152,338 2018-01-01 23:00:00,1.20148,1.20162,1.20019,1.20108,2917 2018-01-02 00:00:00,1.20105,1.20178,1.2009,1.20162,804 2018-01-02 01:00:00,1.20168,1.20236,1.20143,1.20156,1208 2018-01-02 02:00:00,1.2016,1.20166,1.20079,1.20096,766 2018-01-02 03:00:00,1.201,1.20212,1.20095,1.2012,683 2018-01-02 04:00:00,1.20126,1.20196,1.20116,1.20175,497 2018-01-02 05:00:00,1.2018,1.20245,1.2016,1.20245,427 2018-01-02 06:00:00,1.20245,1.20307,1.20226,1.20288,659 2018-01-02 07:00:00,1.20284,1.20382,1.20242,1.2036,2185 2018-01-02 08:00:00,1.20365,1.20475,1.20322,1.2045,1853 2018-01-02 09:00:00,1.20452,1.20706,1.20419,1.20706,1797 2018-01-02 10:00:00,1.20703,1.20812,1.20617,1.20686,2419 2018-01-02 11:00:00,1.20689,1.20708,1.20592,1.20594,1511 2018-01-02 12:00:00,1.20592,1.20686,1.2057,1.2063,1805 2018-01-02 13:00:00,1.20634,1.20666,1.20564,1.20576,1925 2018-01-02 14:00:00,1.20571,1.2059,1.20353,1.2039,2672 2018-01-02 15:00:00,1.20394,1.2046,1.20266,1.20444,3186 2018-01-02 16:00:00,1.20443,1.2059,1.20434,1.20492,3120 2018-01-02 17:00:00,1.20495,1.20538,1.2041,1.20446,1716 2018-01-02 18:00:00,1.2044,1.20496,1.20418,1.20442,837 2018-01-02 19:00:00,1.20442,1.20538,1.20432,1.20532,664 2018-01-02 20:00:00,1.20532,1.20606,1.20518,1.20593,676 2018-01-02 21:00:00,1.20598,1.20656,1.20548,1.2058,965 2018-01-02 22:00:00,1.2059,1.2059,1.20549,1.20574,239 2018-01-02 23:00:00,1.20576,1.20666,1.20569,1.20618,540 2018-01-03 00:00:00,1.20624,1.2064,1.20491,1.20594,1008 2018-01-03 01:00:00,1.20589,1.20636,1.20447,1.20532,981 2018-01-03 02:00:00,1.20527,1.20533,1.2042,1.20425,692 2018-01-03 03:00:00,1.20428,1.20479,1.20418,1.20469,287 2018-01-03 04:00:00,1.20474,1.20538,1.20464,1.20509,351 2018-01-03 05:00:00,1.20509,1.20591,1.20494,1.20585,503 2018-01-03 06:00:00,1.20585,1.20606,1.20442,1.20452,948 2018-01-03 07:00:00,1.20453,1.20492,1.20346,1.20392,2009 2018-01-03 08:00:00,1.20391,1.20428,1.20312,1.20427,1711 2018-01-03 09:00:00,1.20432,1.20434,1.20344,1.20382,1193 2018-01-03 10:00:00,1.20378,1.20404,1.20153,1.20178,2135 2018-01-03 11:00:00,1.20182,1.2019,1.20108,1.2015,2079 2018-01-03 12:00:00,1.2015,1.20237,1.2011,1.20235,1469 2018-01-03 13:00:00,1.20236,1.20346,1.202,1.20328,1507 2018-01-03 14:00:00,1.20326,1.20327,1.20143,1.20174,1788 2018-01-03 15:00:00,1.20174,1.20246,1.20034,1.20236,3330 2018-01-03 16:00:00,1.20231,1.20326,1.202,1.20296,1383 2018-01-03 17:00:00,1.203,1.20318,1.20244,1.2027,832 2018-01-03 18:00:00,1.2027,1.20294,1.2022,1.20275,680 2018-01-03 19:00:00,1.20274,1.20301,1.2001,1.20161,3330 2018-01-03 20:00:00,1.20162,1.20288,1.20103,1.20124,1500 2018-01-03 21:00:00,1.2012,1.20158,1.20102,1.20144,859 2018-01-03 22:00:00,1.20144,1.20162,1.20106,1.20114,898 2018-01-03 23:00:00,1.20114,1.2014,1.20064,1.20111,880 2018-01-04 00:00:00,1.20112,1.20115,1.20042,1.20101,985 2018-01-04 01:00:00,1.20098,1.20114,1.20048,1.20075,803 2018-01-04 02:00:00,1.20075,1.20192,1.20075,1.20177,893 2018-01-04 03:00:00,1.20174,1.20178,1.20124,1.20145,451 2018-01-04 04:00:00,1.20149,1.20224,1.20146,1.2018,425 2018-01-04 05:00:00,1.20178,1.20231,1.20144,1.20192,394 2018-01-04 06:00:00,1.20192,1.20382,1.2017,1.20282,1388 2018-01-04 07:00:00,1.20281,1.2038,1.20254,1.20285,1810 2018-01-04 08:00:00,1.2028,1.20354,1.20216,1.20258,1564 2018-01-04 09:00:00,1.20256,1.20462,1.20251,1.2043,1663 2018-01-04 10:00:00,1.2043,1.20556,1.20415,1.20513,1899 2018-01-04 11:00:00,1.20514,1.2066,1.20467,1.20624,2061 2018-01-04 12:00:00,1.20619,1.20751,1.2059,1.20706,1518 2018-01-04 13:00:00,1.20703,1.2075,1.20552,1.2062,3877 2018-01-04 14:00:00,1.20625,1.20863,1.20603,1.20825,3121 2018-01-04 15:00:00,1.20826,1.2089,1.20716,1.20744,2718 2018-01-04 16:00:00,1.20749,1.2077,1.20612,1.20632,2754 2018-01-04 17:00:00,1.20635,1.20784,1.20618,1.20747,1487 2018-01-04 18:00:00,1.20748,1.20801,1.20611,1.20648,1480 2018-01-04 19:00:00,1.2065,1.2072,1.2064,1.20716,803 2018-01-04 20:00:00,1.2072,1.20738,1.20651,1.20688,768 2018-01-04 21:00:00,1.20684,1.20712,1.20653,1.20683,547 2018-01-04 22:00:00,1.20664,1.20734,1.20635,1.2072,750 2018-01-04 23:00:00,1.2072,1.20728,1.20667,1.20667,589 2018-01-05 00:00:00,1.20661,1.20797,1.20651,1.20772,1192 2018-01-05 01:00:00,1.20774,1.20794,1.20698,1.2072,709 2018-01-05 02:00:00,1.20716,1.20744,1.20682,1.20712,661 2018-01-05 03:00:00,1.2071,1.20756,1.2071,1.20747,504 2018-01-05 04:00:00,1.20748,1.20748,1.20704,1.20739,278 2018-01-05 05:00:00,1.20741,1.20754,1.20696,1.20714,542 2018-01-05 06:00:00,1.20714,1.20744,1.20564,1.20568,1075 2018-01-05 07:00:00,1.20574,1.20674,1.20556,1.20578,1722 2018-01-05 08:00:00,1.20577,1.20626,1.20511,1.20528,1723 2018-01-05 09:00:00,1.20524,1.20575,1.20416,1.2046,1471 2018-01-05 10:00:00,1.2046,1.2054,1.20422,1.20502,1483 2018-01-05 11:00:00,1.20502,1.20554,1.2048,1.20529,836 2018-01-05 12:00:00,1.20524,1.20541,1.20446,1.20475,976 2018-01-05 13:00:00,1.2048,1.20834,1.20446,1.20522,7458 2018-01-05 14:00:00,1.20518,1.20559,1.20252,1.20294,4649 2018-01-05 15:00:00,1.20294,1.20444,1.20208,1.20364,3545 2018-01-05 16:00:00,1.20364,1.20436,1.20302,1.20386,2123 2018-01-05 17:00:00,1.2039,1.2049,1.20361,1.20472,1252 2018-01-05 18:00:00,1.2047,1.20491,1.20434,1.20456,687 2018-01-05 19:00:00,1.20462,1.20508,1.2046,1.20475,607 2018-01-05 20:00:00,1.2047,1.20492,1.20389,1.20417,493 2018-01-05 21:00:00,1.20421,1.20422,1.20283,1.20297,1153 2018-01-07 22:00:00,1.20271,1.20366,1.20266,1.20318,506 2018-01-07 23:00:00,1.20324,1.2039,1.20324,1.20371,909 2018-01-08 00:00:00,1.20369,1.20449,1.20334,1.20428,465 2018-01-08 01:00:00,1.20431,1.20522,1.20416,1.20446,1006 2018-01-08 02:00:00,1.20448,1.20464,1.20309,1.2036,883 2018-01-08 03:00:00,1.20356,1.20356,1.20271,1.20326,513 2018-01-08 04:00:00,1.20329,1.20329,1.2023,1.20252,749 2018-01-08 05:00:00,1.20255,1.20288,1.20122,1.20198,983 2018-01-08 06:00:00,1.20198,1.2023,1.20126,1.20142,689 2018-01-08 07:00:00,1.20144,1.20144,1.19928,1.19931,1583 2018-01-08 08:00:00,1.19928,1.20008,1.19838,1.19906,1726 2018-01-08 09:00:00,1.19906,1.19938,1.19868,1.19899,1163 2018-01-08 10:00:00,1.19902,1.20096,1.19896,1.20038,1440 2018-01-08 11:00:00,1.20041,1.20067,1.19845,1.19858,1134 2018-01-08 12:00:00,1.19861,1.19905,1.1971,1.19715,1717 2018-01-08 13:00:00,1.1971,1.19791,1.19684,1.19699,1944 2018-01-08 14:00:00,1.19703,1.19792,1.19663,1.19673,2027 2018-01-08 15:00:00,1.19669,1.19774,1.19594,1.19753,2125 2018-01-08 16:00:00,1.19756,1.19823,1.19702,1.19726,2049 2018-01-08 17:00:00,1.19724,1.19787,1.19686,1.1977,1181 2018-01-08 18:00:00,1.19773,1.19774,1.19568,1.19624,1176 2018-01-08 19:00:00,1.19622,1.19659,1.19558,1.19628,717 2018-01-08 20:00:00,1.19624,1.19673,1.1961,1.19672,521 2018-01-08 21:00:00,1.19671,1.19687,1.19638,1.19687,412 2018-01-08 22:00:00,1.19689,1.19706,1.1966,1.1968,413 2018-01-08 23:00:00,1.1968,1.1971,1.19663,1.19703,397 2018-01-09 00:00:00,1.19698,1.19737,1.1965,1.19718,521 2018-01-09 01:00:00,1.19718,1.19719,1.19581,1.19673,1376 2018-01-09 02:00:00,1.19674,1.19756,1.19671,1.19728,1535 2018-01-09 03:00:00,1.19731,1.19752,1.19676,1.19719,536 2018-01-09 04:00:00,1.19716,1.19724,1.19662,1.19683,371 2018-01-09 05:00:00,1.1968,1.19716,1.19642,1.19652,482 2018-01-09 06:00:00,1.19652,1.19652,1.19516,1.19559,905 2018-01-09 07:00:00,1.19562,1.1971,1.1951,1.19511,2503 2018-01-09 08:00:00,1.19507,1.19526,1.19326,1.19394,2301 2018-01-09 09:00:00,1.1939,1.19463,1.1932,1.19386,2148 2018-01-09 10:00:00,1.19386,1.19396,1.19252,1.19279,1781 2018-01-09 11:00:00,1.19276,1.193,1.1921,1.19281,1147 2018-01-09 12:00:00,1.19284,1.19357,1.19252,1.19334,1237 2018-01-09 13:00:00,1.1933,1.19338,1.19159,1.19208,2082 2018-01-09 14:00:00,1.19208,1.1933,1.19174,1.19182,2382 2018-01-09 15:00:00,1.19181,1.19338,1.19178,1.19297,2605 2018-01-09 16:00:00,1.19299,1.19314,1.1918,1.1921,2108 2018-01-09 17:00:00,1.19213,1.19259,1.19182,1.19214,1216 2018-01-09 18:00:00,1.19218,1.19314,1.19166,1.19304,821 2018-01-09 19:00:00,1.19304,1.19358,1.19248,1.19318,849 2018-01-09 20:00:00,1.1932,1.19386,1.19299,1.19379,618 2018-01-09 21:00:00,1.19376,1.19394,1.19358,1.19369,348 2018-01-09 22:00:00,1.19369,1.19388,1.19351,1.1936,404 2018-01-09 23:00:00,1.19357,1.19364,1.19278,1.19284,465 2018-01-10 00:00:00,1.19288,1.19386,1.19284,1.19377,847 2018-01-10 01:00:00,1.19372,1.19452,1.1933,1.1945,1725 2018-01-10 02:00:00,1.19446,1.19491,1.19406,1.19486,924 2018-01-10 03:00:00,1.19482,1.19496,1.19414,1.19418,348 2018-01-10 04:00:00,1.1942,1.19428,1.19357,1.1936,408 2018-01-10 05:00:00,1.19365,1.19417,1.19272,1.19281,753 2018-01-10 06:00:00,1.19281,1.19344,1.19235,1.1929,1396 2018-01-10 07:00:00,1.19289,1.19452,1.19278,1.1935,3130 2018-01-10 08:00:00,1.19354,1.1955,1.19341,1.19464,2258 2018-01-10 09:00:00,1.19468,1.19496,1.19382,1.19389,1946 2018-01-10 10:00:00,1.1939,1.20006,1.19317,1.19948,6932 2018-01-10 11:00:00,1.19952,1.20166,1.19872,1.20056,5439 2018-01-10 12:00:00,1.20056,1.20182,1.19957,1.19979,3564 2018-01-10 13:00:00,1.19982,1.2003,1.19901,1.19948,3156 2018-01-10 14:00:00,1.19953,1.2007,1.19733,1.19862,4003 2018-01-10 15:00:00,1.19862,1.19965,1.19771,1.1983,3308 2018-01-10 16:00:00,1.1983,1.1984,1.19556,1.19578,3512 2018-01-10 17:00:00,1.19579,1.19652,1.1951,1.19606,2003 2018-01-10 18:00:00,1.19607,1.19679,1.19558,1.1959,1487 2018-01-10 19:00:00,1.19588,1.19596,1.194,1.19578,3016 2018-01-10 20:00:00,1.19576,1.19653,1.19546,1.1957,2007 2018-01-10 21:00:00,1.19574,1.19586,1.19472,1.19474,690 2018-01-10 22:00:00,1.19479,1.19532,1.19461,1.19494,580 2018-01-10 23:00:00,1.19494,1.19592,1.19492,1.19562,402 2018-01-11 00:00:00,1.19561,1.19604,1.19474,1.19604,1545 2018-01-11 01:00:00,1.19604,1.19629,1.19542,1.19597,1556 2018-01-11 02:00:00,1.19595,1.19692,1.19566,1.19668,814 2018-01-11 03:00:00,1.19665,1.19706,1.19453,1.19536,2059 2018-01-11 04:00:00,1.19534,1.19548,1.19414,1.1947,1169 2018-01-11 05:00:00,1.1947,1.19541,1.19412,1.19521,1048 2018-01-11 06:00:00,1.19521,1.19521,1.19365,1.19424,1048 2018-01-11 07:00:00,1.19424,1.19499,1.19297,1.194,2273 2018-01-11 08:00:00,1.19398,1.19418,1.19295,1.19378,2339 2018-01-11 09:00:00,1.19377,1.19495,1.19331,1.19477,2210 2018-01-11 10:00:00,1.19478,1.19518,1.1939,1.19466,1864 2018-01-11 11:00:00,1.1947,1.19508,1.19406,1.19435,1468 2018-01-11 12:00:00,1.19437,1.2013,1.19364,1.20044,9176 2018-01-11 13:00:00,1.20048,1.20353,1.20026,1.20314,9406 2018-01-11 14:00:00,1.20312,1.20489,1.2031,1.2048,4285 2018-01-11 15:00:00,1.2048,1.20592,1.20394,1.20492,4332 2018-01-11 16:00:00,1.20496,1.20496,1.2032,1.20343,2620 2018-01-11 17:00:00,1.20342,1.2041,1.20313,1.20378,1605 2018-01-11 18:00:00,1.20384,1.20476,1.20304,1.2037,1974 2018-01-11 19:00:00,1.20368,1.20456,1.2034,1.20387,1036 2018-01-11 20:00:00,1.20382,1.20398,1.20276,1.20311,796 2018-01-11 21:00:00,1.2031,1.2035,1.20282,1.2032,811 2018-01-11 22:00:00,1.20332,1.20408,1.20315,1.20386,854 2018-01-11 23:00:00,1.20384,1.2044,1.20308,1.20431,918 2018-01-12 00:00:00,1.20436,1.20669,1.20417,1.20586,2966 2018-01-12 01:00:00,1.20584,1.20616,1.20432,1.20455,1378 2018-01-12 02:00:00,1.20452,1.20508,1.2042,1.20471,844 2018-01-12 03:00:00,1.20473,1.20532,1.20458,1.20516,680 2018-01-12 04:00:00,1.20512,1.2055,1.20458,1.20536,669 2018-01-12 05:00:00,1.2054,1.20573,1.20403,1.20444,685 2018-01-12 06:00:00,1.20442,1.20526,1.20424,1.20482,934 2018-01-12 07:00:00,1.20477,1.2076,1.20454,1.20626,3649 2018-01-12 08:00:00,1.20628,1.21208,1.2052,1.21114,6783 2018-01-12 09:00:00,1.21115,1.21368,1.21109,1.21253,5139 2018-01-12 10:00:00,1.21258,1.21293,1.21148,1.21187,2153 2018-01-12 11:00:00,1.21188,1.21358,1.21188,1.21246,2167 2018-01-12 12:00:00,1.21251,1.21382,1.21228,1.21331,2179 2018-01-12 13:00:00,1.21332,1.21493,1.2112,1.21202,9629 2018-01-12 14:00:00,1.21203,1.21492,1.21112,1.21382,5906 2018-01-12 15:00:00,1.21382,1.21553,1.2133,1.21482,4987 2018-01-12 16:00:00,1.21482,1.21557,1.2122,1.21302,5546 2018-01-12 17:00:00,1.21305,1.21371,1.21242,1.21344,2483 2018-01-12 18:00:00,1.21346,1.21668,1.2133,1.21627,3430 2018-01-12 19:00:00,1.21624,1.21846,1.21608,1.21809,3402 2018-01-12 20:00:00,1.21812,1.21884,1.21768,1.21872,1389 2018-01-12 21:00:00,1.21878,1.22193,1.2187,1.21968,3851 2018-01-14 22:00:00,1.21935,1.22089,1.21931,1.22036,1308 2018-01-14 23:00:00,1.22034,1.22124,1.21909,1.21919,1835 2018-01-15 00:00:00,1.21918,1.21988,1.21878,1.21909,2576 2018-01-15 01:00:00,1.21906,1.22088,1.21892,1.21986,3016 2018-01-15 02:00:00,1.21986,1.22082,1.21924,1.21947,2009 2018-01-15 03:00:00,1.21942,1.22046,1.2193,1.22001,1561 2018-01-15 04:00:00,1.21997,1.22113,1.21962,1.22102,1346 2018-01-15 05:00:00,1.221,1.224,1.2208,1.22228,4610 2018-01-15 06:00:00,1.22228,1.22301,1.22076,1.22087,3372 2018-01-15 07:00:00,1.22086,1.22202,1.22012,1.22117,3001 2018-01-15 08:00:00,1.22114,1.22348,1.22096,1.22309,2685 2018-01-15 09:00:00,1.22308,1.22698,1.22288,1.22604,3836 2018-01-15 10:00:00,1.226,1.22968,1.22576,1.22744,4236 2018-01-15 11:00:00,1.22743,1.22824,1.22522,1.22554,2561 2018-01-15 12:00:00,1.22554,1.22725,1.22513,1.22696,1797 2018-01-15 13:00:00,1.22701,1.22865,1.22654,1.2283,2509 2018-01-15 14:00:00,1.2283,1.22912,1.22598,1.2262,2560 2018-01-15 15:00:00,1.22618,1.22714,1.22546,1.22598,2237 2018-01-15 16:00:00,1.22598,1.22938,1.22592,1.2266,4140 2018-01-15 17:00:00,1.22664,1.22878,1.22664,1.22719,1097 2018-01-15 18:00:00,1.22725,1.22769,1.2268,1.22728,960 2018-01-15 19:00:00,1.22726,1.22746,1.22633,1.22688,1299 2018-01-15 20:00:00,1.22694,1.22784,1.22633,1.22694,2467 2018-01-15 21:00:00,1.22693,1.22699,1.22632,1.22636,1104 2018-01-15 22:00:00,1.22655,1.22678,1.22622,1.2265,423 2018-01-15 23:00:00,1.22656,1.22753,1.22615,1.22668,1185 2018-01-16 00:00:00,1.2267,1.22698,1.22604,1.2262,1375 2018-01-16 01:00:00,1.22622,1.22659,1.22477,1.22655,1908 2018-01-16 02:00:00,1.22651,1.22798,1.22588,1.22678,2016 2018-01-16 03:00:00,1.22672,1.22696,1.22555,1.22573,1179 2018-01-16 04:00:00,1.22574,1.22705,1.22574,1.2267,1051 2018-01-16 05:00:00,1.22668,1.227,1.22616,1.2263,768 2018-01-16 06:00:00,1.2263,1.22832,1.22581,1.22674,2357 2018-01-16 07:00:00,1.22674,1.22682,1.22156,1.22466,10486 2018-01-16 08:00:00,1.22465,1.22678,1.22227,1.2236,4599 2018-01-16 09:00:00,1.22356,1.2241,1.22222,1.22332,3188 2018-01-16 10:00:00,1.22334,1.22389,1.22018,1.22132,5283 2018-01-16 11:00:00,1.22137,1.2224,1.22048,1.22158,1978 2018-01-16 12:00:00,1.22156,1.22406,1.22146,1.22324,2043 2018-01-16 13:00:00,1.22322,1.22357,1.22123,1.2218,2566 2018-01-16 14:00:00,1.22182,1.22184,1.21952,1.22092,4724 2018-01-16 15:00:00,1.2209,1.22344,1.2207,1.22296,3636 2018-01-16 16:00:00,1.22296,1.22486,1.2225,1.22446,2873 2018-01-16 17:00:00,1.22443,1.22456,1.22335,1.22437,2068 2018-01-16 18:00:00,1.22441,1.22695,1.22432,1.22674,1976 2018-01-16 19:00:00,1.22674,1.2281,1.22634,1.22716,2354 2018-01-16 20:00:00,1.22716,1.22744,1.22649,1.22664,1327 2018-01-16 21:00:00,1.2266,1.22665,1.22544,1.22607,720 2018-01-16 22:00:00,1.22591,1.22642,1.2254,1.22562,1113 2018-01-16 23:00:00,1.22564,1.22774,1.22564,1.22708,1207 2018-01-17 00:00:00,1.22705,1.2323,1.22638,1.23034,6019 2018-01-17 01:00:00,1.2304,1.2306,1.22852,1.22911,4049 2018-01-17 02:00:00,1.22907,1.22972,1.22674,1.22684,2513 2018-01-17 03:00:00,1.22688,1.22755,1.2259,1.22672,2314 2018-01-17 04:00:00,1.22672,1.22702,1.22486,1.22494,2208 2018-01-17 05:00:00,1.22496,1.22542,1.2237,1.22439,2581 2018-01-17 06:00:00,1.22439,1.22514,1.22148,1.22201,3325 2018-01-17 07:00:00,1.22203,1.22353,1.22084,1.22172,5452 2018-01-17 08:00:00,1.22174,1.22442,1.22112,1.22268,5240 2018-01-17 09:00:00,1.22264,1.22439,1.22166,1.22366,3126 2018-01-17 10:00:00,1.22372,1.22503,1.22324,1.22344,2907 2018-01-17 11:00:00,1.22343,1.2237,1.22055,1.2207,2521 2018-01-17 12:00:00,1.22069,1.22144,1.2201,1.22095,2195 2018-01-17 13:00:00,1.22096,1.22232,1.21964,1.22194,3596 2018-01-17 14:00:00,1.2219,1.22298,1.22128,1.22216,3855 2018-01-17 15:00:00,1.22216,1.22456,1.22161,1.22428,4933 2018-01-17 16:00:00,1.22427,1.22447,1.22256,1.22288,3177 2018-01-17 17:00:00,1.22285,1.22536,1.22272,1.2252,2059 2018-01-17 18:00:00,1.22518,1.2288,1.22501,1.22768,3954 2018-01-17 19:00:00,1.22766,1.22766,1.22357,1.22376,3769 2018-01-17 20:00:00,1.22371,1.22385,1.22089,1.22131,4593 2018-01-17 21:00:00,1.22133,1.2218,1.21776,1.21854,3194 2018-01-17 22:00:00,1.21872,1.21922,1.21656,1.21656,2343 2018-01-17 23:00:00,1.21653,1.21883,1.21653,1.21803,1293 2018-01-18 00:00:00,1.21809,1.22046,1.2179,1.21901,4246 2018-01-18 01:00:00,1.21906,1.21974,1.218,1.2193,2496 2018-01-18 02:00:00,1.21925,1.22016,1.21908,1.21943,2011 2018-01-18 03:00:00,1.21942,1.21949,1.21735,1.21798,1840 2018-01-18 04:00:00,1.21794,1.21968,1.21788,1.21948,1616 2018-01-18 05:00:00,1.21954,1.2209,1.21942,1.2207,2315 2018-01-18 06:00:00,1.2207,1.22097,1.21962,1.2207,2156 2018-01-18 07:00:00,1.22076,1.22124,1.21864,1.22064,3502 2018-01-18 08:00:00,1.22064,1.22178,1.2202,1.2204,2946 2018-01-18 09:00:00,1.2204,1.2218,1.21961,1.22144,2226 2018-01-18 10:00:00,1.22146,1.22295,1.22126,1.22279,2055 2018-01-18 11:00:00,1.2228,1.22472,1.22274,1.224,2420 2018-01-18 12:00:00,1.22397,1.22528,1.22339,1.22394,2502 2018-01-18 13:00:00,1.22394,1.22503,1.2227,1.22489,2964 2018-01-18 14:00:00,1.2249,1.2265,1.22419,1.22606,3310 2018-01-18 15:00:00,1.22607,1.22607,1.22308,1.22392,4079 2018-01-18 16:00:00,1.2239,1.22546,1.22269,1.22382,5483 2018-01-18 17:00:00,1.22386,1.22556,1.22208,1.22283,3083 2018-01-18 18:00:00,1.22283,1.22359,1.22199,1.2226,2458 2018-01-18 19:00:00,1.22266,1.22448,1.22248,1.22434,1672 2018-01-18 20:00:00,1.22434,1.22475,1.22264,1.22386,2202 2018-01-18 21:00:00,1.22386,1.22436,1.22368,1.2239,1060 2018-01-18 22:00:00,1.2237,1.22444,1.22334,1.22409,885 2018-01-18 23:00:00,1.22412,1.22466,1.22322,1.2234,1274 2018-01-19 00:00:00,1.2234,1.22542,1.22324,1.22368,3295 2018-01-19 01:00:00,1.22366,1.22457,1.2232,1.22436,2560 2018-01-19 02:00:00,1.22442,1.22564,1.22404,1.22448,2669 2018-01-19 03:00:00,1.22454,1.22704,1.22436,1.22667,2546 2018-01-19 04:00:00,1.22666,1.2267,1.22596,1.22625,1310 2018-01-19 05:00:00,1.2263,1.22638,1.22536,1.2261,929 2018-01-19 06:00:00,1.2261,1.22684,1.22576,1.22593,1916 2018-01-19 07:00:00,1.22588,1.22796,1.22514,1.22718,3556 2018-01-19 08:00:00,1.22713,1.22852,1.22624,1.22832,3888 2018-01-19 09:00:00,1.22831,1.22954,1.22649,1.22816,3843 2018-01-19 10:00:00,1.22815,1.22842,1.22666,1.22742,2912 2018-01-19 11:00:00,1.22744,1.22774,1.22561,1.22731,2346 2018-01-19 12:00:00,1.22726,1.22726,1.22532,1.22572,2070 2018-01-19 13:00:00,1.22578,1.22696,1.22392,1.22435,6699 2018-01-19 14:00:00,1.22431,1.22529,1.22304,1.22503,4743 2018-01-19 15:00:00,1.22503,1.22576,1.22183,1.22208,5010 2018-01-19 16:00:00,1.22211,1.22392,1.22206,1.22372,2674 2018-01-19 17:00:00,1.22372,1.22436,1.22311,1.22328,1890 2018-01-19 18:00:00,1.22328,1.22328,1.22191,1.22324,1563 2018-01-19 19:00:00,1.22326,1.22386,1.22316,1.22328,1412 2018-01-19 20:00:00,1.22328,1.22347,1.22204,1.22212,1174 2018-01-19 21:00:00,1.2221,1.22338,1.22152,1.22211,2661 2018-01-21 22:00:00,1.22716,1.22743,1.22552,1.22591,1121 2018-01-21 23:00:00,1.22594,1.22602,1.22342,1.22562,2248 2018-01-22 00:00:00,1.22562,1.22574,1.22413,1.22442,2370 2018-01-22 01:00:00,1.2244,1.22448,1.22253,1.2229,3227 2018-01-22 02:00:00,1.22287,1.22377,1.22265,1.22366,1885 2018-01-22 03:00:00,1.22362,1.22376,1.22248,1.2229,1073 2018-01-22 04:00:00,1.22288,1.223,1.22165,1.2224,1641 2018-01-22 05:00:00,1.22237,1.2233,1.22174,1.22319,1445 2018-01-22 06:00:00,1.22316,1.22342,1.22141,1.22161,2125 2018-01-22 07:00:00,1.22162,1.22427,1.22156,1.22374,3147 2018-01-22 08:00:00,1.2238,1.225,1.22355,1.22418,2631 2018-01-22 09:00:00,1.22422,1.2256,1.2242,1.22495,1695 2018-01-22 10:00:00,1.22498,1.22671,1.22478,1.22621,1932 2018-01-22 11:00:00,1.22624,1.2263,1.22513,1.22552,1431 2018-01-22 12:00:00,1.2255,1.22567,1.2239,1.22414,1314 2018-01-22 13:00:00,1.22414,1.22519,1.22336,1.22494,1691 2018-01-22 14:00:00,1.22498,1.22587,1.22384,1.22402,2521 2018-01-22 15:00:00,1.22404,1.22608,1.22386,1.22537,2677 2018-01-22 16:00:00,1.22535,1.22549,1.2228,1.2228,2934 2018-01-22 17:00:00,1.22281,1.22448,1.2224,1.22438,2860 2018-01-22 18:00:00,1.22438,1.2251,1.22392,1.22494,1295 2018-01-22 19:00:00,1.22498,1.22614,1.22478,1.22584,1325 2018-01-22 20:00:00,1.22586,1.22606,1.22546,1.22588,1002 2018-01-22 21:00:00,1.22588,1.2263,1.2257,1.22614,564 2018-01-22 22:00:00,1.22614,1.22648,1.22571,1.226,967 2018-01-22 23:00:00,1.22598,1.22603,1.22555,1.22564,1299 2018-01-23 00:00:00,1.22562,1.22732,1.22556,1.22674,1313 2018-01-23 01:00:00,1.22672,1.22756,1.22626,1.22664,1515 2018-01-23 02:00:00,1.22662,1.22674,1.22512,1.22615,1250 2018-01-23 03:00:00,1.22611,1.22759,1.22548,1.22578,4124 2018-01-23 04:00:00,1.22573,1.22651,1.22551,1.22596,1189 2018-01-23 05:00:00,1.22598,1.22608,1.22515,1.22581,1066 2018-01-23 06:00:00,1.22581,1.2271,1.22443,1.22482,3058 2018-01-23 07:00:00,1.2248,1.22565,1.22384,1.22512,3427 2018-01-23 08:00:00,1.22514,1.2253,1.2223,1.22386,3903 2018-01-23 09:00:00,1.22382,1.22456,1.22312,1.22376,2345 2018-01-23 10:00:00,1.22374,1.22546,1.22374,1.22465,2376 2018-01-23 11:00:00,1.22468,1.22576,1.22364,1.22364,2334 2018-01-23 12:00:00,1.22366,1.22538,1.22338,1.22494,2294 2018-01-23 13:00:00,1.22496,1.22651,1.22458,1.22586,2634 2018-01-23 14:00:00,1.2259,1.22833,1.22516,1.22784,3708 2018-01-23 15:00:00,1.22786,1.23062,1.22768,1.2293,5201 2018-01-23 16:00:00,1.22929,1.22947,1.22706,1.22763,3521 2018-01-23 17:00:00,1.22758,1.22902,1.22753,1.2279,2019 2018-01-23 18:00:00,1.22791,1.22896,1.2276,1.22808,1890 2018-01-23 19:00:00,1.22806,1.22956,1.228,1.22956,1611 2018-01-23 20:00:00,1.22955,1.2297,1.22883,1.22938,825 2018-01-23 21:00:00,1.22934,1.22998,1.22928,1.2299,746 2018-01-23 22:00:00,1.23004,1.23004,1.22959,1.22974,305 2018-01-23 23:00:00,1.22968,1.23158,1.2294,1.23092,1693 2018-01-24 00:00:00,1.23092,1.2313,1.22914,1.23003,2169 2018-01-24 01:00:00,1.22997,1.23121,1.22932,1.2307,2212 2018-01-24 02:00:00,1.2307,1.23184,1.23016,1.231,3715 2018-01-24 03:00:00,1.23098,1.23164,1.23015,1.23143,1687 2018-01-24 04:00:00,1.23146,1.23356,1.23146,1.23214,2807 2018-01-24 05:00:00,1.23212,1.23255,1.2314,1.23249,1368 2018-01-24 06:00:00,1.23246,1.23291,1.23067,1.23092,3173 2018-01-24 07:00:00,1.23092,1.23198,1.23026,1.23124,3455 2018-01-24 08:00:00,1.23125,1.23449,1.23114,1.23404,5805 2018-01-24 09:00:00,1.23404,1.23563,1.23367,1.23369,5641 2018-01-24 10:00:00,1.23368,1.23482,1.23341,1.23374,3304 2018-01-24 11:00:00,1.23374,1.2349,1.23296,1.234,3164 2018-01-24 12:00:00,1.23401,1.23458,1.23305,1.23384,2801 2018-01-24 13:00:00,1.23384,1.23679,1.23384,1.23658,7281 2018-01-24 14:00:00,1.23654,1.23966,1.2363,1.23925,5598 2018-01-24 15:00:00,1.23927,1.24022,1.23802,1.23808,5367 2018-01-24 16:00:00,1.23814,1.23972,1.23766,1.23897,4256 2018-01-24 17:00:00,1.23893,1.23963,1.23824,1.23915,2432 2018-01-24 18:00:00,1.23917,1.24149,1.23902,1.24126,3465 2018-01-24 19:00:00,1.24122,1.24154,1.23978,1.24048,2544 2018-01-24 20:00:00,1.24047,1.24062,1.23946,1.2399,1624 2018-01-24 21:00:00,1.2399,1.24101,1.23978,1.2408,1573 2018-01-24 22:00:00,1.24104,1.24109,1.24006,1.2405,1216 2018-01-24 23:00:00,1.24048,1.24049,1.23902,1.2393,1043 2018-01-25 00:00:00,1.23926,1.24002,1.23845,1.23985,2900 2018-01-25 01:00:00,1.23986,1.2411,1.23894,1.24104,2965 2018-01-25 02:00:00,1.24102,1.24282,1.24056,1.24231,4617 2018-01-25 03:00:00,1.2423,1.24401,1.24202,1.24314,5198 2018-01-25 04:00:00,1.24312,1.24392,1.24258,1.24364,2532 2018-01-25 05:00:00,1.24362,1.24487,1.2426,1.2448,2441 2018-01-25 06:00:00,1.2448,1.24596,1.24228,1.24357,5894 2018-01-25 07:00:00,1.24352,1.24422,1.2409,1.24194,12088 2018-01-25 08:00:00,1.24191,1.24195,1.2389,1.23968,7607 2018-01-25 09:00:00,1.2397,1.24332,1.2397,1.24318,4369 2018-01-25 10:00:00,1.24316,1.24324,1.2408,1.24134,3774 2018-01-25 11:00:00,1.24133,1.24212,1.23987,1.24034,2468 2018-01-25 12:00:00,1.2403,1.24159,1.23972,1.24044,3963 2018-01-25 13:00:00,1.24045,1.24984,1.24022,1.24972,24445 2018-01-25 14:00:00,1.24973,1.25374,1.24623,1.24899,21503 2018-01-25 15:00:00,1.24898,1.25301,1.2484,1.25039,10393 2018-01-25 16:00:00,1.25041,1.25069,1.24838,1.24871,4975 2018-01-25 17:00:00,1.24866,1.24922,1.24774,1.24922,3503 2018-01-25 18:00:00,1.24923,1.25065,1.24734,1.24844,5107 2018-01-25 19:00:00,1.24846,1.24875,1.23642,1.23904,31054 2018-01-25 20:00:00,1.23905,1.24312,1.23884,1.24065,9978 2018-01-25 21:00:00,1.2407,1.2423,1.23874,1.2398,3213 2018-01-25 22:00:00,1.2394,1.24014,1.23699,1.2373,1307 2018-01-25 23:00:00,1.23718,1.23959,1.23716,1.23912,2730 2018-01-26 00:00:00,1.23914,1.24108,1.2386,1.2406,3564 2018-01-26 01:00:00,1.24058,1.24332,1.24033,1.24306,4653 2018-01-26 02:00:00,1.24304,1.24304,1.24123,1.24272,4517 2018-01-26 03:00:00,1.24272,1.24318,1.24224,1.24294,2188 2018-01-26 04:00:00,1.24294,1.24315,1.24204,1.24213,999 2018-01-26 05:00:00,1.24212,1.24326,1.24131,1.24313,1688 2018-01-26 06:00:00,1.24312,1.24526,1.24284,1.24508,4061 2018-01-26 07:00:00,1.24508,1.2494,1.24477,1.24745,7990 2018-01-26 08:00:00,1.2475,1.24908,1.2452,1.24668,6098 2018-01-26 09:00:00,1.2467,1.24678,1.2452,1.24556,4570 2018-01-26 10:00:00,1.24558,1.24671,1.24423,1.2459,4104 2018-01-26 11:00:00,1.24594,1.24652,1.24298,1.24386,4397 2018-01-26 12:00:00,1.24382,1.24502,1.24344,1.24408,4107 2018-01-26 13:00:00,1.24408,1.24576,1.24207,1.24286,7873 2018-01-26 14:00:00,1.24287,1.24478,1.24127,1.24412,9288 2018-01-26 15:00:00,1.24416,1.24458,1.24155,1.2433,6780 2018-01-26 16:00:00,1.2433,1.24374,1.24077,1.24216,6574 2018-01-26 17:00:00,1.24216,1.2442,1.2416,1.24388,4712 2018-01-26 18:00:00,1.24387,1.24505,1.24334,1.24446,2989 2018-01-26 19:00:00,1.24447,1.2448,1.24176,1.24208,7796 2018-01-26 20:00:00,1.24207,1.24235,1.24059,1.24196,2627 2018-01-26 21:00:00,1.24196,1.24321,1.24174,1.24296,2200 2018-01-28 22:00:00,1.2428,1.2428,1.24134,1.24164,755 2018-01-28 23:00:00,1.24164,1.24311,1.24161,1.2431,1552 2018-01-29 00:00:00,1.24304,1.24324,1.24207,1.2421,2431 2018-01-29 01:00:00,1.24212,1.2422,1.23956,1.23968,4632 2018-01-29 02:00:00,1.23967,1.24014,1.23852,1.23997,3975 2018-01-29 03:00:00,1.23996,1.24195,1.23982,1.24152,3504 2018-01-29 04:00:00,1.24156,1.24221,1.24117,1.2413,1067 2018-01-29 05:00:00,1.24135,1.24191,1.24054,1.24142,2235 2018-01-29 06:00:00,1.24142,1.2427,1.24046,1.24158,3341 2018-01-29 07:00:00,1.24158,1.24292,1.23944,1.24018,5236 2018-01-29 08:00:00,1.2402,1.24096,1.23884,1.24071,4379 2018-01-29 09:00:00,1.24073,1.24166,1.23942,1.24119,3991 2018-01-29 10:00:00,1.24124,1.2428,1.24037,1.24037,3256 2018-01-29 11:00:00,1.24032,1.24044,1.23912,1.23934,2338 2018-01-29 12:00:00,1.2393,1.24043,1.23772,1.23846,2968 2018-01-29 13:00:00,1.23842,1.23893,1.2359,1.23647,4045 2018-01-29 14:00:00,1.23648,1.23714,1.23452,1.23517,4145 2018-01-29 15:00:00,1.23518,1.23742,1.23498,1.23563,4324 2018-01-29 16:00:00,1.23563,1.2368,1.2337,1.23522,7734 2018-01-29 17:00:00,1.23523,1.2373,1.23448,1.23718,3241 2018-01-29 18:00:00,1.23719,1.23802,1.2369,1.23781,2005 2018-01-29 19:00:00,1.2378,1.23902,1.23734,1.23888,2067 2018-01-29 20:00:00,1.23888,1.23899,1.2381,1.23825,1450 2018-01-29 21:00:00,1.2382,1.23838,1.2378,1.23827,620 2018-01-29 22:00:00,1.2381,1.23861,1.23778,1.23823,1043 2018-01-29 23:00:00,1.23826,1.23866,1.23784,1.23808,1004 2018-01-30 00:00:00,1.23809,1.23863,1.23684,1.23763,2660 2018-01-30 01:00:00,1.23766,1.23884,1.23729,1.2385,2669 2018-01-30 02:00:00,1.2385,1.2385,1.23601,1.23694,3793 2018-01-30 03:00:00,1.23698,1.23837,1.23664,1.23785,2606 2018-01-30 04:00:00,1.23784,1.23866,1.2371,1.23717,3131 2018-01-30 05:00:00,1.23718,1.23763,1.23601,1.2364,2048 2018-01-30 06:00:00,1.23635,1.23656,1.2347,1.23484,2557 2018-01-30 07:00:00,1.23481,1.23705,1.2348,1.23555,3429 2018-01-30 08:00:00,1.23554,1.23848,1.23349,1.23824,5082 2018-01-30 09:00:00,1.23822,1.24076,1.23772,1.23986,6087 2018-01-30 10:00:00,1.23988,1.24242,1.23947,1.24126,4784 2018-01-30 11:00:00,1.24126,1.24414,1.24087,1.24396,3446 2018-01-30 12:00:00,1.2439,1.24395,1.24178,1.24264,3480 2018-01-30 13:00:00,1.2427,1.24482,1.24034,1.24454,4536 2018-01-30 14:00:00,1.24454,1.24543,1.24312,1.24418,5943 2018-01-30 15:00:00,1.2442,1.2443,1.2403,1.24103,6573 2018-01-30 16:00:00,1.24102,1.24205,1.23838,1.23907,5325 2018-01-30 17:00:00,1.23908,1.24079,1.2386,1.24052,2888 2018-01-30 18:00:00,1.24056,1.24066,1.2384,1.23956,2694 2018-01-30 19:00:00,1.2396,1.24058,1.23894,1.24056,2370 2018-01-30 20:00:00,1.24056,1.24062,1.23939,1.23942,1114 2018-01-30 21:00:00,1.2394,1.24036,1.2394,1.24024,594 2018-01-30 22:00:00,1.24028,1.24058,1.2401,1.24052,739 2018-01-30 23:00:00,1.24046,1.24135,1.23962,1.24085,1263 2018-01-31 00:00:00,1.24082,1.24248,1.24066,1.24177,3571 2018-01-31 01:00:00,1.24176,1.24208,1.24019,1.24147,4426 2018-01-31 02:00:00,1.24146,1.24152,1.23988,1.2414,4356 2018-01-31 03:00:00,1.24142,1.24338,1.2414,1.24236,4803 2018-01-31 04:00:00,1.24238,1.24279,1.24152,1.24196,3012 2018-01-31 05:00:00,1.24198,1.2438,1.24178,1.24372,2586 2018-01-31 06:00:00,1.24372,1.24504,1.24334,1.24494,3378 2018-01-31 07:00:00,1.24488,1.24628,1.24347,1.24444,5822 2018-01-31 08:00:00,1.24444,1.2451,1.24251,1.24376,4251 2018-01-31 09:00:00,1.24373,1.24561,1.24332,1.24512,4786 2018-01-31 10:00:00,1.24512,1.24601,1.24394,1.24442,4322 2018-01-31 11:00:00,1.24442,1.24561,1.24412,1.24546,2263 2018-01-31 12:00:00,1.24544,1.24602,1.24458,1.24595,2336 2018-01-31 13:00:00,1.24598,1.24728,1.24482,1.2465,5219 2018-01-31 14:00:00,1.2465,1.2466,1.2445,1.24586,3988 2018-01-31 15:00:00,1.24586,1.24748,1.24528,1.24564,5840 2018-01-31 16:00:00,1.2457,1.24634,1.2424,1.24272,5895 2018-01-31 17:00:00,1.24276,1.2437,1.24068,1.2415,3282 2018-01-31 18:00:00,1.24154,1.24232,1.24089,1.24183,3171 2018-01-31 19:00:00,1.24187,1.24384,1.2387,1.24112,17390 2018-01-31 20:00:00,1.24112,1.2424,1.2402,1.2415,5015 2018-01-31 21:00:00,1.2415,1.24176,1.24096,1.24138,882 2018-01-31 22:00:00,1.24098,1.24148,1.24046,1.24122,1411 2018-01-31 23:00:00,1.24128,1.24226,1.24122,1.24166,876 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2004-12-29,191.78,193.52,191.78,192.9,2678100 2004-12-30,192.97,198.23,191.85,197.6,5904300 2004-12-31,199.23,199.88,192.56,192.79,7668500 2005-01-03,197.4,203.64,195.46,202.71,15844200 2005-01-04,201.4,202.93,193.48,194.5,13755900 2005-01-05,193.45,196.9,192.23,193.51,8236600 2005-01-06,195.08,195.9,187.72,188.55,10387100 2005-01-07,190.64,194.25,188.78,193.85,9662900 2005-01-10,194.5,198.1,191.83,195.06,7539600 2005-01-11,195.62,197.71,193.18,193.54,6958700 2005-01-12,194.33,195.93,190.5,195.38,8177800 2005-01-13,195.38,197.39,194.05,195.33,6849400 2005-01-14,196,200.01,194.13,199.97,9640300 2005-01-18,200.97,205.02,198.66,203.9,13172600 2005-01-19,204.65,205.3,196.71,197.3,11257700 2005-01-20,192.5,196.25,192,193.92,9001600 2005-01-21,194.54,195.36,188.12,188.28,9258400 2005-01-24,188.69,189.33,180.32,180.72,14022700 2005-01-25,181.94,182.24,176.29,177.12,10659200 2005-01-26,179.27,189.41,179.15,189.24,12307900 2005-01-27,188.76,188.86,185.2,188.08,6627400 2005-01-28,190.02,194.7,186.34,190.34,12208200 2005-01-31,193.69,196.36,191.72,195.62,9596700 2005-02-01,194.38,196.66,190.63,191.9,18839000 2005-02-02,215.55,216.8,203.66,205.96,32799300 2005-02-03,205.99,213.37,205.81,210.86,12988100 2005-02-04,206.47,207.75,202.6,204.36,14819300 2005-02-07,205.26,206.4,195.51,196.03,12960400 2005-02-08,196.96,200.02,194.53,198.64,11480000 2005-02-09,200.76,201.6,189.46,191.58,17171500 2005-02-10,191.97,192.21,185.25,187.98,18982700 2005-02-11,186.66,192.32,186.07,187.4,13116000 2005-02-14,182.85,193.08,181,192.99,38562200 2005-02-15,193.6,199.84,193.08,195.23,25782800 2005-02-16,194.7,199.33,194.3,198.41,16532300 2005-02-17,197.83,199.75,196.81,197.9,10414400 2005-02-18,198.51,198.84,196.66,197.95,8485900 2005-02-22,196.5,198.9,190.39,191.37,13483700 2005-02-23,193.3,194.48,188.66,193.95,15586000 2005-02-24,183.37,189.85,182.23,188.89,25814300 2005-02-25,189.15,189.92,185.51,185.87,9973500 2005-02-28,186,189.87,185.85,187.99,7818400 2005-03-01,189.29,189.75,182,186.06,9311200 2005-03-02,185.95,187.67,184.36,185.18,7285500 2005-03-03,186.13,187.75,184.31,187.01,7608600 2005-03-04,186.7,187.25,185.07,185.9,6774100 2005-03-07,187.78,189.6,187.03,188.81,8667400 2005-03-08,189.1,189.85,184.97,185.2,8046100 2005-03-09,184.21,184.65,180.16,181.35,11360400 2005-03-10,181.01,181.2,177.4,179.98,10960500 2005-03-11,180.44,180.95,177.15,177.8,8028300 2005-03-14,178.33,178.4,172.57,174.99,11146600 2005-03-15,175.3,180,174.21,178.61,10422100 2005-03-16,176.7,178.61,175.01,175.6,7106300 2005-03-17,177.13,179.64,175.8,179.29,8260600 2005-03-18,178.81,180.4,178.31,180.04,7090000 2005-03-21,179.27,182.17,177.25,180.88,7483700 2005-03-22,181.18,181.94,177.85,178.6,5631700 2005-03-23,177.97,180.24,177.97,178.98,4845000 2005-03-24,180.7,180.86,179.2,179.25,3705200 2005-03-28,181.68,184.8,180.95,181.42,8738000 2005-03-29,181.05,183.28,178.07,179.57,6473000 2005-03-30,180.64,181.45,179.6,180.45,6236100 2005-03-31,177.95,181.39,177.64,180.51,6768600 2005-04-01,181.76,182.95,179.99,180.04,6182000 2005-04-04,179.95,185.32,179.84,185.29,8076400 2005-04-05,187.73,190.26,187.57,188.57,8736700 2005-04-06,189.24,189.65,187.58,189.22,5252600 2005-04-07,188.78,194.62,188.64,193.76,9692200 2005-04-08,193.69,195.1,191.45,192.05,5116600 2005-04-11,193.09,194.8,192.32,193.23,5410500 2005-04-12,193,194.42,189.41,193.96,7319600 2005-04-13,193.47,194.32,189.73,192.93,6555800 2005-04-14,193.27,194.36,190.1,191.45,6152700 2005-04-15,190.1,190.34,184.66,185,11577400 2005-04-18,184.58,187.88,183.49,186.97,6550300 2005-04-19,189.33,192,188.03,191.4,8430000 2005-04-20,198.58,200.5,195.91,198.1,15451500 2005-04-21,200.42,205,199.32,204.22,17751900 2005-04-22,222.9,224,214.26,215.81,33205100 2005-04-25,217.82,224.74,217.52,223.53,19840000 2005-04-26,220.22,222,218.29,218.75,17272000 2005-04-27,217.99,220.85,216.74,219.78,10264800 2005-04-28,219.5,222.08,217.71,219.45,8682800 2005-04-29,221.91,222.25,217.82,220,9170200 2005-05-02,222.05,223.7,220.21,222.29,9767400 2005-05-03,221.85,228.15,221.32,226.19,17780200 2005-05-04,227.23,229.88,227,228.5,12083500 2005-05-05,228.62,228.62,225.88,226.98,7509600 2005-05-06,228.4,229.25,226.47,228.02,6763900 2005-05-09,228,228.5,225.43,226.02,5536800 2005-05-10,225.47,227.8,224.72,227.8,6345800 2005-05-11,228.97,231.98,227.93,231.29,11478800 2005-05-12,230.81,232.23,228.2,228.72,8948200 2005-05-13,229.18,231.09,227.32,229.24,7415500 2005-05-16,229.68,231.62,228.57,231.05,5681400 2005-05-17,230.56,233.45,230.2,233.13,7808900 2005-05-18,233.61,239.97,233.52,239.16,12312000 2005-05-19,240.34,241.17,238.27,239.18,9716500 2005-05-20,241.21,241.67,239.65,241.61,8163500 2005-05-23,243.16,258.1,242.71,255.45,21388300 2005-05-24,256.96,265.44,253.5,256,29043100 2005-05-25,252.73,260.98,250.63,260.81,18057900 2005-05-26,260.96,263.76,258.3,259.2,13546600 2005-05-27,260.46,266.05,259.25,266,12184100 2005-05-31,269.43,278.4,269.37,277.27,22236800 2005-06-01,283.2,292.89,282.02,288,35191700 2005-06-02,288.73,289.78,284.6,287.9,17974100 2005-06-03,286.79,289.3,277.41,280.26,18782300 2005-06-06,282.39,293.75,281.83,290.94,22525900 2005-06-07,297.1,299.59,290.3,293.12,24323000 2005-06-08,292.85,293.19,278,279.56,25700900 2005-06-09,284.72,288.5,280.56,286.31,16441100 2005-06-10,286.99,287.28,280.02,282.5,12696600 2005-06-13,279.82,284.19,276.52,282.75,12803200 2005-06-14,278.59,281.24,277.75,278.35,10091900 2005-06-15,275,277.3,267.43,274.8,20883100 2005-06-16,274.26,278.3,273.07,277.44,12462400 2005-06-17,279,280.3,275.9,280.3,10434400 2005-06-20,276.09,287.67,271.73,286.7,21024700 2005-06-21,288.07,290.3,284.97,287.84,15132300 2005-06-22,289.67,292.32,288.67,289.3,10474000 2005-06-23,288,294.81,286.5,289.71,14056400 2005-06-24,290.9,298,289.58,297.25,17771200 2005-06-27,298.9,304.47,293.86,304.1,17802900 2005-06-28,306.28,309.25,302,302,19036500 2005-06-29,302.5,304.38,292.15,292.72,18298700 2005-06-30,294.34,298.93,291.04,294.15,15094400 2005-07-01,295.04,296.24,289.22,291.25,9227600 2005-07-05,292.1,295.98,290.23,295.71,7494000 2005-07-06,297.3,297.6,291.38,291.52,8000300 2005-07-07,289.39,295.8,288.51,295.54,10672100 2005-07-08,296.25,297.5,294.05,296.23,7457600 2005-07-11,296.4,296.6,291.02,293.35,8390300 2005-07-12,293.39,294.4,290.93,291.78,5864900 2005-07-13,292.51,299.24,292.1,298.86,11437900 2005-07-14,305.34,306.75,300.07,300.89,10667700 2005-07-15,301.24,303.4,299.78,301.19,8438400 2005-07-18,300,301.9,297.75,299.54,6207800 2005-07-19,302.1,310.35,301.8,309.9,12621400 2005-07-20,305.57,312.61,301.8,312,14310400 2005-07-21,314.05,317.8,311.21,313.94,19789400 2005-07-22,306.37,309.25,296.33,302.4,23386800 2005-07-25,302.39,303.29,294.96,295.85,9658800 2005-07-26,295.01,298,292.09,296.09,9816900 2005-07-27,297.74,298.23,292.4,296.93,7217900 2005-07-28,297.41,297.41,293.28,293.5,5925600 2005-07-29,292.14,292.84,286.99,287.76,8363300 2005-08-01,288.12,292.5,288.1,291.61,5662400 2005-08-02,291.6,299.52,291.12,299.19,7290200 2005-08-03,298,299.72,295.6,297.3,5930600 2005-08-04,295.55,299,295.25,297.73,5236500 2005-08-05,297.5,298.51,291.31,292.35,5939700 2005-08-08,293.6,295.65,290.49,291.25,4481800 2005-08-09,291.96,292.68,288.51,291.57,5779300 2005-08-10,291.3,292.33,284.88,285.68,6879000 2005-08-11,285.89,286.58,280.62,284.05,7514900 2005-08-12,283.36,290.2,281.64,289.72,6585900 2005-08-15,289.8,292.77,283.77,284,8174700 2005-08-16,284.88,287.79,283.34,285.65,7109200 2005-08-17,285.51,286.57,284,285.1,3883300 2005-08-18,275.91,280.5,275,279.99,11872800 2005-08-19,280.99,281.45,279.62,280,5542900 2005-08-22,281.24,281.47,273.35,274.01,6813000 2005-08-23,276.16,279.74,274.12,279.58,5821700 2005-08-24,277.57,284.75,276.45,282.57,8593100 2005-08-25,282.55,284,279.97,282.59,4376600 2005-08-26,283.48,285.02,282.66,283.58,3755300 2005-08-29,282.24,289.12,282.24,288.45,5903000 2005-08-30,287.39,289.51,285.88,287.27,4792000 2005-08-31,288.23,288.5,284.36,286,5034000 2005-09-01,285.91,287.5,285,286.25,2742100 2005-09-02,286.51,289.99,286.44,288.45,3434500 2005-09-06,289,289.39,286.8,287.11,4212300 2005-09-07,285.89,295.5,285.28,294.87,7499500 2005-09-08,294.83,299.28,293.36,295.39,6613300 2005-09-09,297.28,299.1,296.56,299.09,4390500 2005-09-12,301.75,311.42,301,309.74,10386500 2005-09-13,309,315.53,306.17,311.68,10299900 2005-09-14,308.73,313.28,300.3,303,11275800 2005-09-15,299.52,306.75,297.91,302.62,15466200 2005-09-16,304.02,304.5,299.87,300.2,7579800 2005-09-19,301,306,300.71,303.79,5761900 2005-09-20,306.15,311.3,305.23,307.91,9351000 2005-09-21,308.41,313.76,305.96,311.9,10119700 2005-09-22,311.5,319.22,310.17,311.37,13006400 2005-09-23,313,317.21,312.59,315.36,8483800 2005-09-26,319.5,320.95,312.56,314.28,9894400 2005-09-27,314.95,318.41,313.38,313.94,6873100 2005-09-28,314.22,315.1,305.6,306,7997400 2005-09-29,306.68,310.72,306.08,309.62,5613800 2005-09-30,314.22,317.5,312.29,316.46,9151300 2005-10-03,313.63,320.11,312.79,318.68,9160300 2005-10-04,319.95,321.28,310.74,311,9144300 2005-10-05,312.69,314.9,308,310.71,8328400 2005-10-06,314.14,314.48,310.09,312.75,7993800 2005-10-07,314.79,316.67,310.54,312.99,6770300 2005-10-10,313.31,314.82,309.15,310.65,5572200 2005-10-11,310.61,312.65,304.86,306.1,8542600 2005-10-12,305.2,307.19,299,300.97,9306200 2005-10-13,302,302,290.68,297.44,10567700 2005-10-14,299.9,300.23,292.54,296.14,8519100 2005-10-17,297.5,305.2,294.56,305,7566700 2005-10-18,304.96,307.96,302.74,303.28,7077800 2005-10-19,304,309.87,303.96,308.7,7010700 2005-10-20,309.99,311.13,301.21,303.2,13911700 2005-10-21,345.8,346.43,333,339.9,22892400 2005-10-24,343.37,349.3,342.19,348.65,9431700 2005-10-25,345.78,347.4,342.86,346.91,6878300 2005-10-26,346.28,356,346.19,355.44,8907500 2005-10-27,356.6,357.09,351.68,353.06,5134400 2005-10-28,355.27,358.95,355.02,358.17,5903500 2005-10-31,360.24,374.75,359.51,372.14,14342900 2005-11-01,371.86,383.9,369.01,379.38,16356100 2005-11-02,381.7,385,377.17,379.68,10565400 2005-11-03,382.41,386.58,381.38,385.95,7448400 2005-11-04,389.98,391.79,385.45,390.43,8824900 2005-11-07,395.1,397.47,392.15,395.03,9591500 2005-11-08,394.25,395.59,388.58,389.9,7897500 2005-11-09,386.67,388.29,378.03,379.15,10466900 2005-11-10,378.36,391.35,377.43,391.1,9128700 2005-11-11,395.12,396.9,388.85,390.4,7063900 2005-11-14,392.12,398.22,391.53,396.97,7807900 2005-11-15,394.38,397,390.95,392.8,8624900 2005-11-16,396.2,398.85,394.11,398.15,8695200 2005-11-17,401.8,403.81,399.53,403.45,9212200 2005-11-18,403.49,404.5,399.85,400.21,7025700 2005-11-21,399.17,409.98,393.49,409.36,10335100 2005-11-22,408.65,417.31,406.23,416.47,9596000 2005-11-23,417.04,424.72,415.78,422.86,10085000 2005-11-25,425.78,428.75,425.3,428.62,4840100 2005-11-28,429.82,431.24,422.44,423.48,11008400 2005-11-29,424.46,426.4,402.14,403.54,21495800 2005-11-30,404.26,408.45,395.56,404.91,15596600 2005-12-01,409.2,415.44,408.29,414.09,9744900 2005-12-02,416.94,419.53,413.86,417.7,7543500 2005-12-05,417,417.5,404.28,405.85,10289400 2005-12-06,408.7,416.41,401.7,404.54,15114700 2005-12-07,406.16,406.7,399.01,404.22,11665900 2005-12-08,405.3,410.65,402.64,410.65,8910100 2005-12-09,415,415.78,408.56,409.2,7643400 2005-12-12,414.63,415.21,409.95,412.61,6950100 2005-12-13,412.5,418,411.64,417.49,8157000 2005-12-14,417.04,419.73,415.49,418.96,6630400 2005-12-15,419.11,423.14,416.5,422.55,6045800 2005-12-16,425.34,432.5,422.75,430.15,16330500 2005-12-19,432.2,446.21,420.11,424.6,21936800 2005-12-20,427.86,432.2,424.67,429.74,10084700 2005-12-21,433.55,436.86,420.71,426.33,11221900 2005-12-22,431.77,432.86,425.93,432.04,7546600 2005-12-23,432.15,432.5,428.78,430.93,4595100 2005-12-27,431.86,431.86,422.76,424.64,6702800 2005-12-28,424.34,427.78,421.26,426.69,7117900 2005-12-29,427.98,428.73,419.17,420.15,6945800 2005-12-30,417.27,418.21,413.74,414.86,7587100 2006-01-03,422.52,435.67,418.22,435.23,13121200 2006-01-04,443.9,448.96,439.75,445.24,15286400 2006-01-05,446,451.55,441.5,451.24,10808300 2006-01-06,456.87,470.5,453.24,465.66,17756900 2006-01-09,466.41,473.4,460.94,466.9,12791900 2006-01-10,464.42,470.25,462.04,469.76,9097100 2006-01-11,471.27,475.11,469.18,471.63,9007400 2006-01-12,473.72,474.99,461.5,463.63,10125300 2006-01-13,464.31,466.89,461.61,466.25,7656600 2006-01-17,463.06,469.9,462.53,467.11,8270300 2006-01-18,447.3,457.36,443.25,444.91,20485700 2006-01-19,451.17,453.49,433,436.45,14537300 2006-01-20,438.7,440.03,394.74,399.46,41116700 2006-01-23,407.38,428.39,405.73,427.5,22741400 2006-01-24,436.03,444.95,434.48,443.03,15464600 2006-01-25,451.26,454.23,429.22,433,18739800 2006-01-26,439.54,439.99,423.56,434.27,12926100 2006-01-27,435,438.22,428.98,433.49,8452200 2006-01-30,429.23,433.28,425,426.82,8588900 2006-01-31,430.57,439.6,423.97,432.66,22066000 2006-02-01,389.03,402,387.52,401.78,27122500 2006-02-02,403.82,406.5,395.98,396.04,11807700 2006-02-03,393.62,393.9,372.57,381.55,18281800 2006-02-06,385.31,389.9,379.56,385.1,8940400 2006-02-07,382.99,383.7,363.35,367.92,16630200 2006-02-08,368.48,370.69,354.67,369.08,20804100 2006-02-09,371.2,374.4,356.11,358.77,11912400 2006-02-10,361.95,364.5,353.14,362.61,15223500 2006-02-13,346.64,350.6,341.89,345.7,19717800 2006-02-14,345.33,351.69,342.4,343.32,14654000 2006-02-15,341.27,346,337.83,342.38,12947000 2006-02-16,345.67,367,344.49,366.46,21315500 2006-02-17,369.86,372.14,363.62,368.75,14320200 2006-02-21,366.44,373.54,365.11,366.59,8686000 2006-02-22,367.15,368.95,363.86,365.49,6476200 2006-02-23,365.61,381.24,365.39,378.07,12551600 2006-02-24,377.3,380.07,373.49,377.4,6484300 2006-02-27,381.27,391.7,380.28,390.38,10212200 2006-02-28,393.2,397.54,338.51,362.62,39437600 2006-03-01,368.56,369.45,361.3,364.8,12061200 2006-03-02,364.28,381.1,362.2,376.45,18330300 2006-03-03,384.3,387.24,375.76,378.18,11962000 2006-03-06,380.91,383.4,367.14,368.1,8939700 2006-03-07,365.02,368.45,358.15,364.45,10378800 2006-03-08,353.93,360.03,350.54,353.88,11745600 2006-03-09,355.39,358.53,341.5,343,13910400 2006-03-10,343.5,344.5,331.55,337.5,19325600 2006-03-13,340.93,346.1,335.45,337.06,13642400 2006-03-14,337.14,352.37,332.62,351.16,18450700 2006-03-15,350.77,352.3,340.53,344.5,12768800 2006-03-16,348.61,348.75,337.9,338.77,10016700 2006-03-17,338.8,341.78,334.93,339.79,8551700 2006-03-20,342.34,350.09,341.54,348.19,10407600 2006-03-21,350.01,351.66,339.08,339.92,9831100 2006-03-22,339.75,344.1,337.5,340.22,7596000 2006-03-23,342.35,345.75,340.2,341.89,7434700 2006-03-24,368.62,370.09,362.51,365.8,15180600 2006-03-27,367.09,371.71,365,369.69,7023700 2006-03-28,371.71,377.86,371.17,377.2,8945800 2006-03-29,379.94,399,379.51,394.98,19027500 2006-03-30,389.19,393.5,383.61,388.44,14711700 2006-03-31,388.74,391.87,384.03,390,36521400 2006-04-03,389.53,392.47,387.93,389.7,8122700 2006-04-04,389.9,404.9,388.14,404.34,15715700 2006-04-05,408.2,414.57,402.82,407.99,13410500 2006-04-06,406.49,413.89,405.43,411.18,8598500 2006-04-07,412.41,412.85,404.02,406.16,7025900 2006-04-10,407.08,417.17,405.25,416.38,9320100 2006-04-11,416.42,419.1,406.22,409.66,11107200 2006-04-12,409,411.33,405.19,408.95,6017000 2006-04-13,408.63,409.76,400.5,402.16,6552900 2006-04-17,403.45,412.5,400.84,406.82,8259500 2006-04-18,407.93,409.83,401.5,404.24,8137600 2006-04-19,412.57,413.64,406.73,410.5,6781700 2006-04-20,411.01,416,408.2,415,12271500 2006-04-21,448.9,450.72,436.17,437.1,22551300 2006-04-24,439.4,444.7,436.52,440.5,8836400 2006-04-25,439.63,441.04,426,427.16,9569000 2006-04-26,427.74,430.04,423.53,425.97,7277800 2006-04-27,422.91,426.91,419.39,420.03,8337900 2006-04-28,418.63,425.73,416.3,417.94,7421300 2006-05-01,418.47,419.44,398.55,398.9,10361200 2006-05-02,401.08,402.49,388.4,394.8,13104300 2006-05-03,396.35,401.5,390.88,394.17,8072200 2006-05-04,395.03,398.87,392.21,394.75,4652000 2006-05-05,397.6,400.68,391.78,394.3,6065000 2006-05-08,395.11,397.12,390.05,394.78,5118600 2006-05-09,395.7,409,393.75,408.8,9140600 2006-05-10,408.31,411.71,401.86,402.98,6187200 2006-05-11,403.42,404.71,384.98,387,8892800 2006-05-12,383.54,384.87,373.55,374.13,10087600 2006-05-15,375.93,380.15,368.25,376.2,8590100 2006-05-16,375.99,376.86,369.89,371.3,6491100 2006-05-17,370.61,379.84,370.22,374.5,10643800 2006-05-18,378.78,381.81,370.71,370.99,5835000 2006-05-19,373.28,374.5,360.57,370.02,11398200 2006-05-22,367.85,373.03,365.25,370.95,8604400 2006-05-23,374.21,383.88,373.56,375.58,8983000 2006-05-24,377.35,383.44,371.61,381.25,9553800 2006-05-25,379.08,383,372.31,382.99,8194600 2006-05-26,384.55,385.88,380.03,381.35,3667000 2006-05-30,378.28,381,371.45,371.94,4316000 2006-05-31,373.8,378.25,366.78,371.82,7981300 2006-06-01,373.54,382.99,371.6,382.62,6278000 2006-06-02,386.84,387.08,377.45,379.44,6386400 2006-06-05,376.18,381.45,374.15,374.44,5558500 2006-06-06,376.58,390,376.3,389.99,10259800 2006-06-07,393.24,394.86,386.5,386.51,8911300 2006-06-08,387.75,394.27,378.59,393.3,10359500 2006-06-09,392.19,395.43,385.35,386.57,6157500 2006-06-12,388.34,390.49,381,381.54,5019100 2006-06-13,380.9,387,378.12,386.52,7659100 2006-06-14,389.83,391.1,378.52,384.39,7772000 2006-06-15,386.62,392.25,383,391,6785700 2006-06-16,389.1,390.93,388,390.7,5304600 2006-06-19,390.85,394.8,386.98,388.14,7633100 2006-06-20,388.03,391.87,386.51,387.17,4039900 2006-06-21,391.06,404,389.75,402.13,8744400 2006-06-22,401.58,406,388,399.95,5911900 2006-06-23,402.76,409.75,400.74,404.86,5314800 2006-06-26,406.75,408.3,403.25,404.22,3551200 2006-06-27,405.71,408,401.01,402.32,4107100 2006-06-28,404.01,406.48,401.13,406.11,3710500 2006-06-29,407.99,418.2,405.82,417.81,6658200 2006-06-30,415.6,419.33,412.33,419.33,6258000 2006-07-03,420.04,423.77,419.45,423.2,2156700 2006-07-05,421.52,422.8,415.64,421.46,4985600 2006-07-06,423.38,425.38,421.98,423.19,3687100 2006-07-07,426.05,427.89,415.88,420.45,6041900 2006-07-10,423.44,425.23,416.38,418.2,4436400 2006-07-11,418.51,425.05,413.03,424.56,5971300 2006-07-12,422.09,422.74,416.73,417.25,4906700 2006-07-13,414,418.34,406.83,408.83,6924500 2006-07-14,410.33,411.49,398.61,403.5,7552100 2006-07-17,404.63,411,403.72,407.89,5811900 2006-07-18,409.75,410.57,397.74,403.05,8536800 2006-07-19,395.01,401.14,394.66,399,8518500 2006-07-20,404.28,404.44,385.66,387.12,12538700 2006-07-21,386.14,391.75,377.69,390.11,11754600 2006-07-24,392.82,393.89,381.21,390.9,8086100 2006-07-25,385.02,391.31,383.8,389.36,5761100 2006-07-26,388.2,391.91,383,385.5,5531900 2006-07-27,387.37,387.49,377.95,382.4,5641100 2006-07-28,382,389.56,381.73,388.12,4083600 2006-07-31,388,389.17,383.31,386.6,4595300 2006-08-01,385.11,385.77,375.51,375.51,5463200 2006-08-02,375.6,377.17,365.2,367.23,7097800 2006-08-03,364.98,377.91,363.36,375.39,6327000 2006-08-04,379.56,380.68,371.75,373.85,5095200 2006-08-07,371.5,379.73,371.15,377.95,3946900 2006-08-08,382.82,384.5,379.09,381,5743200 2006-08-09,382.8,384.68,376.36,376.94,4311000 2006-08-10,373.88,377.67,372.46,374.2,4261900 2006-08-11,374.4,375.28,368,368.5,3766500 2006-08-14,371.5,375.13,368.67,369.43,4968300 2006-08-15,374.11,381.67,372.6,380.97,6698200 2006-08-16,383.48,388.45,382.12,387.72,5853200 2006-08-17,386.39,390,383.92,385.8,5080200 2006-08-18,386.31,387.09,380.75,383.36,4952200 2006-08-21,378.1,379,375.22,377.3,4023300 2006-08-22,377.73,379.26,374.84,378.29,4164100 2006-08-23,377.64,378.27,372.66,373.43,3642300 2006-08-24,374.44,376.4,372.26,373.73,3482500 2006-08-25,373.08,375.32,372.5,373.26,2466700 2006-08-28,375.61,380.95,375,380.95,4164000 2006-08-29,380.78,382.32,377.2,378.95,4460000 2006-08-30,379.21,384.65,378.51,380.75,4044400 2006-08-31,381.49,382.15,378.2,378.53,2959900 2006-09-01,380.99,381.28,377.19,378.6,2672900 2006-09-05,379.87,385.4,377.44,384.36,4074300 2006-09-06,382.1,383.19,379.66,380.14,3724100 2006-09-07,379.39,381.75,377.4,378.49,3842000 2006-09-08,376.72,380.79,376.72,377.85,3083400 2006-09-11,378.26,384.69,377.77,384.09,4529200 2006-09-12,385,392.73,384.88,391.9,5442200 2006-09-13,395.15,406.76,395.1,406.57,9768200 2006-09-14,404.3,406.28,401.93,403.98,5366100 2006-09-15,407.48,410.05,406.74,409.88,7838200 2006-09-18,410,418.69,409.47,414.69,7106700 2006-09-19,415.46,415.49,392.74,403.81,14292900 2006-09-20,407.1,407.39,394.62,397,9147800 2006-09-21,400.3,408.45,399.86,406.85,10692100 2006-09-22,404.98,407.45,401.36,403.78,4649600 2006-09-25,405.58,409.45,402.5,403.98,5737300 2006-09-26,405.5,407.68,401.77,406.87,5289400 2006-09-27,406.3,411.22,402.37,402.92,5876700 2006-09-28,404.08,406.98,400.54,403.58,5107400 2006-09-29,405.13,405.62,401.41,401.9,3310900 2006-10-02,401.9,406,400.8,401.44,3651900 2006-10-03,401.29,406.46,398.19,404.04,5464700 2006-10-04,404.97,415.77,403.05,415.7,6661800 2006-10-05,414.7,418.24,410.86,411.81,5789800 2006-10-06,410.22,421.91,409.75,420.5,7336500 2006-10-09,424.8,431.95,423.42,429,7583300 2006-10-10,431.56,437.85,422.39,426.65,9788600 2006-10-11,425.02,429.91,423.76,426.5,5635400 2006-10-12,428.56,429.68,424,427.44,4844000 2006-10-13,427.76,429.5,425.56,427.3,3622500 2006-10-16,427.7,429.2,421.34,421.75,4319400 2006-10-17,420.3,423.75,416.7,420.64,5211000 2006-10-18,422.99,424.75,417.5,419.31,6017300 2006-10-19,420.23,429.5,419.57,426.06,11503500 2006-10-20,458.99,460.1,453.59,459.67,11647900 2006-10-23,462.28,484.64,460.37,480.78,15104500 2006-10-24,476.28,477.86,471.41,473.31,8660200 2006-10-25,477.49,488.5,475.11,486.6,9187500 2006-10-26,487.68,491.96,484.2,485.1,7031700 2006-10-27,483.9,485.24,472.49,475.2,6604000 2006-10-30,474.82,480.46,470.01,476.57,6563100 2006-10-31,478.06,482.16,473.84,476.39,6285400 2006-11-01,478.76,479.13,465.26,467.5,5426300 2006-11-02,467.5,473.73,466.38,469.91,5236700 2006-11-03,472.23,473.75,465.06,471.8,4907700 2006-11-06,473.77,479.66,472.33,476.95,4991500 2006-11-07,476.95,479.02,471.77,472.57,4897100 2006-11-08,470.35,481.74,468.6,475,7965000 2006-11-09,476.5,479.49,471.86,472.63,4879200 2006-11-10,473.78,474.72,470.29,473.55,2796700 2006-11-13,474.9,481.17,474.14,481.03,4341900 2006-11-14,480.7,489.95,480.5,489.3,7223400 2006-11-15,493.43,499.85,491.93,491.93,8370700 2006-11-16,495,497.68,492.56,495.9,5092600 2006-11-17,493.25,499.66,493,498.79,5511000 2006-11-20,498.4,498.4,492.65,495.05,5124500 2006-11-21,496.54,510,495.83,509.65,8427500 2006-11-22,510.97,513,505.78,508.01,4500700 2006-11-24,504.5,507.5,504,505,1732700 2006-11-27,501.37,501.78,484.75,484.75,7324700 2006-11-28,481.13,489.86,477.03,489.5,7797600 2006-11-29,494.24,494.74,482.25,484.65,6315300 2006-11-30,484.19,490.4,481.55,484.81,5577500 2006-12-01,485.98,488.39,478.5,480.8,5631400 2006-12-04,483,487.43,479.35,484.85,4899900 2006-12-05,487.4,489.44,484.89,487,4103000 2006-12-06,486.96,492.4,484.52,488.71,4450300 2006-12-07,490.23,491.8,482.42,482.64,4664300 2006-12-08,481.94,488.6,480,484.11,3974900 2006-12-11,484.92,488.9,483.8,483.93,3263400 2006-12-12,483.85,486.36,480.28,481.78,4181000 2006-12-13,484.69,485.5,477.02,478.99,4662100 2006-12-14,480.25,483.75,477.26,482.12,4748900 2006-12-15,482.64,484.11,479.84,480.3,5190800 2006-12-18,482.51,482.74,460.72,462.8,8016600 2006-12-19,461.72,469.31,458.5,468.63,6587000 2006-12-20,470,471.5,462.33,462.9,4367800 2006-12-21,464.18,465.25,452.34,456.2,6953300 2006-12-22,457.5,458.64,452.73,455.58,3988300 2006-12-26,456.52,459.47,454.59,457.53,2074300 2006-12-27,460,468.08,459.1,468.03,4231500 2006-12-28,467.12,468.58,462.25,462.56,3116200 2006-12-29,462.1,464.47,459.86,460.48,2559200 2007-01-03,466,476.66,461.11,467.59,7706500 2007-01-04,469,483.95,468.35,483.26,7887600 2007-01-05,482.5,487.5,478.11,487.19,6872100 2007-01-08,487.69,489.87,482.2,483.58,4754400 2007-01-09,485.45,488.25,481.2,485.5,5381400 2007-01-10,484.43,493.55,482.04,489.46,5968500 2007-01-11,497.2,501.75,496.18,499.72,7208200 2007-01-12,501.99,505,500,505,4473700 2007-01-16,507.55,513,503.3,504.28,7568900 2007-01-17,503.39,507.77,494.38,497.28,6699100 2007-01-18,494.52,496.48,487.43,487.83,5932000 2007-01-19,487.98,490.76,486.74,489.75,4978300 2007-01-22,492.5,492.65,478.5,480.84,5404300 2007-01-23,480.79,484.75,477.29,479.05,4665500 2007-01-24,484.45,499.54,483.29,499.07,6059300 2007-01-25,501,504.5,485.66,488.09,6368500 2007-01-26,490.93,497.9,487.03,495.84,5496500 2007-01-29,498,498.75,490.5,492.47,4775700 2007-01-30,494,498,491.22,494.32,4180500 2007-01-31,496.49,505,495.51,501.5,12206100 2007-02-01,506,506.01,481.53,481.75,15658700 2007-02-02,482.61,485,477.81,481.5,6286500 2007-02-05,477.5,478,466.19,467.16,7206900 2007-02-06,468.1,473.3,467.26,471.48,5321900 2007-02-07,473.82,474.35,468.78,470.01,4119800 2007-02-08,468.05,473.75,465.15,471.03,4076700 2007-02-09,471.65,472.68,461.5,461.89,4858600 2007-02-12,460.68,462.39,455.02,458.29,5754500 2007-02-13,459.15,462.78,457.26,459.1,4062600 2007-02-14,460,469.13,459.22,465.93,5698800 2007-02-15,466,466.13,460.72,461.47,4042400 2007-02-16,462.8,470.15,462.06,469.94,6177000 2007-02-20,468.47,472.75,464.71,472.1,4067600 2007-02-21,469.84,478.68,467.74,475.86,5640600 2007-02-22,478.69,484.24,474.39,475.85,5743900 2007-02-23,475.75,476.95,467.8,470.62,3882600 2007-02-26,472.83,475.25,463.75,464.93,3969900 2007-02-27,455,459.8,447.17,448.77,9312800 2007-02-28,450.41,453.67,443.04,449.45,8032300 2007-03-01,442.67,452.42,440,448.23,8685200 2007-03-02,445.11,448.7,438.68,438.68,6583600 2007-03-05,437.02,445.5,437,440.95,6355100 2007-03-06,447.47,459,447.38,457.55,7533700 2007-03-07,462.69,463.14,454.29,455.64,6534100 2007-03-08,459.22,465.5,454.1,454.72,5362800 2007-03-09,458,458.4,450.1,452.96,4977700 2007-03-12,452.57,455.25,451.11,454.75,3465400 2007-03-13,450.11,451.93,442.83,443.03,6377300 2007-03-14,443.23,448.66,439,448,8016900 2007-03-15,447.86,449.82,443.94,446.19,3944200 2007-03-16,445.65,446.7,439.89,440.85,5659100 2007-03-19,443.25,448.5,440.63,447.23,5197700 2007-03-20,445.79,447.6,443.6,445.28,3421500 2007-03-21,445.3,456.57,445.21,456.55,5798300 2007-03-22,455.61,462.17,452.53,462.04,5680700 2007-03-23,461.45,463.39,457.08,461.83,4111300 2007-03-26,460.55,465,455.62,465,4710300 2007-03-27,463.55,465.23,460.34,463.62,3741200 2007-03-28,461.87,465.44,460.15,461.88,4591600 2007-03-29,464.55,466,455,460.92,3988500 2007-03-30,462.1,463.4,456.14,458.16,3380200 2007-04-02,457.76,458.53,452.12,458.53,3448500 2007-04-03,464.05,474.25,464,472.6,6501800 2007-04-04,472.14,473,469.58,471.02,3778800 2007-04-05,471.3,472.09,469.62,471.51,2715800 2007-04-09,472.98,473,465.59,468.21,3062100 2007-04-10,467.09,470.79,465.16,466.5,2979300 2007-04-11,466.06,469.4,462.61,464.53,3812000 2007-04-12,464,468,462.24,467.39,2707900 2007-04-13,468.45,468.77,463.36,466.29,2794800 2007-04-16,468.46,476.99,468.15,474.27,5077900 2007-04-17,473.8,476.39,471.6,472.8,3210100 2007-04-18,471.26,479.9,469.53,476.01,5670500 2007-04-19,474.5,481.95,469.59,471.65,11009600 2007-04-20,490.52,492.5,482.02,482.48,12161500 2007-04-23,480.1,485,478.26,479.08,5674600 2007-04-24,478.61,479.98,475.55,477.53,3694700 2007-04-25,480,481.37,476.11,477.99,3966800 2007-04-26,478.1,484.45,477.11,481.18,4124900 2007-04-27,480.07,482.4,478.33,479.01,2925700 2007-04-30,479.15,481.35,471.38,471.38,3641200 2007-05-01,472.19,472.81,464.17,469,3658200 2007-05-02,468.65,471.08,465.73,465.78,3062700 2007-05-03,466.22,474.07,465.29,473.23,3594200 2007-05-04,470.12,474.84,465.88,471.12,3950000 2007-05-07,472.14,472.82,466.47,467.27,3020100 2007-05-08,466.13,468.17,464.73,466.81,2905100 2007-05-09,466.15,471.73,463.88,469.25,3889900 2007-05-10,467.04,469.49,461.02,461.47,3686300 2007-05-11,461.83,467,461,466.74,2944100 2007-05-14,465.48,467.51,460,461.78,3872700 2007-05-15,461.96,462.54,457.41,458,4119000 2007-05-16,462,473.14,459.02,472.61,6554200 2007-05-17,472.46,475.22,470.81,470.96,4660600 2007-05-18,472.03,472.7,469.75,470.32,3695900 2007-05-21,469.53,479.2,466.72,470.6,6159300 2007-05-22,473,479.01,473,475.86,3839000 2007-05-23,480.82,483.41,473.75,473.97,5060200 2007-05-24,475.15,479.2,471.5,474.33,4173600 2007-05-25,479.7,484.95,477.27,483.52,5348500 2007-05-29,485,491.8,484,487.11,5218000 2007-05-30,484.5,498.84,483,498.6,7245800 2007-05-31,500.56,508.78,497.06,497.91,8924300 2007-06-01,501,505.02,497.93,500.4,4799000 2007-06-04,497.91,510.51,497.59,507.07,7101000 2007-06-05,509.75,519,506.61,518.84,10447100 2007-06-06,516.75,520.78,515.26,518.25,7886700 2007-06-07,519.75,526.5,512.51,515.06,10630500 2007-06-08,516.2,519.64,509.46,515.49,6358200 2007-06-11,514.02,518.25,510,511.34,4647700 2007-06-12,508.71,511.67,503.17,504.77,6419500 2007-06-13,507.09,508.54,498.69,505.24,7034000 2007-06-14,505.38,505.88,501.7,502.84,4621200 2007-06-15,508.19,509,501.23,505.89,6174100 2007-06-18,506.18,516,504.24,515.2,4835900 2007-06-19,514.01,517.25,511.54,514.31,4355300 2007-06-20,516.96,518.75,509.06,509.97,4338200 2007-06-21,510.98,515.29,506.28,514.11,4409700 2007-06-22,516.42,524.99,516.1,524.98,7203700 2007-06-25,528.98,534.99,523.38,527.42,7925000 2007-06-26,532.73,533.2,526.24,530.26,5689500 2007-06-27,525,527.99,519.56,526.29,6123100 2007-06-28,524.88,529.5,523.8,525.01,4168400 2007-06-29,526.02,527.4,519.46,522.7,3880600 2007-07-02,525.49,531.85,524.2,530.38,3487600 2007-07-03,531.06,534.4,527.5,534.34,1871800 2007-07-05,535.56,544.4,532.15,541.63,4942900 2007-07-06,541.25,543.87,538.73,539.4,2747000 2007-07-09,543,548.74,540.26,542.56,3729800 2007-07-10,543.79,547,541.65,543.34,3856000 2007-07-11,543.61,546.5,540.01,544.47,3309300 2007-07-12,545.86,547.32,540.22,545.33,3441600 2007-07-13,547.91,552.67,547.25,552.16,5237100 2007-07-16,550.3,558.58,549.31,552.99,6599500 2007-07-17,555.04,557.73,552.38,555,4328600 2007-07-18,553.89,554.5,543.81,549.5,6080000 2007-07-19,553.46,553.52,542.24,548.59,11127200 2007-07-20,511.9,523.18,509.5,520.12,17772300 2007-07-23,519.01,520,512.15,512.51,6356700 2007-07-24,509.3,518.69,507.11,514,5572100 2007-07-25,516.98,517.02,505.56,509.76,5545000 2007-07-26,508.74,512.59,498.88,508,6883400 2007-07-27,508.53,516.62,505.5,511.89,5509100 2007-07-30,512.92,519.34,510.5,516.11,3963300 2007-07-31,520.23,520.44,510,510,4270500 2007-08-01,510.5,516.51,508.14,512.94,4421500 2007-08-02,513.72,514.99,509,511.01,3154900 2007-08-03,510.05,513.2,503,503,3176200 2007-08-06,503,510.15,502.5,510,3651500 2007-08-07,509.75,519.88,509.04,516.02,4264300 2007-08-08,519.34,525.78,517.09,525.78,4068800 2007-08-09,520.8,526.82,514.63,514.73,4846500 2007-08-10,510.18,518.72,505.63,515.75,5875200 2007-08-13,519.54,519.75,513.03,515.5,3179300 2007-08-14,515.72,517.4,508,508.6,3633700 2007-08-15,509,511.69,496.71,497.55,5409500 2007-08-16,492.02,496.43,480.46,491.52,8645600 2007-08-17,497.44,501,491.65,500.04,5479400 2007-08-20,502.46,502.56,496,497.92,2697300 2007-08-21,498.94,508.16,497.77,506.61,3610600 2007-08-22,509.96,516.25,509.25,512.75,3252700 2007-08-23,516,516.13,507,512.19,3076700 2007-08-24,512.61,515.55,508.5,515,2472700 2007-08-27,514.43,517.45,511.4,513.26,2325100 2007-08-28,511.53,514.98,505.79,506.4,3273900 2007-08-29,507.84,513.3,507.23,512.88,2549300 2007-08-30,512.36,515.4,510.58,511.4,2651700 2007-08-31,513.1,516.5,511.47,515.25,2977600 2007-09-04,515.02,528,514.62,525.15,3693700 2007-09-05,523.4,529.48,522.25,527.8,3312900 2007-09-06,529.36,529.83,518.24,523.52,3625900 2007-09-07,517.86,521.24,516.8,519.35,3663600 2007-09-10,521.28,522.07,510.88,514.48,3225800 2007-09-11,516.99,521.65,515.73,521.33,2703600 2007-09-12,520.53,527.98,519,522.65,2986000 2007-09-13,524.06,527.21,523.22,524.78,1891100 2007-09-14,523.2,530.27,522.22,528.75,2764900 2007-09-17,526.53,529.28,524.07,525.3,2197500 2007-09-18,526.52,537.25,524.27,535.27,4215700 2007-09-19,539.27,549.45,538.86,546.85,5526900 2007-09-20,547,556.8,546.03,552.83,5525000 2007-09-21,556.34,560.79,552.83,560.1,8011700 2007-09-24,561,571.46,560,568.02,5297000 2007-09-25,564,569.56,562.86,569,2730600 2007-09-26,570.4,571.79,563.81,568.16,3346100 2007-09-27,571.73,571.74,565.78,567.5,2056300 2007-09-28,567,569.55,564.12,567.27,2639500 2007-10-01,569.97,584.35,569.61,582.55,4711300 2007-10-02,583.38,596.81,580.01,584.39,7067500 2007-10-03,586.25,588.99,580.36,584.02,3879500 2007-10-04,585.09,585.09,577.06,579.03,2986700 2007-10-05,587.11,596,587.01,594.05,5068700 2007-10-08,595,610.26,593.95,609.62,5028000 2007-10-09,615.11,623.78,608.39,615.18,8767800 2007-10-10,621.36,625.68,616.8,625.39,5385600 2007-10-11,633.64,641.41,609,622,11799000 2007-10-12,623.98,638.4,618.24,637.39,6823700 2007-10-15,638.47,639.86,615.55,620.11,6943800 2007-10-16,618.49,625.92,611.99,616,6025300 2007-10-17,630.45,634,621.59,633.48,6030500 2007-10-18,635.41,641.37,628.5,639.62,12289200 2007-10-19,654.56,658.49,643.23,644.71,15789000 2007-10-22,638.67,655,636.28,650.75,6664400 2007-10-23,661.25,677.6,660,675.77,6793700 2007-10-24,672.71,677.47,659.56,675.82,7404200 2007-10-25,678.68,678.97,663.55,668.51,5795500 2007-10-26,674.03,676.54,668.06,674.6,3353900 2007-10-29,677.77,680,672.09,679.23,3066300 2007-10-30,677.51,699.91,677.51,694.77,6900600 2007-10-31,700.69,707,696.04,707,6876800 2007-11-01,702.79,713.72,701.78,703.21,6527200 2007-11-02,710.51,713.58,697.34,711.25,5841500 2007-11-05,706.99,730.23,706.07,725.65,8883700 2007-11-06,737.56,741.79,725,741.79,8436300 2007-11-07,741.13,747.24,723.14,732.94,8252900 2007-11-08,734.6,734.89,677.18,693.84,16512200 2007-11-09,675.78,681.88,661.21,663.97,11388100 2007-11-12,657.74,669.93,626.21,632.07,10227300 2007-11-13,644.99,660.92,632.07,660.55,8426100 2007-11-14,673.28,675.49,636.27,641.68,8094700 2007-11-15,638.57,647.5,624,629.65,6967700 2007-11-16,633.94,635.49,616.02,633.63,9042800 2007-11-19,629.59,636.77,618.5,625.85,5527400 2007-11-20,636.48,659.1,632.87,648.54,9840600 2007-11-21,643.77,669.97,642.08,660.52,7013500 2007-11-23,670,678.28,668.11,676.7,2738700 2007-11-26,680.2,693.4,665,666,6790100 2007-11-27,674.8,676.43,650.26,673.57,8904500 2007-11-28,682.11,694.3,672.14,692.26,7916500 2007-11-29,690.75,702.79,687.77,697,6208000 2007-11-30,711,711.06,682.11,693,7895500 2007-12-03,691.01,695,681.14,681.53,4325100 2007-12-04,678.31,692,677.12,684.16,4231800 2007-12-05,692.73,698.93,687.5,698.51,4209600 2007-12-06,697.8,716.56,697.01,715.26,4909000 2007-12-07,714.99,718,710.5,714.87,3852100 2007-12-10,715.99,724.8,714,718.42,3856200 2007-12-11,719.94,720.99,698.78,699.2,6139100 2007-12-12,714,714.32,688.5,699.35,6159100 2007-12-13,696.31,697.62,681.21,694.05,5040800 2007-12-14,687.51,699.7,687.26,689.96,3673500 2007-12-17,688,695.42,663.67,669.23,5486000 2007-12-18,674.16,676.71,652.5,673.35,7166700 2007-12-19,674.21,679.5,669,677.37,4421100 2007-12-20,685.83,691,680.61,689.69,4422200 2007-12-21,697.88,699.26,693.24,696.69,5382000 2007-12-24,694.99,700.73,693.06,700.73,1628400 2007-12-26,698.99,713.22,698.21,710.84,2530000 2007-12-27,707.07,716,700.74,700.74,2942500 2007-12-28,704.93,707.95,696.54,702.53,2562700 2007-12-31,698.57,702.49,690.58,691.48,2376200 2008-01-02,692.87,697.37,677.73,685.19,4306900 2008-01-03,685.26,686.85,676.52,685.33,3252500 2008-01-04,679.69,680.96,655,657,5359800 2008-01-07,653.94,662.28,637.35,649.25,6403400 2008-01-08,653,659.96,631,631.68,5339100 2008-01-09,630.04,653.34,622.51,653.2,6739700 2008-01-10,645.01,657.2,640.11,646.73,6334200 2008-01-11,642.7,649.47,630.11,638.25,4977000 2008-01-14,651.14,657.4,645.25,653.82,4447500 2008-01-15,645.9,649.05,635.38,637.65,5568200 2008-01-16,628.97,639.99,601.93,615.95,10560000 2008-01-17,620.76,625.74,598.01,600.79,8216800 2008-01-18,608.36,609.99,598.45,600.25,8539600 2008-01-22,562.03,597.5,561.2,584.35,9501500 2008-01-23,560.71,568,519,548.62,16965700 2008-01-24,558.8,579.69,554.14,574.49,9400900 2008-01-25,591.81,595,566.18,566.4,6966000 2008-01-28,570.97,572.24,548.6,555.98,5816700 2008-01-29,560.47,561.33,540.67,550.52,6283000 2008-01-30,549.19,560.43,543.51,548.27,7939600 2008-01-31,539.01,573,534.29,564.3,14871300 2008-02-01,528.67,536.67,510,515.9,17600500 2008-02-04,509.07,512.78,492.55,495.43,13157100 2008-02-05,489.43,509,488.52,506.8,11203300 2008-02-06,511.14,511.17,497.93,501.71,7636400 2008-02-07,496.86,514.19,494.76,504.95,7928900 2008-02-08,509.41,517.73,508.7,516.69,6828900 2008-02-11,520.52,523.71,513.4,521.16,5826000 2008-02-12,523.39,530.6,513.03,518.09,6662300 2008-02-13,522.5,534.99,518.69,534.62,6624700 2008-02-14,538.35,541.04,531,532.25,6476700 2008-02-15,528.31,532.66,524.33,529.64,5240100 2008-02-19,534.94,535.06,506.5,508.95,6350400 2008-02-20,503.51,511.01,498.82,509,6662200 2008-02-21,512.85,513.21,499.5,502.86,5677800 2008-02-22,502.06,509,497.55,507.8,5515900 2008-02-25,505.95,506.5,485.74,486.44,8372800 2008-02-26,461.2,466.47,446.85,464.19,23287300 2008-02-27,460.13,475.49,459.64,472.86,10121900 2008-02-28,470.5,479.09,467.36,475.39,6586900 2008-02-29,471.87,479.74,464.65,471.18,9425400 2008-03-03,471.51,472.72,450.11,457.02,7554500 2008-03-04,450.95,453.36,435.78,444.6,13621700 2008-03-05,445.25,454.17,444,447.7,7436600 2008-03-06,447.69,453.3,431.18,432.7,7470100 2008-03-07,428.88,440,426.24,433.35,8071800 2008-03-10,428.83,431,413.04,413.62,7987600 2008-03-11,425.26,440.15,424.65,439.84,8826900 2008-03-12,440.01,447.88,438.07,440.18,6651900 2008-03-13,432.67,446.98,428.78,443.01,7726600 2008-03-14,442.98,449.34,430.62,437.92,6574400 2008-03-17,427.99,433.71,412.11,419.87,7888200 2008-03-18,428.98,440.84,425.53,439.16,7237200 2008-03-19,441.11,447.5,431.67,432,6179000 2008-03-20,427.32,435.7,417.5,433.55,9913400 2008-03-24,438.43,465.78,437.72,460.56,6763500 2008-03-25,457.46,457.47,446,450.78,5831600 2008-03-26,452.59,462.87,449.29,458.19,5225700 2008-03-27,446,448.61,440.49,444.08,5832200 2008-03-28,447.46,453.57,434.31,438.08,4376200 2008-03-31,435.64,442.69,432.01,440.47,4446400 2008-04-01,447.74,466.5,446.87,465.71,6093100 2008-04-02,469.9,475.74,460.39,465.7,5999000 2008-04-03,461.73,463.29,448.13,455.12,6778400 2008-04-04,457.01,477.83,456.2,471.09,5897200 2008-04-07,477.03,485.44,473.53,476.82,5943500 2008-04-08,473.04,474.14,462.01,467.81,4547000 2008-04-09,469.13,472,457.54,464.19,6048100 2008-04-10,464.96,473.86,461.85,469.08,5072400 2008-04-11,464.07,467.26,455.01,457.45,4169700 2008-04-14,457.16,457.45,450.15,451.66,3842600 2008-04-15,458.13,459.72,443.72,446.84,4577600 2008-04-16,444.4,458.28,441,455.03,7630700 2008-04-17,455.63,459.37,446.52,449.54,13353000 2008-04-18,535.21,547.7,524.77,539.41,18235600 2008-04-21,539.39,542.59,530.29,537.79,7439700 2008-04-22,537.57,560.83,537.56,555,7938500 2008-04-23,557.94,559.31,540.95,546.49,4921500 2008-04-24,551.29,554.49,540.02,543.04,4135100 2008-04-25,549.02,553,542.73,544.06,4164400 2008-04-28,545.88,556.81,539,552.12,4008600 2008-04-29,550.83,563.4,550.01,558.47,4346000 2008-04-30,562.21,584.86,558.47,574.29,7903000 2008-05-01,578.31,594.93,576.97,593.08,6602800 2008-05-02,598.49,602.45,579.3,581.29,6998800 2008-05-05,598.86,599,587.13,594.9,6281000 2008-05-06,591,592,583,586.36,4629300 2008-05-07,590.27,599.49,576.43,579,6613000 2008-05-08,586.2,589.3,578.91,583.01,5122900 2008-05-09,579,585,571.3,573.2,4484900 2008-05-12,574.75,586.75,568.91,584.94,4863900 2008-05-13,586.23,587.95,578.55,583,5163500 2008-05-14,586.49,591.19,575.25,576.3,4375800 2008-05-15,579,582.95,575.61,581,4342700 2008-05-16,581.43,584.68,578.32,580.07,4274100 2008-05-19,578.55,588.88,573.52,577.52,5604500 2008-05-20,574.63,582.48,572.91,578.6,3313600 2008-05-21,578.52,581.41,547.89,549.99,6468100 2008-05-22,551.95,554.21,540.25,549.46,5076300 2008-05-23,546.96,553,537.81,544.62,4431500 2008-05-27,544.96,562.6,543.85,560.9,3865500 2008-05-28,567.94,571.49,561.1,568.24,4050400 2008-05-29,574.79,585.88,573.2,583,4845000 2008-05-30,583.47,589.92,581.3,585.8,3225200 2008-06-02,582.5,583.89,571.27,575,3674200 2008-06-03,576.5,580.5,560.61,567.3,4305300 2008-06-04,565.33,578,564.55,572.22,3363200 2008-06-05,577.08,588.04,576.21,586.3,3916700 2008-06-06,579.75,580.72,567,567,4734500 2008-06-09,568.06,570,545.4,557.87,5288300 2008-06-10,549.56,558.82,546.78,554.17,3657400 2008-06-11,556.24,557.34,544.46,545.2,3812900 2008-06-12,548.76,558,546.88,552.95,5491600 2008-06-13,561.49,575.7,561.34,571.51,6184400 2008-06-16,566.5,579.1,566.5,572.81,3542800 2008-06-17,576.35,578.07,568.38,569.46,3462900 2008-06-18,564.51,568.99,559.16,562.38,3381200 2008-06-19,555.35,563.78,550.81,560.2,5683100 2008-06-20,556.98,556.98,544.51,546.43,5983100 2008-06-23,545.36,553.15,542.02,545.21,3635900 2008-06-24,545.14,551.19,535.1,542.3,4672600 2008-06-25,544.97,557.8,543.67,551,4122200 2008-06-26,544.1,544.93,528.26,528.82,5659500 2008-06-27,527.68,530,515.09,528.07,5447500 2008-06-30,532.47,538,523.06,526.42,3765300 2008-07-01,519.58,536.72,517,534.73,4959900 2008-07-02,536.51,540.38,526.06,527.04,4223000 2008-07-03,530.88,539.23,527.5,537,2400500 2008-07-07,542.3,549,535.6,543.91,4255200 2008-07-08,545.99,555.19,540,554.53,4932400 2008-07-09,550.76,555.68,540.73,541.55,4154000 2008-07-10,545,549.5,530.72,540.57,4331700 2008-07-11,536.5,539.5,519.43,533.8,4981400 2008-07-14,539,540.06,515.45,521.62,4424800 2008-07-15,516.28,527.5,501.1,516.09,6071000 2008-07-16,514.04,536.5,510.6,535.6,4742200 2008-07-17,534.16,537.05,524.5,533.44,8787400 2008-07-18,498.35,498.98,478.19,481.32,11292400 2008-07-21,480.88,484.09,465.7,468.8,5901500 2008-07-22,466.72,480.25,465.6,477.11,4691500 2008-07-23,481.61,497.23,478.1,489.22,4894100 2008-07-24,496.7,496.87,475.62,475.62,3540900 2008-07-25,486.49,493.13,481.5,491.98,3183500 2008-07-28,492.09,492.09,475.13,477.12,3160000 2008-07-29,479.3,487.26,478,483.11,2802800 2008-07-30,485.5,486.02,472.81,482.7,3490700 2008-07-31,474.56,480.89,471.44,473.75,2865100 2008-08-01,472.51,473.22,462.5,467.86,3007900 2008-08-04,468.12,473.01,461.9,463,2487000 2008-08-05,467.59,480.08,466.33,479.85,3584500 2008-08-06,478.37,489.77,472.51,486.34,3375800 2008-08-07,482,484,476.41,479.12,2773800 2008-08-08,480.15,495.75,475.69,495.01,3739300 2008-08-11,492.47,508.88,491.78,500.84,4239300 2008-08-12,502,506.13,498,502.61,2755700 2008-08-13,501.6,503.54,493.88,500.03,3625500 2008-08-14,497.7,507.61,496.29,505.49,2918600 2008-08-15,506.99,510.66,505.5,510.15,3545700 2008-08-18,509.84,510,495.51,498.3,3333900 2008-08-19,490.43,498.28,486.63,490.5,3046500 2008-08-20,494.72,496.69,482.57,485,3982100 2008-08-21,482.92,489.9,479.27,486.53,3514100 2008-08-22,491.5,494.88,489.48,490.59,2297200 2008-08-25,486.11,497,481.5,483.01,2014300 2008-08-26,483.46,483.46,470.59,474.16,3308200 2008-08-27,473.73,474.83,464.84,468.58,4387100 2008-08-28,472.49,476.45,470.33,473.78,3029700 2008-08-29,469.75,471.01,462.33,463.29,3848200 2008-09-02,476.77,482.18,461.42,465.25,6111500 2008-09-03,468.73,474.29,459.58,464.41,4314600 2008-09-04,460,463.24,449.4,450.26,4848500 2008-09-05,445.49,452.46,440.08,444.25,4534300 2008-09-08,452.02,452.94,417.55,419.95,9017900 2008-09-09,423.17,432.38,415,418.66,7229600 2008-09-10,424.47,424.48,409.68,414.16,6226800 2008-09-11,408.35,435.09,406.38,433.75,6471400 2008-09-12,430.21,441.99,429,437.66,6028000 2008-09-15,424,441.97,423.71,433.86,6567400 2008-09-16,425.96,449.28,425.49,442.93,6990700 2008-09-17,438.48,439.14,413.44,414.49,9126900 2008-09-18,422.64,439.18,410.5,439.08,8589400 2008-09-19,461,462.07,443.28,449.15,10006000 2008-09-22,454.13,454.13,429,430.14,4407300 2008-09-23,433.25,440.79,425.72,429.27,5204200 2008-09-24,430.34,445,430.11,435.11,4242000 2008-09-25,438.84,450,435.98,439.6,5020300 2008-09-26,428,437.16,421.03,431.04,5292500 2008-09-29,419.51,423.51,380.71,381,10762900 2008-09-30,395.98,425.08,392.32,400.52,3086300 2008-10-01,411.15,416.98,403.1,411.72,6234800 2008-10-02,409.79,409.98,386,390.49,5984900 2008-10-03,397.35,412.5,383.07,386.91,7992900 2008-10-06,373.98,375.99,357.16,371.21,11220600 2008-10-07,373.33,374.98,345.37,346.01,11054400 2008-10-08,330.16,358.99,326.11,338.11,11826400 2008-10-09,344.52,348.57,321.67,328.98,8075000 2008-10-10,313.16,341.89,310.3,332,10597800 2008-10-13,355.79,381.95,345.75,381.02,8905500 2008-10-14,393.53,394.5,357,362.71,7784800 2008-10-15,354.65,359,338.83,339.17,6721400 2008-10-16,332.76,356.5,309.44,353.02,16239700 2008-10-17,378.96,386,363.55,372.54,14249200 2008-10-20,379.75,380.98,359.59,379.32,6753400 2008-10-21,372.39,383.78,362,362.75,5782000 2008-10-22,356.99,369.69,344,355.67,6560000 2008-10-23,353.65,358,337.99,352.32,6478900 2008-10-24,326.47,350.47,324.74,339.29,7359000 2008-10-27,334.81,343,325.6,329.49,6200700 2008-10-28,339.05,369.31,328.51,368.75,8105400 2008-10-29,365.79,371,352.37,358,9756600 2008-10-30,368.46,372,358.37,359.69,7988900 2008-10-31,356.16,371.96,354.27,359.36,7423300 2008-11-03,357.58,362.99,341.43,346.49,5954500 2008-11-04,353.44,372.36,345.5,366.94,7349900 2008-11-05,362.15,368.88,341.31,342.24,6946500 2008-11-06,339.97,344.42,325.81,331.22,8574800 2008-11-07,333.12,341.15,325.33,331.14,4681300 2008-11-10,328,329.44,309.47,318.78,8080100 2008-11-11,308.69,316.3,300.52,311.46,10146600 2008-11-12,302.05,312.49,287.76,291,10051100 2008-11-13,291.77,313,280,312.08,13234700 2008-11-14,303.25,324.99,302.56,310.02,9517100 2008-11-17,303,310.16,297.95,300.12,7543800 2008-11-18,301.57,303.73,285.35,297.42,8346100 2008-11-19,295.39,300.19,278.58,280.18,7834600 2008-11-20,274.89,282.94,259.04,259.56,9779400 2008-11-21,262.51,269.37,247.3,262.43,10244500 2008-11-24,269.26,269.95,249.01,257.44,10054700 2008-11-25,268.68,286.66,267.32,282.05,10771200 2008-11-26,280.28,295.46,276.2,292.09,6356600 2008-11-28,290.58,296.45,288.28,292.96,2565500 2008-12-01,286.68,287.38,265.98,265.99,5711200 2008-12-02,269.73,277.78,262.58,275.11,5839700 2008-12-03,269.85,281.36,265.34,279.43,5904800 2008-12-04,276.53,283.49,268.77,274.34,4886600 2008-12-05,271.02,284.24,264.02,283.99,6521200 2008-12-08,289.99,309.44,282,302.11,8144300 2008-12-09,297.69,318,297.01,305.97,6889900 2008-12-10,309.24,314.9,304.51,308.82,5237000 2008-12-11,304.17,312.88,297.8,300.22,6179200 2008-12-12,295.71,316.47,294,315.76,5722100 2008-12-15,314.01,318.49,305.11,310.67,6737900 2008-12-16,314.52,329.5,311.27,325.28,7059600 2008-12-17,318.64,322.13,312.42,315.24,5789700 2008-12-18,316.7,320.35,309.11,310.28,4763500 2008-12-19,310.99,317.79,309,310.17,5612600 2008-12-22,308.56,309.5,290.63,297.11,3917600 2008-12-23,300.43,303.31,296.67,298.02,3777700 2008-12-24,301.48,306.34,298.38,302.95,1921500 2008-12-26,304.07,305.26,298.31,300.36,1959100 2008-12-29,300.22,301.38,291.58,297.42,3701900 2008-12-30,300.8,306.81,298.71,303.11,3843500 2008-12-31,304.2,311,302.61,307.65,2886800 2009-01-02,308.6,321.82,305.5,321.32,3610500 2009-01-05,321,331.24,315,328.05,4889000 2009-01-06,332.98,340.8,326.39,334.06,6425200 2009-01-07,328.32,330.91,318.75,322.01,4494500 2009-01-08,318.28,325.19,317.34,325.19,3600700 2009-01-09,327.5,327.5,313.4,315.07,4340500 2009-01-12,316.31,318.95,310.23,312.69,3304300 2009-01-13,311.77,320.6,310.39,314.32,4432500 2009-01-14,310,313.8,297.75,300.97,5467900 2009-01-15,297.57,303.58,286.79,298.99,5934500 2009-01-16,305.02,308.25,295.7,299.67,5224400 2009-01-20,299.14,299.5,282.75,282.75,5048200 2009-01-21,288.35,303.5,288.35,303.08,4924500 2009-01-22,298.04,309.35,295.15,306.5,8267000 2009-01-23,309.27,331.96,304.22,324.7,10732800 2009-01-26,324.85,328,320.56,323.87,4610700 2009-01-27,326.45,333.87,324.27,331.48,4927300 2009-01-28,337.98,352.33,336.31,348.67,7691400 2009-01-29,344.54,345.05,340.11,343.32,7283800 2009-01-30,344.69,348.8,336,338.53,4672000 2009-02-02,334.29,345,332,340.57,5206900 2009-02-03,342.57,343,333.83,340.45,6556500 2009-02-04,340.07,354.44,339.17,343,6817400 2009-02-05,340.91,355.38,337,353.72,7264400 2009-02-06,356.46,373.81,355.44,371.28,7038100 2009-02-09,371.28,381,367.3,378.77,4977300 2009-02-10,375.98,377.5,357.89,358.51,7103700 2009-02-11,358.95,365,353,358.04,5231600 2009-02-12,353.16,363.62,351.48,363.05,5550300 2009-02-13,362.19,362.99,355.23,357.68,4146700 2009-02-17,346.51,347.09,339.69,342.66,5680400 2009-02-18,347.24,353.38,340.52,353.11,6024500 2009-02-19,357.47,359.8,341.41,342.64,4988700 2009-02-20,338.05,348.92,335,346.45,6217100 2009-02-23,347,349.8,329.55,330.06,5221100 2009-02-24,331.02,349.62,330.89,345.45,6095900 2009-02-25,342.15,352.3,338.92,341.64,6439100 2009-02-26,345.96,352.49,337.16,337.18,5605600 2009-02-27,332.95,343.82,331.11,337.99,5420000 2009-03-02,333.33,340.7,326,327.16,5788500 2009-03-03,330.07,333.69,322.35,325.48,6524800 2009-03-04,323.16,329,315.38,318.92,7818400 2009-03-05,316.48,319.08,302.64,305.64,6529900 2009-03-06,307.22,310.19,294.25,308.57,7234200 2009-03-09,299.98,306.57,289.45,290.89,6471300 2009-03-10,298.25,310.5,294.25,308.17,6730200 2009-03-11,310.1,320,305.71,317.91,5923600 2009-03-12,317.54,325,313.65,323.53,5024100 2009-03-13,326.1,327.46,319.03,324.42,3906400 2009-03-16,325.99,329.73,318.59,319.69,4946800 2009-03-17,320.18,335.34,319.09,335.34,4712500 2009-03-18,334.81,340,328.05,333.1,5012200 2009-03-19,331.68,336,327.38,329.94,4111200 2009-03-20,330.3,332.99,326.34,330.16,4737900 2009-03-23,333.56,349.45,333.03,348.6,4271500 2009-03-24,346.5,353.84,344,347.17,3820000 2009-03-25,350.4,351.34,336.25,344.07,4336300 2009-03-26,353.13,359.16,348.5,353.29,6003300 2009-03-27,350,352,345.47,347.7,3322800 2009-03-30,342.55,343.81,336.05,342.69,3094100 2009-03-31,348.93,353.51,346.18,348.06,3655300 2009-04-01,343.78,355.24,340.61,354.09,3301200 2009-04-02,363.31,369.76,360.32,362.5,4488000 2009-04-03,364.5,371.72,358,369.78,3789800 2009-04-06,367,369.82,361.4,368.24,3280300 2009-04-07,362.6,363.75,355.31,358.65,3680100 2009-04-08,363.5,365,356.21,362,2765200 2009-04-09,369.5,374.35,366.25,372.5,3382600 2009-04-13,371.33,379.1,370.3,378.11,3050100 2009-04-14,376.94,376.99,365.6,368.91,3428600 2009-04-15,367.1,381.06,364.16,379.5,4930700 2009-04-16,381.5,392.9,381.02,388.74,10185100 2009-04-17,386.02,399.82,384.81,392.24,10730800 2009-04-20,386.15,390.65,375.89,379.3,4428900 2009-04-21,376.17,384.3,376.1,381.47,3695400 2009-04-22,381.75,390,379.01,383.86,3501800 2009-04-23,387.51,389.75,381.11,384.69,2609000 2009-04-24,386.05,393.18,380.5,389.49,3385400 2009-04-27,384.34,389.49,382.75,385.95,2290600 2009-04-28,383.75,389.05,381.54,383.71,2943600 2009-04-29,385.97,394.97,385.83,391.47,3610000 2009-04-30,395.76,403.75,394.8,395.97,4355700 2009-05-01,395.03,397.59,391.55,393.69,2427700 2009-05-04,398.17,402.4,394.79,401.98,3203000 2009-05-05,399.98,405,397.25,402.99,2400800 2009-05-06,406.79,408.28,401,403.47,2632900 2009-05-07,404.1,404.99,392.5,396.61,2999800 2009-05-08,402.85,410.13,395,407.33,3865100 2009-05-11,402.8,412,401.2,407.98,2559300 2009-05-12,410.01,410.99,395.11,399.01,3790800 2009-05-13,394.09,396.39,388.35,389.54,2842800 2009-05-14,388.8,392.21,384.69,387.5,2937000 2009-05-15,391.1,394.11,389.09,390,3008700 2009-05-18,394.73,397.31,385.4,396.84,3351700 2009-05-19,396.1,401.64,393,398.88,2837500 2009-05-20,402.09,405.67,395,397.18,2284000 2009-05-21,396.3,402.84,393.84,396.5,2719800 2009-05-22,396.66,398.65,392,393.5,1718600 2009-05-26,391.95,405,390,404.36,3104500 2009-05-27,405.64,411.86,404.81,405.56,3034300 2009-05-28,408.68,411.62,404.61,410.4,2668800 2009-05-29,412.11,417.23,410.7,417.23,2648200 2009-06-01,418.73,429.6,418.53,426.56,3322400 2009-06-02,426.25,429.96,423.4,428.4,2623600 2009-06-03,426,432.46,424,431.65,3532800 2009-06-04,435.3,441.24,434.5,440.28,3638100 2009-06-05,445.07,447.34,439.46,444.32,3680800 2009-06-08,439.5,440.92,434.12,438.77,3098700 2009-06-09,438.58,440.5,431.76,435.62,3254900 2009-06-10,436.23,437.89,426.67,432.6,3358900 2009-06-11,431.77,433.73,428.37,429,2865200 2009-06-12,426.86,427.7,421.21,424.84,2918400 2009-06-15,421.5,421.5,414,416.77,3736900 2009-06-16,419.31,421.09,415.42,416,3049700 2009-06-17,416.19,419.72,411.56,415.16,3490100 2009-06-18,415.68,418.69,413,414.06,3085200 2009-06-19,418.21,420.46,414.58,420.09,4259100 2009-06-22,416.95,417.49,401.89,407.35,4124400 2009-06-23,406.65,408.99,402.55,405.68,2899600 2009-06-24,408.74,412.23,406.56,409.29,2457800 2009-06-25,407,415.9,406.51,415.77,3044500 2009-06-26,413.68,428.23,413.11,425.32,3256700 2009-06-29,426,427.8,422.24,424.14,2169300 2009-06-30,424,427.21,418.22,421.59,2593900 2009-07-01,424.2,426.4,418.15,418.99,2310800 2009-07-02,415.41,415.41,406.81,408.49,2517600 2009-07-06,406.5,410.64,401.66,409.61,2262600 2009-07-07,408.24,409.19,395.98,396.63,3259300 2009-07-08,400,406,398.06,402.49,3441200 2009-07-09,406.12,414.45,405.8,410.39,3275600 2009-07-10,409.58,417.37,408.7,414.4,2926600 2009-07-13,416.17,424.52,415.18,424.3,4045700 2009-07-14,423.71,426.73,420.87,424.69,2895800 2009-07-15,429.66,438.68,428.49,438.17,3777000 2009-07-16,436.68,445.75,434.1,442.6,6554500 2009-07-17,433,435.48,426.7,430.25,6854100 2009-07-20,429.88,432.85,426.25,430.17,3153500 2009-07-21,430.94,431.9,425.72,427.9,2968200 2009-07-22,428,430.2,423.5,427.69,2586700 2009-07-23,428.68,441.21,425.5,437.34,3478200 2009-07-24,435.81,450.49,435,446.72,3626900 2009-07-27,446.04,446.75,437.61,444.8,2504400 2009-07-28,441,442.81,436.1,439.85,2532500 2009-07-29,437.23,437.8,431.89,436.24,1987000 2009-07-30,442.61,451.47,442.61,445.64,3198600 2009-07-31,449.98,452.7,442.43,443.05,2860400 2009-08-03,448.74,453.9,447.64,452.21,2590300 2009-08-04,449.37,454,448.43,453.73,2389800 2009-08-05,456,456.91,447.88,451.14,2342000 2009-08-06,454.3,454.88,448.53,450.36,2110900 2009-08-07,455.67,459.42,454.99,457.1,2543100 2009-08-10,455.14,458.41,453.79,456.61,1742000 2009-08-11,453.67,457.23,452.38,453.94,1712500 2009-08-12,455.41,461.75,454.92,458.58,2341400 2009-08-13,462.15,464.72,458.91,462.28,1995200 2009-08-14,462.78,463.18,456.89,460,1675100 2009-08-17,451.5,451.99,443.12,444.89,2620200 2009-08-18,445.1,447.7,442.32,445.28,2351100 2009-08-19,439.99,445,438.56,443.97,2255000 2009-08-20,452,462.18,451.23,460.41,3998900 2009-08-21,465.54,466.09,462.65,465.24,3560500 2009-08-24,467.35,470.09,464.42,468.73,2453400 2009-08-25,469.13,474.35,468.72,471.37,2341000 2009-08-26,472.76,473,466.7,468,1987800 2009-08-27,468.58,468.58,460.73,466.06,1998900 2009-08-28,469.26,472.37,463.38,464.75,1771600 2009-08-31,459.79,461.86,458,461.67,1957900 2009-09-01,459.68,466.82,454.42,455.76,2594900 2009-09-02,455.82,458.33,452.59,453.01,1804800 2009-09-03,455.82,458.25,455,457.52,1646200 2009-09-04,457.57,462.6,455.78,461.3,1499200 2009-09-08,464.29,466.99,455.84,458.62,2656700 2009-09-09,459.06,466.27,458.8,463.97,2195400 2009-09-10,466.65,470.94,462,470.94,2534600 2009-09-11,470.4,473.3,467.63,472.14,1902900 2009-09-14,470.51,476.8,470.05,475.12,1975700 2009-09-15,475.08,478.91,472.71,477.54,2398100 2009-09-16,479.8,489.37,478.48,488.29,2585300 2009-09-17,490.57,497.37,487.15,491.72,4483100 2009-09-18,496.77,496.98,491.23,491.46,3283500 2009-09-21,487.74,498.9,486.22,497,2116400 2009-09-22,500.92,501.99,497.81,499.06,3041200 2009-09-23,500.78,507,497.71,498.46,2704200 2009-09-24,500.47,501.41,493,496.77,2527600 2009-09-25,494.29,499.93,492,492.48,2049600 2009-09-28,494.84,501.5,493.3,498.53,1839300 2009-09-29,499.53,499.75,493.01,498.53,2099200 2009-09-30,500,500.14,487.24,495.85,3141700 2009-10-01,493,496.47,487,487.2,2813200 2009-10-02,483.74,491.74,482.6,484.58,2600800 2009-10-05,487.65,492.43,483.34,488.52,2144600 2009-10-06,491.7,499.37,491.7,498.74,2732300 2009-10-07,499,518.99,497.81,517.54,4874200 2009-10-08,519.57,523.25,513.34,514.18,4303800 2009-10-09,516.65,521.51,514.5,516.25,2738100 2009-10-12,523.42,525.76,519.32,524.04,3322200 2009-10-13,524.39,527.46,521.38,526.11,3037400 2009-10-14,532.46,535.58,530,535.32,3258400 2009-10-15,533.75,536.9,527.27,529.91,6100400 2009-10-16,547.33,554.75,544.53,549.85,8841900 2009-10-19,552.69,553.6,548.73,552.09,3217900 2009-10-20,551.64,552.95,540.7,551.72,4043700 2009-10-21,549.91,559.35,549,551.1,3670600 2009-10-22,550,555,548,554.09,2336500 2009-10-23,555.25,557.89,551.2,553.69,2392700 2009-10-26,555.75,561.64,550.89,554.21,2970400 2009-10-27,550.97,554.56,544.16,548.29,3216500 2009-10-28,547.87,550,538.25,540.3,2567800 2009-10-29,543.01,551.83,541,551.05,2522600 2009-10-30,550,550.17,534.24,536.12,3468500 2009-11-02,537.08,539.46,528.24,533.99,3202100 2009-11-03,530.01,537.5,528.3,537.29,2380200 2009-11-04,540.8,545.5,536.42,540.33,2332700 2009-11-05,543.49,549.77,542.66,548.65,1847700 2009-11-06,547.72,551.78,545.5,551.1,1826700 2009-11-09,555.45,562.58,554.23,562.51,2649900 2009-11-10,562.73,568.78,562,566.76,2230800 2009-11-11,570.48,573.5,565.86,570.56,2319700 2009-11-12,569.56,572.9,565.5,567.85,1886300 2009-11-13,569.29,572.51,566.61,572.05,1666800 2009-11-16,575,576.99,572.78,576.28,2199200 2009-11-17,574.87,577.5,573.72,577.49,1916700 2009-11-18,576.65,578.78,572.07,576.65,1549600 2009-11-19,573.77,574,570,572.99,2168000 2009-11-20,569.5,571.6,569.4,569.96,2006200 2009-11-23,576.49,586.6,575.86,582.35,2547500 2009-11-24,582.52,584.29,576.54,583.09,1605200 2009-11-25,586.41,587.06,582.69,585.74,1461200 2009-11-27,572,582.46,570.97,579.76,1384600 2009-11-30,580.63,583.67,577.11,583,1725100 2009-12-01,588.13,591.22,583,589.87,2320300 2009-12-02,591,593.01,586.22,587.51,1663200 2009-12-03,589.04,591.45,585,585.74,1428700 2009-12-04,593.02,594.83,579.18,585.01,2513600 2009-12-07,584.21,588.69,581,586.25,1636200 2009-12-08,583.5,590.66,582,587.05,1524000 2009-12-09,587.5,589.33,583.58,589.02,1781000 2009-12-10,590.44,594.71,590.41,591.5,1668300 2009-12-11,594.68,594.75,587.73,590.51,1720000 2009-12-14,595.35,597.31,592.61,595.73,1913400 2009-12-15,593.3,596.38,590.99,593.14,2280400 2009-12-16,598.6,600.37,596.64,597.76,2809400 2009-12-17,596.44,597.64,593.76,593.94,2638800 2009-12-18,596.03,598.93,595,596.42,3531500 2009-12-21,597.61,599.84,595.67,598.68,2571200 2009-12-22,601.34,601.5,598.85,601.12,1880800 2009-12-23,603.5,612.87,602.85,611.68,2072700 2009-12-24,612.93,619.52,612.27,618.48,858700 2009-12-28,621.66,625.99,618.48,622.87,1697900 2009-12-29,624.74,624.84,618.29,619.4,1424800 2009-12-30,618.5,622.73,618.01,622.73,1465600 2009-12-31,624.75,625.4,619.98,619.98,1219800 2010-01-04,626.95,629.51,624.24,626.75,1956200 2010-01-05,627.18,627.84,621.54,623.99,3004700 2010-01-06,625.86,625.86,606.36,608.26,3978700 2010-01-07,609.4,610,592.65,594.1,6414300 2010-01-08,592,603.25,589.11,602.02,4724300 2010-01-11,604.46,604.46,594.04,601.11,7212900 2010-01-12,597.65,598.16,588,590.48,4853300 2010-01-13,576.49,588.38,573.9,587.09,6496600 2010-01-14,583.9,594.2,582.81,589.85,4240100 2010-01-15,593.34,593.56,578.04,580,5434500 2010-01-19,581.2,590.42,576.29,587.62,4316700 2010-01-20,585.98,585.98,575.29,580.41,3250700 2010-01-21,583.44,586.82,572.25,582.98,6307700 2010-01-22,564.5,570.6,534.86,550.01,6800400 2010-01-25,546.59,549.88,535.51,540,4419900 2010-01-26,537.97,549.6,536.29,542.42,4355500 2010-01-27,541.27,547.65,535.31,542.1,3964400 2010-01-28,544.49,547,530.6,534.29,3229100 2010-01-29,538.49,540.99,525.61,529.94,4140500 2010-02-01,534.6,535.81,530.3,533.02,2250800 2010-02-02,534.96,534.96,527.61,531.12,4096200 2010-02-03,528.67,542.1,528.23,540.82,2999100 2010-02-04,537,538,525.56,526.78,3377700 2010-02-05,528.4,533.5,522.46,531.29,3156000 2010-02-08,532.5,542,531.53,533.47,2694300 2010-02-09,539.54,541.53,535.07,536.44,2819600 2010-02-10,534.07,537.79,527.69,534.45,2674500 2010-02-11,533.32,540.49,529.5,536.4,2410000 2010-02-12,532.97,537.15,530.5,533.12,2279700 2010-02-16,536.87,544.13,534.3,541.3,3654400 2010-02-17,542,543.4,537.61,538.21,2029700 2010-02-18,537.54,545.01,536.14,543.22,2336900 2010-02-19,540.53,544.03,539.7,540.76,2553100 2010-02-22,547.35,547.5,541,542.8,2144600 2010-02-23,543,543.63,532.29,535.07,2872600 2010-02-24,533.98,538.44,530.51,531.47,2326600 2010-02-25,527.12,528.49,520,526.43,3309200 2010-02-26,527.42,531.75,523.48,526.8,2049300 2010-03-01,529.2,533.29,527.74,532.69,2237900 2010-03-02,535.48,545.66,535.01,541.06,4356800 2010-03-03,542.36,548.12,539.25,545.32,3089400 2010-03-04,546.5,556.13,546.2,554.59,3183800 2010-03-05,561.35,567.67,559.9,564.21,3912200 2010-03-08,564.78,565.18,561.01,562.48,2386400 2010-03-09,559.85,564.66,556.5,560.19,3176600 2010-03-10,563.76,578.5,562.21,576.45,5654900 2010-03-11,574.26,586.21,574.2,581.14,4233300 2010-03-12,588.14,588.28,579.16,579.54,2753400 2010-03-15,566.68,569.45,556,563.18,4653900 2010-03-16,561.83,568.42,560.76,565.2,3431500 2010-03-17,568.3,571.45,564.25,565.56,3321600 2010-03-18,564.72,568.44,562.96,566.4,1777200 2010-03-19,566.23,568,557.28,560,4792400 2010-03-22,556.11,566.85,554.28,557.5,4004800 2010-03-23,557.04,558.31,542,549,5501300 2010-03-24,545.51,559.85,539.7,557.33,6565200 2010-03-25,559.02,572,558.66,562.88,3930900 2010-03-26,565.27,567.39,560.02,562.69,2696200 2010-03-29,563,564.72,560.57,562.45,3104500 2010-03-30,562.83,567.63,560.28,566.71,1977900 2010-03-31,565.05,569.74,562.81,567.12,3030800 2010-04-01,571.35,573.45,565.55,568.8,2102700 2010-04-05,570.9,574.88,569,571.01,1901500 2010-04-06,569.46,570.89,565.4,568.22,2060100 2010-04-07,567.3,568.75,561.86,563.54,2581000 2010-04-08,563.32,569.85,560.05,567.49,1947500 2010-04-09,567.49,568.77,564,566.22,2056600 2010-04-12,567.35,574,566.22,572.73,2352400 2010-04-13,572.53,588.88,571.13,586.77,3912300 2010-04-14,590.06,592.34,584.01,589,3402700 2010-04-15,592.17,597.84,588.29,595.3,6761800 2010-04-16,563,568.81,549.63,550.15,12235500 2010-04-19,548.75,553.99,545,550.1,3894000 2010-04-20,554.17,559.66,551.06,555.04,2977400 2010-04-21,556.46,560.25,552.16,554.3,2391500 2010-04-22,552,552.5,543.35,547.06,3280700 2010-04-23,547.25,549.32,542.27,544.99,2089400 2010-04-26,544.97,544.99,529.21,531.64,4368800 2010-04-27,528.95,538.33,527.23,529.06,3844700 2010-04-28,532.1,534.83,521.03,529.19,3406100 2010-04-29,533.37,536.5,526.67,532,3058900 2010-04-30,531.13,537.68,525.44,525.7,2435400 2010-05-03,526.5,532.92,525.08,530.6,1857800 2010-05-04,526.52,526.74,504.21,506.37,6076300 2010-05-05,500.98,515.72,500.47,509.76,4582200 2010-05-06,508.75,517.52,460,498.67,5000100 2010-05-07,499.97,505.32,481.33,493.14,5089000 2010-05-10,513.97,522.82,512.6,521.65,4128000 2010-05-11,515.67,519.88,508.22,509.05,3322600 2010-05-12,512.04,512.04,502,505.39,3851800 2010-05-13,516.5,522,510.37,510.88,3325800 2010-05-14,509.77,510.99,496.25,507.53,4116000 2010-05-17,506.78,508.36,498.35,507.97,2793800 2010-05-18,510,510.97,497.07,498.37,2825500 2010-05-19,496.26,499.44,487.74,494.43,3445700 2010-05-20,485.07,485.58,473.8,475.01,4913300 2010-05-21,469.06,485,464.4,472.05,9690800 2010-05-24,480.73,489.79,476.8,477.16,4345600 2010-05-25,468.15,477.45,464.01,477.07,3017400 2010-05-26,482.07,489.76,475,475.47,3475600 2010-05-27,484.95,492.31,481.05,490.46,2809100 2010-05-28,492.74,493.45,483,485.63,2894800 2010-06-01,480.43,491.06,480.12,482.37,2666800 2010-06-02,486.68,493.87,481.46,493.37,2540800 2010-06-03,495.11,508,494.7,505.6,3650700 2010-06-04,499.72,509.25,496.7,498.72,3920300 2010-06-07,499.06,500.91,483.15,485.52,3632700 2010-06-08,487.85,488.84,477.54,484.78,2685100 2010-06-09,487.22,488.88,472,474.02,2729000 2010-06-10,480.37,488.5,475.84,487.01,2577900 2010-06-11,482.5,488.71,481.62,488.5,1781700 2010-06-14,494.48,494.5,483.19,483.19,2040400 2010-06-15,483.08,500.4,482.18,497.99,4259600 2010-06-16,496.17,504,496.11,501.27,2289300 2010-06-17,503.45,505.87,496.69,500.08,1977300 2010-06-18,502.51,503.47,498.13,500.03,2872900 2010-06-21,499.9,500.97,484.89,488.56,2983500 2010-06-22,489.9,496.6,485.73,486.25,2219700 2010-06-23,486.89,486.89,478.16,482.05,2029100 2010-06-24,479.66,482.75,473.26,475.1,1893600 2010-06-25,477.06,477.65,470.56,472.68,2245200 2010-06-28,472.59,477.55,469.01,472.08,1762300 2010-06-29,463.44,464.55,451.12,454.26,3502100 2010-06-30,454.96,457.83,444.72,444.95,3603200 2010-07-01,445.29,448.4,433.63,439.49,3513600 2010-07-02,441.62,442.28,436,436.55,1936000 2010-07-06,444,447.67,433.63,436.07,2560100 2010-07-07,438.31,451.29,435.38,450.2,3129700 2010-07-08,453.55,457.33,449.66,456.56,2668900 2010-07-09,471.96,473.26,462.78,467.49,4331500 2010-07-12,472.37,479.44,471.08,475.83,3334400 2010-07-13,482.25,492.99,480.28,489.2,3976300 2010-07-14,489.88,493.83,486.46,491.34,3118000 2010-07-15,491.73,494.7,482.68,494.02,4858200 2010-07-16,469.12,470.56,459.52,459.61,7824800 2010-07-19,461.01,469.65,457.52,466.18,4550300 2010-07-20,461.03,482.99,460.6,481.59,4054500 2010-07-21,484,485.7,475.43,477.5,3279600 2010-07-22,483.23,488.98,482.48,484.81,2147700 2010-07-23,480.77,490.59,480.01,490.06,2264400 2010-07-26,489.09,490.75,484.88,488.97,1995200 2010-07-27,490.58,497.5,490.17,492.63,2451200 2010-07-28,494.94,495.25,482.67,484.35,2496000 2010-07-29,485.95,488.88,479.33,484.99,2675500 2010-07-30,479.65,487.36,479.14,484.85,2144100 2010-08-02,488.99,493.28,486.94,490.41,1858700 2010-08-03,490.5,492.46,486.76,489.83,1802300 2010-08-04,492.18,507,491.05,506.32,3812500 2010-08-05,505.89,508.6,503.56,508.1,2420400 2010-08-06,505.4,505.74,496.05,500.22,3319500 2010-08-09,502.25,505.5,501.36,505.35,1872200 2010-08-10,502.35,506,498.57,503.71,2074000 2010-08-11,497.73,498,491.5,491.74,2223000 2010-08-12,483.94,494.75,482.51,492.01,2204600 2010-08-13,489,491.19,486.01,486.35,1934700 2010-08-16,483.68,489.87,480.5,485.59,1305000 2010-08-17,488.53,494.7,486.03,490.52,1890700 2010-08-18,490.44,490.87,481.55,482.15,2686400 2010-08-19,481.01,482.51,467.25,467.97,3925000 2010-08-20,467.97,471.59,461.02,462.02,3917600 2010-08-23,461.5,468.25,457.73,464.07,2986200 2010-08-24,457.7,458.37,450.92,451.39,2762700 2010-08-25,450,457.81,450,454.62,2592100 2010-08-26,456.06,457.26,450.44,450.98,1777000 2010-08-27,452.56,459.99,447.65,458.83,2312400 2010-08-30,459.15,459.76,452.42,452.69,1231400 2010-08-31,450.11,454.87,448,450.02,1946800 2010-09-01,454.98,464.94,452.5,460.33,3228300 2010-09-02,462.84,464.43,460.31,463.18,1684200 2010-09-03,470.52,471.88,467.44,470.3,2540400 2010-09-07,464.5,467.59,463.02,464.4,1709900 2010-09-08,465.19,472.5,464.51,470.58,2401800 2010-09-09,477.83,480.4,470.58,476.18,2430600 2010-09-10,479.02,479.79,475.08,476.14,1974300 2010-09-13,480.9,484.35,479.53,482.27,2241500 2010-09-14,482.01,484.75,480.08,480.43,2216500 2010-09-15,479.95,481.89,478.5,480.64,2402800 2010-09-16,479.95,482.45,479.41,481.06,1969500 2010-09-17,483.75,491.2,481.18,490.15,5641600 2010-09-20,492.5,510.41,492.06,508.28,4403500 2010-09-21,509.68,519.98,508.91,513.46,4466300 2010-09-22,512.86,517.78,511.68,516,2537100 2010-09-23,514.61,519.69,511.3,513.48,2317400 2010-09-24,521.74,527.83,518.26,527.29,3356900 2010-09-27,528.85,536.85,528.85,530.41,3107400 2010-09-28,533.48,533.59,518.45,527.17,3654700 2010-09-29,527.85,532.94,524.71,527.69,2172200 2010-09-30,529.16,531.87,518.92,525.79,3244100 2010-10-01,530,530.62,523,525.62,2225000 2010-10-04,524.95,528.25,518.85,522.35,1940500 2010-10-05,528.38,540,526.55,538.23,3507100 2010-10-06,539.26,539.95,529.94,534.35,2762300 2010-10-07,536.21,537.2,529.14,530.01,2395800 2010-10-08,532.77,537.6,527.62,536.35,2859200 2010-10-11,538.48,544.6,537.17,538.84,2626300 2010-10-12,540.12,545.99,537.79,541.39,3753600 2010-10-13,547,547.49,542.33,543.3,3055600 2010-10-14,544.18,545.25,537.11,540.93,6634100 2010-10-15,599.27,601.64,591.6,601.45,14824800 2010-10-18,600.55,619.69,600.55,617.71,7098200 2010-10-19,608.85,614.82,602.86,607.83,4587900 2010-10-20,608.14,617.38,607.5,607.98,3525100 2010-10-21,611.51,616,606,611.99,2920500 2010-10-22,611.92,614.82,610.05,612.53,2256100 2010-10-25,615.59,624.74,614.97,616.5,3158400 2010-10-26,613.1,621.23,611.03,618.6,2512900 2010-10-27,615.77,620,612.33,616.47,2241900 2010-10-28,620.05,621,613.3,618.58,2184300 2010-10-29,617.07,619,612.99,613.7,2278300 2010-11-01,615.73,620.66,611.21,615,3087100 2010-11-02,618.67,620,614.58,615.6,1997600 2010-11-03,617.5,621.83,613.5,620.18,3380500 2010-11-04,624.64,629.92,622.1,624.27,3570800 2010-11-05,623.18,625.49,621.11,625.08,1765700 2010-11-08,624.02,629.49,623.13,626.77,2101300 2010-11-09,630,630.85,620.51,624.82,2237200 2010-11-10,622.08,623,617.51,622.88,2497000 2010-11-11,619.7,619.85,614.21,617.19,2261600 2010-11-12,613.99,616.9,601.21,603.29,3393000 2010-11-15,603.08,604,594.05,595.47,3478900 2010-11-16,592.76,597.89,583.45,583.72,3307400 2010-11-17,585,589.5,581.37,583.55,2442500 2010-11-18,589,599.98,588.56,596.56,2590000 2010-11-19,597,597.89,590.34,590.83,2297500 2010-11-22,587.47,593.44,582.75,591.22,2186600 2010-11-23,587.01,589.01,578.2,583.01,2162600 2010-11-24,587.31,596.6,587.05,594.97,2396400 2010-11-26,590.46,592.98,587,590,1311100 2010-11-29,589.17,589.8,579.95,582.11,2859700 2010-11-30,574.32,574.32,553.31,555.71,7117400 2010-12-01,563,571.57,562.4,564.35,3754100 2010-12-02,568.66,573.33,565.35,571.82,2547900 2010-12-03,569.45,576.48,568,573,2631200 2010-12-06,580.57,582,576.61,578.36,2093800 2010-12-07,591.27,593,586,587.14,3042200 2010-12-08,591.97,592.52,583.69,590.54,1756900 2010-12-09,593.88,595.58,589,591.5,1868900 2010-12-10,593.14,593.99,590.29,592.21,1704700 2010-12-13,597.12,603,594.09,594.62,2398500 2010-12-14,597.09,598.29,592.48,594.91,1643300 2010-12-15,594.2,596.45,589.15,590.3,2167700 2010-12-16,592.85,593.77,588.07,591.71,1596900 2010-12-17,591,592.56,587.67,590.8,3087100 2010-12-20,594.65,597.88,588.66,595.06,1973300 2010-12-21,598.57,604.72,597.61,603.07,1879500 2010-12-22,604,607,603.28,605.49,1207500 2010-12-23,605.34,606,602.03,604.23,1110800 2010-12-27,602.74,603.78,599.5,602.38,1208100 2010-12-28,602.05,603.87,598.01,598.92,1064800 2010-12-29,602,602.41,598.92,601,1019200 2010-12-30,598,601.33,597.39,598.86,989500 2010-12-31,596.74,598.42,592.03,593.97,1539300 2011-01-03,596.48,605.59,596.48,604.35,2365200 2011-01-04,605.62,606.18,600.12,602.12,1824500 2011-01-05,600.07,610.33,600.05,609.07,2532300 2011-01-06,610.68,618.43,610.05,613.5,2057800 2011-01-07,615.91,618.25,610.13,616.44,2101200 2011-01-10,614.8,615.39,608.56,614.21,1579200 2011-01-11,617.71,618.8,614.5,616.01,1439300 2011-01-12,619.35,619.35,614.77,616.87,1632700 2011-01-13,616.97,619.67,614.16,616.69,1334000 2011-01-14,617.4,624.27,617.08,624.18,2365600 2011-01-18,626.06,641.99,625.27,639.63,3617000 2011-01-19,642.12,642.96,629.66,631.75,3406100 2011-01-20,632.21,634.08,623.29,626.77,5485800 2011-01-21,639.58,641.73,611.36,611.83,8904400 2011-01-24,607.57,612.49,601.23,611.08,4599200 2011-01-25,608.2,620.69,606.52,619.91,3646800 2011-01-26,620.33,622.49,615.28,616.5,2038100 2011-01-27,617.89,619.7,613.25,616.79,2019200 2011-01-28,619.07,620.36,599.76,600.99,4231100 2011-01-31,603.6,604.47,595.55,600.36,2804900 2011-02-01,604.49,613.35,603.11,611.04,2745300 2011-02-02,611,614.34,607.53,612,1760700 2011-02-03,609.48,611.45,606.13,610.15,1495100 2011-02-04,610.15,611.44,606.61,610.98,1550800 2011-02-07,610.16,618.39,609.21,614.29,1799600 2011-02-08,614.93,619.63,614.51,618.38,1694900 2011-02-09,616.87,619.45,612.34,616.5,1842500 2011-02-10,613.9,617.5,611.56,616.44,2334400 2011-02-11,613.79,625,613,624.5,2589900 2011-02-14,623.34,629.64,620.04,628.15,2128000 2011-02-15,627.32,630.09,623.1,624.15,2092000 2011-02-16,625.63,626.5,622.1,624.22,1684000 2011-02-17,621.25,627.25,620.28,625.26,1478800 2011-02-18,626,631.18,624.18,630.08,3217900 2011-02-22,620.03,624.93,607.77,610.21,3639900 2011-02-23,610.33,614.7,604,611.32,2889600 2011-02-24,611.39,613.09,601.35,608.82,2711700 2011-02-25,611.86,614.72,609.5,610.04,1932400 2011-02-28,610,616.49,608.01,613.4,2281500 2011-03-01,617.78,619.22,599.3,600.76,3323200 2011-03-02,599.8,606,595.19,600.79,2026700 2011-03-03,606.38,611.49,605,609.56,1945300 2011-03-04,608.33,608.98,600.2,600.62,3011000 2011-03-07,600.55,603.69,587,591.66,3462700 2011-03-08,592.93,597.98,590.2,592.31,2284900 2011-03-09,591,594.51,585.75,591.77,2151000 2011-03-10,585.44,586.62,579.45,580.3,3128100 2011-03-11,578.22,580,573.33,576.71,3032000 2011-03-14,572.8,578.29,568.02,569.99,2816100 2011-03-15,557.5,571,555.5,569.56,4005600 2011-03-16,568.01,569.79,551.28,557.1,3798500 2011-03-17,564.48,569,560.54,561.36,2899300 2011-03-18,564.64,567.99,559.74,561.06,3298600 2011-03-21,570.22,579.8,569.02,576.5,3020900 2011-03-22,577.27,579.23,572.51,577.32,1886900 2011-03-23,575.19,582.45,572,582.16,1816200 2011-03-24,585.43,588.39,578.8,586.89,2098700 2011-03-25,586.88,586.91,579.24,579.74,2858400 2011-03-28,582.07,584.99,574.71,575.36,2218400 2011-03-29,576,581.89,573.01,581.73,1604800 2011-03-30,584.38,585.5,580.58,581.84,1422300 2011-03-31,583,588.16,581.74,586.76,2029400 2011-04-01,588.76,595.19,588.76,591.8,2613200 2011-04-04,593,594.74,583.1,587.68,2054500 2011-04-05,581.08,581.49,565.68,569.09,6047500 2011-04-06,572.18,575.16,568,574.18,2668300 2011-04-07,575.73,580.64,574.19,580,2531500 2011-04-08,584.89,584.89,578.06,578.16,1901800 2011-04-11,576.2,578.1,573,577.37,1858200 2011-04-12,575,576.91,568.05,570.61,2085600 2011-04-13,575.51,577.6,571.75,576.28,2069400 2011-04-14,575.19,579.45,572.1,578.51,5456300 2011-04-15,545.29,545.75,530.06,530.7,14043700 2011-04-18,526.42,527.66,519,526.84,5039800 2011-04-19,529.95,530.88,520.9,521.53,2684100 2011-04-20,525.9,526.82,521.39,525.73,3060000 2011-04-21,527.49,528.28,522.39,525.1,2470100 2011-04-25,525.25,527,522.01,525.05,1630800 2011-04-26,526.52,537.44,525.21,532.82,3500000 2011-04-27,538,538.11,534.35,537.76,2298400 2011-04-28,538.06,539.25,534.08,537.97,2000000 2011-04-29,540,544.1,538.51,544.1,4228500 2011-05-02,545.7,545.73,537.12,538.56,2133700 2011-05-03,537.13,542.01,529.63,533.89,2081500 2011-05-04,535.17,539,533.02,535.79,2117000 2011-05-05,533.86,539.42,531.5,534.27,1997800 2011-05-06,538.15,541.46,535.18,535.3,2056100 2011-05-09,535,538.49,531.1,537.68,1948700 2011-05-10,540,544.43,537.54,542.66,2042900 2011-05-11,540.14,543.55,533.69,535.45,2338800 2011-05-12,535.24,536.94,530.91,535.05,1448300 2011-05-13,534.61,535.92,529.05,529.55,2108700 2011-05-16,526.31,527.27,516.4,518.42,2958200 2011-05-17,515.43,531.22,515.03,530.46,3303600 2011-05-18,529.54,530.33,525.7,529.81,1953200 2011-05-19,532.73,536.54,529.72,531.25,2468700 2011-05-20,531.8,531.99,523.13,524.03,2317500 2011-05-23,516.6,520,513.4,518.39,2300000 2011-05-24,520.37,523.96,518.15,518.26,1900000 2011-05-25,519.67,522.77,517.25,519.67,1301600 2011-05-26,517.7,522.12,515,518.13,2118500 2011-05-27,518.48,521.79,516.3,520.9,1745800 2011-05-31,525,529.05,523.5,529.02,2687300 2011-06-01,528.04,533.2,525.31,525.6,2955900 2011-06-02,527.57,530.3,522.48,528.06,2204500 2011-06-03,522,527.6,521.5,523.08,1748500 2011-06-06,523.54,526.82,519.25,521.06,1942100 2011-06-07,522.89,524.63,518.99,519.03,1907600 2011-06-08,516.53,521.24,515.78,519.17,1653400 2011-06-09,520,520,515.64,516.73,1689100 2011-06-10,514.08,516.69,509.29,509.51,2439900 2011-06-13,510,510.2,502.17,504.73,2427300 2011-06-14,508.15,514.08,506.99,508.37,2341500 2011-06-15,505.03,508.35,500.61,502.95,2073300 2011-06-16,502.81,506.57,496.67,500.37,2757000 2011-06-17,506.18,506.69,484.8,485.02,5245400 2011-06-20,485,486.23,479.23,484.58,3028600 2011-06-21,487.19,493.94,484.73,493,2765400 2011-06-22,491.45,492.35,486.73,487.01,2407100 2011-06-23,482.13,482.86,473.73,480.22,4801700 2011-06-24,480.68,480.75,473.02,474.88,3805600 2011-06-27,474,488.4,473.6,482.8,3444700 2011-06-28,484.02,496.21,484.02,493.65,2715100 2011-06-29,496.54,500.25,492.38,497.57,2343000 2011-06-30,501.99,506.67,501.5,506.38,2428400 2011-07-01,506.74,521.18,506.38,521.03,3636700 2011-07-05,525.3,535.4,525.3,532.44,3849200 2011-07-06,533.5,538.51,533.04,535.36,2695600 2011-07-07,541,550.68,535.88,546.6,3935500 2011-07-08,532.95,537.65,527.27,531.99,4770200 2011-07-11,528.18,535.98,525.5,527.28,2839300 2011-07-12,528.16,539.42,526,534.01,2841200 2011-07-13,537,544,536.48,538.26,2790200 2011-07-14,539.12,542,526.73,528.94,6649500 2011-07-15,597.5,600.25,588.16,597.62,13732100 2011-07-18,592.49,602.05,592,594.94,4468300 2011-07-19,596.14,604.68,595.53,602.55,2967500 2011-07-20,602.18,602.83,595.35,595.35,2227800 2011-07-21,594.03,608.06,594.01,606.99,3469500 2011-07-22,605.39,619.5,604.27,618.23,3528200 2011-07-25,613.36,625.41,613,618.98,3131600 2011-07-26,618.05,627.5,617.22,622.52,2342900 2011-07-27,617.18,620.95,604.75,607.22,3934400 2011-07-28,605.19,615.98,603,610.94,3108400 2011-07-29,604.23,614.96,603.69,603.69,4137400 2011-08-01,611.22,615.5,599.18,606.77,3966100 2011-08-02,606,609.67,591.57,592.4,3200600 2011-08-03,594.27,603,583.63,601.17,3825700 2011-08-04,594.5,598.85,577.47,577.52,4914600 2011-08-05,582.54,590,562,579.04,5929100 2011-08-08,562.98,569,544.35,546.02,7496600 2011-08-09,561,574.61,541.01,573.41,6469700 2011-08-10,561.38,564.12,547.73,549.01,5369600 2011-08-11,553.82,568.5,548.41,562.13,4830600 2011-08-12,569.5,570.5,560.4,563.77,3154300 2011-08-15,553.43,564.99,546.05,557.23,7144900 2011-08-16,552.43,552.44,530.3,539,6939400 2011-08-17,540.03,543.69,530.77,533.15,4079100 2011-08-18,523.47,524.89,500.49,504.88,6290700 2011-08-19,499.34,514.87,490.86,490.92,5410000 2011-08-22,504,507,494.53,498.17,4905900 2011-08-23,503.5,521.41,499.06,518.82,4346800 2011-08-24,519.33,530,517.23,523.29,3594500 2011-08-25,530.38,537.27,518.3,520.04,3293000 2011-08-26,519.99,530.45,513.14,526.86,3596300 2011-08-29,534.56,539.45,533.56,539.08,2335800 2011-08-30,538.19,542.99,530.81,540.7,2989000 2011-08-31,544.74,546.3,536,540.96,2693300 2011-09-01,540.75,543.83,531.22,532.5,2415500 2011-09-02,524.47,527.92,520.73,524.84,2401200 2011-09-06,510.8,522.76,510.5,522.18,2715800 2011-09-07,530.45,535.94,527.4,534.03,2755800 2011-09-08,533.8,539.1,531.9,534.96,2380500 2011-09-09,531.4,534.65,521.19,524.85,3268800 2011-09-12,517.96,531.99,517.5,530.12,2670400 2011-09-13,532,533.88,523.4,529.52,2354200 2011-09-14,532.59,536.95,525.82,532.07,2694400 2011-09-15,535.5,544.98,534.56,542.56,2960800 2011-09-16,544.8,546.84,543.14,546.68,3534300 2011-09-19,540.35,549.9,535.38,546.67,2468400 2011-09-20,549.4,558.52,542.67,546.63,2779100 2011-09-21,547.69,555,538.86,539.2,2514300 2011-09-22,526.25,528.78,514,520.66,4400300 2011-09-23,516.56,526.42,514.5,525.51,2777300 2011-09-26,527.25,532.93,513.25,531.89,2634200 2011-09-27,538.2,547.05,536.05,539.34,3010900 2011-09-28,541.5,544.02,527.7,528.84,2263300 2011-09-29,536.04,537.3,519.41,527.5,2906600 2011-09-30,520.21,524,514.38,515.04,2723600 2011-10-03,509.85,512,495,495.52,4474400 2011-10-04,490.03,503.44,480.6,501.9,4158800 2011-10-05,496.35,507.8,480.77,504.7,4534100 2011-10-06,507.5,515.23,502.6,514.71,3424300 2011-10-07,516.83,520.5,510.3,515.12,2855900 2011-10-10,525.18,537.47,523.2,537.17,2322200 2011-10-11,533.46,546.8,533.46,543.18,2853400 2011-10-12,548.13,555.23,544.63,548.5,3177800 2011-10-13,550.03,559,548.02,558.99,5687600 2011-10-14,599.47,599.6,587.57,591.68,8529900 2011-10-17,583.72,591.83,578,582.41,4008200 2011-10-18,580.19,592.56,577.4,590.51,3800800 2011-10-19,587.34,592.06,579.22,580.7,2931500 2011-10-20,581.9,588.89,579.51,583.67,3379000 2011-10-21,589.51,592.75,586.7,590.49,3391000 2011-10-24,586.72,599.97,586.5,596.42,3426900 2011-10-25,593.1,595,582.85,583.16,2540500 2011-10-26,589.55,590.18,572.86,586.31,2873100 2011-10-27,598.42,602.7,593.3,598.67,3780300 2011-10-28,594.52,602.3,594.25,600.14,2508000 2011-10-31,595.09,599.69,591.67,592.64,2557800 2011-11-01,580.1,585.51,576.75,578.65,3125100 2011-11-02,584.9,587.96,580.48,584.82,2120900 2011-11-03,587,597.5,583.72,597.5,2669800 2011-11-04,593.5,599.74,592.43,596.14,2834800 2011-11-07,593.32,608.78,592.23,608.33,3357400 2011-11-08,609,614.37,603.6,612.34,3029400 2011-11-09,604.26,609.39,598.66,600.95,3711000 2011-11-10,605.93,605.95,591.56,595.08,2868400 2011-11-11,601.3,612.09,598.6,608.35,3977000 2011-11-14,608,618.08,607.78,613,3188700 2011-11-15,612.8,618.08,610.5,616.56,2676000 2011-11-16,612.08,618.3,610.61,611.47,2608500 2011-11-17,610.05,612.29,596.78,600.87,3493300 2011-11-18,602,604.5,593.75,594.88,3287700 2011-11-21,587.76,588.5,572.09,580.94,3001300 2011-11-22,580,584.97,575.24,580,2405300 2011-11-23,575.35,580.25,570.11,570.11,2325800 2011-11-25,565.19,574.27,561.33,563,1562400 2011-11-28,579.37,588.82,576.5,588.19,2828900 2011-11-29,587.88,590.36,581.33,582.93,1831800 2011-11-30,597.95,599.51,592.09,599.39,3397000 2011-12-01,600,616,599,613.77,3620600 2011-12-02,617.05,624,616.26,620.36,4085700 2011-12-05,627.64,631.9,622.4,625.65,3197500 2011-12-06,622.99,628.62,620.24,623.77,2326500 2011-12-07,621.68,625.66,618.1,623.39,2257300 2011-12-08,621.04,627.45,615.3,616.05,2413100 2011-12-09,618,629.13,617.01,627.42,2766200 2011-12-12,621.88,626.18,620.29,625.39,2183900 2011-12-13,628.76,636.56,622.85,625.63,4028200 2011-12-14,621.85,624.32,612.49,618.07,3903700 2011-12-15,622.52,624,618.69,619.54,2408600 2011-12-16,624.32,629.32,621.47,625.96,4459300 2011-12-19,628.01,628.5,620,621.83,2143500 2011-12-20,628,631.84,627.99,630.37,2388200 2011-12-21,630.01,631.82,618.96,625.82,2497900 2011-12-22,627.95,631.73,627.01,629.7,1822300 2011-12-23,632,634.68,630.56,633.14,1453700 2011-12-27,632.05,644.49,632,640.25,1606400 2011-12-28,642.75,645,638.1,639.7,2127200 2011-12-29,641.49,643,635.2,642.4,1575400 2011-12-30,642.02,646.76,642.02,645.9,1782300 2012-01-03,652.94,668.15,652.37,665.41,3676500 2012-01-04,665.03,670.25,660.62,668.28,2864000 2012-01-05,662.13,663.97,656.23,659.01,3282900 2012-01-06,659.15,660,649.79,650.02,2692900 2012-01-09,646.5,647,621.23,622.46,5822600 2012-01-10,629.75,633.8,616.91,623.14,4395600 2012-01-11,623.5,629.39,621.12,625.96,2400000 2012-01-12,631.22,632.89,626.5,629.64,1875200 2012-01-13,626.26,626.95,621.06,624.99,2307300 2012-01-17,631.98,631.98,625.68,628.58,1909300 2012-01-18,626.63,634,622.12,632.91,2761700 2012-01-19,640.99,640.99,631.46,639.57,6305300 2012-01-20,590.53,591,581.7,585.99,10576300 2012-01-23,586,588.66,583.16,585.52,3412900 2012-01-24,586.32,587.68,578,580.93,3055800 2012-01-25,577.51,578.71,566.38,569.49,4987700 2012-01-26,571.98,574.48,564.55,568.1,3226200 2012-01-27,570.78,580.32,569.33,579.98,3617500 2012-01-30,578.05,580,573.4,577.69,2330500 2012-01-31,583,584,575.15,580.11,2142400 2012-02-01,584.94,585.5,579.14,580.83,2320700 2012-02-02,584.87,586.41,582.08,585.11,2414700 2012-02-03,590.66,597.07,588.05,596.33,3168500 2012-02-06,595.01,610.83,594.01,609.09,3679600 2012-02-07,607.15,609.39,603.76,606.77,2092100 2012-02-08,608.64,611.35,604.74,609.85,1836400 2012-02-09,612.02,614.5,609,611.46,2264700 2012-02-10,607.88,608.13,604,605.91,2325200 2012-02-13,610.5,613.84,610.02,612.2,1816300 2012-02-14,611.54,612,604.76,609.76,1803700 2012-02-15,612.93,612.93,602.56,605.56,2425900 2012-02-16,602.82,608.81,597.73,606.52,2530900 2012-02-17,604.97,607.63,602.4,604.64,2449100 2012-02-21,603.87,617.88,602.88,614,2480800 2012-02-22,611.96,616.78,606.71,607.94,1967000 2012-02-23,607,607.94,600.35,606.11,2055000 2012-02-24,607.35,611.65,605.51,609.9,1935600 2012-02-27,606.59,612.36,605.06,609.31,1813900 2012-02-28,610,619.77,607.68,618.39,2847600 2012-02-29,618.6,625.6,615.5,618.25,3136900 2012-03-01,622.26,625.7,618.15,622.4,2237700 2012-03-02,622,624,620.32,621.25,1573300 2012-03-05,620.43,622.49,611.38,614.25,1593300 2012-03-06,608.05,608.81,593.84,604.96,3174400 2012-03-07,609.05,611.19,605.86,606.8,1264500 2012-03-08,610.04,611.5,606.35,607.14,1345500 2012-03-09,607.95,611.9,600,600.25,2670600 2012-03-12,600,607,599.26,605.15,1669000 2012-03-13,608.75,617.85,605.55,617.78,2245800 2012-03-14,615,622.78,613.46,615.99,2936900 2012-03-15,616.6,623.5,614.83,621.13,2435100 2012-03-16,620.89,625.91,620.05,625.04,3050500 2012-03-19,623.12,637.27,621.24,633.98,2172800 2012-03-20,630.92,636.06,627.27,633.49,1540500 2012-03-21,634.61,647.39,632.51,639.98,2469600 2012-03-22,638.5,648.8,631,646.05,2410200 2012-03-23,646.6,648.5,640.9,642.59,1940200 2012-03-26,645,649.49,639.54,649.33,1819200 2012-03-27,647.03,653.5,644.8,647.02,2007200 2012-03-28,652.03,658.59,651.08,655.76,2538900 2012-03-29,653.44,656.59,644.3,648.41,1924300 2012-03-30,651.75,653.49,641,641.24,2310700 2012-04-02,640.77,647.5,634.84,646.92,2284200 2012-04-03,645.41,647.95,638.64,642.62,2044900 2012-04-04,638.45,639,631.1,635.15,1627600 2012-04-05,632.24,636.43,628.57,632.32,2318700 2012-04-09,628.48,635.33,625.29,630.84,2182700 2012-04-10,633.52,634.5,624.55,626.86,2482000 2012-04-11,633.97,636,631.3,635.96,2199000 2012-04-12,642.35,653.14,640.26,651.01,5756600 2012-04-13,647.55,648.99,623.54,624.6,8159300 2012-04-16,623,623.81,601.66,606.07,5692100 2012-04-17,608.56,617.69,607.01,609.57,3023900 2012-04-18,608.05,612.8,602.81,607.45,2669500 2012-04-19,605.69,616.26,599,599.3,3294600 2012-04-20,604.25,608.85,595.83,596.06,3058400 2012-04-23,592.9,598.45,590.2,597.6,2197800 2012-04-24,598.24,606.63,597.32,601.27,1929100 2012-04-25,604,611.35,602.88,609.72,1821100 2012-04-26,610.91,618,609.7,615.47,2092500 2012-04-27,615.02,616.74,610.6,614.98,1636400 2012-04-30,612.99,616.08,600.61,604.85,2407300 2012-05-01,603.79,611.6,600.19,604.43,2002300 2012-05-02,601.2,608.11,600.61,607.26,1611500 2012-05-03,609.62,614.83,608.95,611.02,1868000 2012-05-04,605.92,607.89,596.81,596.97,2207400 2012-05-07,595,610.57,595,607.55,1994500 2012-05-08,605.53,616.9,600.7,612.79,2677300 2012-05-09,606.82,616.38,601.81,609.15,2328800 2012-05-10,612.96,616.19,610.23,613.66,1535900 2012-05-11,610.35,614.55,604.77,605.23,2099400 2012-05-14,600.78,608.5,600.58,604,1824400 2012-05-15,605.35,615,603.75,611.11,2102100 2012-05-16,617.96,630.1,615.94,628.93,4835100 2012-05-17,633.83,637.85,621.23,623.05,3353800 2012-05-18,625.1,632.42,596.7,600.4,5973500 2012-05-21,600.51,615.69,600,614.11,3075400 2012-05-22,613.44,613.81,596,600.8,3051900 2012-05-23,601.65,609.6,597.12,609.46,3178100 2012-05-24,609.16,611.92,598.87,603.66,1891300 2012-05-25,601,601.73,588.28,591.53,3581900 2012-05-29,595.81,599.13,588.32,594.34,2605700 2012-05-30,588.16,591.9,583.53,588.23,1906700 2012-05-31,588.72,590,579,580.86,2968300 2012-06-01,571.79,572.65,568.35,570.98,3057900 2012-06-04,570.22,580.49,570.01,578.59,2432700 2012-06-05,575.45,578.13,566.47,570.41,2339900 2012-06-06,576.48,581.97,573.61,580.57,2095800 2012-06-07,587.6,587.89,577.25,578.23,1758500 2012-06-08,575.85,581,574.58,580.45,1410400 2012-06-11,584.21,585.32,566.69,568.5,2661100 2012-06-12,569.77,570.3,558.58,565.1,3224200 2012-06-13,561.72,567,558.68,561.09,1954200 2012-06-14,561.3,565.07,556.52,559.05,2344900 2012-06-15,560.34,564.52,557.09,564.51,3001200 2012-06-18,562.62,574.21,559.25,570.85,2496900 2012-06-19,573.59,584.28,573.12,581.53,2076200 2012-06-20,579.81,580,573.51,577.51,2346700 2012-06-21,579.84,579.84,563.73,565.21,2011300 2012-06-22,568,571.48,565.82,571.48,2227900 2012-06-25,567.33,568.09,557.35,560.7,1581600 2012-06-26,562.76,566.6,559.48,564.68,1350200 2012-06-27,567.7,573.99,566.02,569.3,1692300 2012-06-28,565.9,566.23,557.21,564.31,1920900 2012-06-29,574.96,580.13,572.2,580.07,2519500 2012-07-02,581.82,583,576.5,580.47,1655500 2012-07-03,580.01,588.41,578,587.83,1189500 2012-07-05,588.76,600.06,588.54,595.92,2345900 2012-07-06,592.45,593.52,582.82,585.98,2161800 2012-07-09,584.95,588.6,581.25,586.01,1715100 2012-07-10,590.19,592.43,578.74,581.7,1923100 2012-07-11,576.3,577.85,564.94,571.19,3499300 2012-07-12,567.12,571.93,562.09,570.48,2309800 2012-07-13,572.15,579.15,568.55,576.52,1976000 2012-07-16,576.37,579.19,571.78,574.92,1462600 2012-07-17,578.43,580.67,568.4,576.73,1680100 2012-07-18,576.98,583.69,576.13,580.76,1548200 2012-07-19,586.14,598.48,586,593.06,4674700 2012-07-20,608.76,612.94,598.18,610.82,6463700 2012-07-23,600.48,618.35,598.25,615.51,3561700 2012-07-24,615,617.93,604.34,607.57,2009400 2012-07-25,608.32,613.38,605.37,607.99,1823000 2012-07-26,615,616.87,610.03,613.36,1685200 2012-07-27,618.89,635,617.5,634.96,3549700 2012-07-30,636.05,642.6,629.5,632.3,2186700 2012-07-31,628.26,636.5,628.22,632.97,1865600 2012-08-01,637.3,639.51,631.38,632.68,1844600 2012-08-02,625.51,638.03,623.41,628.75,1977700 2012-08-03,640,643.72,636.14,641.33,1897100 2012-08-06,639.61,649.38,639.22,642.82,1782400 2012-08-07,641.79,644.26,636.47,640.54,1981800 2012-08-08,639.05,645.87,638.5,642.23,1322200 2012-08-09,644.51,646.37,641.52,642.35,1070300 2012-08-10,638.59,642.24,636.13,642,1434600 2012-08-13,647.42,660.15,646.68,660.01,3267900 2012-08-14,659.25,672.85,659,668.66,3661700 2012-08-15,670.28,674.25,664.1,667.54,2410700 2012-08-16,667.51,674.64,667.08,672.87,1717700 2012-08-17,674.12,677.25,671.7,677.14,2177700 2012-08-20,675.5,678.87,672.66,675.54,1758100 2012-08-21,673.11,678,662.17,669.51,2222200 2012-08-22,667.38,680.6,666.7,677.18,1909200 2012-08-23,674.27,680.48,671,676.8,1784200 2012-08-24,675.6,680.45,674.08,678.63,1426600 2012-08-27,662.99,672,659.24,669.22,2613700 2012-08-28,665,677.62,664.74,677.25,2058600 2012-08-29,677.37,688.99,676.15,688.01,2990300 2012-08-30,684.24,687.39,680.18,681.68,1626900 2012-08-31,684,688.58,680.04,685.09,2127100 2012-09-04,684.55,685,673.5,681.04,1889600 2012-09-05,680,686.5,679.14,680.72,1708200 2012-09-06,685.96,699.89,684.73,699.4,3043500 2012-09-07,700,712.25,697.67,706.15,3233000 2012-09-10,709.76,712.81,698.39,700.77,2560000 2012-09-11,697.96,700.65,691,692.19,1873800 2012-09-12,689.41,694.91,680.88,690.88,2642300 2012-09-13,693.09,709,690.54,706.04,2659000 2012-09-14,709.6,713,707.01,709.68,2618500 2012-09-17,708.11,712.88,705,709.98,1508300 2012-09-18,707.78,718.66,706.78,718.28,2066800 2012-09-19,717.5,728.56,716.41,727.5,3098300 2012-09-20,724.47,731.38,721.22,728.12,2907400 2012-09-21,732.21,734.92,730.12,733.99,6359100 2012-09-24,731,750.04,730.25,749.38,3563800 2012-09-25,753.05,764.89,747.66,749.16,6058500 2012-09-26,749.85,761.24,741,753.46,5672900 2012-09-27,759.95,762.84,751.65,756.5,3931100 2012-09-28,754.15,759.3,751.15,754.5,2783500 2012-10-01,759.05,765,756.21,761.78,3168000 2012-10-02,765.2,765.99,750.27,756.99,2790200 2012-10-03,755.72,763.92,752.2,762.5,2208300 2012-10-04,762.75,769.89,759.4,768.05,2454200 2012-10-05,770.71,774.38,765.01,767.65,2735900 2012-10-08,761,763.58,754.15,757.84,1958600 2012-10-09,759.67,761.32,742.53,744.09,3003200 2012-10-10,741.86,747.53,738.29,744.56,2039900 2012-10-11,752.9,758.5,750.29,751.48,2383900 2012-10-12,751.85,754.87,744.1,744.75,2404200 2012-10-15,741.94,743.83,730.7,740.98,3019100 2012-10-16,740.13,746.99,736.46,744.7,2058200 2012-10-17,743.95,756.34,740.26,755.49,2292900 2012-10-18,755.54,759.42,676,695,12442400 2012-10-19,705.58,706.7,672,681.79,11482200 2012-10-22,681.01,684.63,669.7,678.67,4055600 2012-10-23,672.01,687.33,672,680.35,2916600 2012-10-24,686.8,687,675.27,677.3,2496500 2012-10-25,680,682,673.51,677.76,2401100 2012-10-26,676.5,683.03,671.2,675.15,1950800 2012-10-31,679.86,681,675,680.3,1537000 2012-11-01,679.5,690.9,678.72,687.59,2050100 2012-11-02,694.79,695.55,687.37,687.92,2324400 2012-11-05,684.5,686.86,675.56,682.96,1635900 2012-11-06,685.48,686.5,677.55,681.72,1582800 2012-11-07,675,678.23,666.49,667.12,2232300 2012-11-08,670.2,671.49,651.23,652.29,2597000 2012-11-09,654.65,668.34,650.3,663.03,3114100 2012-11-12,663.75,669.8,660.87,665.9,1405900 2012-11-13,663,667.6,658.23,659.05,1594200 2012-11-14,660.66,662.18,650.5,652.55,1668400 2012-11-15,650,660,643.9,647.26,1848900 2012-11-16,645.99,653.02,636,647.18,3438200 2012-11-19,655.7,668.92,655.53,668.21,2368200 2012-11-20,669.51,678,664.57,669.97,2088700 2012-11-21,668.99,669.8,660.4,665.87,2112200 2012-11-23,669.97,670,666.1,667.97,922500 2012-11-26,666.44,667,659.02,661.15,2204600 2012-11-27,660.17,675,658,670.71,2508700 2012-11-28,668.01,684.91,663.89,683.67,3042000 2012-11-29,687.78,693.9,682,691.89,2776500 2012-11-30,691.31,699.22,685.69,698.37,3163600 2012-12-03,702.24,705.89,694.11,695.25,2192500 2012-12-04,695,695.51,685.7,691.03,1991700 2012-12-05,692.15,694.5,682.33,687.82,1862400 2012-12-06,687.59,695.61,684.51,691.13,1462300 2012-12-07,695,696.88,682.42,684.21,1919300 2012-12-10,685.39,691.65,683.79,685.42,1366700 2012-12-11,690,701.92,687.72,696.88,2687600 2012-12-12,699.23,703.51,693.48,697.56,2426000 2012-12-13,715.92,716.47,699.55,702.7,3444900 2012-12-14,699.17,707.82,698.43,701.96,2130100 2012-12-17,705.5,721.92,704.02,720.78,3035400 2012-12-18,716.6,729.1,715.05,721.07,3004900 2012-12-19,720.71,723,716.68,720.11,1918600 2012-12-20,723.26,724.65,716.97,722.36,1657000 2012-12-21,713.97,718.82,710.52,715.63,3526000 2012-12-24,714.51,715.18,707.47,709.5,840900 2012-12-26,708.07,712.88,702.41,708.87,1182400 2012-12-27,707.14,708.84,698.61,706.29,1647400 2012-12-28,701.69,706.91,700.01,700.01,1402000 2012-12-31,700,710.57,696,707.38,1997400 2013-01-02,719.42,727,716.55,723.25,2541300 2013-01-03,724.93,731.93,720.72,723.67,2318200 2013-01-04,729.34,741.47,727.68,737.97,2763500 2013-01-07,735.45,739.38,730.58,734.75,1655700 2013-01-08,735.54,736.3,724.43,733.3,1676100 2013-01-09,732.27,738.35,728.6,738.12,2024700 2013-01-10,742.83,745,733.5,741.48,1835700 2013-01-11,742,742.43,736.3,739.99,1285200 2013-01-14,737,742.2,722.35,723.25,2863900 2013-01-15,719.33,735,712.1,724.93,3927700 2013-01-16,722.4,724.34,713.67,715.19,2023400 2013-01-17,717.71,719.64,711.02,711.32,2211500 2013-01-18,710.36,712.77,701.33,704.51,3226800 2013-01-22,704.66,705.34,695.52,702.87,3792400 2013-01-23,735.99,749,735.79,741.5,5909100 2013-01-24,741.24,756.83,740.51,754.21,3382700 2013-01-25,750.77,758.48,750.25,753.67,2225900 2013-01-28,751.76,755.6,747.89,750.73,1627100 2013-01-29,746.75,756.95,746.54,753.68,1747100 2013-01-30,753.74,760.95,752.91,753.83,1733000 2013-01-31,750.51,757.62,750.25,755.69,1634200 2013-02-01,758.2,776.6,758.1,775.6,3746100 2013-02-04,767.69,770.47,758.27,759.02,3040500 2013-02-05,761.13,771.11,759.47,765.74,1870700 2013-02-06,759.07,772.96,758.5,770.17,2078100 2013-02-07,769.7,778.81,765.5,773.95,2840200 2013-02-08,780.13,786.67,779.56,785.37,3020100 2013-02-11,778.4,783,773.75,782.42,2167700 2013-02-12,781.75,787.9,779.37,780.7,1859000 2013-02-13,780.13,785.35,779.97,782.86,1198200 2013-02-14,779.73,788.74,777.77,787.82,1735300 2013-02-15,787.4,793.26,787.07,792.89,2729800 2013-02-19,795.99,807,795.28,806.85,2931800 2013-02-20,805.3,808.97,791.79,792.46,2764200 2013-02-21,798,805.45,791.22,795.53,3506400 2013-02-22,799.26,801.25,793.8,799.71,2053900 2013-02-25,802.3,808.41,790.49,790.77,2303900 2013-02-26,795,795.95,784.4,790.13,2202500 2013-02-27,794.8,804.75,791.11,799.78,2026100 2013-02-28,801.1,806.99,801.03,801.2,2265800 2013-03-01,797.8,807.14,796.15,806.19,2175400 ================================================ FILE: backtesting/test/__init__.py ================================================ """Data and utilities for testing.""" from __future__ import annotations import pandas as pd def _read_file(filename): from os.path import dirname, join return pd.read_csv(join(dirname(__file__), filename), index_col=0, parse_dates=True) BTCUSD = _read_file('BTCUSD.csv') """DataFrame of monthly BTC/USD histrical index data from 2012 through 2024 (12 years).""" GOOG = _read_file('GOOG.csv') """DataFrame of daily NASDAQ:GOOG (Google/Alphabet) stock price data from 2004 to 2013.""" EURUSD = _read_file('EURUSD.csv') """DataFrame of hourly EUR/USD forex data from April 2017 to February 2018.""" def SMA(arr: pd.Series, n: int) -> pd.Series: """ Returns `n`-period simple moving average of array `arr`. """ return pd.Series(arr).rolling(n).mean() ================================================ FILE: backtesting/test/__main__.py ================================================ import unittest import warnings if __name__ == '__main__': warnings.filterwarnings('error') unittest.main(module='backtesting.test._test', verbosity=2) ================================================ FILE: backtesting/test/_test.py ================================================ import inspect import multiprocessing as mp import os import sys import time import unittest from concurrent.futures.process import ProcessPoolExecutor from contextlib import contextmanager from glob import glob from runpy import run_path from tempfile import NamedTemporaryFile, gettempdir from unittest import TestCase import numpy as np import pandas as pd from pandas.testing import assert_frame_equal from backtesting import Backtest, Strategy from backtesting._stats import compute_drawdown_duration_peaks from backtesting._util import _Array, _as_str, _Indicator, patch, try_ from backtesting.lib import ( FractionalBacktest, MultiBacktest, OHLCV_AGG, SignalStrategy, TrailingStrategy, barssince, compute_stats, cross, crossover, plot_heatmaps, quantile, random_ohlc_data, resample_apply, ) from backtesting.test import BTCUSD, EURUSD, GOOG, SMA SHORT_DATA = GOOG.iloc[:20] # Short data for fast tests with no indicator lag @contextmanager def _tempfile(): with NamedTemporaryFile(suffix='.html') as f: if sys.platform.startswith('win'): f.close() yield f.name @contextmanager def chdir(path): cwd = os.getcwd() os.chdir(path) try: yield finally: os.chdir(cwd) class SmaCross(Strategy): # NOTE: These values are also used on the website! fast = 10 slow = 30 def init(self): self.sma1 = self.I(SMA, self.data.Close, self.fast) self.sma2 = self.I(SMA, self.data.Close, self.slow) def next(self): if crossover(self.sma1, self.sma2): self.position.close() self.buy() elif crossover(self.sma2, self.sma1): self.position.close() self.sell() class _S(Strategy): def init(self): super().init() class TestBacktest(TestCase): def test_run(self): bt = Backtest(EURUSD, SmaCross) bt.run() def test_run_invalid_param(self): bt = Backtest(GOOG, SmaCross) self.assertRaises(AttributeError, bt.run, foo=3) def test_run_speed(self): bt = Backtest(GOOG, SmaCross) start = time.process_time() bt.run() end = time.process_time() self.assertLess(end - start, .3) def test_data_missing_columns(self): df = GOOG.copy(deep=False) del df['Open'] with self.assertRaises(ValueError): Backtest(df, SmaCross).run() def test_data_nan_columns(self): df = GOOG.copy() df['Open'] = np.nan with self.assertRaises(ValueError): Backtest(df, SmaCross).run() def test_data_extra_columns(self): df = GOOG.copy(deep=False) df['P/E'] = np.arange(len(df)) df['MCap'] = np.arange(len(df)) class S(Strategy): def init(self): assert len(self.data.MCap) == len(self.data.Close) assert len(self.data['P/E']) == len(self.data.Close) def next(self): assert len(self.data.MCap) == len(self.data.Close) assert len(self.data['P/E']) == len(self.data.Close) Backtest(df, S).run() def test_data_invalid(self): with self.assertRaises(TypeError): Backtest(GOOG.index, SmaCross).run() with self.assertRaises(ValueError): Backtest(GOOG.iloc[:0], SmaCross).run() def test_assertions(self): class Assertive(Strategy): def init(self): self.sma = self.I(SMA, self.data.Close, 10) self.remains_indicator = np.r_[2] * np.cumsum(self.sma * 5 + 1) * np.r_[2] self.transpose_invalid = self.I(lambda: np.column_stack((self.data.Open, self.data.Close))) resampled = resample_apply('W', SMA, self.data.Close, 3) resampled_ind = resample_apply('W', SMA, self.sma, 3) assert np.unique(resampled[-5:]).size == 1 assert np.unique(resampled[-6:]).size == 2 assert resampled in self._indicators, "Strategy.I not called" assert resampled_ind in self._indicators, "Strategy.I not called" assert 1 == try_(lambda: self.data.X, 1, AttributeError) assert 1 == try_(lambda: self.data['X'], 1, KeyError) assert self.data.pip == .01 assert float(self.data.Close) == self.data.Close[-1] def next(self, _FEW_DAYS=pd.Timedelta('3 days')): # noqa: N803 assert self.equity >= 0 assert isinstance(self.sma, _Indicator) assert isinstance(self.remains_indicator, _Indicator) assert self.remains_indicator.name assert isinstance(self.remains_indicator._opts, dict) assert not np.isnan(self.data.Open[-1]) assert not np.isnan(self.data.High[-1]) assert not np.isnan(self.data.Low[-1]) assert not np.isnan(self.data.Close[-1]) assert not np.isnan(self.data.Volume[-1]) assert not np.isnan(self.sma[-1]) assert self.data.index[-1] self.position self.position.size self.position.pl self.position.pl_pct self.position.is_long if crossover(self.sma, self.data.Close): for order in self.orders: if not order.is_contingent: order.cancel() price = self.data.Close[-1] sl, tp = 1.05 * price, .9 * price n_orders = len(self.orders) self.sell(size=.21, limit=price, stop=price, sl=sl, tp=tp) assert len(self.orders) == n_orders + 1 order = self.orders[-1] assert order.limit == price assert order.stop == price assert order.size == -.21 assert order.sl == sl assert order.tp == tp assert not order.is_contingent elif self.position: assert not self.position.is_long assert self.position.is_short assert self.position.pl assert self.position.pl_pct assert self.position.size < 0 trade = self.trades[0] if self.data.index[-1] - self.data.index[trade.entry_bar] > _FEW_DAYS: assert not trade.is_long assert trade.is_short assert trade.size < 0 assert trade.entry_bar > 0 assert isinstance(trade.entry_time, pd.Timestamp) assert trade.exit_bar is None assert trade.exit_time is None assert trade.entry_price > 0 assert trade.exit_price is None assert trade.pl / 1 assert trade.pl_pct / 1 assert trade.value > 0 assert trade.sl assert trade.tp # Close multiple times self.position.close(.5) self.position.close(.5) self.position.close(.5) self.position.close() self.position.close() bt = Backtest(GOOG, Assertive) with self.assertWarns(UserWarning): stats = bt.run() self.assertEqual(stats['# Trades'], 131) def test_broker_params(self): bt = Backtest(GOOG.iloc[:100], SmaCross, cash=1000, spread=.01, margin=.1, trade_on_close=True) bt.run() def test_spread_commission(self): class S(Strategy): def init(self): self.done = False def next(self): if not self.position: self.buy() else: self.position.close() self.next = lambda: None # Done SPREAD = .01 COMMISSION = .01 CASH = 10_000 ORDER_BAR = 2 stats = Backtest(SHORT_DATA, S, cash=CASH, spread=SPREAD, commission=COMMISSION).run() trade_open_price = SHORT_DATA['Open'].iloc[ORDER_BAR] self.assertEqual(stats['_trades']['EntryPrice'].iloc[0], trade_open_price * (1 + SPREAD)) self.assertEqual(stats['_equity_curve']['Equity'].iloc[2:4].round(2).tolist(), [9685.31, 9749.33]) stats = Backtest(SHORT_DATA, S, cash=CASH, commission=(100, COMMISSION)).run() self.assertEqual(stats['_equity_curve']['Equity'].iloc[2:4].round(2).tolist(), [9784.50, 9718.69]) commission_func = lambda size, price: size * price * COMMISSION # noqa: E731 stats = Backtest(SHORT_DATA, S, cash=CASH, commission=commission_func).run() self.assertEqual(stats['_equity_curve']['Equity'].iloc[2:4].round(2).tolist(), [9781.28, 9846.04]) def test_commissions(self): class S(_S): def next(self): if len(self.data) == 2: self.buy(size=SIZE, tp=3) FIXED_COMMISSION, COMMISSION = 10, .01 CASH, SIZE, PRICE_ENTRY, PRICE_EXIT = 5000, 100, 1, 4 arr = np.r_[1, PRICE_ENTRY, 1, 2, PRICE_EXIT, 1, 2] df = pd.DataFrame({'Open': arr, 'High': arr, 'Low': arr, 'Close': arr}) with self.assertWarnsRegex(UserWarning, 'index is not datetime'): stats = Backtest(df, S, cash=CASH, commission=(FIXED_COMMISSION, COMMISSION)).run() EXPECTED_PAID_COMMISSION = ( FIXED_COMMISSION + COMMISSION * SIZE * PRICE_ENTRY + FIXED_COMMISSION + COMMISSION * SIZE * PRICE_EXIT) self.assertEqual(stats['Commissions [$]'], EXPECTED_PAID_COMMISSION) self.assertEqual(stats._trades['Commission'][0], EXPECTED_PAID_COMMISSION) self.assertEqual( stats['Equity Final [$]'], CASH + (PRICE_EXIT - PRICE_ENTRY) * SIZE - EXPECTED_PAID_COMMISSION) def test_dont_overwrite_data(self): df = EURUSD.copy() bt = Backtest(df, SmaCross) bt.run() bt.optimize(fast=4, slow=[6, 8]) bt.plot(plot_drawdown=True, open_browser=False) self.assertTrue(df.equals(EURUSD)) def test_strategy_abstract(self): class MyStrategy(Strategy): pass self.assertRaises(TypeError, MyStrategy, None, None) def test_strategy_str(self): bt = Backtest(GOOG.iloc[:100], SmaCross) self.assertEqual(str(bt.run()._strategy), SmaCross.__name__) self.assertEqual(str(bt.run(fast=11)._strategy), SmaCross.__name__ + '(fast=11)') def test_compute_drawdown(self): dd = pd.Series([0, 1, 7, 0, 4, 0, 0]) durations, peaks = compute_drawdown_duration_peaks(dd) np.testing.assert_array_equal(durations, pd.Series([3, 2], index=[3, 5]).reindex(dd.index)) np.testing.assert_array_equal(peaks, pd.Series([7, 4], index=[3, 5]).reindex(dd.index)) def test_compute_stats(self): stats = Backtest(GOOG, SmaCross, finalize_trades=True).run() expected = pd.Series({ # NOTE: These values are also used on the website! # noqa: E126 '# Trades': 66, 'Avg. Drawdown Duration': pd.Timedelta('41 days 00:00:00'), 'Avg. Drawdown [%]': -5.925851581948801, 'Avg. Trade Duration': pd.Timedelta('46 days 00:00:00'), 'Avg. Trade [%]': 2.531715975158555, 'Best Trade [%]': 53.59595229490424, 'Buy & Hold Return [%]': 522.0601851851852, 'Calmar Ratio': 0.4414380935608377, 'Duration': pd.Timedelta('3116 days 00:00:00'), 'End': pd.Timestamp('2013-03-01 00:00:00'), 'Equity Final [$]': 51422.98999999996, 'Equity Peak [$]': 75787.44, 'Expectancy [%]': 3.2748078066748834, 'Exposure Time [%]': 96.74115456238361, 'Max. Drawdown Duration': pd.Timedelta('584 days 00:00:00'), 'Max. Drawdown [%]': -47.98012705007589, 'Max. Trade Duration': pd.Timedelta('183 days 00:00:00'), 'Profit Factor': 2.167945974262033, 'Return (Ann.) [%]': 21.180255813792282, 'Return [%]': 414.2298999999996, 'Volatility (Ann.) [%]': 36.49390889140787, 'CAGR [%]': 14.159843619607383, 'SQN': 1.0766187356697705, 'Kelly Criterion': 0.1518705127029717, 'Sharpe Ratio': 0.5803778344714113, 'Sortino Ratio': 1.0847880675854096, 'Start': pd.Timestamp('2004-08-19 00:00:00'), 'Win Rate [%]': 46.96969696969697, 'Worst Trade [%]': -18.39887353835481, 'Alpha [%]': 394.37391142027462, 'Beta': 0.03803390709192, }) def almost_equal(a, b): try: return np.isclose(a, b, rtol=1.e-8) except TypeError: return a == b diff = {key: print(key) or value # noqa: T201 for key, value in stats.filter(regex='^[^_]').items() if not almost_equal(value, expected[key])} self.assertDictEqual(diff, {}) self.assertSequenceEqual( sorted(stats['_equity_curve'].columns), sorted(['Equity', 'DrawdownPct', 'DrawdownDuration'])) self.assertEqual(len(stats['_trades']), 66) indicator_columns = [ f'{entry}_SMA(C,{n})' for entry in ('Entry', 'Exit') for n in (SmaCross.fast, SmaCross.slow)] self.assertSequenceEqual( sorted(stats['_trades'].columns), sorted(['Size', 'EntryBar', 'ExitBar', 'EntryPrice', 'ExitPrice', 'SL', 'TP', 'PnL', 'ReturnPct', 'EntryTime', 'ExitTime', 'Duration', 'Tag', 'Commission', *indicator_columns])) def test_compute_stats_bordercase(self): class SingleTrade(Strategy): def init(self): self._done = False def next(self): if not self._done: self.buy() self._done = True if self.position: self.position.close() class SinglePosition(_S): def next(self): if not self.position: self.buy() class NoTrade(_S): def next(self): pass for strategy in (SmaCross, SingleTrade, SinglePosition, NoTrade): with self.subTest(strategy=strategy.__name__): stats = Backtest(GOOG.iloc[:100], strategy).run() self.assertFalse(np.isnan(stats['Equity Final [$]'])) self.assertFalse(stats['_equity_curve']['Equity'].isnull().any()) self.assertEqual(stats['_strategy'].__class__, strategy) def test_trade_enter_hit_sl_on_same_day(self): the_day = pd.Timestamp("2012-10-17 00:00:00") class S(_S): def next(self): if self.data.index[-1] == the_day: self.buy(sl=720) self.assertEqual(Backtest(GOOG, S).run()._trades.iloc[0].ExitPrice, 720) class S(_S): def next(self): if self.data.index[-1] == the_day: self.buy(stop=758, sl=720) with self.assertWarns(UserWarning): self.assertEqual(Backtest(GOOG, S).run()._trades.iloc[0].ExitPrice, 705.58) def test_stop_price_between_sl_tp(self): class S(_S): def next(self): if self.data.index[-1] == pd.Timestamp("2004-09-09 00:00:00"): self.buy(stop=104, sl=103, tp=110) with self.assertWarns(UserWarning): self.assertEqual(Backtest(GOOG, S).run()._trades.iloc[0].EntryPrice, 104) def test_position_close_portion(self): class SmaCross(Strategy): def init(self): self.sma1 = self.I(SMA, self.data.Close, 10) self.sma2 = self.I(SMA, self.data.Close, 20) def next(self): if not self.position and crossover(self.sma1, self.sma2): self.buy(size=10) if self.position and crossover(self.sma2, self.sma1): self.position.close(portion=.5) bt = Backtest(GOOG, SmaCross, spread=.002) bt.run() def test_close_orders_from_last_strategy_iteration(self): class S(_S): def next(self): if not self.position: self.buy() elif len(self.data) == len(SHORT_DATA): self.position.close() with self.assertWarnsRegex(UserWarning, 'finalize_trades'): self.assertTrue(Backtest(SHORT_DATA, S, finalize_trades=False).run()._trades.empty) self.assertFalse(Backtest(SHORT_DATA, S, finalize_trades=True).run()._trades.empty) def test_check_adjusted_price_when_placing_order(self): class S(_S): def next(self): self.buy(tp=self.data.Close * 1.01) self.assertRaises(ValueError, Backtest(SHORT_DATA, S, spread=.02).run) class TestStrategy(TestCase): @staticmethod def _Backtest(strategy_coroutine, data=SHORT_DATA, **kwargs): class S(Strategy): def init(self): self.step = strategy_coroutine(self) def next(self): try_(self.step.__next__, None, StopIteration) return Backtest(data, S, **kwargs) def test_position(self): def coroutine(self): yield self.buy() assert self.position assert self.position.is_long assert not self.position.is_short assert self.position.size > 0 assert self.position.pl assert self.position.pl_pct yield self.position.close() assert not self.position assert not self.position.is_long assert not self.position.is_short assert not self.position.size assert not self.position.pl assert not self.position.pl_pct self._Backtest(coroutine).run() def test_broker_hedging(self): def coroutine(self): yield self.buy(size=2) assert len(self.trades) == 1 yield self.sell(size=1) assert len(self.trades) == 2 self._Backtest(coroutine, hedging=True).run() def test_broker_exclusive_orders(self): def coroutine(self): yield self.buy(size=2) assert len(self.trades) == 1 yield self.sell(size=3) assert len(self.trades) == 1 assert self.trades[0].size == -3 self._Backtest(coroutine, exclusive_orders=True).run() def test_trade_multiple_close(self): def coroutine(self): yield self.buy() assert self.trades self.trades[-1].close(1) self.trades[-1].close(.1) yield self._Backtest(coroutine).run() def test_close_trade_leaves_needsize_0(self): def coroutine(self): self.buy(size=1) self.buy(size=1) yield if self.position: self.sell(size=1) self._Backtest(coroutine).run() def test_stop_limit_order_price_is_stop_price(self): def coroutine(self): self.buy(stop=112, limit=113, size=1) self.sell(stop=107, limit=105, size=1) yield stats = self._Backtest(coroutine).run() self.assertListEqual(stats._trades.filter(like='Price').stack().tolist(), [112, 107]) def test_autoclose_trades_on_finish(self): def coroutine(self): yield self.buy() stats = self._Backtest(coroutine, finalize_trades=True).run() self.assertEqual(len(stats._trades), 1) def test_order_tag(self): def coroutine(self): yield self.buy(size=2, tag=1) yield self.sell(size=1, tag='s') yield self.sell(size=1) yield self.buy(tag=2) yield self.position.close() stats = self._Backtest(coroutine).run() self.assertEqual(list(stats._trades.Tag), [1, 1, 2]) class TestOptimize(TestCase): def test_optimize(self): bt = Backtest(GOOG.iloc[:100], SmaCross) OPT_PARAMS = {'fast': range(2, 5, 2), 'slow': [2, 5, 7, 9]} self.assertRaises(ValueError, bt.optimize) self.assertRaises(ValueError, bt.optimize, maximize='missing key', **OPT_PARAMS) self.assertRaises(ValueError, bt.optimize, maximize='missing key', **OPT_PARAMS) self.assertRaises(TypeError, bt.optimize, maximize=15, **OPT_PARAMS) self.assertRaises(TypeError, bt.optimize, constraint=15, **OPT_PARAMS) self.assertRaises(ValueError, bt.optimize, constraint=lambda d: False, **OPT_PARAMS) self.assertRaises(ValueError, bt.optimize, return_optimization=True, **OPT_PARAMS) res = bt.optimize(**OPT_PARAMS) self.assertIsInstance(res, pd.Series) default_maximize = inspect.signature(Backtest.optimize).parameters['maximize'].default res2 = bt.optimize(**OPT_PARAMS, maximize=lambda s: s[default_maximize]) self.assertDictEqual(res.filter(regex='^[^_]').fillna(-1).to_dict(), res2.filter(regex='^[^_]').fillna(-1).to_dict()) res3, heatmap = bt.optimize(**OPT_PARAMS, return_heatmap=True, constraint=lambda d: d.slow > 2 * d.fast) self.assertIsInstance(heatmap, pd.Series) self.assertEqual(len(heatmap), 4) self.assertEqual(heatmap.name, default_maximize) with _tempfile() as f: bt.plot(filename=f, open_browser=False) def test_method_sambo(self): bt = Backtest(GOOG.iloc[:100], SmaCross, finalize_trades=True) res, heatmap, sambo_results = bt.optimize( fast=range(2, 20), slow=np.arange(2, 20, dtype=object), constraint=lambda p: p.fast < p.slow, max_tries=30, method='sambo', return_optimization=True, return_heatmap=True, random_state=2) self.assertIsInstance(res, pd.Series) self.assertIsInstance(heatmap, pd.Series) self.assertGreater(heatmap.max(), 1.1) self.assertGreater(heatmap.min(), -2) self.assertEqual(-sambo_results.fun, heatmap.max()) self.assertEqual(heatmap.index.tolist(), heatmap.dropna().index.unique().tolist()) def test_max_tries(self): bt = Backtest(GOOG.iloc[:100], SmaCross) OPT_PARAMS = {'fast': range(2, 10, 2), 'slow': [2, 5, 7, 9]} for method, max_tries, random_state in (('grid', 5, 0), ('grid', .3, 0), ('sambo', 6, 0), ('sambo', .42, 0)): with self.subTest(method=method, max_tries=max_tries, random_state=random_state): _, heatmap = bt.optimize(max_tries=max_tries, method=method, random_state=random_state, return_heatmap=True, **OPT_PARAMS) self.assertEqual(len(heatmap), 6) def test_optimize_invalid_param(self): bt = Backtest(GOOG.iloc[:100], SmaCross) self.assertRaises(AttributeError, bt.optimize, foo=range(3)) self.assertRaises(ValueError, bt.optimize, fast=[]) def test_optimize_no_trades(self): bt = Backtest(GOOG, SmaCross) stats = bt.optimize(fast=[3], slow=[3]) self.assertTrue(stats.isnull().any()) def test_optimize_speed(self): bt = Backtest(GOOG.iloc[:100], SmaCross) start = time.process_time() bt.optimize(fast=range(2, 20, 2), slow=range(10, 40, 2)) end = time.process_time() print(end - start) handicap = 5 if 'win' in sys.platform else .1 self.assertLess(end - start, .3 + handicap) class TestPlot(TestCase): def test_plot_before_run(self): bt = Backtest(GOOG, SmaCross) self.assertRaises(RuntimeError, bt.plot) def test_file_size(self): bt = Backtest(GOOG, SmaCross) bt.run() with _tempfile() as f: bt.plot(filename=f[:-len('.html')], open_browser=False) self.assertLess(os.path.getsize(f), 500000) def test_params(self): bt = Backtest(GOOG.iloc[:100], SmaCross) bt.run() with _tempfile() as f: for p in dict(plot_volume=False, # noqa: C408 plot_equity=False, plot_return=True, plot_pl=False, plot_drawdown=True, plot_trades=False, superimpose=False, resample='1W', smooth_equity=False, relative_equity=False, reverse_indicators=True, show_legend=False).items(): with self.subTest(param=p[0]): bt.plot(**dict([p]), filename=f, open_browser=False) def test_hide_legend(self): bt = Backtest(GOOG.iloc[:100], SmaCross) bt.run() with _tempfile() as f: bt.plot(filename=f, show_legend=False) # Give browser time to open before tempfile is removed time.sleep(5) def test_resolutions(self): with _tempfile() as f: for rule in 'ms s min h D W ME'.split(): with self.subTest(rule=rule): df = EURUSD.iloc[:2].resample(rule).agg(OHLCV_AGG).dropna().iloc[:1100] bt = Backtest(df, SmaCross) bt.run() bt.plot(filename=f, open_browser=False) def test_range_axis(self): df = GOOG.iloc[:100].reset_index(drop=True) # Warm-up. CPython bug bpo-29620. try: with self.assertWarns(UserWarning): Backtest(df, SmaCross) except RuntimeError: pass with self.assertWarns(UserWarning): bt = Backtest(df, SmaCross) bt.run() with _tempfile() as f: bt.plot(filename=f, open_browser=False) def test_preview(self): class Strategy(SmaCross): def init(self): super().init() def ok(x): return x self.a = self.I(SMA, self.data.Open, 5, overlay=False, name='ok') self.b = self.I(ok, np.random.random(len(self.data.Open))) bt = Backtest(GOOG, Strategy) bt.run() with _tempfile() as f: bt.plot(filename=f, plot_drawdown=True, smooth_equity=True) # Give browser time to open before tempfile is removed time.sleep(5) def test_wellknown(self): class S(_S): def next(self): date = self.data.index[-1] if date == pd.Timestamp('Thu 19 Oct 2006'): self.buy(stop=484, limit=466, size=100) elif date == pd.Timestamp('Thu 30 Oct 2007'): self.position.close() elif date == pd.Timestamp('Tue 11 Nov 2008'): self.sell(stop=self.data.Low, limit=324.90, # High from 14 Nov size=200) bt = Backtest(GOOG, S, margin=.1) stats = bt.run() trades = stats['_trades'] self.assertAlmostEqual(stats['Equity Peak [$]'], 46961) self.assertEqual(stats['Equity Final [$]'], 0) self.assertEqual(len(trades), 2) assert trades[['EntryTime', 'ExitTime']].equals( pd.DataFrame({'EntryTime': pd.to_datetime(['2006-11-01', '2008-11-14']), 'ExitTime': pd.to_datetime(['2007-10-31', '2009-09-21'])})) assert trades['PnL'].round().equals(pd.Series([23469., -34420.])) with _tempfile() as f: bt.plot(filename=f, plot_drawdown=True, smooth_equity=False) # Give browser time to open before tempfile is removed time.sleep(1) def test_resample(self): class S(SmaCross): def init(self): self.I(lambda: ['x'] * len(self.data)) # categorical indicator, GH-309 super().init() bt = Backtest(GOOG, S) bt.run() import backtesting._plotting with _tempfile() as f, \ patch(backtesting._plotting, '_MAX_CANDLES', 10), \ self.assertWarns(UserWarning): bt.plot(filename=f, resample=True) # Give browser time to open before tempfile is removed time.sleep(1) def test_indicator_name(self): test_self = self class S(Strategy): def init(self): def _SMA(): return SMA(self.data.Close, 5), SMA(self.data.Close, 10) test_self.assertRaises(TypeError, self.I, _SMA, name=42) test_self.assertRaises(ValueError, self.I, _SMA, name=("SMA One", )) test_self.assertRaises( ValueError, self.I, _SMA, name=("SMA One", "SMA Two", "SMA Three")) for overlay in (True, False): self.I(SMA, self.data.Close, 5, overlay=overlay) self.I(SMA, self.data.Close, 5, name="My SMA", overlay=overlay) self.I(SMA, self.data.Close, 5, name=("My SMA", ), overlay=overlay) self.I(_SMA, overlay=overlay) self.I(_SMA, name="My SMA", overlay=overlay) self.I(_SMA, name=("SMA One", "SMA Two"), overlay=overlay) def next(self): pass bt = Backtest(GOOG, S) bt.run() with _tempfile() as f: bt.plot(filename=f, plot_drawdown=False, plot_equity=False, plot_pl=False, plot_volume=False, open_browser=False) def test_indicator_color(self): class S(Strategy): def init(self): a = self.I(SMA, self.data.Close, 5, overlay=True, color='red') b = self.I(SMA, self.data.Close, 10, overlay=False, color='blue') self.I(lambda: (a, b), overlay=False, color=('green', 'orange')) def next(self): pass bt = Backtest(GOOG, S) bt.run() with _tempfile() as f: bt.plot(filename=f, plot_drawdown=False, plot_equity=False, plot_pl=False, plot_volume=False, open_browser=False) def test_indicator_scatter(self): class S(Strategy): def init(self): self.I(SMA, self.data.Close, 5, overlay=True, scatter=True) self.I(SMA, self.data.Close, 10, overlay=False, scatter=True) def next(self): pass bt = Backtest(GOOG, S) bt.run() with _tempfile() as f: bt.plot(filename=f, plot_drawdown=False, plot_equity=False, plot_pl=False, plot_volume=False, open_browser=False) class TestLib(TestCase): def test_barssince(self): self.assertEqual(barssince(np.r_[1, 0, 0]), 2) self.assertEqual(barssince(np.r_[0, 0, 0]), np.inf) self.assertEqual(barssince(np.r_[0, 0, 0], 0), 0) def test_cross(self): self.assertTrue(cross([0, 1], [1, 0])) self.assertTrue(cross([1, 0], [0, 1])) self.assertFalse(cross([1, 0], [1, 0])) def test_crossover(self): self.assertTrue(crossover([0, 1], [1, 0])) self.assertTrue(crossover([0, 1], .5)) self.assertTrue(crossover([0, 1], pd.Series([.5, .5], index=[5, 6]))) self.assertFalse(crossover([1, 0], [1, 0])) self.assertFalse(crossover([0], [1])) def test_quantile(self): self.assertEqual(quantile(np.r_[1, 3, 2], .5), 2) self.assertEqual(quantile(np.r_[1, 3, 2]), .5) def test_resample_apply(self): res = resample_apply('D', SMA, EURUSD.Close, 10) self.assertEqual(res.name, 'C[D]') self.assertEqual(res.count() / res.size, .9634) np.testing.assert_almost_equal(res.iloc[-48:].unique().tolist(), [1.242643, 1.242381, 1.242275], decimal=6) def resets_index(*args): return pd.Series(SMA(*args).values) res2 = resample_apply('D', resets_index, EURUSD.Close, 10) self.assertTrue((res.dropna() == res2.dropna()).all()) self.assertTrue((res.index == res2.index).all()) res3 = resample_apply('D', None, EURUSD) self.assertIn('Volume', res3) res3 = resample_apply('D', lambda df: (df.Close, df.Close), EURUSD) self.assertIsInstance(res3, pd.DataFrame) def test_plot_heatmaps(self): bt = Backtest(GOOG, SmaCross) stats, heatmap = bt.optimize(fast=range(2, 7, 2), slow=range(7, 15, 2), return_heatmap=True) with _tempfile() as f: for agg in ('mean', lambda x: np.percentile(x, 75)): plot_heatmaps(heatmap, agg, filename=f, open_browser=False) # Preview plot_heatmaps(heatmap, filename=f) time.sleep(5) def test_random_ohlc_data(self): generator = random_ohlc_data(GOOG, frac=1) new_data = next(generator) self.assertEqual(list(new_data.index), list(GOOG.index)) self.assertEqual(new_data.shape, GOOG.shape) self.assertEqual(list(new_data.columns), list(GOOG.columns)) def test_compute_stats(self): stats = Backtest(GOOG, SmaCross).run() only_long_trades = stats._trades[stats._trades.Size > 0] long_stats = compute_stats(stats=stats, trades=only_long_trades, data=GOOG, risk_free_rate=.02) self.assertNotEqual(list(stats._equity_curve.Equity), list(long_stats._equity_curve.Equity)) self.assertNotEqual(stats['Sharpe Ratio'], long_stats['Sharpe Ratio']) self.assertEqual(long_stats['# Trades'], len(only_long_trades)) self.assertEqual(stats._strategy, long_stats._strategy) assert_frame_equal(long_stats._trades, only_long_trades) def test_SignalStrategy(self): class S(SignalStrategy): def init(self): sma = self.data.Close.s.rolling(10).mean() self.set_signal(self.data.Close > sma, self.data.Close < sma) stats = Backtest(GOOG, S).run() self.assertIn(stats['# Trades'], (1179, 1180)) # varies on different archs? def test_TrailingStrategy(self): class S(TrailingStrategy): def init(self): super().init() self.set_atr_periods(40) self.set_trailing_pct(.1) self.set_trailing_sl(3) self.sma = self.I(lambda: self.data.Close.s.rolling(10).mean()) def next(self): super().next() if not self.position and self.data.Close > self.sma: self.buy() stats = Backtest(GOOG, S).run() self.assertEqual(stats['# Trades'], 56) def test_FractionalBacktest(self): ubtc_bt = FractionalBacktest(BTCUSD['2015':], SmaCross, fractional_unit=1 / 1e6, cash=100) stats = ubtc_bt.run(fast=2, slow=3) self.assertEqual(stats['# Trades'], 41) trades = stats['_trades'] self.assertEqual(len(trades), 41) trade = trades.iloc[0] self.assertAlmostEqual(trade['EntryPrice'], 236.69) self.assertAlmostEqual(stats['_strategy']._indicators[0][trade['EntryBar']], 234.14) def test_MultiBacktest(self): import backtesting assert callable(getattr(backtesting, 'Pool', None)), backtesting.__dict__ for start_method in mp.get_all_start_methods(): with self.subTest(start_method=start_method), \ patch(backtesting, 'Pool', mp.get_context(start_method).Pool): start_time = time.monotonic() btm = MultiBacktest([GOOG, EURUSD, BTCUSD], SmaCross, cash=100_000) res = btm.run(fast=2) self.assertIsInstance(res, pd.DataFrame) self.assertEqual(res.columns.tolist(), [0, 1, 2]) heatmap = btm.optimize(fast=[2, 4], slow=[10, 20]) self.assertIsInstance(heatmap, pd.DataFrame) self.assertEqual(heatmap.columns.tolist(), [0, 1, 2]) print(start_method, time.monotonic() - start_time) plot_heatmaps(heatmap.mean(axis=1), open_browser=False) class TestUtil(TestCase): def test_as_str(self): def func(): pass class Class: def __call__(self): pass self.assertEqual(_as_str('4'), '4') self.assertEqual(_as_str(4), '4') self.assertEqual(_as_str(_Indicator([1, 2], name='x')), 'x') self.assertEqual(_as_str(func), 'func') self.assertEqual(_as_str(Class), 'Class') self.assertEqual(_as_str(Class()), 'Class') self.assertEqual(_as_str(pd.Series([1, 2], name='x')), 'x') self.assertEqual(_as_str(pd.DataFrame()), 'df') self.assertEqual(_as_str(lambda x: x), 'λ') for s in ('Open', 'High', 'Low', 'Close', 'Volume'): self.assertEqual(_as_str(_Array([1], name=s)), s[0]) def test_patch(self): class Object: pass o = Object() o.attr = False with patch(o, 'attr', True): self.assertTrue(o.attr) self.assertFalse(o.attr) def test_pandas_accessors(self): class S(Strategy): def init(self): close, index = self.data.Close, self.data.index assert close.s.equals(pd.Series(close, index=index)) assert self.data.df['Close'].equals(pd.Series(close, index=index)) self.data.df['new_key'] = 2 * close def next(self): close, index = self.data.Close, self.data.index assert close.s.equals(pd.Series(close, index=index)) assert self.data.df['Close'].equals(pd.Series(close, index=index)) assert self.data.df['new_key'].equals(pd.Series(self.data.new_key, index=index)) Backtest(GOOG.iloc[:20], S).run() def test_indicators_picklable(self): bt = Backtest(SHORT_DATA, SmaCross) with ProcessPoolExecutor() as executor: stats = executor.submit(Backtest.run, bt).result() assert stats._strategy._indicators[0]._opts, '._opts and .name were not unpickled' bt.plot(results=stats, resample='2d', open_browser=False) class TestDocs(TestCase): DOCS_DIR = os.path.join(os.path.dirname(__file__), '..', '..', 'doc') @unittest.skipUnless(os.path.isdir(DOCS_DIR), "docs dir doesn't exist") @unittest.skipUnless(sys.platform.startswith('linux'), "test_examples requires mp.start_method=fork") def test_examples(self): import backtesting examples = glob(os.path.join(self.DOCS_DIR, 'examples', '*.py')) self.assertGreaterEqual(len(examples), 4) with chdir(gettempdir()), \ patch(backtesting, 'Pool', mp.get_context('fork').Pool): for file in examples: with self.subTest(example=os.path.basename(file)): run_path(file) def test_backtest_run_docstring_contains_stats_keys(self): stats = Backtest(SHORT_DATA, SmaCross).run() for key in stats.index: self.assertIn(key, Backtest.run.__doc__) def test_readme_contains_stats_keys(self): with open(os.path.join(os.path.dirname(__file__), '..', '..', 'README.md')) as f: readme = f.read() stats = Backtest(SHORT_DATA, SmaCross).run() for key in stats.index: self.assertIn(key, readme) class TestRegressions(TestCase): def test_gh_521(self): class S(_S): def next(self): if self.data.Close[-1] == 100: self.buy(size=1, sl=90) arr = np.r_[100, 100, 100, 50, 50] df = pd.DataFrame({'Open': arr, 'High': arr, 'Low': arr, 'Close': arr}) with self.assertWarnsRegex(UserWarning, 'index is not datetime'): bt = Backtest(df, S, cash=100, trade_on_close=True) self.assertEqual(bt.run()._trades['ExitPrice'][0], 50) def test_stats_annualized(self): stats = Backtest(GOOG.resample('W').agg(OHLCV_AGG), SmaCross).run() self.assertFalse(np.isnan(stats['Return (Ann.) [%]'])) self.assertEqual(round(stats['Return (Ann.) [%]']), -3) def test_cancel_orders(self): class S(_S): def next(self): self.buy(sl=1, tp=1e3) if self.position: self.position.close() for order in self.orders: order.cancel() Backtest(SHORT_DATA, S).run() def test_trade_on_close_closes_trades_on_close(self): def coro(strat): yield strat.buy(size=1, sl=90) and strat.buy(size=1, sl=80) assert len(strat.trades) == 2 yield strat.trades[0].close() yield arr = np.r_[100, 101, 102, 50, 51] df = pd.DataFrame({ 'Open': arr - 10, 'Close': arr, 'High': arr, 'Low': arr}) with self.assertWarnsRegex(UserWarning, 'index is not datetime'): trades = TestStrategy._Backtest(coro, df, cash=250, trade_on_close=True).run()._trades # trades = Backtest(df, S, cash=250, trade_on_close=True).run()._trades self.assertEqual(trades['EntryBar'][0], 1) self.assertEqual(trades['ExitBar'][0], 2) self.assertEqual(trades['EntryPrice'][0], 101) self.assertEqual(trades['ExitPrice'][0], 102) self.assertEqual(trades['EntryBar'][1], 1) self.assertEqual(trades['ExitBar'][1], 3) self.assertEqual(trades['EntryPrice'][1], 101) self.assertEqual(trades['ExitPrice'][1], 40) with self.assertWarnsRegex(UserWarning, 'index is not datetime'): trades = TestStrategy._Backtest(coro, df, cash=250, trade_on_close=False).run()._trades # trades = Backtest(df, S, cash=250, trade_on_close=False).run()._trades self.assertEqual(trades['EntryBar'][0], 2) self.assertEqual(trades['ExitBar'][0], 3) self.assertEqual(trades['EntryPrice'][0], 92) self.assertEqual(trades['ExitPrice'][0], 40) self.assertEqual(trades['EntryBar'][1], 2) self.assertEqual(trades['ExitBar'][1], 3) self.assertEqual(trades['EntryPrice'][1], 92) self.assertEqual(trades['ExitPrice'][1], 40) def test_trades_dates_match_prices(self): bt = Backtest(EURUSD, SmaCross, trade_on_close=True) trades = bt.run()._trades self.assertEqual(EURUSD.Close[trades['ExitTime']].tolist(), trades['ExitPrice'].tolist()) def test_sl_always_before_tp(self): class S(_S): def next(self): i = len(self.data.index) if i == 4: self.buy() if i == 5: t = self.trades[0] t.sl = 105 t.tp = 107.9 trades = Backtest(SHORT_DATA, S).run()._trades self.assertEqual(trades['ExitPrice'].iloc[0], 104.95) def test_stop_entry_and_tp_in_same_bar(self): class S(_S): def next(self): i = len(self.data.index) if i == 3: self.sell(stop=108, tp=105, sl=113) trades = Backtest(SHORT_DATA, S).run()._trades self.assertEqual(trades['ExitBar'].iloc[0], 3) self.assertEqual(trades['ExitPrice'].iloc[0], 105) def test_optimize_datetime_index_with_timezone(self): data: pd.DataFrame = GOOG.iloc[:100] data.index = data.index.tz_localize('Asia/Kolkata') res = Backtest(data, SmaCross).optimize(fast=range(2, 3), slow=range(4, 5)) self.assertGreater(res['# Trades'], 0) def test_sl_tp_values_in_trades_df(self): class S(_S): def next(self): self.next = lambda: None self.buy(size=1, tp=111) self.buy(size=1, sl=99) trades = Backtest(SHORT_DATA, S).run()._trades self.assertEqual(trades['SL'].fillna(0).tolist(), [0, 99]) self.assertEqual(trades['TP'].fillna(0).tolist(), [111, 0]) ================================================ FILE: doc/README.md ================================================ Backtesting.py Documentation ============================ After installing documentation dependencies: pip install .[doc,test] build HTML documentation by running: ./build.sh When submitting pull requests that change example notebooks, commit example _.py_ files too (`build.sh` should tell you how to make them). ================================================ FILE: doc/alternatives.md ================================================ Alternatives ------------ The thing with backtesting is, unless you dug into the dirty details yourself, you can't rely on execution correctness, and you risk losing your house. In addition, everyone has their own preconveived ideas about how a mechanical trading strategy should be conducted, so everyone (and their brother) just rolls their own backtesting frameworks. If after reviewing the docs and examples perchance you find [_Backtesting.py_](https://kernc.github.io/backtesting.py) not your cup of tea, kindly have a look at some similar alternative Python backtesting frameworks: - [AutoTrader](https://github.com/kieran-mackle/AutoTrader) - an automated trading framework with an emphasis on cryptocurrency markets that includes a [robust backtesting API](https://github.com/kieran-mackle/AutoTrader/blob/main/docs/source/tutorials/backtesting.md) - [bt](http://pmorissette.github.io/bt/) - a framework based on reusable and flexible blocks of strategy logic that support multiple instruments and output detailed statistics and useful charts. - [vectorbt](https://polakowo.io/vectorbt/) - a pandas-based library for quickly analyzing trading strategies at scale. - [Backtrader](https://www.backtrader.com/) - a pure-python feature-rich framework for backtesting and live algotrading with a few brokers. - [PyAlgoTrade](https://gbeced.github.io/pyalgotrade/) - event-driven algorithmic trading library with focus on backtesting and support for live trading. - [Pinkfish](http://fja05680.github.io/pinkfish/) - a lightweight backtester for intraday strategies on daily data. - [finmarketpy](https://github.com/cuemacro/finmarketpy) - a library for analyzing financial market data. - [QuantStart QSTrader](https://github.com/mhallsmoore/qstrader/) - a modular schedule-driven backtesting framework for long-short equities and ETF-based systematic trading strategies. - [pysystemtrade](https://github.com/robcarver17/pysystemtrade) - the open-source version of Robert Carver's backtesting engine that implements systems according to his book _Systematic Trading: A unique new method for designing trading and investing systems_. - [QTPyLib](https://github.com/ranaroussi/qtpylib) - a versatile, event-driven algorithmic trading library. - [Quantdom](https://github.com/constverum/Quantdom) - a Qt-based framework that lets you focus on modeling financial strategies, portfolio management, and analyzing backtests. - [Clairvoyant](https://github.com/anfederico/Clairvoyant) - software for identifying and monitoring social / historical cues for short-term stock movement. - [optopsy](https://github.com/michaelchu/optopsy) - a nimble backtesting library for options trading. - [RQalpha](https://github.com/ricequant/rqalpha) - a complete solution for programmatic traders from data acquisition, algorithmic trading, backtesting, real-time simulation, live trading to mere data analysis. Documentation in Chinese. - [zvt](https://github.com/zvtvz/zvt) - a quant trading platform which includes data recorder, factor calculation, stock picking, backtesting, and unified visualization. Documentation in Chinese. - [AwesomeQuant](https://github.com/wilsonfreitas/awesome-quant#trading--backtesting) - A somewhat curated list of libraries, packages, and resources for quants. - [Nautilus Trader](https://github.com/nautechsystems/nautilus_trader) - high-performance, production-grade algorithmic trading platform written in Rust/Python, with event-driven engine to backtest portfolios of automated trading strategies, and also deploy those same strategies live, with no code changes. - [LiuAlgoTrader](https://amor71.github.io/LiuAlgoTrader/) - A scalable, multi-process ML-ready framework for effective algorithmic trading. #### Obsolete / Unmaintained The following projects are mainly old, stale, incomplete, incompatible, abandoned, and here for posterity reference only: - [Zipline](https://www.zipline.io/) - the backtesting and live-trading engine powering Quantopian — the community-centered, hosted platform for building and executing strategies. - [AlephNull](https://github.com/CarterBain/AlephNull) - extends the features of Zipline, for use within an institutional environment. - [ProfitPy](https://code.google.com/p/profitpy/) - a set of libraries and tools for the development, testing, and execution of automated stock trading systems. - [prophet](https://github.com/Emsu/prophet) - a microframework for financial markets, focusing on modeling strategies and portfolio management. - [pybacktest](https://github.com/ematvey/pybacktest) - a vectorized pandas-based backtesting framework, designed to make backtesting compact, simple and fast. - [quant](https://github.com/maihde/quant) - a technical analysis tool for trading strategies with a particularily simplistic view of the market. - [QuantSoftware Toolkit](https://github.com/QuantSoftware/QuantSoftwareToolkit) - a toolkit by the guys that soon after went to form Lucena Research. - [QuantStart QSForex](https://github.com/mhallsmoore/qsforex) - an event-driven backtesting and live-trading platform for use in the foreign exchange markets, - [tia: Toolkit for integration and analysis](https://github.com/PaulMest/tia/) - a toolkit providing Bloomberg data access, PDF generation, technical analysis and backtesting functionality. - [TradingWithPython](https://github.com/sjev/trading-with-python) - boiler-plate code for the (no longer active) course _Trading With Python_. - [Ultra-Finance](https://github.com/panpanpandas/ultrafinance) - real-time financial data collection, analyzing and backtesting trading strategies. - [visualize-wealth](https://github.com/benjaminmgross/visualize-wealth) - a library to construct, backtest, analyze, and evaluate portfolios and their benchmarks, with comprehensive documentation illustrating all underlying methodologies and statistics. - [Gemini](https://github.com/anfederico/Gemini) - a backtester namely focusing on cryptocurrency markets. ================================================ FILE: doc/build.sh ================================================ #!/bin/bash set -eu IS_RELEASE="$([[ "${GITHUB_REF:-}" == refs/tags/* ]] && echo 1 || true)" die () { echo "ERROR: $*" >&2; exit 2; } for cmd in pdoc3 \ jupytext \ jupyter-nbconvert; do command -v "$cmd" >/dev/null || die "Missing $cmd; \`pip install backtesting[doc]\`" done DOCROOT="$(dirname "$(readlink -f "$0")")" BUILDROOT="$DOCROOT/build" echo echo 'Building API reference docs' echo mkdir -p "$BUILDROOT" rm -r "$BUILDROOT" 2>/dev/null || true pushd "$DOCROOT/.." >/dev/null pdoc3 --html \ ${IS_RELEASE+--template-dir "$DOCROOT/pdoc_template"} \ --output-dir "$BUILDROOT" \ backtesting popd >/dev/null echo echo 'Ensuring example notebooks match their py counterparts' echo strip_yaml () { awk -f "$DOCROOT/scripts/strip_yaml.awk" "$@"; } for ipynb in "$DOCROOT"/examples/*.ipynb; do echo "Checking: '$ipynb'" diff <(strip_yaml "${ipynb%.ipynb}.py") <(jupytext --to py --output - "$ipynb" | strip_yaml) || die "Notebook and its matching .py file differ. Maybe run: \`jupytext --to py '$ipynb'\` ?" done echo echo 'Converting example notebooks → py → HTML' echo jupytext --test --update --to ipynb "$DOCROOT/examples"/*.py { mkdir -p ~/.ipython/profile_default/startup cp -f "$DOCROOT/scripts/ipython_config.py" ~/.ipython/profile_default/startup/99-backtesting-docs.py trap 'rm -f ~/.ipython/profile_default/startup/99-backtesting-docs.py' EXIT; } PYTHONWARNINGS='ignore::UserWarning,ignore::RuntimeWarning' \ time jupyter-nbconvert --execute --to=html \ --ExecutePreprocessor.timeout=300 \ --output-dir="$BUILDROOT/examples" "$DOCROOT/examples"/*.ipynb if [ "$IS_RELEASE" ]; then echo -e '\nAdding GAnalytics code\n' ANALYTICS="" find "$BUILDROOT" -name '*.html' -print0 | xargs -0 -- sed -i "s##$ANALYTICS#i" ANALYTICS='' find "$BUILDROOT" -name '*.html' -print0 | xargs -0 -- sed -i "s##$ANALYTICS#i" fi echo echo 'Testing for broken links' echo problematic_urls=' https://www.gnu.org/licenses/agpl-3.0.html ' pushd "$BUILDROOT" >/dev/null WEBSITE='https://kernc\.github\.io/backtesting\.py' grep -PR '(cat 1>&2) | python -c ' import sys from urllib.parse import urljoin for line in sys.stdin.readlines(): base, url = line.split("\t") print(base, urljoin(base, url.strip()), sep="\t") ' | sed "s,$WEBSITE/doc/,," | grep -Pv "$WEBSITE"'/?$' | grep -v $'\t''$' | while read -r line; do while IFS=$'\t' read -r file url; do target_file="$(python -c ' import html, sys # fixes & from urllib.parse import unquote # fixes %20 print(html.unescape(unquote(sys.argv[-1])))' "$url")" if [ -f "$target_file" ]; then continue; fi url="${url// /%20}" echo "$url" curl --silent --fail --retry 2 --retry-delay 2 --connect-timeout 10 \ --user-agent 'Mozilla/5.0 Firefox 128' "$url" >/dev/null 2>&1 || grep -qF "$url" <(echo "$problematic_urls") || die "broken link in $file: $url" done done popd >/dev/null echo echo "All good. Docs in: $BUILDROOT" echo echo " file://$BUILDROOT/backtesting/index.html" echo ================================================ FILE: doc/examples/Multiple Time Frames.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Multiple Time Frames\n", "============\n", "\n", "Best trading strategies that rely on technical analysis might take into account price action on multiple time frames.\n", "This tutorial will show how to do that with _backtesting.py_, offloading most of the work to\n", "[pandas resampling](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling).\n", "It is assumed you're already familiar with\n", "[basic framework usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html).\n", "\n", "We will put to the test this long-only, supposed\n", "[400%-a-year trading strategy](https://web.archive.org/web/20180515044054/http://jbmarwood.com/stock-trading-strategy-300/),\n", "which uses daily and weekly\n", "[relative strength index](https://en.wikipedia.org/wiki/Relative_strength_index)\n", "(RSI) values and moving averages (MA).\n", "\n", "In practice, one should use functions from an indicator library, such as\n", "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n", "[Tulipy](https://tulipindicators.org),\n", "but among us, let's introduce the two indicators we'll be using." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "\n", "def SMA(array, n):\n", " \"\"\"Simple moving average\"\"\"\n", " return pd.Series(array).rolling(n).mean()\n", "\n", "\n", "def RSI(array, n):\n", " \"\"\"Relative strength index\"\"\"\n", " # Approximate; good enough\n", " gain = pd.Series(array).diff()\n", " loss = gain.copy()\n", " gain[gain < 0] = 0\n", " loss[loss > 0] = 0\n", " rs = gain.ewm(n).mean() / loss.abs().ewm(n).mean()\n", " return 100 - 100 / (1 + rs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The strategy roughly goes like this:\n", "\n", "Buy a position when:\n", "* weekly RSI(30) $\\geq$ daily RSI(30) $>$ 70\n", "* Close $>$ MA(10) $>$ MA(20) $>$ MA(50) $>$ MA(100)\n", "\n", "Close the position when:\n", "* Daily close is more than 2% _below_ MA(10)\n", "* 8% fixed stop loss is hit\n", "\n", "We need to provide bars data in the _lowest time frame_ (i.e. daily) and resample it to any higher time frame (i.e. weekly) that our strategy requires." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"
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  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", " \"
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\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
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\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from backtesting import Strategy, Backtest\n", "from backtesting.lib import resample_apply\n", "\n", "\n", "class System(Strategy):\n", " d_rsi = 30 # Daily RSI lookback periods\n", " w_rsi = 30 # Weekly\n", " level = 70\n", " \n", " def init(self):\n", " # Compute moving averages the strategy demands\n", " self.ma10 = self.I(SMA, self.data.Close, 10)\n", " self.ma20 = self.I(SMA, self.data.Close, 20)\n", " self.ma50 = self.I(SMA, self.data.Close, 50)\n", " self.ma100 = self.I(SMA, self.data.Close, 100)\n", " \n", " # Compute daily RSI(30)\n", " self.daily_rsi = self.I(RSI, self.data.Close, self.d_rsi)\n", " \n", " # To construct weekly RSI, we can use `resample_apply()`\n", " # helper function from the library\n", " self.weekly_rsi = resample_apply(\n", " 'W-FRI', RSI, self.data.Close, self.w_rsi)\n", " \n", " \n", " def next(self):\n", " price = self.data.Close[-1]\n", " \n", " # If we don't already have a position, and\n", " # if all conditions are satisfied, enter long.\n", " if (not self.position and\n", " self.daily_rsi[-1] > self.level and\n", " self.weekly_rsi[-1] > self.level and\n", " self.weekly_rsi[-1] > self.daily_rsi[-1] and\n", " self.ma10[-1] > self.ma20[-1] > self.ma50[-1] > self.ma100[-1] and\n", " price > self.ma10[-1]):\n", " \n", " # Buy at market price on next open, but do\n", " # set 8% fixed stop loss.\n", " self.buy(sl=.92 * price)\n", " \n", " # If the price closes 2% or more below 10-day MA\n", " # close the position, if any.\n", " elif price < .98 * self.ma10[-1]:\n", " self.position.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how our strategy fares replayed on nine years of Google stock data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Start 2004-08-19 00:00:00\n", "End 2013-03-01 00:00:00\n", "Duration 3116 days 00:00:00\n", "Exposure Time [%] 2.79\n", "Equity Final [$] 10017.44\n", "Equity Peak [$] 10978.38\n", "Return [%] 0.17\n", "Buy & Hold Return [%] 703.46\n", "Max. Drawdown [%] -10.01\n", "Avg. Drawdown [%] -9.34\n", "Max. Drawdown Duration 2653 days 00:00:00\n", "Avg. Drawdown Duration 1410 days 00:00:00\n", "# Trades 4\n", "Win Rate [%] 25.00\n", "Best Trade [%] 9.69\n", "Worst Trade [%] -4.46\n", "Avg. Trade [%] 0.08\n", "Max. Trade Duration 35 days 00:00:00\n", "Avg. Trade Duration 21 days 00:00:00\n", "Profit Factor 1.11\n", "Expectancy [%] 4.61\n", "SQN 0.01\n", "Sharpe Ratio 0.01\n", "Sortino Ratio 0.06\n", "Calmar Ratio 0.01\n", "_strategy System\n", "_equity_curve Equ...\n", "_trades Size EntryBa...\n", "dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from backtesting.test import GOOG\n", "\n", "backtest = Backtest(GOOG, System, commission=.002)\n", "backtest.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Meager four trades in the span of nine years and with zero return? How about if we optimize the parameters a bit?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5e5f09a4f9c5400da75aab3cf7d6a683", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "CPU times: user 187 ms, sys: 36.2 ms, total: 223 ms\n", "Wall time: 4.05 s\n" ] }, { "data": { "text/plain": [ "Start 2004-08-19 00:00:00\n", "End 2013-03-01 00:00:00\n", "Duration 3116 days 00:00:00\n", "Exposure Time [%] 21.55\n", "Equity Final [$] 22822.75\n", "Equity Peak [$] 23395.59\n", "Return [%] 128.23\n", "Buy & Hold Return [%] 703.46\n", "Max. Drawdown [%] -18.92\n", "Avg. Drawdown [%] -3.80\n", "Max. Drawdown Duration 778 days 00:00:00\n", "Avg. Drawdown Duration 97 days 00:00:00\n", "# Trades 22\n", "Win Rate [%] 63.64\n", "Best Trade [%] 25.03\n", "Worst Trade [%] -6.30\n", "Avg. Trade [%] 3.60\n", "Max. Trade Duration 63 days 00:00:00\n", "Avg. Trade Duration 30 days 00:00:00\n", "Profit Factor 4.75\n", "Expectancy [%] 5.94\n", "SQN 2.40\n", "Sharpe Ratio 0.45\n", "Sortino Ratio 1.58\n", "Calmar Ratio 0.19\n", "_strategy System(d_rsi=30,...\n", "_equity_curve Equ...\n", "_trades Size EntryB...\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "backtest.optimize(d_rsi=range(10, 35, 5),\n", " w_rsi=range(10, 35, 5),\n", " level=range(30, 80, 10))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[2148]},\"SMA(C,50)_2_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Kfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3P32Y7aVRivc/igZZsSAW+D+LVEiUHg/4P6YL4TM66fc/ConkxlXu9z99BC4qCr33PzzEb6X/bvc/xo722pR09z+jeTDEElj3P380zGtNfPc/sJms7OPq9z8LJcOMUeD3PwfyHCzErPc/SaMyYPHd9z9pyROjD1/4P7IrXVwLfPg/g5HHbsed+D8m6F8tn274P0dD9txqX/g/vJvXc/pD+D/3eFFWNJ/4PwpPGC8+zvg/CvwRIdYk+T/M7vn8WAr5P9O9fiPiL/k/TB5/BCY5+T/ZYXwzkkb5P6Bcg1nJUPk/XhKXfuqh+T9Ik7tqxqv5Py54AZGmw/k/vod7x1GC+T+IbFCWgnf5P2mrwgFVJfg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P1noDlj1Vvg/hqGZHWVh+D+FcJAGc1j4P01cUqtrUfg/vUXrnvhO+D9MCrdysvH4P8m+79VKBfk/DiAHG1oB+T/N82vhNcz4P1Zy2d4qbPk/Yqz6UvsR+j93Xu6JMU36P4jNiwntufo/DNpv49KV+j9P98WbuDn7P8ormy+ygfo/KcVSGu1V+j/Hd+3IFBD7P9UUjEB4Ufs/ojRfG62H+z8l3sXx/8f7P39/Dulz0vw/RHmqOfzS/D8YoWP9IoX8P2FEZiT+xfw/Dzdr5kz3/D+lQZWQy5z9P/yBNi8KH/4/T8A6hK32/T/IcADzTEz+P7hveaKn5f4/ly/zE4qR/z+VfRtYSjP/Pz+Cphvkkv0/0DxEmclU/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D8OJb0cNAP8P3vv0voZJvw/qg4cRClp+z+ort/Aomr7P62Z392INvs/svVvC1QO+z/pY6FKi0L6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j/ChoZJ4e/5P5F/RQI2Ffo/avlgnVM++j9A4GDzmkz6P6zfKz4un/o/TMPPA0hi+j9toQoDvzv6PwlpbDM+wfo/gLiKSagO+z8tf50fb+f6P6d9UnEMdvs/EM6GgGyH+z+a52duro/7P+QvDNyqGfs/rL8E8hXS+z/vPNCORN37P5qvcYH0Qvs/Af2dcQqg+j/1Sjh4T5P6P/VKOHhPk/o/9Uo4eE+T+j/1Sjh4T5P6P/VKOHhPk/o/9Uo4eE+T+j/1Sjh4T5P6P/VKOHhPk/o/9Uo4eE+T+j/1Sjh4T5P6P/VKOHhPk/o/vLpOFgMD+z92ySOrZyD7P29qi15SVPs/V+uvSi7e+z8pX7untx78P8drSToPxvs/Ibi1eL2T+z8AuehXf2P7P2VZG1b9Kfs/y8hEPYnn+j/ieClon2b6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/CMUUtOJu+j+MWbQI3iP6P0mWUTQo7fk/End6rZ17+j9w54AsXVT6P8hw+oIv3/o/lJr7szj2+j/wCgIz+M76P8EuSAImw/o/ZgavZUsp+z/fszNU3iH7PxYHVXJn+fo/n6E9Zrk/+z+tJaJnzHf7P/tmIJ4+Vfs/0YUW4T9w+j9U8m+fbnP6P1Tyb59uc/o/VPJvn25z+j9U8m+fbnP6P9lphOHQefo/QnQhuamf+j9eWu3EWW/6P8pQLvCJU/o/I3udN8JA+j9VRGaRmhT6P5b7/cXgv/k/FWa6OnSY+T/qGHD5vMD5P+oYcPm8wPk/6hhw+bzA+T/qGHD5vMD5P+oYcPm8wPk/sNe7P97L+T+1IcZrXvX5Pyq3tBoSF/o/k2qfjses+j+NHOkMjNz6P0it9xvt2Po/53k3FhQm+z/+iCZQxEL7P2H+0TdpOvs/K6uwGeAi+z9S/yCSIef6P/R0dcdiO/s/9HR1x2I7+z89cJUnEBb7P/eh0oiZnfo/Tt7H0Rx5+j/sCRLb3aP6P+wJEtvdo/o/swTPc/qQ+z82+HjXj2P7P5+NEqqKf/s/bAFXaNbo+z/UlvA60QT8P8UGwZlOgfw/zduOXy44/D94Cm/FtEX9P8oSU9j7Y/0/j/qhiPte/T+wk7Almlb9P/CHVXIEf/0/UD2IU5x5/T+IUcauo4D9P9DNtLOeMP0/s7QJ5p7E/D9b6tYh7VX9PwecW9cejPw/vOjhuhiZ/D+86OG6GJn8P7zo4boYmfw/vOjhuhiZ/D+Nx/pwCrf8P0RdWQyYTP0/U/mMrkyE/T9sFbJdG7b9P0DUax2Wkv0/Minz+aLJ/T9qQkiAFAH+P2UuXpbwEP4/ALfcA+4F/j/OLs0X9dX9PybAnhk+rv0/J2GUCqRQ/j9USr0RV1r+P/oCaP4Sff4/CDGFcbEu/j+J/VMUKVn+P/qNZPk0s/4/jD7UIEaU/j/6Amj+En3+PzhbLoWBc/4/dgsSRcKD/j8qfdafPo7+PyrV89gQqP4/pzW7WJLI/j/x6DR1mLv+P95OEN8DAP8/vx4BRhHe/j9kL8lrnxr/P3SQxaCN6P4/7/CMIA8J/z8nssRtrib/P/QMz7WpRv8/2+m8MBPR/z8tWCEaGhUAQCfwxUzfMQBAjhkfmyEbAEA0Jjtr9DAAQMaCOa/uHgBA44MV3ExLAEC/PxxZNjkAQMWDG5M/pP8/Mqe5f6bq/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P03YQ9DYxP4/t7a1Wmfp/j+i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While the strategy doesn't perform as well as simple buy & hold, it does so with significantly lower exposure (time in market).\n", "\n", "In conclusion, to test strategies on multiple time frames, you need to pass in OHLC data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to the lower time frame, filling in the in-betweens.\n", "Which is what the function [`backtesting.lib.resample_apply()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.resample_apply) does for you." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn more by exploring further\n", "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", "or find more framework options in the\n", "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." ] } ], "metadata": { "jupytext": { "text_representation": { "format_version": "1.1" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: doc/examples/Multiple Time Frames.py ================================================ # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.17.1 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %% [markdown] # Multiple Time Frames # ============ # # Best trading strategies that rely on technical analysis might take into account price action on multiple time frames. # This tutorial will show how to do that with _backtesting.py_, offloading most of the work to # [pandas resampling](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling). # It is assumed you're already familiar with # [basic framework usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html). # # We will put to the test this long-only, supposed # [400%-a-year trading strategy](https://web.archive.org/web/20180515044054/http://jbmarwood.com/stock-trading-strategy-300/), # which uses daily and weekly # [relative strength index](https://en.wikipedia.org/wiki/Relative_strength_index) # (RSI) values and moving averages (MA). # # In practice, one should use functions from an indicator library, such as # [TA-Lib](https://github.com/mrjbq7/ta-lib) or # [Tulipy](https://tulipindicators.org), # but among us, let's introduce the two indicators we'll be using. # %% import pandas as pd def SMA(array, n): """Simple moving average""" return pd.Series(array).rolling(n).mean() def RSI(array, n): """Relative strength index""" # Approximate; good enough gain = pd.Series(array).diff() loss = gain.copy() gain[gain < 0] = 0 loss[loss > 0] = 0 rs = gain.ewm(n).mean() / loss.abs().ewm(n).mean() return 100 - 100 / (1 + rs) # %% [markdown] # The strategy roughly goes like this: # # Buy a position when: # * weekly RSI(30) $\geq$ daily RSI(30) $>$ 70 # * Close $>$ MA(10) $>$ MA(20) $>$ MA(50) $>$ MA(100) # # Close the position when: # * Daily close is more than 2% _below_ MA(10) # * 8% fixed stop loss is hit # # We need to provide bars data in the _lowest time frame_ (i.e. daily) and resample it to any higher time frame (i.e. weekly) that our strategy requires. # %% from backtesting import Strategy, Backtest from backtesting.lib import resample_apply class System(Strategy): d_rsi = 30 # Daily RSI lookback periods w_rsi = 30 # Weekly level = 70 def init(self): # Compute moving averages the strategy demands self.ma10 = self.I(SMA, self.data.Close, 10) self.ma20 = self.I(SMA, self.data.Close, 20) self.ma50 = self.I(SMA, self.data.Close, 50) self.ma100 = self.I(SMA, self.data.Close, 100) # Compute daily RSI(30) self.daily_rsi = self.I(RSI, self.data.Close, self.d_rsi) # To construct weekly RSI, we can use `resample_apply()` # helper function from the library self.weekly_rsi = resample_apply( 'W-FRI', RSI, self.data.Close, self.w_rsi) def next(self): price = self.data.Close[-1] # If we don't already have a position, and # if all conditions are satisfied, enter long. if (not self.position and self.daily_rsi[-1] > self.level and self.weekly_rsi[-1] > self.level and self.weekly_rsi[-1] > self.daily_rsi[-1] and self.ma10[-1] > self.ma20[-1] > self.ma50[-1] > self.ma100[-1] and price > self.ma10[-1]): # Buy at market price on next open, but do # set 8% fixed stop loss. self.buy(sl=.92 * price) # If the price closes 2% or more below 10-day MA # close the position, if any. elif price < .98 * self.ma10[-1]: self.position.close() # %% [markdown] # Let's see how our strategy fares replayed on nine years of Google stock data. # %% from backtesting.test import GOOG backtest = Backtest(GOOG, System, commission=.002) backtest.run() # %% [markdown] # Meager four trades in the span of nine years and with zero return? How about if we optimize the parameters a bit? # %% # %%time backtest.optimize(d_rsi=range(10, 35, 5), w_rsi=range(10, 35, 5), level=range(30, 80, 10)) # %% backtest.plot() # %% [markdown] # Better. While the strategy doesn't perform as well as simple buy & hold, it does so with significantly lower exposure (time in market). # # In conclusion, to test strategies on multiple time frames, you need to pass in OHLC data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to the lower time frame, filling in the in-betweens. # Which is what the function [`backtesting.lib.resample_apply()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.resample_apply) does for you. # %% [markdown] # Learn more by exploring further # [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) # or find more framework options in the # [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). ================================================ FILE: doc/examples/Parameter Heatmap & Optimization.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Parameter Heatmap\n", "==========\n", "\n", "This tutorial will show how to optimize strategies with multiple parameters and how to examine and reason about optimization results.\n", "It is assumed you're already familiar with\n", "[basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html).\n", "\n", "First, let's again import our helper moving average function.\n", "In practice, one should use functions from an indicator library, such as\n", "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n", "[Tulipy](https://tulipindicators.org)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jk/PycharmProjects/backtesting/venv/lib/python3.11/site-packages/backtesting/_plotting.py:50: UserWarning: Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support (e.g. PyCharm, Spyder IDE). Reset with `backtesting.set_bokeh_output(notebook=False)`.\n", " warnings.warn('Jupyter Notebook detected. '\n" ] }, { "data": { "text/html": [ " \n", "
\n", " \n", " Loading BokehJS ...\n", "
\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "'use strict';\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", "const JS_MIME_TYPE = 'application/javascript';\n", " const HTML_MIME_TYPE = 'text/html';\n", " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " const CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " const script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " function drop(id) {\n", " const view = Bokeh.index.get_by_id(id)\n", " if (view != null) {\n", " view.model.document.clear()\n", " Bokeh.index.delete(view)\n", " }\n", " }\n", "\n", " const cell = handle.cell;\n", "\n", " const id = cell.output_area._bokeh_element_id;\n", " const server_id = cell.output_area._bokeh_server_id;\n", "\n", " // Clean up Bokeh references\n", " if (id != null) {\n", " drop(id)\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd_clean, {\n", " iopub: {\n", " output: function(msg) {\n", " const id = msg.content.text.trim()\n", " drop(id)\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd_destroy);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " const output_area = handle.output_area;\n", " const output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " const bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " const script_attrs = bk_div.children[0].attributes;\n", " for (let i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " const toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " const events = require('base/js/events');\n", " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " const NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"
    \\n\"+\n", " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", " \"
\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded(error = null) {\n", " const el = document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\");\n", " if (el != null) {\n", " const html = (() => {\n", " if (typeof root.Bokeh === \"undefined\") {\n", " if (error == null) {\n", " return \"BokehJS is loading ...\";\n", " } else {\n", " return \"BokehJS failed to load.\";\n", " }\n", " } else {\n", " const prefix = `BokehJS ${root.Bokeh.version}`;\n", " if (error == null) {\n", " return `${prefix} successfully loaded.`;\n", " } else {\n", " return `${prefix} encountered errors while loading and may not function as expected.`;\n", " }\n", " }\n", " })();\n", " el.innerHTML = html;\n", "\n", " if (error != null) {\n", " const wrapper = document.createElement(\"div\");\n", " wrapper.style.overflow = \"auto\";\n", " wrapper.style.height = \"5em\";\n", " wrapper.style.resize = \"vertical\";\n", " const content = document.createElement(\"div\");\n", " content.style.fontFamily = \"monospace\";\n", " content.style.whiteSpace = \"pre-wrap\";\n", " content.style.backgroundColor = \"rgb(255, 221, 221)\";\n", " content.textContent = error.stack ?? error.toString();\n", " wrapper.append(content);\n", " el.append(wrapper);\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(() => display_loaded(error), 100);\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error(url) {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (let i = 0; i < css_urls.length; i++) {\n", " const url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error.bind(null, url);\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (let i = 0; i < js_urls.length; i++) {\n", " const url = js_urls[i];\n", " const element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error.bind(null, url);\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.6.2.min.js\"];\n", " const css_urls = [];\n", "\n", " const inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " if (root.Bokeh !== undefined || force === true) {\n", " try {\n", " for (let i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", "\n", " } catch (error) {display_loaded(error);throw error;\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " const cell = $(document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\");\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.6.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {display_loaded(error);throw error;\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"c1d9e215-ea64-4085-976e-44c7f1e81bdd\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from backtesting.test import SMA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our strategy will be a similar moving average cross-over strategy to the one in\n", "[Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html),\n", "but we will use four moving averages in total:\n", "two moving averages whose relationship determines a general trend\n", "(we only trade long when the shorter MA is above the longer one, and vice versa),\n", "and two moving averages whose cross-over with daily _close_ prices determine the signal to enter or exit the position." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from backtesting import Strategy\n", "from backtesting.lib import crossover\n", "\n", "\n", "class Sma4Cross(Strategy):\n", " n1 = 50\n", " n2 = 100\n", " n_enter = 20\n", " n_exit = 10\n", " \n", " def init(self):\n", " self.sma1 = self.I(SMA, self.data.Close, self.n1)\n", " self.sma2 = self.I(SMA, self.data.Close, self.n2)\n", " self.sma_enter = self.I(SMA, self.data.Close, self.n_enter)\n", " self.sma_exit = self.I(SMA, self.data.Close, self.n_exit)\n", " \n", " def next(self):\n", " \n", " if not self.position:\n", " \n", " # On upwards trend, if price closes above\n", " # \"entry\" MA, go long\n", " \n", " # Here, even though the operands are arrays, this\n", " # works by implicitly comparing the two last values\n", " if self.sma1 > self.sma2:\n", " if crossover(self.data.Close, self.sma_enter):\n", " self.buy()\n", " \n", " # On downwards trend, if price closes below\n", " # \"entry\" MA, go short\n", " \n", " else:\n", " if crossover(self.sma_enter, self.data.Close):\n", " self.sell()\n", " \n", " # But if we already hold a position and the price\n", " # closes back below (above) \"exit\" MA, close the position\n", " \n", " else:\n", " if (self.position.is_long and\n", " crossover(self.sma_exit, self.data.Close)\n", " or\n", " self.position.is_short and\n", " crossover(self.data.Close, self.sma_exit)):\n", " \n", " self.position.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's not a robust strategy, but we can optimize it.\n", "\n", "[Grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search)\n", "is an exhaustive search through a set of specified sets of values of hyperparameters. One evaluates the performance for each set of parameters and finally selects the combination that performs best.\n", "\n", "Let's optimize our strategy on Google stock data using _randomized_ grid search over the parameter space, evaluating at most (approximately) 200 randomly chosen combinations:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "76f75e81cb0841a98da56b2a544395d4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Backtest.optimize: 0%| | 0/9 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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3013974.91711696.31911757.99115092.99413152.24311518.68711271.35411384.55110649.053
20NaN10102.8679864.21911003.218NaNNaNNaNNaNNaN
\n", "" ], "text/plain": [ "n2 40 60 80 100 120 140 160 \\\n", "n1 \n", "100 NaN NaN NaN NaN 11253.157 7101.260 11323.427 \n", "90 NaN NaN NaN 8958.143 9538.050 9884.416 9685.920 \n", "80 NaN NaN NaN 10863.109 7721.244 9139.946 8813.950 \n", "70 NaN NaN 14712.143 7192.893 10403.015 10065.280 8293.734 \n", "60 NaN NaN 9232.415 8046.486 10838.454 12876.589 10312.955 \n", "50 NaN 8383.465 10180.503 10563.790 9081.947 14272.266 13575.861 \n", "40 NaN 13666.448 NaN 7549.100 10629.479 12860.994 11405.291 \n", "30 13974.917 11696.319 11757.991 15092.994 13152.243 11518.687 11271.354 \n", "20 NaN 10102.867 9864.219 11003.218 NaN NaN NaN \n", "\n", "n2 180 200 \n", "n1 \n", "100 10163.322 11944.455 \n", "90 11343.644 8806.572 \n", "80 10414.656 8908.486 \n", "70 9895.782 9360.478 \n", "60 9427.545 9555.402 \n", "50 11383.465 10053.469 \n", "40 10863.807 10658.140 \n", "30 11384.551 10649.053 \n", "20 NaN NaN " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hm = heatmap.groupby(['n1', 'n2']).mean().unstack()\n", "hm = hm[::-1]\n", "hm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot this table as a heatmap:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "im = ax.imshow(hm, cmap='viridis')\n", "_ = (\n", " ax.set_xticks(range(len(hm.columns)), labels=hm.columns),\n", " ax.set_yticks(range(len(hm)), labels=hm.index),\n", " ax.set_xlabel('n2'),\n", " ax.set_ylabel('n1'),\n", " ax.figure.colorbar(im, ax=ax),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that, on average, we obtain the highest result using trend-determining parameters `n1=30` and `n2=100` or `n1=70` and `n2=80`,\n", "and it's not like other nearby combinations work similarly well — for our particular strategy, these combinations really stand out.\n", "\n", "Since our strategy contains several parameters, we might be interested in other relationships between their values.\n", "We can use\n", "[`backtesting.lib.plot_heatmaps()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.plot_heatmaps)\n", "function to plot interactive heatmaps of all parameter combinations simultaneously.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", "\n" ], "text/plain": [ "GridPlot(id='p1288', ...)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from backtesting.lib import plot_heatmaps\n", "\n", "\n", "plot_heatmaps(heatmap, agg='mean')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model-based optimization\n", "\n", "Above, we used _randomized grid search_ optimization method. Any kind of grid search, however, might be computationally expensive for large data sets. In the follwing example, we will use\n", "[_SAMBO Optimization_](https://sambo-optimization.github.io)\n", "package to guide our optimization better informed using forests of decision trees.\n", "The hyperparameter model is sequentially improved by evaluating the expensive function (the backtest) at the next best point, thereby hopefully converging to a set of optimal parameters with **as few evaluations as possible**.\n", "\n", "So, with `method=\"sambo\"`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "\n", "! pip install sambo # This is a run-time dependency" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d091822151a54ce581c47d7ded59c125", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Backtest.optimize: 0%| | 0/40 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sambo.plot import plot_objective\n", "\n", "names = ['n1', 'n2', 'n_enter', 'n_exit']\n", "_ = plot_objective(optimize_result, names=names, estimator='et')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sambo.plot import plot_evaluations\n", "\n", "_ = plot_evaluations(optimize_result, names=names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn more by exploring further\n", "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", "or find more framework options in the\n", "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." ] } ], "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: doc/examples/Parameter Heatmap & Optimization.py ================================================ # -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.17.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # %% [markdown] # Parameter Heatmap # ========== # # This tutorial will show how to optimize strategies with multiple parameters and how to examine and reason about optimization results. # It is assumed you're already familiar with # [basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html). # # First, let's again import our helper moving average function. # In practice, one should use functions from an indicator library, such as # [TA-Lib](https://github.com/mrjbq7/ta-lib) or # [Tulipy](https://tulipindicators.org). # %% from backtesting.test import SMA # %% [markdown] # Our strategy will be a similar moving average cross-over strategy to the one in # [Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html), # but we will use four moving averages in total: # two moving averages whose relationship determines a general trend # (we only trade long when the shorter MA is above the longer one, and vice versa), # and two moving averages whose cross-over with daily _close_ prices determine the signal to enter or exit the position. # %% from backtesting import Strategy from backtesting.lib import crossover class Sma4Cross(Strategy): n1 = 50 n2 = 100 n_enter = 20 n_exit = 10 def init(self): self.sma1 = self.I(SMA, self.data.Close, self.n1) self.sma2 = self.I(SMA, self.data.Close, self.n2) self.sma_enter = self.I(SMA, self.data.Close, self.n_enter) self.sma_exit = self.I(SMA, self.data.Close, self.n_exit) def next(self): if not self.position: # On upwards trend, if price closes above # "entry" MA, go long # Here, even though the operands are arrays, this # works by implicitly comparing the two last values if self.sma1 > self.sma2: if crossover(self.data.Close, self.sma_enter): self.buy() # On downwards trend, if price closes below # "entry" MA, go short else: if crossover(self.sma_enter, self.data.Close): self.sell() # But if we already hold a position and the price # closes back below (above) "exit" MA, close the position else: if (self.position.is_long and crossover(self.sma_exit, self.data.Close) or self.position.is_short and crossover(self.data.Close, self.sma_exit)): self.position.close() # %% [markdown] # It's not a robust strategy, but we can optimize it. # # [Grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search) # is an exhaustive search through a set of specified sets of values of hyperparameters. One evaluates the performance for each set of parameters and finally selects the combination that performs best. # # Let's optimize our strategy on Google stock data using _randomized_ grid search over the parameter space, evaluating at most (approximately) 200 randomly chosen combinations: # %% # %%time from backtesting import Backtest from backtesting.test import GOOG backtest = Backtest(GOOG, Sma4Cross, commission=.002) stats, heatmap = backtest.optimize( n1=range(10, 110, 10), n2=range(20, 210, 20), n_enter=range(15, 35, 5), n_exit=range(10, 25, 5), constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2, maximize='Equity Final [$]', max_tries=200, random_state=0, return_heatmap=True) # %% [markdown] # Notice `return_heatmap=True` parameter passed to # [`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize). # It makes the function return a heatmap series along with the usual stats of the best run. # `heatmap` is a pandas Series indexed with a MultiIndex, a cartesian product of all permissible (tried) parameter values. # The series values are from the `maximize=` argument we provided. # %% heatmap # %% [markdown] # This heatmap contains the results of all the runs, # making it very easy to obtain parameter combinations for e.g. three best runs: # %% heatmap.sort_values().iloc[-3:] # %% [markdown] # But we use vision to make judgements on larger data sets much faster. # Let's plot the whole heatmap by projecting it on two chosen dimensions. # Say we're mostly interested in how parameters `n1` and `n2`, on average, affect the outcome. # %% hm = heatmap.groupby(['n1', 'n2']).mean().unstack() hm = hm[::-1] hm # %% [markdown] # Let's plot this table as a heatmap: # %% # %matplotlib inline import matplotlib.pyplot as plt fig, ax = plt.subplots() im = ax.imshow(hm, cmap='viridis') _ = ( ax.set_xticks(range(len(hm.columns)), labels=hm.columns), ax.set_yticks(range(len(hm)), labels=hm.index), ax.set_xlabel('n2'), ax.set_ylabel('n1'), ax.figure.colorbar(im, ax=ax), ) # %% [markdown] # We see that, on average, we obtain the highest result using trend-determining parameters `n1=30` and `n2=100` or `n1=70` and `n2=80`, # and it's not like other nearby combinations work similarly well — for our particular strategy, these combinations really stand out. # # Since our strategy contains several parameters, we might be interested in other relationships between their values. # We can use # [`backtesting.lib.plot_heatmaps()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.plot_heatmaps) # function to plot interactive heatmaps of all parameter combinations simultaneously. # # # %% from backtesting.lib import plot_heatmaps plot_heatmaps(heatmap, agg='mean') # %% [markdown] # ## Model-based optimization # # Above, we used _randomized grid search_ optimization method. Any kind of grid search, however, might be computationally expensive for large data sets. In the follwing example, we will use # [_SAMBO Optimization_](https://sambo-optimization.github.io) # package to guide our optimization better informed using forests of decision trees. # The hyperparameter model is sequentially improved by evaluating the expensive function (the backtest) at the next best point, thereby hopefully converging to a set of optimal parameters with **as few evaluations as possible**. # # So, with `method="sambo"`: # %% # %%capture # ! pip install sambo # This is a run-time dependency # %% # #%%time stats, heatmap, optimize_result = backtest.optimize( n1=[10, 100], # Note: For method="sambo", we n2=[20, 200], # only need interval end-points n_enter=[10, 40], n_exit=[10, 30], constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2, maximize='Equity Final [$]', method='sambo', max_tries=40, random_state=0, return_heatmap=True, return_optimization=True) # %% heatmap.sort_values().iloc[-3:] # %% [markdown] # Notice how the optimization runs somewhat slower even though `max_tries=` is lower. This is due to the sequential nature of the algorithm and should actually perform quite comparably even in cases of _much larger parameter spaces_ where grid search would effectively blow up, likely reaching a better optimum than a simple randomized search would. # A note of warning, again, to take steps to avoid # [overfitting](https://en.wikipedia.org/wiki/Overfitting) # insofar as possible. # # Understanding the impact of each parameter on the computed objective function is easy in two dimensions, but as the number of dimensions grows, partial dependency plots are increasingly useful. # [Plotting tools from _SAMBO_](https://sambo-optimization.github.io/doc/sambo/plot.html) # take care of the more mundane things needed to make good and informative plots of the parameter space. # # Note, because SAMBO internally only does _minimization_, the values in `optimize_result` are negated (less is better). # %% from sambo.plot import plot_objective names = ['n1', 'n2', 'n_enter', 'n_exit'] _ = plot_objective(optimize_result, names=names, estimator='et') # %% from sambo.plot import plot_evaluations _ = plot_evaluations(optimize_result, names=names) # %% [markdown] # Learn more by exploring further # [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) # or find more framework options in the # [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). ================================================ FILE: doc/examples/Quick Start User Guide.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "_Backtesting.py_ Quick Start User Guide\n", "=======================\n", "\n", "This tutorial shows some of the features of *backtesting.py*, a Python framework for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies.\n", "\n", "_Backtesting.py_ is a small and lightweight, blazing fast backtesting framework that uses state-of-the-art Python structures and procedures (Python 3.6+, Pandas, NumPy, Bokeh). It has a very small and simple API that is easy to remember and quickly shape towards meaningful results. The library _doesn't_ really support stock picking or trading strategies that rely on arbitrage or multi-asset portfolio rebalancing; instead, it works with an individual tradeable asset at a time and is best suited for optimizing position entrance and exit signal strategies, decisions upon values of technical indicators, and it's also a versatile interactive trade visualization and statistics tool.\n", "\n", "\n", "## Data\n", "\n", "_You bring your own data._ Backtesting ingests _all kinds of \n", "[OHLC](https://en.wikipedia.org/wiki/Open-high-low-close_chart)\n", "data_ (stocks, forex, futures, crypto, ...) as a\n", "[pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/10min.html)\n", "with columns `'Open'`, `'High'`, `'Low'`, `'Close'` and (optionally) `'Volume'`.\n", "Such data is widely obtainable, e.g. with packages:\n", "* [pandas-datareader](https://pandas-datareader.readthedocs.io/en/latest/),\n", "* [Quandl](https://www.quandl.com/tools/python),\n", "* [findatapy](https://github.com/cuemacro/findatapy),\n", "* [yFinance](https://github.com/ranaroussi/yfinance),\n", "* [investpy](https://investpy.readthedocs.io/),\n", " etc.\n", "\n", "Besides these columns, **your data frames can have additional columns which are accessible in your strategies in a similar manner**.\n", "\n", "DataFrame should ideally be indexed with a _datetime index_ (convert it with [`pd.to_datetime()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html));\n", "otherwise a simple range index will do." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\\n\"+\n", " \"
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\\n\"+\n \"

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  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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OpenHighLowCloseVolume
2013-02-25802.30808.41790.49790.772303900
2013-02-26795.00795.95784.40790.132202500
2013-02-27794.80804.75791.11799.782026100
2013-02-28801.10806.99801.03801.202265800
2013-03-01797.80807.14796.15806.192175400
\n", "
" ], "text/plain": [ " Open High Low Close Volume\n", "2013-02-25 802.30 808.41 790.49 790.77 2303900\n", "2013-02-26 795.00 795.95 784.40 790.13 2202500\n", "2013-02-27 794.80 804.75 791.11 799.78 2026100\n", "2013-02-28 801.10 806.99 801.03 801.20 2265800\n", "2013-03-01 797.80 807.14 796.15 806.19 2175400" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Example OHLC daily data for Google Inc.\n", "from backtesting.test import GOOG\n", "\n", "GOOG.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strategy\n", "\n", "Let's create our first strategy to backtest on these Google data, a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover).\n", "\n", "_Backtesting.py_ doesn't ship its own set of _technical analysis indicators_. Users favoring TA should probably refer to functions from proven indicator libraries, such as\n", "[TA-Lib](https://github.com/TA-Lib/ta-lib-python) or\n", "[Tulipy](https://tulipindicators.org),\n", "but for this example, we can define a simple helper moving average function ourselves:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "\n", "def SMA(values, n):\n", " \"\"\"\n", " Return simple moving average of `values`, at\n", " each step taking into account `n` previous values.\n", " \"\"\"\n", " return pd.Series(values).rolling(n).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A new strategy needs to extend \n", "[`Strategy`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy)\n", "class and override its two abstract methods:\n", "[`init()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.init) and\n", "[`next()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.next).\n", "\n", "Method `init()` is invoked before the strategy is run. Within it, one ideally precomputes in efficient, vectorized manner whatever indicators and signals the strategy depends on.\n", "\n", "Method `next()` is then iteratively called by the\n", "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", "instance, once for each data point (data frame row), simulating the incremental availability of each new full candlestick bar.\n", "\n", "Note, _backtesting.py_ cannot make decisions / trades _within_ candlesticks — any new orders are executed on the next candle's _open_ (or the current candle's _close_ if\n", "[`trade_on_close=True`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.__init__)).\n", "If you find yourself wishing to trade within candlesticks (e.g. daytrading), you instead need to begin with more fine-grained (e.g. hourly) data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from backtesting import Strategy\n", "from backtesting.lib import crossover\n", "\n", "\n", "class SmaCross(Strategy):\n", " # Define the two MA lags as *class variables*\n", " # for later optimization\n", " n1 = 10\n", " n2 = 20\n", " \n", " def init(self):\n", " # Precompute the two moving averages\n", " self.sma1 = self.I(SMA, self.data.Close, self.n1)\n", " self.sma2 = self.I(SMA, self.data.Close, self.n2)\n", " \n", " def next(self):\n", " # If sma1 crosses above sma2, close any existing\n", " # short trades, and buy the asset\n", " if crossover(self.sma1, self.sma2):\n", " self.position.close()\n", " self.buy()\n", "\n", " # Else, if sma1 crosses below sma2, close any existing\n", " # long trades, and sell the asset\n", " elif crossover(self.sma2, self.sma1):\n", " self.position.close()\n", " self.sell()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In `init()` as well as in `next()`, the data the strategy is simulated on is available as an instance variable\n", "[`self.data`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data).\n", "\n", "In `init()`, we declare and **compute indicators indirectly by wrapping them in \n", "[`self.I()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.I)**.\n", "The wrapper is passed a function (our `SMA` function) along with any arguments to call it with (our _close_ values and the MA lag). Indicators wrapped in this way will be automatically plotted, and their legend strings will be intelligently inferred.\n", "\n", "In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we close the possible short position and go long; if it did and downwards, we close the open long position and go short. Note, we don't adjust order size, so _Backtesting.py_ assumes _maximal possible position_. We use\n", "[`backtesting.lib.crossover()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.crossover)\n", "function instead of writing more obscure and confusing conditions, such as:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%%script echo\n", "\n", " def next(self):\n", " if (self.sma1[-2] < self.sma2[-2] and\n", " self.sma1[-1] > self.sma2[-1]):\n", " self.position.close()\n", " self.buy()\n", "\n", " elif (self.sma1[-2] > self.sma2[-2] and # Ugh!\n", " self.sma1[-1] < self.sma2[-1]):\n", " self.position.close()\n", " self.sell()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and all declared indicators is adjusted** on each `next()` call so that `array[-1]` (e.g. `self.data.Close[-1]` or `self.sma1[-1]`) always contains the most recent value, `array[-2]` the previous value, etc. (ordinary Python indexing of ascending-sorted 1D arrays).\n", "\n", "**Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are NumPy arrays for performance reasons. If you prefer pandas Series or DataFrame objects, use `Strategy.data..s` or `Strategy.data.df` accessors respectively. You could also construct the series manually, e.g. `pd.Series(self.data.Close, index=self.data.index)`.\n", "\n", "We might avoid `self.position.close()` calls if we primed the\n", "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", "instance with `Backtest(..., exclusive_orders=True)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Backtesting\n", "\n", "Let's see how our strategy performs on historical Google data. The\n", "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", "instance is initialized with OHLC data and a strategy _class_ (see API reference for additional options), and we begin with 10,000 units of cash and set broker's commission to realistic 0.2%." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Start 2004-08-19 00:00:00\n", "End 2013-03-01 00:00:00\n", "Duration 3116 days 00:00:00\n", "Exposure Time [%] 94.27\n", "Equity Final [$] 68935.12\n", "Equity Peak [$] 68991.22\n", "Return [%] 589.35\n", "Buy & Hold Return [%] 703.46\n", "Max. Drawdown [%] -33.08\n", "Avg. Drawdown [%] -5.58\n", "Max. Drawdown Duration 688 days 00:00:00\n", "Avg. Drawdown Duration 41 days 00:00:00\n", "# Trades 93\n", "Win Rate [%] 53.76\n", "Best Trade [%] 57.12\n", "Worst Trade [%] -16.63\n", "Avg. Trade [%] 1.96\n", "Max. Trade Duration 121 days 00:00:00\n", "Avg. Trade Duration 32 days 00:00:00\n", "Profit Factor 2.13\n", "Expectancy [%] 6.91\n", "SQN 1.78\n", "Sharpe Ratio 0.18\n", "Sortino Ratio 0.44\n", "Calmar Ratio 0.06\n", "_strategy SmaCross\n", "_equity_curve Equ...\n", "_trades Size EntryB...\n", "dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from backtesting import Backtest\n", "\n", "bt = Backtest(GOOG, SmaCross, cash=10_000, commission=.002)\n", "stats = bt.run()\n", "stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run)\n", "method returns a pandas Series of simulation results and statistics associated with our strategy. We see that this simple strategy makes almost 600% return in the period of 9 years, with maximum drawdown 33%, and with longest drawdown period spanning almost two years ...\n", "\n", "[`Backtest.plot()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot)\n", "method provides the same insights in a more visual form." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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dAQGDl0CJSd0ABK4cW2Rt3QNwkBoGVB3dAne+nxkv5dkCnxks3ie52QLt0kxgE3nZAD1g5tMjSdkBRuB6F6712QJVDi2znrHZAWDm0yHaYdkBGtvP91Jx2QLKd76fGnXZA2c73U+OadkBkO99PjYV2QMh2vp8adXZAU+Olm8RfdkBkO99PjUt2QPhT46WbLnZAF9nO91MldkCyne+nxhR2QD4K16NwIHZA3iQGgZU4dkAZBFYOLUJ2QBKDwMqhS3ZA8/3UeOlcdkDn+6nx0nx2QCYxCKwcqHZALbKd76fedkDdJAaBlRV3QNajcD0KVXdAo3A9CteDd0BmZmZmZrd3QCYxCKwc6ndA2vl+arwfeEB+arx0k0x4QNajcD0KhXhAvp8aL93AeEB36SYxCA15QGwSg8DKSHlAUuOlm8R2eUAOWDm0yJ15QOb7qfHSsXlAgJVDi2zJeUAFgZVDi9B5QLLIdr6f1HlAcGiR7XzMeUA1iUFg5cF5QAWsHFpktHlAPTVeukmreUBLYhBYOaV5QHgUrkfhn3lAth6F61GOeUA1iUFg5Xd5QGVmZmZmX3lAM166SQxFeUBmke18Pyh5QNWjcD0KBXlASjeJQWDPeEDa+X5qvJd4QLXz/dR4bnhAAlYOLbJKeEDw0k1iEC14QG4Sg8DKD3hATWIQWDn6d0CicD0K1+d3QJiZmZmZ3ndALwisHFrSd0BWObTIdsJ3QBCDwMqhvndAyszMzMysd0CSGARWDqV3QL7KoUW2pHdAbj0K16Oqd0A2iUFg5bJ3QOx8PzVevXdAH7ByaJHKd0BzkxgEVtp3QEW28/3U6HdAKocW2c71d0A1XrpJDAt4QF66SQwCGnhALt0kBoEreEDMzMzMzD14QMh2vp8aVnhAqMZLN4lxeECmm8QgsI14QJqZmZmZrXhA+VPjpZvUeEASWDm0yO14QOWlm8QgC3lABCuHFtkgeUAA1XjpJjp5QCKwcmiRXHlAANV46SZ1eUBkEFg5tIh5QGMQWDm0knlA+n5qvHSgeUCq8dJNYqx5QAIrhxbZtXlApXA9CtepeUCIFtnO96F5QM7MzMzMlnlA9P3UeOmKeUD6fmq8dH15QHJoke18anlA2vl+arxSeUBjO99PjTh5QGZmZmZmEnlAvHSTGATneECyne+nxsB4QNEi2/l+m3hAnu+nxkt7eECaxCCwclh4QDIzMzMzOHhAzyLb+X4ceEDPItv5fgB4QHs/NV664XdA8NJNYhDQd0DWo3A9Csd3QEKLbOf7xXdA3CQGgZXAd0BiEFg5tLV3QAIrhxbZrHdA1XjpJjGjd0DGSzeJQZx3QO58PzVekHdAAiuHFtmLd0CZmZmZmY53QCLb+X5qmXdArnJoke2bd0B7PzVeup93QF7l0CLbpHdAuUkMAiukd0DDILByaKV3QBbZzvdTqHdAzMzMzMy0d0Cxne+nxsZ3QMVLN4lB23dAIgaBlUPod0BHDAIrh/t3QDnfT42XFHhAyszMzMwpeEBS46WbxDh4QC3dJAaBU3hAA4GVQ4treECC61G4HoR4QF7l0CLbmHhAdukmMQiseECb76fGS754QJ9FtvP90HhAIgaBlUPjeEBcukkMAvN4QPd+arx0D3lAJTEIrBwqeUCqHFpkO0x5QFqPwvUocHlAJlyPwvWLeUCTQ4ts55t5QOgmMQisrnlA9lPjpZu8eUDFSzeJQcJ5QB2F61G4z3lAHrByaJHheUALLbKd7/B5QMv3U+OlHXpACgIrhxZbekAG16NwPZB6QHO+nxov03pAY2ZmZmYUe0DVo3A9Ck97QJ0aL90ki3tAFi/dJAbFe0BTObTIdu57QGjn+6nxHHxAPots5/tFfEDOTWIQWGx8QKpH4XoUkXxAdhSuR+G3fEBz6SYxCNx8QHPpJjEIAX1A9X5qvHQwfUCl8dJNYmd9QBCuR+F6oX1ALQisHFrZfUDL91Pjpfh9QO3STWIQBH5APWDl0CIhfkAgBoGVQzJ+QHK+nxovQn5A4vup8dJJfkC1SQwCK1R+QK2d76fGWn5Arsh2vp9ofkBukxgEVnF+QK2d76fGe35Ag0Fg5dCDfkB2PzVeupB+QJ1wPQrXln5AFS/dJAagfkAS2c73U6h+QKtyaJHtqH5AQ+F6FK6gfkBCtvP91Jh+QC0IrBxajH5AvfUoXI9vfkB7lUOLbFp+QBUv3SQGNX5Aemq8dJMLfkDSo3A9CuR9QCAGgZVDzn1ACAIrhxa9fUAe2/l+aqt9QMahRbbzl31ApvHSTWKNfUAjMQisHIx9QCIGgZVDjH1AWo/C9SiIfUCaxCCwcop9QHo/NV66jn1Ai8L1KFybfUAKLbKd7619QLZJDAIrwn1Amu+nxkvOfUC1HoXrUdR9QHgUrkfh6X1AzfdT46XzfUAesHJokQB+QLufGi/dIn5AGlpkO988fkDoUbgehVt+QEuNl24Sb35AEK5H4XqIfkCa76fGS6l+QLDIdr6ftH5A66fGSzezfkDQeOkmMaN+QIiXbhKDmX5AgOtRuB6NfkBIN4lBYH5+QCMxCKwcYH5ANrTIdr46fkCWmZmZmRZ+QOhRuB6F/X1A20+Nl27ofUDaJAaBldh9QDjfT42X0X1A1aNwPQrPfUBWObTIdrx9QHJoke18rn1A9Shcj8KVfUDNzMzMzHJ9QJZDi2znTn1AQDVeukkkfUBGtvP91AF9QAqsHFpk4XxAYxBYObTZfEB66SYxCM18QM3MzMzMwHxA0k1iEFiyfEASg8DKoax8QICVQ4tsoHxABYGVQ4uXfECWbhKDwId8QMZLN4lBd3xACwIrhxZlfEASg8DKoU98QHW+nxovQHxAQGDl0CI1fEDGdr6fGi58QFqPwvUoLnxA1aNwPQo6fEBAi2zn+0N8QD5g5dAiTnxAG4XrUbhdfEANWDm0yGt8QOX7qfHSd3xA4qWbxCCEfEDC9Shcj5F8QPUoXI/CnXxAW4/C9SinfECOwvUoXLh8QBtaZDvfx3xAyqFFtvPXfEClm8QgsPJ8QJ4aL90kB31Aez81XroffUAqhxbZzit9QFqPwvUoPH1AJlyPwvVJfUBCi2zn+1N9QBKuR+F6X31AULgehetufUAGrBxaZH19QIUW2c73h31A0E1iEFiQfUDWzvdT44p9QCqyne+njH1A0E1iEFiMfUA630+Nl4t9QKJwPQrXi31AUw4tsp2PfUB4FK5H4Yp9QAbXo3A9i31AZZHtfD+BfUDCILByaHV9QKObxCCwcn1APmDl0CJyfUDCILByaGh9QLLIdr6fYX1APTVeuklgfUBNjZduEl19QDjfT42XV31AMDMzMzNbfUAOWDm0yGd9QFY5tMh2f31AuEkMAiuZfUCTQ4ts5659QIhs5/upy31ATrgehev0fUBNjZduEh5+QBuF61G4Qn5ACy2yne9tfkBTDi2ynZF+QFu6SQwCtH5AyszMzMzZfkBAi2zn+/F+QK1yaJHtDX9AQ7bz/dQxf0DKzMzMzFR/QAgCK4cWcH9AMDMzMzOQf0AbhetRuLh/QKBwPQrX239Ai8L1KFz+f0DESzeJQQqAQGi8dJMYFYBAunSTGAQegECicD0KVyeAQNWjcD2KLYBABqwcWuQ2gEDhpZvEoECAQPd+arx0S4BAxEs3iUFYgEA+YOXQImiAQOYmMQgseIBAY2ZmZuaLgEAH16NwvZ6AQFmPwvWoroBA2Pl+ary8gEB0vp8aL8yAQAoCK4eWzoBAl5mZmZnJgECqHFpkO8SAQHbpJjEIvIBA7afGS7e0gECEFtnOd6+AQEK28/3UrIBAReF6FK6kgECC61G4HpyAQBpaZDvfj4BAWGQ730+BgEA+NV66SXSAQI7C9ShcaYBAqfHSTeJhgECeGi/dpFWAQK1H4XoUR4BA3CQGgRU4gEAaL90khiWAQDi0yHa+EIBAQ7bz/dTzf0BS46WbxON/QGi8dJMY2H9Adb6fGi/Sf0B9arx0k9R/QK5yaJHt139AItv5fmraf0BAYOXQIth/QMVLN4lB1X9Aju18PzXVf0DoUbgehdV/QLUehetR339AOgrXo3Drf0C6nxov3fR/QI4YBFYO839Ak24Sg8D2f0DW+X5qvPV/QGJmZmZm+n9Ath6F69ECgEC+yqFFtg2AQMZ2vp+aHIBAju18P7UmgEDM91PjpTWAQDa0yHa+RYBAr53vp8ZVgEA6Ctej8GiAQMz3U+MlfoBAauf7qXGUgEDM91PjJa2AQM4i2/n+woBA0U1iEFjZgEAiBoGVQ/SAQCZcj8L1C4FAmsQgsHIigUD81HjppjiBQHbpJjGIVoFACgIrh5Z8gUDipZvEIKKBQExiEFg5y4FAJTEIrBzygUAesHJokR2CQKBFtvN9Q4JADlg5tMhjgkAMLbKdb4aCQPzUeOkmqYJAzyLb+f7KgkAKAiuHFuyCQJzvp8bLFoNAjsL1KNxBg0AjBoGVQ2qDQAJWDi0ylYNA7Hw/Nd67g0DqUbgeBeiDQL/KoUU2GYRAqfHSTeJKhEAkBoGVw3mEQBcEVg4tqIRAth6F69HahECicD0K1wWFQH5qvHSTIoVAkO18PzUthUCgRbbz/TGFQFhkO9/PQ4VAOt9PjRdHhUDGdr6fGkOFQOYmMQisPoVAvsqhRbY0hUC1HoXr0SmFQDAzMzOzI4VA4tAi2/kmhUClxks3iSOFQIDAyqFFIYVAUeOlm0QghUBR46WbRByFQHO+nxovGIVAmu+nxksMhUAwMzMzs/uEQNbO91Nj6oRAhkFg5VDjhEB6PzVeuuuEQFy6SQyCAYVAjsL1KFwchUB6FK5H4SuFQBXZzvfTQIVAyaFFtvNYhUDTeOkmMWeFQNN46SYxeoVATWIQWLmFhUAHrBxaZJGFQNjO91NjmYVA4HoUrkenhUClm8QgMLaFQK1H4XqUuYVAbhKDwMq7hUB2vp8aL7uFQM73U+OlvIVAVQ4tsh29hUCMl24Sg6yFQPZT46UbkoVARLbz/dRwhUA4tMh2vlaFQMVLN4nBQYVAh0Fg5VAphUBMYhBYORmFQMvMzMxMBIVAcmiR7fzuhEBXObTI9tGEQFUOLbIds4RAE65H4fqIhECWbhKDwE2EQMZLN4lBG4RAE65H4Xrhg0B+arx0k6eDQILAyqHFaoNAcmiR7Xwxg0ARg8DKIQGDQI7C9ShcvYJAqhxaZLt8gkCWbhKDwEOCQCIGgZXDD4JAVjm0yHbUgUCaxCCwcqCBQCUxCKyccYFAth6F61E7gUD2U+OlGxKBQOKlm8Sg8IBAuEkMAivUgEDN91Pjpa+AQFy6SQyCkYBAYBBYOTR/gEBHDAIrh2SAQASBlUOLRIBAFdnO99MfgEAjBoGVwwCAQEpiEFg5x39Aej81Xrp8f0CyyHa+n01/QCZcj8L1JH9ARuF6FK71fkC28/3UeL5+QNV46SYxhX5A2/l+arwyfkBI4XoUrvF9QDIIrBxas31A8tJNYhBqfUCamZmZmR59QBJYObTIxnxAy6FFtvOOfEAyCKwcWlF8QJVDi2znGXxAJjEIrBz0e0DdJAaBldd7QBBYObTI0ntA9Shcj8K7e0Dz/dR46Z17QNJNYhBYhXtAm++nxkuMe0AWBFYOLZ17QCUxCKwco3tA5fup8dLBe0CWmZmZmeR7QMahRbbzD3xA20+Nl24jfEBgO99PjTp8QMV2vp8aRnxAZrx0kxhRfEDlJjEIrGZ8QOp8PzVec3xAY2ZmZmaBfEAKAiuHFtZ8QBtaZDvfE31AZpHtfD9nfUDWzvdT4619QJIYBFYO/X1AonA9CtdRfkBbj8L1KKt+QO18PzVe9X5ACtejcD1Mf0D2U+Olm7p/QKrx0k1iCYBAVg4tsp04gECoxks3CWiAQClcj8L1lYBAeekmMYjDgEBGtvP91PGAQKAaL90kJ4FA+FPjpZtdgUBWDi2yHY6BQDIzMzOzwoFAVzm0yPbSgUBg5dAi2+KBQJ7vp8ZL7IFABFYOLbLtgUAlBoGVQ/CBQHNoke188IFAAAAAAAD0gUDFILBy6PeBQAisHFpk+4FAFK5H4Xr4gUBYObTI9vWBQIGVQ4vs6oFAU+Olm0TlgUClm8QgMOiBQA9YObTI4YFA/dR46abbgUDSTWIQWM+BQE1iEFg5wIFAqvHSTeK2gUAMAiuHFrOBQHa+nxovsIFAKFyPwvWsgUC18/3UeKaBQJIYBFaOqoFA0k1iEFipgUCsR+F6lKmBQG49CtcjooFAN7TIdj6bgUBOjZdukoWBQBKuR+F6boFAM166SQxbgUDqUbgeBU6BQJrEILDyO4FAfD81XjoogUChRbbz/R6BQCyyne+nHYFA9lPjpZsYgUDFSzeJwRaBQGi8dJMYD4FAbj0K1yP7gEDJoUW2c+SAQMMgsHLo1oBARbbz/VTLgEDgehSux6uAQHs/NV66jIBAcmiR7XxxgEDGSzeJQVyAQMh2vp8aPoBA7Xw/NV4vgEByaJHt/BqAQIts5/upCYBAYxBYObTpf0BQjZduEr9/QPYoXI/Ch39AeukmMQhHf0BANV66SQt/QIbrUbge335AK1yPwvWtfkCzcmiR7Y5+QP6p8dJNfn5A7lG4HoVzfkD10k1iEFd+QGUQWDm0QH5AVuOlm8RXfkDwfD81Xm9+QLBH4XoUen5AZRBYObR2fkDiT42Xbn9+QLsehetRfn5Ae+kmMQiDfkAeWmQ733t+QIBqvHSTcH5Am5mZmZlwfkBu5/up8Wx+QDu0yHa+bn5ACqwcWmRifkBLDAIrh0V+QBODwMqhKX5A3iQGgZXtfUClcD0K16t9QK5H4XoUZX1AkxgEVg4wfUBuEoPAyvl8QPYoXI/CvHxAWjm0yHaQfEAusp3vp1N8QFO4HoXrLnxAvXSTGAQRfEAtsp3vp+B7QIts5/uptXtAgpVDi2yWe0AL16NwPX97QDZeukkMXXtA8KfGSzcbe0DgT42Xbud6QOJ6FK5HvXpAWDm0yHaNekA730+Nl196QJiZmZmZOHpAE65H4Xr+eUBwPQrXo8F5QOb7qfHSbXlAne+nxksZeUAaL90kBu94QAIrhxbZrnhAO99PjZdyeEA6tMh2vi14QPp+arx08HdAzczMzMzHd0DdJAaBlZJ3QHnpJjEIU3dAke18PzUNd0AqhxbZzsN2QJmZmZmZmnZAdr6fGi+BdkCR7Xw/NVZ2QCCwcmiRPXZASQwCK4cndkCWbhKDwBN2QM8i2/l+JHZAy8zMzMwndkA630+Nlyl2QJRDi2znKHZAJTEIrBz3dUAlMQisHM51QH1qvHSTp3VA5fup8dKGdUAphxbZzlR1QJrEILByFXVAdb6fGi/hdEBtEoPAyqR0QNwkBoGVWnRAarx0kxgddED5fmq8dONzQGq8dJMYnnNA7Xw/NV5pc0BDi2zn+zNzQD81XrpJ6XJAdr6fGi+wckAZBFYOLWpyQGHl0CLbM3JAUI2XbhIOckACK4cW2fZxQJqZmZmZ7HFAcmiR7XzqcUC+nxov3fFxQCuHFtnO9HFAFtnO91P1cUD6fmq8dAlyQMDKoUW2F3JAbhKDwMovckDgehSuR1hyQBkv3SQGdHJAcWiR7XyUckCP7Xw/NaVyQOX7qfHSq3JABqwcWmSvckAKAiuHFs1yQILrUbge53JAD4PAyqEIc0CWmZmZmTNzQCqyne+nW3NAfWq8dJNrc0DIoUW283pzQMihRbbzf3NArXJoke2Jc0Cune+nxohzQLl0kxgEgXNAQIts5/trc0BGDAIrh19zQCzdJAaBSXNAQrbz/dRDc0DQTWIQWEtzQKObxCCwYHNAfpVDi2xxc0C2HoXrUYpzQLYehetRs3NAcGiR7XzTc0DSeOkmMexzQDnfT42X+3NAipduEoMFdECIbOf7qQx0QHbpJjEIJnRAkkOLbOdKdEC7nxov3X10QOlRuB6FonRAziLb+X7FdEBaj8L1KPd0QCUxCKwcJnVALd0kBoFIdUBsEoPAyoB1QDsK16NwoHVAZGZmZmbAdUCkm8QgsMR1QMmhRbbz1XVArUfhehTedUDYzvdT49R1QLPIdr6f0HVASQwCK4fHdUD6fmq8dLt1QKwcWmQ7qnVA001iEFiMdUBOYhBYOWh1QPT91HjpJ3VA4E+Nl27vdEDLoUW28850QNNNYhBYs3RAPgrXo3CUdEA1XrpJDHZ0QNV46SYxcHRAd76fGi9gdEAaL90kBlZ0QKJFtvP9SHRA5/up8dJXdEAzMzMzM1l0QJ8aL90kW3RAeOkmMQhodEDMzMzMzG90QE1iEFg5fHRAPjVeukmOdECAlUOLbKp0QPL91Hjp13RAehSuR+EIdUD0KFyPwkZ1QJ4aL90kb3VAIdv5fmqSdUA630+Nl7l1QASBlUOL5HVAULgehesLdkBmke18Py92QPMoXI/CW3ZAl5mZmZmNdkDy/dR46bR2QI/tfD81z3ZAwPUoXI/sdkCRGARWDg13QLp0kxgEKndAUw4tsp1Id0DdT42Xbml3QFqPwvUojHdAiWzn+6mtd0BTDi2yncZ3QEk3iUFg4HdAFgRWDi38d0B26SYxCCB4QByF61G4O3hA2iQGgZVXeEASrkfhem94QMD1KFyPh3hAOt9PjZePeECYmZmZmY54QCqHFtnOjHhAonA9CteaeEBS46WbxKh4QH+VQ4tss3hAHFpkO9+8eECuR+F6FMB4QCyHFtnOznhAQTVeukngeEDhT42Xbu94QD8K16NwAHlA3vl+arwaeUAgWmQ73y95QNDMzMzMRnlAbJHtfD9keUAn2/l+aop5QCWwcmiRo3lATeF6FK65eUBqO99PjdR5QIrrUbge9HlApkW28/0RekDKILByaCd6QOD5fmq8NnpA+yhcj8JDekAqBoGVQ1F6QEZg5dAiZHpA9afGSzdvekCIwMqhRXB6QM1LN4lBc3pAazvfT413ekAiL90kBn56QBMCK4cWfHpAeD0K16N2ekCVl24Sg2x6QLVH4XoUU3pAYmQ73083ekCgmZmZmRV6QHC8dJMY+3lAG9nO91PpeUDT91Pjpd15QPOnxks33XlAaDvfT43jeUBDNV66SfV5QA7Xo3A9C3pA2njpJjEYekDLSzeJQSB6QAhWDi2yMHpAAKrx0k1CekA9tMh2vlh6QDLdJAaBcXpADgIrhxaBekAwsp3vp416QE43iUFgmXpABlYOLbKuekDVTWIQWMp6QIKVQ4ts7HpAyna+nxoae0AbL90kBkF7QAIAAAAAYXtAXo/C9SiDe0BiukkMAp17QMggsHJotHtA3vl+arzEe0AYrkfhetR7QOV6FK5H7HtAlhgEVg74e0AtXI/C9QV8QEaLbOf7EnxAQArXo3AlfEDKSzeJQTR8QGpmZmZmR3xAWg4tsp1gfEAeL90kBnp8QJPC9ShcinxAI4XrUbibfEBFNV66SaN8QPr91HjppHxAyyCwcmimfEBIYOXQIqx8QNYi2/l+r3xAzna+nxqxfEDopZvEILl8QMJ0kxgEw3xAmkOLbOfKfEAFAAAAANd8QIvrUbge8XxAUAwCK4cTfUCDPzVeujl9QCawcmiRUn1A69Ai2/lrfUAQ16NwPYR9QPj91HjpmX1AtXJoke2wfUD20k1iEMZ9QBICK4cW4X1AhWq8dJP+fUDV91PjpR5+QAYAAAAAOn5A1fdT46VPfkCIlUOLbGV+QPJRuB6FhX5AUjeJQWCwfkCNFtnO99J+QM1LN4lB9n5ADqwcWmQdf0Buke18P0R/QCJaZDvfaX9AhpVDi2yIf0BIYOXQIrd/QJjtfD81439AvkkMAqsGgEBSYhBYuRuAQALVeOmmMoBARmDl0CJLgEDJILByaGGAQF5kO99PdYBAPt9PjReHgEAVg8DKoaCAQGuR7Xw/tYBAES2ynW/HgEBi5dAi29aAQLjz/dT434BA9yhcj8LtgEA1MzMzs/uAQA4CK4cWC4FABCuHFlkbgUBu5/upcSmBQIfrUbieOIFAAgAAAIBBgUAaBFYOLUuBQEaLbOd7VYFAZBBYObRfgUAmBoGVQ2eBQITAyqHFbYFASwwCKwd5gUCdxCCw8oaBQLbIdr4fmYFA+lPjpZukgUCRwvUoXLeBQMLKoUW2zYFAzszMzMzhgUBaObTI9vOBQGC6SQyCAoJAIrByaJEQgkCr8dJNYhqCQEE1XrpJI4JAjWzn+6krgkB+PzVeujSCQDUzMzMzPoJAbuf7qfFEgkBmO99PDU2CQIkW2c73U4JApnA9ClddgkAY2c7302iCQHaTGARWcIJAhcDKocV7gkDvfD813oiCQCkxCKwcmoJA6yYxCKyogkCKQWDl0LWCQP2p8dLNwoJAku18PzXTgkDOzMzMzOKCQMp2vp+a64JAgpVDi2zugkC2yHa+n/OCQIkW2c5394JA+35qvHT3gkACAAAAAPSCQHm+nxqv8oJAsEfhepTrgkBmO99PDemCQNH3U+Ol4oJA73w/NV7cgkCClUOL7MeCQJluEoNAq4JAGNnO99OMgkDtUbgehWyCQOfQItt5SoJAksL1KFwlgkCCarx0kwKCQOr7qfFS3IFAlhgEVg67gUBbObTIdpqBQKdwPQpXgYFAVbgehetlgUBnO99PDUyBQHQ9CtejNYFAwp8aL10hgUDtJjEIrAqBQNh46SYx+4BAsnJokW3ngEBmO99PjdiAQI5s5/upx4BA43oUrsfEgEAthxbZzsKAQLJyaJFtvoBAXo/C9Si4gECNbOf7KbWAQCYGgZVDtoBAFq5H4Xq5gEBdj8L1KL+AQLtJDAKrxIBAcT0K16PTgEBWDi2yHeCAQP2p8dLN6oBALIcW2c76gEBEi2znew2BQArXo3C9HoFAJQaBlcMqgUDo+6nxUjSBQCUGgZVDP4FAqMZLN4lIgUCsHFpkO1CBQCcxCKwcVoFAd76fGq9bgUBKDAIrB2aBQPhT46WbdIFAWTm0yPaCgUDufD813o6BQOSlm8QgmYFAAyuHFtmhgUB66SYxiKeBQJhuEoNAqoFANl66SYysgUDZzvdT462BQPup8dJNqoFAdJMYBFakgUAmMQisnKGBQDVeukkMq4FAILByaJG0gUBXObTIdsCBQFc5tMj2uYFALt0kBgG2gUDrUbgeBbWBQHA9Ctcjt4FAeOkmMQizgUBLN4lB4KuBQPl+arx0n4FAmJmZmRmSgUA630+NF4OBQASBlUMLdYFA+qnx0s1jgUBvPQrXo1OBQGZmZmbmOoFA2M73U2MlgUCOwvUo3AmBQOKlm8Sg7IBAA1YOLTLYgED2U+OlG7mAQIps5/upl4BANolBYOV1gEABK4cW2WSAQM8i2/n+U4BAFdnO91M9gEB6FK5HYSWAQML1KFyPCIBAUuOlm8TWf0B1vp8aL6t/QDrfT42XgX9AUw4tsp1Wf0Cm8dJNYjV/QBLZzvdTFX9ANbTIdr7ufkDOTWIQWOR+QLh0kxgE4X5AjhgEVg7hfkAlXI/C9dp+QFU5tMh2vX5AOwrXo3ChfkDa+X5qvJJ+QEW28/3UgH5AjsL1KFxtfkBLN4lBYGV+QANWDi2yZ35A7qfGSzdsfkCrHFpkO4B+QD0K16NwjX5AwMqhRbaUfkADVg4tsph+QGZmZmZmmH5AGgRWDi2KfkB2kxgEVn9+QAMrhxbZaH5AKDEIrBxCfkBOYhBYOQ1+QHNoke18231AsHJoke2zfUAlBoGVQ5h9QJ3vp8ZLin1ARuF6FK56fUAFgZVDi3B9QDAIrBxadX1AeOkmMQhwfUCrHFpkO2p9QGDl0CLbSX1Asp3vp8YufUAzMzMzMyl9QDMzMzMzIn1AxSCwcmgkfUBLN4lBYDB9QMMgsHJoPX1AACuHFtlNfUBQuB6F62V9QMihRbbzhX1ACNejcD2qfUAV2c73U9V9QHOTGARWAH5AqhxaZDstfkBWObTIdlZ+QM33U+OlcH5AIwaBlUOIfkAaWmQ735N+QNJ46SYxlH5A2iQGgZWSfkDi0CLb+ad+QCvdJAaBt35Ay/dT46W+fkDqfD81XsJ+QDOJQWDltH5AVTm0yHaefkADgZVDi4p+QGI730+NaX5AUuOlm8RRfkDA9ShcjzZ+QDa0yHa+IX5AHrByaJEDfkAdhetRuON9QK5yaJHtvn1AoEW28/2afUCSGARWDoN9QJvvp8ZLYn1AbRKDwMpHfUDSTWIQWDt9QM33U+OlLn1AqPHSTWIrfUDFSzeJQSd9QC4IrBxaH31AEq5H4XoefUBLYhBYOTB9QKocWmQ7VX1ANrTIdr58fUDdT42XbrB9QOhRuB6F331AHbByaJEcfkBCtvP91FV+QB7b+X5qkX5AYDvfT43PfkBOuB6F6wN/QBpaZDvfNX9Ai5duEoNff0B9arx0k5p/QDrfT42XzX9AK7Kd76f4f0Dz/dR4aRSAQNajcD0KK4BAgJVDi2xDgEByaJHtfFyAQA4tsp1vdIBA+n5qvPSggEB8PzVeusyAQOXQItt58oBAU+Olm0QXgUDpJjEIrD6BQFPjpZvEYIFAYhBYOTSDgUCyne+nxqeBQD41XrpJy4FAlUOLbGfwgUBBYOXQohOCQANWDi2yOIJAnxov3aRXgkDl0CLb+XmCQEfhehSun4JA6SYxCCzDgkDSTWIQWOaCQO+nxku3B4NANV66SYwng0BkO99PDUaDQD41XrrJRoNABqwcWuQ9g0Bmke18PzSDQOPQItt5KoNA+qnx0k0kg0CyyHa+nxuDQIqXbhKDEYNAr53vp0YDg0AWBFYOrfqCQDa0yHY+74JA9VPjpZvigkAFrBxa5MqCQAWsHFpktoJAMl66SQyjgkDTo3A9io6CQC4IrBzae4JA/P////9rgkA7NV66SV6CQNf5fmq8UYJANrTIdr5HgkCune+nRkSCQGE7308NRIJAc76fGq9GgkCYxCCw8kmCQJwaL92kR4JAcZMYBFZJgkB7arx0E06CQB2wcmgRV4JAf8DKocVagkBKYhBYuV+CQJfEILByZoJA7dJNYpB4gkDpfD81XoaCQHg/NV46j4JAT+Olm8SbgkB+wMqhRaWCQAKBlUMLroJAeD81Xjq3gkBeEFg5NMGCQCvdJAYByoJAvvUoXI/SgkDPTWIQWNuCQGNmZmbm5YJA1s73U+PygkBPuB6FawaDQGi8dJMYFYNAfWq8dJMeg0AlMQisHCGDQC8IrBzaI4NAvZ8aL90qg0A2iUFg5TGDQL7KoUU2OINA79JNYhA5g0CD61G4njuDQJzvp8ZLPoNAZpHtfD9Cg0BG4XoUrkKDQOcmMQisQYNAV2Q7389Ag0ASrkfhekKDQBYEVg6tQoNALN0kBoFCg0DgpZvEoEWDQOynxks3R4NAFy/dJAZBg0CKl24SAz6DQMEgsHJoPYNAju18P7VEg0CMwvUoXESDQH6VQ4vsQINALgisHNo9g0D61HjpJjuDQFIOLbIdQINArp3vp0ZAg0ATBFYOLTyDQC4zMzMzO4NA1M73U2M3g0BXj8L1qC+DQOh8PzXeJoNASWIQWDkcg0A0tMh2vg2DQCwIrBza/YJAMF66SQzogkAiMQisnNCCQGoSg8DKtYJAyvdT46WcgkCCFtnO94KCQKPGSzeJbYJAdhSuR2FggkDrp8ZLt1SCQGjn+6nxS4JA4/up8dI/gkD0U+OlmzCCQM0i2/n+KIJAHtv5fmohgkCa76fGSxiCQE/jpZvEFIJAFARWDi0TgkDVzvdT4wmCQC0IrBzaAoJAqBxaZLsCgkBWZDvfTwOCQCIGgZVDBoJAdL6fGq8GgkBe5dAiWw6CQO2nxks3FYJATo2XbhIJgkCP7Xw/NfWBQAasHFrk3oFAth6F61HIgUDHdr6fmq+BQHs/NV66mYFAvsqhRbaIgUCyyHa+H3eBQH1qvHSTZYFAi5duEoNUgUDup8ZLNz+BQDEzMzOzKYFAeBSuR2EcgUAg2/l+agyBQKXGSzeJ+oBA0E1iEFjqgEAmXI/CddyAQDSJQWBlzoBAkkOLbOe9gEC0HoXrUaqAQMAgsHJopYBAW+XQItumgEC1SQwCK6qAQEY3iUFgrIBAlsQgsPKrgEALWDm0SKmAQCRcj8J1o4BASWIQWDmcgEA6NV66SZSAQFm6SQwCi4BA0HjpJjGHgEBHN4lB4IOAQAtYObTIgIBAJocW2U58gEAiMQisnHaAQPnUeOkmb4BAwks3icFlgEBO46WbRF6AQF87308NVIBAr8h2vh9KgEDEdr6fGkaAQJWZmZkZO4BA4/up8VIvgEBCtvP91ByAQGA7308NDYBAM4lBYOUCgEBmEoPAyux/QKMcWmQ7zX9ACoPAyqGqf0D11HjpJox/QFbl0CLbb39A1U+Nl25Zf0ACAiuHFkh/QJLEILByRn9AWzvfT41Pf0BNDi2ynVx/QLr1KFyPcn9AS+Olm8R+f0A7i2zn+4x/QL0gsHJopH9AsB6F61G8f0AeMQisHNF/QPZ+arx0D4BAfZVDi2w7gEAiMQisnGqAQGA730+Nk4BAAoGVQ4vDgEBkke18v/qAQNXO91NjNIFArp3vp0ZsgUBeEFg5tJmBQGE7308Nx4FA4tAi2/ntgUCAwMqhRRCCQMNLN4lBKIJAqkfhepRCgkCYxCCw8k6CQFHjpZvEYYJAIgaBlUNpgkAXL90kBnmCQOT7qfFSfYJAlpmZmZmKgkC+9ShcD32CQOHQItv5bYJAjxgEVo5UgkAUBFYOrTuCQEG28/3UEoJAwCCwcujfgUDZJAaBla+BQMR2vp8ahoFAdOkmMYhkgUDkJjEILECBQDgK16NwIYFAuZ8aL10GgUByvp8ar/GAQJSZmZmZ2YBANt9PjZfHgEDu/dR46bGAQEY3iUFgqIBAr8h2vp+YgEAq3SQGAZOAQDffT40XhIBADoPAyqF2gEAyXrpJjGuAQK6d76fGaIBA06NwPYpsgEDyKFyPQn2AQL/1KFyPk4BAaef7qfGmgEBnvHSTGK+AQAOBlUMLroBAqhxaZDuwgEA2tMh2PrKAQP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"metadata": {}, "source": [ "## Optimization\n", "\n", "We hard-coded the two lag parameters (`n1` and `n2`) into our strategy above. However, the strategy may work better with 15–30 or some other cross-over. **We declared the parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)**.\n", "\n", "We optimize the two parameters by calling\n", "[`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize)\n", "method with each parameter a keyword argument pointing to its pool of possible values to test. Parameter `n1` is tested for values in range between 5 and 30 and parameter `n2` for values between 10 and 70, respectively. Some combinations of values of the two parameters are invalid, i.e. `n1` should not be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which takes in the parameters and returns `True` (i.e. admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes return over the observed period. We could instead choose to optimize any other key from the returned `stats` series." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7df5af7a17a64c18a41c5c4125f4d9f6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "CPU times: user 156 ms, sys: 52 ms, total: 208 ms\n", "Wall time: 1.55 s\n" ] }, { "data": { "text/plain": [ "Start 2004-08-19 00:00:00\n", "End 2013-03-01 00:00:00\n", "Duration 3116 days 00:00:00\n", "Exposure Time [%] 98.14\n", "Equity Final [$] 105040.13\n", "Equity Peak [$] 108327.72\n", "Return [%] 950.40\n", "Buy & Hold Return [%] 703.46\n", "Max. Drawdown [%] -44.00\n", "Avg. Drawdown [%] -6.14\n", "Max. Drawdown Duration 690 days 00:00:00\n", "Avg. Drawdown Duration 43 days 00:00:00\n", "# Trades 152\n", "Win Rate [%] 51.32\n", "Best Trade [%] 61.56\n", "Worst Trade [%] -19.78\n", "Avg. Trade [%] 1.52\n", "Max. Trade Duration 83 days 00:00:00\n", "Avg. Trade Duration 21 days 00:00:00\n", "Profit Factor 1.96\n", "Expectancy [%] 6.00\n", "SQN 1.51\n", "Sharpe Ratio 0.15\n", "Sortino Ratio 0.39\n", "Calmar Ratio 0.03\n", "_strategy SmaCross(n1=10,n...\n", "_equity_curve Eq...\n", "_trades Size Entry...\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "stats = bt.optimize(n1=range(5, 30, 5),\n", " n2=range(10, 70, 5),\n", " maximize='Equity Final [$]',\n", " constraint=lambda param: param.n1 < param.n2)\n", "stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look into `stats['_strategy']` to access the Strategy _instance_ and its optimal parameter values (10 and 15)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats._strategy" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strategy optimization managed to up its initial performance _on in-sample data_ by almost 50% and even beat simple\n", "[buy & hold](https://en.wikipedia.org/wiki/Buy_and_hold).\n", "In real life optimization, however, do **take steps to avoid\n", "[overfitting](https://en.wikipedia.org/wiki/Overfitting)**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trade data\n", "\n", "In addition to backtest statistics returned by\n", "[`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run)\n", "shown above, you can look into _individual trade returns_ and the changing _equity curve_ and _drawdown_ by inspecting the last few, internal keys in the result series." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sortino Ratio 0.39\n", "Calmar Ratio 0.03\n", "_strategy SmaCross(n1=10,n2=15)\n", "_equity_curve Equity DrawdownPct DrawdownDura...\n", "_trades Size EntryBar ExitBar EntryPrice Exit...\n", "dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The columns should be self-explanatory." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EquityDrawdownPctDrawdownDuration
2004-08-1910000.000.00NaT
2004-08-2010000.000.00NaT
2004-08-2310000.000.00NaT
2004-08-2410000.000.00NaT
2004-08-2510000.000.00NaT
............
2013-02-25103035.530.05NaT
2013-02-26102952.330.05NaT
2013-02-27104206.830.04NaT
2013-02-28104391.430.04NaT
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" ], "text/plain": [ " Equity DrawdownPct DrawdownDuration\n", "2004-08-19 10000.00 0.00 NaT\n", "2004-08-20 10000.00 0.00 NaT\n", "2004-08-23 10000.00 0.00 NaT\n", "2004-08-24 10000.00 0.00 NaT\n", "2004-08-25 10000.00 0.00 NaT\n", "... ... ... ...\n", "2013-02-25 103035.53 0.05 NaT\n", "2013-02-26 102952.33 0.05 NaT\n", "2013-02-27 104206.83 0.04 NaT\n", "2013-02-28 104391.43 0.04 NaT\n", "2013-03-01 105040.13 0.03 533 days\n", "\n", "[2148 rows x 3 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats['_equity_curve'] # Contains equity/drawdown curves. DrawdownDuration is only defined at ends of DD periods." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SizeEntryBarExitBarEntryPriceExitPricePnLReturnPctEntryTimeExitTimeDuration
0872060114.65185.236140.560.622004-09-172004-11-1256 days
1-876069184.86175.80788.180.052004-11-122004-11-2614 days
2966971176.15180.71437.610.032004-11-262004-11-304 days
3-967175180.35179.13116.980.012004-11-302004-12-066 days
4977582179.49177.99-145.33-0.012004-12-062004-12-159 days
.................................
147-12120562085738.65687.786155.240.072012-10-162012-11-2944 days
14813920852111689.16735.546447.440.072012-11-292013-01-0840 days
149-13921112113734.07742.83-1217.79-0.012013-01-082013-01-102 days
15013621132121744.32735.99-1132.29-0.012013-01-102013-01-2313 days
151-13621212127734.52750.51-2174.91-0.022013-01-232013-01-318 days
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" ], "text/plain": [ " Size EntryBar ExitBar EntryPrice ExitPrice PnL ReturnPct \\\n", "0 87 20 60 114.65 185.23 6140.56 0.62 \n", "1 -87 60 69 184.86 175.80 788.18 0.05 \n", "2 96 69 71 176.15 180.71 437.61 0.03 \n", "3 -96 71 75 180.35 179.13 116.98 0.01 \n", "4 97 75 82 179.49 177.99 -145.33 -0.01 \n", ".. ... ... ... ... ... ... ... \n", "147 -121 2056 2085 738.65 687.78 6155.24 0.07 \n", "148 139 2085 2111 689.16 735.54 6447.44 0.07 \n", "149 -139 2111 2113 734.07 742.83 -1217.79 -0.01 \n", "150 136 2113 2121 744.32 735.99 -1132.29 -0.01 \n", "151 -136 2121 2127 734.52 750.51 -2174.91 -0.02 \n", "\n", " EntryTime ExitTime Duration \n", "0 2004-09-17 2004-11-12 56 days \n", "1 2004-11-12 2004-11-26 14 days \n", "2 2004-11-26 2004-11-30 4 days \n", "3 2004-11-30 2004-12-06 6 days \n", "4 2004-12-06 2004-12-15 9 days \n", ".. ... ... ... \n", "147 2012-10-16 2012-11-29 44 days \n", "148 2012-11-29 2013-01-08 40 days \n", "149 2013-01-08 2013-01-10 2 days \n", "150 2013-01-10 2013-01-23 13 days \n", "151 2013-01-23 2013-01-31 8 days \n", "\n", "[152 rows x 10 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats['_trades'] # Contains individual trade data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn more by exploring further\n", "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", "or find more framework options in the\n", "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." ] } ], "metadata": { "jupytext": { "text_representation": { "format_version": "1.1" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: doc/examples/Quick Start User Guide.py ================================================ # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.17.1 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %% [markdown] # _Backtesting.py_ Quick Start User Guide # ======================= # # This tutorial shows some of the features of *backtesting.py*, a Python framework for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies. # # _Backtesting.py_ is a small and lightweight, blazing fast backtesting framework that uses state-of-the-art Python structures and procedures (Python 3.6+, Pandas, NumPy, Bokeh). It has a very small and simple API that is easy to remember and quickly shape towards meaningful results. The library _doesn't_ really support stock picking or trading strategies that rely on arbitrage or multi-asset portfolio rebalancing; instead, it works with an individual tradeable asset at a time and is best suited for optimizing position entrance and exit signal strategies, decisions upon values of technical indicators, and it's also a versatile interactive trade visualization and statistics tool. # # # ## Data # # _You bring your own data._ Backtesting ingests _all kinds of # [OHLC](https://en.wikipedia.org/wiki/Open-high-low-close_chart) # data_ (stocks, forex, futures, crypto, ...) as a # [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/10min.html) # with columns `'Open'`, `'High'`, `'Low'`, `'Close'` and (optionally) `'Volume'`. # Such data is widely obtainable, e.g. with packages: # * [pandas-datareader](https://pandas-datareader.readthedocs.io/en/latest/), # * [Quandl](https://www.quandl.com/tools/python), # * [findatapy](https://github.com/cuemacro/findatapy), # * [yFinance](https://github.com/ranaroussi/yfinance), # * [investpy](https://investpy.readthedocs.io/), # etc. # # Besides these columns, **your data frames can have additional columns which are accessible in your strategies in a similar manner**. # # DataFrame should ideally be indexed with a _datetime index_ (convert it with [`pd.to_datetime()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)); # otherwise a simple range index will do. # %% # Example OHLC daily data for Google Inc. from backtesting.test import GOOG GOOG.tail() # %% [markdown] # ## Strategy # # Let's create our first strategy to backtest on these Google data, a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover). # # _Backtesting.py_ doesn't ship its own set of _technical analysis indicators_. Users favoring TA should probably refer to functions from proven indicator libraries, such as # [TA-Lib](https://github.com/TA-Lib/ta-lib-python) or # [Tulipy](https://tulipindicators.org), # but for this example, we can define a simple helper moving average function ourselves: # %% import pandas as pd def SMA(values, n): """ Return simple moving average of `values`, at each step taking into account `n` previous values. """ return pd.Series(values).rolling(n).mean() # %% [markdown] # A new strategy needs to extend # [`Strategy`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy) # class and override its two abstract methods: # [`init()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.init) and # [`next()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.next). # # Method `init()` is invoked before the strategy is run. Within it, one ideally precomputes in efficient, vectorized manner whatever indicators and signals the strategy depends on. # # Method `next()` is then iteratively called by the # [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) # instance, once for each data point (data frame row), simulating the incremental availability of each new full candlestick bar. # # Note, _backtesting.py_ cannot make decisions / trades _within_ candlesticks — any new orders are executed on the next candle's _open_ (or the current candle's _close_ if # [`trade_on_close=True`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.__init__)). # If you find yourself wishing to trade within candlesticks (e.g. daytrading), you instead need to begin with more fine-grained (e.g. hourly) data. # %% from backtesting import Strategy from backtesting.lib import crossover class SmaCross(Strategy): # Define the two MA lags as *class variables* # for later optimization n1 = 10 n2 = 20 def init(self): # Precompute the two moving averages self.sma1 = self.I(SMA, self.data.Close, self.n1) self.sma2 = self.I(SMA, self.data.Close, self.n2) def next(self): # If sma1 crosses above sma2, close any existing # short trades, and buy the asset if crossover(self.sma1, self.sma2): self.position.close() self.buy() # Else, if sma1 crosses below sma2, close any existing # long trades, and sell the asset elif crossover(self.sma2, self.sma1): self.position.close() self.sell() # %% [markdown] # In `init()` as well as in `next()`, the data the strategy is simulated on is available as an instance variable # [`self.data`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data). # # In `init()`, we declare and **compute indicators indirectly by wrapping them in # [`self.I()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.I)**. # The wrapper is passed a function (our `SMA` function) along with any arguments to call it with (our _close_ values and the MA lag). Indicators wrapped in this way will be automatically plotted, and their legend strings will be intelligently inferred. # # In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we close the possible short position and go long; if it did and downwards, we close the open long position and go short. Note, we don't adjust order size, so _Backtesting.py_ assumes _maximal possible position_. We use # [`backtesting.lib.crossover()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.crossover) # function instead of writing more obscure and confusing conditions, such as: # %% magic_args="echo" language="script" # # def next(self): # if (self.sma1[-2] < self.sma2[-2] and # self.sma1[-1] > self.sma2[-1]): # self.position.close() # self.buy() # # elif (self.sma1[-2] > self.sma2[-2] and # Ugh! # self.sma1[-1] < self.sma2[-1]): # self.position.close() # self.sell() # %% [markdown] # In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and all declared indicators is adjusted** on each `next()` call so that `array[-1]` (e.g. `self.data.Close[-1]` or `self.sma1[-1]`) always contains the most recent value, `array[-2]` the previous value, etc. (ordinary Python indexing of ascending-sorted 1D arrays). # # **Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are NumPy arrays for performance reasons. If you prefer pandas Series or DataFrame objects, use `Strategy.data..s` or `Strategy.data.df` accessors respectively. You could also construct the series manually, e.g. `pd.Series(self.data.Close, index=self.data.index)`. # # We might avoid `self.position.close()` calls if we primed the # [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) # instance with `Backtest(..., exclusive_orders=True)`. # %% [markdown] # ## Backtesting # # Let's see how our strategy performs on historical Google data. The # [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) # instance is initialized with OHLC data and a strategy _class_ (see API reference for additional options), and we begin with 10,000 units of cash and set broker's commission to realistic 0.2%. # %% from backtesting import Backtest bt = Backtest(GOOG, SmaCross, cash=10_000, commission=.002) stats = bt.run() stats # %% [markdown] # [`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run) # method returns a pandas Series of simulation results and statistics associated with our strategy. We see that this simple strategy makes almost 600% return in the period of 9 years, with maximum drawdown 33%, and with longest drawdown period spanning almost two years ... # # [`Backtest.plot()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot) # method provides the same insights in a more visual form. # %% bt.plot() # %% [markdown] # ## Optimization # # We hard-coded the two lag parameters (`n1` and `n2`) into our strategy above. However, the strategy may work better with 15–30 or some other cross-over. **We declared the parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)**. # # We optimize the two parameters by calling # [`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize) # method with each parameter a keyword argument pointing to its pool of possible values to test. Parameter `n1` is tested for values in range between 5 and 30 and parameter `n2` for values between 10 and 70, respectively. Some combinations of values of the two parameters are invalid, i.e. `n1` should not be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which takes in the parameters and returns `True` (i.e. admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes return over the observed period. We could instead choose to optimize any other key from the returned `stats` series. # %% # %%time stats = bt.optimize(n1=range(5, 30, 5), n2=range(10, 70, 5), maximize='Equity Final [$]', constraint=lambda param: param.n1 < param.n2) stats # %% [markdown] # We can look into `stats['_strategy']` to access the Strategy _instance_ and its optimal parameter values (10 and 15). # %% stats._strategy # %% bt.plot(plot_volume=False, plot_pl=False) # %% [markdown] # Strategy optimization managed to up its initial performance _on in-sample data_ by almost 50% and even beat simple # [buy & hold](https://en.wikipedia.org/wiki/Buy_and_hold). # In real life optimization, however, do **take steps to avoid # [overfitting](https://en.wikipedia.org/wiki/Overfitting)**. # %% [markdown] # ## Trade data # # In addition to backtest statistics returned by # [`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run) # shown above, you can look into _individual trade returns_ and the changing _equity curve_ and _drawdown_ by inspecting the last few, internal keys in the result series. # %% stats.tail() # %% [markdown] # The columns should be self-explanatory. # %% stats['_equity_curve'] # Contains equity/drawdown curves. DrawdownDuration is only defined at ends of DD periods. # %% stats['_trades'] # Contains individual trade data # %% [markdown] # Learn more by exploring further # [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) # or find more framework options in the # [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). ================================================ FILE: doc/examples/Strategies Library.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Library of Composable Base Strategies\n", "======================\n", "\n", "This tutorial will show how to reuse composable base trading strategies that are part of _backtesting.py_ software distribution.\n", "It is, henceforth, assumed you're already familiar with\n", "[basic package usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html).\n", "\n", "We'll extend the same moving average cross-over strategy as in\n", "[Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html),\n", "but we'll rewrite it as a vectorized signal strategy and add trailing stop-loss.\n", "\n", "Again, we'll use our helper moving average function." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from backtesting.test import SMA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Part of this software distribution is\n", "[`backtesting.lib`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)\n", "module that contains various reusable utilities for strategy development.\n", "Some of those utilities are composable base strategies we can extend and build upon.\n", "\n", "We import and extend two of those strategies here:\n", "* [`SignalStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.SignalStrategy)\n", " which decides upon a single signal vector whether to buy into a position, akin to\n", " [vectorized backtesting](https://www.google.com/search?q=vectorized+backtesting)\n", " engines, and\n", "* [`TrailingStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.TrailingStrategy)\n", " which automatically trails the current price with a stop-loss order some multiple of\n", " [average true range](https://en.wikipedia.org/wiki/Average_true_range)\n", " (ATR) away." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from backtesting.lib import SignalStrategy, TrailingStrategy\n", "\n", "\n", "class SmaCross(SignalStrategy,\n", " TrailingStrategy):\n", " n1 = 10\n", " n2 = 25\n", " \n", " def init(self):\n", " # In init() and in next() it is important to call the\n", " # super method to properly initialize the parent classes\n", " super().init()\n", " \n", " # Precompute the two moving averages\n", " sma1 = self.I(SMA, self.data.Close, self.n1)\n", " sma2 = self.I(SMA, self.data.Close, self.n2)\n", " \n", " # Where sma1 crosses sma2 upwards. Diff gives us [-1,0, *1*]\n", " signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0)\n", " signal = signal.replace(-1, 0) # Upwards/long only\n", " \n", " # Use 95% of available liquidity (at the time) on each order.\n", " # (Leaving a value of 1. would instead buy a single share.)\n", " entry_size = signal * .95\n", " \n", " # Set order entry sizes using the method provided by \n", " # `SignalStrategy`. See the docs.\n", " self.set_signal(entry_size=entry_size)\n", " \n", " # Set trailing stop-loss to 2x ATR using\n", " # the method provided by `TrailingStrategy`\n", " self.set_trailing_sl(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, since the strategies in `lib` may require their own intialization and next-tick logic, be sure to **always call `super().init()` and `super().next()` in your overridden methods**.\n", "\n", "Let's see how the example strategy fares on historical Google data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Start 2004-08-19 00:00:00\n", "End 2013-03-01 00:00:00\n", "Duration 3116 days 00:00:00\n", "Exposure Time [%] 46.69\n", "Equity Final [$] 69390.94\n", "Equity Peak [$] 71890.64\n", "Return [%] 593.91\n", "Buy & Hold Return [%] 703.46\n", "Max. Drawdown [%] -22.49\n", "Avg. Drawdown [%] -3.45\n", "Max. Drawdown Duration 576 days 00:00:00\n", "Avg. Drawdown Duration 39 days 00:00:00\n", "# Trades 38\n", "Win Rate [%] 60.53\n", "Best Trade [%] 51.60\n", "Worst Trade [%] -7.89\n", "Avg. Trade [%] 7.16\n", "Max. Trade Duration 183 days 00:00:00\n", "Avg. Trade Duration 46 days 00:00:00\n", "Profit Factor 5.47\n", "Expectancy [%] 11.86\n", "SQN 2.25\n", "Sharpe Ratio 0.45\n", "Sortino Ratio 3.19\n", "Calmar Ratio 0.32\n", "_strategy SmaCross\n", "_equity_curve Equ...\n", "_trades Size EntryB...\n", "dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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Application\",\"version\":\"2.1.1\"}};\n", " var render_items = [{\"docid\":\"660da296-3d62-48c3-bdea-8ed684922b5d\",\"root_ids\":[\"1476\"],\"roots\":{\"1476\":\"5eb27549-7526-4ec7-88f1-a3a3a92f4886\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "1476" } }, "output_type": "display_data" } ], "source": [ "from backtesting import Backtest\n", "from backtesting.test import GOOG\n", "\n", "bt = Backtest(GOOG, SmaCross, commission=.002)\n", "\n", "bt.run()\n", "bt.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how managing risk with a trailing stop-loss secures our gains and limits our losses.\n", "\n", "For other strategies of the sort, and other reusable utilities in general, see\n", "[**_backtesting.lib_ module reference**](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn more by exploring further\n", "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", "or find more framework options in the\n", "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: doc/examples/Strategies Library.py ================================================ # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.17.1 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %% [markdown] # Library of Composable Base Strategies # ====================== # # This tutorial will show how to reuse composable base trading strategies that are part of _backtesting.py_ software distribution. # It is, henceforth, assumed you're already familiar with # [basic package usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html). # # We'll extend the same moving average cross-over strategy as in # [Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html), # but we'll rewrite it as a vectorized signal strategy and add trailing stop-loss. # # Again, we'll use our helper moving average function. # %% from backtesting.test import SMA # %% [markdown] # Part of this software distribution is # [`backtesting.lib`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html) # module that contains various reusable utilities for strategy development. # Some of those utilities are composable base strategies we can extend and build upon. # # We import and extend two of those strategies here: # * [`SignalStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.SignalStrategy) # which decides upon a single signal vector whether to buy into a position, akin to # [vectorized backtesting](https://www.google.com/search?q=vectorized+backtesting) # engines, and # * [`TrailingStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.TrailingStrategy) # which automatically trails the current price with a stop-loss order some multiple of # [average true range](https://en.wikipedia.org/wiki/Average_true_range) # (ATR) away. # %% import pandas as pd from backtesting.lib import SignalStrategy, TrailingStrategy class SmaCross(SignalStrategy, TrailingStrategy): n1 = 10 n2 = 25 def init(self): # In init() and in next() it is important to call the # super method to properly initialize the parent classes super().init() # Precompute the two moving averages sma1 = self.I(SMA, self.data.Close, self.n1) sma2 = self.I(SMA, self.data.Close, self.n2) # Where sma1 crosses sma2 upwards. Diff gives us [-1,0, *1*] signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0) signal = signal.replace(-1, 0) # Upwards/long only # Use 95% of available liquidity (at the time) on each order. # (Leaving a value of 1. would instead buy a single share.) entry_size = signal * .95 # Set order entry sizes using the method provided by # `SignalStrategy`. See the docs. self.set_signal(entry_size=entry_size) # Set trailing stop-loss to 2x ATR using # the method provided by `TrailingStrategy` self.set_trailing_sl(2) # %% [markdown] # Note, since the strategies in `lib` may require their own intialization and next-tick logic, be sure to **always call `super().init()` and `super().next()` in your overridden methods**. # # Let's see how the example strategy fares on historical Google data. # %% from backtesting import Backtest from backtesting.test import GOOG bt = Backtest(GOOG, SmaCross, commission=.002) bt.run() bt.plot() # %% [markdown] # Notice how managing risk with a trailing stop-loss secures our gains and limits our losses. # # For other strategies of the sort, and other reusable utilities in general, see # [**_backtesting.lib_ module reference**](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html). # %% [markdown] # Learn more by exploring further # [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) # or find more framework options in the # [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). ================================================ FILE: doc/examples/Trading with Machine Learning.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Trading with Machine Learning Models\n", "\n", "This tutorial will show how to train and backtest a \n", "[machine learning](https://en.wikipedia.org/wiki/Machine_learning)\n", "price forecast model with _backtesting.py_ framework. It is assumed you're already familiar with\n", "[basic framework usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html)\n", "and machine learning in general.\n", "\n", "For this tutorial, we'll use almost a year's worth sample of hourly EUR/USD forex data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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OpenHighLowCloseVolume
2017-04-19 09:00:001.071.071.071.071413
2017-04-19 10:00:001.071.071.071.071241
2017-04-19 11:00:001.071.071.071.071025
2017-04-19 12:00:001.071.071.071.071460
2017-04-19 13:00:001.071.071.071.071554
..................
2018-02-07 11:00:001.231.241.231.242203
2018-02-07 12:00:001.241.241.231.232325
2018-02-07 13:00:001.231.231.231.232824
2018-02-07 14:00:001.231.231.231.234065
2018-02-07 15:00:001.231.231.231.236143
\n", "

5000 rows × 5 columns

\n", "
" ], "text/plain": [ " Open High Low Close Volume\n", "2017-04-19 09:00:00 1.07 1.07 1.07 1.07 1413\n", "2017-04-19 10:00:00 1.07 1.07 1.07 1.07 1241\n", "2017-04-19 11:00:00 1.07 1.07 1.07 1.07 1025\n", "2017-04-19 12:00:00 1.07 1.07 1.07 1.07 1460\n", "2017-04-19 13:00:00 1.07 1.07 1.07 1.07 1554\n", "... ... ... ... ... ...\n", "2018-02-07 11:00:00 1.23 1.24 1.23 1.24 2203\n", "2018-02-07 12:00:00 1.24 1.24 1.23 1.23 2325\n", "2018-02-07 13:00:00 1.23 1.23 1.23 1.23 2824\n", "2018-02-07 14:00:00 1.23 1.23 1.23 1.23 4065\n", "2018-02-07 15:00:00 1.23 1.23 1.23 1.23 6143\n", "\n", "[5000 rows x 5 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from backtesting.test import EURUSD, SMA\n", "\n", "data = EURUSD.copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In\n", "[supervised machine learning](https://en.wikipedia.org/wiki/Supervised_learning), \n", "we try to learn a function that maps input feature vectors (independent variables) into known output values (dependent variable):\n", "\n", "$$ f\\colon X \\to \\mathbf{y} $$\n", "\n", "That way, provided our model function is sufficient, we can predict future output values from the newly acquired input feature vectors to some degree of certainty.\n", "In our example, we'll try to map several price-derived features and common technical indicators to the price point two days in the future.\n", "We construct [model design matrix](https://en.wikipedia.org/wiki/Design_matrix) $X$ below:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def BBANDS(data, n_lookback, n_std):\n", " \"\"\"Bollinger bands indicator\"\"\"\n", " hlc3 = (data.High + data.Low + data.Close) / 3\n", " mean, std = hlc3.rolling(n_lookback).mean(), hlc3.rolling(n_lookback).std()\n", " upper = mean + n_std*std\n", " lower = mean - n_std*std\n", " return upper, lower\n", "\n", "\n", "close = data.Close.values\n", "sma10 = SMA(data.Close, 10)\n", "sma20 = SMA(data.Close, 20)\n", "sma50 = SMA(data.Close, 50)\n", "sma100 = SMA(data.Close, 100)\n", "upper, lower = BBANDS(data, 20, 2)\n", "\n", "# Design matrix / independent features:\n", "\n", "# Price-derived features\n", "data['X_SMA10'] = (close - sma10) / close\n", "data['X_SMA20'] = (close - sma20) / close\n", "data['X_SMA50'] = (close - sma50) / close\n", "data['X_SMA100'] = (close - sma100) / close\n", "\n", "data['X_DELTA_SMA10'] = (sma10 - sma20) / close\n", "data['X_DELTA_SMA20'] = (sma20 - sma50) / close\n", "data['X_DELTA_SMA50'] = (sma50 - sma100) / close\n", "\n", "# Indicator features\n", "data['X_MOM'] = data.Close.pct_change(periods=2)\n", "data['X_BB_upper'] = (upper - close) / close\n", "data['X_BB_lower'] = (lower - close) / close\n", "data['X_BB_width'] = (upper - lower) / close\n", "data['X_Sentiment'] = ~data.index.to_series().between('2017-09-27', '2017-12-14')\n", "\n", "# Some datetime features for good measure\n", "data['X_day'] = data.index.dayofweek\n", "data['X_hour'] = data.index.hour\n", "\n", "data = data.dropna().astype(float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since all our indicators work only with past values, we can safely precompute the design matrix in advance. Alternatively, we would reconstruct the matrix every time before training the model.\n", "\n", "Notice the made-up _sentiment_ feature. In real life, one would obtain similar features by parsing news sources, Twitter sentiment, Stocktwits or similar.\n", "This is just to show input data can contain all sorts of additional explanatory columns.\n", "\n", "As mentioned, our dependent variable will be the price (return) two days in the future, simplified into values $1$ when the return is positive (and significant), $-1$ when negative, or $0$ when the return after two days is roughly around zero. Let's write some functions that return our model matrix $X$ and dependent, class variable $\\mathbf{y}$ as plain NumPy arrays:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def get_X(data):\n", " \"\"\"Return model design matrix X\"\"\"\n", " return data.filter(like='X').values\n", "\n", "\n", "def get_y(data):\n", " \"\"\"Return dependent variable y\"\"\"\n", " y = data.Close.pct_change(48).shift(-48) # Returns after roughly two days\n", " y[y.between(-.004, .004)] = 0 # Devalue returns smaller than 0.4%\n", " y[y > 0] = 1\n", " y[y < 0] = -1\n", " return y\n", "\n", "\n", "def get_clean_Xy(df):\n", " \"\"\"Return (X, y) cleaned of NaN values\"\"\"\n", " X = get_X(df)\n", " y = get_y(df).values\n", " isnan = np.isnan(y)\n", " X = X[~isnan]\n", " y = y[~isnan]\n", " return X, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how our data performs modeled using a simple\n", "[k-nearest neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)\n", "(kNN) algorithm from the state of the art\n", "[scikit-learn](https://scikit-learn.org)\n", "Python machine learning package.\n", "To avoid (or at least demonstrate)\n", "[overfitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html),\n", "always split your data into _train_ and _test_ sets; in particular, don't validate your model performance on the same data it was built on." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(n_neighbors=7)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Classification accuracy: 0.4210960032962505\n" ] }, { "data": { "image/png": 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q8H8xy3mh+krccPPHce8vfoWr+n4GALb6AsCq6nfhwuHHkTGo+c4AoGsEf9//VfSNpJi0AeDNi3+Ad2/+DHYcGzTvRUMawrqGoWTanWHGhcDuZwEAIY1gXnsNNh3sZ9I+Hp6MltQh5rP2ujgO94267tfGw+gfLdR3SWcdXjxMUZXpB0AAsHneks46ALB9r7mTqjGSTONA7yjGSBQRmsSayguxuupi/PCW0/HF37yBG3Z9HnNaq/BW1zAyBsWiybXQo5VAKju+dncNYyCRwrSmSuy19GVn2esPjUAzxgAAp3bU4g1Om7DwVnQ+Hq+7iTsWpjZW4Pejp6I71IqLBh5Gb6gJ9eluVxorT1jyhSex6d+vMPkZDw/VfwSHItMxd3QDLhtYad5/tvpqfKZ5LTbsPoIhvRZVmX6srP8o/o/+c9TFI8BNDwK/eD+S6Qy2HRnEs52fwIUHvsMs4+7WOzFvdL05FvIYI1Hc2/IV89r5/s7xm//GAPDJxCfw0a5/RWVUx8AYQe0tD2JWSxUAYMO/XW7m57UpCx31cYyMZXBieAyTamM42p9w1YdFr6amBjM+/msc+e7VODaQlC6Ph7baGHqGx3Bf3WdwS/d/CtPe3XonpiW34919P0N4yjKk9nvvTb+79U7c2vct1Ka6XfNZHr+ruwV/1Xe/eb3kui9h22//zeTx05sq8Vn9Szi1ow5/dzzbJjuODmI0leGW+8faa/H5v/0oHnvjCB5af9Bsy8drb8Q7+3+JuoowDtSvQPWhVWaeBe01COs556ObHsS3fv0Uzt+dbRMKgtM6a80xn+8vZ81qwtKX/hYA0B1qQ1OabXPOc5MhvRatoSEMJ+11j4d1zJ1UDQDceeDu1jtRWxFG/0jKfJ9hrRqVxiC6Qu14oPETAIC6dDdu7vlvbts4kZdT7m3+B9zWdSc33S8bPoEbT2TH3bdbvob/N/ov6BpMYlvsNMxLvI43Y6diTuINM30srCGRMlx0Hqr/CN7X+0Nk6mfgN+QyXH/ie8zytsSXYcFooY9pBKCUzZWXdNbhYO8IuofGuPW/d/K/4h/OiqHr159lPn+i9v24sv9B273XK87GaSMvmc93xRbhng+8A7HX7gX2PI9tRwZcsogViybXYvtwBVJ9hX6Rn88B2OZQHv/gyW4RXUPCILin5f/hlLZqbD8yiPf0/gSnhfehZ5jdDjNuvhs1z33F1cd4/GbT2d/Gc7sKaU8deRUfDD+NAydGpXne3a13ojIzgFu777Ldr4joGBnLoPGa/0Tnmn8269SvN6A2c8L1/Wm4Etqi64DqSdCIBsOik1AQEEvPGNGqkCEhREM6qmIh9AwlTRl8UM/y9vx1WM/SYs1fTtkdADRCMEziiBsjiFQ3AZru2QalQCwWQ0dHB8LhsO0+IWQdpXQZK894rcgdAtBpue7I3bOBUnofgPsAYNmyZcFomAEiUzcDYyQGguwAJ0zWo15tL2WaXU4WGtjMRpSHXxHHXwAazTASOPOplUWsWSg121OGSv69qOL7EUs76bArFwalJl3ZdtNonp4gfRE9OJVmCTN8gqI+RChAiD2/+z3Z+SmUPy+AbJvyvhGlBkjud4Y3g3vUSxUhmp8AfYzP3F+NkTdhETpt3yBVUIjMdvAa59RKS62OPD5g0gZAqAGSI0wY9AOy6dlhZN9JpwyFPoDyKQlgdwC1j4egjJtKXY3K80CV8ol3KgCFdCrvHjaSqK0Io0cgZNvrw+6jaaNw3/AoP/+Yz1sAUL4imE1TKI/XPtZ2CFG+ESufilCjqI9nOITN/Bzlax7PQTPnVdlekE2dL7LAU+TqkK+rbqQAXTAfOehR6RLYoBSghpj/iVDo+1Aah6FMAtaeofoOvG9rUGry53y3IDCE9FVZlnOckZzKpArWO1DGL6DQn5x5tEXXoWnaAtRVRhHSNaHhsDfUhBSJojIaQnN1FHu7h01D7/HwZAAwr2NhHWnDML0FrDgebkdL6rDtnq4R9JI61GR6UdEyDdAj3HqUCpRS9PT04ODBg5g+fbp0vvE6R+4RAB8iWZwJoJ9S+rZyqwSywjAlfrp7AUHLSrKMXioV8UpbzJvz4DFxO56T/CSsqjxy0msmgwzu3WSFofGzVLhLKkZAcNMqHbTAtAuftbQIwyIDzklndXKgtPWTpx5kvwP447qMLAhHeQoClKqpCE6erWqMs4KAMOcAQvJGK0FeWz6BQjiOIF6VLiGsBqrsCqPXnKz+pNQQzbksAxyThqMdvBA0LysG6kpk6UuW5j3Vk1BXGZ3QMeDGxNSFEILGxkYkEgnvxBYEdfzALwFcAKCJEHIQwD8BCAMApfR7AB5H9uiBXcgeP/DhIModb+TdKEyBrsTcXmaiOxmYSfF1KM2qlmzZ1klA9l1E6QoLmx608tY2lkDCVL78wVxtLGF/VRHKbO828d1XGqqrtk68jV5VCeJ+SW2piMXiHwyKsuWz7wa2IDeBgi1K398IoGjFp8zLwNmSHNsVwmtlMKCqmJhYEdY6/7mFb1fdOC81kUYVMU/28ISwPVdaRldI6w+yTarqRRCceZRBSZk4OcmUuImFn7YIRJGjlN7o8ZwC+GQQZU0kCAEoNEvnnXjRzA/z9FxE55CknA6mKtjaUiu7ZeZ9DsTuM9x8rvuA4cMrxt0ShTtBzGcsa5ZfBYLknGuI7Z68pd7rfViMR5zH7VrDpS2oJ98tilVi8RMFy53WfHeJT+PdL0rLT7L9oITKvGAC4rnbKJbguiNrbWfB2hZWPjoRXN1suYCV3NIa+qiaBsKbV3zUkRA+b/CqksxKgce2UAVIEiqhHOvVB6xFa6x5R3JlZSKNyuKtBZIrchTSkzc1/3PeDBayY0NZfglopV7UbUvZH4odLn7qtnfvXrz88su46aabiiw9eIyXa+WfCXJWZdlBwGQKCm5ZEn1NRSAvZOIpZP6grsjZSxKV66SsUpZdceEpctm9XAUhVFWxzOXy0Xj5kpj7mwLkgQTUpWxN5KRbSnevoEFtwmp+Ra4Af71Gpty3IxiDwLUXI1DqCK6lgqFjrePJ8A1PBo8NE45xX2zNZMwGfhHUipwstJKuSHi9i9eKnJzhzUw3EYsrQtdKr/nG34pcqcYW8cFE1PsrSw71YVARzOUl5T0l62P8Ou/duxe/+MUvmM/Sae894KVEWZFTQHZFrrCa5aejBj0/SNdBwZuN95yfrxiro0+rsd+VPEb5VuurPH/ICfWMegTzjVnLSn4Ju1eQWcEulOpigfqqsGP1Q2RN9TUZsPIEx/lt76tA1muiPKmEbiV415taVN4g37KoNuN8xsCCnUwkqOtHCcoIYp3bH1vLehjwvSxs9F0pTj5DUil1H+8VykILsVa4VcfYhOhxgjp6rkjm3t+HNKeco2RQrAqTxwUtm55EzSPCP9/1LXz7+/eb11/9xjfxrf+525Xui1/8Il544QUsWbIE3/zmN3H//ffj6quvxkUXXYSLL74Yzz33HK666ioz/ac+9Sncf3+W7rp163D++edj6dKluPzyy3HkSLAhQsqKnAI0kndSk+uhvlbLFOFbkJH0rbTTF7lWqjBS/6y+QEuibSWKIcjvkfNbp/wk4MOMZhow5VZuefB2nWEJXKwJm1ELmVVh1aajwks77YCm12JUYKcjste45poMFCpRKmWiFMpiQRDidwSpfZoeVQuan3pb6n3gJFuSO6mMAxzrvcp6iNmPHFF4C8+lKuKZwhlFstSQ4aHFuNbLPpdZkeORGw95hwUKKlwZ0jyimDIh8z3eRp4lMvDTu0R9i0ywJier3H/oxmvwi18/nM1jGFj58GP44Ac+4MrzjW98A+eeey42bNiAz3zmMwCA9evXY+XKlXj++ee5ZaVSKdx+++1YuXIl1q1bh1tvvRVf/vKX/b4WE+N1/MCfEQjjl1fKHKiaNVou7UkwURdTBclw03mYA1BG97JZ3DlMl/q1CFN7fdjF+gY72InPiZzm95TwFPRiodaviUL6oDbQl8pKnFfyTvZFHEq9v/l4BOcopt/JBgWSBztvUa1wkvUD9X4v/wI026l80y52zPj+9hIFB6XHnQzdQUXB0qg7zP1JZQzgQPRJpSN7UwVhYJwYvmwp6k4xAdTfi4YPRfdXm/qwr49/3EdCG4aBECIhDfGIjv6RFCqM7Pm1I1r2DL/8dUgjMCjF5JowrltY66hb4efUzg401Ndhw6at2N09isUL56OxUe4w8EsvvRQNDQ3CNDt27MDmzZtx6aWXAgAymQza2tqk6MuirMgpQMsdP1DAxLtKsTYns2C3evJmX7dQKuM84y0g5iPWueuirEmZ1n9Fdw9OcvdZO9KsM0u3UILjiYwLazYF02Ko0Cbe30i2Rm5IWYqVGXZpx0Wg4jkjm2d/56zwen7SkgsHpQl+UTjbkVUkZablEFIvu4g2sxdXoDMRx8gVk4dLi0L5ZVTbs5jjB/zQK/A64n9lSOIVT8Y9csE4sjLKtrQX04AoubdxohS+7FEU/D1KsivvAi7GTCu7d7AYWD0zinBmYGRwt4lql896+nh5p5z8RgAAuOWm6/C/Dz6EA8f78Nc3XCPN2CorK83foVAIhuU8w/wRApRSLFiwAK+88kqgdbairMgpQP0EOacQww6PWMyc4S9qmzOGYeGuCLyJhMAQL7E7GV4RgTcKLlpGUdZg3n11QaY0gUqCPefNTUt6cqMS7k4MLdlLtZdNK5wouGErhYUXDdvKZmnCDZYEnsOlJK/gJOpfGglylTqb9+RxjSrdirHaO6q1p1rbU55rpQIZa/14PMzLFVCmTQIbCpKESupa6W1BspQhEy2ZTW+ijh/w3nss/t7+j/op1fuqT2zcJ9xvUjzv40VBNu9Q9XDg1y2qE7o15w8Er4qF0FQlPhA8Gs4eLs46ENxZsauvvAR3/sf/IJnK4Gf3fINZdnV1NQYHB7l1mzp1KrZu3YpkMonR0VE8/fTTOOecczB37lx0dXXhlVdewYoVK5BKpfDmm29iwYIFoqZQQlmRU0A+2Il5rbgiV5rJWm0FKQ8/dRHvgfEJH1Yg2WzWNLIb4+Xr4SzLuiKnqgzKpT9ZXVw421X46RVW8Cb6nWVX91T7Y5BpTx7wo1YWVuWpb8NV0H1B1ptBBb5rGHDUQv+WcDWjWlHnyJl3/XoK5PPZ48gGE4BlfEdgKc/RUj5+wKX4ys5PEwmBIdnjW9rPNVXo/zJpi+xHsi6+QfRWv0sCPEqiSOETDWe9I5EIzj97OWI1TdB1nZnn1FNPha7rWLx4MW655RbU19fbnnd2duL666/HwoULMX36dJx22mkm7ZUrV+KOO+5Af38/0uk0Pv3pT5cVuYmEUlALBkMMWpgrhVVZdZhpEG82diuAKrEhnQqoZUVOAaKoldn/iUc6Nj3hgZiS3YN9/EBw58hpMGBQp7gj+Z5SrpW+qlVAwDIna5QFu8LJ/u2FiYyGSE3rqQ+rrgeIYwyJ0zLKZcnjgnIcubMEfAjCvD4RmDvQxMsr6h4bSm7uqqSdK/FUmY7VTZ/1xZ17gTkV8SxnvF0rs3ppqcpUWZHz5p38asobWYMEARFqPCreJyqulaV7U4p875bth7xVLO5Zi8xo26oGaEPYZwnP+zmAfi7D7bMmHdn2M7Bm3Qbc+4MfcksIh8N45plnbPduueUW2/Vdd92Fu+66y5V3yZIlWLVqlVRd/KActVIBzhU5L7gZiOrMJ1GGr0ERNAOS23tjBoYoQhku3PfBdJj3i2sLVsQ+aYrU9cNSL1WISqUwqP0bMQNH+DSpqkanUnHB8Xc0hfRNX7AaEVjHDwZSUvBDFMDEWUML5RZTvs+8nP7G4wnB7ZE7CTS5EnkH+AE1HO1dZFE87wznd3UXI+FaOc6fTo73qlRKISib5WUJWMFOZA8Ez6WbgG5PBXWUNnbb6l18bAClsjkoPiBQiQgjvyIvaHdOGf62AwWNQh22vbkLi866FBeeswIzZsyYwDr5R3lFTgGF4wdkwV5NChbqNIOuR3FnOYmVQPek4k8g5Cm8xbuDMCxb3CdssOoW5N4WApZlL8g+oErLaZnnQ6Vv+K2NCEEKdCqkglYCssbmibD4O3hgUcFJeGOYKvLlLPICxckgVpgIuDKljFoJKN/5x0sAACAASURBVK6EulbkioUsBfU+6CdqZbHvo7pVQx7ydFnuxv6MxeMLURW965/nA0pLw1L3ipUvbMFOhOnY9/leB4xgJ17EuBl8J5gwWL/LvDmzsOXVp6FrBAMANm/bgY9deb0tfTQaxerVq8e1jiooK3IKyM5Z8kPTffyAj0JV/LsDg9VCZ2UknHenHvUIdCLIMd2gVuSoPWqldHvmyme2iDIfLHJVEFRIgpgRS9jfVQSpc+SKjlp58jJ8FuzHOORXmS0YDw3zJEKhPfxZsanrByevQFgRGwN4Y9/H+VIlQin2F/lSU5VXy+VrznMTo5zf/DKzRlVevwsi6EZQbtCyVAiIJ0/2+17FniMna3ibqJUWr3PkvIOdWIkJ5nVXwTIrcsW1iXxu3jtyKAgOBFfZXsL65laFkFXMeAWZ8mcMpVg4by42rF8HaOy9cicjyq6VCiAgoMR67ZW+uBU5mdRq/t8eIF5peVErKYqRUpWiVpqMVlWR8xYR/NHLi0v+RTH2HjnWKp1o1U28KlLMuUieLUfUTtNTcq0UxlzmCIaBWkapcNFBaUFCoc+WygheyjNaxT0wkBAUvnLxSi614OlPjAhWgShlQAbePjU+eK6sPt85l80dBdlJ3+sGl/S4QXNUmrn/T4V/WPMptC9rTAQ9TsbbvVtWRspC0sDJvCe5TKcA2T1yLrflHLjfnqnHqclWXLnPFI1OZotmsPLgRKOsyCnAuSLnHCTugexU5NQtEZ7WJB+ToJpzqJyVXXw+lPPEJoU9coyyAPXJ3ytUdVCrY1kKxSuZapO22JpLkNsjZ9sLIcmsZTqLah90jhthdv5DlVL9WrNZufyenTPR05emaHDhCQfc9BKdRXhch69gJzIWd16+PC+xVyQwJZop141vL5Dbx2PhCyWI5JmHDM9WOQKHL6B7lCPjWunD8lXMpyWE2Poxm18pcTzLL68VOYP5W3SPSUe27wQ8BggI92iL7HO5FUmqxB5VlB01WJtHuhsGcJarj5oyx+ufl4r09kBZkVNANgaOimulU5HzAMNy6L3q52PilaDLzMbJRECFgrLZDsKQdZwyObRkGKTdKlmaYCfMcpVJFqfIeaVnuZ6x07s/kMzkpmqxVXm3Cd8Y7bG6l9ddpGqp8CpBv7Wv1SHJjiwXQSw/bkUFytGQve9FWOO4VhazKkY5vycKyopcKWsdwB45sx9Zjh9wzmbe7+A9ZxbjweAXXi6OSgZYoqLIFcA+kkNsoGalFh3tEXQfo6BCXiWrYJbkkxdJVJYHGxyjG9fYwWoTU0yTay+uYdBckRs/mUsV3LmEmo3wtkJZkVOBK2qlozN48DvuxlNOv6aCPF40feWhtj/jBI/VPFcb5u9LMBtLXq/jB7jpSmhBz5OW3UxeDAN0W5hZyqNfMKx7wk+q8h7qK3KlcHGxghsG3wNKNSiRJqe00ivd9+VperkA+0JACmAgQ91nXQKHj+9tyyiZVi3WCXu+9H+uILckfwQtCOz4ARkyNNeOtvnKjdLtLRIr8kHPT6WQkaUMyVz4WIuSzFL0HjnK/u1OWPweufwdP1tdeGUzn4qFgzJ8oKzIKUBz+eKLLYyqxw9w1DxxnUq8cVQm2AmBIZxk3PN3Mazc30jXwLa+F1YLC+cTMZ973LdaQOVlgPwqhaQ7CzfypkcYYBjZo7asE7aDVjETjvLeTwXLvIqSL0NPFd4TkXtJ7qTeGiCAS9VXdteR8sN1Te9UMA7s1H1aeDmP8/Tyj8djFYZb15JZgL1fyq+C4BXsxG0U4+yR4/wWQXNoPqLVLCdNZ4Arv3DtxSuKltM9vtSGJ8tzC8NiyROu9uR24YIlWOwhUgqZhV8eb+7Pwz4XBssExPOyt1FL2qDA8S5Q6uHU8dcDWbmPpRBm7/Hmj5NhRY6Pgjr7dkIgihwh5ApCyA5CyC5CyBcZz28hhHQRQjbk/v1NEOVOBKhSkxUvLHszYZ9MUdRPFaupvundf7mFyUKtklZmbZ3AnefAudtTTpHzzsFPx9wjF+BKYFbkcZwjN46CggtKfdZHPRlZilNU+bRKFuykJJOd2uq3rCJXGEPiks20XEW3GIWCD56xq+D+la9/XgDxVQ0pjK8QI3e+pz1L6eoXVCTIPPjuUc6CnZe570z4c7mUAB1IWOqcQOw4yDyIfdPWGU9cvtXA51/JIpZxVJKVdxFFoWulXHmU0kL/L/7cgCIJZCG/R05NThEpYNL9zMt+RtkuryeDIsdvl/HD3r17sXDhwkBoFX38ACFEB3APgEsBHASwlhDyCKV0qyPpg5TSTxVb3kTCeY6c9/41B7j9l/2ACvPk6iQ5KOxrCGoTgjUnO5HsgeAMmh55Wewmn08Fmm1Dt8ha6FS+Jb1jbAq+opLJnLRZ6XhlE8/ojs4Jwb2aTHxzMQLFz8FxsWJB5NajouwEyaD9bPCeaGTXTtRqOV5xOcxiPIRIfl/wqqicMJMnT1k3A4NYgAi6NNFeJbNsH0GQCunlwVuJt95WoWedTwrHx2TfQfQW+XwGNO5qjYwiJzM3yLSmxZdDnE75yJ0sVPYZM10rPQ9YZ0E0x5YC/H4uHdnb8ts76I7cPVHbi1aEzb4tq1MZvFVHjmxZxHENhXSU2bamLuxjRU52TlcxoI43MpkMdH18jy4I4hy5MwDsopTuBgBCyAMA3gPAqcj9WYAX8AMQKB05eDEUytDcvPKUwk2Bp/TxXSsphIzbK8KBSnhkM23xylL2vvgcuexztUGpKvv53XNlzS8TtdJ+T77feDFXtnVPQK/EZzSVWv/wjlrJua9QsdK8gxpVgysc2ME00LjSZMsW8TPfQUs8+yfvvt21kn1shf8vETQ9pbJz5cuMc1udAoxaKbvf2MpfpFuHWAVF9/EDQjoOLwwWZFZCCCG2d/L7ZQtjQ6xQ++47HozHM2ql9B45Kx1RuhKMAeEeOX9RbcXliR8TGKDQvRN6kJadK7nHD/DKZ+2R8yW3BPst9a2/BRk4xH1eo1UgAx3RsAaEddSNpBA2BgEAdVo1AJjXRCPQKKBXtyMz771cmv9817dQX1+Lv7vtwwAovvqNb2Ly9Ln4u09/2pbuueeewz/+4z+iuroau3btwoUXXojvfOc70DQNVVVVuO222/CnP/0J99xzD+LxOD772c9iaGgITU1NuP/++9HW1oZ169bh1ltvBQBcdtllxTSVDUG4Vk4GcMByfTB3z4lrCCFvEEJWEkI6Ayh33KE5jh9g+GzYUDTDyil2gTBFV92Cg5ciIQumwhDQhiP5jdjBMSYvxiiyXKkrd2JFjjrCT05kNEg1RU6QVqlr+HtfnuiZB9Nyyps7iy/YP3IVLI0ioUbTldrUpEogcAnyuVcacgJ+YHtmWPysCHI+IDPOg27XAuzP3cFO/DeGeP5ylMtRKEXbJIJbiJUhxFDaAnWtVzCUwn2Is0sR4pCT3mtWgqV+EUlpr6WcvBUEiPnXL71svqL3yCnIT/k78rKBx/EDiu6eE4UP3XgNfvHrhwFko3+ufPgxfPDmm5lp16xZg7vvvhtbt27FW2+9hYceeggAMDw8jOXLl2Pjxo1Yvnw5br/9dqxcudJU3L785S8DAD784Q/j7rvvxsaNGwN9hyBW5GTwewC/pJQmCSG3AfgJgIuciQghHwPwMQCYMmXKOFVNHiS3mFy4Fls1ZANnWG16LNc+EXPhhdAWwb9rpT+4aFiX6DyOQnDW1JxUJCzH1ry8dnIyLVf7Z83aLogC3ajaaOVdK3n9R3yOHJC1MNucZGX3P0l8/iwtBebscrES9G/BMxU9rqgJ1dl9fVJSLDVwel68xJVH8Rw5mZYRrhz74GVZml715ChyDl9Klmul6rf2al2/gVn8Qsa10m6YUDbLCx6JFTfKuO3t0sYOq18IWiWvYBqE8I0ukq6VDLLKyNeXeLj/qwQ2Uzmr1cpjNQsvJ46/8mWLFdGgBXkCIpzP5I8foMUpmbZ+zGAmPiC9R05xRS6Ib+A1n1DKntlFZWfmv1f4CQZCTUiRKGriIcQro+jrHkYklV3B6wtn14/y11pYQ8agyGTEhpKpnR1oqK/D65u24kDXABYvnI/GxkZm+WeccQZmzJgBALjxxhvx4osv4tprr4Wu67jmmmsAADt27MDmzZtx6aWXZt8pk0FbWxv6+vrQ19eH8847DwBw880344knnuC/rAKCUOQOAbCusHXk7pmglPZYLn8A4C4WIUrpfQDuA4Bly5adXGo7oDyrywbOsD61uzJmFTvRPiH/AzI4cZTAEAsMrgmcXbYKD1XfLyAn9ju/GX9DvfN95SdOmbqpusyKJ06DYWVVsVJ6lK3aB0sd7CRIQaGgAZnwHezk5ONoQkgHO1Fob5EbrteY5p+JpF5m9r53NL5iXNfZQkxpowz7Kc/m0qfQSYmHEc6tyPlT1Hk0C8E1Cv/D9ZtvDBStyAV2/IAMqOOv+8IH5Pfy2xV5w3VfZLS0l1iEQaAIZNUF/zKSn7p67+XKPi82qri0HscdW1xLBfeW/B45Q3xwPW8MBTK2gjWn3nLTdfjfBx/C4eMncPMN1/BLdUauz13HYjFzXxylFAsWLMArr7xiS9vX1xdona0IwrVyLYDZhJDphJAIgBsAPGJNQAhps1xeDWBbAOWOOwgBDCWnDkd+X9bY4piUQkESrMnf4PGOYOVHEFRVlngWqzw13h45nhDIh7qvefACsz2fWzAJUqAkrjBxEHvWSAoGgMc7l3KecNSCVwBh9AT+JC9fsVLIkV79x/lUdS8jiz+Y7oqWkcYnUJqolbzHdkXg5EDQdVHnGSrp1WgHqcISy/EDzgBksoK7UaRrpdOI49+kylaYeOnU6Xu4LHsEu/GzLUGUpxRHJgmjVsqWR83/fEk61hoEpcjK8mB+YBEeYcHB7yoGdZZhzrxFmbROxjglV195Cf747At4fcNGXHrBOdx0a9aswZ49e2AYBh588EGcc4477dy5c9HV1WUqcqlUClu2bEFdXR3q6urw4osvAgB+/vOfB1b/olfkKKVpQsinADyJbFSIH1FKtxBCvgbgNUrpIwDuIIRcDSAN4ASAW4otdyKgeo4c201SDCfT8dp/Juc6w4LAkuIov/CU/QaaRx2Jgzk66ahErSy4LKgxSn472YUBN9OXtOZb3km2ZqJIxyrfNdsm4iASWQ/RQs2c9P0oTGZeNr/mk1MKbiM4n3AcRHAKt7XL7yStqhYFiTw1Jbdq6e/knc7kAcIw4R7nPXHHorh8nuBYcNPO/skbO2yufkVo1Kys47Y3leYXk2WCnbhXYWTgNZ85PUmcwqZpkvMRMMSusBFxoBCXhSL3R3j8gEwd7GFV/Cty7nM+i0X226tDoxmzFprEmLXCXv9xNo8I6ug1l9qD/QRU79wHkJGLRLAaYMWBqdWMh6zkqnME4UQcL+hxHnx3AuGsdyQSwflnL0dlTT20EF8tOv300/GpT33KDHby3ve6g6hEIhGsXLkSd9xxB/r7+5FOp/HpT38aCxYswI9//GPceuutIIQEGuwkkD1ylNLHATzuuPePlt9fAvClIMqaaAhXpahdopWN2lWg7VScbP8x4WdQBO72IHn8AC+v+VMiuZpgxZ7crd/QrdBAeM2ixyvWu6b5ibJ4xiZsFpqPWilnMZUnbCaSo8WjKcjuz+0lGDoyyNt2ZCLvTXjUSkrdZ2yJkivvkWMRkUhCnT+CLoT93Dn2qeuHn9UDf3UpFZRd0BV5kYp/ShDuiqZBgDjfTUUYtxvvmCnkfMpN8BR0WTJO/sxMp/htZAVzr6N55GUMufllvAMuSW8jCKS0LIpbdSxQl2bBAeyRKxiW/SjubmT3yLH60/gZs2RhGAbWrNuAH37/e8J0NTU1ePTRR133h4aGbNdLlizBqlWrXOmWLl1qC3Ry113MXWbKCORA8L8UaASggk0xAtsEAMkO7JibNE9nTjlQG5NVWDWSKD37XhLKJpOU2iHFhftqbSl/jpzkHjnHfT8rcuIyqLB8Z9miydZckbO0mTtiH3/jvxSUZsFgpszxmA7YqyqMMNtBV0aRnox7oXcaZ5+WLZtfB+cd8Vj3cgFTdB/yeO7eI5cX8H0YPEwiXnXhCPxqpUiDd06aFUFGSLTR9TJkMopV2c9lyyeImOjKketHItdK2XPkvOrFT2+9T4XPnelkQFFw+vb+vtb+buFtuXZSFuyp+F1KIciLFG/pc+QoYLZF0UJX/puKPEpkeju/X9sTcqJWStF15pH/3sz3K9owFww81Ezz17Y3d2HRWZfignNWYOaM6aWuVkkwXlEr/4wgb4OU3W8lThNMaH+/II43YsODJZkkildJC4xD1XIsp5A5Jz3u3rogVtEEZbD2TYjfQcSY3YKJ7OSWPQ9HDI2RRpSHUrtxwnfv5mYc//FCuReW2woTWzDGADW4vqGkObjYYCcyQo+oHN9RK82VGcpNFbgbENcwFXQxWYLqxw8oGlnkGH/2itP/VUovGA0KxiuncVV2lhEePyBDgFh/UlDRIbMeNaGUIxT7RsFc670iR5m/C5Qk+YAMEwRKIuCL5mPvuc5HfTwNNva/fiF9+gBvRY5n/GLc93O+q/D4AUmZa6Ixb84sbHn1aWgaQQLA5m07cNsV19nSRKNRrF69GhdccMGE1NELZUVOARohNpXFy9IlP6FY87Bc/YKe5YO1ChOIGanYCl+6QW1rV0v9WCGs8/fkFZzg6i0StuztKkonKsFtf5Pf93DygisTuwz/tGTnMjmjWIlQ0unL023bNJMq0AxOsLQGE+ItzIzbypBZnkTUSuWAL2KUItADF1TW4OS/3VnBfgrPnMWw1wGUohXnlRNRJFlZ10rB2JXbI+em6QdEuD5iT6cC2dTWPmL7LblS6CyRQvwNSjOniOZGLznN8jwgHuR3L78T0scPcPcBc2Q9xm2RYZlPm19BSt3Rsu0lqTwJFqI+uHDeXGzYsGGcahIMyq6VRcGj21E7g/AaIOxofsEKoQ7ialk4vV/Vks5fv3NXytkmNgapEjSD61qZ+004USs5ZQQatIBRRr6+8oKfSJE2PKNW+g0kAfhYnQxQQVAoNDBKqtEfxwOywpFKYCH1PXLusUotv9mlFG4RDzdAvkLmz6CWH1u2KGuwD0cZ10QeVIWYUkCGf/g5X1IGLr7v2ENeWAmVbxNr/Qorqlalw+1m7jp6RcK1Uu4cOetea4m3EEx7BuXN8xYFVeUcOVKonVcfsD7XLEfVFAIUqRo3DSGfKUWwiyCiVnodY2BPK5fCz15Bu9goOTYMNT4lrpcsPGRTTr9xH6dFfa0GSoFLlmsGLk09FOCnLcqKnAKy54fyz2dxrXo4lAfu4LHIOKwDqoNeivashzI9NYblPKzUXBWTsoL6qyTPfYgVMF2uPMc7May7Xu+Tf8xWCr0nAiu8GDN1BKSR2c8kuquexprceyWkGLjIUf9WYKZ45nOVRiWXakROueiEkh1SuQ7svkpAXTSFiqSH4sg/QsRDkePurbPny1u/qSCNGtx5x3uFW86d31/USk+6zkBSPIUggCA3dt4mly3oc+T8Hs+TV6QmyuWMNy/kf8saLWW2AXg98w2hIiepnAVYLdbWiGIhosR1reTmckfnLtiz5OrsFa2cb7B15Bk8ir7hpJICU1o+OnF+SJRS9PT0IBaLKeUru1YqgIAImb8YPtg89VaSFEhxr0Swli1aSRPXMc/UWI/EVjC3cmyfLGRb1b6Jm63UAWxrEZueu2bKMC2fjD1yjGc86xcFEfJeAupy0WALxSzBU0YQVFVSgmGVXMbvUkiKLMdxzdwX4N391RspQGTbnN/vmXkkrby8fsnqO6IjB/2ucvs37ngrv0EfFzCeobepj/JKqUwYYIduUFHC2Gm9crGNc4bg+AEq4dNmt935N4MBWSMCu+3d2wBkoGKMsBucWfu1VfuseEYozRjwXx6LJ4lchousjiVJgApDkQeCW41u0v3M01ItZwo0Nv0a3QC6qyfBayyPaAPIkBB6IjoqIiH0DCXRn8ketD2ojwCAeR3SCQyDbZRJaL2IGaO2e4QQpKFDoxlEe1MSNS8NYrEYOjo6lPKUFTkFEFdgP7HEqDknHanjB6x7uWjOijkxlrp8LQpgDzLioYz52Ssovgnz3B3ZltEYvv9AQQjlnSPHdcsKJNgJX8LP18N+WCsbWSYsqA+VOEdOoBR5tXG2nyv0UZf1MNj+HSg15iRv6UuMj8JvSn+CmAxUV9vYSZz8rNg+zu/XLHieI+fTZM5rm/wYyD81GMK75lEnEVil8vhGqTi8TJvZ0ihWRLRF1O2m7pjLcj99nSNHCqltUXuJu4+5OWg+X5EHglt+awpGRTcd975RZroSuZ9Z+6TG2S8nAzP4DBXP+UHMnS6Igp14lmftk3Jt7JWssMrqM3aAKhSMaaz02bNgqVK9uFEr8zAyzAHt8jpLDYOuvz/7jIj3BT5e/zc4HJmGd53ahPfN68BH7l+L2499HQDw49Y7AcC8ntxYgf7RFHpH3ErZa1WX4+yhJ233KqMh7DLaUJfpxpLP/45fiZMQZddKBRBAOHO5+591CpF0kWQKjsFDXgGSWZHxWmIPjmGxNmPLgBeZqxDshEeTxyADUEaouz5OaJJuT+I9Am4fdGeZ/EOTJTQ5cdUYNHmClhrU8vkrxd473JCx3JoK0kTaY3IQufy4er7iHjn3GYxWUwV7D6qjQA/6isKKYj6zrtTNH4KC5/7KgIrLk5HbI2d93xLvkWNC/qV5xiiRlwWjYwOA8GAfKqNsOFbkvN5CLDl4ywdqckCBmpdC5uUS6ce9X5Qn6FVuL8iW58XrPfPbsgb/jkKKvOMHpBlKYZ6X53le6RRXCSUoBgXRnODf627i8Par8QTCGbXSDYelwfVMTilyXgcvTKjAai1ViFBmeyaqv2XDrNJreren9SnPfcTd3nJBQJzwYy0VTbQFgdtboMieAScuP+MZ7ISnyMkIgmrCn0ug89m9pUMzBzw72ASWXMdnqwSQuJt/5hR6WeWK6uQVSCmfTmB1dX4WxQ30Lv7HfAfBcRYq0eUE5co+5wXF4PENGXgL8yUIhV5UeT6VValB5VbkvCKD+jtLzXH8AK9uDiOs0LVSMdhJMXN0YUXOXz9mp7ScI+epIIrnRtkVNNnjB5SCtsgqYQKjk2wwNrUgE6y07n4sUiJ58pSvnqS8yumWMwtzhGxwG6+I2z4Mbx4vb/ZJiWjRIgmxGHnnZERZkVOAM9iJNywDm1Jupzejd1F7J6fIKXJBuCJYiuZGS2Lck5mgslZf/9K4vGrMjlQmA577iFXEzT5zCqPsMpyWbj8Tude+NlZ9+GkFriUwAEffck7OvHJkeruKiysAH5POBMPxcp6rKrz7JTQ3SrnQKfZRqVUJIV1qvrRIKCy49PjrF14Wdy/XSmc9bHmL+GisnCVxKxMULsM/7PnUvrnYuOCkzTZ3qHl2FrRtwpkL+EHI7O7zQsu7j+/k37Uy2wbe/bg0fcfaRzSbUm1+IUlKdnmHX57Ce0h3X//lFRQ52bJU+mmATF+k/3CDnXDuO7dWoGBkkT+ayEPuo85dsVmIvkcJp0hHHXhzgn8X6YlEWZErIdwDwo+wVYquLU9TtkurCAy8d5S0vTF/epcZ7Iqc7MHhMhC5P4nqaoNHW3gFOyFQP/NFql6s9C4BurQInj7Lguv/20inEe5HkoVdsRJWQfFAcPeYYJxfKLTg+nOt9FzJ87QMU/sfm9GrCOGZUSyXXom0fNUVOWW3KoU9cvx9PPJtzDPkqQbyyaZSMSG6YV0UUDZm2Yqi2dwB94GCiVJeQZSeb5h05BQ/tRU51bKLK89y9oJHOrn6+FvtVs/Cda30UYRKewmVPo5HR3EGCfnGERvK2QbFrMdIWZH7swbxcK10nVdjXQEReuTzCOZW5MbNTsGphPmLE+wEFFIDjFjTKxfvyqu2R44T7MQshn0guKw7l5+hL9pcbK5g2OrNh3giywkKjv5ohXCPnAfyFmVpBLQqUcoVLlEZZoAcauk/AjmyIN4oTELKY94jvWAccbPQjDddAVhlMPsZFTzzoCe67wXnOXIsKkEf4M2jV6quLFd/deOY1Eq965gRzsBQmg/cAy3rRpjnpYL+kD8r1AxwxRd/1I1TvIAlcoYwftTKAtT2llndPsV9gHeOnCoKdKiw3wU9prJF8iutVl4wI7HQ+uJ5WQXCOYF7xIrsilxh9Uy2vbz3+7LpBPH9JTwrheDVIXu/rMj92UNFW3dbCWWYeqGDZa2O/gWVICBX9ngqm1YlzIMhWC3rlJevYLfM/i/3Hi5FiGER84zOa9aNsUfO4ZLmDfGE4RQUWKsnvLzeRQc4ISlSkkoVsMaX5wAGpeyolbx6qBTCXM0R1cljUs2NUeEahNP1TTrYSUGI5qewjq9gFTK/KwBOFx/DFPD9GYskqiLxnYKB7GqMO41cDcyouiprwZSzP9J3NFIbEQV6OQVeJA3K2yVzvyXamVOcvAujiiFIHm7jqOrc46DjNQZKYIETBvtS2Xtrzrse5Xncy8/p46YSSJ6VacnAvaMSF0DsJcSe6Yv5/kG1p8jTSsgXTlKUFTlF+FXkCLw7MNM7NyCmRy20uG8gWH0Qwev4ATMds0wxM3CyAnsAaXlYhTl+tDO3Qiar4BTjQsHOSxn1oXz3JNF3otR1lorLtdI6ETg2rXi/mar1OqAVuUCo+CnHZ8miT1RkCVJcyWrMkPoGctZJnvWZFdhEo+6Q1NT6TFQOV1iRrZ/zPs/NsfCzmD1tqvtDSgGZ4xPsRi71vi08FsUK1x45WQWmAI1YV1Hl6u0+ViP7x5BckeOuasgEXJB8NcNjrzigJgBbqXmu9Fn6iEYzltRqfSFPx8uLqBSulWJDplx5pfDwEJWt7MInqh/H6MblNy65pzDRSwe38TLgc909x48H8r4pjy+WXSv/UlCK4wes8xlD4A5+tUt+TA6nwAAAIABJREFUTUTWouu/66vlzU5m2QP9VFxN+K6VeSu8+xlsTxz1CECRE1nA8u/mrKuflTPLkUsW+nzXSqdLsBeULWxcge7kA6tm+W9j0IIwJ3oDX0FZGRCf2SUbWY5vbXf1/Iz/M9ScZcjsGfHey8Pr+8W5ZOafss4vGi/XylJBeUVONtCBr+A6nDwSh287SWTPWC1wUGvERMILe553rcyPX6EiV9gKIeueKqGKccsCLZ2Aq7IiZeP9inzdurIo6ndKY0C2CqJz5Bjl8Veh/XNp+97aPL/jI1C5TtmN0T02TYOayh45ET+nBrM5i+OBuXaVilqpONfQ8efPQaGsyClCyKwdfYPr+iEAe79UMJ3LjwuNjNVTdh+fSHHxLj2f12oxkafDU07cCpnfYCesdB71o9555erjbQHNGNQh7PDfW1oAM58q9s9xXpErlZqorICqyKsBVzpn/kChX3ES2a7VvpOrT1G4+jhrla4QX8BLIXPkkzYBeSiAeeXAdK20piniQzCyjrebvNzcYVXkgtkfw0pDHZ4bTAoeZJlGOEJsGV2KC6fDefYfjz3dTlmyqAPBJdP5o+/13B7sxOMLedLx8tAZ/zHAkqlYhqzg6iXvLusGL4eQkqprJUt+EcojbngpPTxX6mK+v6cBSSniJuv+X3DUSkLIFYSQHYSQXYSQLzKeRwkhD+aeryaETAui3ImAShd0Hl7ttdGdwt3JfR+eyejQpjDF6ezFDTqJdEwdRMz0WcqxQTTk9/yIi7NO7gbzd74AM9iJcxOwZKQ1P8q2TNh1GTcH7zWh/P4fdntkrzn+7DLf1cgoToIegpZPMkWnk8xshi+nFiGJinLk7xcrKAjOcZTakEmFAoZbj1MV6vkGgHwaUX8W9nUGnzCI7iifVz+58sy3sBo8FJVZ6zdmjqdxd62U5R95yApCAoMAF45AGqbgqLLym+PXlLOSRNz8lPdGzHPkchXULOKc7DfzO7qzwaJkDgRXkj4s30acz3U0j6JA76ZDhfyoFGNAeI4cszwG/6OArCLgzcvFslY2RYAKA/dAcDnXymx98nVWWZETpOVFrQzg+0tt0xF8IvYxONm6/UUqcoQQHcA9AK4EMB/AjYSQ+Y5kHwHQSymdBeCbAP6t2HInDj73yEnuI7Mib8H0szlU1gpVLM3sfUOpjsXVw2IxUSBjVYitv51fU9Zlkrn6oAjRuS2FYAJ2yYfFOAkVR0RlWXzFrpWsYDACWPzrZTD+xw/4L8FfJDqOoUShGqo1Vg2iIWV4kNhfxaZeqJNTrSnOtdLZb+xRCP3Uz/aUkWy8o1YGvRIrt3Jm3R8rS9mq7UrQhejdxMqvFRpLE4TjPbmCYr6/5AwxDPGHZZTgR/R1wLdBKheMSiUohxdJy3Khiuuxc1+/CqyG4sBcKyHXrKrlFbsvlJWF7VoZ3IAWUeIpsrLRqK3RSlWiVvpR2EsVtdLpGSZqL9530d6me+RCAdA4A8AuSuluACCEPADgPQC2WtK8B8BXc79XAvg2IYTQoMPJjQNEIYsHk2nbdVtqv/l7VnILqowBZr6RsQwGE2kMJtKYo20y76cyFLMTmxE3hpXrOW3sTdt1/2gK07VtAIDm9FGMaG4hLZVxD7DG9DFMT26DTtOYkdzGLKstdQB1mR5uXdIGRd/IGAYSKQDA1OSbMAAMJ9MY2fEyqjN93Lz9oynbtQYDaYQwnExhRnQ7EqSCm3fI8j1CdMz8HTeGzN+tqYNIkxCmje0EANRmTthoTB3b6bqXpWH/JrOTm1xpBhNp9Dnqb0W+8zdmjruehXJC9JxEgW51ph/Tk9tdaesz3ajJ9HLLiRvDOLDlBdv3c9a/NnMCmdxelenJ7ciQLGuoS/cgkRIz3uGdqzCQSLvvJ9mKAE2nbKzS+Y1lMZx0l8nCIKNuLPSNjsEZhW8wkUYkZB/zLanDmJ7chl3rB4D9+7J1GUubW31GkhmAjkHXNFBKzXZNC/YC9Y3Y22BkzF3njCB/U/oIOsbe4j5PpDJoTB9DtZEdazFj1JXG+R2O7FgLTWL/0rTkm0iSGGYlt9rut6f2oW9kzFbe5NQeJIiB/tEUDq99Gu2jKbNc1jjLY2ZyK2Y6+E9+wp2V3AJKCKoz/a5805Pb0JY6YF4blGI4mf2mVv4MAKN7XsWOoSp0DSVRlbvnTOOFsTQ1+RyLn850tJGZLzGKXeufw6hkn/bCYK4OHWN7PNPOSWxCzBgBAIyMys0105M7sj8EMk9j2s7Xuo4dtm2HG0ymMT28DTUjfeiPpkBBmW1mxazkZry59k/oPtxvfteqTL9Z1nAyjUTC/Q6JVAaJ0SEcXvs0alLHABT6T76tAGBGchsIDMSM0ew+HEq5869zn06EJlxpDJqlb1D++O1MvYWh/i60pg67njWlj2BWYhMM6GhmPOehyjIWZic2C9Nax11baj9GMtk+2GoZNzLI09FGuzE1upObztmeIimwfzSFIQ/+PXV4CxJvvShdHgBMsfDKSamDmJnYgrdeP47arsPQx1Kec0bfaMrWbwAgadlT3Dm2Gw3p42hP7ePS4MmDeUzP8TudptE59hbcHNuC/S9jMM7iN1uYyeu712EWGUSahJEmEcxKbDHnp9kJtyzDQvvYPrSmD3Kfdx89AGLhZ3k5z488m8es5BaE6Rh6tu7Cm4lGzEwUxsTs5GabEpZIZTCUZMsWrLkmlTEQoikkSRyJVAaxsO67nuMNUqwuRQi5FsAVlNK/yV3fDGA5pfRTljSbc2kO5q7fyqXpdtD6GICPAcCUKVOW7tvHHwQThZ/c/U9YPPLqRFfjLxpdoXY0p+UntTLKKOPPD2MkxhSeRWiqiqB7aMw74UkGjajFBBkvzGyuxO7u4XE509EvpjRUYP+JEdu9UKwKa7XFOG3kJW6+eFjHaIq/Kt1cHUXXYDKwep5MsO86LOMvHbpGhIbEPyccC3fi1I/cjbba+ERXxQZCyDpK6TLWsyBW5AIDpfQ+APcBwLJly07KXvNS1RWmIveLxttBaAYZEkaKRPD5y+YAhKAyTJFJpxDTDYxlgNAfvoDd3QUrRPSCz+Ag6cAf39iLEa0Sf9P1DWZZHfVxHOwdxeO1N2FQr8H7T3wPADCpJoajA1kB5pcNn0C/3ojLBlaaKy7aomtgbPoNACBz1h346ZsRRI0ETh953mZteaH6SpzQW/BxPIieoTHcW/cZ3NL9nwCA9roYtOa5OLhzo0tger3ibHMCrLvmm/jusztx44nv2Oq+ZenX8PL+UdSle3Bd733CNm278vOoef17GEsb2NPtttasrrwYy4efRr/egMdrb0RPqAWdY7vxnr6fmGn2Rufi5cpLMaJXIUxTaEkdxJX9D2bf5QPfwSPr92FTV3bZ/Lbef8fUxkr8a/oDyEDH2UNPYcrYLrM9KdHRNrYPFw4+Iqx3a00UTVVRaISgZziJw30JxMM6pjRUYEOXgV/V3wYNBnSazu3ry+68+JD+B8wPHUJKi6Ovrw9dQ0kM6bX4fe0HMKzXIGaMQKdpzEluxtLhVWZ5T9Vci55QCyg0fPHas/G1h17DLd3/hfyU+2jdB3As1IGPdGc9l39bfysuO3cFHnnxdQDA54wfob4igr4zPoeqV/8DEV1D+tJ/QYqEcecjG2FQDQ2VEcRCwJH+BGYkt2P58DMAgF80fAo1Ri+u6vs5AGDNgq/g4jkN2PHof3Otjs9XX4Xd0XkI0yQ6m2tx/vZ/BgCsrP8oru39vi2tfvmdGAnV4IHV+zB5bA8uGXjIfDa5Lo6aeBiZM/4WvQ2LQTQNB358K6LGKE6ZVI3tRwfRG2rGo7UfwPzEeiwdXoXeUDPObMz22eODCfSOpPCjps8jbgwjQ0L4YM//QNcIBlGJCmMIm+OnY0PFCpzb0I+pO3+C2njYXCn6WeMdOGP4OcxJvAE9HEUmlYQRqULdzNMxsO1Z23scWPHPeP7NLrSkDuOygZUAssJuKkOxMrkcZww/i6poyLZazEJlRMeUxkokV3wWA4ij+6EvAAD+VPM+s206z7kJqWkXIHJ4Naq3/xpbDtstvLPOvxEvZ+aj5cWvuOjPba3GjmOD5vWu2EK8UHUFLh74HaaM7cL+yGw0ZI5jQV0K+3pG8HLVZThr6CkAwOO1N+Ez8d8DFNA0YNuRQRvtJ2uvw7LhVWhMH2O+m7XsNZUX4ozhbBv+suETMIiOmxu3w3jzj9m0k6px7/AFuGDw9zYanfVx/DhzBSqMYbNeADC7tQq9F38TNVt/hoFdr+CPxuk2YT2ZtlutJzXUojZsIDH3aoy0no5DfSM4tOqnmJ7cjl2xhVhbcR4o0WFAwwd7vgUAmN5UCWPBNdgWXYQ1r6wy6zaqVdqszU/Uvh+LyS60960DAMxprYJGCMamnIvRU96LjBZDy1OfxObcd3u47q8xolWaq+Hzp3fg9b1ZT4f/inwXGYNi9Nwv4esv9OKmo/9uts8rc76AX28bRaUxiA/2/A8A4P6mz+E2+iskewqrKroGPFb9fpx/7oX43cubAFD8a/zn2HTIvZIJAIcWfgLnHP0J9nQP45WqS7Bi6E+oiYWQPPtzuP/1QdRmenE77N9l+MzPYOvWLTh94Cm0VEdR8d5vIbbp5xjY8xqeTczBc9VX4YuRX6H30E7sii1EVaYfk1IHMNR+Fh5JvANpEsbtI9/GpJoY/jN9Ha7q+ZFJu7k6ioaKCJLpDPb2jOCBhk/ghty8M+3s63Dn9rbse9K0az7KY3P8dGyKn46zFs7Cy9sHsT22GO/p+ykqjCFsrDjTZqSd1VKF/SdGcDgZx4mZ70bnzp/baFVFQ+gaTOL+ps+hJXUYt0d+j/0nhpHKUJux8cdNn8d5g4/ZVmONSDX+t/qjCCOF9/b+2PQSCesEqUxB9GmpjiIW1rH/xAh+2fAJ871mXPJRrEl2YsPGDbhk4CHMaqlCSCMYbTgF+7autdUzr5De3/Q5XNP7Q1Rn+tBeF8fhvlE8UXsD+vQG1Gd6cEVuvhzSa3FGC0XaMPDV1C246cS3s3RqmzEllgAhQOjd/wXy+P/Bnu5h/Cp2PZYsOAVnz6jHky+8hDn7HgAA/L7ugxjQ60EoRb/egP+4MIrwqqysM3bZ1xF+8kvYeXwQj9bciH2R2ag2sv0wP87MPqVVo9IYzLWPhsYbvoNND37NZcztDTUjZowgbgxj5mW3Idy5FP1dh5B65us2401nQxwDo2n0j6ZQGw+jMqLjcH92rniy9npc3v8rANkxVJ3pxzW9PwCQHWs7jmbrkf++p0yqxh+aP4Jpm/4bEV3DPQ1fQKUxhFNGX8c7Rl7ErJYqoGEGtu47isjocZC5V+Ln3bNybocUGRKCTtPmnARk+Uaf3ggAuHF2Blh9L5x4Zu4/4a+Xd6BCG8ORIwcx8OTXAQBb40sxf3QdjlbNw4XVh0ApxYHeEQwns99fpxnc3PPfLnoA0BNqxeyWStQkj+KPwzPRFWrDWUNPIVrdiCnXfR300OuIvPFzZLQwvpL4ACI0iRv77jMVvD69CU/UXo8xLY6wkQSBgduvfAd++9puRA6uxvLhp82yftZ4B9pT+/FB8gfUxMNIIIJ/SH8UIDC9A94x8hLmj67D8fBknN3Qj1hIRzKdwZvHhpj1t+LAgtvw/NFoNjYFzSBDQvj4+TNw96oDCNGU2eYJrQLnDj6BuYmNGK2agt9FrsKyBXPRWBn1LONkQhCK3CEAnZbrjtw9VpqDhJAQgFoAfF+8kxj5SRbIdnwrOjqnMPMkHUu0FbUtaKvuRM9W9mJ53hoWzbl09YRaMKTVmM+trl7d4XYAsD2vamxHXqSrb+nE0FtJDOnAiFZpK6dHb0V3aBKi0BGP6BjU681n0ZAOGq8pvLNFrR7SC2W1d85Ad9itfFU1dSBx8DD6Q976eOvUecBmHbrG9tPpCbVk3zU0Cd3h7GSdZ3QAENIITujNSNV0YnQ0hVEAlRENyMknLR0zMbrDwNCJPpCcy2JYJxig9RjVqpDQCpaXVM1U9I+mUC1wVcwjomsI61quDlqOroZ4REefXo+BUAMnYw0iOIJIywyMDG3IlkvC5rcc1bKOXU7XpOPhdvTm2qKhvgGDej2GtSpzkuvVmzGiV5vpu0OtqK2pNelGMzrCuobmyTOAXJ/Um2ciqmlA/QB6BpNoa65FOKShe6QXzemjAIABvR494UlIp8Mm7WhDByZN7cDLoVa0p/ZhVKuyuawCQJqEMaTXAgAWtzQD2wvv4URN2wykkxoG9AHU6Pa2j4X17FiorkNFSxMAYLtWjagxaro/DGk16As14YTebF7HIynzmwzqdRjWazGcq09vqBmTSA/60IgKYwhpEkZvqAVGTbbtQ3qhL/aGWjCaGzskHAdSSUSiFaisbcRA7nvFjSGMalWob5uB3t3Etpk6FtZBiIGuTLbvRsMahpJZP3/eSkZI1xANaYhOmo5IqBp51wVrv69unYbI5KnA2H5A11z0qlpnoFmfzaQfj9h5Uo/eiiG9Dt2hNkwZ24XeUCOqjH5URLLv0W/hDYPxVsQFbiddoXb0hpq5ipy17HxfoCBmP61uGkR/zjNcJwTdoUkuGiFdQ9qIoFe3W01jIT3Lh4+2YCysYyhd48prRbiuDfHUUcQnz0X9lHkYOjqIt0jcbJNuRl+NhTVEmyahrmIaRrT15v0RvcamyPWGWpCOjwA5RS6c4xexSTNR05abKxqmAIezrm/doUm28dvS3IyRA2OWvECsrgFDlllb1wiida0Y047BQKFdB/V6RGONdkWOaDihN6Oipt7kozyeCwB64zREujSzbtl311HRPhvdm49iLMc3ras3VQ1toDV9wACghSKoa5sOHO5E8sB6jGhVGNPi0GvbgUM70RWahLCRXdFKVXeiJzMp964E8YgOI9JokxJCFXWIR5KmO2dXuB1Dei2qMv2oa5xk8kYApoDsxJBeg+5wO1KRWhhkBN3hdoyRGCowhK5Qm/39NYKIrqE7PAmhaC23nQb1eoRoChURHdGQjlQmjePhrKCfISEM6bVmYJ48ElUd5vwwRiLI9+JYOJs/j2ioMMd0h9sxolWhwhhCvK4VTZG5OLElq8xEQlrWMFdZDyciIQ2jqQwG9XoMabWozvQhFs7SHNaq0R1ut9WvX69HJNQHI0UxbJnr0/FGxMPZOQENUwCNIBLS0BtqRLRpGmontyJWU9jS0RNqtckU1W1zzHkn1jIdiOiIhDScCDUjrUXQqzWbafMKbUKL40SoBZVjOWNRpBKTOqbjFb3GpcgNaTXZb0mHUd0wCWiaihjGcDxkb/toSEdYz+TaW7O50B0PFcb7oF6PFCkI9Fa5ayhXfiyso64lm0fXCBJaJRJapTnvxcM69OapMLoywOhx1DS1o6fPLjc6MaQV5uzGNp0pKMfqWtCY4yE1mbAp7+XlQCNSi2joiC3PiFZtk1+dGNDrEWmYhPDx4+gKtWFQrwMApCuaUdk8FUj3AyENiFaiN90CnaZsK3UnQs0Ffqln57eOjk7E96TQe2SHrazeUAtCNI0wybZ/Mj4Zo/3ZuTcv/+Tf5XioHdHQEHSNMN0dI7qGMYdLdlXrDPR2290q69pmYFB3K4F5OT4Vb0a31o7JrU2u7RQnO4Ko7VoAswkh0wkhEQA3AHAuZTwC4K9zv68F8MzbcX+cX7imSla0LEFOmg3F5ZGS2q7MXzY/fkd8MkIgIp2/7wylEfhmUPN8H3lQ4nwXzbYBln/MiBmLjPkehXPBin1HQYRB6y/ivqtCkwr7EmG3gzVPvu0Z6YTnLBF7Hla4FVuPFPTDXE25ZbHy8TYrU3PMeI2xbErq+N6E0xdNumbbUeTZp2Ebb4ISffRzdq3zZWkehRKp83aydcv/Zbef9ZpSMU3KGVuykDujLPsF/n97Xx6s11Hd+Tv3LdJ7T9vTvsuyLNuSLPAibGxjbGPihcU2CQacIjiBxKGAkJCQwEAWJiuVmmRqUjWVqmRCFZlJQqWYYSAzFAQYKBIcjGU2Izu25VW2tVqStW/v9fxxt15Ob/fe73vv2f2rkt797u0+3bfv6e5zTp8+rY8DerFePijzF39lvcb+mjnz51nk2IB6m1m+taUvGO9iyWs2Dyl/GNLK7yyQJ5ANVHmEXEZW8ojJegTmgAljjBJVansaZkwo0vjb1I6qn7NP2QgKQfTNb9/RHOkg08z9sQzCUf4y57vyXYjC3kOAap5yHnZpG/cj2sozNoSGagmbb/Q84fdt7RZ0/pkcqMYyvyv1Z4wx3NgV8i256tny+edYNw1/iKrAb8QmY8YTT9t3LNX2Fa1X5IQQ54joQwC+CmAAwGeEEDuI6PcBbBdCfAnA3wD470S0E8BB5MreKxa6EOxKBzSYFCwaTdPJRVcW5A6cZXxnrg9B7o1lg3sXWUDJtOdqW5bDIEejyuGvBJtEi4THogxXKQnH0UoaRzX0+9o13mAhryyzqKOvrpmHD+Vire8RUTe9/XWaZJGysyqkvfqwFmRrwYdKgVZ6d1Zvrupdcl3NfV5BTO97vGbuo+JFLdCZ9/QywoS7uHJlkHrgIY9KiSLzNlANQDp9Lj6sWra7bnWOXJGblPnaUCLJ0i/Cxme+L7rHPe2JcSdIWWTLkoRLj4GHUyxzXmeUnUqBdihRZXrXGON4psZOrZUUM12sEtJ7kMUYR5lmuHRQcN0X2tikQ+0vIQK9qRCy+T1zHzVWUzVaNr5o+fGE5V2M50Y5IQXb52c2tTL/5G02KZrKXfX85puHrYYqjRbgH3f4xy36HKPc2vLqc/tMRCd75IQQXwbwZe3e70rXpwDc1UVZMxL62BLLMKR2KlvoVZ6+SwCjarXPVSN9Zab78KxN6JmCdsa/dv6TsRi6lMEg61XDdqjPPKophFo+Xfd8NGod1THt17KVueppCM2+sut7Dg8uI21Ymi4W9BlFwDf/KSty5RUjUTFtLPQ0QbCvqlFpRLF9TwoXh2qhS1JUpak6RKDj6TWBi++ouuM7eYutpb5YVH8wJpdbsDf7v/mb/XaOFVQvOKHRMmZlnKXeohhwEMz4RPAbe4zjYSpjD6c9FStESrh8yxjMKVPWcz7l4wic1ZWSxSlyrrz6SpdxXE1DoVHlznhBtwqLX08GAMJWXVztwxmdzYXjCAVFqp9v3pNT8gVbSMM/h9vbxeRXOal15TdkRc6bwo7qSJWGIhoVU6I6ttoUnRjlLL5CTd8hL41bkbOU45BrZgpmliPoywWUuZlFe8S5sPjoV5fejHxNVJHZLlhlGb9XprSCuk84qyrpT2PQV7MLkLLfw2UBouI/bsAsZeNYZVUvzm2BKhU5d3jboLZzlJkL45wgaV8l0Ic09ScpT1TXypgVOa5K9XOdVm00jhiuQiyZ4FZBsvqhhNq1sv5mVFzL9a2FULWcvCzVNSyI7Yk09xrpG1Bm3NMyBxSgwhRAi/vRK/thZfNneanX9r7kceGkgLpqHyNsZTj/LzPoM+NkxAqqyYtcVrOHWgWUlqu3VkONvCqt58nqb0Jy21rG25okI6wbq9EDKt2iFkXBDupyavM7h+Vj6Ou8EsI7ZtZGyLIBw7WXg63NyfG87vu6AdnOt7WhlCul/Gnx3nEopKUHTZ3WB03pCHJ9stcl6Ay7gZh5yZ9W5jWyeDzJ4GQw4ZmT/HCNHeocEULBZ+jlngrueWjHsbrxsol9Wac9kiLXB+h8YXNHtOXLO2X4p1IGXIcLg30Pk1qBSYd7l3Vwb7D8EKk6qeVBtX673H/qPGaaakWuYW8OO7y0vJAnKI8LVlDZ5vdlF2e5QU6a98oBz9wbaWYQ1TOHewn41QEZQS6d8ve1NDS7OsEK9EL7VQgvNlfhqg9kVfp6P6XkakwqPbkO5nU7hagWnnyubn6Ye2Xq1Xr1vp+ob6WMK1cG2T6uVr/J0nws560SlmuKvgbQlSeL8qLnIDL2EPEGL25ctbmiuetW3tOb31xV1O/XT6MMVJa+QJXgaCpFRJk0btR/SUlv8hIxwqgpeLn3A9vAuQtzQl2sK7siqBIZv+VUhtU/4jOoAnGZ3+Q/Dvo76UOPkMazuoyMyWBvH1mhd6/gMWMIWegqypX5wL9Kx/cJVzlNwW3UiOUlGbHeD6oMxruUB9MqqVjbH3FKso0GeEMQXxs32DflZBzLJ9FX/magHpcUualBFq/1a6shoWktEqT2mxM2dEWyRozFKEpwiBoYzPeSdYUQzw2Xa2X77uzKH1aGayU0vkx5EncJ/qGSv7ZpnnUlrO+pepx7krG/ZzvFR02lTy2MkCmhtkuUS7aiVmYD28zqbuOsqGqg8Bpf9LyBpRm85qAdslLcyv3acJeT3l8b25wKM1svm4ubTzDUq5QVeTyDDiuk2vIECPtkBjuxBWiyjc1RSkQp8CteHnICLT07l+TtVPE/s3/R9q4KqvexlSH9tAbFiOPLeg5sJhjXKcPE81CK5Wq8HjZGHxO8ip4rXYCiWBTKfj8zEBEjXAOskG+XdtwHxk+yZRr+1NafTuXTBosLM5c7zOVc7hf+9Oxqtsew6iq6lo00hS1SaI1yreT4p80cYuE1Dk1X6acTkiI3BQgWlstJVJ8e2TG3HuAUwS/Aeu5YjKvS6HlCEeOGFdOPlFVB5APXgLJSqJXACR9MgfWKih+KPKbXz/E21beirCrHmIBsFXQg3O3E3e0r4S1ieIhzrTTfKwvhWcbyaIPuluG3XOfPfavlgnFbnkQtVNlWHuU6dOWDX61itLD+StQAmN+8FmQjy4hUblXhxS7GyZOugOu7hq0JGrpCsPARsCKnR9L10DVcii3l+u/Yn8SN3fLqSkmRGH5Tx93yPaq7ldJhn0c4gdVQSpiolQRdldFrrN1lFY78r8tA4VeIyndU62PnwrDvwCw6Vw8UL4ZgA6TQUhX1tBiLQuopIHuyxK+a8q6VzcdIvf6uQJcdjcT2OhhySEh7uudKs3gz2Fbstgw9v2z063NuAAAgAElEQVR4caUKLcbvhhk2VoXyRRYwnshUZzqSItcHGAxksWBVj5l7MdEEZbdCbtkdzjtmLXQhzie0A7VRPTSkdiuU+onLuh04UNSCeGSdmrxCltlHfNiUu5hKBBw/oOXMyD5Ucu5BLtdKGT7XSnmPXMgEEu68Z+Yt83OuSd49CYxbnGx95YT1elWDlEdONaFiw8zJ12pi44GjBBX18QNybvcRD1ZaihDgU6DN4DFKuWT/0gJkjC8V95arPnrbCP2n/k0kPvR8IILWFsZqiNYviPvw9kL4SI6uMUtPan4D4dufbeQ3adQrgOyII/2QA3BZCilX9pl+Z/KZYz+lPsWaRRQ0zaiV5BiDqyechd9VpjY2dOVaKRPgjQS6y7huHOBdyrn9SCZ/h+9x48q2r5LYG0NY8vn3vpZ9rZ0Bit2nZWRx7Tk3Zb+YOoQofmzfaSxT8fwqPwtlXvXdPWMZ8zhuRU5L2yRqZVWtmafYJUWuL2g2iJfJJpEpw68vWhUF+hiGdhR9j1wI2m+s9dHXlEvdQum1APFpYqJWtkeI8oLgNJzFW+aVunn8QglfnkVohp9H1Iii4YOsmqh9G/ngjcgnB6hhBOV6NdM+EUbV0OUqFWCFDx1rbAInW26AEOV02fLApWiYoc599E3lSnmqK4Ikp7UoSGUtPW5sNm8HsN8wVPiyC0R2hVe9Dp5/JKOOKtA6+I4kF0qjHrog6BZYTbdZLhCRP9hJm/2a1bMAzxawvOPiIf8TV6nRZ7AZeeEdQ9S2YxQGUVBzjnt1qdwt1rVSose7fVrVKvVvZJ/S6xIyfnFzhm3/V8jpycpqWqw7Y2nAko8fqJokQClUvmPA/BJYL84oLSQCsb3EpGWXTap7FnJtjCzTBUmR6wd0YcES6dGe37+ioRbHTboFIQkuaxOR3Kn1YCfhbNOPc+TKK7mJXAsrBCgChwxrsAobneq6Qe/3KJ6mIONTMtyCZU3H3jjqgciO8oyy3FZB74qy4tpj26AfEsAmHARZgSmrEVZPIQnyIUpOGcw/HnrUSjaJ5X4MT5qTts3Fxy/U1t8wSAA2aqIHvubSlEKegzLFKJJcOlv/yQdHgj4WGqMoz7OBK6jsGEbm/mrbPj/DUh/VHvbvLxsKjYAqkSvr5csErT6SjRvsd8ua6HXi+3mkEqKUEDj2doWCD0wBVoXPxa58bjcCSO61Fr61jX9G2RGeA7aUwe3qUFD9o7WNpqVOjMuv1SAWqyUEpFe3A1g8ESLB9v1YpVJJHiDf6M8jhm5zPya3eu/m3xmov1VIilw/0EGnipqAlX08vk/sUkHyJ2b0wh6xfARZlxUvR+Z4ZqcRdSC2I6l7j1x5Ye5BcaGJIhDuWlkKOHIe/bvLP9S9Fj6BwdeuFNkWPrXI3K9ky69O+JxvvZKvEiYl4VBxheHqql5FuW6QPdhJ/R3tAlLsipxJoXgu1yGCEbsYK2QaMp+pVuNQWipqVif5j1GuCd0DgDOkKCVIWW1RK3WhnFdu5PZ3Co8sf4S3FxHx1bfseStRfiNbqdxZV+w85WlfV1pFeVNcK+00WLYmk4b2qKLrMpo2NeOA7DN02Je0t0ssNZsYnD/jhH2/cJ1TcA2amuzjGXxijZ96cjO/OzsQKTMEELQdsB4D1YMrHPmIalPu7coxS0sZS10l8hRV7xN3WSHukbGecDMJSZHrA/RQ+Fk24FSf6nx1x/EJfyRswU4Cokp65CHj+IGQwYiJTGaF09JqzWT8Vjd/+1Lzq0jlymcTd1IgUglxHA0BS/3cZXuUkOrCpWSSYyDX0nqEXjlynG9B2We9y2/HrATL6pOFwbliAisqmKTcniEZehCIsInFrUxEEHLCvnprTt4hK/I+V80S9X5bWRl2cR4pf6z0heWJ6Vup/Gxz/AA3ysgKiu8rmWG+raWrdzwrHXogi3B2qWegScmtkRx9JCNAiHi+DI28Z9bQFuxEpiNfM+WUvORw+fbyfJVQpWUP3GS5bdy3j4WszuRRfklrLv74gZIUBX3DSSkKd3SgDqaOefXceduttMc8DymC4ynb0R2RR1xErshVRpI2p2kj/yaxvOuk58sUONaF0o+ZEtuuXk4HJEWuH9CVikjGmXTa+rjiZIUmbEDkFJ06jf34ATtdlb4b8R1JCbhCRUkOV75Qe2gto8Qo2ibCFNgOj27g0pNtfOQiOtV/bS61OrVcGc4x6Tnc3Ge1zDxKrV6+3cId1mbVhKeV6T1/UA77XSl1tYLG7rG06JHu+pUXuhGH6dsdRK30rshFjECyB0E4D+vulNyTetIl5MKSf9+oG/pKUPCKnGdVCijGKO6xXGaIS7GtgsVv64qNLsQX/we7gVuDqEjvro0xRMSuyHHso7prOgKZVODGmNIo5ngnz2paqdw4vSjcS+3MHKn/dc2wdjhqpChaVTptxcpcgebXifUo0HyRdsGeyx26OuZakbO7c/ogTWi+lFZG5QyCDgL62NFQP1DaI0Dx41azmxqiubNR3W3ZRqF27rbV2t5HWRvrmO1LNhlED4A0E/W6pMj1A8ahnPGcEmXJCbQC+3yDeSEuMppiH3pFNRjEFsVkqAXxZnUIQ20WjSknVmg1gi245NLyb6wffGDdBiL2eJrCquV+BPi9WE3oFX1GQDoPy5dDjXBoc111lVeCV6jsAkZXRx2odfChtub6jD4+vtE/uR64w1MNhPfMWs0JTa+vyLFuWY6Idt4SAvm9mht0xS3jVlti+IHPR4zgWiNkFc28Ctkj5yw28L0qQ0Bkt4h3EdaV6IaulSap4p5lnjLS+uYMl6roh3DQCF/p4MbmZqj3c9WUWrR8EDjjn3XPYYQ3Q047RBE103Sz/aXgsYakyDPu+OWZNu/A8JSX3AzU4AokRa4PMDpaRtAPppWhR0MWyOzWzeqW5MYmb0Z3cK+w7IHQy9CPGwjqYFFRJ9wKJUveeC9yngGlGCrJXljrA8FDQlmHWJHRxIobWOfACda3gT9G6fMllXm26Rk4OSKnbW0FzSd8KO9c9c9aqHJFPTVD5YfUL7MbcYgX4NX6hhTif295DIiZYH1GH8H0N7dYp17bLc+8o6t13GUEfKdrETHKh+EFQPwHsNJV77O2jwZuaEqdKHzfJCx7dVzuokQ2VzlfUSHtRJb7sCjMOeRvzgdlsj+z10V9Jz1oiO+Inhj1wqa4ZmTunY8/ELysd9weZVsNYwKh6XXgKeqGiEhEBL3g6fs7Cx+IzqLcBmhFSv8J6axMmrbnyKmBpPQx2jVWM8QQYLBroOipaTV6EWNQzwMT9QFJkesHDPedsGaXO5VyokBwTr0s15ZTl8LXYI9cEztYK8uPUAQrc0ITWh4eJY3ogVBL3oUVMHZSNIUHVWN1vVHZXplj8FXdBVXBxbfy4luRC3IeVgTIyGAnvPUjWllWXZDUiY0gzakONxRSf7oRJHVb0kREJeG+n7WNvS7BNb3wFTmXayU5ru3vTmD4QHslPeKjKkBa9rlI/086vR9se5vDxhY+2Il5yLjNZcicZ+KCZiGzHD/gMFZkGVXCapD7c/FBuCBDIa55WQCPq8FOyhpI7xPgWtl8POfbKvQrWNORaty1qTrmOxWulaXyynzL2GlYSHyu9s+w+Ytv25oe/118tXQo/SH5qrqFKF4877I5o2WcEEWydiOstww0VORKvmDHHrNNA3Yz9wEMj0QcCJ4UuYT+wOAzt7VBtuC7mZRQRdvS+4FEtKl/fy/BCp6xliwGUR6Almt/Pv8GfQ6xwoThWhmAXBkpJlGbiyNQCcqmGC4nl4OdeCriUCAbfUlm5Ux5bBEAQ1fkhETXekaOVh0liIAXpP2VCFXXHa7IeaqhCGgBPo3CVv8AcLbgqmxNkWttMHHxuCefuQ+X+80rY9byHVUrK2i0f6AiV6pX4UoEGQYbghxcgRewOSOYWqopbHLGTfMwZTPMu/7Mjzh+rAI/MSspqusYb+ysty907+BH8iRteS1zLLWMe8Hl8Xk5A1abNUffuGV154Q+JvZeVinHgbBIBgGKocRLrsBCThoRQZYMkN6+7eU/m7LbgqSWVeNzdtuMpW76uN1rX9weICly/YDGxN6olUL/aQoN3kxa2cqgHwl9pcfnNgI06wxRLkJsfulaF7IEn05H6SYRf/6dQ+A2ELbTJzrYiU8JCaBnDazBprX9KHML12M7LWsi/zfJgsUHtXWDFY+qDobDc/7YmT1cQbWt7Lms1yai/JuZ3NKKjCygRVgHfCvbfD/z9wn3Nvmahqlc6VCfZ8GCqCqoA8CkliJX9n0861Dk2JsRPMeuksQYd2xjgayA6UXKwllAQaUQHORa6afDgYtaySV3B7fx8XGcsBs8tpOlaMoC58vQ/se1v5HYzC9cc2XYO4bIE6G0zT2D1GY5NbAqZl1sLuWxe9BDRHQuauVkw41tvVJ7/fvIu0UTevVq5MxDK0WOiBYS0deI6PHi77gl3QQR/bD496U2Zc5ExHfeMl91pXCmXyiWOrZMQwMfv0qiU6bTXSsbvo+9oEbdzrijulbG5u6kCoHZSitq7CTTe+siSazWZSAW7zlyEbQAWFfUbLBbcOMEMO6pYkF15K+i+UV9Rl0RkQeCgBW5zngm0qhQKjoBbpjOkgi6xaC6mgyIWmmUZ7hW6hd+5DaxLGD1nqC8TVVW4LTLa3LmHUs9uIPC44Ybi2ul9uKkfbDQADZsABWFkq4lutrNochJH93pPunigdDjBypa7nq1nkJJDbZD2t+6HlYtX03H8ZXx27aaR97rpmhMg50fLN/CUV5If4lZNAuRnbgtDH1D2TeJ+45xdYkxCgdWKxxM1Eqra2UfV297hbYrch8H8A0hxEYA3yh+czgphLi0+Hd7yzJnHjQGyrIMMXvIKvdHVxHy6occHKGYgJzLysQN2pLAFDDot0M8PXNZXrc0x0281W1mIAshUQsnAT7qgUpIrKWSC4Ed2rKV+7t6V6OnC3A+6uGulcROHHqiZufIhUFSrp3JOCNJWSYvpObUNaE+uFou4bKss6VdOvQRUVc1Qva9BXOe9ld/qoqP/KHbXCXsqrurHkGH+0pVdrWFfL6WnK0VQhQeR1pDuXQWJfVJ2UhWKukMrfz4AYcLpAXsHjmrhqqkKguOKE0T3j1KVwgROeCR/NuaNbBr2qnEfHN7bt0FNL+OhCw/BLoNy/DNta3kjZB5xVlWQB7m6B2v62dgHcLOkZPLb7lHzmoSgDTfyGOBi5ZcKzOl4hrfcnA0AolF0Zu5ClyJtorcHQA+W1x/FsCdLem9LBFnWVRzArHuMDwNXkAh6X87hHF+j78yk310ZzCnIkbINsJWWsiy1Oypm6Cqm4gLCeOPQGaUpLk6OeokKTJlFj54Cqp0cpm+ug14WF4JsmJfYpB++IKdSNmk/+t7fI1Do2vJxhUhleCKeir0mgSxWMgqhIOfW85RtcCnn9toh0B9/IDPGMEGOzEUZJtQRA7BSOSKlsPFOi9L54twGEqmMc6D/34NhF2lVL0Ya3cxH8QYh2yCmOyCZIi9JAtntrHHrFfGRGY13eRMBbFypXZGrZSula0GRdaQYCesAi2XkSmF1cF+LLSD9TCLEZeIjZaoeypY98iR+jzkHDnbfVk+aaZAMO8h02PcnQNC3BSEar6QD7UPrUqQdwhjBbXLbLHKZET5cuqGq2H1aEywG0nM7+GDPXhK6RfmqaN3zlHBjSehdXsl7pFbJoTYXVzvAbDMkm42EW0nou8SUVL2vBaoIln1O2QbrSwISR27GOw5vvZtNi1v9XxFrgfuAz53B9vAEXMguAtBbRRpifTTbP6dyqoEu4kIn2Ou+qype3FOx6+w1AgbhdUJS7bK+zLKikeE9ZPqvGVbNGsRU5m281EHPVUS2iuqvsYi3tLPp+UE5NB9o0GacOBzNZqfOwehjh5pr48o0pkEmglZefLM7h5qFKMqE770PBH1guCusc1bXFF6BNNvmEoZSnrDMUSNWhnb3iWNhmVX7da1dKieTSndZsuvH/uiWlpJwbea3RRR38SjdDc5/qDtIBkXmTZwrnDS1pJzRpBO2a3hTBWpfHWL+DpXM8EM1OQGfQmI6OsAljOPPin/EEIIIqvDwDohxPNEdD6A/0dEDwkhnmDKuhfAvQCwdu1ab+VnKojiD+qN2vMVcXaKNX6iKoVrebpXvNqAE4ubBjup0gdZwuJo1sQniwweRS5ScPGvJvkhc2bb7xwTtVINMmE7N00yUFimgdoi7eNZdT2heu7dC8N99FJpcSvCjc72cfW9qq4Wup1OSExbBcCXtm4T+duG0ZNX/syEthVXDZXpP9AyLEE/x8tcXw+wyndgxLLVmQ0J39AFse4f2thq6BG1qhelL3Krh4by5KAoWfdd4OKhlsW4Viv9ez17MyeSTXOmsBUfW720oQvsOXLGt3XtkTN5LRThu9lsqR3JqN7GEh/ALAwZmSG2rO0eYClVvR8i61wZSRqO/XLXts0fga6VMqznfTbzjOZIKLCevfoyhVeRE0K80faMiPYS0QohxG4iWgFgn4XG88XfJ4noWwAuA2AockKIvwLwVwCwbdu2macWByJ4daKScTTFz5tdSpDJriS6NS5Py1WHUE/6zQbndhHz4mEKUU3QWq4KeO36+0fGcvLVzeP+EwKSBDVzP1LzxsnIjOoXj/AJxFjN5Hic4i3t1bcTspBb10l2O1PysdcdCX8Opm0ziJaHk+R04pSPUigOVxw4Edu8IysUQq+XQc+vyhljcaxxxrG6Zt0rGN8kUl59lcrxOSxuvqHF2+iaXgu1EkUkKechhoRSgWb3yPEV0ls5Dgx3hYzZnuc6H9ZKYbcKnsptQaYK53NXkBOdvrOnSbJKPFzjl8UJ3ruSb7ZDFWzKURNzhTPgfewdkKEficjm5I42ivkk9tFMJiSPx2G02sJtYDPH+jZlz0TFo63a+iUA9xTX9wD4op6AiMaJaFZxvRjAtQAeblnujEYTNzOfgcG6QlEOXpYymwjnVutKU3S0kUeVqZrRzFoor12j9YBitdoySbW/3C9XfXzrH/5gJz5aWqKWrWO1LkesyNV7I7jpj5vE1T0arTmsBy7JXZQV4/7IWslJT8MoclXeuDbw6RbBr0lUGCc8SjTLTy2+G7Onz2bkZ5UjZI3mHy5yne7yXz8v5xy5LgzNaI8Dxx4n1x45JmolWx/Xt+QUB2f1u+ubLKXCtdd0nVQR6o7fZr5T+3Ab3pLRrD6mIhZbt/hymS1yjvb005fHFGICqQShJ9pIGx7h5kPpPRtT5um32coxAz0rWytynwbwU0T0OIA3Fr9BRNuI6L8VaTYB2E5EPwLwTQCfFkK8ohW50KiVtiHaOzTLM7sStdIwcVZ/bVNySM049L8zqIE9jDDMkdRCfO3J+sMzMQY2ju2Q2RiQMik40kk6iW1VyQXWsisiFDmLsK4mCvkmNmfhMIHGl0452qP4K0esC9tj1RyskmRtF9GqH8plxbmF1lb08OAaEp86K01KauceH0bRMveLkfIk5hw5PRKpvrqrK018TV2dMvSmhbK2sm7bv2ZDRvI5gnK0VlX4VzhyIJMCXQX0uQiBi/ccsW4OqKCtwRqpy8/mDmjiqafuveCg6aLmG7XkQDLElGvWQ5MiNAYo+0erqJVSPZvtUXMpybzJzacYmDWzn+3WFpzsZGuH6CBqDavc1NhOivG3cNN0tJurev7jB3rzPZpS7qddtGt4XStdEEK8COAm5v52AL9YXN8HYGubchICmEwSfjjhnROkuYlYzmcb0EMG+n7qcbaoXjJCBdpaSGmGOqqWS8CULcR+WlLOhrUKhzusTvPyvSvKEuk21kxu2udz8S5GocE5VCceU9xzCxodfUfvbvKpNC2GvWPgTrb6icwnrve3vbt1UGvQVhSg7MbsuQwq0yX0hpQdW17+J7R1KOBHKC3jk1RRK/3jvY2OW5h3KYNxgmjvPTqo4L+AdCF3g/jQLE2EWq8aIu/vDZTM8qMTSXNyXOj/qUJ8QCc5daF8dXr0TFEH1gPFVReGhkGbSdwImsGiAb167p55S3KvrB2B0wRZNsBPRg74bZu2jalmuGX1edk5u6xNLLofPGMO6ZThEsStiWPLsATk6KyAFvSI6lSNgnNYEHMguF2PkxUm9447pTeQ+T2t39lazfKBeY6cGprdIjgRs9fD0SRRU6VrRa6jSSk2UECo9ZtdyQ0Utq37Z4qnIXfl0D5AxLgR4rYsL2/r95uCDWTlhm50aCLk2Yx+xm/iVwOUdEKlaS8zBu3HqljXcbcJwvNusT7vxn0KOvOQD/IEySAVcxyFvYW4w+d7CbuRuRyUy/mBzGd9gK1+IZ89dj9yl1Dcoe1vEU3XHw2g+XvywXJatNvM0+OSIjclaGDRiOrPisBbulbGSo0trY0xwkIne+SENgipNEMFWtmlo/dw16nriccpHlfuV7Lwqbdhc55oFbWywTcxrZqcwgCG9xznklmqIAu5pSIQskG/yddlJ/nA1Yh+ISqqpebaCJgChLrqpe55cbly8aqHnkj/JsGaHEirm1egqF5VwLzZHNb9z2yFIgLhk1x/jp+hWn6QzzeTVVfpdvzivnV9Lpprj5x0XXKFPEdWimXImY3uW5U7Gkj5G5SVeZI3MVd2ZineHcykbC+rISuibtyT7pUlm7HGwvOGsUyOxumOnKhdBoOXLSztEDIgK0N8UxG96XeQ85lmr/yPe8zjSHFt3+UoaLrMtyQ4w5AUuSlAeNRKWSFryJmlgO6lrz2y5cEMMVg0XZIrEPKOuqoQDmWEcyQLV6TaghPXjMiP0W8p75HzpA0h3dD9hyXVUMNRolaysl052en3I/uN7VUVa0VvIuNxiDtyoJ64e8mzwrlGUKdxwTgQPKK67Cqrmajd84D0fgrNvoFLpMvvccYRy8zBDHmxPbDZHKj2PNd2goih2IumfB/Lf23y5xliEvNcoPzqiQRtMk9U+5Z5pL2ivUBXZEM8PHxoGiGaL840tnWD7j6EGeykDa2Zh6TITQGaWFlcq00u+uQYvLwOfjajUpCveQzadehyalb2Bur1iTb/tFUEXfnDXCt9gRrC6iHlCngldX9ylFThfBziBlQixKXTqucEl5JDt5zHnBelh+b3bRfp4hwjLnCDtdBe7JPoGIIJ0x5zaLH1Wey7x6anPGqlLLjyC2C1iaTrFaqcqr9vdfPteBqGATBEuQ10LTf3yDnSO4JLsH1GTkP2Z3WaOJfWLvsLL1tbVuSMAFnx59/ZHepizZvN0d6I7F+pjSvZlSOsb+Q3/fTbnCPX9uB5h9mfHSNDW8un4LfiGuaVezHWTmckRW4KEGpliWFFsgjslSLHDtiuiTGi8LbohYnM63LlRshEzLdp+dCev+1gG4Ng+VTSCaz2t9jFA2VFLm5lREEl90nCfmQceWabvvarFrjdZGulzVaWSs9By5vCR8BX53ZRK/myghIHtyef2162HD7eek5bkTKoLM210/aaTYeo3qxQNEP1pg14Iti9jaRv5E4ZVKoPvnFgUjusOfb4gTZtZqMp0/XBJZA2C+hgcZdric7ZvKDXWCFmPlgIrekQ7ERGfG3aD/jmyG2YawLpmOO2jHy/bhRJZ2nKryZ9o5zbZ+CSXFLkItHFgNXmjAsrTU8H7rLIEOFkKjqDumqpIrY6jQWwDt+76xWckDci1EEc4tzpfApQMCn7e0ccP6BHkrTtFQn/XGV+8/gB9dwfO4XeBTtxrCR0xJDR/SfwDeIFJ0k40IR0jbCR3kcv/xUuYmdECq/yrobcOXntVRwrOQn2bx+r5LrHQ/lJRr4wRC2GSLb/B6y8MHvkePq8Ygr4V+qZonoLyoJ41X6Ui+c5m8VuDnPupWyA2rwSZ/Z0nyNnyzO9oO6Djl2RyxE6J9ny59d6y6iBcry0lGT9VZDbuVZON47wIylyU4Asa3jIYygYd5/YWCeufhBk2YrS5Np1cm4KMQSoSM2yrWXOefxA44Gid4NhfXB8fR1zjh2/3bu+OxCxZ7HN8QNNVDPll9FR1BUbZVWQ6VT1yqNpIezC2ssHO7Ft4u/StTJ8qpBXyvyRCTnlR18tteUFfDzhbQFpzyMQF7VSp6+fIweoAqmU0Cy/KcilyGm1iCxKTi4YGgJk0CR5NdZTnpc3AlwryfEsL4M/s5VLzrt0t1NQrCtyHaxAeG0EbC5/sJM2aHp+mQvuo1w06A0gBfeZKStyXeyRazz0M8VVw1qL+cT3Ddu6QhrnJU6D79hPJEUuGl1YUHvLZDFW5fwPnyPMBvtyRcA7yjK1lj50yHNZf9oGG4lBWY9e8WbMHjm7HqeKli74vPlJu2JkcE8dhKQDhFrru5hsyHI9fSBA0oHI/rStyvEpA53tzeAUzgAKhWBrr0fv55PGMl2ARd24m5kr1vp1jGCtEXc842kZ+4xdZ9G5wCTvS++rbSLa/W5Kj1uRczybdkORa/Vo2lVWheJa1LSuzfKFSYJhtKeylZu5Hc9cJEVuGqNpH1ZcYhyzeNOIa2GWrQi0ngXM/Lri0AvXsOZDbFhtulDk5JVRZ25hpjHLa/6dYoKIhqzI2dowNhpleJvm6Xihub5X8h1//EBU1QKqlKl/DXS3Ry6W94JX8NhmCv8mdgUp8MWr7Hl6m8GB97rTFUnGfMAxfodSb0gIFWMcabFHzmdKIehHRJQPmoxdDHEbHIqyevxAUwR+Ry1CYlvY9Dgg7By5ThHAOM3D5Wt02g6Wjro6KU8zaZ4iRfRO9t9rY2IX6K0R2kSboOVpj9wrAJ3skct4d49wAk0zWYRfhmZ/x7N2peXfRD1HzrUSEFJab/tyvarjrEMHzOYLyqGDyJ7MPRi7aWdRrpW2PXIx7RGqyIeuZpi7svWvSIC0SmeW0rvJrDd0GwsFASs5JepvLRscwqPjtY1aGXr8ALMel7vLVsGkbP2V4eWOJQWrQG8pJjz+kWQQtKQw7sghZFMAABrDSURBVFD9ekHf0dEWxhzp2iNnW5EjS9RKedEjiN04DvCjbbATKyxRK310ddfKqPcKqHTTsPe2sgQydn+irV0bRbiNTGPm4RhI8Hp+bN+P1UgEv0c8FMpioC0SCbN1x0er/y6r00wj7zGSIheJ6coepAhCckdzR2OTKag/7TaxsD1y3iSdQzl+QKtifH3afukecEofmK9pkHSeJwJXA4MRPjkZqxBGBxAKldB9efX+Qb4sd726aAV5f1W5ImdXZrrqh+wqiwUhLo8u+APGl+W4nvpylyjeq8HxAwZHcXvkKoHUpiH2slPr7VjwbuhipUchFyVFfazlaIUV2RyOPXJyheLPwswxGex7XeTrLhyfvYw2pDuuVl2XPklI1u+dAERsFQihZXzTBsR7vZ1IG9TaBTuZeUiK3BQhRFwOEy3Le/wGepW/u+lMIRG8+t8ZSLUmGa6Vcvv4qYUIrlERCuV8ld2y9wcRiMDXnhShCr8NnGVXUuQiCJsupVxkOtsaAXOfKZoMi6lFMNCDl3BW1nIflJ0MQGHn49XJbZb8GDG5HXepgVVa5HWgbhPFfKuAhGnnL9fB7OUI6X87ah6I3yOqR63kGY0JbNVx1Mo2bkShsI6Huv0Pkrur7Ww32f3QFXE1Kl5W2ApN9VtW7oLar9tGDvW2yINP8Q9id7aHwtbsXD80PQ96y4xChPVryHUt+c02xrechGO+Q+/jIxTt04UzhQ7rOGyj1SelXlT/SWXHozK6zUDfyqTIRWL6beo14YiirMCtbNjROZv3oFF7PWAG1KBziv1wTyA4Fnh6XnpZTrfvSdL/ZlmRtDxVs4Xh7smkNuU8bod+0HoMYl6rXzzJjqnd6mMzFqY9JLQxIle5nMntitxUhhOPXRnhlTZLWh9POstp0C8D6HW5NxAov1+TnI5nDfjolQSjBZhIoEF0prApp17+6y+SIjedEcOLLawIsTzfvULRjh5f/3as3XRCCvoKfbX4RE6DzveOFLy6fk2SI+J1Q7wpL9s+ocuVrotzAaMgbPs3eo1wDad9m9isDpF75DzpbYKv9/zErPff3NZna+c+od2P5wl2PLSsCk0ygZOadLMuAmYJypRP2zg8PvcdudX5qlzPC0+R1T82GFSDEnpMPxTN3nM6HD+gINbju2Vx8ljSe3+h7uanUHf8EMy89bikyEWjn+dTNC9LPqS2oMVOOnKnjYE/9VQvTxtRK3uwR075PpqLVjDipJU42hHZqwAF1Exo5V+jaeSwCBO0Qdfc7M39NoXbsHf21cAVtTJKSLA1QTiFvMyOumGXh8P70pqTMu8uK0BeF2j/QfUN+xQRSAukwTu9elYmO7Ac+1eJVQTvkXNSsddlUpjfJNYjhK9PvJHJtr8nVoDk+TQELY2UsO9a5oLcBMzMrepjI+fdCxqLkp51hTd8jpq6Fdn2bdG07k231KgmOK3syMmkn4tielGtIrrOQE0uKXIvEyhsK22acCpUvVx1ikFX7hiu/RaRtNofCG5HU8rNjh8IK7ft1vz+hBfuQRlTOds0QdRk2mVP7dFYwZINq7eb50I5unbiikOgoq7vsQygEwsbhZpV2m+Y4Y4fsMF/0HfYPqeu9shxynasgSNa6ezJJqWpRT9WaPhSJXmm+uvpT0z7l26uXM81r9pjaj5l/gbyfuy2U1xvWqc7koG7Jr2Yxl3Pi1aKHBHdRUQ7iGiSiLY50t1KRI8S0U4i+nibMqca09f1VmZkWZEr79grztq73EsmMRXrObjauCwyISudQcMCtyBX5XeV378JMX4PGHX2eTt/zwC3D9uKnJFOm+hDBTXfCo4r8EQXym5Um4qp3B0UhuYrHXlCqwtbsGtlXHq2ErDXuR97NYLLsMu4XvgcT+UrQWXgmPhy1DIjKmoLrJIv3coJ8/uhpMm4cENfxLCuHMWWb6JJkJtezT3Vilxn58i1Bb8+PtMQHUy35fdV+6zFGBbcsfvV3uY7Nxp7Km+a6T5rmmjb634C4KcBfNuWgIgGAPxXALcB2AzgbiLa3LLcKcN0UOS8rjRWw7AuvDZD98cPdLJUoVLUZrl+HT8Q67DTa8RPBI4375L5G5GKyGRELQ13xeGfC28NyPrDebM5Aj5uGxdnWSiIc5eUr5usiHXRL8IUetc3cduyykmfjHtcOrVqkcvkHlhX5KrnzfbIqQYv63KGmoqkA7il97S+Zo9d8AXUM1tZw4HLaaXKZ3/mLt9maHCX56uPq0Q1ffzeoUYyMJj+0AFixu2mYW3a7q6ynSPHJ+61hNDWt6ZGyMjo9DwKqEJXvX9qVounDwbbZBZCPAJ4LYJXAtgphHiySPs5AHcAeLhN2QkqWjFy3FzTWWSquvxu6HVZq5AJaRro9FFwyozaXod44vzE2ik65buWdbMJYl3tkZvRiFH6OL4h7TdjcW1Q1tSgH8FOel5EM95l9MA2cMdhsqzIEbhI9A3K9miwPYKriFb7gBqgn8JyP2MRyHi5jNE9WVWaQWH5sxZnssyg16zQjz1yqwDskn4/V9ybkVgwOhyfadZc49aAg9G4JyPDA9JzM8Vpml1dZwO1fl6WMzo8iLM0S8lTRowjEG9MHh4FAJyx5HNh1lCGeSND3nSDng53joYxUZzFpNejxEBGOEdDGJXaaNZQfn2W8u81MjSgpJdxRmq7Kl3kJFkmHywuzmR8XQEARbtiaLSajM9ks41kIWfZnczGnM8HB8LeY6AoKiPC7KINyghvp7IRJe1ZGsZQkaFsX5n/Spyj+vvLtRgZHmC/+6Alwl/t3VTnOUVqnUreOEeDxe+6n2ZERv1OZWMYyEjKl9d1eDAr8pTp8m9VvieG8t8TQ6PA4Gyl7FPZiPNg3FJIKGm7xoABC/8pUfeq87ryv0abDgxiaCDDJHeWmYaJot3OFu1wDkM4OzBq1B0Axmb57X+li9VZ4sfLkt/KOfNUNoY5s3O6NKR+K7lsmRfz1RZLGw6NggjV2GFF8T2R1e9U9l2ZfxeMamNZkV5UbZ/hLNOHS8+A49mc+uaARGtozOoiNzTI94ecR+tMNqElG8zbvuTPAamuIRgeNFcbOeVB5uOMCAtG8vLEcDHvDdbtOTZrUPm+p4s2Gxis26Ss3tiwymdiUO3zAHAym1Pwklqvkp9P0SgmJuUVuUx+nTxNwVPyvFY+z8eOWdV84kM53tKgOi6e1ueuobpvlWNMWZ4LJ8u0lOWBZfQ5QqKb0x5Rvo8+lnM4zfAxgHreklDWtyxjZDhubcA2zpW3T9OIMj9PDOXzHTeunKbZdd0ziZ8KYqeorv9Axc98m7PtJI8R0vcs50K5H05gsLYtDM2GGB4rSMTJj7axYfYQ34fLthoartvM1sZs3qFREBEmMFDNpfUYWfSB4VrmkOccXb4cK+SxWUMDrCwjiOq2HzLbuxx/bXNICXkeL0Gov4sP52gw54OiDqH5phO8vY6Ivg5gOfPok0KIL3ZZGSK6F8C9ALB27douSXeG9157Hr4x8WG8/rwxfHjZRowMD+DFY2ewetwxQN74CSw/+58hjuzG4fVvAQBsWDKGGy5agoVjs7B49M/wL9+7H+sGX8K6hbMxe+Eq7D90GLhkG8Z378eWffPw3tetxwvjH8HyE49iePYATm/YjItWzMPbjq3EdRuX4Bs/nIsXHvsiFi1dgdGFq4BbP42V2UEsmT8b73zNGly1fhEe2/gerDqwBj85uQhndz+MdYvX4OIV84A5H8Tw7NV4465BzLv0kzj7/I+QXXUrhuetxOCpBXjLa9+BZ//1czgxbwOOHNyL9269BOd+8jOYv+piAMBvv2Uz/vlbH8aCs/vw1vFn8cCcG/GObXmZn39wF85e+DEcfuIBzLv4esw6sQdP7j2M2fOXYu/h43j91vPzUfvVdwPH92Poitdj3qGHcWzXjzB09hi+Nf9tuHLd+ZjcdQ5vuvpt2P/gfly9YREGM8KzD30Qq5aMY+msvdggrsKtr1qNeSNDOHrqLO64dBV+cvyXMLAs9+L96ctXYd5Izu4jS/8jMJbh3lNrseOFIzh49C6sPjeIx+dfg3dsW4OR4QHs3HcML85/B7Isw7yDO/C17GqsWLQQc8/ux8JFSyEmzmDwggXA7AXA0Rcw69DTOLlxAy4YPQQMnMP1i6/H5lOE0eEB/I/vPotrL1iEebOHsOOFI1j5mk8Au+4DLrwVi0WG/c/uxubNt+G80VV44fBJrFs0hoeeP4zx0TUY2PvzODW2BrNO7MFvv2or7nviRVy8PBeQPnDjBVhy5tcwuf1vcWzdjfjQ6gvw/OGTWDrvL/CdB7bj93/qEqycP7viM1r1J8CxfTlPXv+xShEBgDsuXYUf7DqMV62ej7mzh/D0geO4cv35OPbIu3Fq4WX4pWUr8J2dB7B/0T04Om8j3rBpKQBg6y2/gNUvbsDstbdg34PrsGDOHByftQST+x/FwiU34bfOX4rnDp3A+Ngwnrr5D/Hi3ufxO9dsxtDe38NjB87h8UMTuHT+CQDAJavm4W2Xr8Jje45i3+L3YnzJKgwcfgojV98E7HtE6VLzb/9jLDj8MDB0DCuXH8HJBdfijecWYsWcOzG2Zw5uvuStgNgNnDuJBYd3YezUFrx7znxMTAg8c/AEzmz9FJbP2Y1Tz63CCM7Dmy++Hbciw8XL5+J7r/kAFm3diomnHsJ39y/FO89bg+cO3I4Th+dj0xvuwSOD47jo0huB8cUY3XYQN73qDuz+/kacWrAVc2cN4vfeugWff3AXXpz3M1i1Zj2wchyYNRc/++ICzH7hPRg9/zxkOx7CioUZDmSbsffJH2Hu2FyIVVdgbO/9eH7Blbjk1P8BLrihel963a/im89nuHTVRpza8yasWTQXGF2YPxxdCGy+E+cPfxsPDl2Go0eP48rzFwHrr8cWyvDP1/0h5j7zNSycNwez11yGyWP7gBMPYf048IMV78LQM9/E2JzX4Zb5o9iw6G6s2L0EWxZej43jA8BzX8GcdfPw0Qsvw0vP/gEOHNiDj11zMfDSb+YGqkPPYH32eRxZdClOjKzA9589iLsvOw9HDs3B0ItnMTHvapx+8d9w3vwB3D9wKbbQ08DwYawfP4DPn74Kv3PdVgw8+UG8NO8ifHLFOjyy5yho/VYsPz2EfYuvAiYfxW8svwHbfzgPo6f24t9Pr8KqyX/D4JZNuGdoK5bNm4Xj2+/CyLFnka18NbBhS94mV/4SRhaejwuPXIq9YgPmnXgGGYCnaRWWHtyOiRMvYeiCG4DLLwPmLgNW11u8z7vpF/Gj78zH6WVX4cOXrcaje4/isjULsHfl72LRub3AklnAvNwWeddVF+DpB9+Mm9bPxtHld+Lwt89g1uwxPJatxw3rlmDTRUswePadeHr2ZmB0P/DS88CyS2pGvuIeLN+/F/90ciuuW70YKxeMYGggw859x3DF2nF8+KZ8jsHgH1d99z1Xr8P9o5/AtfgB8KptmDtrELdfuhKjw4M48Oz7cXLWUnzg/A1YunAt9h8+hoF1N2Pt7v+NkfNfi7efXo0Ll83BB27ckBsl6Q+x7MffwFd2DWDLxL9j99q3YuvqcezZ/RyuXjsO4Fcx64mnsProCB5d+lG8aus4AOB9163HyTMTwPD7seLkaTzwzEGMjwxidM58/ML1F2L3nJ/H7NfdmL/jRW8CTU5g6+BrcdfKhVg8uRSHzg5j29rXA3Q9Jnd/G+df8lq8f95h3LfzRSza+qfAmSP4yMIL8ZWJe7Fh5WJM7HsUC665B3h6LZANQVw4itedXozH578Xty87AMxZhl+8bhjHT5/D+iVjOLTzo9j95I9wfPlVOHrgPuyk87BtdA9evXAzssEhXLFuHBuWzMHf3f8M7pt1D64afAy3bbkc48N/jqPP/zvWndwBbH0z5h/ahblntuDGrWvxkyPvw7EFF2Pw+e9hyfAZ0AUbseDKVXjjwTnYumo+MPHrmLf3Bby061m8+6Z3Ydf9I1iy5hqc/+QpXPaGD+PQD/4JK87bBDz2Zax582/gtkeOYmiAcPLER/DQ4/+Ca659A5Yc/A52P/sidp/IMLDxJrx60V5geA7OHRrBz42uxov7P4oLBp8ERhaAALzpqkswevB9wBVvAJ7+F+DCW3F8YjP2HdiP8clDOLFoG5YuPwSBRfjA0Ars2/MxzJ94DNi0DYM7fow3Di/DivmzsWhsFvY9/AEcHRjH1Vs2AY/8NXDRbXj/mXXY/uOPYePIEVxyzfXA9/4S2HR7/l1v+WMsO7QHd51agw1LcuH+zstW4WtPvR1DYwvxllUrcHZCYOe+Y/i5167L89zw8VrJuvmPsOzwXmx5aj5u3rwM9z1xAGsXjuHw4d/G1rWz8dRDO/AULsGl65dh9ZEL8MJxgQ2XXAcAuO2d78eOrw7h2oVHcfLsBA4NLcPydW9GNjSMNYc2AWuvrrrY4jd8EM/uOoI166/E2Ye+gMEtm7BoYBTPPPECxue/hGzyLMZGnsPOpbfgrqVrMHTwlzGy8hL8+ugyLBgdxuP/+h6sHT4CXPN2rPz6X+DEOYH9Y2/H9Rt/Fph1Chcvn4t/e+1HsPGCNXj30SXY/sxBLJ97OUbOvRuYMwFsvQtbLzqFx+77ItZvfg0+MPclLBgdwhP7jmHHC0ewacVcHDs9gcdW/Q4WnNmDa+cvxvBghuXzZmPB6DCW3/Ib+N7huchOHwFe+D4Ojr8a73qNKh8P3vBbePQw4cL1F2LomY1411U/AxzZBJw8hKWHnsXTx5bg7uVr8eo1C/DdH/4BTj33ECZPHcO6CzZh7OhTWL1kIU7MuQrYsBgDY0txydnX4JHdR3DirMDlN/9sXsiiC4DL3g2sfz1+d+sg9h45jWX0R9j/3AEsPLMbixe/DncPjmJ01gCOn57Aa87Lx4q7rliN8dEhHN31bjz54mksXnU+fnPLRdi57xjmD/8ycHwPsPlO3Lt7Ak8eOI4lc2bhu0++iGXjN+HQnkls2no7sOAl4Ht/DazehuxVWzF+4kk89MTzyM4dx3UrBU6vuhNHH/0WBpZcjBPHj2DrvNn41O1b8Pf3P4MVC0Zw/YVLAAC/ctNGjA0PYP+x0zhzbhLzR4bw1P7lGD85gKVX3I63PX4U116wGDMN1EWYeCL6FoCPCiG2M8+uBvApIcQtxe//AABCiD9x0dy2bZvYvt0gl5CQkJCQkJCQkJCQ8IoAET0ohGCDSvZjDfEBABuJaD0RDQN4F4Av9aHchISEhISEhISEhISElyXaHj/wNiJ6DsDVAP4vEX21uL+SiL4MAEKIcwA+BOCrAB4B8I9CiB3tqp2QkJCQkJCQkJCQkPDKRduolV8A8AXm/gsA3iT9/jKAL7cpKyEhISEhISEhISEhISFHJ3vkegEi2g/gmamuB4PFAA5MdSUSXlFIPJfQbySeS+g3Es8l9BuJ5xL6jaY8t04IsYR7MG0VuekKItpu23CYkNALJJ5L6DcSzyX0G4nnEvqNxHMJ/UYveG7mHZiQkJCQkJCQkJCQkJDwCkdS5BISEhISEhISEhISEmYYkiIXj7+a6gokvOKQeC6h30g8l9BvJJ5L6DcSzyX0G53zXNojl5CQkJCQkJCQkJCQMMOQVuQSEhISEhISEhISEhJmGJIiFwEiupWIHiWinUT08amuT8LLB0T0NBE9REQ/JKLtxb2FRPQ1Inq8+Dte3Cci+ouCD39MRJdPbe0TZgKI6DNEtI+IfiLdi+YxIrqnSP84Ed0zFe+SMP1h4bdPEdHzxTj3QyJ6k/TsPxT89igR3SLdT/NuQhCIaA0RfZOIHiaiHUT0q8X9NM4l9AQOnuvbWJdcKwNBRAMAHgPwUwCeA/AAgLuFEA9PacUSXhYgoqcBbBNCHJDu/SmAg0KITxedelwI8bFiQPgVAG8CcBWA/yKEuGoq6p0wc0BErwdwDMDfCiEuKe5F8RgRLQSwHcA2AALAgwCuEEIcmoJXSpjGsPDbpwAcE0L8Jy3tZgD/AOBKACsBfB3AhcXjNO8mBIGIVgBYIYT4PhHNRT4+3Qng55HGuYQewMFz70Cfxrq0IheOKwHsFEI8KYQ4A+BzAO6Y4jolvLxxB4DPFtefRT44lPf/VuT4LoAFxWCSkGCFEOLbAA5qt2N57BYAXxNCHCyEmq8BuLX3tU+YabDwmw13APicEOK0EOIpADuRz7lp3k0IhhBitxDi+8X1UQCPAFiFNM4l9AgOnrOh87EuKXLhWAVgl/T7Obg/VkJCDASAfyaiB4no3uLeMiHE7uJ6D4BlxXXixYSuEMtjifcS2uJDhRvbZ0oXNyR+S+gYRHQegMsA3I80ziX0ARrPAX0a65Iil5AwPfA6IcTlAG4D8MHCLamCyH2gkx90Qs+QeCyhD/hLABsAXApgN4A/m9rqJLwcQURzAPxPAL8mhDgiP0vjXEIvwPBc38a6pMiF43kAa6Tfq4t7CQmtIYR4vvi7D8AXkC+z7y1dJou/+4rkiRcTukIsjyXeS2gMIcReIcSEEGISwF8jH+eAxG8JHYGIhpAL1H8nhPhfxe00ziX0DBzP9XOsS4pcOB4AsJGI1hPRMIB3AfjSFNcp4WUAIhorNsmCiMYA3AzgJ8j5q4yWdQ+ALxbXXwLwniLi1msBvCS5jSQkxCCWx74K4GYiGi9cRW4u7iUkeKHt5X0b8nEOyPntXUQ0i4jWA9gI4HtI825CBIiIAPwNgEeEEH8uPUrjXEJPYOO5fo51g+1f45UBIcQ5IvoQ8s48AOAzQogdU1ythJcHlgH4Qj4eYBDA3wshvkJEDwD4RyJ6H4BnkEdBAoAvI4+ytRPACQC/0P8qJ8w0ENE/ALgBwGIieg7A7wH4NCJ4TAhxkIj+APmkAwC/L4QIDWiR8AqChd9uIKJLkbu2PQ3glwFACLGDiP4RwMMAzgH4oBBioqCT5t2EUFwL4OcAPEREPyzufQJpnEvoHWw8d3e/xrp0/EBCQkJCQkJCQkJCQsIMQ3KtTEhISEhISEhISEhImGFIilxCQkJCQkJCQkJCQsIMQ1LkEhISEhISEhISEhISZhiSIpeQkJCQkJCQkJCQkDDDkBS5hISEhISEhISEhISEGYakyCUkJCQkJCQkJCQkJMwwJEUuISEhISEhISEhISFhhiEpcgkJCQkJCQkJCQkJCTMM/x9j9C4TdvfLqQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import train_test_split\n", "\n", "X, y = get_clean_Xy(data)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)\n", "\n", "clf = KNeighborsClassifier(7) # Model the output based on 7 \"nearest\" examples\n", "clf.fit(X_train, y_train)\n", "\n", "y_pred = clf.predict(X_test)\n", "\n", "_ = pd.DataFrame({'y_true': y_test, 'y_pred': y_pred}).plot(figsize=(15, 2), alpha=.7)\n", "print('Classification accuracy: ', np.mean(y_test == y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see the forecasts are all over the place (classification accuracy 42%), but is the model of any use under real backtesting?\n", "\n", "Let's backtest a simple strategy that buys the asset for 20% of available equity with 20:1 leverage whenever the forecast is positive (the price in two days is predicted to go up),\n", "and sells under the same terms when the forecast is negative, all the while setting reasonable stop-loss and take-profit levels. Notice also the steady use of\n", "[`data.df`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data)\n", "accessor:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8.24 s, sys: 7.25 ms, total: 8.25 s\n", "Wall time: 8.25 s\n" ] }, { "data": { "text/plain": [ "Start 2017-04-25 12:00:00\n", "End 2018-02-07 15:00:00\n", "Duration 288 days 03:00:00\n", "Exposure Time [%] 79.41\n", "Equity Final [$] 14176.63\n", "Equity Peak [$] 14979.13\n", "Return [%] 41.77\n", "Buy & Hold Return [%] 12.87\n", "Max. Drawdown [%] -9.44\n", "Avg. Drawdown [%] -1.11\n", "Max. Drawdown Duration 41 days 23:00:00\n", "Avg. Drawdown Duration 2 days 15:00:00\n", "# Trades 353\n", "Win Rate [%] 52.69\n", "Best Trade [%] 0.58\n", "Worst Trade [%] -0.52\n", "Avg. Trade [%] 0.02\n", "Max. Trade Duration 3 days 09:00:00\n", "Avg. Trade Duration 0 days 19:00:00\n", "Profit Factor 1.22\n", "Expectancy [%] 0.25\n", "SQN 1.85\n", "Sharpe Ratio 0.08\n", "Sortino Ratio 0.14\n", "Calmar Ratio 0.00\n", "_strategy MLTrainOnceStrategy\n", "_equity_curve ...\n", "_trades Size Entr...\n", "dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "from backtesting import Backtest, Strategy\n", "\n", "N_TRAIN = 400\n", "\n", "\n", "class MLTrainOnceStrategy(Strategy):\n", " price_delta = .004 # 0.4%\n", "\n", " def init(self): \n", " # Init our model, a kNN classifier\n", " self.clf = KNeighborsClassifier(7)\n", "\n", " # Train the classifier in advance on the first N_TRAIN examples\n", " df = self.data.df.iloc[:N_TRAIN]\n", " X, y = get_clean_Xy(df)\n", " self.clf.fit(X, y)\n", "\n", " # Plot y for inspection\n", " self.I(get_y, self.data.df, name='y_true')\n", "\n", " # Prepare empty, all-NaN forecast indicator\n", " self.forecasts = self.I(lambda: np.repeat(np.nan, len(self.data)), name='forecast')\n", "\n", " def next(self):\n", " # Skip the training, in-sample data\n", " if len(self.data) < N_TRAIN:\n", " return\n", "\n", " # Proceed only with out-of-sample data. Prepare some variables\n", " high, low, close = self.data.High, self.data.Low, self.data.Close\n", " current_time = self.data.index[-1]\n", "\n", " # Forecast the next movement\n", " X = get_X(self.data.df.iloc[-1:])\n", " forecast = self.clf.predict(X)[0]\n", "\n", " # Update the plotted \"forecast\" indicator\n", " self.forecasts[-1] = forecast\n", "\n", " # If our forecast is upwards and we don't already hold a long position\n", " # place a long order for 20% of available account equity. Vice versa for short.\n", " # Also set target take-profit and stop-loss prices to be one price_delta\n", " # away from the current closing price.\n", " upper, lower = close[-1] * (1 + np.r_[1, -1]*self.price_delta)\n", "\n", " if forecast == 1 and not self.position.is_long:\n", " self.buy(size=.2, tp=upper, sl=lower)\n", " elif forecast == -1 and not self.position.is_short:\n", " self.sell(size=.2, tp=lower, sl=upper)\n", "\n", " # Additionally, set aggressive stop-loss on trades that have been open \n", " # for more than two days\n", " for trade in self.trades:\n", " if current_time - trade.entry_time > pd.Timedelta('2 days'):\n", " if trade.is_long:\n", " trade.sl = max(trade.sl, low)\n", " else:\n", " trade.sl = min(trade.sl, high)\n", "\n", "\n", "bt = Backtest(data, MLTrainOnceStrategy, commission=.0002, margin=.05)\n", "bt.run()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4901]},\"High\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4901]},\"Open\":{\"__ndarray__\":\"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gurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/L7hILq5I9D8vuEgurkj0Py+4SC6uSPQ/8Iq2de5G9D/wirZ17kb0P/CKtnXuRvQ/8Iq2de5G9D+l0jMP5Df0P6XSMw/kN/Q/px3rLOUw9D+nHess5TD0P7V5cNhULfQ/tXlw2FQt9D+1eXDYVC30P7V5cNhULfQ/tXlw2FQt9D+1eXDYVC30P7V5cNhULfQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P/sMEWPrJvQ/+wwRY+sm9D/7DBFj6yb0P3gQQVR1FPQ/cW9SipAD9D83rUA/IgH0PzetQD8iAfQ/N61APyIB9D83rUA/IgH0PzetQD8iAfQ/N61APyIB9D83rUA/IgH0PzetQD8iAfQ/N61APyIB9D83rUA/IgH0PzetQD8iAfQ/N61APyIB9D83rUA/IgH0PzetQD8iAfQ/N61APyIB9D83rUA/IgH0PzetQD8iAfQ/uqwZJEb88z/iANVzafvzP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z9EyJy+2u/zP0TInL7a7/M/RMicvtrv8z/s5pIi6uPzP1xA+eIs0vM/XED54izS8z8F7Ir5wL/zP38rulT6uvM/fyu6VPq68z9/K7pU+rrzP38rulT6uvM/EvCR45ut8z8S8JHjm63zPxLwkeObrfM/EvCR45ut8z8S8JHjm63zPxLwkeObrfM/s3azu6qe8z+zdrO7qp7zP7N2s7uqnvM/s3azu6qe8z+zdrO7qp7zP7N2s7uqnvM/z5mmM4OD8z/PmaYzg4PzPzkItdX7cvM/OQi11fty8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/tU+VPIhh8z+1T5U8iGHzP7VPlTyIYfM/5/DJBF1X8z9+S1yCmU3zP35LXIKZTfM/fktcgplN8z9+S1yCmU3zPy1eBFhcQPM/LV4EWFxA8z8tXgRYXEDzPy1eBFhcQPM/LV4EWFxA8z8tXgRYXEDzPy1eBFhcQPM/LV4EWFxA8z8tXgRYXEDzPy1eBFhcQPM/LV4EWFxA8z8tXgRYXEDzPy1eBFhcQPM/LV4EWFxA8z8tXgRYXEDzPy1eBFhcQPM/LV4EWFxA8z8tXgRYXEDzPy1eBFhcQPM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z9Hajjv8zbzP0dqOO/zNvM/R2o47/M28z8ZazJHLDPzPyuj92kzMfM/imA5IIsw8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/YcAhfcIu8z9hwCF9wi7zP2HAIX3CLvM/uR39yZUo8z+9Rp5TgBXzP71GnlOAFfM/vUaeU4AV8z+9Rp5TgBXzP71GnlOAFfM/vUaeU4AV8z+9Rp5TgBXzP71GnlOAFfM/vUaeU4AV8z+9Rp5TgBXzP71GnlOAFfM/vUaeU4AV8z+9Rp5TgBXzP71GnlOAFfM/vUaeU4AV8z9+RFkGEATzP35EWQYQBPM/sRGAYjjl8j+xEYBiOOXyP+CIEFqF1PI/4IgQWoXU8j/giBBahdTyP+CIEFqF1PI/z/6rj9K78j8STbRuFqDyPxJNtG4WoPI/Ek20bhag8j8STbRuFqDyPxJNtG4WoPI/Ek20bhag8j8STbRuFqDyPxJNtG4WoPI/Ek20bhag8j8STbRuFqDyPxJNtG4WoPI/Ek20bhag8j/SKciuiYLyP9IpyK6JgvI/BKsbtTRw8j+KkL7rsmzyP8MZpNvBZ/I/wxmk28Fn8j/iggcNG2DyP+KCBw0bYPI/4oIHDRtg8j/iggcNG2DyP+KCBw0bYPI/4oIHDRtg8j/iggcNG2DyP+KCBw0bYPI/4oIHDRtg8j/iggcNG2DyP+KCBw0bYPI/4oIHDRtg8j/sz7F0cFzyP8i+1laAWfI/yL7WVoBZ8j/IvtZWgFnyP3wlm8RYU/I/8Bhyg6458j/wGHKDrjnyP/AYcoOuOfI/NBE8NaEm8j+HCpmq1yXyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j+q7O++dCLyP6rs7750IvI/quzvvnQi8j87dEtQTx/yPzt0S1BPH/I/yf5wU5Qd8j9h2/1rdxvyP2Hb/Wt3G/I/Ydv9a3cb8j9h2/1rdxvyP2Hb/Wt3G/I/Ydv9a3cb8j9h2/1rdxvyP2Hb/Wt3G/I/Ydv9a3cb8j9h2/1rdxvyP2Hb/Wt3G/I/Ydv9a3cb8j9h2/1rdxvyP2Hb/Wt3G/I/Ydv9a3cb8j9h2/1rdxvyP2Hb/Wt3G/I/0R1dQd0A8j/RHV1B3QDyP9EdXUHdAPI/0R1dQd0A8j/RHV1B3QDyP9EdXUHdAPI/0R1dQd0A8j/RHV1B3QDyP9EdXUHdAPI/0R1dQd0A8j/RHV1B3QDyP9EdXUHdAPI/0R1dQd0A8j/RHV1B3QDyP65DDrPI+fE/4fW7F2Xw8T9/8N38ddnxP4NerujN1vE/g16u6M3W8T+DXq7ozdbxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb3S8T+VvF6FvdLxP5W8XoW90vE/lbxehb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Let's see how it performs under\n", "[walk-forward optimization](https://en.wikipedia.org/wiki/Walk_forward_optimization),\n", "akin to k-fold or leave-one-out\n", "[cross-validation](https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.4 s, sys: 8.24 ms, total: 10.4 s\n", "Wall time: 10.4 s\n" ] }, { "data": { "text/plain": [ "Start 2017-04-25 12:00:00\n", "End 2018-02-07 15:00:00\n", "Duration 288 days 03:00:00\n", "Exposure Time [%] 71.72\n", "Equity Final [$] 5885.37\n", "Equity Peak [$] 10052.84\n", "Return [%] -41.15\n", "Buy & Hold Return [%] 12.87\n", "Max. Drawdown [%] -41.47\n", "Avg. Drawdown [%] -14.36\n", "Max. Drawdown Duration 261 days 18:00:00\n", "Avg. Drawdown Duration 88 days 05:00:00\n", "# Trades 324\n", "Win Rate [%] 41.67\n", "Best Trade [%] 0.38\n", "Worst Trade [%] -0.51\n", "Avg. Trade [%] -0.05\n", "Max. Trade Duration 3 days 07:00:00\n", "Avg. Trade Duration 0 days 18:00:00\n", "Profit Factor 0.67\n", "Expectancy [%] 0.25\n", "SQN -2.79\n", "Sharpe Ratio -0.17\n", "Sortino Ratio -0.29\n", "Calmar Ratio -0.00\n", "_strategy MLWalkForwardStr...\n", "_equity_curve ...\n", "_trades Size Entr...\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "\n", "class MLWalkForwardStrategy(MLTrainOnceStrategy):\n", " def next(self):\n", " # Skip the cold start period with too few values available\n", " if len(self.data) < N_TRAIN:\n", " return\n", "\n", " # Re-train the model only every 20 iterations.\n", " # Since 20 << N_TRAIN, we don't lose much in terms of\n", " # \"recent training examples\", but the speed-up is significant!\n", " if len(self.data) % 20:\n", " return super().next()\n", "\n", " # Retrain on last N_TRAIN values\n", " df = self.data.df[-N_TRAIN:]\n", " X, y = get_clean_Xy(df)\n", " self.clf.fit(X, y)\n", "\n", " # Now that the model is fitted, \n", " # proceed the same as in MLTrainOnceStrategy\n", " super().next()\n", "\n", "\n", "bt = Backtest(data, MLWalkForwardStrategy, commission=.0002, margin=.05)\n", "bt.run()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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(max - min) * .03 : 0;\\n range.start = min - pad;\\n range.end = max + pad;\\n } else console.error('backtesting: scale range error:', min, max, range);\\n };\\n}\\n\\nclearTimeout(window._bt_autoscale_timeout);\\n\\nwindow._bt_autoscale_timeout = setTimeout(function () {\\n /**\\n * @variable cb_obj `fig_ohlc.x_range`.\\n * @variable source `ColumnDataSource`\\n * @variable ohlc_range `fig_ohlc.y_range`.\\n * @variable volume_range `fig_volume.y_range`.\\n */\\n \\\"use strict\\\";\\n\\n let i = Math.max(Math.floor(cb_obj.start), 0),\\n j = Math.min(Math.ceil(cb_obj.end), source.data['ohlc_high'].length);\\n\\n let max = Math.max.apply(null, source.data['ohlc_high'].slice(i, j)),\\n min = Math.min.apply(null, source.data['ohlc_low'].slice(i, j));\\n _bt_scale_range(ohlc_range, min, max, true);\\n\\n if (volume_range) {\\n max = Math.max.apply(null, source.data['Volume'].slice(i, j));\\n _bt_scale_range(volume_range, 0, max * 1.03, false);\\n }\\n\\n}, 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WEVb2TxP4PF4cyvZvE/KxiV1Alo8T+ze/KwUGvxP/JBz2bVZ/E/avZAKzBk8T+jzAaZZGTxP5C93v3xXvE/zCN/MPBc8T9rJt9sc2PxP94CCYofY/E/wFsgQfFj8T/6eVORCmPxPxWRYRVvZPE/+nlTkQpj8T/E6/oFu2HxP8YzaOifYPE/VFc+y/Ng8T+ndLD+z2HxP1IP0egOYvE/bm5MT1hi8T/4GRcOhGTxPzDYDdsWZfE/avZAKzBk8T8UeZJ0zWTxP2nGounsZPE/xks3iUFg8T/+2Y8UkWHxP6ZEEr2MYvE/hA1Pr5Rl8T+7m6c65GbxPzDw3Hu4ZPE/iQyreCNz8T99XBsqxnnxP5rrNNJSefE/Ja/OMSB78T8O+PwwQnjxP7Uy4Zf6efE/X7Uy4Zd68T+wcmiR7XzxP+gwX16AffE/Wd3qOel98T/swDkjSnvxP7KiBtMwfPE/tAJDVrd68T+WQ4ts53vxPyR/MPDce/E/lBPtKqR88T+U+x2KAn3xPyNPkq6ZfPE/c/T4vU1/8T8Z/z7jwoHxPzQWTWcng/E/SZ2AJsKG8T9224XmOo3xPyTusfShi/E/qdkDrcCQ8T83/dmPFJHxP8ZQTrSrkPE/VHQkl/+Q8T8Cnx9GCI/xP5NS0O0ljfE/48eYu5aQ8T//PuPCgZDxPwGHUKVmj/E/499nXDiQ8T+okZbK25HxP8QgsHJokfE/UPwYc9eS8T/c14FzRpTxP6LRHcTOlPE/orlOIy2V8T8RNjy9UpbxP/W+8bVnlvE/OsyXF2Cf8T9oImx4eqXxPyOERxtHrPE/zk4GR8mr8T/l1TkGZK/xPx3EzhQ6r/E/KJtyhXe58T/aVUj5SbXxP7NeDOVEu/E/nwd3Z+228T9juaXVkLjxPzzCacGLvvE/cSAkC5jA8T+xFp8CYLzxP3pwd9Zuu/E/e6AVGLK68T8b0xOWeMDxP1bZd0Xwv/E/ODKP/MHA8T+oxks3icHxPy2yne+nxvE/gLdAguLH8T/bxMn9DsXxP0pBt5c0xvE/EmvxKQDG8T91yM1wA77xP+nsZHCUvPE/G7tE9dbA8T/YfFwbKsbxP51GWipvx/E/P8HFihrM8T9a8KKvIM3xP72MYrml1fE/ObnfoSjQ8T+KjuTyH9LxPwwCK4cW2fE/LjnulA7W8T9GIF7XL9jxP9CbilQY2/E/W0dVE0Td8T9AMEeP39vxP3/eVKTC2PE/kzXqIRrd8T8MAiuHFtnxPwvqW+Z02fE/swxxrIvb8T8xmSoYldTxPxiyutVz0vE/yTzyBwPP8T8cQpWaPdDxPyHqPgCpzfE/mG4Sg8DK8T/9gt2wbdHxPwkbnl4py/E/2HxcGyrG8T/ye5v+7MfxP1rY0w5/zfE/OxkcJa/O8T93Z+22C83xP66ek943vvE/pDZxcr/D8T/ye5v+7MfxP0/pYP2fw/E/wt1Zu+3C8T/ZlCu8y8XxPxE7U+i8xvE/vAUSFD/G8T+ERxtHrMXxP0/pYP2fw/E/SkG3lzTG8T+dRloqb8fxP5wWvOgryPE/lialoNvL8T81KQXdXtLxP7tE9dbA1vE/Jhk5C3va8T947j1cctzxPzSAt0CC4vE/3gIJih/j8T+eJF0z+ebxP30Facai6fE/9HFtqBjn8T8OuRluwOfxPwCpTZzc7/E/dhVSflLt8T92FVJ+Uu3xPx+A1CZO7vE/sTOFzmvs8T8fgNQmTu7xPwVpxqLp7PE/2UKQgxLm8T+4I5wWvOjxP39lpUkp6PE/f2WlSSno8T8sYAK37ubxP0VHcvkP6fE/hSUeUDbl8T/HYwYq49/xP65M+KV+3vE/6znpfePr8T+fk943vvbxP0Ruhhvw+fE/kbOwpx3+8T/eyDzyBwPyP8lxp3Sw/vE/7iB2ptD58T/vOEVHcvnxPwbYR6eu/PE/7ginBS/68T8mx53SwfrxP5eL+E7M+vE/Jsed0sH68T9BDkqYafvxP+3YCMTr+vE/XW3F/rL78T857pQO1v/xP/7PYb68API/dTxmoDL+8T/HEWvxKQDyP65Cyk+q/fE/2bERiNf18T+paoKo+wDyP1Z9rrZi//E/bOwS1VsD8j9ehZSfVPvxP/vo1JXP8vE/7zhFR3L58T8SiNf1C/bxP7raiv1l9/E/tcNfkzXq8T9HdxA7U+jxP9I6qpog6vE/TDeJQWDl8T/4GRcOhOTxP79DUaBP5PE/oZxoVyHl8T+D3bBtUebxPzRo6J/g4vE/2UKQgxLm8T8wwD46deXxP/bRqSuf5fE/wXPv4ZLj8T8W2c73U+PxP4nNx7Wh4vE//Knx0k3i8T9vnuqQm+HxP/bRqSuf5fE/p3Sw/s/h8T/yQc9m1efxP0rvG1975vE/JnDrbp7q8T8ukKD4MebxP2nGouns5PE/G2mpvB3h8T9R9wFIbeLxPxIZVvFG5vE/X16AfXTq8T9v9Zz0vvHxP/vQBfUt8/E/VK7wLhfx8T8012mkpfLxP9UhN8MN+PE/hpSfVPv08T+h8xq7RPXxP3nkDwae+/E/QQ5KmGn78T+VK7zLRfzxP5PjTulg/fE/1jkGZK/38T+vWpnwS/3xP1slWBzO/PE/aO22C8318T9RTrSrkPLxP/5IERlW8fE/zZIANbXs8T9vDWyVYPHxPxvYKsHi8PE/NNdppKXy8T/C+j+H+fLxP1g+y/Pg7vE/PSe9b3zt8T/HuriNBvDxPx0gmKPH7/E/Adnr3R/v8T9Uxr/PuPDxPzqX4qqy7/E/IJijx+/t8T9J10y+2ebxP3qlLEMc6/E/IxCv6xfs8T/P2m0XmuvxP7bz/dR46fE/q0Nuhhvw8T+JDKt4I/PxP6HzGrtE9fE/UU60q5Dy8T/cEU4LXvTxP9bKhF/q5/E/aybfbHPj8T/mOo20VN7xP0w3iUFg5fE/rTQpBd3e8T+K5ZZWQ+LxP+BKdmwE4vE/NrBVgsXh8T8BUps4ud/xP9zSakjc4/E/b57qkJvh8T+jzAaZZOTxP8Tr+gW74fE/48KBkCzg8T+uZMdGIN7xPyKJXkax3PE/kzXqIRrd8T+xxAPKptzxPyFZwARu3fE/AVKbOLnf8T+lLEMc6+LxP6K0N/jC5PE/oFT7dDzm8T/biv1l9+TxP7zLRXwn5vE/TDeJQWDl8T/HYwYq49/xPzawVYLF4fE/M1AZ/z7j8T9wzojS3uDxP4+NQLyu3/E/qwSLw5nf8T/HYwYq49/xP5VliGNd3PE/nYU97fDX8T+n7V9ZadLxP3UHsTOFzvE/AMtKk1LQ8T+VDtb/OczxP3UHsTOFzvE/d2fttgvN8T9ZwARu3c3xP7dFmQ0yyfE/c9cS8kHP8T/ek4eFWtPxP96rVib80vE/kzXqIRrd8T+4zOmymNjxP1RXPsvz4PE/6spneR7c8T/yQc9m1efxP/yp8dJN4vE/v0NRoE/k8T834PPDCOHxP8Fz7+GS4/E/SddMvtnm8T9J10y+2ebxP9laXyS05fE/M1AZ/z7j8T84+MJkquDxP41donpr4PE/AVKbOLnf8T/hehSuR+HxP4wVNZiG4fE/i/1l9+Th8T8bmUf+YODxP65M+KV+3vE/9Ik8Sbrm8T++++O9auXxPxtpqbwd4fE/SKeufJbn8T/FihpMw/DxP9wRTgte9PE/93DJcaf08T9FniRdM/nxP8qJdhVS/vE/Qj7o2az68T9ftTLhl/rxP+uoaoKo+/E/WMUbmUf+8T8pJ9pVSPnxPz2WPnRB/fE/PZY+dEH98T9BDkqYafvxP5ZbWg2J+/E/KA8Ltab58T8fv7fpz/7xP8zpspjY/PE/yXGndLD+8T/m6PF7m/7xP+3YCMTr+vE/WvW52or98T/U8ZiByvjxP0tenWNA9vE/C4DxDBr68T9lpUkp6PbxP8L6P4f58vE/qOMxA5Xx8T+GrG71nPTxP6DDfHkB9vE/cCU7NgLx8T8dOGdEae/xP6tDboYb8PE/Nh/Xhorx8T82H9eGivHxP/94r1qZ8PE/GsBbIEHx8T9PHhZqTfPxP8WKGkzD8PE/bcX+snvy8T9RTrSrkPLxP23F/rJ78vE/iQyreCPz8T8WMIFbd/PxP6abxCCw8vE/pYP1fw7z8T+LbOf7qfHxP6M7iJ0p9PE/MnctIR/08T+JDKt4I/PxP5Jc/kP67fE/K97IPPIH8j8Jv9TPmwryP9HoDmJnCvI/Dmd+NQcI8j/x7zMuHAjyP7VxxFp8CvI/7C+7Jw8L8j+TUtDtJQ3yP1pkO99PDfI/shGI1/UL8j+yEYjX9QvyP5fK2xFOC/I/0zB8REwJ8j8r3sg88gfyP7WhYpy/CfI/fxMKEXAI8j+7YduizAbyP6AaL90kBvI/aFw4EJIF8j8S34lZLwbyP7fRAN4CCfI/vJF55A8G8j/pSC7/If3xPx+/t+nP/vE/eJyiI7n88T857pQO1v/xP8cRa/EpAPI/AAAAAAAA8j9TBaOSOgHyP1CNl24SA/I/bATidf0C8j/8byU7NgLyP4vDmV/NAfI/b0xPWOIB8j9PdcjNcAPyP/s/h/nyAvI/96+sNCkF8j9+42vPLAnyP5tattYXCfI/8gcDz70H8j+BW3fzVAfyP++P96qVCfI/K97IPPIH8j+rsu+K4P/xPxtHrMWnAPI/yEEJM23/8T+OO6WD9f/xP8hBCTNt//E/GedvQiEC8j+e0sH6PwfyPy0+BcB4BvI/g4sVNZgG8j/VeOkmMQjyP5NS0O0lDfI/uRluwOcH8j9BZfz7jAvyP9C4cCAkC/I/elORCmML8j9BZfz7jAvyPyrG+ZtQCPI/YWwhyEEJ8j8OZ341BwjyP2Ysms5OBvI/2QjE6/oF8j/ZCMTr+gXyP4Oj5NU5BvI/fuNrzywJ8j/EsS5uowHyPxMn9zsUBfI/1XjpJjEI8j//eK9amfDxP4pUGFsI8vE/DxDM0eP38T/4x3vVygTyP77BFyZTBfI/7dgIxOv68T8uVtRgGgbyP0q1T8djBvI//p/DfHkB8j8Plxx3SgfyP1GlZg+0AvI/Tl35LM8D8j/6J7hYUQPyP8OBkCxgAvI/qpog6j4A8j8Z529CIQLyP7/xtWeWBPI/Uu3T8ZgB8j9QjZduEgPyP4TTghd9BfI/qWqCqPsA8j+kqgmi7gPyP3PcKR2s//E/mgMEc/T48T8LmMCtu/nxP6HbSxqj9fE/vCL430r28T/70AX1LfPxPxUA4xk09PE/bcX+snvy8T9xVdl3RfDxP1TejnBa8PE/aTUk7rH08T+mm8QgsPLxP/5IERlW8fE/BTkoYabt8T8Facai6ezxP0nXTL7Z5vE/v0NRoE/k8T96pSxDHOvxP7XbLjTX6fE/smMjEK/r8T+wA+eMKO3xP8x6MZQT7fE/9HFtqBjn8T+dDI6SV+fxP8Tr+gW74fE//yH99nXg8T8Wwf9WsuPxP1DHYwYq4/E/V+cYkL3e8T834PPDCOHxPzDw3Hu45PE/9HFtqBjn8T8mcOtunurxP5qUgm4v6fE/7rH0oQvq8T99BWnGounxP7XbLjTX6fE/IbByaJHt8T+wA+eMKO3xP86qz9VW7PE/IbByaJHt8T8mcOtunurxPzqX4qqy7/E/rrt5qkPu8T9Ybmk1JO7xP+MBZVOu8PE/x7q4jQbw8T8VAOMZNPTxP7w6x4Ds9fE/p7OTwVHy8T/DKt7IPPLxPzOny2Ji8/E/qOMxA5Xx8T8eUDblCu/xPz9XW7G/7PE/IuAQqtTs8T97vfvjverxPz4/jBAe7fE/dLUV+8vu8T/MYmLzce3xP+nxe5v+7PE/rtNIS+Xt8T9c5nRZTOzxP0PnNXaJ6vE/YabtX1np8T+cxCCwcujxP+6x9KEL6vE/1uJTAIzn8T/UmuYdp+jxP9gqweJw5vE/KdAn8iTp8T8012mkpfLxP/94r1qZ8PE/O99PjZfu8T889x4uOe7xP/vo1JXP8vE/55E/GHju8T8C8bp+we7xP1huaTUk7vE/uCOcFrzo8T/qIRrdQezxP7Ezhc5r7PE/jswjfzDw8T8DCYofY+7xP4/k8h/S7/E/kBSRYRXv8T8eUDblCu/xPzqvsUtU7/E/NjemJyzx8T82Bwjm6PHxP/94r1qZ8PE/b/Wc9L7x8T/9MEJ4tPHxPxYwgVt38/E/ak3zjlP08T/lYaHWNO/xP1hWmpSC7vE/duWzPA/u8T9angd3Z+3xPyMQr+sX7PE/vsEXJlMF8j9FDaZh+AjyPyPW4lMADPI/Jh5QNuUK8j8ofoy5awnyP0YldQKaCPI/Un5S7dPx8T+lg/V/DvPxP8L6P4f58vE/iiR6GcXy8T9WDi2yne/xP6kT0ETY8PE/F0hQ/Bjz8T/ByqFFtvPxPxl47j1c8vE/91j60AX18T9qTfOOU/TxP1A25Qrv8vE//3ivWpnw8T9FR3L5D+nxPzNQGf8+4/E/JemayTfb8T+ufJbnwd3xP85THXIz3PE/ufyH9NvX8T/1SlmGONbxPxGq1OyB1vE/LNmxEYjX8T9KmGn7V9bxP8Dsnjws1PE/m1Wfq63Y8T/aG3xhMtXxP4bOa+wS1fE/2evdH+/V8T/ZA63AkNXxP/QyiuWW1vE/m1Wfq63Y8T9/9iNFZNjxP0hQ/Bhz1/E/93r3x3vV8T8UIuAQqtTxPxVSflLt0/E/KZFEL6PY8T9ZF7fRAN7xPxuZR/5g4PE/rBxaZDvf8T9R9wFIbeLxP9CbilQY2/E/VYfcDDfg8T8ZOQt72uHxP5ccd0oH6/E/m6xRD9Ho8T+7m6c65ObxP30Facai6fE/8PlhhPDo8T8kKH6MuevxP9I6qpog6vE/Xi7iOzHr8T/MejGUE+3xP7AD54wo7fE/nfS+8bXn8T9D5zV2ierxP3zVyoRf6vE/Q+c1donq8T9/NQcI5ujxP9Sa5h2n6PE/Wp4Hd2ft8T9+HThnROnxPwtBDkqY6fE/Dol7LH3o8T/KMsSxLu7xP2QGKuPf5/E/+THmriXk8T/gSnZsBOLxP1ovhnKi3fE/QWDl0CLb8T/tKqT8pNrxP2Svd3+81/E/LSEf9GzW8T+f5Xlwd9bxP2cPtAJD1vE/Zd8Vwf/W8T9HOC140dfxP9szSwLU1PE/MIFbd/PU8T9hTzv8NdnxP0TAIVSp2fE/YTdsW5TZ8T+3nEtxVdnxP9CzWfW52vE/ZJeo3hrY8T8HQrKACdzxP7RU3o5w2vE/7RLVWwPb8T9GIF7XL9jxP9nTDn9N1vE/ZMdGIF7X8T9vRzgteNHxP+gTeZJ0zfE/eJeL+E7M8T8Gu2HboszxP83MzMzMzPE/ymyQSUbO8T9a8KKvIM3xP6pla32R0PE/je4gdqbQ8T83iUFg5dDxP1PQ7SWN0fE/cF8HzhnR8T//snvysNDxP3F31m670PE/jL6CNGPR8T8Ay0qTUtDxP+azPA/uzvE/Oul942vP8T9yv0NRoM/xP9xj6UMX1PE/2zNLAtTU8T+fzarP1dbxP/ryAuyj0/E/EcKjjSPW8T+m1ZC4x9LxP09AE2HD0/E/3GPpQxfU8T9oJ4Oj5NXxPyzZsRGI1/E/zlMdcjPc8T8E4nX9gt3xP5HVrZ6T3vE/ysNCrWne8T9ZF7fRAN7xP3WOAdnr3fE/eO49XHLc8T890AoMWd3xP+Y6jbRU3vE/kdWtnpPe8T8dyeU/pN/xPzawVYLF4fE/quy7Ivjf8T/qymd5HtzxPzyInSl03vE/W0dVE0Td8T8hWcAEbt3xP6+UZYhj3fE/swxxrIvb8T+zJEBNLdvxPwvSjEXT2fE/EtpyLsXV8T+3nEtxVdnxPxQ6r7FL1PE/EtpyLsXV8T+BJsKGp9fxP52dDI6S1/E/8uocA7LX8T/zGrtE9dbxP/Mau0T11vE/n82qz9XW8T/Qm4pUGNvxP3xmSYCa2vE/lWWIY13c8T/Pa+wS1dvxP5RNucK73PE/x0s3iUHg8T/gYkUNpuHxP8djBirj3/E/cy7FVWXf8T+SBUzg1t3xP+Oqsu+K4PE/xOv6Bbvh8T8noImw4enxPwjJAiZw6/E/7rH0oQvq8T8KEXAIVerxPwn5oGez6vE/J4i6D0Dq8T981cqEX+rxP2G+vAD76PE/mpSCbi/p8T9E/wQXK+rxPwjJAiZw6/E/0jqqmiDq8T9lHvmDgefxPyi4WFGD6fE/0jqqmiDq8T8OoUrNHujxPw6Jeyx96PE/Y+5aQj7o8T9d/kP67evxP9laXyS05fE/p1zhXS7i8T8zUBn/PuPxPzIge7374/E/q0Nuhhvw8T9bzqW4quzxP82SADW17PE/Cyk/qfbp8T/YKsHicObxP7lTOlj/5/E/+Um1T8fj8T/d6jnpfePxP6Tk1TkG5PE/at5xio7k8T/Bc+/hkuPxP2recYqO5PE/vhOzXgzl8T8VkWEVb+TxP7uzdtuF5vE/ZAYq49/n8T9lHvmDgefxP0QX1LfM6fE/ZAYq49/n8T8SGVbxRubxPwEYz6Ch//E/i8OZX80B8j9NFYxK6gTyP4BDqFKzB/I/0LhwICQL8j+PqiaIug/yP8rIWdjTDvI/HVVNEHUf8j99Oh4zUBnyP/1NKETAIfI/K7zLRXwn8j/UJk7udyjyP2pq2VpfJPI/+wW7Ydsi8j/1LXO6LCbyP2uad5yiI/I/v58aL90k8j9hGoaPiCnyP2Eaho+IKfI/1CZO7nco8j/yzTY3pifyP3VZTGw+LvI/YOrnTUUq8j9NSkG3lzTyP1oqb0c4LfI/kwA1tWwt8j9cWg2JeyzyP8Fu2LYoM/I//mX35GEh8j86I0p7gy/yP1jiAWVTLvI/5e0IpwUv8j+GONbFbTTyPzIDlfHvM/I/aJHtfD818j/ZJaq3BjbyP/kUAOMZNPI/pN++Dpwz8j/AJmvUQzTyP01KQbeXNPI/Rloqb0c48j8dA7LXuz/yP8e1oWKcP/I/clDCTNs/8j882jhiLT7yP/9byY6NQPI/Vtl3RfA/8j9GsdzSakjyP/J7m/7sR/I/iO/ErBdD8j/HndLB+j/yP46vPbMkQPI/rrZif9k98j9agSGrWz3yP7q9pDFaR/I/oKaWrfVF8j+Ztn9lpUnyP63ddqG5TvI/7Lsi+N9K8j9a2NMOf03yP5KWytsRTvI/rg0V4/xN8j9XeJeL+E7yPyHqPgCpTfI/RYE+kSdJ8j9YqDXNO07yPyNKe4MvTPI/kpbK2xFO8j+uDRXj/E3yP2HI6lbPSfI/SREZVvFG8j+KH2PuWkLyP1ch5SfVPvI/4bTgRV9B8j+thsQ9lj7yPwK8BRIUP/I/+8vuycNC8j9McVXZd0XyP+VEuwopP/I/bsDnhxFC8j+mft5UpELyP4jX9Qt2Q/I/THFV2XdF8j8uymyQSUbyPytqMA3DR/I/EVMiiV5G8j8O8+UF2EfyP0kRGVbxRvI/FMstrYZE8j8V4/xNKETyP4jvxKwXQ/I/VgkWhzM/8j9QGf8+40LyP1Sp2QOtQPI/ppat9UVC8j+W58HdWTvyP0wychb2NPI/pics8YAy8j8zMzMzMzPyPwJlU67wLvI/OPOrOUAw8j+L4H8r2THyPxx8YTJVMPI/x0YgXtcv8j+VYHE48yvyP1kSoKaWLfI/lUiil1Es8j/nHafoSC7yP1jKMsSxLvI/rS8S2nIu8j+PWItPATDyP4rIsIo3MvI/woanV8oy8j+NKO0NvjDyPzojSnuDL/I/bVFmg0wy8j/EtkWZDTLyPw4V4/xNKPI/pz/7kSIy8j+vX7Abti3yP3tJY7SOKvI/RdPZyeAo8j+2Z5YEqCnyP9P2r6w0KfI/LhwIyQIm8j8t7GmHvybyP7ZnlgSoKfI/Di2yne8n8j9+kdCWcynyP2Eaho+IKfI/YTJVMCop8j+693DJcSfyP2WqYFRSJ/I/nGhXIeUn8j9Dc51GWiryP+l942vPLPI/zh5oBYYs8j85C3va4S/yPwOV8e8zLvI/zh5oBYYs8j+YqN4a2CryP7U3+MJkKvI/kxgEVg4t8j+tLxLaci7yP71vfO2ZJfI/IJ268lke8j8+XHLcKR3yP/1NKETAIfI/lMFR8uoc8j9U46WbxCDyP8e/z7hwIPI/NNyAzw8j8j8xlBPtKiTyPxPVWwNbJfI/oRABh1Al8j+h+DHmriXyP2Eaho+IKfI/0/avrDQp8j9g6udNRSryPyu8y0V8J/I/vW987Zkl8j82PL1SliHyP6RwPQrXI/I/wM+4cCAk8j8xfERMiSTyP8DPuHAgJPI/LzTXaaQl8j+Z8Ev9vCnyP7ivA+eMKPI/Htydtdsu8j9WmpSCbi/yP5y/CYUIOPI/feiC+pY58j+SV+cYkD3yPzl6/N6mP/I/cTjzqzlA8j++NbBVgkXyP0kRGVbxRvI/n3b4a7JG8j8Tg8DKoUXyPyyazk4GR/I/ZqAy/n1G8j9JKej2kkbyPy7KbJBJRvI/iL8ma9RD8j810lJ5O0LyP4jvxKwXQ/I/NKK0N/hC8j9P6WD9n0PyP3EgJAuYQPI/+ZtQiIBD8j9McVXZd0XyPxFTIoleRvI/FxObj2tD8j9SSZ2AJkLyP1Sp2QOtQPI/PNo4Yi0+8j8HZK93fzzyP0PiHksfOvI/6xwDstc78j8bu0T11kDyP3SAYI4eP/I/kQ96Nqs+8j8eG4F4XT/yP6/OMSB7PfI/IatbPSc98j+vzjEgez3yP8nlP6TfPvI/6KT3ja898j+q9ul4zEDyP1bxRuaRP/I/rFYm/FI/8j+Q96qVCT/yP6neGtgqQfI/eBA7U+g88j9O0ZFc/kPyPyQLmMCtO/I/8jy4O2s38j8og6Pk1TnyPynLEMe6OPI/mS8vwD468j9gWWlSCjryP/H0SlmGOPI/s0Y9RKM78j+zRj1EozvyP+QUHcnlP/I/dJgvL8A+8j89CtejcD3yP5NvtrkxPfI/lZ9U+3Q88j8k88gfDDzyP86luKrsO/I/JCNnYU878j+YF2AfnTryP9DtJY3ROvI/XhH8byU78j+bj2tDxTjyP4Ko+wCkNvI/76zddqE58j+31hcJbTnyP13hXS7iO/I/CKwcWmQ78j+Wz/I8uDvyP14R/G8lO/I/rp6T3jc+8j+q9ul4zEDyP2jon+BiRfI/fT81XrpJ8j8h6j4AqU3yPw0a+ie4WPI/eNZuu9Bc8j9IUPwYc1fyP7i0GhL3WPI/gA7z5QVY8j/S4/c2/VnyP+viNhrAW/I/lE25wrtc8j9CeLRxxFryP5C93v3xXvI/HxFTIole8j/jqrLvimDyP17XL9gNW/I/S8gHPZtV8j82cXK/Q1HyPxQKEXAIVfI/wQRu3c1T8j+JRncQO1PyP09Y4gFlU/I/fsaFAyFZ8j9seHqlLEPyP75Nf/YjRfI//RNcrKhB8j+gppat9UXyP9vEyf0ORfI/3SQGgZVD8j/2C3bDtkXyPzXSUnk7QvI/bZBJRs5C8j9O0ZFc/kPyP9vEyf0ORfI/ti3KbJBJ8j/si4S2nEvyP3h/vFetTPI/s7W+SGhL8j+xVYLF4UzyP+M7MevFUPI/GyrG+ZtQ8j+gjscMVEbyP61u9Zz0PvI/6xwDstc78j9juaXVkDjyP2/YtiizQfI/ox6i0R1E8j/p1JXP8jzyPzuqmiDqPvI/LUMc6+I28j996IL6ljnyP7EWnwJgPPI/yM1wAz4/8j9xOPOrOUDyP+nsZHCUPPI/klfnGJA98j+zRj1EozvyPzqSy39IP/I/PNo4Yi0+8j8gY+5aQj7yP4x/n3HhQPI/b9i2KLNB8j/LLa2GxD3yP8Q9lj50QfI/akjcY+lD8j/c9Gc/UkTyP1Sp2QOtQPI/FxObj2tD8j9QATCeQUPyP799HThnRPI/u0T11sBW8j+9jGK5pVXyP3K/Q1GgT/I/bf/KSpNS8j8S2nIuxVXyPyl5dY4BWfI/f/YjRWRY8j+0VN6OcFryP9L7xteeWfI/C7q9pDFa8j8nMQisHFryP12nkZbKW/I/gA7z5QVY8j+7RPXWwFbyPy0hH/RsVvI/SFD8GHNX8j/0MorlllbyP2b35GGhVvI/pKXydoRT8j91H4DUJk7yP1TovMYuUfI/L1G9NbBV8j9LyAc9m1XyP52dDI6SV/I/Rdjw9EpZ8j+4tBoS91jyP7RU3o5wWvI/mg0yychZ8j/tEtVbA1vyP6+UZYhjXfI/WP/nMF9e8j86WP/nMF/yP5MdG4F4XfI/6Gor9pdd8j9bXyS05VzyP7MMcayLW/I/jV2iemtg8j+KPEm6ZnLyPxl47j1ccvI/4Ln3cMlx8j/9MEJ4tHHyP3KFd7mIb/I//kgRGVZx8j9RZoNMMnLyP6kT0ETYcPI/zzEge7178j89ZqAy/n3yP5eL+E7MevI/r1qZ8Et98j/XwFYJFofyP9eoh2h0h/I/2PD0SlmG8j851sVtNIDyPwAAAAAAgPI/Vn2utmJ/8j9Z3eo56X3yP1s9J71vfPI/6njMQGV88j8lr84xIHvyP5m7lpAPevI/Yi0+BcB48j/WOQZkr3fyP4M0Y9F0dvI/n6ut2F928j9o7bYLzXXyPxUYsrrVc/I/34lZL4Zy8j8faAWGrG7yP4yEtpxLcfI/whIPKJty8j8b2CrB4nDyP8ISDyibcvI/jZyFPe1w8j8cCMkCJnDyP3b9gt2wbfI/dv2C3bBt8j+si9toAG/yP6/rF+yGbfI/PQ/uztpt8j+v6xfshm3yP82SADW1bPI/58HdWbtt8j/9MEJ4tHHyP1Fmg0wycvI/AKlNnNxv8j8iyEEJM23yP8e6uI0GcPI/KdAn8iRp8j+ze/KwUGvyPwnh0cYRa/I/OpfiqrJv8j8teNFXkGbyPyoAxjNoaPI/R4/f2/Rn8j9hvrwA+2jyP1WH3Aw3YPI/VmXfFcF/8j9WZd8VwX/yP8x/SL99nfI/Plxy3Cmd8j9au+1Cc53yP0LUfQBSm/I/thDkoISZ8j+XOV0WE5vyPyQtlbcjnPI/za/mAMGc8j/tnjws1JryP9An8iTpmvI/teBFX0Ga8j96whIPKJvyPyjVPh2PmfI/s7CnHf6a8j9eS8gHPZvyP6t4I/PIn/I/YTJVMCqp8j8C9tGpK5/yP2uad5yio/I/NAwfEVOi8j/h7qzddqHyP1X7dDxmoPI/HT1+b9Of8j/DR8SUSKLyP9nO91PjpfI/9UVCW86l8j+YwK27earyPx/0bFZ9rvI/VpqUgm6v8j9BKzBkdavyP7ZnlgSoqfI/l5APejar8j/oZRTLLa3yPxtkkpGzsPI/6pWyDHGs8j8ktOVciqvyP5eQD3o2q/I/lDDT9q+s8j8GDf0TXKzyP3npJjEIrPI/p+hILv+h8j86tMh2vp/yP6Io0CfypPI/Ztr+lZWm8j8zxLEubqPyP28Sg8DKofI/ZvLNNjem8j9XW7G/7J7yP3BCIQIOofI/xqcAGM+g8j9XW7G/7J7yP44B2evdn/I/cEIhAg6h8j/FjzF3LaHyPxsN4C2QoPI/rthfdk+e8j+RSUbOwp7yP8gHPZtVn/I/V1uxv+ye8j/H155ZEqDyP0zD8BExpfI/EY3uIHam8j/whclUwajyP2EyVTAqqfI/KnReY5eo8j+/t+nPfqTyP6EQAYdQpfI/vW987Zml8j/cLjTXaaTyP0JbzqW4qvI/3bWEfNCz8j8tW+uLhLbyP2Ni83FtqPI/hIHn3sOl8j+hEAGHUKXyPxBdUN8yp/I/Tgte9BWk8j/7Bbth26LyPzT0T3CxovI/whcmUwWj8j+KWS+GcqLyP6gAGM+gofI/4zYawFug8j+m0HmNXaLyP4bJVMGopPI/N1SM8zeh8j/lfoeiQJ/yP+V+h6JAn/I/V1uxv+ye8j9BvK5fsJvyP7hAguLHmPI/W+uLhLac8j/iHksfuqDyPzhsW5TZoPI/qmBUUieg8j/MlxdgH53yP10z+Wabm/I/eGLWi6Gc8j+14EVfQZryP1FrmnecovI/7pQO1v+58j+MZ9DQP8HyP2agMv59xvI/zywJUFPL8j+DF30FacbyP9qs+lxtxfI/MUJ4tHHE8j/wS/28qcjyPwkbnl4py/I/Oul942vP8j/O5JttbszyP5z+7EeKyPI/h6dXyjLE8j8Zc9cS8sHyP8CV7NgIxPI/jZduEoPA8j8K9Ik8SbryP4XwaOOItfI/u37Bbti28j98uOS4U7ryP9GuQspPqvI/2c73U+Ol8j+Z8Ev9vKnyP65H4XoUrvI/GjT0T3Cx8j90QX3LnK7yP1eyYyMQr/I/ktCWcymu8j+L4H8r2bHyP2rZWl8ktPI/5gXYR6eu8j9uaTUk7rHyP4XwaOOItfI/xM4UOq+x8j8ZBFYOLbLyP2dhTzv8tfI/ZRniWBe38j+7fsFu2LbyP5gXYB+duvI/VyHlJ9W+8j/Q1VbsL7vyP+QUHcnlv/I/yeU/pN++8j+IvyZr1MPyP11Q3zKny/I/yTzyBwPP8j9h+IiYEsnyPwgDz72Hy/I/b0c4LXjR8j9xj6UPXdDyP+lDF9S3zPI/5ssLsI/O8j+N7iB2ptDyP+yLhLacy/I/0oxF09nJ8j+c/uxHisjyP/TDCOHRxvI/ob5lTpfF8j9lcJS8OsfyP88sCVBTy/I/uqXVkLjH8j9uqBjnb8LyP4RHG0esxfI/gxd9BWnG8j8wEtpyLsXyPzJyFva0w/I/vAUSFD/G8j+xhSAHJczyPwYSFD/G3PI/QIf58gLs8j+94xQdyeXyP2U2yCQj5/I/WYY41sXt8j8AkX77OvDyPzTXaaSl8vI/x7q4jQbw8j+u00hL5e3yPyegibDh6fI/sTOFzmvs8j89J71vfO3yP1vOpbiq7PI/IxCv6xfs8j9ZhjjWxe3yP2GOHr+36fI/RBfUt8zp8j8p0CfyJOnyP0T/BBcr6vI/1/oioS3n8j9KB+v/HObyP4QNT6+U5fI/KgDGM2jo8j+XBKipZevyP2iu00hL5fI/OPjCZKrg8j/eAgmKH+PyP2PuWkI+6PI/+5EiMqzi8j+ojH+fceHyP/3BwHPv4fI/+Um1T8fj8j+IhVrTvOPyP0sfuqC+5fI/SddMvtnm8j82yCQjZ+HyPxR5knTN5PI/oWzKFd7l8j/sUbgehevyP9AKDFnd6vI/xVp8CoDx8j/JAiZw6+7yPw8om3KF9/I/cT0K16Pw8j90tRX7y+7yP86qz9VW7PI/CMkCJnDr8j87x4Ds9e7yP0bOwp52+PI/mbuWkA/68j9bDYl7LP3yP2zsEtVbA/M/mtNlMbH58j8sn+V5cPfyP7iq7Lsi+PI/K4cW2c738j+fq63YX/byP0tenWNA9vI/4zEDlfHv8j9U3o5wWvDyPzO/mgME8/I/wyreyDzy8j8VAOMZNPTyP/q4NlSM8/I/S16dY0D28j8ZkL3e/fHyP/vQBfUt8/I/a2XCL/Xz8j/jAWVTrvDyP2WNeohG9/I/tkqwOJz58j8SoKaWrfXyP1Lt0/GYAfM/i8OZX80B8z8gByXMtP3yP636XG3F/vI/Pq4NFeP88j9yxFp8CgDzP8y5FFeV/fI/WMUbmUf+8j+paoKo+wDzP8JR8uocA/M/G0esxacA8z+paoKo+wDzPxx3Sgfr//I/Gf8+48IB8z9S1QRR9wHzP8AhVKnZA/M/3IDPDyME8z/7P4f58gLzPxe30QDeAvM/3sg88gcD8z+Rs7CnHf7yP+JYF7fRAPM/58HdWbvt8j8fEVMiid7yP7FVgsXhzPI/ymyQSUbO8j/mywuwj87yP3UHsTOFzvI/NBE2PL3S8j+aJQFqatnyP7xcxHdi1vI/nrXbLjTX8j+WlSaloNvyP7n8h/Tb1/I/eR7cnbXb8j91djI4St7yP1fnGJC93vI/qrwd4bTg8j+QpQ9dUN/yP17XL9gN2/I/JemayTfb8j9ortNIS+XyP3HmV3OA4PI/iuWWVkPi8j8bmUf+YODyP/yp8dJN4vI/XaeRlsrb8j9ZF7fRAN7yP+UK73IR3/I/zlMdcjPc8j8/AKlNnNzyPyBB8WPM3fI/AoI5evze8j8Cgjl6/N7yP6wcWmQ73/I/AWpq2Vrf8j89oGzKFd7yPxtpqbwd4fI/iuWWVkPi8j/WyoRf6ufyP4PF4cyv5vI/FakwthDk8j+D9X8O8+XyP01nJ4Oj5PI/+mGE8Gjj8j/cupunOuTyPzDw3Hu45PI/2VpfJLTl8j8OuRluwOfyPzDw3Hu45PI/nQyOklfn8j8wwD46deXyP7mNBvAWyPI//OO9amXC8j/n+6nx0s3yPzUpBd1e0vI/k66ZfLPN8j+xbVFmg8zyP64NFeP8zfI/BaOSOgHN8j//snvysNDyPzsZHCWvzvI/fCdmvRjK8j9JERlW8cbyP2agMv59xvI/L+I7MevF8j9I4XoUrsfyP0WBPpEnyfI/e/fHe9XK8j+1FfvL7snyP9qs+lxtxfI/Dat4I/PI8j9I+Um1T8fyPyo6kst/yPI/mG4Sg8DK8j/p1JXP8rzyPwpjC0EOyvI/zvxqDhDM8j/t0/GYgcryP5OumXyzzfI/djdPdcjN8j8eigJ9Is/yP8bctYR80PI/4zsx68XQ8j/EfHkB9tHyP1GgT+RJ0vI/5IOezarP8j/n49pQMc7yP878ag4QzPI/nBa86CvI8j+eXinLEMfyP/XzpiIVxvI/aOif4GLF8j9xICQLmMDyP+CcEaW9wfI/W5nwS/288j9uwOeHEcLyP/vL7snDwvI/flcE/1vJ8j9lcJS8OsfyP7FVgsXhzPI/PnlYqDXN8j866X3ja8/yP8k88gcDz/I/okW28/3U8j+8XMR3YtbyPy457pQO1vI/hJ7Nqs/V8j/BHD1+b9PyP6JFtvP91PI/9mIoJ9rV8j+GzmvsEtXyP00QdR+A1PI/nrXbLjTX8j8QkgVM4NbyP8JM27+y0vI/VACMZ9DQ8j/KbJBJRs7yP6zFpwAYz/I/ObnfoSjQ8j8bEvdY+tDyP8bctYR80PI/8kHPZtXn8j/GM2jon+DyP3V2MjhK3vI/9tGpK5/l8j8LQQ5KmOnyP/94r1qZ8PI/tJPBUfLq8j+XBKipZevyPyeIug9A6vI/eqUsQxzr8j8niLoPQOryP8/abRea6/I/RUdy+Q/p8j8FOShhpu3yPwaBlUOL7PI/zqrP1Vbs8j+brFEP0ejyP/DhkuNO6fI/rtNIS+Xt8j+GVbyReeTyP/uRIjKs4vI/ceZXc4Dg8j/+CS5W1ODyP65kx0Yg3vI/Vrd6Tnrf8j8E4nX9gt3yP9Lj9zb92fI/lpUmpaDb8j8fEVMiid7yP7xcxHdi1vI/Q5CDEmba8j9CYOXQItvyP9MTlnhA2fI/uhRXlX3X8j9jf9k9edjyP4E+kSdJ1/I/RMAhVKnZ8j+WrfVFQtvyP18HzhlR2vI/YB+duvLZ8j+ADvPlBdjyP1TovMYu0fI/RFGgT+TJ8j/so1NXPsvyPx1yM9yAz/I/Brth26LM8j8Gu2HboszyP5z+7EeKyPI/TDJyFva08j+zXgzlRLvyPzliLT4FwPI/utVz0vvG8j+Bzw8jhMfyP9UEUfcByPI/vAUSFD/G8j9/h6JAn8jyPy2yne+nxvI/9MMI4dHG8j/WHCCYo8fyPw2reCPzyPI/gLdAguLH8j/xY8xdS8jyP2VwlLw6x/I/nP7sR4rI8j/vA5DaxMnyP/AbX3tmyfI/hXe5iO/E8j/lXIqryr7yPzSitDf4wvI/GXPXEvLB8j8ps0EmGbnyP+nUlc/yvPI/feiC+pa58j+8rl+wG7byP++s3XahufI/t+7mqQ658j8ps0EmGbnyP4MXfQVpxvI/hv4JLlbU8j8UIuAQqtTyP/naM0sC1PI/aCeDo+TV8j+dhT3t8NfyP/QyiuWW1vI/MrH5uDbU8j8/AKlNnNzyP7VsrS8S2vI/ZJeo3hrY8j9/3lSkwtjyPw0a+ie42PI/bhea6zTS8j+PTl35LM/yP9mUK7zLxfI/7Eyh8xq78j8QtAJDVrfyP/IMGvonuPI/aHke3J218j9P6WD9n8PyP3v3x3vVyvI/gv+tZMfG8j+azk4GR8nyP2HgufdwyfI/8Ev9vKnI8j+kNnFyv8PyPzXSUnk7wvI/pn7eVKTC8j9QATCeQcPyP8nlP6TfvvI/FvvL7snD8j/ZlCu8y8XyP2owDcNHxPI/ou4DkNrE8j/WHCCYo8fyPy/iOzHrxfI/K2owDcPH8j/6sx8pIsPyP11Q3zKny/I/n3b4a7LG8j8H0zB8RMzyP2agMv59xvI/sYUgByXM8j/vA5DaxMnyP1wgQfFjzPI/B+v/HObL8j8iMqzijczyPwRbJVgczvI/AMtKk1LQ8j81KQXdXtLyP1TovMYu0fI/qn06HjPQ8j+Rfvs6cM7yP49OXfksz/I/6BN5knTN8j8cQpWaPdDyP+M7MevF0PI/WJBmLJrO8j+TxmgdVc3yP7TNjekJy/I/nC6Lic3H8j9GmQ0yycjyP9O84xQdyfI/f4eiQJ/I8j8cQpWaPdDyPzQRNjy90vI/8IrgfyvZ8j8/AKlNnNzyP4GVQ4ts5/I/Su8bX3vm8j+XHHdKB+vyP381Bwjm6PI/u7N224Xm8j+FJR5QNuXyP7qD2JlC5/I/SddMvtnm8j8w2A3bFuXyP2d+NQcI5vI/uoPYmULn8j+fPCzUmubyP93qOel94/I/MPDce7jk8j8yIHu9++PyP1IP0egO4vI/IVnABG7d8j+R1a2ek97yP72MYrml1fI/5rM8D+7O8j8EWyVYHM7yP6N1VDVB1PI/4QuTqYLR8j+t9UVCW87yP6cFL/oK0vI/i6azk8HR8j/7OnDOiNLyP64NFeP8zfI/6BN5knTN8j83iUFg5dDyP+ELk6mC0fI/bef7qfHS8j+m1ZC4x9LyP1PQ7SWN0fI/IjKs4o3M8j9xp3Sw/s/yP8JM27+y0vI/4iNiSiTR8j9VMCqpE9DyP+NTAIxn0PI/jzaOWIvP8j9EwCFUqdnyP+mC+pY53fI/f95UpMLY8j+2hHzQs9nyP+F6FK5H4fI/yJOkaybf8j+NXaJ6a+DyPyswZHWr5/I//cHAc+/h8j9SJ6CJsOHyP6BU+3Q85vI/fQVpxqLp8j/UmuYdp+jyP2r2QCsw5PI/vhOzXgzl8j8SMSWS6OXyPw65GW7A5/I/hSUeUDbl8j82sFWCxeHyP8Fz7+GS4/I/bm5MT1ji8j+K5ZZWQ+LyP4idKXRe4/I/zPPg7qzd8j90XmOXqN7yPwdCsoAJ3PI/zPPg7qzd8j9ZF7fRAN7yP01/9iNF5PI/VFc+y/Pg8j9SJ6CJsOHyP2w+rg0V4/I/xOv6Bbvh8j+JtfgUAOPyP2d+NQcI5vI/+mGE8Gjj8j82sFWCxeHyP8UDyqZc4fI/Uz9vKlLh8j/Co40j1uLyPxuZR/5g4PI/5NpQMc7f8j+TNeohGt3yP3RGlPYG3/I/cv4mFCLg8j9x5ldzgODyPxyxFp8C4PI/Pbg7a7fd8j99rrZif9nyP+TaUDHO3/I/pSxDHOvi8j9HdxA7U+jyP7prCfmg5/I/3eo56X3j8j8bmUf+YODyP8qJdhVS/vI/yVnY0w7/8j+GAyFZwATzP/s/h/nyAvM/4Ch5dY4B8z/c14FzRhTzPxbe5SK+E/M/Bp57D5cc8z8dVU0QdR/zPwL20akrH/M/SgwCK4cW8z/3Bl+YTBXzP/g2/dmPFPM/3h/vVSsT8z+mYfiImBLzP/JG5pE/GPM/SMSUSKIX8z+/SGjLuRTzP9cv2A3bFvM/uXAgJAsY8z99Oh4zUBnzP9u/stKkFPM/Kh2s/3MY8z/wFkhQ/BjzP3Ba8KKvIPM/ut+hKNAn8z/vVSsTfinzP+89XHLcKfM/0ZZzKa4q8z+cOLnfoSjzPyf8Uj9vKvM/fGEyVTAq8z/YtiizQSbzP03zjlN0JPM/Tdu/stIk8z+fyJOkaybzPxB1H4DUJvM/g1FJnYAm8z9iSiTRyyjzP0ATYcPTK/M/YFlpUgo68z+gjscMVEbzP5YmpaDbS/M/UAEwnkFD8z9v8IXJVEHzP4aPiCmRRPM/dIBgjh4/8z8EHEKVmj3zP3bgnBGlPfM/PQrXo3A98z9gcTjzqznzP4hodAexM/M/Haz/c5gv8z+DaRg+IibzP/LNNjemJ/M/vVetTPgl8z9/wW7YtijzP/T91HjpJvM/KVyPwvUo8z8pXI/C9SjzP0SjO4idKfM/662BrRIs8z/xnZj1YijzP4WxhSAHJfM/VOOlm8Qg8z+MoZxoVyHzP83HtaFiHPM/kDF3LSEf8z+u8C4X8R3zP/cGX5hMFfM/4X8r2bER8z/kDwaeew/zPzt15bM8D/M/MPXzpiIV8z/r501FKgzzP0TdByC1CfM/9Wc/UkQG8z+gGi/dJAbzP92YnrDEA/M/K/aX3ZMH8z9inL8JhQjzPy0+BcB4BvM/nbryWZ4H8z+M8zehEAHzP2987ZklAfM/j1N0JJf/8j/EsS5uowHzP8hBCTNt//I/LT4FwHgG8z9mFMstrQbzPw034PPDCPM/b3ztmSUB8z+Zu5aQD/ryPxzw+WGE8PI/pYP1fw7z8j/zyB8MPPfyPy5W1GAaBvM/nbryWZ4H8z/DgZAsYALzP/Q3oRABB/M/JO6x9KEL8z8jvhOzXgzzPwTnjCjtDfM/Fw6EZAET8z8Ab4EExQ/zP8r49xkXDvM/WBzO/GoO8z/on+BiRQ3zP81YNJ2dDPM/QpWaPdAK8z+bWrbWFwnzP/HvMy4cCPM/hzO/mgME8z/caABvgQTzP4Fbd/NUB/M/Dmd+NQcI8z+q8dJNYhDzP1R0JJf/EPM/lGqfjscM8z94nKIjufzyP2987ZklAfM/jPM3oRAB8z+aAwRz9PjyP+lILv8h/fI/zQGCOXr88j8HCObo8fvyP31cGyrG+fI/Uu3T8ZgB8z8aFw6EZAHzP6XydoTTAvM/NV66SQwC8z8WhzO/mgPzPxaHM7+aA/M/w5lfzQEC8z8Xt9EA3gLzP9yAzw8jBPM/K97IPPIH8z/udygK9AnzP8QI4dHGEfM/HLYtymwQ8z/mP6Tfvg7zP5EKYwtBDvM/8L+V7NgI8z96O8JpwQvzP1fsL7snD/M//yYUIuAQ8z/1fw7z5QXzPwmnBS/6CvM/0LhwICQL8z+bWrbWFwnzP7jpz36kCPM/8L+V7NgI8z8ofoy5awnzP/Mf0m9fB/M/CtejcD0K8z+bWrbWFwnzP3fzVIfcDPM/PgXAeAYN8z/NWDSdnQzzP5SCbi9pDPM/elORCmML8z/EsS5uowHzPxZvZB75A/M/3sg88gcD8z9+42vPLAnzPy9uowG8BfM/0egOYmcK8z8whnKiXQXzP8QI4dHGEfM/kNrEyf0O8z8k7rH0oQvzPw034PPDCPM/30+Nl24S8z82zTtO0RHzP6rx0k1iEPM/AFeyYyMQ8z+K6q2BrRLzPzI9YYkHFPM/M23/ykoT8z8gXtcv2A3zP8mwijcyD/M/yuAoeXUO8z+OklfnGBDzPzD186YiFfM/3h/vVSsT8z8qNXugFRjzP3h6pSxDHPM/RnwnZr0Y8z+EEmba/hXzP0jcY+lDF/M/LGUZ4lgX8z9qEyf3OxTzP5hp+1dWGvM/e9rhr8ka8z+iuU4jLRXzP+F/K9mxEfM/V+wvuycP8z+q8dJNYhDzP6ipZWt9EfM/pmH4iJgS8z/bv7LSpBTzP0xsPq4NFfM/wZDVrZ4T8z+jAbwFEhTzP6LRHcTOFPM/v0hoy7kU8z+jAbwFEhTzP/p+arx0E/M/9u6P96oV8z8cJa/OMSDzPxiV1AloIvM/Jcy0/Ssr8z/L1voioS3zP9QmTu53KPM/owbTMHxE8z8XvOgrSDPzP0iKyLCKN/M/dcjNcAM+8z8CZVOu8C7zP20hyEEJM/M/FFysqME08z8IrBxaZDvzPz4ipkQSPfM/s3bbheY68z+zdtuF5jrzP5EPejarPvM/U3k7wmlB8z/MXUvIBz3zP8bctYR8UPM/VBhbCHJQ8z9WSPlJtU/zPzLhl/p5U/M/y4Rf6udN8z+61XPS+0bzP1rwoq8gTfM/lialoNtL8z+srdhfdk/zP1ioNc07TvM/owbTMHxE8z/mdFlMbD7zP7WmeccpOvM/wcWKGkxD8z91yM1wAz7zP46vPbMkQPM/PNo4Yi0+8z91yM1wAz7zP/0TXKyoQfM/Pzp15bM88z8lOzYC8TrzP5kvL8A+OvM/ZNF0djI48z+gT+RJ0jXzP7WmeccpOvM/8Nx7uOQ48z+DwMqhRTbzP5pfzQGCOfM/EOSghJk28z/vxKwXQznzPyTzyB8MPPM/uAa2SrA48z/yJOmayTfzP21RZoNMMvM/6X3ja88s8z91WUxsPi7zPz/7kSIyLPM/Tgte9BUk8z+7Jw8LtSbzP8DPuHAgJPM/pHA9Ctcj8z8dPX5v0x/zP/+tZMdGIPM/VOOlm8Qg8z+I+fIC7CPzP6foSC7/IfM/IM1YNJ0d8z9wWvCiryDzP2yyRj1EI/M/cFrwoq8g8z8b9RCN7iDzP1KDaRg+IvM/KVyPwvUo8z9iSiTRyyjzPxmto6oJIvM/wf9WsmMj8z+asWg6OxnzP7qgvmVOF/M/2EenrnwW8z8+XHLcKR3zP+M2GsBbIPM/LhwIyQIm8z/cRgN4CyTzP/sFu2HbIvM/Lexph78m8z/a5sb0hCXzP9rmxvSEJfM/hbGFIAcl8z/WVuwvuyfzP0gzFk1nJ/M/gjl6/N4m8z8G9S1zuizzPz6zJEBNLfM/zjY3pics8z+VSKKXUSzzP3tJY7SOKvM/Y2LzcW0o8z/NBplk5CzzP7QfKSLDKvM/1lbsL7sn8z/L1voioS3zP8zuycNCLfM/1CZO7nco8z/u5qkOuRnzP+KvyRr1EPM/74/3qpUJ8z8Yz6ChfwLzP6J6a2CrBPM/j1N0JJf/8j/AIVSp2QPzPxVXlX1XBPM/aFw4EJIF8z+AQ6hSswfzP77BFyZTBfM/iGNd3EYD8z84vvbMkgDzP40j1uJTAPM/AAAAAAAA8z83jliLTwHzP40j1uJTAPM/pKoJou4D8z/qz36kiAzzPwdfmEwVDPM/Ypy/CYUI8z97gy9MpgrzP4JzRpT2BvM/D5ccd0oH8z+IY13cRgPzPx6n6Egu//I/3GgAb4EE8z/5D+m3rwPzP8QI4dHGEfM/j6omiLoP8z+Ugm4vaQzzP+F/K9mxEfM/Hc78ag4Q8z9SLLe0GhLzP6VJKej2EvM/WUxsPq4N8z+x4emVsgzzP/8+48KBEPM/WUxsPq4N8z8Bh1ClZg/zP99PjZduEvM/+WabG9MT8z9tc2N6whLzP4P6ljldFvM/0m9fB84Z8z9XW7G/7B7zPyCduvJZHvM/KnReY5co8z9hGoaPiCnzP4fhI2JKJPM/bvqzHyki8z8+dEF9yxzzP7cos0EmGfM/QuxMofMa8z8i/fZ14BzzP3Yaaam8HfM/1c+bilQY8z95knTN5BvzP2CrBIvDGfM/XANbJVgc8z/KT6p9Oh7zPyL99nXgHPM/XANbJVgc8z8ldQKaCBvzP7JoOjsZHPM/ILWJk/sd8z+/SGjLuRTzPwouVtRgGvM/kDF3LSEf8z9vEoPAyiHzP5Xx7zMuHPM/wf9WsmMj8z9tyhXe5SLzP+EGfH4YIfM/7rYLzXUa8z/Vz5uKVBjzP306HjNQGfM/5ZZWQ+Ie8z9yio7k8h/zP43ROqqaIPM/yAc9m1Uf8z9vKlJhbCHzP23KFd7lIvM/MXxETIkk8z8Spb3BFybzP/ZdEfxvJfM/EF1Q3zIn8z+2f2WlSSnzP7G/7J48LPM//tR46SYx8z+pnzcVqTDzP5Qw0/avLPM/WMoyxLEu8z+PiCmRRC/zP5xoVyHlJ/M/6X3ja88s8z9aKm9HOC3zP5jYfFwbKvM/l5APejYr8z+4rwPnjCjzP8Xm49pQMfM/GjT0T3Ax8z+KyLCKNzLzPzfDDfj8MPM/qG+Z02Ux8z/+1HjpJjHzP1Vq9kArMPM/q8/VVuwv8z8ps0EmGTnzP7yWkA96NvM/nx9GCI828z8R/G8lOzbzP0mil1EsN/M/T6p9Oh4z8z+mD11Q3zLzPy5zuiwmNvM/KuPfZ1w48z+hZ7PqczXzP0wao3VUNfM/j1iLTwEw8z+L+E7MejHzP+JdLuI7MfM/isiwijcy8z++9sySADXzPymzQSYZOfM/SrVPx2MG8z+7YduizAbzP9eoh2h0B/M/Dmd+NQcI8z+gGi/dJAbzP8cpOpLL//I/Gy/dJAYB8z/HEWvxKQDzP9yAzw8jBPM/TkUqjC0E8z/BOSNKewPzP4hjXdxGA/M/8L+V7NgI8z9BfcucLgvzP3Iz3IDPD/M/j6omiLoP8z96U5EKYwvzP9C4cCAkC/M/jErqBDQR8z8xJZLoZRTzP4i6D0BqE/M/dzI4Sl4d8z+dEaW9wRfzP0Z8J2a9GPM/Zmt9kdAW8z+fQUP/BBfzP5YhjnVxG/M/mFEst7Qa8z8ldQKaCBvzPwaeew+XHPM/0A8jhEcb8z+qSIWxhSDzP+EGfH4YIfM/bxKDwMoh8z9tyhXe5SLzPy4cCMkCJvM/14aKcf4m8z9x4UBIFjDzPx3EzhQ6L/M/4l0u4jsx8z8wTKYKRiXzP/m9TX/2I/M/nYAmwoYn8z/1LXO6LCbzPxKlvcEXJvM/n8iTpGsm8z8GnnsPlxzzP+j2ksZoHfM/B84ZUdob8z9cA1slWBzzP1mjHqLRHfM/OC140VcQ8z8zbf/KShPzPzD186YiFfM/SzygbMoV8z8tlbcjnBbzP59BQ/8EF/M/1v85zJcX8z+dEaW9wRfzP/buj/eqFfM/A8+9h0sO8z90kxgEVg7zPyO+E7NeDPM/onprYKsE8z84vvbMkgDzP1jFG5lH/vI/kGtDxTj/8j8Z/z7jwgHzP+p4zEBl/PI/vCL430r28j+kU1c+y/PyPzYHCObo8fI/wcqhRbbz8j+/gjRj0fTyPxTQRNjw9PI/+IiYEkn08j+cM6K0N/jyPw74/DBC+PI/DyibcoX38j8pJ9pVSPnyP2IVb2Qe+fI/mKPH72368j9ljXqIRvfyPxmQvd798fI/ozuInSn08j9K7xtfe+byP2PuWkI+6PI/nQyOklfn8j93vp8aL93yPyKJXkax3PI/kgVM4Nbd8j99rrZif9nyP2zPLAlQ0/I/aFch5SfV8j+1bK0vEtryP4UlHlA25fI/jEXT2cng8j91djI4St7yPwKaCBue3vI/rnyW58Hd8j9bR1UTRN3yP5fFxObj2vI/lWWIY13c8j941m670NzyP5RNucK73PI/Wi+GcqLd8j9G8L+V7NjyPxCSBUzg1vI/41MAjGfQ8j8A++jUlc/yPyyazk4Gx/I/lQ7W/znM8j9daK7TSMvyP7TlXIqryvI/jZduEoPA8j+JB5RNucLyP9qs+lxtxfI/JXoZxXLL8j+Trpl8s83yPzm536Eo0PI/yAxUxr/P8j8Gu2HboszyPwkzbf/KyvI/0VynkZbK8j9EaW/whcnyP5T2Bl+YzPI/sYUgByXM8j+5jQbwFsjyP0aZDTLJyPI/ou4DkNrE8j/2I0VkWMXyP22QSUbOwvI/KPIk6ZrJ8j8CK4cW2c7yP6Sl8naE0/I/3Xu45LjT8j+bVZ+rrdjyP4JuL2mM1vI/T0ATYcPT8j8BamrZWt/yPyUBamrZ2vI/8uocA7LX8j/Qs1n1udryPwYSFD/G3PI/Pbg7a7fd8j/xun7BbtjyP5aVJqWg2/I/0LNZ9bna8j9EwCFUqdnyP32utmJ/2fI/eR7cnbXb8j8peXWOAdnyP7oUV5V91/I/tDwP7s7a8j9/3lSkwtjyPy0JUFPL1vI/j41AvK7f8j8Cgjl6/N7yP1RXPsvz4PI/GTkLe9rh8j8teNFXkObyP0rvG1975vI/Wp4Hd2ft8j9Wt3pOet/yP+/Jw0Kt6fI/ZmZmZmbm8j80gLdAguLyPxfxnZj14vI/g92wbVHm8j8MWd3qOenyP4GVQ4ts5/I/iIVa07zj8j+hbMoV3uXyP01nJ4Oj5PI/sKw0KQXd8j+7RPXWwNbyP2THRiBe1/I/f/YjRWTY8j/y6hwDstfyP9/b9Gc/0vI/wkzbv7LS8j8o8iTpmsnyP3rf+Nozy/I/E5uPa0PF8j9CCTNt/8ryP0fJq3MMyPI/AOMZNPTP8j89YYkHlM3yP/TDCOHRxvI/Cks8oGzK8j98J2a9GMryP9XsgVZgyPI/YLAbti3K8j/0wwjh0cbyPy/6CtKMxfI/Z7gBnx/G8j8rUmFsIcjyP0xxVdl3xfI/jZduEoPA8j9CsoAJ3LryP3f4a7JGvfI/fLjkuFO68j97iEZ3ELvyP+1kcJS8uvI//RNcrKjB8j+1FfvL7snyP0tZhjjWxfI/86s5QDDH8j9z1xLyQc/yP3h/vFetzPI/eJeL+E7M8j/mszwP7s7yPwRbJVgczvI/AMtKk1LQ8j8EWyVYHM7yP88sCVBTy/I/Brth26LM8j8iMqzijczyPyfChqdXyvI/Dwu1pnnH8j+ZnrDEA8ryP+tztRX7y/I/8xq7RPXW8j9LsDic+dXyPzKx+bg21PI/+dozSwLU8j8yychZ2NPyP/d698d71fI/inYVUn7S8j+rlQm/1M/yP6yt2F92z/I/41MAjGfQ8j8tCVBTy9byP37GhQMh2fI/hJ7Nqs/V8j9t5/up8dLyPzeJQWDl0PI/U9DtJY3R8j9uF5rrNNLyP9/DJced0vI/UIiAQ6jS8j9uF5rrNNLyP3F31m670PI/yAxUxr/P8j9U6LzGLtHyP+SDns2qz/I/AiuHFtnO8j+rlQm/1M/yP3GndLD+z/I/HqLRHcTO8j+qZWt9kdDyP3UHsTOFzvI/UaBP5EnS8j8vUb01sNXyP0loy7kU1/I/w2SqYFTS8j/PLAlQU8vyP0j5SbVPx/I/ou4DkNrE8j8ai6azk8HyPzzaOGItvvI/mBdgH5268j94QNmUK7zyP+58PzVeuvI/CvSJPEm68j97oBUYsrryP1uZ8Ev9vPI/PiKmRBK98j+w/s9hvrzyP+eMKO0NvvI/IatbPSe98j9cyY6NQLzyPweUTbnCu/I/nwd3Z+228j9k0XR2MrjyPw6EZAETuPI/LVvri4S28j/5LM+Du7PyP7t+wW7YtvI/CvSJPEm68j96cHfWbrvyPwnE6/oFu/I/24XmOo208j8QtAJDVrfyPzAS2nIuxfI/S1mGONbF8j9SSZ2AJsLyP2fQ0D/BxfI/9fOmIhXG8j8v4jsx68XyP4RHG0esxfI/ob5lTpfF8j+6pdWQuMfyP/FjzF1LyPI/YcjqVs/J8j8PC7WmecfyP7rVc9L7xvI/t0WZDTLJ8j9lcJS8OsfyP7rVc9L7xvI/Dwu1pnnH8j9rYKsEi8PyP/mDgefew/I/utVz0vvG8j9DOdGuQsryP3v3x3vVyvI/escpOpLL8j8O2xZlNsjyP02JJHoZxfI/8nub/uzH8j/SjEXT2cnyP1sIclDCzPI/BYvDmV/N8j8Gu2HboszyP5PGaB1VzfI/KAr0iTzJ8j/TvOMUHcnyP5rOTgZHyfI/RYE+kSfJ8j+xhSAHJczyP4Emwoan1/I/oBUYsrrV8j9rt11ortPyPy9pjNZR1fI/aD9SRIbV8j9kl6jeGtjyP23n+6nx0vI/Y3/ZPXnY8j/D0ytlGeLyPwvSjEXT2fI/AVKbOLnf8j8kufyH9NvyPzSAt0CC4vI/KgDGM2jo8j/Co40j1uLyP4rlllZD4vI/wqONI9bi8j80aOif4OLyP2Ue+YOB5/I/MPDce7jk8j9q3nGKjuTyP9laXyS05fI/EOm3rwPn8j+alIJuL+nyPxIxJZLo5fI/p1zhXS7i8j/4GRcOhOTyPyxgArfu5vI/nNzvUBTo8j+YTBWMSuryP3ldv2A37PI/sTOFzmvs8j/AWyBB8ePyPyZw626e6vI/Pj+MEB7t8j8WMIFbd/PyPyuHFtnO9/I/+rg2VIzz8j8U6BN5kvTyP2tlwi/18/I/pYP1fw7z8j+62or9ZffyP5QT7Sqk/PI/7iB2ptD58j+Zu5aQD/ryP5dzKa4q+/I/CCC1iZP78j/rqGqCqPvyPz1moDL+/fI/VmXfFcH/8j8cd0oH6//yP3Gsi9toAPM/qFKzB1oB8z8G2EenrvzyPwbYR6eu/PI/CVBTy9b68j+8Cik/qfbyP0gWMIFb9/I/gez17o/38j+GrG71nPTyP3S1FfvL7vI/Ttaoh2j08j9SflLt0/HyPx0gmKPH7/I/v5oDBHP08j+rWz0nve/yP6tbPSe97/I/y0qTUtDt8j8fgNQmTu7yPzqX4qqy7/I/BCFZwATu8j8zp8tiYvPyPzOny2Ji8/I/ofMau0T18j+iC+pb5vTyP90pHaz/8/I/hqxu9Zz08j/i6ZWyDPHyP8x6MZQT7fI/klz+Q/rt8j89J71vfO3yPyMQr+sX7PI/+THmriXk8j8pV3iXi/jyP80Bgjl6/PI/HSCYo8fv8j+OzCN/MPDyP86qz9VW7PI/XRYTm4/r8j/UmuYdp+jyP0VHcvkP6fI/0AoMWd3q8j+hbMoV3uXyP/kx5q4l5PI/uoPYmULn8j+fPCzUmubyP/uRIjKs4vI/o8wGmWTk8j9rDhDM0ePyP74Ts14M5fI/5NpQMc7f8j/Qs1n1udryP+Y6jbRU3vI/BfpEniTd8j+TNeohGt3yP+BKdmwE4vI/FsH/VrLj8j9q9kArMOTyP4rlllZD4vI/quy7Ivjf8j/biv1l9+TyP/bRqSuf5fI/FsH/VrLj8j9ZF7fRAN7yPyApIsMq3vI/5NpQMc7f8j85EJIFTODyPz2gbMoV3vI/5QrvchHf8j8HWoEhq9vyP0hQ/Bhz1/I/tFTejnDa8j9iZwqd19jyP2SXqN4a2PI/FAoRcAjV8j9WSPlJtc/yP0uwOJz51fI/F4IclDDT8j8Ay0qTUtDyPxsS91j60PI//FI/byrS8j8Fi8OZX83yP7O1vkhoy/I/WvCiryDN8j/KbJBJRs7yPyWS6GUUy/I/bf/KSpPS8j9LyAc9m9XyP6N1VDVB1PI/a7ddaK7T8j+kpfJ2hNPyP2qHvyZr1PI/93r3x3vV8j/1SlmGONbyP/eqlQm/1PI/EcKjjSPW8j8UChFwCNXyP4p2FVJ+0vI/UHCxogbT8j+Rfvs6cM7yP1PQ7SWN0fI/0ETY8PTK8j8o8iTpmsnyP5OumXyzzfI/7Lsi+N/K8j9t5/up8dLyP4leRrHc0vI/K8HicObX8j/Z0w5/TdbyP7L0oQvq2/I/ejarPlfb8j80gLdAguLyPztwzojS3vI/XaeRlsrb8j+uZMdGIN7yP5ClD11Q3/I/kL3e/fHe8j+hbMoV3uXyP2w+rg0V4/I/jEXT2cng8j/hehSuR+HyP/yp8dJN4vI/OPjCZKrg8j8LKT+p9unyP0nXTL7Z5vI/TpfFxObj8j+DxeHMr+byP3qlLEMc6/I/coV3uYjv8j/nwd1Zu+3yP6RTVz7L8/I/vAopP6n28j8YYB+duvLyP3KFd7mI7/I/q0Nuhhvw8j8+P4wQHu3yP/wYc9cS8vI/2IFzRpT28j8VAOMZNPTyP7+CNGPR9PI/E7h1N0/18j8vF/GdmPXyPwn5oGez6vI/oZxoVyHl8j/w+WGE8OjyP4GVQ4ts5/I/viuC/63k8j+c3O9QFOjyP/phhPBo4/I/3xrYKsHi8j/ZQpCDEubyP2r2QCsw5PI/KzBkdavn8j8ZOQt72uHyP7L0oQvq2/I/eR7cnbXb8j/bM0sC1NTyPwqi7gOQ2vI/7PoFu2Hb8j+cbW5MT9jyP/VKWYY41vI/mj3QCgzZ8j+aPdAKDNnyPy457pQO1vI/NlmjHqLR8j8eotEdxM7yP/6arFEP0fI/F4IclDDT8j8UIuAQqtTyP/eSxmgd1fI/vrwA++jU8j8RwqONI9byP1QAjGfQ0PI/ARO4dTfP8j/so1NXPsvyP9dMvtnmxvI/Jqq3BrbK8j9jKCfaVcjyP9dMvtnmxvI/ea9amfDL8j9CCTNt/8ryP+IjYkok0fI/X5hMFYzK8j/zk2qfjsfyP/OTap+Ox/I/P6n26XjM8j9bCHJQwszyPz+p9ul4zPI/lPYGX5jM8j8eotEdxM7yPzeJQWDl0PI/xawXQznR8j+oNc07TtHyP8EcPX5v0/I/NlmjHqLR8j/JPPIHA8/yP+SDns2qz/I/kGYsms7O8j/fwyXHndLyP4qO5PIf0vI/qmVrfZHQ8j/IDFTGv8/yP/2C3bBt0fI/cXfWbrvQ8j9T0O0ljdHyP96Th4Va0/I/inYVUn7S8j/jUwCMZ9DyP16/YDds2/I/N4lBYOXQ8j/iI2JKJNHyPzUpBd1e0vI/ptWQuMfS8j83iUFg5dDyPwDLSpNS0PI/VACMZ9DQ8j83iUFg5dDyP49OXfksz/I/cad0sP7P8j/6CtKMRdPyP4y+gjRj0fI//YLdsG3R8j9rn47HDNTyP9xj6UMX1PI/D2JnCp3X8j9/3lSkwtjyPw9iZwqd1/I/jnVxGw3g8j8SMSWS6OXyPzSAt0CC4vI/g8XhzK/m8j8UeZJ0zeTyP93qOel94/I/3xrYKsHi8j9J10y+2ebyPw65GW7A5/I/SKeufJbn8j/TakjcY+nyPw6Jeyx96PI/BWnGouns8j9Bt5c0RuvyPwjJAiZw6/I/Xf5D+u3r8j92FVJ+Uu3yP+2ZJQFq6vI/Rl9BmrHo8j9J10y+2ebyP/cBSG3i5PI/2CrB4nDm8j/cSxqjddTyP6JFtvP91PI/AHSYLy/A8j8LJCh+jLnyPyv7rgj+t/I/jBAebRyx8j8MzXUaaanyP0gzFk1np/I/F30Facai8j8UBfpEnqTyP2gibHh6pfI/p+hILv+h8j9XW7G/7J7yP3mSdM3km/I/P4wQHm2c8j91ApoIG57yP8gHPZtVn/I/PSzUmuad8j+pGOdvQqHyP2LboswGmfI/Plxy3Cmd8j/QP8HFipryP3cyOEpenfI/0VeQZiya8j9RFOgTeZLyP5DaxMn9jvI/ZeQs7GmH8j9l5CzsaYfyP2XkLOxph/I/elORCmOL8j99s82N6YnyP5EKYwtBjvI/WAT/W8mO8j8YJlMFo5LyP8PwETElkvI/kzoBTYSN8j/99nXgnJHyPzbNO07RkfI/h6JAn8iT8j9P5EnSNZPyP/cGX5hMlfI/9b7xtWeW8j/ZX3ZPHpbyPxE2PL1SlvI/SNxj6UOX8j+TqYJRSZ3yP3OiXYWUn/I/kUlGzsKe8j+V2SCTjJzyP4VCBBxClfI/ZDvfT42X8j/1vvG1Z5byP197ZkmAmvI/rJDyk2qf8j+zgAncupvyP8gHPZtVn/I/9RWkGYum8j8xfERMiaTyP91e0hito/I/cEIhAg6h8j9N845TdKTyPxd9BWnGovI/WrvtQnOd8j875Ga4AZ/yPyCduvJZnvI/tvgUAOOZ8j+ReeQPBp7yP1PL1voiofI/qAAYz6Ch8j//rWTHRqDyP5OpglFJnfI/eZJ0zeSb8j8B3gIJip/yP1mLTwEwnvI/qmBUUieg8j875Ga4AZ/yP4gpkUQvo/I/12670Fyn8j/7Bbth26LyP6NYbmk1pPI/Mqzijcyj8j/8NVmjHqLyP+C+DpwzovI/wOeHEcKj8j+GyVTBqKTyPzq0yHa+n/I/PBQF+kSe8j/pJjEIrJzyP+o+AKlNnPI/QKTfvg6c8j+zmNh8XJvyPwDGM2jon/I/alIKur2k8j8dPX5v05/yP+kmMQisnPI/4zYawFug8j875Ga4AZ/yP59ZEqCmlvI/3NeBc0aU8j94YtaLoZzyP9hHp658lvI/tvgUAOOZ8j+zsKcd/pryP0jcY+lDl/I/SMSUSKKX8j+6oL5lTpfyP7hAguLHmPI/SfQyiuWW8j/QJ/Ik6ZryPyDNWDSdnfI/nFCIgEOo8j+EmbZ/ZaXyP4NpGD4ipvI/LNSa5h2n8j8Rje4gdqbyP2bCL/XzpvI/OIQqNXug8j9b64uEtpzyP+M2GsBboPI/whcmUwWj8j+FsYUgB6XyP9sWZTbIpPI/Tgte9BWk8j+bIOo+AKnyPyJUqdkDrfI/fqmfNxWp8j8Rje4gdqbyP2SSkbOwp/I/8Z2Y9WKo8j/z5QXYR6fyPxGN7iB2pvI/EHUfgNSm8j/XbrvQXKfyP2bCL/XzpvI/RuuoaoKo8j+693DJcafyP2bCL/XzpvI/8IXJVMGo8j9gArfu5qnyP7nH0ocuqPI//B2KAn2i8j+JQWDl0KLyP8Mv9fOmovI/bypSYWyh8j/7Bbth26LyP/VFQlvOpfI/w0fElEii8j+J0t7gC5PyPxJOC170lfI/GoaPiCmR8j9UjPM3oZDyP1EU6BN5kvI/URToE3mS8j9sQ8U4f5PyP7wA++jUlfI/vRjKiXaV8j+LMhtkkpHyP0/MejGUk/I/vNBcp5GW8j+FQgQcQpXyPzNt/8pKk/I/wGA3bFuU8j+J0t7gC5PyP6QZi6azk/I/T8x6MZST8j//PuPCgZDyPzNt/8pKk/I/znADPj+M8j9d3EYDeIvyPyh+jLlrifI/RPXWwFaJ8j/TMHxETInyPzuNtFTejvI/ahMn9zuU8j/BkNWtnpPyP8QI4dHGkfI/bVuU2SCT8j8zbf/KSpPyP94HILWJk/I/Mj1hiQeU8j9p44i1+JTyPxqGj4gpkfI/FJZ4QNmU8j/c14FzRpTyP99PjZdukvI/b7vQXKeR8j84LXjRV5DyP5j6eVORivI/qFKzB1qB8j/gEKrU7IHyP1Lt0/GYgfI/+z+H+fKC8j/+t5IdG4HyP3LEWnwKgPI/Et+JWS+G8j8TJ/c7FIXyP4O7s3bbhfI/8e8zLhyI8j+E04IXfYXyP5gSSfQyivI/JQaBlUOL8j99s82N6YnyPz8djxmojPI/HebLC7CP8j+x4emVsozyPz3VITfDjfI/Bf9byY6N8j92q+ek943yP1gE/1vJjvI/r5l8s82N8j8HX5hMFYzyPyF2ptB5jfI/c2N6whKP8j/r501FKozyP41iuaXVkPI/51dzgGCO8j9E3QcgtYnyP3o7wmnBi/I/WAT/W8mO8j/TMHxETInyPwmPNo5Yi/I/Px2PGaiM8j+wyRr1EI3yPyF2ptB5jfI/Arfu5qmO8j+wyRr1EI3yP5Rqn47HjPI/Pu3w12SN8j9DrWnecYryPwUv+grSjPI/yxDHuriN8j/r501FKozyP8sQx7q4jfI/xlBOtKuQ8j9P5EnSNZPyP2+70FynkfI/zUBl/PuM8j9VpMLYQpDyP9zXgXNGlPI/HT1+b9Of8j/Nx7WhYpzyPxqGj4gpkfI/B7ZKsDic8j8MdsO2RZnyP96OcFrwovI/AMYzaOif8j9PO/w1WaPyP087/DVZo/I/VOOlm8Sg8j9V+3Q8ZqDyP8R3YtaLofI/ilkvhnKi8j8wZHWr56TyP+C+DpwzovI/Ga2jqgmi8j/7Bbth26LyP6gAGM+gofI//DVZox6i8j/B/1ayY6PyPwaGrG71nPI/dzI4Sl6d8j/VPh2PGajyP2qCqPsApPI/VisTfqmf8j8VHcnlP6TyPxdlNsgko/I/fXkB9tGp8j9jYvNxbajyP0p7gy9MpvI/nYAmwoan8j8OFeP8TajyP1/SGK2jqvI/RIts5/up8j9Ei2zn+6nyP/PlBdhHp/I/8Z2Y9WKo8j/edqG5TqPyP/m9TX/2o/I/3V7SGK2j8j+DaRg+IqbyP03zjlN0pPI/8+UF2Een8j/67evAOaPyPzq0yHa+n/I/NPRPcLGi8j8cJa/OMaDyP23i5H6HovI/o1huaTWk8j9MqyFxj6XyP11y3CkdrPI/K4wtBDmo8j9gArfu5qnyP2byzTY3pvI/fqmfNxWp8j9Ei2zn+6nyP+z17o/3qvI/QSswZHWr8j8m5IOezaryPycs8YCyqfI/fXkB9tGp8j/OHmgFhqzyP842N6YnrPI/Imx4eqWs8j8G9S1zuqzyPwXdXtIYrfI/y9b6IqGt8j8KndfYJaryP2rZWl8ktPI/B2Svd3+88j85evzepr/yPx4bgXhdv/I/4IRCBBzC8j+ztb5IaMvyP8ucLouJzfI/y5wui4nN8j87AU2EDc/yP4qO5PIf0vI/C9KMRdPZ8j+ojH+fceHyPx/5g4Hn3vI/ysNCrWne8j//If32deDyP+Oqsu+K4PI/6Gor9pfd8j+R7Xw/Nd7yP8wjfzDw3PI/HxFTIone8j+OdXEbDeDyPwOy17s/3vI/QDBHj9/b8j/mOo20VN7yP73jFB3J5fI/VK7wLhfx8j/FWnwKgPHyP/iImBJJ9PI/v5oDBHP08j9U3o5wWvDyP91B7Eyh8/I/tvP91Hjp8j82sFWCxeHyP5HVrZ6T3vI/VYfcDDfg8j81mIbhI+LyPzyInSl03vI/PaBsyhXe8j/M8+DurN3yP/eqlQm/1PI/JzEIrBza8j99rrZif9nyP85THXIz3PI/miUBamrZ8j9GIF7XL9jyP52FPe3w1/I/YmcKndfY8j/+8V61MuHyP/d698d71fI/sKw0KQXd8j9kx0YgXtfyP2hXIeUn1fI/NBE2PL3S8j/FrBdDOdHyPyzxgLIp1/I/m1Wfq63Y8j+Cbi9pjNbyP/ZiKCfa1fI/vFzEd2LW8j9qh78ma9TyPzP5Zpsb0/I/vqQxWkfV8j/3qpUJv9TyP6Et51Jc1fI/DjLJyFnY8j+wrDQpBd3yPxEBh1Cl5vI/mnyzzY3p8j/3AUht4uTyP4m1+BQA4/I/NGjon+Di8j8SGVbxRubyP3gGDf0T3PI/XaeRlsrb8j87cM6I0t7yP/3BwHPv4fI/O3DOiNLe8j91jgHZ693yPwdCsoAJ3PI/5QrvchHf8j8L6lvmdNnyP8dLN4lB4PI//cHAc+/h8j/hehSuR+HyP451cRsN4PI/rBxaZDvf8j/oaiv2l93yP09AE2HD0/I/9dvXgXPG8j+1/SsrTcryP5huEoPAyvI/ZXCUvDrH8j8rgv+tZMfyP8CV7NgIxPI/2HxcGyrG8j/zqzlAMMfyP5F++zpwzvI/4pLjTung8j8Cgjl6/N7yP1t381SH3PI/SFD8GHPX8j9DkIMSZtryPy9pjNZR1fI/AOMZNPTP8j/rc7UV+8vyP0aZDTLJyPI/escpOpLL8j/XNO84RcfyP9qs+lxtxfI/ajANw0fE8j+ERxtHrMXyP7wFEhQ/xvI/85Nqn47H8j+ZnrDEA8ryP/XzpiIVxvI/SSno9pLG8j/VBFH3AcjyPytSYWwhyPI/ms5OBkfJ8j+1FfvL7snyP2VwlLw6x/I/DtsWZTbI8j+jHqLRHcTyPw8LtaZ5x/I/U3k7wmnB8j8fSx+6oL7yP1N5O8JpwfI/85Nqn47H8j+mft5UpMLyP/AzLhwIyfI/0oxF09nJ8j9daK7TSMvyP9KMRdPZyfI/8WPMXUvI8j8O2xZlNsjyP6COxwxUxvI/YeC593DJ8j/wMy4cCMnyP3+fceFAyPI/K4L/rWTH8j8rUmFsIcjyP/AbX3tmyfI/X5hMFYzK8j8+eVioNc3yP48ev7fpz/I/96qVCb/U8j8A++jUlc/yP7FVgsXhzPI/INJvXwfO8j/vA5DaxMnyPz55WKg1zfI/LNmxEYjX8j9BSBYwgdvyPz8AqU2c3PI/rBxaZDvf8j/ikuNO6eDyP8/abRea6/I/KLhYUYPp8j/tgVZgyOryP0VHcvkP6fI/LEgzFk3n8j/X+iKhLefyP3u9++O96vI/J6CJsOHp8j8730+Nl+7yP1YOLbKd7/I/dhVSflLt8j+TpGsm3+zyP5S8OseA7PI/3UHsTKHz8j/ecYqO5PLyP7+aAwRz9PI/a2XCL/Xz8j+lg/V/DvPyPy8X8Z2Y9fI/FQDjGTT08j/C+j+H+fLyP2fV52or9vI/LxfxnZj18j8t51JcVfbyP2a9GMqJ9vI/SkbOwp728j8RcAhVavbyPy7/If329fI/Lv8h/fb18j/ecYqO5PLyP75qZcIv9fI/8piByvj38j8wL8A+OvXyP4IclDDT9vI/Zr0Yyon28j9pNSTusfTyP02+2ebG9PI/C7CPTl358j+vWpnwS/3yP1s9J71v/PI/Y0UNpmH48j+PU3Qkl//yP5tyhXe5CPM/NbVsrS8S8z8OvjCZKhjzP2HD0ytlGfM/oaF/gosV8z/bv7LSpBTzPzD186YiFfM/SfQyiuUW8z9kO99PjRfzP0Z8J2a9GPM/L90kBoEV8z/jx5i7lhDzP4rqrYGtEvM/GW7A54cR8z8xJZLoZRTzP8B4Bg39E/M/LsVVZd8V8z/6fmq8dBPzP4QqNXugFfM/fQqA8Qwa8z/0piIVxhbzP+7O2m0XGvM/fToeM1AZ8z/rVs9J7xvzP4pZL4ZyIvM/7rYLzXUa8z9SLLe0GhLzPx8uOe6UDvM/0LhwICQL8z+0WfW52grzP2A8g4b+CfM/YDyDhv4J8z99y5wuiwnzP2A8g4b+CfM/shGI1/UL8z+TUtDtJQ3zP9IA3gIJCvM/tXHEWnwK8z9ATS1b6wvzP5WaPdAKDPM/0LhwICQL8z/nhxHCow3zP4TTghd9BfM/I74Ts14M8z+7YduizAbzPzHO34RCBPM/hgMhWcAE8z9vTE9Y4gHzP4BDqFKzB/M/jPM3oRAB8z/nGJC93v3yP/63kh0bAfM/y6FFtvP98j+a02UxsfnyPzdPdcjN8PI/ijxJumby8j/goSjQJ/LyP7+aAwRz9PI/90ArMGT18j++amXCL/XyP4o8Sbpm8vI/EqCmlq318j/zyB8MPPfyP0t2bATi9fI/vCL430r28j+2SrA4nPnyP5LLf0i//fI/V5V9VwT/8j+UE+0qpPzyP4asbvWc9PI/NNdppKXy8j9xVdl3RfDyP3E9Ctej8PI/x7q4jQbw8j8vF/GdmPXyP0WeJF0z+fI/LedSXFX28j8N4C2QoPjyPwlQU8vW+vI/vCL430r28j+/gjRj0fTyP9nJ4Ch59fI/vAopP6n28j+eY0D2evfyP7UaEvdY+vI/zQGCOXr88j8jZ2FPO/zyP0I+6Nms+vI/XW3F/rL78j94nKIjufzyP+uoaoKo+/I/Tu53KAr08j/KGvUQje7yP1huaTUk7vI/cVXZd0Xw8j/ByqFFtvPyP+84RUdy+fI/XKyowTQM8z9sQ8U4fxPzP18ktOVcCvM/gnNGlPYG8z+sOUAwRw/zP5pC5zV2CfM/0LhwICQL8z9ATS1b6wvzP4Oj5NU5BvM/mkLnNXYJ8z8E54wo7Q3zP8r49xkXDvM/BRcrajAN8z9zY3rCEg/zP+Y/pN++DvM/hCo1e6AV8z9RFOgTeRLzPzcVqTC2EPM/vOgrSDMW8z96U5EKYwvzPxL3WPrQBfM/Q61p3nEK8z8Qr+sX7AbzPz1+b9Of/fI/0ZFc/kP68j+yutVz0vvyPz7t8NdkDfM/W3wKgPEM8z9jtI6qJgjzP9aQuMfSB/M/nwJgPIMG8z9+42vPLAnzPz1+b9Of/fI/DeAtkKD48j+uEiwOZ/7yPwAAAAAAAPM/Oh4zUBn/8j88TtGRXP7yP1jFG5lH/vI/IjfDDfj88j9cVfZdEfzyP0uOO6WD9fI/5dAi2/n+8j/ysFBrmvfyP0uOO6WD9fI/t2J/2T358j9GzsKedvjyP2tlwi/18/I/ppvEILDy8j8vF/GdmPXyP4aUn1T79PI/iQyreCPz8j+9UpYhjvXyP7iq7Lsi+PI/KVd4l4v48j/qkJvhBvzyP3eE04IX/fI/d4TTghf98j9YxRuZR/7yP3P0+L1N//I/yEEJM23/8j+qmiDqPgDzPwSQ2sTJ/fI/6WD9n8P88j+UE+0qpPzyP2IVb2Qe+fI/FNBE2PD08j9lpUkp6PbyP9BhvrwA+/I/I2dhTzv88j9NvtnmxvTyP1YOLbKd7/I/sRu2Lcrs8j/KGvUQje7yP+/Jw0Kt6fI/pPyk2qfj8j/cupunOuTyP9f6IqEt5/I/Xi7iOzHr8j9Ah/nyAuzyP8x6MZQT7fI/WYY41sXt8j+ZZOQs7OnyP5XUCWgi7PI/BoGVQ4vs8j+KJHoZxfLyPzY3pics8fI/Tx4Wak3z8j8eUDblCu/yP9AKDFnd6vI/7ZklAWrq8j/QCgxZ3eryPyLgEKrU7PI/LXjRV5Dm8j+DxeHMr+byP6PMBplk5PI/W0dVE0Td8j9bd/NUh9zyP807TtGR3PI/HxFTIone8j9ZF7fRAN7yP8zz4O6s3fI/Vrd6Tnrf8j8dyeU/pN/yP21Wfa624vI/F/GdmPXi8j/Ie9XKhN/yPzj4wmSq4PI/jC0EOSjh8j/Co40j1uLyP1RvDWyV4PI/51JcVfbd8j9Y/+cwX97yP1a3ek563/I/zPPg7qzd8j/pmsk329zyP2JnCp3X2PI/lWWIY13c8j/VWwNbJdjyP/7xXrUy4fI/HLEWnwLg8j+wrDQpBd3yPz7o2az63PI/ADrMlxfg8j9yFva0w9/yP/VKWYY41vI/16NwPQrX8j8uOe6UDtbyP4E+kSdJ1/I/EcKjjSPW8j9qh78ma9TyPzUpBd1e0vI/T0ATYcPT8j/52jNLAtTyP6gd/pqs0fI/+yKhLefS8j+vJeSDns3yP5PGaB1VzfI/9MMI4dHG8j8PC7WmecfyP0M50a5CyvI/gLdAguLH8j+C/61kx8byPxnKiXYV0vI/j05d+SzP8j8iAg6hSs3yP3BfB84Z0fI/c9cS8kHP8j81KQXdXtLyP6ONI9bi0/I/GJrrNNLS8j/2Yign2tXyP4bOa+wS1fI/a5+OxwzU8j/ZA63AkNXyP7n8h/Tb1/I/nYU97fDX8j+aJQFqatnyP9VDNLqD2PI/8IrgfyvZ8j+4zOmymNjyP7RU3o5w2vI/rTQpBd3e8j9Vh9wMN+DyP8YzaOif4PI/Pbg7a7fd8j9X5xiQvd7yPyqpE9BE2PI/kKUPXVDf8j+QpQ9dUN/yPxk5C3va4fI/kgVM4Nbd8j+SBUzg1t3yP3ke3J212/I/9UpZhjjW8j+G5jqNtNTyP9nTDn9N1vI/LNmxEYjX8j+BPpEnSdfyP/eqlQm/1PI/TRB1H4DU8j9NEHUfgNTyPy9RvTWw1fI/Zw+0AkPW8j8s2bERiNfyP9kDrcCQ1fI/dqbQeY3d8j98fhghPNryPyzZsRGI1/I/SWjLuRTX8j8QkgVM4NbyPwWjkjoBzfI/YeC593DJ8j8xQni0ccTyP4s3Mo/8wfI/p658lufB8j8Uyy2thsTyPw7bFmU2yPI/tOVciqvK8j+1FfvL7snyPyuC/61kx/I/7wOQ2sTJ8j9dUN8yp8vyPyfaVUj5yfI/sYUgByXM8j+PHr+36c/yP5F++zpwzvI/WJBmLJrO8j+sxacAGM/yPytSYWwhyPI/t0WZDTLJ8j9cIEHxY8zyPz55WKg1zfI/1eyBVmDI8j9jEFg5tMjyP48ev7fpz/I/IgIOoUrN8j9xp3Sw/s/yP/6arFEP0fI/pwUv+grS8j/0MorlltbyP5p8s82N6fI/XRYTm4/r8j/Owp52+OvyP3l1jgHZ6/I/jswjfzDw8j9TliGOdfHyPzTXaaSl8vI/WFaalILu8j8AqU2c3O/yP8oa9RCN7vI/6iEa3UHs8j/QCgxZ3eryPyno9pLG6PI/B5lk5Czs8j8p0CfyJOnyP+VhodY07/I/z9ptF5rr8j90tRX7y+7yP2GOHr+36fI/Ug/R6A7i8j9kl6jeGtjyPyvB4nDm1/I/QBh47j3c8j+q1OyBVuDyP3WOAdnr3fI/lWWIY13c8j8rweJw5tfyP/oK0oxF0/I/Zd8Vwf/W8j/Pg7uzdtvyP17XL9gN2/I/QUgWMIHb8j8L0oxF09nyP7Q8D+7O2vI/mN2Th4Xa8j+XxcTm49ryP3WOAdnr3fI/Jhk5C3va8j8AOsyXF+DyP8Fz7+GS4/I/G4F4Xb/g8j+L/WX35OHyP6mkTkAT4fI/gA7z5QXY8j+n7V9ZadLyP0YgXtcv2PI/+yKhLefS8j907+GS487yPwD76NSVz/I/kX77OnDO8j8iGt1B7MzyP7O1vkhoy/I/fCdmvRjK8j/pQxfUt8zyPx1yM9yAz/I/HXIz3IDP8j/CTNu/stLyP8WsF0M50fI/4iNiSiTR8j8ysfm4NtTyPylhpu1f2fI/IoleRrHc8j8/AKlNnNzyP5ClD11Q3/I/ysNCrWne8j+QpQ9dUN/yP+YivhOz3vI/hlW8kXnk8j8ukKD4MebyP2w+rg0V4/I/PdAKDFnd8j+R1a2ek97yP0J4tHHE2vI/YTdsW5TZ8j99rrZif9nyP18HzhlR2vI/X+/+eK/a8j8Kou4DkNryP7VsrS8S2vI/QDBHj9/b8j8iiV5GsdzyPyApIsMq3vI/5jqNtFTe8j8Cgjl6/N7yP/lJtU/H4/I/MzhKXp3j8j/AWyBB8ePyP4ZVvJF55PI/TDeJQWDl8j/p8Xub/uzyP+ohGt1B7PI/vhOzXgzl8j8wwD46deXyP9I6qpog6vI/sAPnjCjt8j8zp8tiYvPyP08GR8mr8/I/GZC93v3x8j+o4zEDlfHyPzTXaaSl8vI//3ivWpnw8j/70AX1LfPyP/sApDZx8vI/ba0vEtry8j+/mgMEc/TyP6ezk8FR8vI/3SkdrP/z8j89J71vfO3yP6ezk8FR8vI/hqxu9Zz08j/FihpMw/DyP/q4NlSM8/I/HAjJAibw8j+KPEm6ZvLyP2yVYHE48/I/YOXQItv58j+IY13cRgPzP1K9NbBVAvM/4lgXt9EA8z+mCkYldQLzPzeOWItPAfM//p/DfHkB8z8g14aKcf7yPx6n6Egu//I/qWqCqPsA8z857pQO1v/yP1jFG5lH/vI/c9wpHaz/8j8H8BZIUPzyP1slWBzO/PI/zNHj9zb98j8gByXMtP3yP5GDEmba/vI//p/DfHkB8z/DgZAsYALzP1jFG5lH/vI/6njMQGX88j9CJhk5C/vyPwuwj05d+fI/QfZ698f78j9EboYb8PnyPwJgPIOG/vI/lPsdigL98j94nKIjufzyP8lxp3Sw/vI/qoJRSZ0A8z8djxmojP/yPwfwFkhQ/PI/n5PeN7728j/B4nDmV/PyP/q4NlSM8/I/Z9Xnaiv28j8TuHU3T/XyP/dY+tAF9fI/EqCmlq318j9GzsKedvjyP5+T3je+9vI/R/5g4Ln38j9oBYasbvXyP02+2ebG9PI/Zr0Yyon28j9Ljjulg/XyP0f+YOC59/I/LedSXFX28j8BwRw9fu/yP3AlOzYC8fI/SS7/If328j+DTDJyFvbyP4NMMnIW9vI/RZ4kXTP58j/u8NdkjfryPyDXhopx/vI/rhIsDmf+8j+T407pYP3yP0Deq1Ym/PI/dCSX/5D+8j/KiXYVUv7yPwSQ2sTJ/fI/dTxmoDL+8j+wcmiR7fzyP3QMyF7v/vI/PX5v05/98j/RkVz+Q/ryPwuYwK27+fI/S16dY0D28j+/mgMEc/TyP51LcVXZ9/I/Gy/dJAYB8z8HCObo8fvyP7GKNzKP/PI/kbOwpx3+8j89ZqAy/v3yP+zAOSNK+/I/s+pztRX78j9FniRdM/nyPyf3OxQF+vI/1PGYgcr48j/QYb68APvyP5m7lpAP+vI/0EnvG1/78j96/N6mP/vyPznulA7W//I/4UBIFjAB8z/+n8N8eQHzPwXAeAYN/fI/y6FFtvP98j++wRcmUwXzP57Swfo/B/M/LA5nfjUH8z+BW3fzVAfzP18M5US7CvM/PgXAeAYN8z9jtI6qJgjzPyYeUDblCvM/mSoYldQJ8z/vj/eqlQnzP37ja88sCfM/Dmd+NQcI8z8whnKiXQXzP0TFOH8TCvM/0egOYmcK8z+yKVd4lwvzPz8djxmoDPM/dqvnpPcN8z9YHM78ag7zPzblCu9yEfM/1xcJbTkX8z+BsilXeBfzP7TIdr6fGvM/RUyJJHoZ8z8sZRniWBfzP4HK+PcZF/M/DHbDtkUZ8z+YUSy3tBrzP8x/SL99HfM/WaMeotEd8z9FTIkkehnzP3S6LCY2H/M/x9eeWRIg8z9u+rMfKSLzP6t4I/PIH/M/QuxMofMa8z8GnnsPlxzzP5mByvj3GfM/7rYLzXUa8z8/jBAebRzzP0G8rl+wG/M/dhppqbwd8z8kRWRYxRvzP7FQa5p3HPM/yk+qfToe8z84bFuU2SDzP48ZqIx/H/M/Zwqd19gl8z+WeEDZlCvzPyXMtP0rK/M/WfrQBfUt8z9wmdNlMTHzPwStwJDVLfM/HHxhMlUw8z+d76fGSzfzP+oENBE2PPM/XeFdLuI78z/w3Hu45DjzP4GQLGACN/M/NWPRdHYy8z+GIAclzDTzP7fWFwltOfM/KxN+qZ838z8og6Pk1TnzP33ogvqWOfM/19081SE38z+ASL99HTjzP+58PzVeOvM/BTQRNjw98z8eM1AZ/z7zPxlz1xLyQfM/EoPAyqFF8z/H9IQlHlDzP+WbbW5MT/M/CAPPvYdL8z8Fo5I6AU3zPwkzbf/KSvM/bHh6pSxD8z9o6J/gYkXzP7q9pDFaR/M/THFV2XdF8z+FX+rnTUXzP39v05/9SPM/lj50QX1L8z/QRNjw9ErzPyaqtwa2SvM/sW1RZoNM8z8IA8+9h0vzP39v05/9SPM/+FPjpZtE8z+DF30FaUbzP0fJq3MMSPM/CRueXilL8z/2C3bDtkXzP1ABMJ5BQ/M/3NyYnrBE8z/fVKTC2ELzP0LKT6p9OvM/001iEFg58z+Uh4Va0zzzP+LkfoeiQPM/fLjkuFM68z8GTODW3TzzP6xWJvxSP/M/IGPuWkI+8z9YObTIdj7zP7a+SGjLOfM/Rloqb0c48z8ps0EmGTnzP9WtnpPeN/M/gWCOHr838z8QtAJDVjfzP4PAyqFFNvM/0QX1LXM68z+ad5yiIznzPyZTBaOSOvM/e4hGdxA78z90mC8vwD7zP8nlP6TfPvM/yy2thsQ98z8wKqkT0ETzPw7bFmU2SPM/WwhyUMJM8z/H9IQlHlDzP5T2Bl+YTPM/okW28/1U8z+oHf6arFHzP+krSDMWTfM//WoOEMxR8z8Ec/T4vU3zPzihEAGHUPM/yCQjZ2FP8z+PTl35LE/zP/+ye/KwUPM/dQexM4VO8z8Ymus00lLzP/+ye/KwUPM/cY+lD11Q8z/9ag4QzFHzP29HOC14UfM/VBhbCHJQ8z97D5ccd0rzP+y7IvjfSvM/t11ortNI8z+DL0ymCkbzPytqMA3DR/M/KSLDKt5I8z8tsp3vp0bzPw7DR8SUSPM/yM1wAz4/8z/ghEIEHELzP6VmD7QCQ/M/2GSNeohG8z+9HeG04EXzPy2yne+nRvM/ajANw0dE8z8eG4F4XT/zP3E486s5QPM//OO9amVC8z9NofMau0TzP0xxVdl3RfM/GXPXEvJB8z//W8mOjUDzPyGTjJyFPfM/JCNnYU878z9GKowtBDnzP45AvK5fMPM/cxHfiVkv8z9XsmMjEC/zPxRcrKjBNPM/ITzaOGIt8z+dgCbChifzP4I5evzeJvM/9UVCW84l8z9+qZ83FSnzP4DxDBr6J/M/C7Wmeccp8z8yrOKNzCPzP/m9TX/2I/M/hIHn3sMl8z8uBDkoYSbzPxKlvcEXJvM/SEvl7Qgn8z/yzTY3pifzP/VFQlvOJfM/Y3rCEg8o8z9kkpGzsCfzP2byzTY3JvM/oijQJ/Ik8z9Ty9b6IiHzPwL20akrH/M/Ci5W1GAa8z99CoDxDBrzP/buj/eqFfM/2XdF8L8V8z9kIxCv6xfzP21zY3rCEvM/4X8r2bER8z/1vvG1ZxbzP1D8GHPXEvM/Fw6EZAET8z8QHm0csRbzP0jcY+lDF/M/YKsEi8MZ8z/v/nivWhnzP/AWSFD8GPM/LsVVZd8V8z99Ik+SrhnzP+kmMQisHPM/58b0hCUe8z+zmNh8XBvzP/AWSFD8GPM/2XdF8L8V8z/ZX3ZPHhbzP39qvHSTGPM/IeUn1T4d8z/RV5BmLBrzPxpMw/ARMfM/EywOZ3418z/fFcH/VjLzPxpMw/ARMfM/6E1FKowt8z+xv+yePCzzP8NfkzXqIfM/NNyAzw8j8z8YldQJaCLzP8NfkzXqIfM/Njy9UpYh8z+SkbOwpx3zP3Tqymd5HvM/qRjnb0Ih8z9QU8vW+iLzPzT0T3CxIvM/vW987Zkl8z8cJa/OMSDzP8x/SL99HfM/c6JdhZQf8z9dM/lmmxvzP7TIdr6fGvM/7uapDrkZ8z/o3sMlxx3zPwVu3c1THfM/QKTfvg4c8z++9sySADXzP6omiLoPQPM/ZohjXdxG8z+6vaQxWkfzP4xn0NA/QfM/F0M50a5C8z+KH2PuWkLzPxcrajANQ/M/Vtl3RfA/8z9VwaikTkDzP6VmD7QCQ/M/ou4DkNpE8z963/jaM0vzP0tZhjjWRfM/ZqAy/n1G8z9jKCfaVUjzP0WBPpEnSfM/aOif4GJF8z9JERlW8UbzPz+RJ0nXTPM//gkuVtRg8z+7s3bbhWbzP2sOEMzRY/M/oFT7dDxm8z9hjh6/t2nzPxWRYRVvZPM/CLEzhc5r8z9ybagY52/zP39N1qiHaPM/J4i6D0Bq8z/ZsRGI13XzP69amfBLffM/q7LviuB/8z9OXfksz4PzP4OLFTWYhvM/i8OZX82B8z8aFw6EZIHzP00tW+uLhPM/+z+H+fKC8z/3r6w0KYXzP9MwfERMifM/yuAoeXWO8z8qrir7rojzP32zzY3pifM/xAjh0caR8z/LZ3ke3J3zP/nVHCCYo/M/lgm/1M+b8z+oABjPoKHzPyzUmuYdp/M/5q4l5IOe8z8gzVg0nZ3zP6pgVFInoPM/36Y/+5Gi8z/ejnBa8KLzPxrdQexMofM/xHdi1ouh8z8C9tGpK5/zPzm0yHa+n/M/OIQqNXug8z/mriXkg57zP3KKjuTyn/M/qTC2EOSg8z8GnnsPl5zzPxsN4C2QoPM/V1uxv+ye8z/iHksfuqDzP4CCixU1mPM/ayv2l92T8z/7rgj+t5LzPyc2H9eGivM/silXeJeL8z9tizIbZJLzP3gLJCh+jPM/YoTwaOOI8z/+DkWBPpHzP54pdF5jl/M/n0FD/wSX8z/iHksfuqDzP4pZL4ZyovM/cXK/Q1Gg8z8gtYmT+53zPyQtlbcjnPM//DVZox6i8z/IXu/+eK/zPyf8Uj9vqvM/U8vW+iKh8z//lZUmpaDzP+/+eK9amfM/gcr49xmX8z/MKJZbWo3zP+vnTUUqjPM/jpJX5xiQ8z8VxhaCHJTzP0/kSdI1k/M/nKIjufyH8z+30QDeAonzP7DJGvUQjfM/dqvnpPeN8z+fWRKgppbzP3Drbp7qkPM/fPKwUGua8z+iQJ/Ik6TzP4Zyol2FlPM/tXHEWnyK8z+s4o3MI3/zP7vyWZ4Hd/M/BcB4Bg198z+M8zehEIHzP240gLdAgvM/NC4cCMmC8z/M6bKY2HzzP2wE4nX9gvM/nKIjufyH8z+coiO5/IfzP/IHA8+9h/M/SZ2AJsKG8z96a2CrBIvzP3EDPj+MkPM/L90kBoGV8z+FQgQcQpXzP7cos0EmmfM/rvAuF/Gd8z+iuU4jLZXzPzENw0fElPM/NxWpMLaQ8z8Ab4EExY/zP0n0MorllvM/9x4uOe6U8z++MJkqGJXzPy7FVWXflfM/idLe4AuT8z/4Tsx6MZTzPy196IL6lvM/gbIpV3iX8z/Hv8+4cKDzPx+F61G4nvM/dQKaCBue8z/o9pLGaJ3zP23i5H6HovM/EF1Q3zKn8z/1FaQZi6bzPxdNZyeDo/M/NNyAzw+j8z+V2SCTjJzzP7u4jQbwlvM/7uapDrmZ8z/nhxHCo43zP8BgN2xblPM/NJ2dDI6S8z9tizIbZJLzPzSdnQyOkvM/r5l8s82N8z891SE3w43zPwVu3c1TnfM/JC2VtyOc8z/XFwltOZfzP1R0JJf/kPM/FcYWghyU8z9UdCSX/5DzPx4Wak3zjvM/NbVsrS+S8z8Id2fttovzP6Pp7GRwlPM/La2GxD2W8z9hw9MrZZnzP1hzgGCOnvM/s4AJ3Lqb8z9nmxvTE5bzP+/+eK9amfM/E2ba/pWV8z8J/reSHZvzP+PHmLuWkPM/EAaeew+X8z/v/nivWpnzP+kOYmcKnfM/ryDNWDSd8z88FAX6RJ7zPwRWDi2ynfM/B7ZKsDic8z/iHksfuqDzP3Fyv0NRoPM/rthfdk+e8z8+dEF9y5zzPz1EozuInfM/lMFR8uqc8z+byTfb3JjzP+7O2m0XmvM/9x4uOe6U8z+GcqJdhZTzPw6+MJkqmPM/Mj1hiQeU8z/v/nivWpnzP8yXF2AfnfM/vodLjjul8z+XkA96NqvzP2pq2VpfpPM/aCJseHql8z9m8s02N6bzPyOERxtHrPM/JLTlXIqr8z89m1Wfq63zPyJUqdkDrfM/i+B/K9mx8z8DlfHvM67zP8X+snvysPM/GARWDi2y8z8yA5Xx77PzP7t+wW7YtvM/Rloqb0e48z+L4H8r2bHzP8Fu2LYos/M/H0sfuqC+8z+Tb7a5Mb3zP8xFfCdmvfM/WDm0yHa+8z898gcDz73zP/AzLhwIyfM/hxbZzvfT8z+PTl35LM/zPxia6zTS0vM/FmpN847T8z/OUx1yM9zzPyl5dY4B2fM/ZvfkYaHW8z9fB84ZUdrzPwwCK4cW2fM/wOyePCzU8z8tIR/0bNbzPwhyUMJM2/M/ceZXc4Dg8z9Ol8XE5uPzP4P1fw7z5fM/CfmgZ7Pq8z9olgSoqeXzPwKCOXr83vM/oBUYsrrV8z8VqTC2EOTzP1t381SH3PM/8bp+wW7Y8z9iZwqd19jzP5GDEmba/vM/JH8w8Nz78z9S7dPxmAH0P7QCQ1a3+vM/WyVYHM788z+2SrA4nPnzPzP5Zpsb0/M/toR80LPZ8z/1SlmGONbzP+tztRX7y/M/wRw9fm/T8z99rrZif9nzP8Fz7+GS4/M/p1zhXS7i8z9sPq4NFePzP3IW9rTD3/M/3NJqSNzj8z95dY4B2evzP71SliGO9fM/ijxJumby8z/LSpNS0O3zP+VhodY07/M/SddMvtnm8z8OuRluwOfzP98a2CrB4vM/1sqEX+rn8z9q3nGKjuTzP8djBirj3/M/LGACt+7m8z/w4ZLjTunzPzlAMEeP3/M/5QrvchHf8z9Qx2MGKuPzP658lufB3fM/iIVa07zj8z8dyeU/pN/zP6AVGLK61fM/glZgyOrW8z9bR1UTRN3zP5VliGNd3PM/6ZrJN9vc8z8E4nX9gt3zP9Vz0vvG1/M/YB+duvLZ8z+y9KEL6tvzP0YIjzaO2PM/hv4JLlbU8z9xd9Zuu9DzPw7DR8SUyPM/UAEwnkHD8z++TX/2I8XzP4jX9Qt2w/M/lj50QX3L8z+SlsrbEc7zPxiyutVz0vM/HVpkO9/P8z8A4xk09M/zPznRrkLKz/M/j05d+SzP8z+TxmgdVc3zPzeJQWDl0PM/7dPxmIHK8z9YqDXNO87zPyV6GcVyy/M/8WPMXUvI8z+nrnyW58HzPzAqqRPQxPM/q5UJv9TP8z+f5Xlwd9bzP85THXIz3PM/uoPYmULn8z8ZOQt72uHzP30Facai6fM/f2WlSSno8z+WrfVFQtvzP4lGdxA70/M/8IrgfyvZ8z9M4NbdPNXzP7ecS3FV2fM/FCLgEKrU8z+ADvPlBdjzP/CK4H8r2fM/mN2Th4Xa8z91djI4St7zPyJxj6UP3fM/BhIUP8bc8z+qvB3htODzP+UK73IR3/M/EhlW8Ubm8z9Bt5c0RuvzPwxZ3eo56fM/2CrB4nDm8z9Ah/nyAuzzPynQJ/Ik6fM/Wp4Hd2ft8z8dOGdEae/zP4ts5/up8fM/HlA25Qrv8z9Ybmk1JO7zP6dc4V0u4vM/d76fGi/d8z8fEVMiid7zP5aVJqWg2/M/d76fGi/d8z8iiV5GsdzzPwdCsoAJ3PM/kgVM4Nbd8z8dyeU/pN/zPx/5g4Hn3vM/ke18PzXe8z8baam8HeHzP6rsuyL43/M/j41AvK7f8z9NEHUfgNTzP7jkuFM62PM/+5EiMqzi8z987ZklAerzPxbZzvdT4/M/LXjRV5Dm8z8+P4wQHu3zPyVATS1b6/M/nMQgsHLo8z/6uDZUjPPzP4C8V61M+PM/EEBqEyf38z92bATidf3zP1MFo5I6AfQ/oBov3SQG9D8XnwJgPAP0P+D42jNLAvQ/FVeVfVcE9D/7P4f58gL0P1Zl3xXB//M/WvW52or98z9yxFp8CgD0P+lILv8h/fM/A3gLJCj+8z+uKvuuCP7zP4czv5oDBPQ/k+NO6WD98z/u8NdkjfrzP7K61XPS+/M/ecxAZfz78z9cVfZdEfzzP30Facai6fM/bVZ9rrbi8z8SSfQyiuXzP30Facai6fM/M6fLYmLz8z+kaybfbPPzP3l1jgHZ6/M/VN6OcFrw8z889x4uOe7zPxWpMLYQ5PM/7rH0oQvq8z/meXB31u7zP5OMnIU97fM/q0Nuhhvw8z+v6xfshu3zP+VhodY07/M/j+TyH9Lv8z8i4BCq1OzzP4GVQ4ts5/M/cT0K16Pw8z/jMQOV8e/zP+MBZVOu8PM/JnDrbp7q8z8OuRluwOfzP2ZmZmZm5vM/mfViKCfa8z86WP/nMN/zP1ovhnKi3fM/wqONI9bi8z8BUps4ud/zPxGq1OyB1vM/bhea6zTS8z+2LcpskMnzP1rwoq8gzfM/YeC593DJ8z/H9IQlHtDzP5PeN772zPM/zywJUFPL8z8o8iTpmsnzP2kAb4EExfM/d2fttgvN8z8A++jUlc/zP/d698d71fM/ICkiwyre8z9GIF7XL9jzP+0qpPyk2vM/JGJKJNHL8z90sP7PYb7zPzliLT4FwPM/W5nwS/288z/j/E0oRMDzP49OXfksz/M/wRw9fm/T8z+Cbi9pjNbzP4guqG+Z0/M/zywJUFPL8z+vJeSDns3zP1d4l4v4zvM/rMWnABjP8z/9gt2wbdHzP23/ykqT0vM/x/SEJR7Q8z82cXK/Q9HzP24Xmus00vM/bM8sCVDT8z+hLedSXNXzP3GndLD+z/M/PEm6ZvLN8z9tkElGzsLzP+eMKO0NvvM/NLqD2JnC8z8eG4F4Xb/zP+m8xi5RvfM/5SzsaYe/8z8KndfYJarzPw==\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4901]},\"High\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4901]},\"Open\":{\"__ndarray__\":\"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More proper data science is an exercise for the keen reader.\n", "\n", "Some instant optimization tips that come to mind are:\n", "* **Data is king.** Make sure your design matrix features as best as possible model and correlate with your chosen target variable(s) and not just represent random noise.\n", "* Instead of modelling a single target variable $y$, model a multitude of target/class variables, possibly better designed than our \"48-hour returns\" above.\n", "* **Model everything:** forecast price, volume, time before it \"takes off\", SL/TP levels,\n", " [optimal position size](https://en.wikipedia.org/wiki/Kelly_criterion#Application_to_the_stock_market)\n", " ...\n", "* Reduce\n", " [false positives](https://en.wikipedia.org/wiki/False_positive_rate)\n", " by increasing the conviction needed and imposing extra domain expertise and discretionary limitations before entering trades.\n", "\n", "Also make sure to familiarize yourself with the full\n", "[Backtesting.py API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: doc/examples/Trading with Machine Learning.py ================================================ # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.17.1 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %% [markdown] # # Trading with Machine Learning Models # # This tutorial will show how to train and backtest a # [machine learning](https://en.wikipedia.org/wiki/Machine_learning) # price forecast model with _backtesting.py_ framework. It is assumed you're already familiar with # [basic framework usage](https://kernc.github.io/backtesting.py/doc/examples/Quick%20Start%20User%20Guide.html) # and machine learning in general. # # For this tutorial, we'll use almost a year's worth sample of hourly EUR/USD forex data: # %% from backtesting.test import EURUSD, SMA data = EURUSD.copy() data # %% [markdown] # In # [supervised machine learning](https://en.wikipedia.org/wiki/Supervised_learning), # we try to learn a function that maps input feature vectors (independent variables) into known output values (dependent variable): # # $$ f\colon X \to \mathbf{y} $$ # # That way, provided our model function is sufficient, we can predict future output values from the newly acquired input feature vectors to some degree of certainty. # In our example, we'll try to map several price-derived features and common technical indicators to the price point two days in the future. # We construct [model design matrix](https://en.wikipedia.org/wiki/Design_matrix) $X$ below: # %% def BBANDS(data, n_lookback, n_std): """Bollinger bands indicator""" hlc3 = (data.High + data.Low + data.Close) / 3 mean, std = hlc3.rolling(n_lookback).mean(), hlc3.rolling(n_lookback).std() upper = mean + n_std*std lower = mean - n_std*std return upper, lower close = data.Close.values sma10 = SMA(data.Close, 10) sma20 = SMA(data.Close, 20) sma50 = SMA(data.Close, 50) sma100 = SMA(data.Close, 100) upper, lower = BBANDS(data, 20, 2) # Design matrix / independent features: # Price-derived features data['X_SMA10'] = (close - sma10) / close data['X_SMA20'] = (close - sma20) / close data['X_SMA50'] = (close - sma50) / close data['X_SMA100'] = (close - sma100) / close data['X_DELTA_SMA10'] = (sma10 - sma20) / close data['X_DELTA_SMA20'] = (sma20 - sma50) / close data['X_DELTA_SMA50'] = (sma50 - sma100) / close # Indicator features data['X_MOM'] = data.Close.pct_change(periods=2) data['X_BB_upper'] = (upper - close) / close data['X_BB_lower'] = (lower - close) / close data['X_BB_width'] = (upper - lower) / close data['X_Sentiment'] = ~data.index.to_series().between('2017-09-27', '2017-12-14') # Some datetime features for good measure data['X_day'] = data.index.dayofweek data['X_hour'] = data.index.hour data = data.dropna().astype(float) # %% [markdown] # Since all our indicators work only with past values, we can safely precompute the design matrix in advance. Alternatively, we would reconstruct the matrix every time before training the model. # # Notice the made-up _sentiment_ feature. In real life, one would obtain similar features by parsing news sources, Twitter sentiment, Stocktwits or similar. # This is just to show input data can contain all sorts of additional explanatory columns. # # As mentioned, our dependent variable will be the price (return) two days in the future, simplified into values $1$ when the return is positive (and significant), $-1$ when negative, or $0$ when the return after two days is roughly around zero. Let's write some functions that return our model matrix $X$ and dependent, class variable $\mathbf{y}$ as plain NumPy arrays: # %% import numpy as np def get_X(data): """Return model design matrix X""" return data.filter(like='X').values def get_y(data): """Return dependent variable y""" y = data.Close.pct_change(48).shift(-48) # Returns after roughly two days y[y.between(-.004, .004)] = 0 # Devalue returns smaller than 0.4% y[y > 0] = 1 y[y < 0] = -1 return y def get_clean_Xy(df): """Return (X, y) cleaned of NaN values""" X = get_X(df) y = get_y(df).values isnan = np.isnan(y) X = X[~isnan] y = y[~isnan] return X, y # %% [markdown] # Let's see how our data performs modeled using a simple # [k-nearest neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) # (kNN) algorithm from the state of the art # [scikit-learn](https://scikit-learn.org) # Python machine learning package. # To avoid (or at least demonstrate) # [overfitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html), # always split your data into _train_ and _test_ sets; in particular, don't validate your model performance on the same data it was built on. # %% import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split X, y = get_clean_Xy(data) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0) clf = KNeighborsClassifier(7) # Model the output based on 7 "nearest" examples clf.fit(X_train, y_train) y_pred = clf.predict(X_test) _ = pd.DataFrame({'y_true': y_test, 'y_pred': y_pred}).plot(figsize=(15, 2), alpha=.7) print('Classification accuracy: ', np.mean(y_test == y_pred)) # %% [markdown] # We see the forecasts are all over the place (classification accuracy 42%), but is the model of any use under real backtesting? # # Let's backtest a simple strategy that buys the asset for 20% of available equity with 20:1 leverage whenever the forecast is positive (the price in two days is predicted to go up), # and sells under the same terms when the forecast is negative, all the while setting reasonable stop-loss and take-profit levels. Notice also the steady use of # [`data.df`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data) # accessor: # %% # %%time from backtesting import Backtest, Strategy N_TRAIN = 400 class MLTrainOnceStrategy(Strategy): price_delta = .004 # 0.4% def init(self): # Init our model, a kNN classifier self.clf = KNeighborsClassifier(7) # Train the classifier in advance on the first N_TRAIN examples df = self.data.df.iloc[:N_TRAIN] X, y = get_clean_Xy(df) self.clf.fit(X, y) # Plot y for inspection self.I(get_y, self.data.df, name='y_true') # Prepare empty, all-NaN forecast indicator self.forecasts = self.I(lambda: np.repeat(np.nan, len(self.data)), name='forecast') def next(self): # Skip the training, in-sample data if len(self.data) < N_TRAIN: return # Proceed only with out-of-sample data. Prepare some variables high, low, close = self.data.High, self.data.Low, self.data.Close current_time = self.data.index[-1] # Forecast the next movement X = get_X(self.data.df.iloc[-1:]) forecast = self.clf.predict(X)[0] # Update the plotted "forecast" indicator self.forecasts[-1] = forecast # If our forecast is upwards and we don't already hold a long position # place a long order for 20% of available account equity. Vice versa for short. # Also set target take-profit and stop-loss prices to be one price_delta # away from the current closing price. upper, lower = close[-1] * (1 + np.r_[1, -1]*self.price_delta) if forecast == 1 and not self.position.is_long: self.buy(size=.2, tp=upper, sl=lower) elif forecast == -1 and not self.position.is_short: self.sell(size=.2, tp=lower, sl=upper) # Additionally, set aggressive stop-loss on trades that have been open # for more than two days for trade in self.trades: if current_time - trade.entry_time > pd.Timedelta('2 days'): if trade.is_long: trade.sl = max(trade.sl, low) else: trade.sl = min(trade.sl, high) bt = Backtest(data, MLTrainOnceStrategy, commission=.0002, margin=.05) bt.run() # %% bt.plot() # %% [markdown] # Despite our lousy win rate, the strategy seems profitable. Let's see how it performs under # [walk-forward optimization](https://en.wikipedia.org/wiki/Walk_forward_optimization), # akin to k-fold or leave-one-out # [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29): # %% # %%time class MLWalkForwardStrategy(MLTrainOnceStrategy): def next(self): # Skip the cold start period with too few values available if len(self.data) < N_TRAIN: return # Re-train the model only every 20 iterations. # Since 20 << N_TRAIN, we don't lose much in terms of # "recent training examples", but the speed-up is significant! if len(self.data) % 20: return super().next() # Retrain on last N_TRAIN values df = self.data.df[-N_TRAIN:] X, y = get_clean_Xy(df) self.clf.fit(X, y) # Now that the model is fitted, # proceed the same as in MLTrainOnceStrategy super().next() bt = Backtest(data, MLWalkForwardStrategy, commission=.0002, margin=.05) bt.run() # %% bt.plot() # %% [markdown] # Apparently, when repeatedly retrained on past `N_TRAIN` data points in a rolling manner, our basic model generalizes poorly and performs not quite as well. # # This was a simple and contrived, tongue-in-cheek example that shows one way to use machine learning forecast models with _backtesting.py_ framework. # In reality, you will need a far better feature space, better models (cf. # [deep learning](https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_networks)), # and better money management strategies to achieve # [consistent profits](https://en.wikipedia.org/wiki/Day_trading#Profitability) # in automated short-term forex trading. More proper data science is an exercise for the keen reader. # # Some instant optimization tips that come to mind are: # * **Data is king.** Make sure your design matrix features as best as possible model and correlate with your chosen target variable(s) and not just represent random noise. # * Instead of modelling a single target variable $y$, model a multitude of target/class variables, possibly better designed than our "48-hour returns" above. # * **Model everything:** forecast price, volume, time before it "takes off", SL/TP levels, # [optimal position size](https://en.wikipedia.org/wiki/Kelly_criterion#Application_to_the_stock_market) # ... # * Reduce # [false positives](https://en.wikipedia.org/wiki/False_positive_rate) # by increasing the conviction needed and imposing extra domain expertise and discretionary limitations before entering trades. # # Also make sure to familiarize yourself with the full # [Backtesting.py API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules) ================================================ FILE: doc/pdoc_template/config.mako ================================================ <%! html_lang = 'en' show_inherited_members = False extract_module_toc_into_sidebar = True list_class_variables_in_index = True sort_identifiers = True show_type_annotations = False show_source_code = False google_search_query = ''' inurl:kernc.github.io/backtesting.py inurl:github.com/kernc/backtesting.py ''' from pdoc.html_helpers import glimpse as _glimpse # Make visible the code block from the first paragraph of the # `backtesting.backtesting` module def glimpse(text, *args, **kwargs): return _glimpse(text, max_length=180, paragraph=False) %> ================================================ FILE: doc/pdoc_template/credits.mako ================================================ <%! from backtesting import __version__ %>

backtesting ${__version__} 卐

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Backtesting.py
================================================ FILE: doc/scripts/ipython_config.py ================================================ # In build.sh, this file is copied into (and removed from) # ~/.ipython/profile_default/startup/ import pandas as pd pd.set_option("display.max_rows", 30) # This an alternative to setting display.preceision=2, # which doesn't work well for our dtype=object Series. pd.set_option('display.float_format', '{:.2f}'.format) del pd ================================================ FILE: doc/scripts/logo.py ================================================ from bokeh.io import show, output_file from bokeh.models import ColumnDataSource from bokeh.plotting import figure output_file("backtesting_logo.html") source = ColumnDataSource(data=dict( colors=[['#00a618', '#d0d000', 'tomato'][i] for i in [0, 0, 1, 0, 1, 0, 0, 1, 0, 2]], x=list(range(10)), bottom=[1, 3, 4, 3, 2, 3, 5, 5, 7, 6.5], top= [4, 7, 6, 5, 4, 6, 8, 7, 9, 8])) # noqa: E222,E251 p = figure(plot_height=800, plot_width=1200, tools='wheel_zoom,save') p.vbar('x', .6, 'bottom', 'top', source=source, line_color='black', line_width=2, fill_color='colors') p.xgrid.grid_line_color = None p.ygrid.grid_line_color = None p.y_range.start = -2 p.y_range.end = 12 p.x_range.start = -2 p.x_range.end = 11 p.background_fill_color = None p.border_fill_color = None show(p) ================================================ FILE: doc/scripts/strip_yaml.awk ================================================ #!/usr/bin/awk -f # Remove YAML front matter from jupytext-converted .py notebooks BEGIN { drop = 0; } /^# ---$/ { if (NR <= 3) { drop = 1 } else { drop = 0; next } } drop == 0 { print } ================================================ FILE: pyproject.toml ================================================ [tool.ruff] exclude = [ '.git', '.eggs', '__pycache__', 'doc/examples', ] ignore = [ 'UP006', 'UP007', 'UP009', 'N802', 'N806', 'C901', 'B008', 'B011', 'RUF002', ] line-length = 100 select = [ 'I', 'E', 'F', 'W', 'UP', 'N', 'C', 'B', 'T', 'RUF', 'YTT', ] [tool.ruff.pep8-naming] ignore-names = [ 'l', 'h', ] ================================================ FILE: requirements.txt ================================================ # To run example notebooks, install required and test dependencies .[test] ================================================ FILE: setup.cfg ================================================ [flake8] # F824 `nonlocal x` is unused: name is never assigned in scope # W503 Line break before a binary operator # W504 Line break after a binary operator -- https://www.flake8rules.com/rules/W504.html ignore = F824, W503, W504 max-line-length = 120 exclude = .git, .eggs, __pycache__, doc/examples [mypy] warn_unused_ignores = True warn_redundant_casts = True ignore_missing_imports = True [coverage:run] parallel = 1 concurrency = multiprocessing,thread disable_warnings = no-data-collected source = backtesting doc/examples omit = [coverage:report] exclude_lines = ^\s*continue\b ^\s*return\b ^\s*raise\b ^\s*except\b ^\s*warnings\.warn\( ^\s*warn\( ================================================ FILE: setup.py ================================================ import os import sys if sys.version_info < (3, 9): sys.exit('ERROR: Backtesting.py requires Python 3.9+') if __name__ == '__main__': from setuptools import setup, find_packages setup( name='backtesting', description="Backtest trading strategies in Python", license='AGPL-3.0', url='https://kernc.github.io/backtesting.py/', project_urls={ 'Documentation': 'https://kernc.github.io/backtesting.py/doc/backtesting/', 'Source': 'https://github.com/kernc/backtesting.py/', 'Tracker': 'https://github.com/kernc/backtesting.py/issues', }, long_description=open(os.path.join(os.path.dirname(__file__), 'README.md'), encoding='utf-8').read(), long_description_content_type='text/markdown', packages=find_packages(), include_package_data=True, setup_requires=[ 'setuptools_git', 'setuptools_scm', ], use_scm_version={ 'write_to': os.path.join('backtesting', '_version.py'), }, install_requires=[ 'numpy >= 1.17.0', 'pandas >= 0.25.0, != 0.25.0', 'bokeh >= 3.0.0, != 3.0.*, != 3.2.*', ], extras_require={ 'doc': [ 'pdoc3', 'jupytext >= 1.3', 'nbconvert', 'ipykernel', # for nbconvert 'jupyter_client', # for nbconvert ], 'test': [ 'matplotlib', 'scikit-learn', 'sambo', 'tqdm', 'ipywidgets', # for tqdm ], 'dev': [ 'flake8', 'coverage', 'mypy', ], }, test_suite="backtesting.test", python_requires='>=3.9', author='Zach Lûster', classifiers=[ 'Intended Audience :: Financial and Insurance Industry', 'Intended Audience :: Science/Research', 'Framework :: Jupyter', 'License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Topic :: Office/Business :: Financial :: Investment', 'Topic :: Scientific/Engineering :: Visualization', ], keywords=[ 'algo', 'algorithmic', 'ashi', 'backtest', 'backtesting', 'bitcoin', 'bokeh', 'bonds', 'candle', 'candlestick', 'cboe', 'chart', 'cme', 'commodities', 'crash', 'crypto', 'currency', 'doji', 'drawdown', 'equity', 'etf', 'ethereum', 'exchange', 'finance', 'financial', 'forecast', 'forex', 'fund', 'futures', 'fx', 'fxpro', 'gold', 'heiken', 'historical', 'indicator', 'invest', 'investing', 'investment', 'macd', 'market', 'mechanical', 'money', 'oanda', 'ohlc', 'ohlcv', 'order', 'price', 'profit', 'quant', 'quantitative', 'rsi', 'silver', 'simulation', 'stocks', 'strategy', 'ticker', 'trader', 'trading', 'tradingview', 'usd', ], )