commit b1066f16fb7b3f81cfafe16c49e67962d3739508 Author: Kernc Date: Sun Jan 21 11:25:28 2018 +0100 Add initial Backtesting.py (squashed dev branch) diff --git a/.codecov.yml b/.codecov.yml new file mode 100644 index 0000000..17a9901 --- /dev/null +++ b/.codecov.yml @@ -0,0 +1,10 @@ +coverage: + range: 75..95 + precision: 0 + status: + patch: + default: + target: '95' + project: + default: + target: auto diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..460a54f --- /dev/null +++ b/.coveragerc @@ -0,0 +1,15 @@ +[run] +parallel = 1 +concurrency = + multiprocessing +source = + backtesting + doc/examples +omit = + +[report] +exclude_lines = + return + raise + except + warnings.warn diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5f5f951 --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +*.py[cod] +*.html +*.png +_version.py + +*.egg-info +__pycache__/* +dist/* + +.coverage +.coverage.* +htmlcov/* + +doc/build/* + +.idea/* +**/.ipynb_checkpoints +*~* diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..cae68e5 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,40 @@ +language: python +dist: trusty +sudo: false +cache: + pip: true + +matrix: + fast_finish: true + include: + - python: '3.5' + - python: '3.7' + + - python: '3.6' + name: 'Lint, Test w/ Coverage' + before_script: + - pip install flake8 coverage + script: + - flake8 --max-line-length=120 --exclude doc/examples . + - BOKEH_BROWSER=none catchsegv coverage run setup.py test + after_success: + - bash <(curl -s https://codecov.io/bash) + + - python: '3.6' + name: 'Docs' + stage: deploy + install: + - pip install .[doc] + script: + - doc/build.sh + after_success: + - if [ "$TRAVIS_BRANCH" = "$TRAVIS_TAG" ]; then bash doc/deploy.sh; fi + +before_install: + - set -eu + +install: + - pip install . + +script: + - time catchsegv python setup.py test diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..cba6f6a --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,660 @@ +### GNU AFFERO GENERAL PUBLIC LICENSE + +Version 3, 19 November 2007 + +Copyright (C) 2007 Free Software Foundation, Inc. + + +Everyone is permitted to copy and distribute verbatim copies of this +license document, but changing it is not allowed. + +### Preamble + +The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + +The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Additional Terms. + +"Additional permissions" are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. +Additional permissions that are applicable to the entire Program shall +be treated as though they were included in this License, to the extent +that they are valid under applicable law. If additional permissions +apply only to part of the Program, that part may be used separately +under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + +When you convey a copy of a covered work, you may at your option +remove any additional permissions from that copy, or from any part of +it. (Additional permissions may be written to require their own +removal in certain cases when you modify the work.) 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Termination. + +You may not propagate or modify a covered work except as expressly +provided under this License. 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You may not convey a covered work if you +are a party to an arrangement with a third party that is in the +business of distributing software, under which you make payment to the +third party based on the extent of your activity of conveying the +work, and under which the third party grants, to any of the parties +who would receive the covered work from you, a discriminatory patent +license (a) in connection with copies of the covered work conveyed by +you (or copies made from those copies), or (b) primarily for and in +connection with specific products or compilations that contain the +covered work, unless you entered into that arrangement, or that patent +license was granted, prior to 28 March 2007. + +Nothing in this License shall be construed as excluding or limiting +any implied license or other defenses to infringement that may +otherwise be available to you under applicable patent law. + +#### 12. No Surrender of Others' Freedom. + +If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under +this License and any other pertinent obligations, then as a +consequence you may not convey it at all. For example, if you agree to +terms that obligate you to collect a royalty for further conveying +from those to whom you convey the Program, the only way you could +satisfy both those terms and this License would be to refrain entirely +from conveying the Program. + +#### 13. Remote Network Interaction; Use with the GNU General Public License. + +Notwithstanding any other provision of this License, if you modify the +Program, your modified version must prominently offer all users +interacting with it remotely through a computer network (if your +version supports such interaction) an opportunity to receive the +Corresponding Source of your version by providing access to the +Corresponding Source from a network server at no charge, through some +standard or customary means of facilitating copying of software. This +Corresponding Source shall include the Corresponding Source for any +work covered by version 3 of the GNU General Public License that is +incorporated pursuant to the following paragraph. + +Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the work with which it is combined will remain governed by version +3 of the GNU General Public License. + +#### 14. Revised Versions of this License. + +The Free Software Foundation may publish revised and/or new versions +of the GNU Affero General Public License from time to time. Such new +versions will be similar in spirit to the present version, but may +differ in detail to address new problems or concerns. + +Each version is given a distinguishing version number. If the Program +specifies that a certain numbered version of the GNU Affero General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU Affero General Public License, you may choose any version ever +published by the Free Software Foundation. + +If the Program specifies that a proxy can decide which future versions +of the GNU Affero General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + +Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + +#### 15. Disclaimer of Warranty. + +THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT +WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND +PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE +DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR +CORRECTION. + +#### 16. Limitation of Liability. + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR +CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, +INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES +ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT +NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR +LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM +TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER +PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. + +#### 17. Interpretation of Sections 15 and 16. + +If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + +END OF TERMS AND CONDITIONS + +### How to Apply These Terms to Your New Programs + +If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these +terms. + +To do so, attach the following notices to the program. It is safest to +attach them to the start of each source file to most effectively state +the exclusion of warranty; and each file should have at least the +"copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as + published by the Free Software Foundation, either version 3 of the + License, or (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper +mail. + +If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for +the specific requirements. + +You should also get your employer (if you work as a programmer) or +school, if any, to sign a "copyright disclaimer" for the program, if +necessary. For more information on this, and how to apply and follow +the GNU AGPL, see . diff --git a/README.md b/README.md new file mode 100644 index 0000000..388a6ad --- /dev/null +++ b/README.md @@ -0,0 +1,19 @@ +Backtesting.py +============== +Backtest trading strategies with Python. + +[![Build Status](https://travis-ci.org/kernc/backtesting.py.svg?branch=master)](https://travis-ci.org/kernc/backtesting.py) +[![Code Coverage](https://codecov.io/gh/kernc/backtesting.py/branch/master/graph/badge.svg)](https://codecov.io/gh/kernc/backtesting.py) +[![Backtesting on PyPI](https://img.shields.io/pypi/pyversions/backtesting.svg)](https://pypi.org/project/backtesting/) + +[**Project website**](https://kernc.github.io/backtesting.py/) + +[Documentation](https://kernc.github.io/backtesting.py/doc/backtesting/) + +Development +----------- +Fork the project. Then: + + git clone git@github.com:YOUR_USERNAME/backtesting.py + cd backtesting.py + pip3 install -e .[doc] diff --git a/backtesting/__init__.py b/backtesting/__init__.py new file mode 100644 index 0000000..cd518d8 --- /dev/null +++ b/backtesting/__init__.py @@ -0,0 +1,40 @@ +""" +# Backtesting.py Documentation + +## Manuals + +* [Quick Start User Guide](../examples/Quick Start User Guide.html) + +## Tutorials + +* [Library of Utilities and Composable Base Strategies](../examples/Strategies Library.html) +* [Multiple Time Frames](../examples/Multiple Time Frames.html) +* [Parameter Heatmap](../examples/Parameter Heatmap.html) + +You can also [try these out] live. + +[try these out]: https://mybinder.org/v2/gh/kernc/backtesting.py/master?urlpath=lab%2Ftree%2Fdoc%2Fexamples + +## Example Strategies + +* (contributions welcome) + +## License + +This software is licensed under the terms of [AGPL 3.0], +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` +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__ # noqa: F401 +except ImportError: + pass # Package not installed + +from .backtesting import Backtest, Strategy, Orders, Position # noqa: F401 +from . import lib # noqa: F401 diff --git a/backtesting/_plotting.py b/backtesting/_plotting.py new file mode 100644 index 0000000..b58f193 --- /dev/null +++ b/backtesting/_plotting.py @@ -0,0 +1,599 @@ +import os +import warnings +from itertools import cycle, combinations +from functools import partial + +import numpy as np +import pandas as pd + +from bokeh.plotting import figure as _figure +from bokeh.models import ( + CustomJS, + ColumnDataSource, + NumeralTickFormatter, + Span, + HoverTool, + Range1d, + DatetimeTickFormatter, + FuncTickFormatter, + WheelZoomTool, + LinearColorMapper, +) +from bokeh.io import 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 + +from backtesting._util import _data_period + + +IS_JUPYTER_NOTEBOOK = 'JPY_PARENT_PID' in os.environ + +if IS_JUPYTER_NOTEBOOK: + warnings.warn('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 `bokeh.io.reset_output()`.') + output_notebook() + + +with open(os.path.join(os.path.dirname(__file__), 'autoscale_cb.js'), + encoding='utf-8') as _f: + _AUTOSCALE_JS_CALLBACK = _f.read() + + +def _bokeh_reset(filename=None): + curstate().reset() + # Test if we are in Jupyter notebook + if IS_JUPYTER_NOTEBOOK: + curstate().output_notebook() + elif filename: + if not filename.endswith('.html'): + filename += '.html' + + output_file(filename, title=filename) + + +def colorgen(): + yield from cycle(Category10[10]) + + +def plot(*, results, df, indicators, filename='', plot_width=1200, + plot_equity=True, plot_pl=True, + plot_volume=True, plot_drawdown=False, + smooth_equity=False, relative_equity=True, omit_missing=True, + superimpose=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). + _bokeh_reset(filename) + + COLORS = ['tomato', 'lime'] + COLORS_LIGHT = ['#ffe3e3', '#e3ffe3'] + + orig_trade_data = trade_data = results._trade_data.copy(False) + + orig_df = df = df.copy(False) + df.index.name = None # Provides source name @index + index = df.index + time_resolution = getattr(index, 'resolution', None) + is_datetime_index = index.is_all_dates + + # If all Volume is NaN, don't plot volume + plot_volume = plot_volume and not df.Volume.isnull().all() + + # OHLC vbar width in msec. + # +1 will work in case of non-datetime index where vbar width should just be =1 + width = 1 + dict(day=86400, + hour=3600, + minute=60, + second=1).get(time_resolution, 0) * 1000 * .85 + + if is_datetime_index: + # Add index as a separate data source column because true .index is offset to align vbars + df['datetime'] = index + df.index = df.index + pd.Timedelta(width / 2, unit='ms') + + if omit_missing: + width = .8 + df = df.reset_index(drop=True) + trade_data = trade_data.reset_index(drop=True) + index = df.index + + new_bokeh_figure = partial( + _figure, + x_axis_type='datetime' if is_datetime_index and not omit_missing else 'linear', + plot_width=plot_width, + plot_height=400, + tools="xpan,xwheel_zoom,box_zoom,undo,redo,reset,crosshair,save", + active_drag='xpan', + active_scroll='xwheel_zoom') + + pad = (index[-1] - index[0]) / 20 + + fig_ohlc = new_bokeh_figure( + x_range=Range1d(index[0], index[-1], + bounds=(index[0] - pad, + index[-1] + pad)) if index.size > 1 else None) + figs_above_ohlc, figs_below_ohlc = [], [] + + source = ColumnDataSource(df) + source.add((df.Close >= df.Open).values.astype(np.uint8).astype(str), 'inc') + returns = trade_data['Returns'].dropna() + trade_source = ColumnDataSource(dict( + index=returns.index, + datetime=orig_trade_data['Returns'].dropna().index, + exit_price=trade_data['Exit Price'].dropna(), + returns_pos=(returns > 0).astype(np.int8).astype(str), + )) + + inc_cmap = factor_cmap('inc', COLORS, ['0', '1']) + cmap = factor_cmap('returns_pos', COLORS, ['0', '1']) + + if is_datetime_index and omit_missing: + fig_ohlc.xaxis.formatter = FuncTickFormatter( + args=dict(axis=fig_ohlc.xaxis[0], + formatter=DatetimeTickFormatter(days=['%d %b', '%a %d'], + months=['%m/%Y', "%b'%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 = ' ' * 4 + ohlc_extreme_values = df[['High', 'Low']].copy(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('plot_height', 90) + 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 + return fig + + def set_tooltips(fig, tooltips=(), vline=True, renderers=(), show_arrow=True): + tooltips = list(tooltips) + renderers = list(renderers) + + if is_datetime_index: + formatters = dict(datetime='datetime') + tooltips = [("Date", "@datetime{%c}")] + tooltips + else: + formatters = {} + tooltips = [("#", "@index")] + tooltips + fig.add_tools(HoverTool( + point_policy='follow_mouse', + renderers=renderers, formatters=formatters, show_arrow=show_arrow, + tooltips=tooltips, mode='vline' if vline else 'mouse')) + + def _plot_equity_section(): + """Equity section""" + # Max DD Dur. line + equity = trade_data['Equity'] + argmax = trade_data['Drawdown Duration'].idxmax() + try: + dd_start = equity[:argmax].idxmax() + except Exception: # ValueError: attempt to get argmax of an empty sequence + dd_start = dd_end = equity.index[0] + timedelta = 0 + else: + dd_end = (equity[argmax:] > equity[dd_start]).idxmax() + if dd_end == argmax: + dd_end = index[-1] + if is_datetime_index and omit_missing: + # "Calendar" duration + timedelta = df.datetime.iloc[dd_end] - df.datetime.iloc[dd_start] + else: + timedelta = dd_end - dd_start + # Get point intersection + if dd_end != index[-1]: + x1, x2 = index.get_loc(dd_end) - 1, index.get_loc(dd_end) + y, y1, y2 = equity[dd_start], equity[x1], equity[x2] + dd_end -= (1 - (y - y1) / (y2 - y1)) * (dd_end - index[x1]) # y = a x + b + + if smooth_equity: + select = (trade_data[['Entry Price', + 'Exit Price']].dropna(how='all').index | + # Include beginning + equity.index[:1] | + # Include max dd end points. Otherwise, the MaxDD line looks amiss. + pd.Index([dd_start, dd_end])) + equity = equity[select].reindex(equity.index) + equity.interpolate(inplace=True) + + if relative_equity: + equity /= equity.iloc[0] + + source.add(equity, 'equity') + fig = new_indicator_figure( + y_axis_label="Equity", + **({} if plot_drawdown else dict(plot_height=110))) + + # 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', 'equity', source=source, line_width=1.5, line_alpha=1) + if relative_equity: + tooltip_format = '@equity{+0,0.[000]%}' + tick_format = '0,0.[00]%' + legend_format = '{:,.0f}%' + else: + tooltip_format = '@equity{$ 0,0}' + tick_format = '$ 0.0 a' + legend_format = '${:,.0f}' + set_tooltips(fig, [('Equity', tooltip_format)], renderers=[r]) + fig.yaxis.formatter = NumeralTickFormatter(format=tick_format) + + # Peaks + argmax = equity.idxmax() + fig.scatter(argmax, equity[argmax], + legend='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='Final ({})'.format( + legend_format.format(equity.iloc[-1] * (100 if relative_equity else 1))), + color='blue', size=8) + + if not plot_drawdown: + drawdown = trade_data['Drawdown'] + argmax = drawdown.idxmax() + fig.scatter(argmax, equity[argmax], + legend='Max Drawdown (-{:.1f}%)'.format(100 * drawdown[argmax]), + color='red', size=8) + fig.line([dd_start, dd_end], equity[dd_start], + line_color='red', line_width=2, + legend='Max Dd Dur. ({})'.format(timedelta) + .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") + drawdown = trade_data['Drawdown'] + argmax = drawdown.idxmax() + source.add(drawdown, 'drawdown') + r = fig.line('index', 'drawdown', source=source, line_width=1.3) + fig.scatter(argmax, drawdown[argmax], + legend='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") + fig.add_layout(Span(location=0, dimension='width', line_color='#666666', + line_dash='dashed', line_width=1)) + position = trade_data['Exit Position'].dropna() + returns_long = returns.copy() + returns_short = returns.copy() + returns_long[position < 0] = np.nan + returns_short[position > 0] = np.nan + trade_source.add(returns_long, 'returns_long') + trade_source.add(returns_short, 'returns_short') + MARKER_SIZE = 13 + r1 = fig.scatter('index', 'returns_long', source=trade_source, fill_color=cmap, + marker='triangle', line_color='black', size=MARKER_SIZE) + r2 = fig.scatter('index', 'returns_short', source=trade_source, fill_color=cmap, + marker='inverted_triangle', line_color='black', size=MARKER_SIZE) + set_tooltips(fig, [("P/L", "@returns_long{+0.[000]%}")], vline=False, renderers=[r1]) + set_tooltips(fig, [("P/L", "@returns_short{+0.[000]%}")], vline=False, renderers=[r2]) + fig.yaxis.formatter = NumeralTickFormatter(format="0.[00]%") + return fig + + def _plot_volume_section(): + """Volume section""" + fig = new_indicator_figure(y_axis_label="Volume") + 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', 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""" + resample_rule = (superimpose if isinstance(superimpose, str) else + dict(day='W', + hour='D', + minute='H', + second='T', + millisecond='S').get(time_resolution)) + if not resample_rule: + warnings.warn( + "'Can't superimpose OHLC data with rule '{}' (index datetime resolution: '{}'). " + "Skipping.".format(resample_rule, time_resolution), + stacklevel=4) + return + + orig_df['_width'] = 1 + from .lib import OHLCV_AGG + df2 = orig_df.resample(resample_rule, label='left').agg(dict(OHLCV_AGG, _width='count')) + + # Check if resampling was downsampling; error on upsampling + orig_freq = _data_period(orig_df) + resample_freq = _data_period(df2) + 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 + + if omit_missing: + width2 = '_width' + df2.index = df2['_width'].cumsum().shift(1).fillna(0) + df2.index += df2['_width'] / 2 - .5 + df2['_width'] -= .1 # Candles don't touch + else: + del df['_width'] + width2 = dict(day=86400 * 5, + hour=86400, + minute=3600, + second=60)[time_resolution] * 1000 + df2.index += pd.Timedelta( + width2 / 2 + + (width2 / 5 if resample_rule == 'W' else 0), # Sunday week start + unit='ms') + df2['inc'] = (df2.Close >= df2.Open).astype(np.uint8).astype(str) + df2.index.name = None + source2 = ColumnDataSource(df2) + fig_ohlc.segment('index', 'High', 'index', 'Low', source=source2, color='#bbbbbb') + fig_ohlc.vbar('index', width2, 'Open', 'Close', source=source2, line_color=None, + fill_color=factor_cmap('inc', COLORS_LIGHT, ['0', '1'])) + + def _plot_ohlc(): + """Main OHLC bars""" + fig_ohlc.segment('index', 'High', 'index', 'Low', source=source, color="black") + r = fig_ohlc.vbar('index', width, 'Open', 'Close', source=source, + line_color="black", fill_color=inc_cmap) + return r + + def _plot_ohlc_orders(): + """Order entry / exit markers on OHLC plot""" + exit_price = trade_data['Exit Price'].dropna() + entry_price = trade_data['Entry Price'].dropna().iloc[:exit_price.size] # entry can be one more at the end # noqa: E501 + trade_source.add(np.column_stack((entry_price.index, exit_price.index)).tolist(), + 'position_lines_xs') + trade_source.add(np.column_stack((entry_price, exit_price)).tolist(), + 'position_lines_ys') + fig_ohlc.multi_line(xs='position_lines_xs', ys='position_lines_ys', + source=trade_source, line_color=cmap, + legend='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("Can't plot indicators with >2D ('{}')".format(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. + # This also prevents legend items named the same as some + # ColumnDataSource's column to be replaced with that column's + # values. + def __eq__(self, other): + return self is other + + ohlc_colors = colorgen() + + for value in indicators: + value = np.atleast_2d(value) + + # Use .get()! A user might have assigned a Strategy.data-evolved + # _Array without Strategy.I() + if not value._opts.get('plot') or _too_many_dims(value): + continue + + color = value._opts['color'] + tooltips = [] + + # Overlay indicators on the OHLC figure + if value._opts['overlay']: + color = color or next(ohlc_colors) + legend = LegendStr(value.name) + for i, arr in enumerate(value): + source_name = '{}_{}'.format(value.name, i) + source.add(arr, source_name) + fig_ohlc.line('index', source_name, source=source, + line_width=1.3, line_color=color, legend=legend) + ohlc_extreme_values[source_name] = arr + tooltips.append('@{{{}}}{{0,0.0[0000]}}'.format(source_name)) + ohlc_tooltips.append((value.name, NBSP.join(tooltips))) + else: + # Standalone indicator sections at the bottom + color = color or colorgen() + fig = new_indicator_figure() + for i, arr in enumerate(value, 1): + legend = '{}-{}'.format(value.name, i) if len(value) > 1 else value.name + name = legend + '_' # Otherwise fig.line(legend=) is interpreted as col of source # noqa: E501 + tooltips.append('@{{{}}}'.format(name)) + source.add(arr.astype(int if arr.dtype == bool else float), name) + r = fig.line('index', name, source=source, + line_color=next(color), line_width=1.3, legend=LegendStr(legend)) + + # Add dashed centerline just because + mean = float(pd.Series(arr).mean()) + if not np.isnan(mean) and (abs(mean) < .1 or + round(abs(mean), -1) in (50, 100, 200)): + fig.add_layout(Span(location=float(mean), dimension='width', + line_color='#666666', line_dash='dashed', + line_width=.5)) + + set_tooltips(fig, [(value.name, 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 + + figs_below_ohlc.append(fig) + + # Construct figure ... + + if plot_equity: + _plot_equity_section() + + 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() + _plot_ohlc_orders() + _plot_indicators() + + 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.callback = CustomJS(args=custom_js_args, + code=_AUTOSCALE_JS_CALLBACK) + + plots = figs_above_ohlc + [fig_ohlc] + figs_below_ohlc + for f in plots: + if f.legend: + f.legend.location = 'top_left' if show_legend else None + 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.min_border_left = 0 + f.min_border_top = 3 + f.min_border_bottom = 6 + f.min_border_right = 10 + f.outline_line_color = '#666666' + + wheelzoom_tool = next(wz for wz in f.tools if isinstance(wz, WheelZoomTool)) + wheelzoom_tool.maintain_focus = False + + fig = gridplot( + plots, + ncols=1, + toolbar_location='right', + # sizing_mode='stretch_width', + toolbar_options=dict(logo=None), + merge_tools=True, + ) + show(fig, browser=None if open_browser else 'none') + return fig + + +def plot_heatmaps(heatmap: pd.Series, agg: 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)`') + + _bokeh_reset() + if filename: + output_file(filename) + + param_combinations = combinations(heatmap.index.names, 2) + dfs = [heatmap.groupby(list(dims)).agg(agg).to_frame(name='_Value') + for dims in param_combinations] + plots = [] + 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, + y_range=level2, + x_axis_label=name1, + y_axis_label=name2, + plot_width=plot_width // ncols, + plot_height=plot_width // ncols, + tools='box_zoom,reset,save', + tooltips=[(name1, '@' + name1), + (name2, '@' + name2), + ('Value', '@_Value{0.[000]}')]) + fig.grid.grid_line_color = None + fig.axis.axis_line_color = None + fig.axis.major_tick_line_color = None + fig.axis.major_label_standoff = 0 + + fig.rect(x=name1, + y=name2, + width=1, + height=1, + source=df, + line_color=None, + fill_color=dict(field='_Value', + transform=cmap)) + plots.append(fig) + + fig = gridplot( + plots, + ncols=ncols, + toolbar_options=dict(logo=None), + toolbar_location='above', + merge_tools=True, + ) + + show(fig, browser=None if open_browser else 'none') + return fig diff --git a/backtesting/_util.py b/backtesting/_util.py new file mode 100644 index 0000000..a2eb448 --- /dev/null +++ b/backtesting/_util.py @@ -0,0 +1,135 @@ + +from numbers import Number + +import numpy as np + + +def _as_str(value): + if isinstance(value, (Number, str)): + return str(value) + name = str(getattr(value, 'name', '') or '') + if callable(value): + name = value.__name__.replace('', '') + if name in ('Open', 'High', 'Low', 'Close'): + name = name[:1] + if len(name) > 10: + name = name[:9] + '…' + return name + + +def _data_period(df): + """Return data index period as pd.Timedelta""" + return df.index[:100].to_series().diff().median() + + +class _Array(np.ndarray): + """ + ndarray extended to supply .name and other arbitrary properties + in ._opts dict. + """ + def __new__(cls, array, name=None, write=False, **kwargs): + obj = np.asarray(array).view(cls) + obj.name = name or array.name + obj._opts = kwargs + if not write: + obj.setflags(write=False) + return obj + + def __array_finalize__(self, obj): + if obj is not None: + self.name = getattr(obj, 'name', '') + self._opts = getattr(obj, '_opts', {}) + + 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__() + + +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): + self.__i = len(df) + self.__pip = None + self.__cache = {} + + self.__arrays = {col: _Array(arr, data=self) + for col, arr in df.items()} + # Leave index as Series because pd.Timestamp nicer API to work with + self.__arrays['__index'] = df.index.copy() + + def __getitem__(self, item): + return getattr(self, item) + + def __getattr__(self, item): + try: + return self.__get_array(item) + except KeyError: + raise KeyError("Column '{}' not in data".format(item)) from None + + def _set_length(self, i): + self.__i = i + self.__cache.clear() + + def __len__(self): + return self.__i + + @property + def pip(self): + if self.__pip is None: + self.__pip = 10**-np.median([len(s.partition('.')[-1]) + for s in self.__arrays['Close'].astype(str)]) + return self.__pip + + def __get_array(self, key): + arr = self.__cache.get(key) + if arr is None: + arr = self.__cache[key] = self.__arrays[key][:self.__i] + return arr + + @property + def Open(self): + return self.__get_array('Open') + + @property + def High(self): + return self.__get_array('High') + + @property + def Low(self): + return self.__get_array('Low') + + @property + def Close(self): + return self.__get_array('Close') + + @property + def Volume(self): + return self.__get_array('Volume') + + @property + def index(self): + 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 diff --git a/backtesting/autoscale_cb.js b/backtesting/autoscale_cb.js new file mode 100644 index 0000000..09f75b1 --- /dev/null +++ b/backtesting/autoscale_cb.js @@ -0,0 +1,64 @@ +if (!window._bt_extremes) + window._bt_extremes = function (arr, initial, agg_func) { + const CHUNK = 32768; + let extreme = initial; + for (let i = 0, len = arr.length; i < len; i += CHUNK) { + const subarr = CHUNK >= len ? arr : arr.slice(i, i + CHUNK); + extreme = agg_func(extreme, agg_func.apply(null, subarr)); + } + return extreme; + }; + +if (!window._bt_bin_search) + window._bt_bin_search = function (index, value) { + let mid, + min = 0, + max = index.length - 1; + + while (min < max) { + mid = (min + max) / 2 | 0; + if (index[mid] < value) + min = mid + 1; + else + max = mid - 1; + } + return min; + }; + +if (!window._bt_scale_range) + window._bt_scale_range = function (range, highs, lows) { + const max = _bt_extremes(highs, -Infinity, Math.max), + min = lows && _bt_extremes(lows, Infinity, Math.min); + if (min !== Infinity && max !== -Infinity) { + const pad = (max - min) * .03; + range.start = min - pad; + range.end = max + pad; + } + }; + +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`. + */ + + let index = source.data['index'], + i = Math.max(_bt_bin_search(index, cb_obj.start) - 1, 0), + j = Math.min(_bt_bin_search(index, cb_obj.end) + 1, index.length); + + _bt_scale_range( + ohlc_range, + source.data['ohlc_high'].slice(i, j), + source.data['ohlc_low'].slice(i, j)); + try { + _bt_scale_range( + volume_range, + source.data['Volume'].slice(i, j), + 0); + } catch (e) {} // volume_range may be undefined + +}, 50); diff --git a/backtesting/backtesting.py b/backtesting/backtesting.py new file mode 100644 index 0000000..6303893 --- /dev/null +++ b/backtesting/backtesting.py @@ -0,0 +1,987 @@ +""" +Core backtesting data structures. +Stuff from this module can be imported from the top-level +module directly, e.g. + + from backtesting import Backtest, Strategy +""" +import os +import re +import sys +import warnings +from abc import abstractmethod, ABCMeta +from collections import Sequence +from concurrent.futures import ProcessPoolExecutor, as_completed +from functools import partial +from itertools import repeat, product, chain +from numbers import Number +from typing import Callable, Union, Tuple + +import numpy as np +import pandas as pd + +from ._plotting import plot +from ._util import _as_str, _Indicator, _Data, _data_period + + +__pdoc__ = { + 'Strategy.__init__': None, + 'Orders.__init__': None, + 'Position.__init__': None, +} + + +_MARKET_PRICE = 'market' + + +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): + self._indicators = [] + self._broker = broker # type: _Broker + self._data = data # type: _Data + self._params = {} + + def __repr__(self): + return '' + + def __str__(self): + params = ','.join('{}={}'.format(*p) for p in zip(self._params.keys(), + map(_as_str, self._params.values()))) + if params: + params = '(' + params + ')' + return '{}{}'.format(self.__class__.__name__, params) + + def _set_params(self, **kwargs): + for k, v in kwargs.items(): + if not hasattr(self, k): + raise AttributeError( + "Strategy '{}' is missing parameter '{}'. Strategy class " + "should define parameters as class variables before they " + "can be optimized or run with.".format(self.__class__.__name__, k)) + + setattr(self, k, v) + self._params = kwargs + + def I(self, # noqa: E743 + func: Callable, *args, + name=None, plot=True, overlay=None, color=None, + **kwargs) -> np.ndarray: + """ + Declare indicator. An indicator is just an array of values, + 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 of + same length as `backtesting.backtesting.Strategy.data`. + + In the plot legend, the indicator is labeled with + function name, unless `name` overrides it. + + 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. By default, the next + available color is assigned. + + 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) + """ + if name is None: + params = ','.join(filter(None, map(_as_str, chain(args, kwargs.values())))) + func_name = func.__name__.replace('', 'λ') + name = ('{}({})' if params else '{}').format(func_name, params) + else: + name = name.format(*map(_as_str, args), + **dict(zip(kwargs.keys(), map(_as_str, kwargs.values())))) + + value = func(*args, **kwargs) + + try: + value = np.asarray(value) + except Exception: + raise ValueError('Indicators must return array-like sequences of values') + if value.shape[-1] != len(self._data.Close): + raise ValueError('Indicators must be arrays of same length as `data`') + + if plot and overlay is None: + 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, plot=plot, overlay=overlay, color=color, + # lib.resample_apply() uses this: + data=self.data) + 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() + """ + + def buy(self, price=None, *, sl=None, tp=None): + """ + Let the strategy close any current position and + use _all available funds_ to + buy the asset for `price`, + optionally entering two other orders: + one at stop-loss price (`sl`; stop-limit order) and + one at take-profit price (`tp`; limit order). + + If `price` is not set, market price is assumed. + """ + self._broker.buy(price and float(price), + sl and float(sl), + tp and float(tp)) + + def sell(self, price=None, *, sl=None, tp=None): + """ + Let the strategy close any current position and + use _all available funds_ to + short sell the asset for `price`, + optionally entering two other orders: + one at stop-loss price (`sl`; stop-limit order) and + one at take-profit price (`tp`; limit order). + + If `price` is not set, market price is assumed. + """ + self._broker.sell(price and float(price), + sl and float(sl), + tp and float(tp)) + + @property + def equity(self): + """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. + + """ + return self._data + + @property + def position(self): + """Instance of `backtesting.backtesting.Position`.""" + return self._broker.position + + @property + def orders(self): + """Instance of `backtesting.backtesting.Orders`.""" + return self._broker.orders + + +class Orders: + """ + Orders waiting for execution, available as + `backtesting.backtesting.Strategy.orders` within + `backtesting.backtesting.Strategy.next`. + + Implied limit and stop-limit orders (taking profits and stopping loss) + are always present; set the limit price with + `backtesting.backtesting.Orders.set_sl` and + `backtesting.backtesting.Orders.set_tp`. + """ + def __init__(self, broker): + self._broker = broker + self._entry = self._sl = self._tp = self._close = self._is_long = None + + def _update(self, entry, sl, tp, is_long=True): + self._entry = entry and float(entry) or _MARKET_PRICE + self._sl = sl and float(sl) or None + self._tp = tp and float(tp) or None + self._close = False + self._is_long = is_long + + @property + def is_long(self): + """True if the waiting entry order is long.""" + return self._is_long + + @property + def is_short(self): + """True if the waiting entry order is short.""" + return not self._is_long + + @property + def entry(self): + """Price at which to enter the position if hit.""" + return self._entry + + @property + def sl(self): + """Stop-loss (stop-limit) price at which to exit the position if hit.""" + return self._sl + + @property + def tp(self): + """Take-profit (limit) price at which to exit the position if hit.""" + return self._tp + + def __is_price_ok(self, price, is_limit_order): + assert price is None or price > 0 + if not price: + return + market_price = self._broker.last_close + # Entry (market/limit) or TP are limit orders, SL is stop order + if (is_limit_order and (self._is_long and price < market_price or + not self._is_long and price > market_price) or + not is_limit_order and (self._is_long and price > market_price or + not self._is_long and price < market_price)): + raise ValueError("Setting the target price as sepcified would trigger " + "the order immediately -- this is forbidden. " + "Use `position.close()` to close the position, or similar.") + + def set_entry(self, price): + """Set new entry price of the implied limit order).""" + if self._entry is None and price is not None: + raise RuntimeError("Can't reset order for position entry. " + "The order has been already executed or no " + "buy/sell order was put in place.") + self.__is_price_ok(price, True) + self._entry = price and float(price) + + def set_sl(self, price): + """Set new stop-loss price (of the implied stop-limit order).""" + if self._entry is None and not self._broker._position: + raise RuntimeError("You don't currently hold a position to set " + "stop-loss for.") + self.__is_price_ok(price, False) + self._sl = price and float(price) + + def set_tp(self, price): + """Set new take-profit price (of the implied limit order).""" + if self._entry is None and not self._broker._position: + raise RuntimeError("You don't currently hold a position to set " + "take-profit limit for.") + self.__is_price_ok(price, True) + self._tp = price and float(price) + + def cancel(self): + """Cancel all implied orders.""" + self._entry = self._sl = self._tp = self._close = self._is_long = None + + def __bool__(self): + return bool(self._entry or self._sl or self._tp or self._close) + + def __repr__(self): + return '' % (self._entry or np.nan, + self._sl or np.nan, + self._tp or np.nan, + self._close or 0) + __str__ = __repr__ + + +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): + self._broker = broker + + def __bool__(self): + return self.size != 0 + + @property + def size(self): + """Position size in units of asset. Negative if position is short.""" + return self._broker._position + + @property + def open_price(self): + """Price at which the position was opened.""" + return self._broker._position_open_price + + @property + def open_time(self): + """Data index value at which the position was opened.""" + i = self._broker._position_open_i + return i if i is None else self._broker._data.index[i] + + def _pl(self, price): + open, size = self.open_price, self.size + pl = (price - open) * size + pl -= open * self._broker._commission * abs(size) + return pl + + @property + def pl(self): + """Profit (positive) or loss (negative) of current position.""" + return self._pl(self._broker._data.Close[-1]) + + @property + def pl_pct(self): + """ + Profit (positive) or loss (negative) of current position, + in percent of position open price. + """ + return self.pl / (self.open_price * abs(self.size)) + + @property + def is_long(self): + """True if the position is long (position size is positive).""" + return self.size > 0 + + @property + def is_short(self): + """True if the position is short (position size is negative).""" + return self.size < 0 + + def close(self): + """Close the position at current market price.""" + self._broker.close() + + def __repr__(self): + return '' % self.size + + +class _OutOfMoneyError(Exception): + pass + + +class _Broker: + class _Log: + def __init__(self, length): + self.equity = np.tile(np.nan, length) + self.exit_entry = np.tile(np.nan, length) + self.exit_position = np.tile(np.nan, length) + self.entry_price = np.tile(np.nan, length) + self.exit_price = np.tile(np.nan, length) + self.pl = np.tile(np.nan, length) + + def __init__(self, *, data, cash, commission, margin, trade_on_close, length): + assert 0 < cash, "cash shosuld be >0, is {}".format(cash) + assert 0 <= commission < .1, "commission should be between 0-10%, is {}".format(commission) + assert 0 < margin <= 1, "margin should be between 0 and 1, is {}".format(margin) + self._data = data # type: _Data + self._cash = cash + self._commission = commission + self._leverage = 1 / margin + self._trade_on_close = trade_on_close + self._position = 0 + self._position_open_price = 0 + self._position_open_i = None + self.log = self._Log(length) + self.position = Position(self) + self.orders = Orders(self) + + def __repr__(self): + return ''.format(self._cash, self.position.pl) + + def buy(self, price=None, sl=None, tp=None): + assert (sl or -np.inf) <= (price or self.last_close) <= (tp or np.inf), (sl, price or self.last_close, tp) # noqa: E501 + self.orders._update(price, sl, tp) + + def sell(self, price=None, sl=None, tp=None): + assert (tp or -np.inf) <= (price or self.last_close) <= (sl or np.inf), (tp, price or self.last_close, sl) # noqa: E501 + self.orders._update(price, sl, tp, is_long=False) + + def close(self): + self.orders.cancel() + self.orders._close = True + + def _get_market_price(self, price): + i = self._i + if price in (_MARKET_PRICE, None): + price = self._data.Open[-1] + if self._trade_on_close: + price = self._data.Close[-2] + i -= 1 + return i, price + + @property + def last_close(self): + """Return price at the last (current) close. + Used e.g. in `Orders._is_price_ok()` to see if the set price is reasonable. + """ + return self._data.Close[-1] + + def _open_position(self, price, is_long): + assert not self._position + self.orders.set_entry(None) + + i, price = self._get_market_price(price) + + position = float(self._cash * self._leverage / (price * (1 + self._commission))) + self._position = position if is_long else -position + self._position_open_price = price + self._position_open_i = i + + self.log.entry_price[i] = price + + def _close_position(self, price=None): + if not self._position: + return + + i, price = self._get_market_price(price) + pl = self.position._pl(price) + + self.log.pl[i] = pl + self.log.exit_entry[i] = self._position_open_i + self.log.exit_price[i] = price + self.log.exit_position[i] = self._position + + self._cash += pl + self._position = 0 + + @property + def equity(self): + return self._cash + self.position.pl + + def next(self): + data = self._data + i = self._i = len(data) - 1 + + if self.orders: + orders = self.orders + is_long = orders._is_long + entry, sl, tp = orders._entry, orders._sl, orders._tp + open, high, low = data.Open[-1], data.High[-1], data.Low[-1] + + if entry or orders._close: + self._close_position() + orders._close = False + + # First make the entry order, if hit + if entry: + if entry is _MARKET_PRICE or high > orders._entry > low: + self._open_position(entry, is_long) + + # Check if stop-loss threshold was hit + if sl and self._position: + price = (sl if low <= sl <= high else # hit + open if (is_long and open < sl or # gapped hit + not is_long and open > sl) else + None) # not hit + if price is not None: + self._close_position(price) + self.orders.cancel() + + # Check if take-profit threshold was hit + if tp and self._position: + price = (tp if low < tp < high else + open if (is_long and open > tp or + not is_long and open > sl) else + None) + if price is not None: + self._close_position(price) + self.orders.cancel() + + # Log account equity for the equity curve + equity = self.equity + self.log.equity[i] = equity + + # Hovever, if negative, set all to 0 and stop the simulation + if equity < 0: + self._close_position() + self._cash = 0 + self.log.equity[i:] = 0 + raise _OutOfMoneyError + + +class Backtest: + """ + Backtest a particular (parameterized) strategy + on particular data. + + Upon initialization, call method + `backtesting.backtesting.Backtest.run` to run a backtest + instance, or `backtesting.backtesting.Backtest.optimize` to + optimize it. + """ + def __init__(self, + data: pd.DataFrame, + strategy: type(Strategy), + *, + cash: float = 10000, + commission: float = .0, + margin: float = 1., + trade_on_close=False + ): + """ + Initialize a backtest. Requires data and a strategy to test. + + `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'] + + DataFrame index can be either datetime index (timestamps) + or a monotonic range index (i.e. a sequence of periods). + + `strategy` is a `backtesting.backtesting.Strategy` + _subclass_ (not instance). + + `cash` is the initial cash to start with. + + `commission` is the commision ratio. E.g. if your broker's commission + is 1% of trade value, set commission to `0.01`. Note, if you wish to + account for bid-ask spread, you approximate doing so by increasing + the commission, e.g. set it to `0.0002` for commission-less forex + trading where average spread is roughly 0.2‰ of asking price. + + `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 leverge your broker allows, + set margin to `0.02`. + + If `trade_on_close` is `True`, market orders will be executed + with respect to the current bar's closing price instead of the + next bar's open. + """ + + if not (isinstance(strategy, type) and issubclass(strategy, Strategy)): + raise TypeError('`strategy` must be a Strategy sub-type') + if not isinstance(commission, Number): + raise TypeError('`commission` must be a float value, percent of ' + 'entry order price') + + data = data.copy(False) + + # Convert index to datetime index + if (not data.index.is_all_dates 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.columns & {'Open', 'High', 'Low', 'Close', 'Volume'}) != 5: + raise ValueError("`data` must be a pandas.DataFrame with columns " + "'Open', 'High', 'Low', 'Close', and (optionally) 'Volume'") from None + if data[['Open', 'High', 'Low', 'Close']].max().isnull().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 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 data.index.is_all_dates: + warnings.warn('Data index is not datetime. Assuming simple periods.', + stacklevel=2) + + self._data = data # type: pd.DataFrame + self._broker = partial( + _Broker, cash=cash, commission=commission, margin=margin, + trade_on_close=trade_on_close, length=len(data) + ) + self._strategy = strategy + self._results = None + + def run(self, **kwargs) -> pd.Series: + """ + Run the backtest. Returns `pd.Series` with results and statistics. + + Keyword arguments are interpreted as strategy parameters. + """ + data = _Data(self._data) + broker = self._broker(data=data) # type: _Broker + strategy = self._strategy(broker, data) # type: Strategy + + strategy._set_params(**kwargs) + + strategy.init() + indicator_attrs = {attr: indicator + for attr, indicator in strategy.__dict__.items() + if isinstance(indicator, _Indicator)}.items() + + # Skip first few candles where indicators are still "warming up" + # +1 to have at least two entries available + start = 1 + max((np.isnan(indicator).argmin() + for _, indicator in indicator_attrs), default=0) + + # Disable "invalid value encountered in ..." warnings. Comparison + # np.nan >= 3 is not invalid; it's False. + with np.errstate(invalid='ignore'): + + for i in range(start, len(self._data)): + # 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() + + self._results = self._compute_stats(broker, strategy) + return self._results + + def optimize(self, + maximize: Union[str, Callable[[pd.Series], float]] = 'SQN', + constraint: Callable[[dict], bool] = None, + return_heatmap: bool = False, + **kwargs) -> Union[pd.Series, Tuple[pd.Series, pd.Series]]: + """ + Optimize strategy parameters to an optimal combination using + parallel exhaustive search. 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). + + `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. + + Additional keyword arguments represent strategy arguments with + list-like collections of possible values. For example: + + backtest.optimize(sma1=[5, 10, 15], sma2=[10, 20, 40], + constraint=lambda p: p.sma1 < p.sma2) + + finds and returns the "best" of the 7 admissible (of the + 9 possible) parameter combinations. + """ + if not kwargs: + raise ValueError('Need some strategy parameters to optimize') + + if isinstance(maximize, str): + + stats = self._results if self._results is not None else self.run() + if maximize not in stats: + 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') + + 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") + + def _tuple(x): + return x if isinstance(x, Sequence) and not isinstance(x, str) else (x,) + + class AttrDict(dict): + def __getattr__(self, item): + return self[item] + + param_combos = tuple(map(dict, # back to dict so it pickles + filter(constraint, # constraints applied on our fancy dict + map(AttrDict, + product(*(zip(repeat(k), _tuple(v)) + for k, v in kwargs.items())))))) + if not param_combos: + raise ValueError('No admissible parameter combinations to test') + + if len(param_combos) > 300: + warnings.warn('Searching best of {} configurations.'.format(len(param_combos)), + stacklevel=2) + + heatmap = pd.Series(np.nan, + index=pd.MultiIndex.from_tuples([p.values() for p in param_combos], + names=next(iter(param_combos)).keys())) + + # TODO: add parameter `max_tries:Union[int, float]=None` which switches + # exhaustive grid search to random search. This might need to avoid + # returning NaNs in stats on runs with no trades to differentiate those + # from non-tested parameter combos in heatmap. + + def _batch(seq): + n = np.clip(len(param_combos) // (os.cpu_count() or 1), 5, 300) + for i in range(0, len(seq), n): + yield seq[i:i + n] + + with ProcessPoolExecutor() as executor: + for future in as_completed(executor.submit(self._mp_task, params) + for params in _batch(param_combos)): + for params, stats in future.result(): + heatmap[tuple(params.values())] = maximize(stats) + + best_params = heatmap.idxmax() + + if pd.isnull(best_params): + # No trade was made in any of the runs. Just make a random + # run so we get some, if empty, results + self.run(**param_combos[0]) + else: + # Re-run best strategy so that the next .plot() call will render it + self.run(**dict(zip(heatmap.index.names, best_params))) + + if return_heatmap: + return self._results, heatmap + return self._results + + def _mp_task(self, param_combos): + return [(params, stats) for params, stats in ((params, self.run(**params)) + for params in param_combos) + if stats['# Trades']] + + def _compute_stats(self, broker: _Broker, strategy: Strategy) -> pd.Series: + data = self._data + + def _drawdown_duration_peaks(dd, index): + # XXX: possible to vectorize any of this? + durations = [np.nan] * len(dd) + peaks = [np.nan] * len(dd) + i = 0 + for j in range(1, len(dd)): + if dd[j] == 0: + if dd[j - 1] != 0: + durations[j - 1] = index[j] - index[i] + peaks[j - 1] = dd[i:j].max() + i = j + return pd.Series(durations), pd.Series(peaks) + + df = pd.DataFrame() + df['Equity'] = pd.Series(broker.log.equity).bfill().fillna(broker._cash) + equity = df.Equity.values + df['Exit Entry'] = broker.log.exit_entry + exits = df['Exit Entry'] + df['Exit Position'] = broker.log.exit_position + df['Entry Price'] = broker.log.entry_price + df['Exit Price'] = broker.log.exit_price + df['P/L'] = broker.log.pl + pl = df['P/L'] + df['Returns'] = returns = pl.dropna() / equity[exits.dropna().values.astype(int)] + df['Drawdown'] = dd = 1 - equity / np.maximum.accumulate(equity) + dd_dur, dd_peaks = _drawdown_duration_peaks(dd, data.index) + df['Drawdown Duration'] = dd_dur + dd_dur = df['Drawdown Duration'] + + df.index = data.index + + def _round_timedelta(value, _period=_data_period(df)): + return value.ceil(_period.resolution) if isinstance(value, pd.Timedelta) else value + + s = pd.Series() + s['Start'] = df.index[0] + s['End'] = df.index[-1] + # Assigning Timedeltas needs the key to exist beforehand, + # otherwise the value is interpreted as nanosec *int*. See: + # https://github.com/pandas-dev/pandas/issues/22717 + s['Duration'] = 0 + s['Duration'] = s.End - s.Start + exits = df['Exit Entry'] # After reindexed + durations = (exits.dropna().index - df.index[exits.dropna().values.astype(int)]).to_series() + s['Exposure [%]'] = np.nan_to_num(durations.sum() / (s['Duration'] or np.nan) * 100) + s['Equity Final [$]'] = equity[-1] + s['Equity Peak [$]'] = equity.max() + s['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100 + c = data.Close.values + s['Buy & Hold Return [%]'] = abs(c[-1] - c[0]) / c[0] * 100 # long OR short + s['Max. Drawdown [%]'] = max_dd = -np.nan_to_num(dd.max()) * 100 + s['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100 + s['Max. Drawdown Duration'] = 0 + s['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max()) + s['Avg. Drawdown Duration'] = 0 + s['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean()) + s['# Trades'] = n_trades = pl.count() + s['Win Rate [%]'] = win_rate = np.nan if not n_trades else (pl > 0).sum() / n_trades * 100 + s['Best Trade [%]'] = returns.max() * 100 + s['Worst Trade [%]'] = returns.min() * 100 + mean_return = returns.mean() + s['Avg. Trade [%]'] = mean_return * 100 + s['Max. Trade Duration'] = 0 + s['Max. Trade Duration'] = _round_timedelta(durations.max()) + s['Avg. Trade Duration'] = 0 + s['Avg. Trade Duration'] = _round_timedelta(durations.mean()) + s['Expectancy [%]'] = ((returns[returns > 0].mean() * win_rate - + returns[returns < 0].mean() * (100 - win_rate))) + pl = pl.dropna() + s['SQN'] = np.sqrt(n_trades) * pl.mean() / pl.std() + s['Sharpe Ratio'] = mean_return / (returns.std() or np.nan) + s['Sortino Ratio'] = mean_return / (returns[returns < 0].std() or np.nan) + s['Calmar Ratio'] = mean_return / ((-max_dd / 100) or np.nan) + + s['_strategy'] = strategy + s._trade_data = df # Private API + return s + + def plot(self, *, results: pd.Series = None, filename=None, plot_width=1200, + plot_equity=True, plot_pl=True, + plot_volume=True, plot_drawdown=False, + smooth_equity=False, relative_equity=True, + omit_missing=True, superimpose: Union[bool, str] = True, + show_legend=True, open_browser=True): + """ + Plot the progression of the last backtest run. + + If `results` is proided, it should be a particular result + `pd.Series` such as returned by + `backtesting.backtesting.Backtest.run` or + `backtesting.backtesting.Backtest.optimize`. + + `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. The height is + currently non-adjustable. FIXME: If someone can make the Bokeh + plot span 100% browser width by default, a contribution would + be appreciated. + + If `plot_equity` is `True`, the resulting plot will contain + an equity (cash plus assets) graph section. + + 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 `smooth_equity` is `True`, the equity graph will be + interpolated between points of cash-only positions, + 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 `omit_missing` is `True`, skip missing candlestick bars on the + datetime axis. + + If `superimpose` is `True`, superimpose downsampled candlesticks + over the original candlestick chart. Default downsampling is: + weekly for daily data, daily for hourly data, hourly for minute data, + and minute for second and sub-second data. + `superimpose` can also be a string, in which case it is a valid + [Pandas offset string], such as `'5T'` or `'5min'`. + Note, this only works for data with a datetime index. + + [Pandas offset string]: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases + + 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 + + def _windos_safe_filename(filename): + if sys.platform.startswith('win'): + return re.sub(r'[^a-zA-Z0-9,_-]', '_', filename.replace('=', '-')) + return filename + + plot( + results=results, + df=self._data, + indicators=results._strategy._indicators, + filename=filename or _windos_safe_filename(str(results._strategy)), + plot_width=plot_width, + plot_equity=plot_equity, + plot_pl=plot_pl, + plot_volume=plot_volume, + omit_missing=omit_missing, + plot_drawdown=plot_drawdown, + smooth_equity=smooth_equity, + relative_equity=relative_equity, + superimpose=superimpose, + show_legend=show_legend, + open_browser=open_browser) diff --git a/backtesting/lib.py b/backtesting/lib.py new file mode 100644 index 0000000..d80f590 --- /dev/null +++ b/backtesting/lib.py @@ -0,0 +1,309 @@ +""" +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 collections import OrderedDict +from itertools import compress +from numbers import Number +from inspect import currentframe +from typing import Sequence, Optional, Union, Callable + +import numpy as np +import pandas as pd + +from .backtesting import Strategy +from ._plotting import plot_heatmaps as _plot_heatmaps +from ._util import _Indicator, _as_str + + +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) +""" + + +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, series2) -> bool: + """ + Return `True` if `series1` and `series2` just crossed (either + direction). + + >>> cross(self.data.Close, self.sma) + True + + """ + return crossover(series1, series2) or crossover(series2, series1) + + +def crossover(series1, series2) -> bool: + """ + Return `True` if `series1` just crossed over + `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] + 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`. + + `heatmap` is a Series as returned by + `backtesting.backtesting.Backtest.optimize` when its parameter + `return_heatmap=True`. + + When projecting the n-dimensional 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. + """ + return _plot_heatmaps(heatmap, agg, ncols, filename, plot_width, open_browser) + + +def quantile(series, quantile=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 resample_apply(rule: str, + func: Callable, + series, + *args, **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. + + 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) + """ + if not isinstance(series, pd.Series): + series = pd.Series(series, + index=series._opts['data'].index, + name=series.name) + + resampled = series.resample(rule, label='right').agg('last').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): + strategy_I = frame.f_locals['self'].I + break + else: + def strategy_I(func, *args, **kwargs): + return func(*args, **kwargs) + + # Resample back to data index + def wrap_func(resampled, *args, **kwargs): + ind = func(resampled, *args, **kwargs) + ind = ind.reindex(index=series.index | ind.index, + method='ffill').reindex(series.index) + return ind + + wrap_func.__name__ = func.__name__ + + array = strategy_I(wrap_func, resampled, *args, **kwargs) + return array + + +class SignalStrategy(Strategy): + """ + A simple helper strategy that operates on position entry/exit signals. + + 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: Sequence[int], exit: Optional[Sequence[bool]] = None, + plot: bool = True): + """ + Set entry/exit signal vectors (arrays). An long entry signal is considered + present wherever `entry` is greater than zero. A short entry signal + is considered present wherever `entry` is less than zero. If `exit` + is provided, a nonzero value closes the position, if any; otherwise + the position is held until a reverse signal in `entry`. + + If `plot` is `True`, the signal entry/exit indicators are plotted when + `backtesting.backtesting.Backtest.plot` is called. + """ + self.__entry_signal = _Indicator(pd.Series(entry, dtype=float).fillna(0), + name='entry', plot=plot, overlay=False) + if exit is not None: + self.__exit_signal = _Indicator(pd.Series(exit, dtype=float).fillna(0), + name='exit', plot=plot, overlay=False) + + def next(self): + super().next() + + if self.position and self.__exit_signal[-1]: + self.position.close() + + signal = self.__entry_signal[-1] + + if signal > 0: + self.buy() + elif signal < 0: + self.sell() + + +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). + + 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. + """ + h, l, c_prev = self.data.High, self.data.Low, pd.Series(self.data.Close).shift(1) + tr = np.max([h - l, (c_prev - h).abs(), (c_prev - l).abs()], axis=0) + atr = pd.Series(tr).rolling(periods).mean().bfill().values + self.__atr = atr + + def set_trailing_sl(self, n_atr: float = 6): + """ + Sets 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 next(self): + super().next() + + if self.__n_atr and self.position: + if self.position.is_long: + self.orders.set_sl(self.data.Close[-1] - self.__atr[-1] * self.__n_atr) + else: + self.orders.set_sl(self.data.Close[-1] + self.__atr[-1] * self.__n_atr) + + +# 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 v.__module__ == __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. diff --git a/backtesting/test/EURUSD.csv b/backtesting/test/EURUSD.csv new file mode 100644 index 0000000..3d3c223 --- /dev/null +++ b/backtesting/test/EURUSD.csv @@ -0,0 +1,5001 @@ +,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 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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 +2018-02-01 00:00:00,1.24169,1.24289,1.24165,1.2421,1831 +2018-02-01 01:00:00,1.2421,1.24266,1.24056,1.24192,2936 +2018-02-01 02:00:00,1.2419,1.2428,1.2415,1.24175,1625 +2018-02-01 03:00:00,1.24176,1.2426,1.24145,1.24246,1265 +2018-02-01 04:00:00,1.24246,1.24286,1.2417,1.24218,1063 +2018-02-01 05:00:00,1.24216,1.24282,1.24186,1.24211,1094 +2018-02-01 06:00:00,1.24208,1.24224,1.23934,1.23938,3482 +2018-02-01 07:00:00,1.23942,1.24046,1.23849,1.24029,5339 +2018-02-01 08:00:00,1.24028,1.24394,1.24021,1.24284,5876 +2018-02-01 09:00:00,1.24279,1.245,1.24278,1.24463,4987 +2018-02-01 10:00:00,1.2446,1.24512,1.24286,1.243,3100 +2018-02-01 11:00:00,1.24302,1.24382,1.24246,1.24379,2997 +2018-02-01 12:00:00,1.24374,1.24602,1.24324,1.24539,4027 +2018-02-01 13:00:00,1.24541,1.24622,1.24444,1.24496,5012 +2018-02-01 14:00:00,1.24495,1.24536,1.2437,1.24425,4274 +2018-02-01 15:00:00,1.24423,1.24745,1.24316,1.24696,7045 +2018-02-01 16:00:00,1.24696,1.2489,1.24624,1.24812,6037 +2018-02-01 17:00:00,1.24812,1.24984,1.24749,1.24784,4718 +2018-02-01 18:00:00,1.24787,1.24954,1.24776,1.24938,2794 +2018-02-01 19:00:00,1.24933,1.25067,1.24836,1.2503,4106 +2018-02-01 20:00:00,1.25031,1.25233,1.25012,1.2515,5909 +2018-02-01 21:00:00,1.25144,1.25182,1.25046,1.25079,1983 +2018-02-01 22:00:00,1.25135,1.25135,1.25028,1.25056,1161 +2018-02-01 23:00:00,1.25057,1.25158,1.25052,1.25106,1294 +2018-02-02 00:00:00,1.25108,1.25126,1.24978,1.25072,1626 +2018-02-02 01:00:00,1.25066,1.25149,1.2492,1.24994,5434 +2018-02-02 02:00:00,1.24995,1.25044,1.2489,1.2494,2742 +2018-02-02 03:00:00,1.24934,1.25006,1.24924,1.25001,1737 +2018-02-02 04:00:00,1.25002,1.25008,1.24919,1.2493,1061 +2018-02-02 05:00:00,1.24932,1.24985,1.24908,1.24955,1042 +2018-02-02 06:00:00,1.24955,1.24982,1.24865,1.24952,2638 +2018-02-02 07:00:00,1.24947,1.25144,1.24942,1.25098,3598 +2018-02-02 08:00:00,1.25096,1.25184,1.2485,1.24936,4891 +2018-02-02 09:00:00,1.24932,1.24955,1.24802,1.24867,3824 +2018-02-02 10:00:00,1.24864,1.24913,1.24759,1.24898,2590 +2018-02-02 11:00:00,1.24894,1.24992,1.24855,1.24902,2462 +2018-02-02 12:00:00,1.249,1.24934,1.24853,1.24904,2174 +2018-02-02 13:00:00,1.24909,1.24982,1.24382,1.24454,11821 +2018-02-02 14:00:00,1.2445,1.24601,1.2427,1.24285,7653 +2018-02-02 15:00:00,1.24288,1.2438,1.24092,1.24354,7367 +2018-02-02 16:00:00,1.24354,1.24547,1.24354,1.24454,6536 +2018-02-02 17:00:00,1.24458,1.24782,1.24456,1.24692,6212 +2018-02-02 18:00:00,1.24692,1.2491,1.24674,1.24693,5717 +2018-02-02 19:00:00,1.24692,1.24719,1.24487,1.24508,5625 +2018-02-02 20:00:00,1.24508,1.2465,1.2449,1.24618,5330 +2018-02-02 21:00:00,1.24612,1.24628,1.24514,1.24566,2431 +2018-02-04 22:00:00,1.24359,1.24455,1.2429,1.24318,1870 +2018-02-04 23:00:00,1.2431,1.2455,1.24242,1.24464,4083 +2018-02-05 00:00:00,1.24465,1.24584,1.24404,1.24581,2901 +2018-02-05 01:00:00,1.24578,1.24584,1.24447,1.24542,2331 +2018-02-05 02:00:00,1.24547,1.24626,1.2444,1.24612,2271 +2018-02-05 03:00:00,1.2461,1.24665,1.24538,1.24549,1608 +2018-02-05 04:00:00,1.24555,1.24614,1.24536,1.2459,1201 +2018-02-05 05:00:00,1.24595,1.24641,1.24532,1.24605,1257 +2018-02-05 06:00:00,1.24602,1.24627,1.2447,1.24532,1700 +2018-02-05 07:00:00,1.2453,1.2463,1.24398,1.244,3907 +2018-02-05 08:00:00,1.24394,1.24679,1.24392,1.24625,5412 +2018-02-05 09:00:00,1.24626,1.2475,1.24549,1.24608,3328 +2018-02-05 10:00:00,1.2461,1.24721,1.24586,1.24626,2002 +2018-02-05 11:00:00,1.24625,1.24634,1.24464,1.24478,3326 +2018-02-05 12:00:00,1.24474,1.24502,1.24392,1.24408,3278 +2018-02-05 13:00:00,1.24411,1.24492,1.2436,1.24375,3791 +2018-02-05 14:00:00,1.24374,1.24381,1.23998,1.24076,5495 +2018-02-05 15:00:00,1.24072,1.24295,1.23988,1.24199,5707 +2018-02-05 16:00:00,1.24198,1.2428,1.2404,1.24161,4813 +2018-02-05 17:00:00,1.24163,1.24303,1.2406,1.24288,2727 +2018-02-05 18:00:00,1.24284,1.24286,1.24115,1.24212,3928 +2018-02-05 19:00:00,1.24208,1.24218,1.23942,1.23987,3671 +2018-02-05 20:00:00,1.23992,1.24221,1.2388,1.23882,22699 +2018-02-05 21:00:00,1.23877,1.23952,1.23625,1.23671,8122 +2018-02-05 22:00:00,1.23671,1.23865,1.2367,1.23758,1076 +2018-02-05 23:00:00,1.23756,1.23838,1.23652,1.23668,3567 +2018-02-06 00:00:00,1.23668,1.23872,1.23639,1.23831,7362 +2018-02-06 01:00:00,1.2383,1.23888,1.2369,1.23754,7568 +2018-02-06 02:00:00,1.23753,1.23786,1.23547,1.23714,10085 +2018-02-06 03:00:00,1.23711,1.23786,1.2363,1.23672,4577 +2018-02-06 04:00:00,1.2367,1.2376,1.23509,1.2356,4033 +2018-02-06 05:00:00,1.2356,1.23759,1.23558,1.23756,4582 +2018-02-06 06:00:00,1.23756,1.239,1.23712,1.23818,4132 +2018-02-06 07:00:00,1.23816,1.24046,1.23754,1.23962,8702 +2018-02-06 08:00:00,1.2396,1.24346,1.23924,1.24174,8584 +2018-02-06 09:00:00,1.24178,1.24208,1.23902,1.24028,5033 +2018-02-06 10:00:00,1.24026,1.24172,1.23959,1.24088,3414 +2018-02-06 11:00:00,1.24092,1.24118,1.23709,1.23726,4143 +2018-02-06 12:00:00,1.23721,1.23732,1.23352,1.23398,6541 +2018-02-06 13:00:00,1.23402,1.23475,1.23222,1.23438,8835 +2018-02-06 14:00:00,1.23444,1.23581,1.23266,1.23364,7365 +2018-02-06 15:00:00,1.2336,1.23468,1.23138,1.23444,8429 +2018-02-06 16:00:00,1.23444,1.23842,1.23414,1.23808,5641 +2018-02-06 17:00:00,1.23809,1.24052,1.23734,1.23912,6150 +2018-02-06 18:00:00,1.23914,1.24052,1.23864,1.23988,4328 +2018-02-06 19:00:00,1.23992,1.23995,1.23852,1.23916,2665 +2018-02-06 20:00:00,1.23918,1.23956,1.23698,1.23714,4940 +2018-02-06 21:00:00,1.23716,1.23818,1.23698,1.2377,1606 +2018-02-06 22:00:00,1.23763,1.23832,1.23706,1.23803,1050 +2018-02-06 23:00:00,1.23803,1.2386,1.23774,1.23806,1558 +2018-02-07 00:00:00,1.23802,1.23936,1.23758,1.23863,2770 +2018-02-07 01:00:00,1.23862,1.23908,1.23782,1.23891,2075 +2018-02-07 02:00:00,1.23888,1.23902,1.23784,1.23831,1341 +2018-02-07 03:00:00,1.23834,1.2393,1.23833,1.23859,1750 +2018-02-07 04:00:00,1.23857,1.23934,1.23832,1.23882,880 +2018-02-07 05:00:00,1.23877,1.2392,1.2384,1.23909,1127 +2018-02-07 06:00:00,1.23907,1.23983,1.2377,1.23959,2417 +2018-02-07 07:00:00,1.23958,1.24064,1.2381,1.23828,4369 +2018-02-07 08:00:00,1.23833,1.23886,1.23628,1.23778,5513 +2018-02-07 09:00:00,1.23777,1.23819,1.235,1.23506,3705 +2018-02-07 10:00:00,1.2351,1.23526,1.23375,1.2339,2872 +2018-02-07 11:00:00,1.2339,1.23548,1.23386,1.23501,2203 +2018-02-07 12:00:00,1.23501,1.23508,1.23342,1.23422,2325 +2018-02-07 13:00:00,1.23422,1.23459,1.23338,1.23372,2824 +2018-02-07 14:00:00,1.23374,1.23452,1.23238,1.23426,4065 +2018-02-07 15:00:00,1.23427,1.23444,1.22904,1.22904,6143 diff --git a/backtesting/test/GOOG.csv b/backtesting/test/GOOG.csv new file mode 100644 index 0000000..2378976 --- /dev/null +++ b/backtesting/test/GOOG.csv @@ -0,0 +1,2149 @@ +,Open,High,Low,Close,Volume 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+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 diff --git a/backtesting/test/__init__.py b/backtesting/test/__init__.py new file mode 100644 index 0000000..2f4b287 --- /dev/null +++ b/backtesting/test/__init__.py @@ -0,0 +1,23 @@ +"""Data and utilities for testing.""" +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, infer_datetime_format=True) + + +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() diff --git a/backtesting/test/_test.py b/backtesting/test/_test.py new file mode 100644 index 0000000..811d98a --- /dev/null +++ b/backtesting/test/_test.py @@ -0,0 +1,512 @@ +import os +import time +import unittest +import warnings +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 backtesting import Backtest, Strategy +from backtesting.lib import ( + OHLCV_AGG, + barssince, + cross, + crossover, + quantile, + SignalStrategy, + TrailingStrategy, + resample_apply, + plot_heatmaps +) +from backtesting.test import GOOG, EURUSD, SMA +from backtesting._util import _Indicator, _as_str, _Array + + +@contextmanager +def _tempfile(): + with NamedTemporaryFile(suffix='.html') as f: + 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.buy() + elif crossover(self.sma2, self.sma1): + self.sell() + + +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, .2) + + def test_data_missing_columns(self): + df = GOOG.copy() + 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() + 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_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] + + 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" + + try: + self.data.X + except KeyError: + pass + else: + assert False + + assert self.data.pip == .01 + + assert float(self.data.Close) == self.data.Close[-1] + + def next(self, FIVE_DAYS=pd.Timedelta('3 days')): + 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.orders.is_long + self.orders.is_short + self.orders.entry + self.orders.sl + self.orders.tp + + self.position + self.position.size + self.position.pl + self.position.pl_pct + self.position.open_price + self.position.open_time + self.position.is_long + + if crossover(self.sma, self.data.Close): + self.orders.cancel() + self.sell() + assert not self.orders.is_long + assert self.orders.is_short + assert self.orders.entry + assert not self.orders.sl + assert not self.orders.tp + price = self.data.Close[-1] + sl, tp = 1.05 * price, .9 * price + self.sell(price, sl=sl, tp=tp) + self.orders.set_entry(price) + self.orders.set_sl(sl) + self.orders.set_tp(tp) + assert self.orders.entry == price + assert self.orders.sl == sl + assert self.orders.tp == tp + + elif self.position: + assert not self.orders.entry + assert not self.position.is_long + assert not not self.position.is_short + assert self.position.open_price + assert self.position.pl + assert self.position.pl_pct + assert self.position.size < 0 + if self.data.index[-1] - self.position.open_time > FIVE_DAYS: + self.position.close() + + bt = Backtest(GOOG, Assertive) + stats = bt.run() + self.assertEqual(stats['# Trades'], 144) + + def test_broker_params(self): + bt = Backtest(GOOG.iloc[:100], SmaCross, + cash=1000, commission=.01, margin=.1, trade_on_close=True) + bt.run() + + 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_stats(self): + stats = Backtest(GOOG, SmaCross).run() + self.assertEqual( + stats.filter(regex='^[^_]').to_dict(), + { + # NOTE: These values are also used on the website! + '# Trades': 65, + 'Avg. Drawdown Duration': pd.Timedelta('33 days 00:00:00'), + 'Avg. Drawdown [%]': -5.494714447812327, + 'Avg. Trade Duration': pd.Timedelta('46 days 00:00:00'), + 'Avg. Trade [%]': 3.0404430275631444, + 'Best Trade [%]': 54.05363186670138, + 'Buy & Hold Return [%]': 703.4582419772772, + 'Calmar Ratio': 0.0631443286380662, + 'Duration': pd.Timedelta('3116 days 00:00:00'), + 'End': pd.Timestamp('2013-03-01 00:00:00'), + 'Equity Final [$]': 52624.29346696951, + 'Equity Peak [$]': 76908.27001642012, + 'Expectancy [%]': 8.774692825628644, + 'Exposure [%]': 93.93453145057767, + 'Max. Drawdown Duration': pd.Timedelta('477 days 00:00:00'), + 'Max. Drawdown [%]': -48.15069053929621, + 'Max. Trade Duration': pd.Timedelta('183 days 00:00:00'), + 'Return [%]': 426.2429346696951, + 'SQN': 0.91553210127173, + 'Sharpe Ratio': 0.23169782960690408, + 'Sortino Ratio': 0.7096713270577958, + 'Start': pd.Timestamp('2004-08-19 00:00:00'), + 'Win Rate [%]': 46.15384615384615, + 'Worst Trade [%]': -18.85561318387153} + ) + self.assertTrue( + stats._trade_data.columns.equals( + pd.Index(['Equity', 'Exit Entry', 'Exit Position', + 'Entry Price', 'Exit Price', 'P/L', 'Returns', + 'Drawdown', 'Drawdown Duration']))) + + def test_compute_stats_bordercase(self): + + class SingleTrade(Strategy): + def init(self): + self._done = False + + def next(self): + if not self._done: + self.buy() + self._done = True + if self.position: + self.position.close() + + class SinglePosition(Strategy): + def init(self): + pass + + def next(self): + if not self.position: + self.buy() + + class NoTrade(Strategy): + def init(self): + pass + + def next(self): + pass + + for strategy in (SmaCross, + SingleTrade, + SinglePosition, + NoTrade): + with self.subTest(strategy=strategy.__name__): + stats = Backtest(GOOG.iloc[:100], strategy).run() + + self.assertFalse(np.isnan(stats['Equity Final [$]'])) + self.assertFalse(stats._trade_data['Equity'].isnull().any()) + self.assertEqual(stats['_strategy'].__class__, strategy) + + +class TestOptimize(TestCase): + def test_optimize(self): + bt = Backtest(GOOG.iloc[:100], SmaCross) + OPT_PARAMS = dict(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) + + res = bt.optimize(**OPT_PARAMS) + self.assertIsInstance(res, pd.Series) + + res2 = bt.optimize(**OPT_PARAMS, maximize=lambda s: s['SQN']) + self.assertSequenceEqual(res.filter(regex='^[^_]').to_dict(), + res2.filter(regex='^[^_]').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) + + with _tempfile() as f: + bt.plot(filename=f, open_browser=False) + + def test_optimize_invalid_param(self): + bt = Backtest(GOOG.iloc[:100], SmaCross) + self.assertRaises(AttributeError, bt.optimize, foo=range(3)) + + 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=(2, 5, 7), slow=[10, 15, 20, 30]) + end = time.process_time() + self.assertLess(end - start, .2) + + +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, + plot_equity=False, + plot_pl=False, + plot_drawdown=True, + superimpose=False, + omit_missing=False, + smooth_equity=False, + relative_equity=False, + show_legend=False).items(): + with self.subTest(param=p[0]): + bt.plot(**dict([p]), filename=f, open_browser=False) + + def test_resolutions(self): + with _tempfile() as f: + for rule in 'LSTHDWM': + with self.subTest(rule=rule): + df = EURUSD.iloc[:2].resample(rule).agg(OHLCV_AGG).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) + + +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) + self.assertEqual(res.iloc[-48:].unique().tolist(), + [1.2426429999999997, 1.2423809999999995, 1.2422749999999998]) + + 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_SignalStrategy(self): + class S(SignalStrategy): + def init(self): + sma = pd.Series(self.data.Close).rolling(10).mean() + self.set_signal(self.data.Close > sma, + self.data.Close < sma) + + stats = Backtest(GOOG, S).run() + self.assertGreater(stats['# Trades'], 1000) + + def test_TrailingStrategy(self): + class S(TrailingStrategy): + def init(self): + super().init() + self.set_atr_periods(40) + self.set_trailing_sl(3) + self.sma = self.I( + lambda: pd.Series(self.data.Close, + index=self.data.index).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.assertGreater(stats['# Trades'], 6) + + +class TestUtil(TestCase): + def test_as_str(self): + def func(): + pass + + class Class: + 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(lambda x: x), '') + for s in ('Open', 'High', 'Low', 'Close'): + self.assertEqual(_as_str(_Array([1], name=s)), s[0]) + + +class TestDocs(TestCase): + def test_examples(self): + examples = glob(os.path.join(os.path.dirname(__file__), + '..', '..', 'doc', 'examples', '*.py')) + self.assertGreaterEqual(len(examples), 4) + with chdir(gettempdir()): + for file in examples: + run_path(file) + + +if __name__ == '__main__': + warnings.filterwarnings('error') + unittest.main() diff --git a/doc/README.md b/doc/README.md new file mode 100644 index 0000000..041f269 --- /dev/null +++ b/doc/README.md @@ -0,0 +1,13 @@ +Backtesting.py Documentation +============================ +After installing documentation dependencies: + + pip install .[doc] + +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). diff --git a/doc/build.sh b/doc/build.sh new file mode 100755 index 0000000..f6778f6 --- /dev/null +++ b/doc/build.sh @@ -0,0 +1,86 @@ +#!/bin/bash +set -eu +IS_RELEASE=${TRAVIS_TAG+1} + +die () { echo "ERROR: $*" >&2; exit 2; } + +for cmd in pdoc \ + jupytext ; do + command -v "$cmd" >/dev/null || + die "Missing $cmd; \`pip install $cmd\`" +done +command -v "jupyter-nbconvert" >/dev/null || + die "Missing jupyter-nbconvert; \`pip install nbconvert\`" + +DOCROOT="$(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 +pdoc --html --html-no-source \ + ${IS_RELEASE+--template-dir "$DOCROOT/pdoc_template"} \ + --html-dir "$BUILDROOT" \ + backtesting +popd >/dev/null + + +echo +echo 'Ensuring example notebooks match their py counterparts' +echo +for ipynb in "$DOCROOT"/examples/*.ipynb; do + echo "Checking: '$ipynb'" + diff -q "${ipynb%.ipynb}.py" <(jupytext --to py --output - "$ipynb") || + die "Notebook and its matching .py file differ. Maybe run: \`jupytext --to py '$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/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' \ + jupyter-nbconvert --execute --to=html \ + --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##$ANALYTICS#i" +fi + + +echo +echo 'Testing for broken links' +echo +pushd "$BUILDROOT" >/dev/null +tmpdir="$(mktemp -d)" +python3 -m http.server 51296 & sleep 1 +trap '{ rm -r "$tmpdir"; kill %1; wait; } >/dev/null 2>&1' EXIT +[ ! "$(jobs -p)" ] && die 'Server not running. See above.' +find -name '*.html' -print0 | + sed --null-data 's/^/http:\/\/127.0.0.1:51296\//' | + xargs -0 -- \ + wget --user-agent "Mozilla/5.0 Firefox 61" -e 'robots=off' --random-wait \ + --no-verbose --recursive --span-hosts --level=1 --tries=2 \ + --directory-prefix "$tmpdir" --no-clobber \ + --reject-regex='\bfonts\b|\.css\b|\bjs\b|\.png\b' |& + grep -B1 'ERROR 404' +popd >/dev/null + + +echo +echo "All good. Docs in: $BUILDROOT" +echo +echo " file://$BUILDROOT/backtesting/index.html" +echo diff --git a/doc/deploy.sh b/doc/deploy.sh new file mode 100644 index 0000000..8a11ab7 --- /dev/null +++ b/doc/deploy.sh @@ -0,0 +1,20 @@ +#!/bin/bash +set -eu + +if [ ! -d doc/build ]; then + echo 'Error: invalid directory. Deploy from repo root.' + exit 1 +fi + +[ "$GH_PASSWORD" ] || exit 12 + +head=$(git rev-parse HEAD) + +git clone -b gh-pages "https://kernc:$GH_PASSWORD@github.com/$TRAVIS_REPO_SLUG.git" gh-pages +mkdir -p gh-pages/doc +cp -R doc/build/* gh-pages/doc/ +cd gh-pages +git add * +git diff --quiet && echo "$0: No changes to commit." && exit 0 +git commit -a -m "CI: Update docs for $TRAVIS_TAG ($head)" +git push diff --git a/doc/examples/Multiple Time Frames.ipynb b/doc/examples/Multiple Time Frames.ipynb new file mode 100644 index 0000000..13fefb2 --- /dev/null +++ b/doc/examples/Multiple Time Frames.ipynb @@ -0,0 +1,676 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Multiple Time Frames\n", + "============\n", + "\n", + "The best trading strategies relying on technical analysis take into account the 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 _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html).\n", + "\n", + "We will test this supposed long-only\n", + "[400%-a-year trading strategy](http://jbmarwood.com/stock-trading-strategy-300/),\n", + "which daily and weekly\n", + "[relative strength index](https://en.wikipedia.org/wiki/Relative_strength_index)\n", + "(RSI) values and moving averages (MA).\n", + "\n", + "Let's introduce the two indicators we'll be using.\n", + "In practice, one can use functions from any indicator library, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib),\n", + "[Tulipy](https://tulipindicators.org),\n", + "PyAlgoTrade, ..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "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", + "* Close 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 frames (i.e. weekly) that our strategy requires." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jk/PycharmProjects/backtesting/backtesting/_plotting.py:34: 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 `bokeh.io.reset_output()`.\n", + " warnings.warn('Jupyter Notebook detected. '\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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\\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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\\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) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"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 have to resample\n", + " # the daily values.\n", + " \n", + " # Strategy exposes `self.data` as raw NumPy arrays.\n", + " # Let's make closing prices back a pandas Series.\n", + " \n", + " close = pd.Series(self.data.Close,\n", + " index=self.data.index,\n", + " name='Close')\n", + " \n", + " # Resample to weekly resolution, ending weeks on\n", + " # fridays. Aggregate groups using their last value\n", + " # (i.e. week's closing price).\n", + " # Notice `label='right'`. If it were set to 'left' (default),\n", + " # the strategy would exhibit look-ahead bias.\n", + " \n", + " weekly_close = close.resample('W-FRI', label='right').agg('last')\n", + " \n", + " # We apply RSI(30) to weekly close\n", + " # prices, then reindex it back to original daily\n", + " # index, forward-filling the missing values in each\n", + " # week.\n", + " # We make a separate function that returns the final\n", + " # indicator array.\n", + " \n", + " def W_RSI(series, n):\n", + " return RSI(series, n).reindex(close.index).ffill()\n", + " \n", + " self.weekly_rsi = self.I(W_RSI, weekly_close, self.w_rsi)\n", + " \n", + " \n", + " # ... And, now that you know what goes on behind the scenes,\n", + " # we could achieve the whole *exact* same thing with simpler:\n", + " \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 [%] 2.59949\n", + "Equity Final [$] 10032.7\n", + "Equity Peak [$] 11012.5\n", + "Return [%] 0.32725\n", + "Buy & Hold Return [%] 703.458\n", + "Max. Drawdown [%] -10.1606\n", + "Avg. Drawdown [%] -4.78424\n", + "Max. Drawdown Duration 163 days 00:00:00\n", + "Avg. Drawdown Duration 84 days 00:00:00\n", + "# Trades 4\n", + "Win Rate [%] 25\n", + "Best Trade [%] 9.51454\n", + "Worst Trade [%] -4.46166\n", + "Avg. Trade [%] 0.210738\n", + "Max. Trade Duration 35 days 00:00:00\n", + "Avg. Trade Duration 21 days 00:00:00\n", + "Expectancy [%] 4.54653\n", + "SQN 0.0261005\n", + "Sharpe Ratio 0.0333333\n", + "Sortino Ratio 0.140578\n", + "Calmar Ratio 0.0207407\n", + "_strategy System\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from backtesting.test import GOOG\n", + "\n", + "backtest = Backtest(GOOG, System, commission=.002)\n", + "backtest.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Meager four trades in a span of nine years with effectively zero return? How about if we optimize the parameters a bit?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 250 ms, sys: 61.7 ms, total: 312 ms\n", + "Wall time: 4.09 s\n" + ] + }, + { + "data": { + "text/plain": [ + "Start 2004-08-19 00:00:00\n", + "End 2013-03-01 00:00:00\n", + "Duration 3116 days 00:00:00\n", + "Exposure [%] 20.5071\n", + "Equity Final [$] 23090.8\n", + "Equity Peak [$] 23686.3\n", + "Return [%] 130.908\n", + "Buy & Hold Return [%] 703.458\n", + "Max. Drawdown [%] -19.2731\n", + "Avg. Drawdown [%] -3.6514\n", + "Max. Drawdown Duration 778 days 00:00:00\n", + "Avg. Drawdown Duration 95 days 00:00:00\n", + "# Trades 22\n", + "Win Rate [%] 63.6364\n", + "Best Trade [%] 25.0905\n", + "Worst Trade [%] -6.21969\n", + "Avg. Trade [%] 3.83033\n", + "Max. Trade Duration 63 days 00:00:00\n", + "Avg. Trade Duration 30 days 00:00:00\n", + "Expectancy [%] 5.88815\n", + "SQN 2.39317\n", + "Sharpe Ratio 0.484418\n", + "Sortino Ratio 1.69146\n", + "Calmar Ratio 0.19874\n", + "_strategy System(d_rsi=30,level=60,w_rsi=10)\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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N7UdW0xHQCI3tR1bTEdAIje1HVtMR0AiN7UdW0xHQCI3tR1bTEdA2gABHuLURkDaAAEe4tRGQNoAAR7i1EZA2gABHuLURkDaAAEe4tRGQHGF4e8nY0hAcYXh7ydjSEBxheHvJ2NIQHGF4e8nY0hAXRLG544hSEBdEsbnjiFIQF0SxueOIUhAXRLG544hSEBdEsbnjiFIQHAtT753M0lAcC1PvnczSUBwLU++dzNJQHAtT753M0lAcC1PvnczSUCeQCK1etZJQJ5AIrV61klAnkAitXrWSUCeQCK1etZJQJ5AIrV61klAFlHuN5j4S0AWUe43mPhLQBZR7jeY+EtAFlHuN5j4S0AWUe43mPhLQM+V3Gq+9U1Az5Xcar71TUDPldxqvvVNQM+V3Gq+9U1Ard0mBuEcTkCt3SYG4RxOQK3dJgbhHE5Ard0mBuEcTkCt3SYG4RxOQHrpj087uEpAeumPTzu4SkB66Y9PO7hKQHrpj087uEpAeumPTzu4SkDP5IltMVZIQM/kiW0xVkhAz+SJbTFWSEDP5IltMVZIQM/kiW0xVkhA+OsR1an8R0D46xHVqfxHQPjrEdWp/EdA+OsR1an8R0D46xHVqfxHQDinYT95i0VAOKdhP3mLRUA4p2E/eYtFQDinYT95i0VAOKdhP3mLRUDgelmOOa1EQOB6WY45rURA4HpZjjmtREDgelmOOa1EQOB6WY45rURAlgp2DB7kR0CWCnYMHuRHQJYKdgwe5EdAlgp2DB7kR0CWCnYMHuRHQHj2PV/UFEZAePY9X9QURkB49j1f1BRGQHj2PV/UFEZAePY9X9QURkBkOocraEJHQGQ6hytoQkdAZDqHK2hCR0BkOocraEJHQJb864TXG0dAlvzrhNcbR0CW/OuE1xtHQJb864TXG0dAlvzrhNcbR0DbJd1gyWFNQNsl3WDJYU1A2yXdYMlhTUDbJd1gyWFNQNsl3WDJYU1AT7TgW1IDTEBPtOBbUgNMQE+04FtSA0xAT7TgW1IDTEBPtOBbUgNMQIYJd0bOk0tAhgl3Rs6TS0CGCXdGzpNLQIYJd0bOk0tAhgl3Rs6TS0BKFgkxotJOQEoWCTGi0k5AShYJMaLSTkBKFgkxotJOQEoWCTGi0k5A7FNGa6zgT0DsU0ZrrOBPQOxTRmus4E9A7FNGa6zgT0DsU0ZrrOBPQA/+HZkN91FAD/4dmQ33UUAP/h2ZDfdRQA/+HZkN91FAD/4dmQ33UUBlEpQOJ7VSQGUSlA4ntVJAZRKUDie1UkBlEpQOJ7VSQGUSlA4ntVJAKRP7020+UkApE/vTbT5SQCkT+9NtPlJAKRP7020+UkApE/vTbT5SQErnlFdeVlJASueUV15WUkBK55RXXlZSQErnlFdeVlJASueUV15WUkBIGYzoeYlTQEgZjOh5iVNASBmM6HmJU0BIGYzoeYlTQBTWg891ylNAFNaDz3XKU0AU1oPPdcpTQBTWg891ylNAFNaDz3XKU0AvNj7s7IBQQC82PuzsgFBALzY+7OyAUEAvNj7s7IBQQC82PuzsgFBAnC/ixfS1UECcL+LF9LVQQJwv4sX0tVBAnC/ixfS1UECcL+LF9LVQQAfKLTvZNVBAB8otO9k1UEAHyi072TVQQAfKLTvZNVBAB8otO9k1UECRFaw+NLZKQJEVrD40tkpAkRWsPjS2SkCRFaw+NLZKQO4lbr6kkktA7iVuvqSSS0DuJW6+pJJLQDEiBU6NpE9AMSIFTo2kT0AxIgVOjaRPQDEiBU6NpE9AMSIFTo2kT0D0rPJXTOZQQPSs8ldM5lBA9KzyV0zmUED0rPJXTOZQQAqk/Jmdbk5ACqT8mZ1uTkAKpPyZnW5OQAqk/Jmdbk5ACqT8mZ1uTkANomNspEBPQA2iY2ykQE9ADaJjbKRAT0ANomNspEBPQA2iY2ykQE9A7t/Cu+kwTEDu38K76TBMQO7fwrvpMExA7t/Cu+kwTEDu38K76TBMQG608WEMkkhAbrTxYQySSEButPFhDJJIQG608WEMkkhAbrTxYQySSEDGKsLX4vZJQMYqwtfi9klAxirC1+L2SUDGKsLX4vZJQGFN7SARFkpAYU3tIBEWSkBhTe0gERZKQGFN7SARFkpAYU3tIBEWSkDjdQBCY6xEQON1AEJjrERA43UAQmOsREDjdQBCY6xEQON1AEJjrERAogBwZJJjR0CiAHBkkmNHQKIAcGSSY0dAogBwZJJjR0CiAHBkkmNHQIZxW/t+h0VAhnFb+36HRUCGcVv7fodFQIZxW/t+h0VAhnFb+36HRUBBdYryjUZJQEF1ivKNRklAQXWK8o1GSUBBdYryjUZJQEF1ivKNRklAA1COacSmSEADUI5pxKZIQANQjmnEpkhAA1COacSmSEADUI5pxKZIQB+5ymFR7kpAH7nKYVHuSkAfucphUe5KQB+5ymFR7kpAwdGfYpbtSUDB0Z9ilu1JQMHRn2KW7UlAwdGfYpbtSUDB0Z9ilu1JQIKLbVI8pExAgottUjykTECCi21SPKRMQIKLbVI8pExAgottUjykTEA4Q2I1a+ZLQDhDYjVr5ktAOENiNWvmS0A4Q2I1a+ZLQDhDYjVr5ktAekUZgxI0SkB6RRmDEjRKQHpFGYMSNEpAekUZgxI0SkB6RRmDEjRKQG9Q1YPjQ0lAb1DVg+NDSUBvUNWD40NJQG9Q1YPjQ0lAb1DVg+NDSUAOytrOXwlKQA7K2s5fCUpADsrazl8JSkAOytrOXwlKQA7K2s5fCUpAvYDDWju6TEC9gMNaO7pMQL2Aw1o7ukxAvYDDWju6TEB4gyWHFKFPQHiDJYcUoU9AeIMlhxShT0B4gyWHFKFPQHiDJYcUoU9AVHKCXKfmUEBUcoJcp+ZQQFRyglyn5lBAVHKCXKfmUEBUcoJcp+ZQQOJGi9/XN09A4kaL39c3T0DiRovf1zdPQOJGi9/XN09A4kaL39c3T0AZ8Pc6UPRQQBnw9zpQ9FBAGfD3OlD0UEAZ8Pc6UPRQQBnw9zpQ9FBAK2ea8SKkUEArZ5rxIqRQQCtnmvEipFBAK2ea8SKkUEAZZ65aXLlRQBlnrlpcuVFAGWeuWly5UUAZZ65aXLlRQBlnrlpcuVFAc6NcxkJwUkBzo1zGQnBSQHOjXMZCcFJAc6NcxkJwUkBzo1zGQnBSQFZEfelaAk1AVkR96VoCTUBWRH3pWgJNQFZEfelaAk1AVkR96VoCTUA1TFsLH1xLQDVMWwsfXEtANUxbCx9cS0A1TFsLH1xLQDVMWwsfXEtA3hhayOKgSUDeGFrI4qBJQN4YWsjioElA3hhayOKgSUDeGFrI4qBJQKs7yt0510tAqzvK3TnXS0CrO8rdOddLQKs7yt0510tAqzvK3TnXS0BEdVoGbMpIQER1WgZsykhARHVaBmzKSEBEdVoGbMpIQER1WgZsykhATid9ScNiS0BOJ31Jw2JLQE4nfUnDYktATid9ScNiS0BOJ31Jw2JLQHrmH3mybUtAeuYfebJtS0B65h95sm1LQHrmH3mybUtAF5kQqxcsTEAXmRCrFyxMQBeZEKsXLExAF5kQqxcsTEAXmRCrFyxMQMqtrQbQ2E1Ayq2tBtDYTUDKra0G0NhNQMqtrQbQ2E1Ayq2tBtDYTUAKW3zD/yJRQApbfMP/IlFAClt8w/8iUUAKW3zD/yJRQApbfMP/IlFAUfzhkmiMUUBR/OGSaIxRQFH84ZJojFFAUfzhkmiMUUBR/OGSaIxRQB8dM0sl3lJAHx0zSyXeUkAfHTNLJd5SQB8dM0sl3lJAHx0zSyXeUkDKLYiXXFZUQMotiJdcVlRAyi2Il1xWVEDKLYiXXFZUQMotiJdcVlRATWqv3z2JVEBNaq/fPYlUQE1qr989iVRATWqv3z2JVEBNaq/fPYlUQCkcZYTgQFVAKRxlhOBAVUApHGWE4EBVQCkcZYTgQFVAKRxlhOBAVUDYygl4uutVQNjKCXi661VA2MoJeLrrVUDYygl4uutVQNjKCXi661VAzb845mx/UUDNvzjmbH9RQM2/OOZsf1FAzb845mx/UUDNvzjmbH9RQF6XoZPvoU5AXpehk++hTkBel6GT76FOQF6XoZPvoU5AmDItdfDkUECYMi118ORQQJgyLXXw5FBAmDItdfDkUECYMi118ORQQIonXsXDaFFAiidexcNoUUCKJ17Fw2hRQIonXsXDaFFAiidexcNoUUBAHRh8WA9SQEAdGHxYD1JAQB0YfFgPUkBAHRh8WA9SQEAdGHxYD1JAdM9icPFOUEB0z2Jw8U5QQHTPYnDxTlBAdM9icPFOUEB0z2Jw8U5QQEzcwLZHjVBATNzAtkeNUEBM3MC2R41QQEzcwLZHjVBA+4y2ApzFUED7jLYCnMVQQPuMtgKcxVBA+4y2ApzFUEBAzVqkWmtLQEDNWqRaa0tAQM1apFprS0BAzVqkWmtLQEDNWqRaa0tA+dIm9yNTSUD50ib3I1NJQPnSJvcjU0lA+dIm9yNTSUD50ib3I1NJQKfodC+5o0VAp+h0L7mjRUCn6HQvuaNFQKfodC+5o0VAZjRqO2XwQkBmNGo7ZfBCQGY0ajtl8EJAZjRqO2XwQkBmNGo7ZfBCQNh+pPh9cD9A2H6k+H1wP0DYfqT4fXA/QNh+pPh9cD9A2H6k+H1wP0AAeE2GrqM/QAB4TYauoz9AAHhNhq6jP0AAeE2GrqM/QAB4TYauoz9AVkP4PQWHQUBWQ/g9BYdBQFZD+D0Fh0FAVkP4PQWHQUD43rO9aApAQPjes71oCkBA+N6zvWgKQED43rO9aApAQPjes71oCkBAcM5LN7y9O0Bwzks3vL07QHDOSze8vTtAcM5LN7y9O0Bwzks3vL07QAA4S95xCzhAADhL3nELOEAAOEvecQs4QAA4S95xCzhAADhL3nELOEAMEn3cv105QAwSfdy/XTlADBJ93L9dOUAMEn3cv105QAwSfdy/XTlApJ1uhgvpOECknW6GC+k4QKSdboYL6ThApJ1uhgvpOEDU46oHEWs6QNTjqgcRazpA1OOqBxFrOkDU46oHEWs6QNTjqgcRazpAdShzd2FPQkB1KHN3YU9CQHUoc3dhT0JAdShzd2FPQkB1KHN3YU9CQOBz8EZ/OUFA4HPwRn85QUDgc/BGfzlBQOBz8EZ/OUFA4HPwRn85QUABAey+qXNKQAEB7L6pc0pAAQHsvqlzSkABAey+qXNKQAEB7L6pc0pAFT18MsDbSkAVPXwywNtKQBU9fDLA20pAFT18MsDbSkAVPXwywNtKQKIQ/5u46U1AohD/m7jpTUCiEP+buOlNQKIQ/5u46U1AohD/m7jpTUCcj6Cigf9MQJyPoKKB/0xAnI+gooH/TECcj6Cigf9MQJyPoKKB/0xAIlnNE9mUTUAiWc0T2ZRNQCJZzRPZlE1AIlnNE9mUTUAiWc0T2ZRNQGq7veZ8jUlAaru95nyNSUBqu73mfI1JQGq7veZ8jUlA2XGv8y8tTUDZca/zLy1NQNlxr/MvLU1A2XGv8y8tTUDZca/zLy1NQK8j0Y61J0tAryPRjrUnS0CvI9GOtSdLQK8j0Y61J0tAryPRjrUnS0DOgU+mtZBLQM6BT6a1kEtAzoFPprWQS0DOgU+mtZBLQM6BT6a1kEtATctVamTWSEBNy1VqZNZIQE3LVWpk1khATctVamTWSEBNy1VqZNZIQGbpbiYbAUdAZuluJhsBR0Bm6W4mGwFHQGbpbiYbAUdAZuluJhsBR0AaHLN/ogdIQBocs3+iB0hAGhyzf6IHSEAaHLN/ogdIQKKrHUfhrEdAoqsdR+GsR0Ciqx1H4axHQKKrHUfhrEdAoqsdR+GsR0CxEo0h4rJCQLESjSHiskJAsRKNIeKyQkCxEo0h4rJCQLESjSHiskJAhSTLUlkaRECFJMtSWRpEQIUky1JZGkRAhSTLUlkaRECFJMtSWRpEQKGK/kZ1FUJAoYr+RnUVQkChiv5GdRVCQKGK/kZ1FUJAoYr+RnUVQkCqRPRsK51FQKpE9GwrnUVAqkT0bCudRUCqRPRsK51FQKpE9GwrnUVAn03xtd9qR0CfTfG132pHQJ9N8bXfakdAn03xtd9qR0CfTfG132pHQIqC0+RnekVAioLT5Gd6RUCKgtPkZ3pFQIqC0+RnekVAioLT5Gd6RUAi0d07LA5DQCLR3TssDkNAItHdOywOQ0Ai0d07LA5DQLjKbxCkiUFAuMpvEKSJQUC4ym8QpIlBQLjKbxCkiUFAuMpvEKSJQUC0RuNxgQVBQLRG43GBBUFAtEbjcYEFQUC0RuNxgQVBQLRG43GBBUFA3VSNCJfIQkDdVI0Il8hCQN1UjQiXyEJA3VSNCJfIQkDdVI0Il8hCQCZafrPoMEFAJlp+s+gwQUAmWn6z6DBBQCZafrPoMEFAJlp+s+gwQUAc7GeR9gM8QBzsZ5H2AzxAHOxnkfYDPEAc7GeR9gM8QBzsZ5H2AzxAyHk/EMtZNkDIeT8Qy1k2QMh5PxDLWTZAyHk/EMtZNkDIeT8Qy1k2QFY58RLPpkBAVjnxEs+mQEBWOfESz6ZAQFY58RLPpkBAVjnxEs+mQEDEJ1wPK4s9QMQnXA8riz1AxCdcDyuLPUDEJ1wPK4s9QMQnXA8riz1AFKVJs2w6QUAUpUmzbDpBQBSlSbNsOkFAFKVJs2w6QUAUpUmzbDpBQAjp84wlGj9ACOnzjCUaP0AI6fOMJRo/QAjp84wlGj9ACOnzjCUaP0AkQ3X5Yss8QCRDdfliyzxAJEN1+WLLPEAkQ3X5Yss8QCRDdfliyzxArBHJIlRSOECsEckiVFI4QKwRySJUUjhArBHJIlRSOEAs3i/vO8w/QCzeL+87zD9ALN4v7zvMP0As3i/vO8w/QCzeL+87zD9ADFfPd2DQPkAMV893YNA+QAxXz3dg0D5ADFfPd2DQPkAMV893YNA+QCdB++zXIENAJ0H77NcgQ0AnQfvs1yBDQCdB++zXIENAJ0H77NcgQ0BRP0mo97xCQFE/Saj3vEJAUT9JqPe8QkBRP0mo97xCQOOuIaZAB0JA464hpkAHQkDjriGmQAdCQOOuIaZAB0JAvjmrVV+kREC+OatVX6REQL45q1VfpERAvjmrVV+kREC+OatVX6REQKt9TylfGkRAq31PKV8aRECrfU8pXxpEQKt9TylfGkRAq31PKV8aRECF6W6NLsZCQIXpbo0uxkJAhelujS7GQkCF6W6NLsZCQKNl96IPE0ZAo2X3og8TRkCjZfeiDxNGQKNl96IPE0ZAo2X3og8TRkBnWEvGk8JHQGdYS8aTwkdAZ1hLxpPCR0BnWEvGk8JHQGdYS8aTwkdAzi5dqUJTS0DOLl2pQlNLQM4uXalCU0tAzi5dqUJTS0DOLl2pQlNLQDEznql6uklAMTOeqXq6SUAxM56perpJQDEznql6uklAO3PsjX9uSEA7c+yNf25IQDtz7I1/bkhAO3PsjX9uSEA7c+yNf25IQCY8xxC8c0dAJjzHELxzR0AmPMcQvHNHQCY8xxC8c0dAJjzHELxzR0BNCfneY1VEQE0J+d5jVURATQn53mNVREBNCfneY1VEQE0J+d5jVURAQtvAXiiARkBC28BeKIBGQELbwF4ogEZAQtvAXiiARkBC28BeKIBGQK6rKQk4R0dArqspCThHR0CuqykJOEdHQK6rKQk4R0dArqspCThHR0AVIsZY+ZhJQBUixlj5mElAFSLGWPmYSUAVIsZY+ZhJQBUixlj5mElAgA8+8zw3TECADz7zPDdMQIAPPvM8N0xAgA8+8zw3TECADz7zPDdMQO6EOiQoh0xA7oQ6JCiHTEDuhDokKIdMQO6EOiQoh0xAA7RTZXS7TkADtFNldLtOQAO0U2V0u05AA7RTZXS7TkADtFNldLtOQJ6w3pOaQU5AnrDek5pBTkCesN6TmkFOQJ6w3pOaQU5AnrDek5pBTkALlRNsyMFOQAuVE2zIwU5AC5UTbMjBTkALlRNsyMFOQAuVE2zIwU5A9gtEN4AtUED2C0Q3gC1QQPYLRDeALVBA9gtEN4AtUED2C0Q3gC1QQL400Thn/UxAvjTROGf9TEC+NNE4Z/1MQL400Thn/UxAvjTROGf9TEBKPImn43ZNQEo8iafjdk1ASjyJp+N2TUBKPImn43ZNQO52fdhlNlBA7nZ92GU2UEDudn3YZTZQQO52fdhlNlBA7nZ92GU2UECmD0UdPI9RQKYPRR08j1FApg9FHTyPUUCmD0UdPI9RQKYPRR08j1FAz09VEUJST0DPT1URQlJPQM9PVRFCUk9Az09VEUJST0DPT1URQlJPQMGh9Rk+cE5AwaH1GT5wTkDBofUZPnBOQMGh9Rk+cE5AwaH1GT5wTkD/g73KjxVPQP+DvcqPFU9A/4O9yo8VT0D/g73KjxVPQP+DvcqPFU9ARL7UwPXUS0BEvtTA9dRLQES+1MD11EtARL7UwPXUS0Bd1AKfabFMQF3UAp9psUxAXdQCn2mxTEBd1AKfabFMQF3UAp9psUxAt8VbuTviTkC3xVu5O+JOQLfFW7k74k5At8VbuTviTkC3xVu5O+JOQCLF+DRwclBAIsX4NHByUEAixfg0cHJQQCLF+DRwclBAIsX4NHByUEAY2n9YoghQQBjaf1iiCFBAGNp/WKIIUEAY2n9YoghQQBjaf1iiCFBAccjcgErkUEBxyNyASuRQQHHI3IBK5FBAccjcgErkUEBxyNyASuRQQMtSFYJuEFFAy1IVgm4QUUDLUhWCbhBRQMtSFYJuEFFAy1IVgm4QUUAoxNQx0WJRQCjE1DHRYlFAKMTUMdFiUUAoxNQx0WJRQCjE1DHRYlFAsXvWD1RQUUCxe9YPVFBRQLF71g9UUFFAsXvWD1RQUUCxe9YPVFBRQAYJgpEsxlBABgmCkSzGUEAGCYKRLMZQQAYJgpEsxlBASsuepPiSUUBKy56k+JJRQErLnqT4klFASsuepPiSUUBKy56k+JJRQJF8i8Jjw1JAkXyLwmPDUkCRfIvCY8NSQJF8i8Jjw1JAkXyLwmPDUkBc59LQGdJSQFzn0tAZ0lJAXOfS0BnSUkBc59LQGdJSQFzn0tAZ0lJAmW2BLmlzUUCZbYEuaXNRQJltgS5pc1FAmW2BLmlzUUCZbYEuaXNRQEWvvFz9SFNARa+8XP1IU0BFr7xc/UhTQEWvvFz9SFNARa+8XP1IU0DWJMinMoxUQNYkyKcyjFRA1iTIpzKMVEDWJMinMoxUQNYkyKcyjFRAz706ev+qVEDPvTp6/6pUQM+9Onr/qlRAz706ev+qVEDPvTp6/6pUQDDvUw2MMVJAMO9TDYwxUkAw71MNjDFSQDDvUw2MMVJAMO9TDYwxUkAv1BlFceFSQC/UGUVx4VJAL9QZRXHhUkAv1BlFceFSQC/UGUVx4VJA/+OQEQW0U0D/45ARBbRTQP/jkBEFtFNA/+OQEQW0U0D/45ARBbRTQG7MLH+yalNAbswsf7JqU0BuzCx/smpTQG7MLH+yalNA3Hw8gmPOU0DcfDyCY85TQNx8PIJjzlNA3Hw8gmPOU0DcfDyCY85TQGqtZWbhAlRAaq1lZuECVEBqrWVm4QJUQGqtZWbhAlRAaq1lZuECVEDf0lpcdDpUQN/SWlx0OlRA39JaXHQ6VEDf0lpcdDpUQN/SWlx0OlRAYn7/BzZ2VEBifv8HNnZUQGJ+/wc2dlRAYn7/BzZ2VEBifv8HNnZUQNIIoISIOFVA0gighIg4VUDSCKCEiDhVQNIIoISIOFVAp8VluHxEVUCnxWW4fERVQKfFZbh8RFVAp8VluHxEVUD8fD9de0pSQPx8P117SlJA/Hw/XXtKUkD8fD9de0pSQPx8P117SlJAC5gYD/HFTkALmBgP8cVOQAuYGA/xxU5AC5gYD/HFTkB/1FViC9xIQH/UVWIL3EhAf9RVYgvcSEB/1FViC9xIQH/UVWIL3EhAH5CXTVXHRUAfkJdNVcdFQB+Ql01Vx0VAH5CXTVXHRUAfkJdNVcdFQNeO0wj7CEZA147TCPsIRkDXjtMI+whGQNeO0wj7CEZA147TCPsIRkDm9UaZrmhGQOb1RpmuaEZA5vVGma5oRkDm9UaZrmhGQHGLxZA4AUhAcYvFkDgBSEBxi8WQOAFIQHGLxZA4AUhAcYvFkDgBSECM/9YjN4FFQIz/1iM3gUVAjP/WIzeBRUCM/9YjN4FFQIz/1iM3gUVAYJC+Z7AyTEBgkL5nsDJMQGCQvmewMkxAYJC+Z7AyTEBgkL5nsDJMQDzGlaAOSU5APMaVoA5JTkA8xpWgDklOQDzGlaAOSU5APMaVoA5JTkAZ+QEeeLNKQBn5AR54s0pAGfkBHnizSkAZ+QEeeLNKQBn5AR54s0pAytPCSnwcS0DK08JKfBxLQMrTwkp8HEtAytPCSnwcS0DK08JKfBxLQOcNYY5jE0xA5w1hjmMTTEDnDWGOYxNMQOcNYY5jE0xAvbOp0WqJS0C9s6nRaolLQL2zqdFqiUtAvbOp0WqJS0C9s6nRaolLQNyWRhQEVkhA3JZGFARWSEDclkYUBFZIQNyWRhQEVkhA3JZGFARWSEA9MdXBU2BHQD0x1cFTYEdAPTHVwVNgR0A9MdXBU2BHQD0x1cFTYEdAsZIEyUQdRECxkgTJRB1EQLGSBMlEHURAsZIEyUQdRECxkgTJRB1EQCgXwLdd8z9AKBfAt13zP0AoF8C3XfM/QCgXwLdd8z9AKBfAt13zP0AQmvlryxFDQBCa+WvLEUNAEJr5a8sRQ0AQmvlryxFDQBCa+WvLEUNAcOMkbuiXPkBw4yRu6Jc+QHDjJG7olz5AcOMkbuiXPkBw4yRu6Jc+QHS7YzMT90FAdLtjMxP3QUB0u2MzE/dBQHS7YzMT90FAqOUes0thRECo5R6zS2FEQKjlHrNLYURAqOUes0thRECo5R6zS2FEQPw1WqkOJENA/DVaqQ4kQ0D8NVqpDiRDQPw1WqkOJENA/DVaqQ4kQ0BxTwxbWk5FQHFPDFtaTkVAcU8MW1pORUBxTwxbWk5FQHFPDFtaTkVA90uuvk0CQkD3S66+TQJCQPdLrr5NAkJA90uuvk0CQkD3S66+TQJCQCQ16T/WZz1AJDXpP9ZnPUAkNek/1mc9QCQ16T/WZz1AaNLPOC7oQ0Bo0s84LuhDQGjSzzgu6ENAaNLPOC7oQ0Bo0s84LuhDQIV5f7MHHkNAhXl/swceQ0CFeX+zBx5DQIV5f7MHHkNAhXl/swceQ0ApwVxouZJHQCnBXGi5kkdAKcFcaLmSR0ApwVxouZJHQCnBXGi5kkdAOzQoVi7zRkA7NChWLvNGQDs0KFYu80ZAOzQoVi7zRkA7NChWLvNGQF/a6VZWFklAX9rpVlYWSUBf2ulWVhZJQF/a6VZWFklAX9rpVlYWSUCCJAbzP0NHQIIkBvM/Q0dAgiQG8z9DR0CCJAbzP0NHQIIkBvM/Q0dAZydWW19mREBnJ1ZbX2ZEQGcnVltfZkRAZydWW19mREBnJ1ZbX2ZEQNjTdSBMC0RA2NN1IEwLREDY03UgTAtEQNjTdSBMC0RA2NN1IEwLRECujtY6H/pFQK6O1jof+kVAro7WOh/6RUCujtY6H/pFQM0zAW5V9EZAzTMBblX0RkDNMwFuVfRGQM0zAW5V9EZAzTMBblX0RkC8WnpT3ztJQLxaelPfO0lAvFp6U987SUC8WnpT3ztJQLxaelPfO0lAg5bDma0eTkCDlsOZrR5OQIOWw5mtHk5Ag5bDma0eTkCDlsOZrR5OQGhHLCsq1E1AaEcsKyrUTUBoRywrKtRNQGhHLCsq1E1AaEcsKyrUTUAslenOTR5PQCyV6c5NHk9ALJXpzk0eT0AslenOTR5PQCyV6c5NHk9AiV8vk/VhUkCJXy+T9WFSQIlfL5P1YVJAiV8vk/VhUkCJXy+T9WFSQKH2ekjIu1JAofZ6SMi7UkCh9npIyLtSQKH2ekjIu1JAofZ6SMi7UkAme2AimsVSQCZ7YCKaxVJAJntgIprFUkAme2AimsVSQCZ7YCKaxVJAFX9Xe5onU0AVf1d7midTQBV/V3uaJ1NAFX9Xe5onU0AVf1d7midTQELmRM2J9VBAQuZEzYn1UEBC5kTNifVQQELmRM2J9VBAQuZEzYn1UEA2yq93JKNPQDbKr3cko09ANsqvdySjT0A2yq93JKNPQL5nrJlte09AvmesmW17T0C+Z6yZbXtPQL5nrJlte09AvmesmW17T0DKBKKpoFlMQMoEoqmgWUxAygSiqaBZTEDKBKKpoFlMQMoEoqmgWUxAI4gyoZO7TkAjiDKhk7tOQCOIMqGTu05AI4gyoZO7TkAjiDKhk7tOQD7cQPJIdk5APtxA8kh2TkA+3EDySHZOQD7cQPJIdk5APtxA8kh2TkA8CkGrag5QQDwKQatqDlBAPApBq2oOUEA8CkGrag5QQOFEnyjk+01A4USfKOT7TUDhRJ8o5PtNQOFEnyjk+01A4USfKOT7TUBGWSbkvl5QQEZZJuS+XlBARlkm5L5eUEBGWSbkvl5QQEZZJuS+XlBAujsttkzMUEC6Oy22TMxQQLo7LbZMzFBAujsttkzMUEDe2j03+OdOQN7aPTf4505A3to9N/jnTkDe2j03+OdOQN7aPTf4505ADsc+CjiwTEAOxz4KOLBMQA7HPgo4sExADsc+CjiwTEAOxz4KOLBMQMSCjnovIk5AxIKOei8iTkDEgo56LyJOQMSCjnovIk5AxIKOei8iTkDArPNZhvRPQMCs81mG9E9AwKzzWYb0T0DArPNZhvRPQMCs81mG9E9A8ofnI5JWUEDyh+cjklZQQPKH5yOSVlBA8ofnI5JWUECEl1+5gzhMQISXX7mDOExAhJdfuYM4TECEl1+5gzhMQISXX7mDOExA7X8si7NcSkDtfyyLs1xKQO1/LIuzXEpA7X8si7NcSkDtfyyLs1xKQD731Oh2REZAPvfU6HZERkA+99TodkRGQD731Oh2REZAPvfU6HZERkDSJ2xFFwdEQNInbEUXB0RA0idsRRcHREDSJ2xFFwdEQNInbEUXB0RA/EEuEjGGR0D8QS4SMYZHQPxBLhIxhkdA/EEuEjGGR0D8QS4SMYZHQKbKMuq/jElApsoy6r+MSUCmyjLqv4xJQKbKMuq/jElApsoy6r+MSUANzs16R1RHQA3OzXpHVEdADc7NekdUR0ANzs16R1RHQA3OzXpHVEdA3HYwUYKBQUDcdjBRgoFBQNx2MFGCgUFA3HYwUYKBQUDcdjBRgoFBQAgjDCvf9EBACCMMK9/0QEAIIwwr3/RAQAgjDCvf9EBAkecg3r1rRECR5yDevWtEQJHnIN69a0RAkecg3r1rRECR5yDevWtEQN9Cf0+qYkNA30J/T6piQ0DfQn9PqmJDQN9Cf0+qYkNA30J/T6piQ0D2gqJ3KbRCQPaConcptEJA9oKidym0QkD2gqJ3KbRCQPaConcptEJAMIUmWbsIQkAwhSZZuwhCQDCFJlm7CEJAMIUmWbsIQkAwhSZZuwhCQAQuxbzio0FABC7FvOKjQUAELsW84qNBQAQuxbzio0FAOsTSWD0sQkA6xNJYPSxCQDrE0lg9LEJAOsTSWD0sQkA6xNJYPSxCQBRxz/wFVUBAFHHP/AVVQEAUcc/8BVVAQBRxz/wFVUBAFHHP/AVVQED44FB6ITI7QPjgUHohMjtA+OBQeiEyO0D44FB6ITI7QPjgUHohMjtAyHwYSuNEOUDIfBhK40Q5QMh8GErjRDlAyHwYSuNEOUDIfBhK40Q5QB4wadTqakZAHjBp1OpqRkAeMGnU6mpGQB4wadTqakZAkVUD7fQuSECRVQPt9C5IQJFVA+30LkhAkVUD7fQuSECRVQPt9C5IQMA9wNND109AwD3A00PXT0DAPcDTQ9dPQMA9wNND109AwD3A00PXT0B+f9BLp8NQQH5/0Eunw1BAfn/QS6fDUEB+f9BLp8NQQH5/0Eunw1BA+7Yo5eZFT0D7tijl5kVPQPu2KOXmRU9A+7Yo5eZFT0D7tijl5kVPQApeXtCCyUtACl5e0ILJS0AKXl7QgslLQApeXtCCyUtACl5e0ILJS0BP2Fm2V9NJQE/YWbZX00lAT9hZtlfTSUBP2Fm2V9NJQE/YWbZX00lA8O9+8ifYQkDw737yJ9hCQPDvfvIn2EJA8O9+8ifYQkDw737yJ9hCQIbFeqyB1EZAhsV6rIHURkCGxXqsgdRGQIbFeqyB1EZAhsV6rIHURkA53pyspKZGQDnenKykpkZAOd6crKSmRkA53pyspKZGQLbcNEPxpkZAttw0Q/GmRkC23DRD8aZGQLbcNEPxpkZAttw0Q/GmRkBMVlOndjJJQExWU6d2MklATFZTp3YySUBMVlOndjJJQExWU6d2MklAwKC1oPHrRkDAoLWg8etGQMCgtaDx60ZAwKC1oPHrRkDAoLWg8etGQJbVkVAo2UVAltWRUCjZRUCW1ZFQKNlFQJbVkVAo2UVAltWRUCjZRUCT7n8v/ttFQJPufy/+20VAk+5/L/7bRUCT7n8v/ttFQJPufy/+20VAjARWidcZTkCMBFaJ1xlOQIwEVonXGU5AjARWidcZTkCMBFaJ1xlOQFSwDZ5y801AVLANnnLzTUBUsA2ecvNNQFSwDZ5y801AVLANnnLzTUD4+aPU2s1OQPj5o9TazU5A+Pmj1NrNTkD4+aPU2s1OQPj5o9TazU5AL67H58I3TkAvrsfnwjdOQC+ux+fCN05AL67H58I3TkAvrsfnwjdOQGuFPDv4Z09Aa4U8O/hnT0BrhTw7+GdPQGuFPDv4Z09Aa4U8O/hnT0Aylij6tUVNQDKWKPq1RU1AMpYo+rVFTUAylij6tUVNQJSoe01r30hAlKh7TWvfSECUqHtNa99IQJSoe01r30hAlKh7TWvfSEB1NUKEMqJOQHU1QoQyok5AdTVChDKiTkB1NUKEMqJOQHU1QoQyok5AKxe6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\",\"dtype\":\"float64\",\"shape\":[2148]},\"SMA(C,20)_0\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[2148]},\"ohlc_low\":{\"__ndarray__\":\"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"backtest.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Better. 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 data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to lower time frame, filling in the in-betweens.\n", + "Or simply use [`backtesting.lib.resample_apply()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.resample_apply) function." + ] + } + ], + "metadata": { + "jupytext_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": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/Multiple Time Frames.py b/doc/examples/Multiple Time Frames.py new file mode 100644 index 0000000..bca59ce --- /dev/null +++ b/doc/examples/Multiple Time Frames.py @@ -0,0 +1,172 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.3' +# jupytext_version: 0.8.6 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# Multiple Time Frames +# ============ +# +# The best trading strategies relying on technical analysis take into account the 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 _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html). +# +# We will test this supposed long-only +# [400%-a-year trading strategy](http://jbmarwood.com/stock-trading-strategy-300/), +# which daily and weekly +# [relative strength index](https://en.wikipedia.org/wiki/Relative_strength_index) +# (RSI) values and moving averages (MA). +# +# Let's introduce the two indicators we'll be using. +# In practice, one can use functions from any indicator library, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib), +# [Tulipy](https://tulipindicators.org), +# PyAlgoTrade, ... + +# + +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) +# - + +# 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: +# * Close 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 frames (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 have to resample + # the daily values. + + # Strategy exposes `self.data` as raw NumPy arrays. + # Let's make closing prices back a pandas Series. + + close = pd.Series(self.data.Close, + index=self.data.index, + name='Close') + + # Resample to weekly resolution, ending weeks on + # fridays. Aggregate groups using their last value + # (i.e. week's closing price). + # Notice `label='right'`. If it were set to 'left' (default), + # the strategy would exhibit look-ahead bias. + + weekly_close = close.resample('W-FRI', label='right').agg('last') + + # We apply RSI(30) to weekly close + # prices, then reindex it back to original daily + # index, forward-filling the missing values in each + # week. + # We make a separate function that returns the final + # indicator array. + + def W_RSI(series, n): + return RSI(series, n).reindex(close.index).ffill() + + self.weekly_rsi = self.I(W_RSI, weekly_close, self.w_rsi) + + + # ... And, now that you know what goes on behind the scenes, + # we could achieve the whole *exact* same thing with simpler: + + 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() +# - + +# 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() +# - + +# Meager four trades in a span of nine years with effectively 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() + +# 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 data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to lower time frame, filling in the in-betweens. +# Or simply use [`backtesting.lib.resample_apply()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.resample_apply) function. diff --git a/doc/examples/Parameter Heatmap.ipynb b/doc/examples/Parameter Heatmap.ipynb new file mode 100644 index 0000000..50f5af0 --- /dev/null +++ b/doc/examples/Parameter Heatmap.ipynb @@ -0,0 +1,906 @@ +{ + "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 Start User Guide.html).\n", + "\n", + "First, let's again import a helper moving average function.\n", + "In practice, one can use functions from any indicator library, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib),\n", + "[Tulipy](https://tulipindicators.org),\n", + "PyAlgoTrade, ..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\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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i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"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 Start User Guide.html),\n", + "but there will be 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 Close prices determine the signal to enter or exit the position." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "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. Let's optimize our strategy on Google stock data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 585 ms, sys: 102 ms, total: 687 ms\n", + "Wall time: 11.7 s\n" + ] + } + ], + "source": [ + "%%time \n", + "\n", + "from backtesting import Backtest\n", + "from backtesting.test import GOOG\n", + "\n", + "\n", + "backtest = Backtest(GOOG, Sma4Cross, commission=.002)\n", + "\n", + "stats, heatmap = backtest.optimize(\n", + " n1=range(10, 110, 10),\n", + " n2=range(20, 210, 20),\n", + " n_enter=range(15, 35, 5),\n", + " n_exit=range(10, 25, 5),\n", + " constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,\n", + " maximize='Equity Final [$]',\n", + " return_heatmap=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice `return_heatmap=True` parameter passed to\n", + "[`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize).\n", + "It makes the function return a heatmap series along with the usual stats of the best run.\n", + "`heatmap` is a pandas Series indexed with a MultiIndex, a cartesian product of all permissible parameter values.\n", + "The series vales are from the `maximize=` field we provided." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "n_exit n_enter n2 n1 \n", + "10 15 40 20 8742.77\n", + " 30 10662.30\n", + " 60 20 10020.29\n", + " 30 11386.84\n", + " 40 13179.02\n", + " 50 8903.43\n", + " 80 20 9877.11\n", + " 30 8675.47\n", + " 40 9671.30\n", + " 50 12621.72\n", + " 60 12754.77\n", + " 70 15869.19\n", + " 100 20 11005.88\n", + " 30 10293.02\n", + " 40 12212.70\n", + " ... \n", + "20 30 160 100 9392.84\n", + " 180 40 8080.16\n", + " 50 8080.16\n", + " 60 8080.16\n", + " 70 7727.38\n", + " 80 7727.38\n", + " 90 7727.38\n", + " 100 7727.25\n", + " 200 40 7923.41\n", + " 50 7923.41\n", + " 60 7923.41\n", + " 70 7222.44\n", + " 80 7863.62\n", + " 90 7651.35\n", + " 100 7418.69\n", + "Length: 486, dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This heatmap contains the results of all the runs,\n", + "and it's very easy to obtain parameter combinations for e.g. three best runs:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "n_exit n1 n_enter n2 \n", + "15 40 25 60 21137.84\n", + " 50 20 120 22012.50\n", + " 40 20 160 22644.29\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heatmap.sort_values().iloc[-3:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But people have this enormous faculty of vision we use to make judgements on much larger data sets much faster.\n", + "Let's plot the whole heatmap by projecting it on two chosen dimensions.\n", + "Say we're mostly interested how parameters `n1` and `n2`, on average, affect the outcome." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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n1
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3011685.4511947.2412507.6413247.9812471.1910955.3312672.7011049.3810599.47
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50NaN9268.0810761.4311780.6511928.5610022.4211639.7810783.379737.71
60NaNNaN9901.7711012.149550.5210839.2111360.9610512.389653.58
70NaNNaN11015.249408.209828.1810759.5111366.779972.348627.18
80NaNNaNNaN10148.8310225.9210272.4911111.8410230.379560.67
90NaNNaNNaN11175.1011240.1610239.0411171.3310561.5510258.52
100NaNNaNNaNNaN11954.449413.2011689.4710094.5110564.84
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" + ], + "text/plain": [ + "n2 40 60 80 100 120 140 160 \\\n", + "n1 \n", + "20 8742.77 10020.29 9877.11 11005.88 12832.05 12115.57 13380.55 \n", + "30 11685.45 11947.24 12507.64 13247.98 12471.19 10955.33 12672.70 \n", + "40 NaN 14522.63 11314.04 8852.70 11252.55 9538.35 12615.52 \n", + "50 NaN 9268.08 10761.43 11780.65 11928.56 10022.42 11639.78 \n", + "60 NaN NaN 9901.77 11012.14 9550.52 10839.21 11360.96 \n", + "70 NaN NaN 11015.24 9408.20 9828.18 10759.51 11366.77 \n", + "80 NaN NaN NaN 10148.83 10225.92 10272.49 11111.84 \n", + "90 NaN NaN NaN 11175.10 11240.16 10239.04 11171.33 \n", + "100 NaN NaN NaN NaN 11954.44 9413.20 11689.47 \n", + "\n", + "n2 180 200 \n", + "n1 \n", + "20 9462.92 9993.13 \n", + "30 11049.38 10599.47 \n", + "40 10804.31 10094.83 \n", + "50 10783.37 9737.71 \n", + "60 10512.38 9653.58 \n", + "70 9972.34 8627.18 \n", + "80 10230.37 9560.67 \n", + "90 10561.55 10258.52 \n", + "100 10094.51 10564.84 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hm = heatmap.groupby(['n1', 'n2']).mean().unstack()\n", + "hm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot that using the excellent [_Seaborn_](https://seaborn.pydata.org) package:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import seaborn as sns\n", + "\n", + "\n", + "sns.heatmap(hm[::-1], cmap='viridis')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that, on average, we obtain the highest result using trend-determining parameters `n1=40` and `n2=60`,\n", + "and it's not like other nearby combinations work similarly well — in our particular strategy, this combination really stands 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." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": 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\n", + "\n" + ], + "text/plain": [ + "Column(id='2574', ...)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from backtesting.lib import plot_heatmaps\n", + "\n", + "\n", + "plot_heatmaps(heatmap, agg='mean')" + ] + } + ], + "metadata": { + "jupytext_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": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/Parameter Heatmap.py b/doc/examples/Parameter Heatmap.py new file mode 100644 index 0000000..b53989e --- /dev/null +++ b/doc/examples/Parameter Heatmap.py @@ -0,0 +1,152 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.3' +# jupytext_version: 0.8.6 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# 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 Start User Guide.html). +# +# First, let's again import a helper moving average function. +# In practice, one can use functions from any indicator library, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib), +# [Tulipy](https://tulipindicators.org), +# PyAlgoTrade, ... + +from backtesting.test import SMA + +# 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 Start User Guide.html), +# but there will be 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 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() +# - + +# It's not a robust strategy, but we can optimize it. Let's optimize our strategy on Google stock data. + +# + +# %%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 [$]', + return_heatmap=True) +# - + +# 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 parameter values. +# The series vales are from the `maximize=` field we provided. + +heatmap + +# This heatmap contains the results of all the runs, +# and it's very easy to obtain parameter combinations for e.g. three best runs: + +heatmap.sort_values().iloc[-3:] + +# But people have this enormous faculty of vision we use to make judgements on much larger data sets much faster. +# Let's plot the whole heatmap by projecting it on two chosen dimensions. +# Say we're mostly interested how parameters `n1` and `n2`, on average, affect the outcome. + +hm = heatmap.groupby(['n1', 'n2']).mean().unstack() +hm + +# Let's plot that using the excellent [_Seaborn_](https://seaborn.pydata.org) package: + +# + +# %matplotlib inline + +import seaborn as sns + + +sns.heatmap(hm[::-1], cmap='viridis') +# - + +# We see that, on average, we obtain the highest result using trend-determining parameters `n1=40` and `n2=60`, +# and it's not like other nearby combinations work similarly well — in our particular strategy, this combination really stands 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') diff --git a/doc/examples/Quick Start User Guide.ipynb b/doc/examples/Quick Start User Guide.ipynb new file mode 100644 index 0000000..86a78f0 --- /dev/null +++ b/doc/examples/Quick Start User Guide.ipynb @@ -0,0 +1,891 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Backtesting.py_ Quick Start User Guide\n", + "=======================\n", + "\n", + "This tutorial will show off some of the features of _backtesting.py_, yet another Python package for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies.\n", + "\n", + "Firstly, what _backtesting.py_ is not: It is not a data source — you bring your own data. It does _not_ support strategies that rely on multiple orders, hedging, position sizing, or portfolio rebalancing. Instead, _backtesting.py_ works with a single asset at a time, a single position at a time (long or short), and the position size is (as yet) non-adjustable, corresponding to 100% of available funds. _Backtesting.py_ is not aware of order types and does not properly simulate, nor can be connected to, a broker. \n", + "\n", + "As a trade-off, _backtesting.py_ is a _blazing fast_, small and lightweight backtesting library that uses state-of-the-art Python data structures and procedures, and whose whole API easily fits into memory of a single human. It's best suited for optimizing position entrence and exit strategies, decisions upon values of technical indicators, and it's also a versatile interactive trading strategy visualization tool.\n", + "\n", + "### Data\n", + "\n", + "_You bring your own data._ Backtesting ingests data 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 easily obtainable (see e.g. \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", + "Your data frame can have other columns, but these are necessary.\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)), otherwise a simple range index will do.\n", + "Let's see it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jk/PycharmProjects/backtesting/backtesting/_plotting.py:34: 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 `bokeh.io.reset_output()`.\n", + " warnings.warn('Jupyter Notebook detected. '\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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OpenHighLowCloseVolume
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\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 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, and let it be a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover).\n", + "\n", + "_Backtesting.py_ doesn't contain its own set of technical indicators. In practice, one should probably use functions from their favorite indicator library, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib),\n", + "[Tulipy](https://tulipindicators.org),\n", + "PyAlgoTrade, ...\n", + "But for this example, let's define a simple helper moving average function. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "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": [ + "Note, this is the exact same helper function as the one used in the project unit tests." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from backtesting.test import SMA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A custom strategy needs to extend `backtesting.Strategy` class and override two 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 at the beginning, before the strategy is run. Within it, one ideally precomputes in efficient, vectorized fashion whatever indicators and signals the strategy depends on.\n", + "\n", + "Method `next()` is iteratively called by the 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/trade within candlesticks. If you need to trade within candlesticks, instead start with more fine-grained data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from backtesting import Strategy\n", + "from backtesting.lib import crossover\n", + "\n", + "\n", + "class SmaCross(Strategy):\n", + " \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 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, buy the asset\n", + " if crossover(self.sma1, self.sma2):\n", + " self.buy()\n", + "\n", + " # Else, if sma1 crosses below sma2, sell it\n", + " elif crossover(self.sma2, self.sma1):\n", + " self.sell()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In `init()` as well as in `next()`, the data we are simulating the strategy 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 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", + "We pass the wrapper a function (here, our `SMA` function) and any additional arguments to call it with (here, our _Close_ values and the MA lag). Indicators wrapped in this way will be plotted, and their names, intelligently inferred, will appear in the plot legend.\n", + "\n", + "In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we go long; if it did and downwards, we sell any open long position and go short. Note, there is no position size to adjust; _Backtesting.py_ always 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": "raw", + "metadata": { + "collapsed": true + }, + "source": [ + " def next(self):\n", + " if (self.sma1[-2] < self.sma2[-2] and\n", + " self.sma1[-1] > self.sma2[-1]):\n", + " self.buy()\n", + "\n", + " elif (self.sma1[-2] > self.sma2[-2] and\n", + " self.sma1[-1] < self.sma2[-1]):\n", + " self.sell() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ugh!\n", + "\n", + "In `init()`, the whole series of points was available, whereas in `next()`, the _length of `self.data` and any indicator arrays 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 need them to be `pandas.Series`, use, e.g., `pd.Series(self.data.Close, index=self.data.index)`.\n", + "\n", + "Let's see now how our strategy performs on historical Google data. We'll begin with ¤10,000 in 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 [%] 94.29\n", + "Equity Final [$] 69665.12\n", + "Equity Peak [$] 69722.15\n", + "Return [%] 596.65\n", + "Buy & Hold Return [%] 703.46\n", + "Max. Drawdown [%] -33.61\n", + "Avg. Drawdown [%] -5.68\n", + "Max. Drawdown Duration 689 days 00:00:00\n", + "Avg. Drawdown Duration 41 days 00:00:00\n", + "# Trades 93\n", + "Win Rate [%] 53.76\n", + "Best Trade [%] 56.98\n", + "Worst Trade [%] -17.03\n", + "Avg. Trade [%] 2.44\n", + "Max. Trade Duration 121 days 00:00:00\n", + "Avg. Trade Duration 32 days 00:00:00\n", + "Expectancy [%] 6.92\n", + "SQN 1.77\n", + "Sharpe Ratio 0.22\n", + "Sortino Ratio 0.54\n", + "Calmar Ratio 0.07\n", + "_strategy SmaCross\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from backtesting import Backtest\n", + "\n", + "bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002)\n", + "bt.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We initialize the\n", + "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", + "instance with data and strategy _class_ (see API reference for additional options).\n", + "\n", + "As we call\n", + "[`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run)\n", + "method, we instantaneously get returned a pandas Series of results and statistics associated with our strategy. We see that this simple strategy makes 600% return in the period of 9 years, with maximal drawdown 33%, and with longest drawdown period spanning almost two years ...\n", + "\n", + "If we call\n", + "[`Backtest.plot()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot)\n", + "method, we can review these results 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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2vl+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+AQAOBlUMLroBAqhxaZDuwgEA2tMh2PrKAQP8qhxZZsoBAxna+n5qtgEAbhetROKiAQNj5fmo8oYBAW7pJDIKVgEA0iUFgZY2AQIhs5/upgYBA7tJNYpB5gEDmJjEIrHWAQJ5FtvN9eIBA9n5qvPR9gEBGDAIrh4SAQJaZmZkZi4BAlpmZmRmdgEA0iUFgZauAQJnEILDyvIBAMl66SYzNgECVbhKDwOaAQDa0yHa+AIFAkRgEVo4agUDaJAaBFSyBQE2Nl24SQ4FA1KNwPYpfgUCrR+F6lIGBQK1yaJFtqIFA4KWbxCDHgUAVBFYOLeeBQGsSg8BKCIJALzMzM7MogkCHbOf7KUWCQBLZzvfTYIJAJ4cW2c51gkA2tMh2PoSCQPD9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AAQH8Cl3BCAAAA5VSXcEIAAAB8nphwQgAAwOHwmHBCAACAR0OZcEIAAECtlZlwQgAAgN6MmnBCAABARN+acEIAAACqMZtwQgAAwA+Em3BCAACAddabcEIAAMCmzZxwQgAAgAwgnXBCAABAcnKdcEIAAADYxJ1wQgAAwD0XnnBCAAAAbw6fcEIAAMDUYJ9wQgAAgDqzn3BCAABAoAWgcEIAAAAGWKBwQgAAQDdPoXBCAAAAnaGhcEIAAMAC9KFwQgAAgGhGonBCAABAzpiicEIAAID/j6NwQgAAQGXio3BCAAAAyzSkcEIAAMAwh6RwQgAAgJbZpHBCAADAx9ClcEIAAIAtI6ZwQgAAQJN1pnBCAAAA+cemcEIAAMBeGqdwQgAAAJARqHBCAADA9WOocEIAAIBbtqhwQgAAQMEIqXBCAABAWFKqcEIAAAC+pKpwQgAAwCP3qnBCAACAiUmrcEIAAEDvm6twQgAAgCCTrHBCAABAhuWscEIAAADsN61wQgAAwFGKrXBCAACAt9ytcEIAAMDo065wQgAAgE4mr3BCAABAtHivcEIAAAAay69wQgAAwH8dsHBCAAAAsRSxcEIAAMAWZ7FwQgAAgHy5sXBCAABA4guycEIAAABIXrJwQgAAQHlVs3BCAAAA36ezcEIAAMBE+rNwQgAAgKpMtHBCAABAEJ+0cEIAAIBBlrVwQgAAQKfotXBCAAAADTu2cEIAAMByjbZwQgAAgNjftnBCAACAbym4cEIAAEDVe7hwQgAAADvOuHBCAADAoCC5cEIAAADSF7pwQgAAwDdqunBCAACAnby6cEIAAEADD7twQgAAAGlhu3BCAABAmli8cEIAAAAAq7xwQgAAwGX9vHBCAACAy0+9cEIAAEAxor1wQgAAgGKZvnBCAABAyOu+cEIAAAAuPr9wQgAAwJOQv3BCAACA+eK/cEIAAMAq2sBwQgAAgJAswXBCAABA9n7BcEIAAABc0cFwQgAAwMEjwnBCAAAA8xrDcEIAAIC+v8NwQgAAQCQSxHBCAAAAimTEcEIAAEC7W8VwQgAAACGuxXBCAADAhgDGcEIAAIDsUsZwQgAAQFKlxnBCAACAg5zHcEIAAEDp7sdwQgAAAE9ByHBCAADAtJPIcEIAAIAa5shwQgAAwEvdyXBCAACAsS/KcEIAAEAXgspwQgAAAH3UynBCAADA4ibLcEIAAAAUHsxwQgAAwHlwzHBCAACA38LMcEIAAEBFFc1wQgAAAKtnzXBCAABA3F7OcEIAAABCsc5wQgAAwKcDz3BCAACADVbPcEIAAEBzqM9wQgAAgKSf0HBCAABACvLQcEIAAABwRNFwQgAAwNWW0XBCAACAO+nRcEIAAMBs4NJwQgAAgNIy03BCAABAOIXTcEIAAACe19NwQgAAwAMq1HBCAAAANSHVcEIAAMCac9VwQgAAgADG1XBCAABAZhjWcEIAAADMatZwQgAAAGO013BCAADAyAbYcEIAAIAuWdhwQgAAQJSr2HBCAACAxaLZcEIAAEAr9dlwQgAAAJFH2nBCAADA9pnacEIAAIBc7NpwQgAAwI3j23BCAACA8zXccEIAAEBZiNxwQgAAAL/a3HBCAADAJC3dcEIAAABWJN5wQgAAwLt23nBCAACAIcnecEIAAECHG99wQgAAAO1t33BCAABAHmXgcEIAAACEt+BwQgAAwOkJ4XBCAACAT1zhcEIAAEC1ruFwQgAAgOal4nBCAABATPjicEIAAACySuNwQgAAwBed43BCAACAfe/jcEIAAMCu5uRwQgAAgBQ55XBCAABAeovlcEIAAADg3eVwQgAAwEUw5nBCAAAAdyfncEIAAMDceedwQgAAgELM53BCAABAqB7ocEIAAAAOcehwQgAAQD9o6XBCAAAApbrpcEIAAMAKDepwQgAAgHBf6nBCAABA1rHqcEIAAIAHqetwQgAAQG3763BCAAAA003scEIAAMA4oOxwQgAAgJ7y7HBCAADAz+ntcEIAAIA1PO5wQgAAQJuO7nBCAAAAAeHucEIAAMBmM+9wQgAAAJgq8HBCAADA/XzwcEIAAIBjz/BwQgAAAC908XBCAABAYGvycEIAAADGvfJwQgAAwCsQ83BCAACAkWLzcEIAAED3tPNwQgAAgCis9HBCAABAjv70cEIAAAD0UPVwQgAAwFmj9XBCAACAv/X1cEIAAMDw7PZwQgAAgFY/93BCAABAvJH3cEIAAAAi5PdwQgAAwIc2+HBCAAAAuS35cEIAAMAegPlwQgAAgITS+XBCAABA6iT6cEIAAABQd/pwQgAAAOfA+3BCAADATBP8cEIAAICyZfxwQgAAQBi4/HBCAAAAFVT+cEIAAMB6pv5wQgAAgOD4/nBCAADAEfD/cEIAAIB3QgBxQgAAQN2UAHFCAAAAQ+cAcUIAAMCoOQFxQgAAwD+DAnFCAACApdUCcUIAAEALKANxQgAAAHF6A3FCAABAonEEcUIAAAAIxARxQgAAwG0WBXFCAACA02gFcUIAAEA5uwVxQgAAgGqyBnFCAABA0AQHcUIAAAA2VwdxQgAAwJupB3FCAACAAfwHcUIAAMAy8whxQgAAgJhFCXFCAABA/pcJcUIAAABk6glxQgAAwMk8CnFCAAAA+zMLcUIAAMBghgtxQgAAgMbYC3FCAABALCsMcUIAAACSfQxxQgAAACnHDXFCAADAjhkOcUIAAID0aw5xQgAAQFq+DnFCAACAi7UPcUIAAEDxBxBxQgAAAFdaEHFCAADAvKwQcUIAAIAi/xBxQgAAwFP2EXFCAACAuUgScUIAAEAfmxJxQgAAAIXtEnFCAADA6j8TcUIAAAAcNxRxQgAAwIGJFHFCAACA59sUcUIAAEBNLhVxQgAAALOAFXFCAABA5HcWcUIAAABKyhZxQgAAwK8cF3FCAACAFW8XcUIAAEB7wRdxQgAAgKy4GHFCAABAEgsZcUIAAAB4XRlxQgAAwN2vGXFCAACAQwIacUIAAMB0+RpxQgAAgNpLG3FCAABAQJ4bcUIAAACm8BtxQgAAAD06HXFCAADAoowdcUIAAIAI3x1xQgAAQG4xHnFCAAAA1IMecUIAAEAFex9xQgAAAGvNH3FCAADA0B8gcUIAAIA2ciBxQgAAQJzEIHFCAACAzbshcUIAAEAzDiJxQgAAAJlgInFCAADA/rIicUIAAIBkBSNxQgAAwJX8I3FCAACA+04kcUIAAEBhoSRxQgAAAMfzJHFCAADALEYlcUIAAABePSZxQgAAwMOPJnFCAACAKeImcUIAAECPNCdxQgAAAPWGJ3FCAABAJn4ocUIAAACM0ChxQgAAwPEiKXFCAACAV3UpcUIAAEC9xylxQgAAgO6+KnFCAABAVBErcUIAAAC6YytxQgAAwB+2K3FCAACAhQgscUIAAIAcUi1xQgAAQIKkLXFCAAAA6PYtcUIAAMBNSS5xQgAAAH9AL3FCAADA5JIvcUIAAIBK5S9xQgAAQLA3MHFCAAAAFoowcUIAAEBHgTFxQgAAAK3TMXFCAADAEiYycUIAAIB4eDJxQgAAQN7KMnFCAACAD8IzcUIAAEB1FDRxQgAAANtmNHFCAADAQLk0cUIAAICmCzVxQgAAwNcCNnFCAACAPVU2cUIAAECjpzZxQgAAAAn6NnFCAADAbkw3cUIAAACgQzhxQgAAwAWWOHFCAABA0To5cUIAAAA3jTlxQgAAQGiEOnFCAAAAztY6cUIAAMAzKTtxQgAAgJl7O3FCAABA/807cUIAAIAwxTxxQgAAQJYXPXFCAAAA/Gk9cUIAAMBhvD1xQgAAgMcOPnFCAADA+AU/cUIAAIBeWD9xQgAAQMSqP3FCAAAAKv0/cUIAAMCPT0BxQgAAAMFGQXFCAADAJplBcUIAAICM60FxQgAAQPI9QnFCAAAAWJBCcUIAAECJh0NxQgAAAO/ZQ3FCAADAVCxEcUIAAIC6fkRxQgAAQCDRRHFCAACAUchFcUIAAEC3GkZxQgAAAB1tRnFCAADAgr9GcUIAAIDoEUdxQgAAwBkJSHFCAACAf1tIcUIAAEDlrUhxQgAAAEsASXFCAADAsFJJcUIAAADiSUpxQgAAwEecSnFCAACAre5KcUIAAEATQUtxQgAAAHmTS3FCAAAAEN1McUIAAMB1L01xQgAAgNuBTXFCAABAQdRNcUIAAIByy05xQgAAQNgdT3FCAAAAPnBPcUIAAMCjwk9xQgAAgAkVUHFCAADAOgxRcUIAAICgXlFxQgAAQAaxUXFCAAAAbANScUIAAMDRVVJxQgAAAANNU3FCAADAaJ9TcUIAAIDO8VNxQgAAQDREVHFCAAAAmpZUcUIAAEDLjVVxQgAAADHgVXFCAADAljJWcUIAAID8hFZxQgAAQGLXVnFCAACAk85XcUIAAED5IFhxQgAAAF9zWHFCAADAxMVYcUIAAIAqGFlxQgAAwFsPWnFCAACAwWFacUIAAEAntFpxQgAAAI0GW3FCAADA8lhbcUIAAAAkUFxxQgAAwImiXHFCAACA7/RccUIAAEBVR11xQgAAALuZXXFCAABA7JBecUIAAABS415xQgAAwLc1X3FCAACAHYhfcUIAAECD2l9xQgAAgLTRYHFCAABAGiRhcUIAAACAdmFxQgAAwOXIYXFCAACASxticUIAAMB8EmNxQgAAgOJkY3FCAABASLdjcUIAAACuCWRxQgAAwBNcZHFCAAAARVNlcUIAAMCqpWVxQgAAgBD4ZXFCAAAA3JxmcUIAAEANlGdxQgAAAHPmZ3FCAADA2DhocUIAAIA+i2hxQgAAQKTdaHFCAACA1dRpcUIAAEA7J2pxQgAAAKF5anFCAADABsxqcUIAAIBsHmtxQgAAwJ0VbHFCAACAA2hscUIAAEBpumxxQgAAAM8MbXFCAADANF9tcUIAAABmVm5xQgAAwMuobnFCAACAMftucUIAAECXTW9xQgAAAP2fb3FCAABALpdwcUIAAMD5O3FxQgAAgF+OcXFCAABAxeBxcUIAAID213JxQgAAAMJ8c3FCAADAJ89zcUIAAICNIXRxQgAAwL4YdXFCAACAJGt1cUIAAECKvXVxQgAAAPAPdnFCAADAVWJ2cUIAAACHWXdxQgAAwOyrd3FCAACAUv53cUIAAEC4UHhxQgAAAB6jeHFCAAAAtex5cUIAAMAaP3pxQgAAgICRenFCAABA5uN6cUIAAIAX23txQgAAQH0tfHFCAAAA4398cUIAAMBI0nxxQgAAgK4kfXFCAADA3xt+cUIAAIBFbn5xQgAAQKvAfnFCAAAAERN/cUIAAMB2ZX9xQgAAAKhcgHFCAADADa+AcUIAAIBzAYFxQgAAQNlTgXFCAAAAP6aBcUIAAADW74JxQgAAwDtCg3FCAACAoZSDcUIAAEAH54NxQgAAgDjehHFCAABAnjCFcUIAAAAEg4VxQgAAwGnVhXFCAACAzyeGcUIAAMAAH4dxQgAAgGZxh3FCAABAzMOHcUIAAAAyFohxQgAAwJdoiHFCAAAAyV+JcUIAAMAusolxQgAAgJQEinFCAABA+laKcUIAAABgqYpxQgAAQJGgi3FCAAAA9/KLcUIAAMBcRYxxQgAAgMKXjHFCAACAWeGNcUIAAEC/M45xQgAAACWGjnFCAADAitiOcUIAAIDwKo9xQgAAwCEikHFCAACAh3SQcUIAAEDtxpBxQgAAAFMZkXFCAADAuGuRcUIAAADqYpJxQgAAwE+1knFCAACAtQeTcUIAAEAbWpNxQgAAAIGsk3FCAABAsqOUcUIAAAAY9pRxQgAAwH1IlXFCAACA45qVcUIAAEBJ7ZVxQgAAgHrklnFCAABA4DaXcUIAAABGiZdxQgAAwKvbl3FCAACAES6YcUIAAMBCJZlxQgAAgKh3mXFCAABADsqZcUIAAAB0HJpxQgAAwNlumnFCAAAAC2abcUIAAMBwuJtxQgAAgNYKnHFCAABAPF2ccUIAAACir5xxQgAAQNOmnXFCAAAAOfmdcUIAAMCeS55xQgAAgASennFCAABAavCecUIAAICb559xQgAAQAE6oHFCAAAAZ4ygcUIAAMDM3qBxQgAAgDIxoXFCAACAyXqicUIAAEAvzaJxQgAAAJUfo3FCAADA+nGjcUIAAAAsaaRxQgAAwJG7pHFCAACA9w2lcUIAAEBdYKVxQgAAAMOypXFCAABA9KmmcUIAAABa/KZxQgAAwL9Op3FCAACAJaGncUIAAECL86dxQgAAgLzqqHFCAABAIj2pcUIAAACIj6lxQgAAwO3hqXFCAACAUzSqcUIAAMCEK6txQgAAgOp9q3FCAABAUNCrcUIAAAC2IqxxQgAAwBt1rHFCAAAATWytcUIAAMCyvq1xQgAAgBgRrnFCAABAfmOucUIAAEAVra9xQgAAAHv/r3FCAADA4FGwcUIAAIBGpLBxQgAAQKz2sHFCAACA3e2xcUIAAEBDQLJxQgAAAKmSsnFCAADADuWycUIAAIB0N7NxQgAAwKUutHFCAACAC4G0cUIAAEBx07RxQgAAANcltXFCAADAPHi1cUIAAABub7ZxQgAAwNPBtnFCAACAORS3cUIAAECfZrdxQgAAAAW5t3FCAABANrC4cUIAAACcArlxQgAAwAFVuXFCAACAZ6e5cUIAAEDN+blxQgAAgP7wunFCAABAZEO7cUIAAADKlbtxQgAAwC/ou3FCAACAlTq8cUIAAMDGMb1xQgAAgCyEvXFCAABAkta9cUIAAAD4KL5xQgAAwF17vnFCAAAAj3K/cUIAAMD0xL9xQgAAgFoXwHFCAABAwGnAcUIAAAAmvMBxQgAAAL0FwnFCAADAIljCcUIAAICIqsJxQgAAQO78wnFCAACAH/TDcUIAAECFRsRxQgAAAOuYxHFCAADAUOvEcUIAAIC2PcVxQgAAwOc0xnFCAACATYfGcUIAAECz2cZxQgAAABksx3FCAADAfn7HcUIAAACwdchxQgAAwBXIyHFCAACAexrJcUIAAEDhbMlxQgAAAEe/yXFCAABAeLbKcUIAAADeCMtxQgAAwENby3FCAACAqa3LcUIAAEAPAMxxQgAAgED3zHFCAABApknNcUIAAAAMnM1xQgAAwHHuzXFCAACA10DOcUIAAMAIOM9xQgAAgG6Kz3FCAABA1NzPcUIAAAA6L9BxQgAAwJ+B0HFCAAAA0XjRcUIAAMA2y9FxQgAAgJwd0nFCAABAAnDScUIAAABowtJxQgAAQJm503FCAAAA/wvUcUIAAMBkXtRxQgAAgMqw1HFCAABAMAPVcUIAAIBh+tVxQgAAQMdM1nFCAAAALZ/WcUIAAMCS8dZxQgAAgPhD13FCAADAKTvYcUIAAICPjdhxQgAAQPXf2HFCAAAAWzLZcUIAAMDAhNlxQgAAAPJ72nFCAADAV87acUIAAIC9INtxQgAAQCNz23FCAAAAicXbcUIAAEC6vNxxQgAAACAP3XFCAADAhWHdcUIAAEBRBt5xQgAAgIL93nFCAABA6E/fcUIAAABOot9xQgAAwLP033FCAACAGUfgcUIAAMBKPuFxQgAAgLCQ4XFCAABAFuPhcUIAAAB8NeJxQgAAwOGH4nFCAAAAE3/jcUIAAMB40eNxQgAAgN4j5HFCAABARHbkcUIAAACqyORxQgAAQNu/5XFCAAAAQRLmcUIAAMCmZOZxQgAAQHIJ53FCAACAowDocUIAAEAJU+hxQgAAAG+l6HFCAACAOkrpcUIAAMBrQepxQgAAgNGT6nFCAABAN+bqcUIAAACdOOtxQgAAwAKL63FCAAAANILscUIAAMCZ1OxxQgAAgP8m7XFCAABAZXntcUIAAADLy+1xQgAAAGIV73FCAADAx2fvcUIAAIAtuu9xQgAAQJMM8HFCAACAxAPxcUIAAEAqVvFxQgAAAJCo8XFCAADA9frxcUIAAIBbTfJxQgAAwIxE83FCAACA8pbzcUIAAEBY6fNxQgAAAL479HFCAADAI470cUIAAABVhfVxQgAAwLrX9XFCAACAICr2cUIAAECGfPZxQgAAAOzO9nFCAAAAgxj4cUIAAMDoavhxQgAAgE69+HFCAABAtA/5cUIAAIDlBvpxQgAAQEtZ+nFCAAAAsav6cUIAAMAW/vpxQgAAgHxQ+3FCAADArUf8cUIAAIATmvxxQgAAQHns/HFCAAAA3z79cUIAAMBEkf1xQgAAAHaI/nFCAADA29r+cUIAAIBBLf9xQgAAQKd//3FCAAAADdL/cUIAAEA+yQByQgAAAKQbAXJCAADACW4BckIAAIBvwAFyQgAAQNUSAnJCAACABgoDckIAAEBsXANyQgAAANKuA3JCAADANwEEckIAAICdUwRyQgAAwM5KBXJCAACANJ0FckIAAECa7wVyQgAAAABCBnJCAADAZZQGckIAAACXiwdyQgAAwPzdB3JCAACAYjAIckIAAEDIgghyQgAAQF/MCXJCAAAAxR4KckIAAMAqcQpyQgAAgJDDCnJCAABA9hULckIAAIAnDQxyQgAAQI1fDHJCAAAA87EMckIAAMBYBA1yQgAAgL5WDXJCAADA700OckIAAIBVoA5yQgAAQLvyDnJCAAAAIUUPckIAAMCGlw9yQgAAALiOEHJCAADAHeEQckIAAICDMxFyQgAAQOmFEXJCAAAAT9gRckIAAECAzxJyQgAAAOYhE3JCAADAS3QTckIAAICxxhNyQgAAQBcZFHJCAACASBAVckIAAECuYhVyQgAAABS1FXJCAADAeQcWckIAAIDfWRZyQgAAgHajF3JCAABA3PUXckIAAABCSBhyQgAAwKeaGHJCAAAA2ZEZckIAAMA+5BlyQgAAgKQ2GnJCAABACokackIAAABw2xpyQgAAQKHSG3JCAAAAByUcckIAAMBsdxxyQgAAgNLJHHJCAABAOBwdckIAAIBpEx5yQgAAQM9lHnJCAAAANbgeckIAAMCaCh9yQgAAgABdH3JCAADAMVQgckIAAICXpiByQgAAQP34IHJCAAAAY0shckIAAMDInSFyQgAAAPqUInJCAADAX+cickIAAIDFOSNyQgAAQCuMI3JCAABAwtUkckIAAAAoKCVyQgAAwI16JXJCAACA88wlckIAAEBZHyZyQgAAgIoWJ3JCAABA8GgnckIAAABWuydyQgAAwLsNKHJCAACAIWAockIAAMBSVylyQgAAgLipKXJCAABAHvwpckIAAACETipyQgAAwOmgKnJCAAAAG5grckIAAMCA6ityQgAAgOY8LHJCAABATI8sckIAAACy4SxyQgAAQOPYLXJCAAAASSsuckIAAMCufS5yQgAAgBTQLnJCAABAeiIvckIAAICrGTByQgAAQBFsMHJCAAAAd74wckIAAMDcEDFyQgAAgEJjMXJCAADAc1oyckIAAIDZrDJyQgAAQD//MnJCAAAApVEzckIAAMAKpDNyQgAAADybNHJCAADAoe00ckIAAIAHQDVyQgAAQG2SNXJCAAAA0+Q1ckIAAEAE3DZyQgAAAGouN3JCAADAz4A3ckIAAIA10zdyQgAAQJslOHJCAABAMm85ckIAAACYwTlyQgAAwP0TOnJCAACAY2Y6ckIAAMCUXTtyQgAAgPqvO3JCAABAYAI8ckIAAADGVDxyQgAAwCunPHJCAAAAXZ49ckIAAMDC8D1yQgAAgChDPnJCAABAjpU+ckIAAAD05z5yQgAAQCXfP3JCAAAAizFAckIAAMDwg0ByQgAAgFbWQHJCAABAvChBckIAAIDtH0JyQgAAQFNyQnJCAAAAucRCckIAAMAeF0NyQgAAgIRpQ3JCAADAtWBEckIAAIAbs0RyQgAAQIEFRXJCAAAA51dFckIAAMBMqkVyQgAAAH6hRnJCAADA4/NGckIAAIBJRkdyQgAAQK+YR3JCAAAAFetHckIAAEBG4khyQgAAAKw0SXJCAADAEYdJckIAAIB32UlyQgAAQN0rSnJCAACADiNLckIAAEB0dUtyQgAAANrHS3JCAADAPx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2yne9tfkBTDi2ynZF+QFu6SQwCtH5AyszMzMzZfkBAi2zn+/F+QK1yaJHtDX9AQ7bz/dQxf0DKzMzMzFR/QAgCK4cWcH9AMDMzMzOQf0AbhetRuLh/QKBwPQrX239Ai8L1KFz+f0DESzeJQQqAQGi8dJMYFYBAunSTGAQegECicD0KVyeAQNWjcD2KLYBABqwcWuQ2gEDhpZvEoECAQPd+arx0S4BAxEs3iUFYgEA+YOXQImiAQOYmMQgseIBAY2ZmZuaLgEAH16NwvZ6AQFmPwvWoroBA2Pl+ary8gEB0vp8aL8yAQAAAAAAA2H9AMzMzMzMBgED2KFyPwrF/QClcj8L1mH9ArkfhehQuf0AAAAAAAJh/QAAAAAAA6H9AAAAAAADgf0AK16NwPcJ/QAAAAAAA0H9AAAAAAABwf0AAAAAAAGh/QHE9Ctej0H9Az8zMzMwDgEB6vp8aLwmAQK5H4XoUmn9ACtejcD0IgEAAAAAAAMB/QI/C9ShcC39Aj8L1KFwHfkBmZmZmZrp+QAAAAAAAAH9AuB6F61Ecf0BrO99PjaF/QHpoke18nX9ARjVeukmcf0BqEFg5tJh/QH2+nxovlX9AxsqhRbatf0DwUbgehc1/QLUehetR339AOgrXo3Drf0C6nxov3fR/QI4YBFYO839Ak24Sg8D2f0CuR+F6FO5/QGJmZmZm+n9Ath6F69ECgEC+yqFFtg2AQMZ2vp+aHIBAju18P7UmgEDM91PjpTWAQDa0yHa+RYBAr53vp8ZVgEA6Ctej8GiAQMz3U+MlfoBAauf7qXGUgEDM91PjJa2AQM4i2/n+woBA0U1iEFjZgEAiBoGVQ/SAQCZcj8L1C4FAmsQgsHIigUD81HjppjiBQHbpJjGIVoFACgIrh5Z8gUDipZvEIKKBQExiEFg5y4FAJTEIrBzygUAesHJokR2CQKBFtvN9Q4JADlg5tMhjgkAMLbKdb4aCQPzUeOkmqYJAzyLb+f7KgkAKAiuHFuyCQJzvp8bLFoNAjsL1KNxBg0AjBoGVQ2qDQAJWDi0ylYNA7Hw/Nd67g0DqUbgeBeiDQL/KoUU2GYRAqfHSTeJKhEAkBoGVw3mEQBcEVg4tqIRAth6F69HahECicD0K1wWFQH5qvHSTIoVASOF6FK6phEBI4XoUrpGDQMP1KFyPwINAXI/C9Sjig0AAAAAAAICDQFyPwvUoQINAAAAAAABUg0ApXI/C9caDQHE9CtejEIRATAwCK4c6hEAA1XjpJjyEQK5H4XoUUoRAIYXrUbh2hEDn0CLb+aKEQC+yne+n1YRA59Ai2/n7hEAwMzMzs/uEQNbO91Nj6oRAhkFg5VDjhEB6PzVeuuuEQFy6SQyCAYVAjsL1KFwchUB6FK5H4SuFQBXZzvfTQIVAyaFFtvNYhUCPwvUoXL2EQAAAAAAAZIRAAAAAAADohEB7FK5H4USFQAYrhxbZlYVACFYOLbKHhUBiukkMApGFQInrUbgekoVAmUOLbOeYhUBxPQrXo5SFQKRwPQrXLYVAXI/C9SgkhUAAAAAAAHiEQM3MzMzM6oNAAAAAAAC4g0CuR+F6FHSDQHsUrkfhAIRAexSuR+Gwg0AAAAAAACqEQNejcD0K24NAPQrXo3DPgkCuR+F6FLCCQJqZmZmZs4JAmpmZmZmJgUAAAAAAADiAQIXrUbgeUYFAPQrXo3CxgUDNzMzMzCSBQI/C9Shc5YBArkfhehT8gEC4HoXrUbKAQAAAAAAA4H9AzczMzMzIfkC4HoXrUYh+QHsUrkfhHn9AXI/C9SjsfkAzMzMzM8t/QDMzMzMzC4BACtejcD0IgEAihetRuC+AQLFH4XoUFoBAU42XbhIhgEAAAAAAAKh/QIXrUbgeLX9AAAAAAAA4f0DNzMzMzBh/QKRwPQrXW35AmpmZmZnte0AK16NwPbp8QPYoXI/CNX1AZmZmZmYKfUD2KFyPwiF8QBSuR+F6PHtAAAAAAADAe0B7FK5H4fJ6QKRwPQrXo3pAcT0K16PQeUBmZmZmZop6QIXrUbgeYXtAFK5H4XrMekBSuB6F6+l6QPYoXI/CwXlAFK5H4XqYekAfhetRuPp6QAAAAAAAGHpAsMZLN4k/e0CqRbbz/Xp7QDgIrBxamHtApHA9CteHe0ApXI/C9SR7QFyPwvUoAHtAm++nxkuMe0AWBFYOLZ17QCUxCKwco3tA5fup8dLBe0CWmZmZmeR7QMahRbbzD3xA20+Nl24jfEBgO99PjTp8QMV2vp8aRnxAZmZmZmYifEDsUbgehbt7QAAAAAAAkHtAuB6F61Hoe0AKAiuHFtZ8QBtaZDvfE31AZpHtfD9nfUDWzvdT4619QJIYBFYO/X1AonA9CtdRfkBbj8L1KKt+QO18PzVe9X5ACtejcD1Mf0D2U+Olm7p/QKrx0k1iCYBAVg4tsp04gECoxks3CWiAQClcj8L1lYBAeekmMYjDgEBGtvP91PGAQKAaL90kJ4FA+FPjpZtdgUBWDi2yHY6BQDIzMzOzwoFAVzm0yPbSgUBg5dAi2+KBQOF6FK5H54FAhetRuB4fgUAAAAAAAOKAQBSuR+F6zoBAzczMzMz+gEDNzMzMzIiBQCGF61G4uoFAxfUoXI++gUAy3SQGgbqBQHsUrkfhhIFAZmZmZmakgUCGwMqhRcqBQAAAAAAAuIFAMzMzMzMLgUAK16NwPRaBQEjhehSuA4FA16NwPQoXgUAfhetRuIqBQMKfGi/dpYFAPt9PjZengUDhehSuR3mBQBSuR+F6NoFArkfhehQEgUBcj8L1KPCAQM3MzMzMuIBAj8L1KFz9gECuR+F6FIKAQB+F61G4GIBAFK5H4XpYgEAAAAAAACiAQBSuR+F6cIBAAAAAAAB8gEDNzMzMzLyAQJUYBFYOy4BA+35qvHTKgED2KFyPwpWAQD0K16NwO4BAmpmZmZkbgECamZmZmVF/QJqZmZmZ6X9AAAAAAABkgEDXo3A9CuN9QDMzMzMzG31AmpmZmZkZfUCamZmZmeF9QFK4HoXruX1AAAAAAAAYfkCuR+F6FLJ9QAAAAAAA4H1AKVyPwvWMfUDXo3A9Cnd9QAAAAAAA6HxAZmZmZmbefEDhehSuRyV9QFyPwvUoiH1Aw/UoXI/GfUDXo3A9Crt9QFIMAiuHEn5AhT81XroxfkACf2q8dE1+QGUQWDm0QH5AVuOlm8RXfkDwfD81Xm9+QK5H4XoUan5AhetRuB4pfkC4HoXrUfR9QLsehetRfn5AAAAAAAAYfkA9CtejcGl9QD0K16NwDX1A4XoUrkdlfUDhehSuR+V8QB+F61G41nxA4XoUrke5fEBmZmZmZhZ8QOF6FK5HgXtAzczMzMwYekAAAAAAAPB5QHsUrkfhmnlArkfhehRmeUAAAAAAANB6QI/C9Shce3pApHA9CteXekDXo3A9Ctd5QAAAAAAAqHlAm0OLbOflekAAAAAAANB6QOxRuB6Fm3pA9ihcj8LhekD40k1iEDJ7QBSuR+F6UHpAj8L1KFzLd0CF61G4HoV4QJqZmZmZMXlAAAAAAAAgeECF61G4HvF3QMP1KFyPUnZAUrgeheuVdUD2KFyPwmF0QB+F61G4GnRAzczMzMxkc0AAAAAAAJx1QAAAAAAAUHZA4XoUrkctdUDXo3A9CldzQBiDwMqhB3ZA/lPjpZsUdkAOrBxaZC92QAAAAAAAgHVApHA9CtcfdUCkcD0K10t0QJqZmZmZWXRAXI/C9SiIdEBSuB6F6wV2QCCwcmiRPXZAuB6F61EkdkB7FK5H4VZ1QAAAAAAAmHVAKVyPwvVUdUApXI/C9Vx0QOF6FK5HVXRA7FG4HoVXc0C4HoXrUchyQFyPwvUo/HFAAAAAAACAcUApXI/C9ehyQDMzMzMzn3JAmpmZmZnVcUDhehSuR2lxQHE9CtejMHBAmpmZmZnpbkC4HoXrUSBvQIXrUbgetXBAMzMzMzNDcUBLtvP91LZxQEjhehSun3BA4XoUrkdpcEA9CtejcJVwQLgehetRzHBAuB6F61GAcECPQWDl0IhxQF45tMh21nFAcmiR7XzqcUC+nxov3fFxQCuHFtnO9HFAFtnO91P1cUD6fmq8dAlyQMDKoUW2F3JAbhKDwMovckDgehSuR1hyQK5H4XoUKnJAH4XrUbiKckCP7Xw/NaVyQClcj8L1pHJA4XoUrkc5ckCPwvUoXKtyQILrUbge53JAD4PAyqEIc0Bo5dAi2ylzQLZH4XoUUHNAfWq8dJNrc0DIoUW283pzQMihRbbzf3NASOF6FK5jc0AK16NwPWZzQAAAAAAAnHJAcT0K16PscUAzMzMzM3tyQAAAAAAArHFAmpmZmZkFckBmZmZmZnJyQOxRuB6FA3NA+NJNYhAsc0DqpZvEIEpzQBICK4cWgXNACCuHFtnEc0DSeOkmMexzQDnfT42X+3NAipduEoMFdECIbOf7qQx0QHbpJjEIJnRAkkOLbOdKdEC7nxov3X10QOlRuB6FonRAziLb+X7FdEBaj8L1KPd0QCUxCKwcJnVA16NwPQo7dUC4HoXrUUh1QMP1KFyPVnVAAAAAAADwdEDNzMzMzJh0QArXo3A9rnRAH4XrUbgudUDD9ShcjxJ1QPYoXI/CsXRAAAAAAABgdECamZmZmSV0QK5H4XoUtnNACtejcD3qckAAAAAAAGRyQDMzMzMzF3JAAAAAAABkckCPwvUoXBtzQGZmZmZmmnNAzXa+nxqxc0AC1XjpJqVzQLVyaJHttHNAWg4tsp3Lc0DWItv5fvJzQNujcD0KFXRA5/up8dJXdEAzMzMzM1l0QJ8aL90kW3RAeOkmMQhodEDMzMzMzG90QE1iEFg5fHRAPjVeukmOdECAlUOLbKp0QPL91Hjp13RAehSuR+EIdUD0KFyPwkZ1QJ4aL90kb3VAIdv5fmqSdUA630+Nl7l1QASBlUOL5HVAULgehesLdkBmke18Py92QPMoXI/CW3ZAl5mZmZmNdk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ttributes\":{\"formatter\":{\"id\":\"1106\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1047\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1056\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"1055\",\"type\":\"LinearAxis\"},{\"attributes\":{\"source\":{\"id\":\"1040\",\"type\":\"ColumnDataSource\"}},\"id\":\"1094\",\"type\":\"CDSView\"},{\"attributes\":{\"factors\":[\"0\",\"1\"],\"palette\":[\"#ffe3e3\",\"#e3ffe3\"]},\"id\":\"1283\",\"type\":\"CategoricalColorMapper\"},{\"attributes\":{},\"id\":\"1180\",\"type\":\"LinearScale\"},{\"attributes\":{\"args\":{\"axis\":{\"id\":\"1011\",\"type\":\"LinearAxis\"},\"formatter\":{\"id\":\"1044\",\"type\":\"DatetimeTickFormatter\"},\"source\":{\"id\":\"1040\",\"type\":\"ColumnDataSource\"}},\"code\":\"\\nthis.labels = this.labels || formatter.doFormat(ticks\\n .map(i => source.data.datetime[i])\\n .filter(t => t !== undefined));\\nreturn this.labels[index] || \\\"\\\";\\n \"},\"id\":\"1045\",\"type\":\"FuncTickFormatter\"},{\"attributes\":{\"active_drag\":{\"id\":\"1248\",\"type\":\"PanTool\"},\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":{\"id\":\"1249\",\"type\":\"WheelZoomTool\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1248\",\"type\":\"PanTool\"},{\"id\":\"1249\",\"type\":\"WheelZoomTool\"},{\"id\":\"1250\",\"type\":\"BoxZoomTool\"},{\"id\":\"1251\",\"type\":\"UndoTool\"},{\"id\":\"1252\",\"type\":\"RedoTool\"},{\"id\":\"1253\",\"type\":\"ResetTool\"},{\"id\":\"1254\",\"type\":\"CrosshairTool\"},{\"id\":\"1255\",\"type\":\"SaveTool\"},{\"id\":\"1273\",\"type\":\"HoverTool\"}]},\"id\":\"1256\",\"type\":\"Toolbar\"},{\"attributes\":{\"args\":{\"ohlc_range\":{\"id\":\"1005\",\"type\":\"DataRange1d\"},\"source\":{\"id\":\"1040\",\"type\":\"ColumnDataSource\"},\"volume_range\":{\"id\":\"1232\",\"type\":\"DataRange1d\"}},\"code\":\"if (!window._bt_extremes)\\n window._bt_extremes = function (arr, initial, agg_func) {\\n const CHUNK = 32768;\\n let extreme = initial;\\n for (let i = 0, len = arr.length; i < len; i += CHUNK) {\\n const subarr = CHUNK >= len ? arr : arr.slice(i, i + CHUNK);\\n extreme = agg_func(extreme, agg_func.apply(null, subarr));\\n }\\n return extreme;\\n };\\n\\nif (!window._bt_bin_search)\\n window._bt_bin_search = function (index, value) {\\n let mid,\\n min = 0,\\n max = index.length - 1;\\n\\n while (min < max) {\\n mid = (min + max) / 2 | 0;\\n if (index[mid] < value)\\n min = mid + 1;\\n else\\n max = mid - 1;\\n }\\n return min;\\n };\\n\\nif (!window._bt_scale_range)\\n window._bt_scale_range = function (range, highs, lows) {\\n const max = _bt_extremes(highs, -Infinity, Math.max),\\n min = lows && _bt_extremes(lows, Infinity, Math.min);\\n if (min !== Infinity && max !== -Infinity) {\\n const pad = (max - min) * .03;\\n range.start = min - pad;\\n range.end = max + pad;\\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\\n let index = source.data['index'],\\n i = Math.max(_bt_bin_search(index, cb_obj.start) - 1, 0),\\n j = Math.min(_bt_bin_search(index, cb_obj.end) + 1, index.length);\\n\\n _bt_scale_range(\\n ohlc_range,\\n source.data['ohlc_high'].slice(i, j),\\n source.data['ohlc_low'].slice(i, j));\\n try {\\n _bt_scale_range(\\n volume_range,\\n source.data['Volume'].slice(i, j),\\n 0);\\n } catch (e) {} // volume_range may be undefined\\n\\n}, 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+ }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization\n", + "\n", + "We hard-coded the two lag parameters into our strategy above, but perhaps the strategy works better with 15–30 cross-over, or some other combination. We defined the two parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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 values to test. Parameter `n1` is tested from 5 to 30, and parameter `n2` from 10 to 70. Some combinations of the two parameters are invalid, i.e. we don't ever want `n1` to be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which returns `True` (admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes final equity (we can choose any key from the returned `stats` series)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 143 ms, sys: 54.8 ms, total: 198 ms\n", + "Wall time: 1.38 s\n" + ] + } + ], + "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 p: p.n1 < p.n2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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 [%] 98.14\n", + "Equity Final [$] 106429.70\n", + "Equity Peak [$] 109515.30\n", + "Return [%] 964.30\n", + "Buy & Hold Return [%] 703.46\n", + "Max. Drawdown [%] -43.98\n", + "Avg. Drawdown [%] -5.70\n", + "Max. Drawdown Duration 690 days 00:00:00\n", + "Avg. Drawdown Duration 36 days 00:00:00\n", + "# Trades 152\n", + "Win Rate [%] 51.32\n", + "Best Trade [%] 60.81\n", + "Worst Trade [%] -20.80\n", + "Avg. Trade [%] 1.90\n", + "Max. Trade Duration 83 days 00:00:00\n", + "Avg. Trade Duration 21 days 00:00:00\n", + "Expectancy [%] 5.97\n", + "SQN 1.51\n", + "Sharpe Ratio 0.19\n", + "Sortino Ratio 0.49\n", + "Calmar Ratio 0.04\n", + "_strategy SmaCross(n2=15,n1=10)\n", + "dtype: object" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can look into `stats._strategy` field for the Strategy instance and its optimal parameter values (10 and 15)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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%d\"],\"months\":[\"%m/%Y\",\"%b'%y\"]},\"id\":\"1762\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"formatter\":{\"id\":\"1824\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1765\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1774\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"1773\",\"type\":\"LinearAxis\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1749\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"source\":{\"id\":\"1834\",\"type\":\"ColumnDataSource\"}},\"id\":\"1838\",\"type\":\"CDSView\"},{\"attributes\":{\"below\":[{\"id\":\"1773\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"1778\",\"type\":\"LinearAxis\"}],\"min_border_bottom\":6,\"min_border_left\":0,\"min_border_right\":10,\"min_border_top\":3,\"outline_line_color\":{\"value\":\"#666666\"},\"plot_height\":110,\"plot_width\":1200,\"renderers\":[{\"id\":\"1773\",\"type\":\"LinearAxis\"},{\"id\":\"1777\",\"type\":\"Grid\"},{\"id\":\"1778\",\"type\":\"LinearAxis\"},{\"id\":\"1782\",\"type\":\"Grid\"},{\"id\":\"1793\",\"type\":\"BoxAnnotation\"},{\"id\":\"1806\",\"type\":\"GlyphRenderer\"},{\"id\":\"1811\",\"type\":\"GlyphRenderer\"},{\"id\":\"1832\",\"type\":\"Legend\"},{\"id\":\"1820\",\"type\":\"GlyphRenderer\"},{\"id\":\"1837\",\"type\":\"GlyphRenderer\"},{\"id\":\"1855\",\"type\":\"GlyphRenderer\"},{\"id\":\"1875\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1822\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1791\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1720\",\"type\":\"Range1d\"},\"x_scale\":{\"id\":\"1769\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1767\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1771\",\"type\":\"LinearScale\"}},\"id\":\"1765\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"args\":{\"axis\":{\"id\":\"1729\",\"type\":\"LinearAxis\"},\"formatter\":{\"id\":\"1762\",\"type\":\"DatetimeTickFormatter\"},\"source\":{\"id\":\"1758\",\"type\":\"ColumnDataSource\"}},\"code\":\"\\nthis.labels = this.labels || formatter.doFormat(ticks\\n .map(i => source.data.datetime[i])\\n .filter(t => t !== undefined));\\nreturn this.labels[index] || \\\"\\\";\\n 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+/9yzJGrnwP/DH4pgDgfA/N61/BkO08T8rZaIPB0vyP2JnuQInFvI/n1H2B56A8j8L7cEPONnyP2I5Y0F5T/M//hby8GIh8z+HmWlTn2DzP9pjBdSbOPM/xT/vQV3g8j/BAaGn0SzzP3wpupXcqfM/fHCzKSnR8z92zEQuixz0P1M1PWH20PQ/SlbCSGCl9D+yDKq7NpvzP7p21X7S2PQ/EfW8VVEQ+D+ey+OnJCf6Pz+/7ioTX/k/gyKG5w30+T8U1Bzw7fn6P5jUjqHlmvo/5hahdnZb+z/jA2cgBTL7Pz7Ykf2xv/o/yu5/gq7G+T92YWm3R6L3PxqNPtqaFPg/mBRW1A6L9z9Y7Mo0DG33P0meaUOpivk/SpgC+ZI/+j9TkTZnPtn5P1qNRD/fkPs/KNoqW0yS+z9WDrjeJUP8P8QaY03UAPw/nluYhCWa/D+8NKts3EP8P4Tm1rK3Qfs/QUa65juc+z8Ltsr1o937P1zHd4UXkPs/xPoRJ4bf+z/xA+R5i/X7Px8YttM4zvs/XpasJW+A+z/4YrKMV776P8IuhA9vhPo/BHbgHjcO+z9N70vIIMf6P3l9Yz00l/o/Bnp9O0jg+z8E0vZVy277P2Zi+4S48vs/F+N5UtZi+z8YyZKh8Z36P2iaEuyP0Po/j6tsTu1q+j+leAb6xj/6P6g5CVkvz/o/TyyYRZXt+j8dKiIAl/L6P0rgg3asmvs/1yuR7afu+j8HkDCPa1H8P/sQAlTPK/s/iCDprGcI+z9wbhncBVf6PzAb73GQFPs/MAgqPtY/+z8PIOJUegn7PygDiuhHS/s/8QPrJX5J+z8Pu1pMbu/7P1V9xUD6e/w/LqRbWoWO/T8emu8tKAf+P7zlQnh00P4/J2ygqDBm/T8Lsop6NFL+PypWqc13Wvw/xaYBMa6K/D8S+I7Qvyz8P9GaQ6tMUfs/s1ZEbunr+z+GWeUsi6P5PzH8zDnj1/g/kpyuKAvm+T+jofFFQkD7PzmamcXQRvs/gURdbqJO+j++ahxRFdD5P/+cIcixu/k/zJkFTTOA+j+gdluB8c76P/s2zPS6Pvs/Ihh0Xc0s+z/SY06j9kf7P4bonSvPLvw/ZcbnlEvU+z8BNbCi0IX8Pw/Q0PfD7/w/+bdyv2Ol/D/eLeplIO37PzA+k0pQy/s/EcIQD6ER/D8g5wSeExb8P4wJe5R8pvs/7kvFaesw/D9ddmdKjsT8P/dCfWAX+fw/ur7bHLBM/T+e+wB3cbj9P5EPk3agLf0/1Y2BLw2h/T8Z/hkBjRP9P/RIp13K9vw/ypzzNZTW/D88ubahAi79P9WJazpwH/0/xqSorRUV/T+u0m0c38H8P2FvM0nQCP0/ihzxdBHn/D/0uH9VlaP9PxNiKMnTj/0/+THirIFs/j+Zb8bnYPb+PwA0DmOzEf8/UHCFJonQ/z/NX5lIqIj/P0JKM/VBuv8/6CJ0TPHY/z9jPk28pa3/PwpIzdZvb/8/oUh7D1Fg/j/m2lC4H7P+P2abUc1Vbf8/Bv0GYntDAEAAQ/4/G8QAQL/sSL6xtwFAEv36M/JZAkCPwEkIfPUBQNIyXdAhCwJAFXmrSjIEAkCkrtN+wQ8CQECgMIviPwJAeO0H/dmRAkCbAeSkZsICQGDBJXt0ogJA58T4EFC4AkD5R09TR44CQBVJgmKwswJAc1Kr4An9AkBufVpTBscCQDL/Qx700QJAGbS1lv73AkAlu1vCtSMDQL7llmRxogNARQgWAN2iA0CW6+xM79UDQOFBlqDP+ARAcHe+1F4EBUC+Z65pdmkFQCEMNASgRwVAGugNiYrWBUBhaWZPZ8MGQF5GVq/qpAdAuJnapdCiB0CDEyWePgIHQC0aV/m04gdA4M14LoYQCED9WsNbiPMGQKRg39tlgQdAzS7EpVIxB0BrXnm9kzYHQPexNxwa2gZAA0GRy32PBkDhDh/5+cYGQJNYI9UVAwdA9YEONJiJB0CxTa2qIJ8HQBiSVAGBgAdAFrXXdOd3B0CGavhvwNkGQKiNv3YSSgZACPHajxvCBUAgOoZd8hYFQHotYl5SMQVAUrSnftX7BEDHfMYwGE4FQNAQOVnQAAVAZY47gfVKBUCM7Od2r1cFQJy8LgaRIgVAsshJ/JsFBUA2CRF8MYgFQGoyrdVhRgZAuFtnKNxABkCNeGdhO18GQKzXHWiIoAVAEplUgvhaBkD1u/hNjn4GQE/X2xvhqgVAc8hGz7syBUCxM3DLIjcFQAwrAT6LRgVAWY6Gv6AHBUBvSf0FV54EQM5hwL315ARA7dwxr/9vBUDSqTgaE1wFQIeuhfMzVAVAxd7ZxbC2BUCK6ovP08oFQKAj0Sn4xAVA4wBHDMswBkAbZnbgoU4GQJnx8P/V5gVAMJVbMoxPBkCJg9CjVzEGQGuJqShpOwZA1CEop/SYBkCdS/rJxZgGQAmHOlBtBgdAsHB/E3agBkAVPva+uWkGQKiRmgRcaQZAQ7rgezxXBkAKN665Ff4FQIn0zqyvEwZA+TmMZu8qBkCTThLNWyYGQAo3rrkV/gVAz1f5Uh7EBUBVM+SzuFoGQCA6WD7QZAZAOrlWIZ+sBkDk4agIUHsHQMWY46D2oAdAxU61XoH4BkBErNZlIfEGQPV4Cf8pwgZAjLtbXdYHB0CGZxr1y1cHQMwRPKo7pQdAaqjPcPKaB0CxUvElYugHQLy6J1Vs0wdAmqIqGNPMB0Br2MhpujIHQMlCBNX7eAdA1K+rVLv9B0BKLxEQ0SgIQIFo6SbEkwdAVD3GVCOOB0AizrTCurUHQFgM/iljugdAx+3zOvmMB0BYsnw/qzQHQDKyf0VPaQdAYTZUiK6tB0BuYyF+3MYHQIqmg8tAGwdA6jDOS5E8B0DGiMWZltMGQBJbePgdPgdAh0+Jjkd3BECZjFVJzc0DQIOhyvSA7wNA3PIHdYx4BEDBIF1VdVUEQHR64qDMoARAVcbHTMVuBUCBcPochNkFQBAMkXDw3QVAQb9cEGI6BkBH0iV5b3wGQIUjK9BBwAZAdL+bB590BkA9+Qb/H9YFQLEt9lVQhgZAuMKWPf57BkB6Efkw3NwGQDKfS9pgnwZAVnVJAkLuBkCOMa5xZjwHQIOhU1GhDAdAFCrFRomTB0DXkDZqXfwHQEKCPviTWghAAC2Ko4CvCEC+w3UcuGMIQDpgWyS6PQdAnSaLGe1RB0C8votKRtkHQKEOIPR/DghACQ5jDslfB0DCZlGQeEwHQNTAsL/ARwdA+8bMR46mB0Ad13ReLZEHQLW/8tzB8gdAWYYsc/OqB0C/2OYfUpUHQAn5M/9+YAdAvnUDpqrwBkCrhpbpTmAGQMXudACnpQZAf99j1qV3BkBcHDCLrsQGQKs62uu0tQZAxjueD9lgBkBzPaksgXwGQKQSveJFJAZAZMNBvefcBUCMxxbkt+8GQEqRH5HDdgdAK6+j2bXHB0DVN35nQIoIQBwmbQL7mghALTuUoZDBCEDhjXwTy9oIQLUQd13dbghAJOdvGDaSCEDqnBVK0J0IQCv7X9dPcgdAR9sBbC0AB0CjZaqLJA0FQNdQxa5uhwZAMbv12/JYB0DVw6oJr7EIQLa2eJOVoAhA91eJGBarCED0Iwdj5AQJQNeG5vRCtghAyi78wwhWCkCpsIODUaMKQN4BYPBpZgtA6bokup02C0A7z0oZ7h0MQKz5jclPDgxAhwsEgSCZDEAtj9m2bGUMQMGmyHEaSQ1A0vtq4CVpDUBCYj3jzXUNQIL20nSWMQxAWqDtPMESDEBMhqVe1i8MQOyxOd+lPgxAj4KW30SVC0BC7DE+Sp4LQPBPKoSG7wpA23L9PkplDEDw9j3cw70LQM5hl+ePoAxAnk9Xsj3CDECKUJoNAv4LQIZKMJHztgtARI3G6S3pCkAcBFhQXxUKQNL7Pa5UhgpAjUQjIeGOCkBDHuRK73wJQAmOFXNU/glA/UaFBahtCkDvTKDM1lkKQDHJb1KjtglAz+tQNlBXCkCsC4YWfFEKQAAsFO4JMQpAimTRoX9gCEBGCk/B6xQIQHD3mLi9OwhAtxjRYypgCUCGgJ52mvQIQJRLgKpCDglAmt/UjFMJCUAJ2xubG/oJQAfSFoQjNgpAM9/5pZpqCkCa8k2CChgKQGDZP6ggwApAnDNz3JpRCkAkxaF87UUKQE+otfZA1glAvXktXeUiCkBhDDiRdvgJQFTI7JVrXwpA6Rv4Um6pCkAug6Hc5xQMQIj14izTTAxApHgtn2xxC0CfkBZD2l0LQF/QOpEo/ApAb6Qh3MjZCkAUJLMao6AJQCELN4U0XQlAltsb+tdSCUDMI0UAYlwJQNgBxFP7VAlAGx1g579aCUBRBnQcoHQIQPI3AqYn1AhADFuZU3PaCUBHOurWsq0KQONvbLy4iwpAqw4YcybcCkDVXSUboKcKQM33aRA94QpAaFWs/CjxCkAEmrYk5eEKQCdCEPfllQpAznChudQ4CkCm4JtGRRwKQEfb4YYwNwpATetNLqXRCkDv0TNrndMKQBm9YAJYIgpA1RwtUIpWCkBoknnoHF8KQJ1QzE16dwtANgtV1bs4C0AhnxEdKLMLQP4HwcXQOQtAtRVkCiDfCkCtCmf96TgLQOJ8baaCEgtAdRrmHLCJC0CKKspqR4QLQM+zylgfVgtA0uf4KaJEC0CqxSVTXFIMQI460jMOKwxARzNXDpWDDECaVdcJC3gMQHq7e0zJVQxAQvAgnx6aDEDlfGLBEW0NQGQrEox5iA1A4VtHH0DODUAtuQvj4K4NQGpcNfcRzg1AVu/xE6ucDUD7ZyDcGXQNQPUS12jF5g1A7Zpy/fhiDUBoXkAyKcsMQDN8x8QPawxAzpCkeza6DEBVlJ258mIMQH5tJFXtGQxACMHm5VmfDUADYMmkjGkNQMD8cY5VWw1A+ccveQt3DUDegoZUgLwNQPiW+kpI9A1AajZedVuNDUD9gyM/tagNQC+uX6lCcA5AOuI6+z8FD0AZoDJva3IOQFJr8Fkhjg5AnMzz8lpEDkCh8uxOeQ0OQAuW+HOHVg5AadTzGNWHDkBPt5fNZe4OQHvkdNCp3Q5AG69hgAMODkB1B8gIjpQNQBQm92Uatw1AqQdjHALjDUDiKMYE1yEOQMuEYo8UNg5AD5Pu5rfSDUAYae8Q2tgNQE6idiQ+zw1Anv96rnZsDkARwsrrkkMOQETGz+dfaA5An8n8sPw6DkD49grkf44OQDqrHOr4GA5AZF2qzRNvDkAWS+13vpAOQNWoQHfNnQ5AmAZOtj3+DkCUVwn7kp8PQPlcfdZOZxBA4IBSjI5MEEC3pDPmfokQQAlizCYtuxBABevmC81KEECVOyCKdgQQQGTqiDEzahBA3hsBuX1KEEDggFKMjkwQQH50dxNoahBAhOnvxZtBEEDbtq/lbEgQQFxmasMSNxBACP77dlIyEEByZhEfJwwQQOP1GkXsJw9AQuQVhR54D0CbQVFQ98QOQPgKCAGfqQ5ARGqrPJ9+DkBYcBKd4HsOQKGwargTjQ5A6U9C8IOKDkD5MCbf9iQOQGLEU3WnEA5Ahi3Uh1H4DUDKG7iV03MOQDaxgQlnbRBA+lFBToguEUDYz3n7MOoQQJJ1PzPHYxFA/ZNCFQ5WEUC4XIqCe/sQQIpaKygECBFAy/c1lV4GEUDZ+DOCCbUQQMkUtOoVyxBAqaHEcWDcEEA8uYYlfgsRQKarM9hr4xBAxmAkGKf5EEA7d4Ve+OMQQNB9LIjw5xBAlj9ewaSjEEB/Lx9uIlgQQK2oPA8fQBBA0BEISVcjEECELbHjajsQQAZk5XVCHBBAMg05ZOSVEEAU8cK0OogQQHFWeywnbxBAFFSClOaMD0B96gWQC9wPQDAregg8iw9ABNMgguaND0DQBED5FUsPQPl8iiCRjg9AK+w542OyD0AdmjapbYQPQJveaGJd6Q9Af0+yqOvQD0B8mjvp6dMPQFph3F2q9w9A3zUWowcTEEB5RWcAA/IPQMLWsFQjCBBACQZRsqOZEEB0wGirKmkQQMNUR9nOmBBAPrrQpoPQEED0ntlOq9UQQBOenMZ0xRBAHOiiwSduEECz4gGIopsQQG+w/sPtrBBAUidnSdBxEEAvFVcSDt8PQIHa9ju0nQ9Aa3N/2LvZD0DTqHF9zrkPQHxOkJ7ZBxBA/n2A3d9dEEAKYmnzJIoQQDZasocbhBBAsXEim2pJEED8VcDqXfQPQDGVnRRIChBAuChJ/yZ/D0BfpV7SImEPQF+zl7FsWBBAFPjgDbr4D0ALYrTDVz0QQE+WNFYWIRBA0+j+cfgiEEB9Dqnax80PQCteFxAnjBBAeO+hmD6OEEBHiD3KPAYRQO3NAcYW4hBAf4/qpGSjEEBcPFR1Nm4QQFkUwJLgwBBAoPu/wm/hEEBjNPNXHNoQQADE/qNWnBBAulUEA63EEEAvL/kqEngQQDdxLA6JZBBAdUxDaodCEEBN4IGRnkIQQObpWb7rcRBAP4Rw+GGlEEChxXWziTcRQFiA1kZjMRFAm04Fm72dEUDIBVPvpz0RQFlhBrZ+VBFAEeoQq+QnEUCDCwhiEjsRQD3TjUVPRBFAunIeaPtVEUA/ud8nAkQRQNINOsiwuRFARpHkPcmHEUC/BkLn9ZkRQC8ZUIqUzxFADdcDjYSPEUDgfq1m048RQI3aNWGdARJALj4QlQs5EkD7KqPJ7DYSQM5Odijh8xFAOT5++cgBEkBUuIVSSAoSQOlwaf+uABJAlyN4MBblEUAdX4BIyegRQNDVlnJ4dRJARqDNGaxlEkC6ZDlckmoSQDsviE6TSRJABtB9Mno4EkB3HfGPxyQSQDuyildgQRJAZqBPqGA2EkAMsiorK4YSQLJk52N4dxJAfvPNEZGXEkAkL1i4+GsSQNA072lC2BJA4LeVY0O2EkCRZGXVw6YSQMpGOE1dqxJAGZRjyULLEkCrUoY1kLUSQA47+JhFaRJAAcrsenlREkB1mig0RzESQK5kIRHFexJAt+d1EqxmEkDs0LKjC5gSQPan3MGinBJAR9oJhkmEEkBdGqxX7NESQDR1XZRVnRJAtR9NatTOEkDvanjRjPQSQIVe4w3CYhJAudaaJjlzEkDST+sYnHkSQNca6M7QdhJAk7dil1NCEkAwXfjKL1USQBIZqyKYURJAxTY0Jpk3EkBI04ZhOVoSQCKZXNwLyRJAKOiHJmTCEkDycYdSaNoSQBF2j4W9GhNAKYu7XEOME0BWrQWRkoYTQHRdLv7NZxNAbemyufNrE0BwrrPF7EMTQHp9ynyOBBNA6V1b/BYJE0AokncJ8fESQG26I+5bDxNAE4VZrCdpE0DHLUcikmATQJphDQu2NhNAeIcJTqReE0DNcjAxlRgTQESGv4AYARNAYzx/IMLlEkCG5uXYnhoTQDwxPNa5DhNAEOxti0L5EkDPK2iboEATQK40pRttZRNAMb/PZCupE0BZFSxqcpQTQOkMv8jPsRNAY3deVg+5E0BvSbiCj8MTQD1PgmCNyxNAmNy3XAULFEAn6HHcuxIUQNdmoHppJRRA2S8vc03yE0D3iEqj2OkTQKXaD/JI4RJAz+I6aJGaEkDZb1T9aagSQAj3LNd5/RJAdLxtX8wNE0ClNurduOkSQNGL2uKVwhJAYfNpCkD7EkCuBhX++98SQFuZCiLi8RJAJDP3Mys8E0Bh82kKQPsSQDCto49rwxJAeMNTs+doEkDN4Ds8Ys8SQMklDBnQJBNAesVP828iE0BvlgAV3+ASQMxbevfedxJAK4aHc5I+EkCljgn4u1QSQJHj6sPWaBJAmplowW0WEkAdbMF3M9wRQD1Suf+C4RFAovjhEN3GEUDSzMhSXdcRQOaPZZRrGBJAjE3D3PfaEUCb5K3oAOkRQE8mBmbe4hFA9/SJ1dg6EkAwTyCRZVISQPXLQJhcLBJAoGQy300HEkAJY06nBtwRQMFa5lvmGBJAgqmzWaEkEkBnricWjzcSQGNXi8/WWhJAuwNKFS48EkBp+6/Ex5QSQKqiZzGw+xJA7Xdyp9QwE0DKZVfbb3ETQE8+J87RtxNAqXyFTIfAE0BdkOtNELkTQPzoBM8ysxNAefRmjyexE0BSXfV18TgUQGcB5XJLSRRA17qCswFGFECl4NF7qRkUQNQez+IjnxRAfujhiIEpFUCbx59/6loVQIRwny6mtRVALIlE9IWXFUDqDaQaSiAWQIA1aje6hhVAeDpC/jNiFUA+0Sbhiv0VQP5x7VYbNBZAX6tzDldhFkDw7GQEBJcWQPRYu1pcdRdAjAimGs51F0DPJ9Lm1zQXQPgYrpz2ahdAU2n41BuUF0DW/Bc7NR4YQL+OumbkihhAsCoKLTZpGEDT26YfqbAYQIFlrAehMBlAFTTMbA/AGUDOzIi7aXEZQBRupHzxFRhA6nlhbn8MF0AH4H8jA/EVQGpcE/Qa7hZA3U1/02lGFkBPeoengdsVQGZWLY4i+RVA0ywxrSI+FkCeSHUb5nQVQE5u0RWmChVAHZCNKSZ7FEDEykkBDNoUQIf0za3olhRAQeOF6yXxE0AEKIj5G8cTQFRsxXpMxBJASe87Hsh0EkBlCq60A4cSQHGZi1d/6hJAfDevX55eE0AM1dM46lsTQJMg9pOGdBNAwb0P60fvEkCxHmQhUvASQOQSFPeTyxJA/gPCPjmvEkBl4jS3gh8SQHzRl60SPBJAnSptKvFXEkBNIioQWq0SQFlZrc605BJA1znWIsHIEkC1A/M+zoISQHnSjGyvyBJAHow590y8EkCTthDKwAgTQDuva6xFNBNA2YG7H3oqE0BhfvOBp18SQN2fTnkrKBJAe3gCjmGqEUCoMPryckQSQLyq2M1O9BFAFGOLvuUwEkB4u1FXpsERQIWPbDcvNRJAFayRIDvQEkB2l1OXjDwTQEIM61BoQBNA5yU6VAOyE0Dj1TTMTbEUQBBejHR2+BNAuezfCUQyFEA5LOJ4t3wUQNRzFni6oxRAedpi/c2zFEBDCPQwRUEUQGRePZ0WmxVAQkb7tFgtFkC61embG9wVQDCG19R5ABZAaslKe1PpFUC+wfKbcZUVQEbQ/nCBdRVAY68613CLFUB6jWV7VRUVQGljRX9EJhVA7BdMgOo4FUDRSxgAv8wVQHLdWotjzBVArZvSV0L4FUBDrUjj+TIWQAv47rPtQxVASNtGretKFECEDxUFBoUTQK49MBDEaBNAqoqFmMmXE0AQdPDg8DUUQKFLggepwBRAS+K7SQmeFEBECd9bkkUVQNyoHNVPPhVAvjbKQa0aFkDj/UAp0/UUQAKoRQIH8hRAweRVRWvSFEDE01P2RAsVQIHmkM/e1BVAoqk3d2v9FEA/503ZY00VQJOyavoTPBVAt7nVDmcOFEAxq227onsUQL6Tucx+nRRAv9Xscfn6E0COaxCW3bUTQJfR4ddk0RNAX6GamBz0FEDt3BMc//MUQIv/G3ciehRADnjTSxQyFUBFKnkFFHQVQIrGJ79MDBVA8P2VqJriFEDhOgOG7RoVQOnKxM34lBRACn72KEhSFEAyoDizwxoUQIwXXxYZeRRA/M9bCd05FEAXD26Y/0QYQDi/JctWMhhAwF4k8ZD4GEAzWSX9i5YYQCOchivPbhhAZOE4yY56GED6XofZZNcYQLOk0LuIIBlAjpqiRMDWGUAweM5FLa8aQB3BI8VgJxpA7CCezSPEGkAMzgpkxmEaQIQHlmAADRpAbL2vbzA7GkA09kA/MsoZQA7XSVFrURpA+/go8xI7GkC613IK5+0ZQECA36kJJBpA/SLqbFMZGkDbdaxi9PsZQMZVh+tkCBpAaJfqAyFCG0DQTUWtOEgbQGYrrZ/bfxtALZ+Vh7fEGkAZvEywV3AaQD7fDZasxhlAnMRj3XymGUCfvaqloiIaQN6GfuElexpAlJJpg5dCGkC29ml8vaAZQAKcR7WYfhpAel9U47DrGUA3GLk8rr8ZQCdsujj8VBlAR+KXSSuxGUC9vnM+740aQGgX4pb8+RlAxaBoHcEhGkDun8P4zHUaQBJYJOmtjxpAUrvNbSQzG0C/9az2n0EbQNR6bVgrZBtAhfdnXuT8GkBtFX2QMAQcQKQ/WskXDRxAuM9AE64gHEB750dLCL4bQNyDEutRGRxAjQsz2RWjG0BTpYGdDlEbQJYmedT80hpAC6VKkRJtG0ALgEnAtHgbQEZZjYcSyRtATUTIGKlZHEDQha+PTpscQI6NemW0sxtA0JfR9QutG0AZKNuJzPgYQHFFZ55UhhlAg4tA1S0YGUDDnrBLqHcYQDFH2O7tKxlAzvVLohJTGEB/9kLmCxgZQL1I67KlyBhAeiaI+RTOGECNIRSCt0QZQCH9g1/KkhlA3OPsFDbTGUCYDNcJ3PMYQPBD+CjVnRhA6IomMIn9GECTm5ilUagZQFc5umO7ChlAH3Oi6UrzGEB1zNpedRUZQEuUY485jBlAqAuOiYTIGUAe3B+mMi8ZQFIUg0dHyhhAb4cUCB6DGECgrhm96ZYYQKAWOGFxyxhAdlQ5s15pGEDEcQd+3fYXQMYEeTeRJxhAT37vA2zkF0CV7GTs32sYQHQWseqQUhhAp01HomldGEDeh10YIBQZQGp6XNi6YRlA8iQfToGbGkDbQqGMKawaQP5F2NFE5hpAZiwyz0/pGUDXwyra0rYZQKVDDzHk5xlAcWP/QcZyGUC7i2/3AeIaQMU1xeB8pBlAGOMaVHUiGUDQpfEU7RcaQCCoqPcoIxpAjTQOLMDXGUAmeN9D1KUZQHV0N0v6JRlAYz1tnJU6FkByN18uIl4XQNMgUAcm6BdAXDGC65okGUB3GiUR+FkZQJ0lDK/+QxpAHFic8qC7G0AZ8uINYzEcQJC1i8t6uRxAiBdYlnaMHEBEGZMWxLEZQJ20N16ywhpA19o0IpYhHEAow08MI1MbQE3TA3QrMBpAd8+bORvLGUCtCsK6GcIaQAWJHL+iKxtAAOHaOJJdG0A5xLTByvEdQNr06NxfFB1AE2DtPLNFIEAl1tvPhJgfQBQbGbizvh9AIw9rAj+3H0Alg33CezwgQEo3YHPZqx5ACZ0XKWZsIEAvfFk3pOggQC5l+ByL6SBAAANgseR0IUB8n5EmiwYiQJ5n6BaVFSJATbX5QePdIEAh/XjyWPwgQJ97qEe7jiFAHp5vjae2IUBxMK/ikbUiQJg9Unx25iNAAwXktga8I0DtQdBKzwUkQIc9gWbrmSJAMweqIHcFIkD0WbztmfghQK5UhBhjhyNAsGhESYkAI0CxZJ/ZqMAiQMbb3vTrCyNAsm7/8Tt9IkC1D32PTnEhQEukv1BiJyJA/hU0OaxSIkAQZXYUDNAhQB94P6QVvCJAUFshvsVuIkBVj1Aar0wjQGfw7q0vtCJAhPVkRdloIkAexV2MLWciQH+v2TDPoCFAAp3mn6GuIUCZLZ1hg/khQML/UHQs0iFALU9il4SlIUDSuQSG8fshQNnZgLr6ziFAl9DcjML/IEB6YU/tvZkgQJnzBVujPiBAEvrQ6kz1IEDxSY+FGMUgQKpJsMyU8B9Afk33/NCyH0CrcvwVHt0fQFMZSMipgh5AaikRa0dPHkB3cSEU7WAeQNmd1w7TqRxAvuczbbauG0BYopSaIlYbQHtKyhjCfhlAFFYoS0GUGUASZ9ZFKM8YQPwQSm3wERdA3F711oCcF0CIOdtR2gEXQMUs7WpYJRdAhS378EEjF0CQnVGQn08XQHtaGR4iChhAl+zC0qU7GUC4PS4O9r0ZQIG8v6F4XRhAcj8hREtVGEAYoFindawYQLJBS9YHTxhAsZ/ONrVJF0BM3jUyhf8XQNCfeyJcSRdA+Qc/wqWLF0CK7owyfW4WQDsOCco/ehdAVECK6c49F0C1o2n3P4sXQOPFvnMvfRdAIGWFqTs5GEB8mUZlZ1YYQE9+1x1PyBhAkzRtxeWuGUC8V0KwBXwZQKX4S7ECrxpAPydkuPeCGUDiQ5Ol2NkYQB5t60xDeBhA/5gGLs9oGECmm8ln8LoYQKzEnukyqxdAJbX14xfSF0AbNmdX9ggYQMXQh2wkBRhAz0++PADQGEDw2ai58bUYQNdntSx1fRhAAzyipnUlGUD5V04Dmr8YQExjtOVPZBhAgiF7TSnGGEAyUelnCTQZQOpf8XpHzRlAtKBec+5RGkDsl4LS3jUaQPVJy2gghxlArPIkIivEGUC+6OlqfoMaQE4teVm36RpArLnHKhRCGkAe4hdGCwMbQNEWQAtpqxtAghPXeC/rG0DSHMZVZv8aQEuGcLjwJhtAlRWoVn1SG0CPO8gInWEbQK26cpATuRtA7vxkjJJ4G0CbwWqiwU8bQB4Paagn3RtASeaYxyYvHEDo6SdHmwUcQHK1y5apnBxAMUAC7hCvHEDnTJP7z7ccQDv3BC/QOhxAIBNXsyX+HEBX1GDA/QkdQMo22nKLZhxAAiyHzPy5G0D6td7S0JQbQDDY3I5ewhtAU80vEAE/HEBZQ9gJLWQcQFQ2IQ8zRRxAFHIKMh0jHEBb7ylYq1kcQKZ2o+ItlBtAi0T9bFt+G0BLWEVfVyYbQNE2QBsjqhpAZKA8dSsAG0CQkM9m/B0bQKjtzoCmUhtAnGcPq37eG0Bk8R/19R8cQBToKK0GxhtAmp2jaPuSG0CJntZsC2IbQEYArGe1JxtAW5OiUEzkGkAQ6EA4h2EaQOeLrv8MVRpA6lXgZHBHGkAciaE4nTUaQLVJcbDuTBpAWMriCfcaG0D6VdXv+DUbQKGcElOX+xpA4WpLvsuSGkDsw3wuWvgZQJzKdHNvCxpAv+Hamaw0GkAUuAHBuF4aQM+PHl+HCBtAXDt0gWr2GkDyhBx4VMgbQKgveLmPaRtAjunqr/xpG0Cjy6Woj64bQHZ57NzbVxxAFQFRNIdeHEDddRC9nRMcQHh4Lc0CyBtA1cMrssiaHEC7i4Y4JpwcQLTQYpfxTBxAKZENXGNJHECjWjatxuwcQJEbFrKXix1AK/uckxVrHUCoswwdRhcdQGr1wMsm2hxAVvnxgE55HUBPlNZPdE0dQDB0OMGL6B1AgFhYGUgCHkB28zzobdYdQE6+2ws5yR1AsPLUJlc7HkAXCQ5HCzMeQAqYrVTWBR5A/lBSHmZUHkDAvAuJC5MeQEnm2wRxbB5Ar6gKrZtsHUBEQzsbNnMdQH+ecP4HXR1A3sNdI2JzHkBbeQZ4KcUeQJOlaqdAAB9AHTJRkfMsH0Dsl9SD5PMeQIY0CrUL0x5Awo8/mN28HkAgQV2Ht4geQGppkAGnJB5AWOzqTRf2HUDGcX+dc0IeQIzpfclzgh5AANf0fhNVHkCZWt67qK8eQIYVZibGhB5A0pgdNX9wHkCc2u1dJD4eQKtnV/c/FR5AObXYxZlfHUAnAaHipDsdQP6XJ+OvNx1Ad+IuiQCMHUApwbWYWqsdQKVEseg4oh1AIhkcbICIHUAR0sj0NUcdQJO5oKNJox1Ak7mgo0mjHUDQ89D3f3odQMDwOk7a9hxAo2o4Z/rOHEATad438QodQFoYb9R7ph1AxFWmX5vEHkBLacBSeJEeQER9aVj5sB5AqpqBUognH0ChripYCUcfQB1ZPNk00x9ANsPxst6AH0ABXbLsK1ggQNSrqZs3aSBAPdIw0mZmIEC0XgwEr2EgQFp7BsFveCBA2VFahmR1IEAAu9OFWXkgQIkhRu5MTCBAFf7j038PIECD2bacTWEgQCH/7MNh3x9Agqt2+fa+H0BUWI9BMPEfQABOF0W6DyBA0WrZy6nwH0Dpb65Kc8sfQIizwf8kHh9Ae6cObpfdHkC2n32g36MeQIRegxsJzR5AksD/o4PNHkA5NrHF0gcfQGd9TrWAGB9A1DaEdOwMH0B+TO7HeNoeQBYX8Eu0sB5A+6eXB39bH0AyqT1lsmUfQBHXfug5ih9AuqPSP8s3H0DHagPMdGQfQFwHDCcowx9AnqJNA6CiH0AR137oOYofQMjcwtQpgB9AxTgeokGRH0CXY0C7SJwfQNu5V5xwtx9A03EON6DZH0A6w42h+ssfQL3zX6z3CSBAOOP6kDvwH0Css6l69RcgQAwOHapC+x9AAeNpIrkOIECz8rbCTB4gQNBAPvwdLyBArY7sbuZ3IECyxL2ZxKYgQAG35nkGxSBAuQPL4RutIEB1hoB0D8QgQNnMTUEbsSBAdvg/fcTfIEBiOxilvswgQLzsmQ9UYCBAA329SHf9H0DneGH6UTUgQFk9SVcMLyBAa67Pw5DLH0BWf+ji1ZwfQPLlghvxDh9A8sStH3OWH0DfjEnJni0fQN8PgCzPkB9AQvi4EnRtH0BO44Bjg5kgQGF8pSRe3iBAjSfgurjNIECzev447M8gQB2YEvekBSFAc9J+8ZAjIUAe8ppTYQ4hQNT/bhBzGyFATsd3MbrYIECRR+puTTkhQKjjbrVHGiFAME1A6kgLIUBw2bb/2vYgQIluAziLBCFA1aN6byH3IEAieDE8sQ0hQJRKSvRs1SBAgh0fLq7qIEBU9cmUWhQhQBDitKuNACFA2LlPKPkQIUB8TlU9wdIgQAwsYlLHtSBAPWKnPDaNIEBaBFCiMJAgQGj99SuZvyBARHqe1PcCIUDI4Gbh8OwgQDe/RN55oiBAYRjqGzNVIEA1hzQ6TzcgQOEXco9oJiBAMDYeyWmeIED5dmrzBsEgQKpAufM3tSBA9w4Synk8IEBUMEhTYksgQPJCA4AKTiBADxkNkz1UIEDUP0iUASUgQPlawySOEiBA7NTL7KSnH0BnIDLzTBEgQMnUIPpCOiBAhVkNsJNWIEDjyYA7WFggQFW/P5D3OCBACIpad/I1IEBzdzKnkikgQGF3a3hLGSBArHGJTdgtIECYfkcg5hAgQImtoTG6LSBA1PmoXHUkIEADkwR2+FMgQLZV7GVwuiBARJHl3bbKIEC8DJmbsfggQL4v0EJZXh9AmCGAbJ5dH0Ckl2XvuaUfQDfGxL7smh9AQcV8uT4xH0Ab4t0v/0QfQHtUepG8AyBA15JtWIcWIEAx0CHxlBUgQGH+CkwdASBAE2EniwAvIECmadjLTwsgQDm+TxTMuyBAJf4h8RqjIEAwjDnT4fMgQElaC0wpHCFAq+lLWIBMIEAOr4TWRqggQLXvxW3vwiBAvA7kivKaIECx4SEYWbMgQFmx0qYksCBAmf7ANxH2IEAbu5HowxIhQMftFUM3oCFAy3hwmsa1IUB55PVHjpAhQP+5URk2kSFA4MbOht2cIUBzadaJri8hQOuBwcLcUiFAWn+jfptqIUDVxudtfBohQNJSmtpnwSBAyKPNl4TzIEABThXfqVMhQMLw67QNWSFAAkfikdrGIEA1LR6wjDwhQDaztbgMSiFAdYG/QPAZIUAu1dWjCKAhQD6ag43AvSFASM1qXfiyIUBPP8MUU9YhQLmecHoKPiJA+wigy+1SIkAYbbAw6HgiQLHKeOn2VyJA3VkiegtAIkAyMSRFHToiQIqsp4X6iSFAnwkSJarqIUAgDphAhiAiQNz8Lp6GPSJAGodcwkJCIkCY8V9Z4LYhQOKJBHojeCFA/Y03AlIMIUAHTKANDbogQGjR690pNiBAE8mgvA0hIEA4haLynQYgQHcrI1QNWiFApz5Z6PZ0IUBx8W2ssAgiQGFLy2F74SFAC2ev5I4nIkC8TX7y4lkiQH8r5PhvTyJAo4SX/4VyIkA+VjgBJiMiQFD+3aFIKSJAeKa9Tiq7IUBEi/vAnZchQJIABzVKkiFAVYIZQLkpIkCBEZ7tEToiQEEWKIC08SFAlVwJ1tAgIkAzEk1KwREiQMMY6oHUoyFAkeYQQ0+mIUBlwEvtVHIhQNMB6DVaayFAvpyGW6CYIUC7sNP3wkshQPydKZqKySBA7JFF1373IEBY+TkHteQgQEfmtHrrWCFAw21vF1E7IUBlnh1xrVwhQOo28WC7FCFAxIutWgFNIUD1conXeWUhQMq66AD8BiFAvZvYMN3sIECQiOPjm6sgQEboBMSu4SBA57+u8AqpIECdEo/wt3UgQGYSoi80OiBAeGItxK9vIECfUwcPol8gQPjM9hl3YSBAqb/VLyFlIEAX+y4JcLQgQK08nA6eUiFAj5gEx89/IUA6kfP1+JUhQBLhuXL8fyFAqvQxl3n4IUAZJKo5shMiQJxB8pBt9yFA2p5bAff7IUB6thVJY+shQCfOkJXn6SFA5f5I61/NIUAy44t77FciQGrwKlwSNiJA63mAzzQQIkBVQ/tuhUciQLoGpfA+XSJApqNudX5zIkBGMI+DKIQiQPAfJQh7byJAwzT4rIB/JEC9eq/7XQ0lQDKP3aMqtyRABC+tq3m4JEAbeSgndtskQNseR0Ms4CRA7tdXZ88CJUBllbLUIRUlQCvrx2WMAiVA5Ez9KPUUJUClMDSAYeokQL6ZOxm59SRABRl6OPX6JECf9aPR6iIlQMFh9PuZRiVAYFoiJqtNJUBnyXjTaVwlQMKr7W1mSyVAXTI9Xnk6JUB/HUaL1AglQJtDHmKOjyRAg4Q8IFRLJEBcnbQR0OQjQJ8qYSNr8iNAAF9l0yKBI0ApITJyB7MjQPFS/RqiryNAxSA01x73I0CqbIsP+44jQJZ10JRBuiNAXBDpAvX+I0A7hKSv1OQkQMwhdXKZmSRAByrkOo5YJEDGzdTqR04kQKP2Wg6cHyRA3l3+vijTI0Bvf/3gjbUjQEbmovYxrSNA/+RPUQOnI0CBdPw8B5IjQD0WMc6AjyNAiQQsLdZqI0Ai1prFtXYjQODEEwwMbyNAz154geuSI0BNG6f+XtYjQKAkmOO/6iNAugyipyPgI0ASxXuIj9AjQOBe8KpssyNAnBglnPDEI0CYFMBS67IjQLZuYL+9iSNASqECWCbhI0DaLVUKX84jQGEfhHHlCCRAHOHGYDMuJEDfGAVC9UYkQG+lV/QtNCRA21kASlZDJEDi1wk6lEokQAr5eDEQSSRAF4dasiKIJEBlPu3mPAolQITN0ajhxyRANrgpEfKdJEBLeDFKIyAkQMnX1XvSGSRAGzZDri1kJEAmYzWadkckQIEfdMTnSSRAkWFyDdvEI0CeVXfvjL8jQBrWwi0foSNA2tA3zA2ZI0D4Yob1magjQG6G20OgoSNAVNzeAM+FI0D1PaGeb2MjQD0o/FI8cyNAkdgUab1zI0B9/MrQADAjQFXgILbUVSNA3kixk082I0AKR2rJ3DYjQNwrAsMOPyNA5q2fEQplI0ArtaSkdsgiQCmY9dM10SJAftlvnoK9IkCbuXW0H8ciQMxhJceZ4SJAIKwWUP99IkDvGid+NmUiQOrzVjo+ICJArDUav4xmIkDt1L2HA60iQDv2wenmpyJAr9JrEiasIkD7R40hWgYjQO7WXrmVIiNAX47Zz25XI0CXaDGD0FojQAzyNLLNvCNA5yaHOE2bI0BhoawyqZ0jQKqsEoI8JCNAO7Mjo8kdI0CiUAt5ufciQEfwRMSG0iJA74EXjsEKI0ARfgefMy0jQNkqYywb+yJAmygRtz36IkBLDMaupgYjQHHFc958MCNArRN6t0kOI0AWXZ1H+XMiQOUsxLc5niJALOHSbonOIkBm0mpdQ78iQIqf8ZW0uCJAanT+VZeAIkDPvjrtWZkiQE6pko3ghiJAmWiAx/MSJEDvpaUM7jIkQP32B3nrXiRAPXfbISA8JECavbs7WEEkQBT86UXCQSRA70JxsWMBJED7jMv2dtgjQDEg1pi51iNAxwEXH/GjI0BppedT1tEjQDY9LXGG+CNAOvdP7sjoI0A2LzSKYPUjQBH14R7Y7CNAOd0mFKj+I0DfpxLNEC4kQBY9l1xt6SNARKtIhp7lI0BAVpCDQrEjQKOJrKtNRyNASmBcFT+SIkD++SkIbJgiQNEPfp+9iiJA9AXcT1bPIkAkW30U7AQjQEjgDEUoBiNA/Irk08L4IkBtZZx1ZAcjQPay0S8T7SJAd0oIfu2fIkCilqC2a8AiQJbmem4MqSJAcXDN7VzYIkDxmL4HjusiQBHzMHTX/iJATDwg8YL9IkAYiUOUsRQjQDt/oURKWSNAs7xvLLkXI0Agd/X1+DojQOVAyQv4JCNA6qm5Quc9I0AK9dbpQNIjQFbVD4aB1iNA6FZyeyGFI0A9ihttBb8jQHiyWqmkACRA0Q5qbkA0JEBzSWpytecjQOx/2E7ZfiNAFoPGx/ZYI0Cqg5nV0QMjQNVuvw88diJAdxMra/YHIkANQa3ujAQiQHbLgltkZSJAdQGE6oPnIUBbg6VW774hQOHwhVDr+CFAmELlS4kdIkAA95y0PM0hQGLD3kD4HCRAXkZ0IeEFJEACxooSckckQHIauXFpCSRASGdhILNtJECx8TaNis4kQDseosgA1SRADIpewoDzJEB5/aBsrm8kQC6nYmq7jyRAUKNWgURRJEC/4pPizWskQJjARsz+7yNA3EANSY47JEDCGUDVym8jQCeW+mjjfCNA6WBgUKt6I0AP2GpXJ48iQB11+5r1YCNAHBkUVSXwIkCcLVdMC+IiQKsj8WhHGiNAp5VjBQi3I0CaQeZGVekjQAtoR1dq3CRAvvHmp3NUJUCi+dupHBYlQEmWAQ2NZCRAjn6tgR0+JECjwaCJD1okQFi8HhRrHyRA3rX861e2I0BQgpnpaagjQAcrC5ctpiNA0RtKFezuI0DOToSVyTAkQD0td/yPFCVAGztdRAePJUBLLVC/o5glQIrkF/InMCVAkuvNqp5mJUBST7VCa2AlQF/nHb3FeiVAvj3d5S7nJUC/bxVLtN8lQH/1f7LO3yVAfgwqUDjgJUCzmBFeqXMlQNAheLXVtiRAbdtZMjvoJEBxZC/hNSklQBIQUGQWdSVAThcDkCUKJUCfV/bBf/wkQKvXPpaYfSRAGd2RgwfQJEBeg7EILo8kQFDEMx64ciRAfmzl9/gMJEC4oyEazggkQCtjg4OtKCNASRzE0JbrIkCulzuTg7UiQLa4TGnjSiJA9W6dV5z+IEA5/bDTvzwhQO8/aeIeeiFAaDNRDMsvIUCqGOrES0YhQOWYeBf4eSFA5g8yI+amIUAtdfGNbkIhQMk5AGlMWiFAw3fzUfK3IUAdbQuWFcMhQJG9hk9CiiFA7lH2ykIgIUBmnfdZAk8hQP5L9fEUryFAW+0ICsekIUCAn/41IwEiQMhyeSeFHyJAMNodsTjJIUBNWDYGv5whQIpG7wBKASJAD/t+UIUkIkD2H9Nrfj8iQJgQlavtGCJABz5bhZ3IIUDp0c8YO5shQN8HmY+cMSFADF1qTX0qIUBdtI9Ljt8gQMEAPnVKwiBAOkNbiNADIEB9Y1eolisgQBghw+w/Xh9AOMxYUceEHkAQPrXuHSEeQImCYokd0R1Abp+IA4ztHUCdtQIyS/MdQJXAn7KwyR1Apw0kpW5WHkCGBY/bTT0eQLzNXWJGQB5ACivsy7HiHUBWtZ4F5PQdQAghuhtcRB5ACcGTtjwRHkDuv9HI8noeQJOBwXegQh5ArHvQpadyHkANXGSHPJ0eQFlHEiE/9R5AM2d4QXDuHkCWsw4u3A8fQARGVjEvOx9Ae/df1VcWIEDsWRQmGyggQKG+NfV23R9AVQg05i5uH0CZjnIYCRkeQIhwvOtzIR5ACCG6G1xEHkCfyRyE6XEeQIFiNlBaOB5AA4ALhspkHkCI64YKl+8dQA8km41QnR1Ag/Hbm5wZIECzPgLOgxwgQFR3ANPdOCBACgpTh4d/IEDVhq59HYggQBfHWAe8PiBAtgjSIeFMIEAPkcB27j0gQDKNdfN7OSBAkUviHwwfIEC6niKZfLMfQCNraqTaFR9Asy6juYMyH0BQuHYddwwfQLSRvG6T+B5AUAArDRr7HkDMc9Zmb00fQA+c3SF/LR9AKPLUOYX2HkA/NmkeFgMfQJLbxl566h5AdnNsE/9kH0CjhYRwrBUfQNfjyTy4/R5AUjszQ1QvH0AkHLg2mycfQDifkzEZsx5AnSQyY+20HkDHvuzUtXIeQOZGqN9OZB5Ao+cohaQMHk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"id": "2146" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "bt.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optimizing the strategy, we managed to up its initial performance _on in-sample data_ by almost 70% and beat \n", + "[buy & hold](https://en.wikipedia.org/wiki/Buy_and_hold).\n", + "In real life, however, do take steps to avoid\n", + "[overfitting](https://en.wikipedia.org/wiki/Overfitting)\n", + "before putting real money at risk.\n", + "\n", + "Learn more by reviewing further\n", + "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials),\n", + "or find more program options in the\n", + "[full API documentation](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." + ] + } + ], + "metadata": { + "jupytext_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": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/Quick Start User Guide.py b/doc/examples/Quick Start User Guide.py new file mode 100644 index 0000000..bc3a7fe --- /dev/null +++ b/doc/examples/Quick Start User Guide.py @@ -0,0 +1,186 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.3' +# jupytext_version: 0.8.6 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# _Backtesting.py_ Quick Start User Guide +# ======================= +# +# This tutorial will show off some of the features of _backtesting.py_, yet another Python package for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies. +# +# Firstly, what _backtesting.py_ is not: It is not a data source — you bring your own data. It does _not_ support strategies that rely on multiple orders, hedging, position sizing, or portfolio rebalancing. Instead, _backtesting.py_ works with a single asset at a time, a single position at a time (long or short), and the position size is (as yet) non-adjustable, corresponding to 100% of available funds. _Backtesting.py_ is not aware of order types and does not properly simulate, nor can be connected to, a broker. +# +# As a trade-off, _backtesting.py_ is a _blazing fast_, small and lightweight backtesting library that uses state-of-the-art Python data structures and procedures, and whose whole API easily fits into memory of a single human. It's best suited for optimizing position entrence and exit strategies, decisions upon values of technical indicators, and it's also a versatile interactive trading strategy visualization tool. +# +# ### Data +# +# _You bring your own data._ Backtesting ingests data 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 easily obtainable (see e.g. +# [pandas-datareader](https://pandas-datareader.readthedocs.io/en/latest/), +# [Quandl](https://www.quandl.com/tools/python), +# [findatapy](https://github.com/cuemacro/findatapy), ...). +# Your data frame can have other columns, but these are necessary. +# 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. +# Let's see it. + +# + +# Example OHLC data for Google Inc. +from backtesting.test import GOOG + +GOOG.tail() +# - + +# ### Strategy +# +# Let's create our first strategy to backtest on these Google data, and let it be a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover). +# +# _Backtesting.py_ doesn't contain its own set of technical indicators. In practice, one should probably use functions from their favorite indicator library, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib), +# [Tulipy](https://tulipindicators.org), +# PyAlgoTrade, ... +# But for this example, let's define a simple helper moving average function. + +# + +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() +# - + +# Note, this is the exact same helper function as the one used in the project unit tests. + +from backtesting.test import SMA + +# A custom strategy needs to extend `backtesting.Strategy` class and override two 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 at the beginning, before the strategy is run. Within it, one ideally precomputes in efficient, vectorized fashion whatever indicators and signals the strategy depends on. +# +# Method `next()` is iteratively called by the 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/trade within candlesticks. If you need to trade within candlesticks, instead start with more fine-grained 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 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, buy the asset + if crossover(self.sma1, self.sma2): + self.buy() + + # Else, if sma1 crosses below sma2, sell it + elif crossover(self.sma2, self.sma1): + self.sell() +# - + +# In `init()` as well as in `next()`, the data we are simulating the strategy 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 compute indicators indirectly by wrapping them in +# [`self.I()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.I). +# We pass the wrapper a function (here, our `SMA` function) and any additional arguments to call it with (here, our _Close_ values and the MA lag). Indicators wrapped in this way will be plotted, and their names, intelligently inferred, will appear in the plot legend. +# +# In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we go long; if it did and downwards, we sell any open long position and go short. Note, there is no position size to adjust; _Backtesting.py_ always 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: + +# + {"active": ""} +# def next(self): +# if (self.sma1[-2] < self.sma2[-2] and +# self.sma1[-1] > self.sma2[-1]): +# self.buy() +# +# elif (self.sma1[-2] > self.sma2[-2] and +# self.sma1[-1] < self.sma2[-1]): +# self.sell() +# - + +# Ugh! +# +# In `init()`, the whole series of points was available, whereas in `next()`, the _length of `self.data` and any indicator arrays 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 need them to be `pandas.Series`, use, e.g., `pd.Series(self.data.Close, index=self.data.index)`. +# +# Let's see now how our strategy performs on historical Google data. We'll begin with ¤10,000 in cash and set broker's commission to realistic 0.2%. + +# + +from backtesting import Backtest + +bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002) +bt.run() +# - + +# We initialize the +# [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) +# instance with data and strategy _class_ (see API reference for additional options). +# +# As we call +# [`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run) +# method, we instantaneously get returned a pandas Series of results and statistics associated with our strategy. We see that this simple strategy makes 600% return in the period of 9 years, with maximal drawdown 33%, and with longest drawdown period spanning almost two years ... +# +# If we call +# [`Backtest.plot()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot) +# method, we can review these results in a more visual form. + +bt.plot() + +# ### Optimization +# +# We hard-coded the two lag parameters into our strategy above, but perhaps the strategy works better with 15–30 cross-over, or some other combination. We defined the two 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 values to test. Parameter `n1` is tested from 5 to 30, and parameter `n2` from 10 to 70. Some combinations of the two parameters are invalid, i.e. we don't ever want `n1` to be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which returns `True` (admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes final equity (we can choose any 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 p: p.n1 < p.n2) +# - + +stats + +# We can look into `stats._strategy` field for the Strategy instance and its optimal parameter values (10 and 15). + +bt.plot() + +# Optimizing the strategy, we managed to up its initial performance _on in-sample data_ by almost 70% and beat +# [buy & hold](https://en.wikipedia.org/wiki/Buy_and_hold). +# In real life, however, do take steps to avoid +# [overfitting](https://en.wikipedia.org/wiki/Overfitting) +# before putting real money at risk. +# +# Learn more by reviewing further +# [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials), +# or find more program options in the +# [full API documentation](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). diff --git a/doc/examples/Strategies Library.ipynb b/doc/examples/Strategies Library.ipynb new file mode 100644 index 0000000..82504a2 --- /dev/null +++ b/doc/examples/Strategies Library.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Library of Composable Base Strategies\n", + "======================\n", + "\n", + "This tutorial will show how to reuse composable base strategies that are part of this software distribution.\n", + "It is assumed you're already familiar with\n", + "[basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.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 Start User Guide.html),\n", + "but we'll rewrite it as a vectorized signal strategy and add trailing stop-loss.\n", + "\n", + "We'll again use a helper moving average function.\n", + "In practice, one can use functions from any indicator library, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib),\n", + "[Tulipy](https://tulipindicators.org),\n", + "PyAlgoTrade, ..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jk/PycharmProjects/backtesting/backtesting/_plotting.py:34: UserWarning: Jupyter Notebook detected. 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\"}};\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) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };var element = document.getElementById(\"1001\");\n", + " if (element == null) {\n", + " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n", + " return false;\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var 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\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 var 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() {\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) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\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.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"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": [ + "_Backtesting.py_ package includes\n", + "[_lib_](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)\n", + "module that contains various reusable utilities for developing strategies.\n", + "Some of those utilities are composable base strategies one 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": { + "collapsed": true + }, + "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 = 20\n", + " \n", + " def init(self):\n", + " # In init() and in next() it is important to call the\n", + " # super method to properly initialize all the 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", + " # Taking a first difference (`.diff()`) of a boolean\n", + " # series results in +1, 0, and -1 values. In our signal,\n", + " # as expected by SignalStrategy, +1 means buy,\n", + " # -1 means sell, and 0 means to hold whatever current\n", + " # position and wait. See the docs.\n", + " signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0)\n", + " \n", + " # Set the signal vector using the method provided\n", + " # by SignalStrategy\n", + " self.set_signal(signal)\n", + " \n", + " # Set trailing stop-loss to 4x ATR\n", + " # using the method provided by TrailingStrategy\n", + " self.set_trailing_sl(4)" + ] + }, + { + "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": {}, + "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 [%] 95.86\n", + "Equity Final [$] 110002.61\n", + "Equity Peak [$] 110092.67\n", + "Return [%] 1000.03\n", + "Buy & Hold Return [%] 703.46\n", + "Max. Drawdown [%] -37.68\n", + "Avg. Drawdown [%] -5.52\n", + "Max. Drawdown Duration 689 days 00:00:00\n", + "Avg. Drawdown Duration 39 days 00:00:00\n", + "# Trades 94\n", + "Win Rate [%] 54.26\n", + "Best Trade [%] 56.98\n", + "Worst Trade [%] -17.03\n", + "Avg. Trade [%] 3.00\n", + "Max. Trade Duration 121 days 00:00:00\n", + "Avg. Trade Duration 32 days 00:00:00\n", + "Expectancy [%] 7.43\n", + "SQN 1.89\n", + "Sharpe Ratio 0.25\n", + "Sortino Ratio 0.67\n", + "Calmar Ratio 0.08\n", + "_strategy SmaCross\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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(!window._bt_extremes)\\n window._bt_extremes = function (arr, initial, agg_func) {\\n const CHUNK = 32768;\\n let extreme = initial;\\n for (let i = 0, len = arr.length; i < len; i += CHUNK) {\\n const subarr = CHUNK >= len ? arr : arr.slice(i, i + CHUNK);\\n extreme = agg_func(extreme, agg_func.apply(null, subarr));\\n }\\n return extreme;\\n };\\n\\nif (!window._bt_bin_search)\\n window._bt_bin_search = function (index, value) {\\n let mid,\\n min = 0,\\n max = index.length - 1;\\n\\n while (min < max) {\\n mid = (min + max) / 2 | 0;\\n if (index[mid] < value)\\n min = mid + 1;\\n else\\n max = mid - 1;\\n }\\n return min;\\n };\\n\\nif (!window._bt_scale_range)\\n window._bt_scale_range = function (range, highs, lows) {\\n const max = _bt_extremes(highs, -Infinity, Math.max),\\n min = lows && _bt_extremes(lows, Infinity, Math.min);\\n if (min !== Infinity && max !== -Infinity) {\\n const pad = (max - min) * .03;\\n range.start = min - pad;\\n range.end = max + pad;\\n }\\n 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2hAElg5tMiqaEBaObTIdqpoQJYYBFYOoWhAcz0K16OYaECg76fGS6NoQENg5dAii2hAjWzn+6lhaED+1HjpJk1oQArXo3A9PmhArRxaZDsvaEC1yHa+nyZoQB1aZDvfH2hA9Shcj8IhaEDcJAaBlQtoQICVQ4ts62dAbOf7qfHGZ0AFgZVDi6BnQJzvp8ZLg2dAYzvfT41vZ0CqHFpkO0dnQB2F61G4KmdANYlBYOUQZ0Cb76fGS+dmQPh+arx0z2ZAyszMzMywZkBovHSTGJhmQNBNYhBYhWZAhRbZzvdrZkD/KocW2VZmQCIGgZVDT2ZArnJoke1MZkCLl24Sg1hmQLhJDAIrZ2ZAkhgEVg5tZkBS46WbxHxmQLlJDAIrf2ZAhhbZzvePZkB9arx0k6hmQMH1KFyPymZAXuXQItv5ZkCHQWDl0CJnQPZT46WbSGdAKrKd76d2Z0A530+Nl55nQJeZmZmZwWdAs/P91HjRZ0D+KocW2dZnQJJDi2zn32dAth6F61H8Z0BsEoPAyh1oQOb7qfHSaWhADlg5tMjKaEAlMQisHBppQHfpJjEIcGlAEoPAyqHJaUASg8DKoTlqQC2yne+nqmpAQ4ts5/sZa0AlBoGVQ3trQGu8dJMYxGtAu0kMAivra0BCYOXQIvNrQGu8dJMYEGxAsHJoke00bEA730+Nl1JsQF2PwvUocGxA1njpJjGMbEBoZmZmZqJsQI6XbhKDxGxAZTvfT43rbEDYo3A9ChdtQPCnxks3dW1A+n5qvHTPbUBSuB6F6y1uQPp+arx0j25ADQIrhxYFb0CiRbbz/ZhvQD81XrpJJHBAg8DKoUVycEABAAAAALRwQLFyaJHtAnFANDMzMzM/cUA4iUFg5WRxQAVWDi2yjXFA5tAi2/mycUCzne+nxs1xQDDdJAaBz3FAq/HSTWK6cUDQ91PjpalxQMLKoUW2qXFAsXJoke2icUAWrkfheppxQPPSTWIQqnFAMd0kBoGvcUDKdr6fGsdxQCYGgZVD6XFAyna+nxoPckCzne+nxityQI6XbhKDRnJAeukmMQhYckCdxCCwcmZyQHaTGARWbHJAuR6F61F2ckCYbhKDwIByQI6XbhKDenJAzszMzMxmckCzne+nxmFyQAMrhxbZbnJAKDEIrBx6ckBNN4lBYIdyQK9H4XoUnnJAAyuHFtm+ckBBNV66SdxyQF6PwvUo5nJAXo/C9SjqckCVGARWDvFyQIgW2c737XJAoRov3STickAusp3vp8xyQIgW2c73v3JARrbz/dSuckBbZDvfT5dyQE03iUFgfXJAnu+nxkttckAPLbKd72VyQGMQWDm0XnJAYxBYObRMckDeJAaBlT1yQCCF61G4QHJAB4GVQ4s0ckB8FK5H4R5yQJDC9ShcC3JA5tAi2/nucUDwp8ZLN9txQBODwMqhv3FAnMQgsHKscUC38/3UeKdxQEJg5dAipXFAW2Q730+bcUDgT42XbqJxQBsv3SQGpXFAWTm0yHamcUAVrkfherBxQAAAAAAAvnFAKlyPwvXScUCClUOLbOtxQA4tsp3v/3FAdZMYBFYackDVeOkmMURyQI/C9ShcaXJASgwCK4eCckCF61G4Hp1yQD0K16Nws3JAQ4ts5/vLckAkBoGVQ+1yQI2XbhKDCHNArkfhehQic0AmMQisHDxzQEs3iUFgQ3NAokW28/1Gc0BuEoPAyktzQKJFtvP9VnNAXrpJDAJxc0BGtvP91IhzQLKd76fGjXNAHVpkO9+Lc0CuR+F6FI5zQD81XrpJinNAFa5H4XqEc0AOLbKd73dzQNrO91Pjb3NAZ2ZmZmZcc0DazvdT4ztzQAEAAAAAJnNA0PdT46UZc0DgT42XbhZzQP7UeOkmB3NAku18PzUyc0BeukkMAm9zQPyp8dJNsHNA+n5qvHQHdECbxCCwcmB0QARWDi2yw3RAhetRuB4vdUB7FK5H4ah1QJzEILByGnZAAiuHFtmedkAEVg4tsu92QJZDi2znOXdAppvEILB+d0C0yHa+n6R3QHJoke184XdAkxgEVg4VeEARWDm0yDx4QMZLN4lBUnhAKocW2c5veEArhxbZzot4QPp+arx0m3hAq/HSTWKyeEA5iUFg5dx4QEe28/3UInlAwZ8aL91eeUBxEoPAypN5QP6p8dJNnnlASuF6FK6xeUB6vp8aL8t5QEaLbOf74XlAYrpJDALreUCi76fGS+N5QAhWDi2yz3lAYI/C9Si8eUAQAiuHFp15QAdWDi2yi3lAwHSTGASieUDVItv5frh5QH3pJjEIxnlAzqFFtvPZeUDOoUW28/d5QIbAyqFFIHpA0vdT46VDekAgWmQ732V6QHU9CtejiHpA3s73U+ObekD9U+Olm6p6QJKXbhKDrHpAle18PzWgekB6kxgEVqh6QJPC9ShcyXpA+ihcj8LrekCqm8QgsCp7QFs5tMh2YntAPt9PjZege0C1ne+nxut7QH4UrkfhJnxAdT0K16NwfEAO16NwPcR8QIA/NV6603xAjmzn+6nFfECNQWDl0HJ8QPooXI/CNXxAJbByaJEPfECbbhKDwNR7QOjQItv5mHtAmm4Sg8Boe0CNbOf7qSl7QAiBlUOL8npAqsZLN4mtekB9FK5H4Wx6QFBiEFg5UHpArfHSTWIMekCV7Xw/NZR5QBItsp3vLXlARGDl0CK1eECAPzVeukN4QBEtsp3vwXdApUW28/0yd0DOoUW289N2QLjIdr6fpHZAoxov3SSQdkBODAIrh3J2QHU9CtejbnZAHy/dJAZ9dkBKtvP91Jp2QCoGgZVDx3ZAepMYBFbidkBnEFg5tAR3QPSnxks3O3dAixbZzvdNd0C1cmiR7Ux3QDPdJAaBSXdAtXJoke02d0COQWDl0P52QOrQItv5vnZAj2zn+6lpdkAa2c73U1d2QAYrhxbZNnZAxvUoXI/6dUBGYOXQIr11QOZ6FK5HnXVAfukmMQh2dUAdBFYOLWB1QGpmZmZmXnVATOF6FK6LdUDezvdT4791QGk730+N6XVAvR6F61E6dkBzEoPAyol2QOilm8Qg2nZACoGVQ4scd0BaDi2ynYN3QDheukkM8HdA9/3UeOleeEB2aJHtfJ94QNl46SYx6nhA6KWbxCAeeUAZrkfhejR5QORPjZduSnlA2E1iEFhleUD9U+Olm3x5QF05tMh2hnlATeF6FK6ReUAD1XjpJrt5QHC8dJMY8nlA2E1iEFgDekBDCtejcB16QMBJDAIrL3pAKNv5fmpIekA+tMh2vjt6QHY9CtejLHpAk5duEoMSekAtMQisHPJ5QBiDwMqhrXlAGq5H4XpkeUDOdr6fGkd5QL4ehetRInlAvfP91HjteEBSN4lBYKd4QJoYBFYOg3hAAH9qvHRdeEBKi2zn+z14QI0W2c73F3hAi+tRuB7xd0CoRbbz/cp3QKpwPQrXlXdAVo2XbhJzd0Azsp3vp2x3QJjtfD81eHdADqwcWmRxd0BTYhBYOXJ3QPWnxks3f3dA4Pl+aryMd0AvhxbZzpN3QIjAyqFFsndA+yhcj8LDd0Ddo3A9Ctd3QFrjpZvE3HdAVo2XbhLdd0BsZmZmZvR3QJKXbhKDCHhAWLgehesVeEAULbKd7yd4QGbl0CLbPXhA2E1iEFg5eEB6kxgEVlJ4QOrQItv5XHhADtejcD16eEBNDAIrh554QC6HFtnOt3hAxvUoXI/aeED9fmq8dAV5QCgGgZVDM3lAksL1KFxreUBQYhBYOaJ5QFO4HoXrw3lAIIXrUbjkeUDz0k1iEPp5QPpT46WbGnpA0yLb+X4yekAFK4cW2TZ6QM2hRbbzH3pA0vdT46UNekDIILByaO15QLrz/dR4yXlA+Shcj8KPeUDzp8ZLN195QKvGSzeJM3lA8qfGSzf7eEAm2/l+ash4QOalm8QgnnhA7lG4HoWFeED+fmq8dGN4QAusHFpkN3hAxvUoXI8EeEBzEoPAyvF3QLed76fG13dAmRgEVg7Dd0BO4XoUrrV3QEqLbOf7p3dAxJ8aL92ad0C78/3UeHt3QAYAAAAAYHdA4Pl+arxod0DxUbgehYl3QB4EVg4tmndADqwcWmSpd0A2CKwcWqh3QMJ0kxgEpHdAbGZmZmaed0DV91PjpZ13QCsGgZVDpXdAClYOLbK3d0BeObTIdrR3QGBkO99PqXdATeF6FK6dd0D30k1iEJZ3QJXC9ShcoXdAvh6F61Gkd0Ap2/l+aqx3QGS6SQwCs3dAepMYBFbEd0Bl5dAi29V3QDpeukkMAnhA86fGSzcneEBQN4lBYFl4QInrUbgek3hADtejcD2yeEDyp8ZLN814QD7fT42X+nhAsUfhehQkeUCYQ4ts50N5QGLl0CLbW3lAvnSTGARWeUBON4lBYFV5QPpT46WbSHlAxiCwcmgzeUC0ne+nxjN5QAZWDi2yUXlAFoPAyqFZeUCu8dJNYnR5QCbb+X5qnHlA9dJNYhC8eUByEoPAyuF5QIoW2c73B3pA+1PjpZswekDNdr6fGlF6QJBs5/upa3pAQwrXo3BxekAQ16NwPYh6QPr91HjpxnpA+yhcj8IZe0Aq2/l+amR7QIZqvHSTxHtATrbz/dQgfEC+8/3UeG18QH6+nxovxXxAsvHSTWIefUB6aJHtfGt9QNX3U+OlsX1AmhgEVg7FfUC2cmiR7b59QD60yHa+vX1Ae76fGi+rfUCwHFpkO5d9QEDfT42XlH1Agj81XrqbfUCu8dJNYrB9QLvz/dR4131AmBgEVg4BfkAO16NwPSx+QDgzMzMzSX5ATgwCK4eEfkDYTWIQWLl+QEZg5dAi7X5AmBgEVg7/fkD9U+Olmwx/QL5JDAIrBX9Atp3vp8b5fkBbDi2yneF+QGBkO99Py35A5U+Nl26+fkC2cmiR7Zx+QHuTGARWdH5A+v3UeOlSfkCgmZmZmVF+QG6R7Xw/RX5A23jpJjE8fkDezvdT4zd+QBICK4cWN35AMIcW2c4TfkAo2/l+avZ9QIrrUbgezX1AjUFg5dCifUC+SQwCK3V9QLNyaJHtSn1As3Joke00fUB1PQrXoxp9QMJ0kxgE+HxATeF6FK7jfEAo2/l+agR9QCsxCKwcIn1AODMzMzNDfUCzR+F6FHJ9QEU1XrpJqH1AdRKDwMrrfUBz5/up8SZ+QApWDi2yaX5Ag2q8dJOkfkAtXI/C9cR+QNZNYhBYz35Ae76fGi/FfkATLbKd771+QNP3U+Ol035A/n5qvHTRfkBtke18P8t+QDgzMzMzt35AKgaBlUOnfkDCdJMYBK5+QEU1XrpJpH5AEgIrhxaXfkA4MzMzM4F+QBICK4cWdX5AQt9PjZdGfkCj76fGSyt+QOvQItv59H1ARTVeukm+fUAVLbKd74V9QCIv3SQGTX1Ahmq8dJMsfUC1R+F6FBp9QEuLbOf7IX1AqkW28/0ofUB9kxgEVjJ9QFDhehSuMX1ADoGVQ4s2fUBiZDvfTyd9QCNaZDvfF31AajvfT437fEASAiuHFtd8QGgQWDm0qHxAhpVDi2yRfEBWjZduEnF8QCsGgZVDT3xAZbpJDAIzfEAlhetRuCJ8QK3GSzeJGXxA9afGSzcXfEDQoUW28xN8QIaVQ4tsF3xAu/P91HghfECocD0K1w18QKLvp8ZLD3xAYrpJDAIbfEA2MzMzMyl8QJ2ZmZmZOXxACoGVQ4tafECbbhKDwHB8QL0ehetRiHxAwHSTGASkfEBtvHSTGLZ8QC6HFtnO4XxAK1yPwvX4fEAqMQisHAh9QLsehetREn1AIoXrUbgUfUAbBFYOLRZ9QKVFtvP9Hn1A4CQGgZUnfUCTwvUoXEF9QNp46SYxWH1A0/dT46VdfUAwsp3vp159QJPtfD81cH1Am5mZmZmBfUBqke18P5N9QBJYObTIqH1AAyuHFtm+fUA1MzMzM9N9QIBqvHSTzn1A0iLb+X7IfUDjpZvEILh9QC2yne+nun1AE65H4XqofUDdJAaBlZV9QOBPjZduhH1AncQgsHJ2fUD1/dR46VZ9QOJ6FK5HQ31AUrgeheszfUC4HoXrUSJ9QGiR7Xw/LX1AVg4tsp0pfUB1kxgEVih9QI1s5/upLX1AoBov3SQ8fUAFVg4tskN9QIPAyqFFWH1AhutRuB5zfUDQ91PjpZt9QPpT46Wb3H1ADgIrhxYFfkDWeOkmMTR+QKNFtvP9bn5AGgRWDi28fkACAAAAAAB/QDu0yHa+QX9Am5mZmZmDf0AqMQisHLB/QPJ8PzVezH9ApUW28/3Wf0B+FK5H4d5/QJBs5/up539A7iYxCKz0f0CGlUOLbO1/QB4EVg4t4H9AM7Kd76fef0CqcD0K1+1/QBNYObTIA4BAGwRWDi0YgEC/dJMYBCmAQJpuEoPAOoBANjMzMzNIgEDWTWIQWFSAQPB8PzVeZIBABlYOLbJ9gECycmiR7ZGAQKVFtvP9n4BAgD81XrqsgEBuvHSTGLiAQBrZzvdTx4BAOF66SQzdgEDkehSuR/WAQOjQItv5CIFAy3a+nxoVgUDsJjEIrBqBQGuR7Xw/C4FAle18PzXzgEDe+X5qvNuAQIoW2c73v4BAzHa+nxqigEDdzvdT44GAQK7x0k1iZIBArvHSTWJAgEBFYOXQIiOAQFONl24SBYBAnm4Sg8Duf0Di+X5qvOp/QI0W2c737X9Az8zMzMwDgEB6vp8aLwmAQIbAyqFFDIBAE1g5tMgLgECObOf7qQqAQGoQWDm0/H9A81G4HoXdf0AYWDm0yNh/QAJ/arx0xX9AbWZmZma2f0BrO99PjaF/QHpoke18nX9ARjVeukmcf0BqEFg5tJh/QH2+nxovlX9AxsqhRbatf0DwUbgehc1/QGXl0CLb5X9AwsqhRbYIgECObOf7qRmAQON6FK5HIoBAYLpJDAIogEA930+NlyeAQDZeukkMLoBANl66SQw7gEAisHJokUSAQJ3EILByUoBAdGiR7XxagEDeJAaBlWKAQOn7qfHScYBAhcDKoUWJgEAeWmQ736mAQGUQWDm01IBApnA9Ctf6gEBrke18Px+BQISVQ4tsQYFADdejcD1ggUDao3A9Co6BQHiTGARWtYFAU42XbhLTgUB86SYxCOiBQNh46SYxA4JAuvP91HgkgkAm2/l+akmCQDczMzMzd4JA0czMzMyigkA7iUFg5dqCQHDn+6nx+IJArxxaZDsSg0DQzMzMzDmDQIbAyqFFaoNA0MzMzMySg0BmEFg5tLODQBsEVg4t5INA7lG4HoUMhEB/PzVeujGEQNMi2/l+T4RAcRKDwMp+hEAthxbZzr2EQLbIdr6f+IRA0yLb+X4rhUB/PzVeumCFQNH3U+OlnIVAWjm0yHbRhUBej8L1KP+FQIOVQ4tsE4ZAVLgehesKhkDWeOkmMeWFQItBYOXQyYVAxfUoXI+VhUDCyqFFtlqFQFgOLbKdHIVAtZ3vp8bMhEAbBFYOLYKEQA3Xo3A9SIRATAwCK4c6hEAA1XjpJjyEQDMIrBxaXYRAIYXrUbh2hEDn0CLb+aKEQC+yne+n1YRA59Ai2/n7hEC2yHa+nyqFQJuZmZmZUoVACqwcWmR+hUCycmiR7ZyFQMCfGi/dxoVA73w/NV7bhUDZo3A9CuGFQErhehSu3oVAapHtfD/chUDHILByaNKFQPkoXI/CyYVABSuHFtm4hUALrBxaZKSFQAYrhxbZlYVACFYOLbKHhUBiukkMApGFQInrUbgekoVAmUOLbOeYhUCJ61G4HpqFQN3O91PjpoVAuvP91HiwhUAcBFYOLaCFQOv7qfHSf4VAjUFg5dBLhUByEoPAyiWFQDLdJAaB8oRAkJduEoPAhEAJgZVDi5mEQBauR+F6boRADgIrhxY3hED8fmq8dPODQJYYBFYOxoNARGDl0CKSg0Com8QgsE+DQCGF61G4EINA+35qvHTQgkByPQrXo46CQAIAAAAAPIJA0yLb+X70gUAbBFYOLcuBQCcGgZVDh4FAxyCwcmgzgUDwfD81XvWAQKZwPQrXz4BAlO18PzWYgEBACtejcHCAQOAkBoGVVIBAcz0K16M6gEAihetRuC+AQLFH4XoUFoBAU42XbhIhgEDczvdT4yuAQNL3U+OlLYBAI7ByaJEugEAFK4cW2TCAQNL3U+OlGIBApRov3STWf0C2ne+nxo1/QGO6SQwCL39Ao++nxkvNfkDOdr6fGll+QKYaL90k8n1A+NJNYhCQfUAyhxbZzh99QK2bxCCwqHxAIARWDi00fED4p8ZLNw18QLpyaJHt2HtAjutRuB6le0CeQ4ts5297QGA5tMh2NHtA/Shcj8Ire0B4PQrXoxJ7QAgAAAAAFHtAsMZLN4k/e0CqRbbz/Xp7QDgIrBxamHtAQt9PjZeee0BoEFg5tJZ7QBZYObTImntA6qWbxCDke0BC30+Nlw58QOMkBoGVM3xA1fdT46VvfECQbOf7qYl8QPr91HjppHxAk5duEoOufECTl24Sg9Z8QDXdJAaB9XxAyiCwcmgHfUD1p8ZLN+l8QKUaL90k2HxA9afGSzfPfEBODAIrhzx9QLNH4XoUnn1A4iQGgZUpfkAqBoGVQ61+QJ6ZmZmZI39AHgRWDi2uf0D9fmq8dCeAQPkoXI/CgIBAvkkMAivggEADAAAAAFOBQDLdJAaBdIFA2HjpJjGigUDkehSuR7uBQEI1XrpJ1YFAKAaBlUP1gUCCarx0kwyCQEm28/3UJoJA/X5qvHQ6gkD10k1iEDyCQGpmZmZmMoJAhJVDi2wxgkDuUbgehSOCQF5kO99PHYJAzHa+nxoGgkAoBoGVQ+uBQGpmZmZm1IFAvUkMAivBgUCSwvUoXLWBQCGF61G4uoFAxfUoXI++gUAy3SQGgbqBQBrZzvdTsoFAUWIQWDmtgUCGwMqhRcqBQP+p8dJN2IFAmUOLbOfigUDvUbgehd2BQBACK4cWy4FAOV66SQyzgUBaDi2ynaeBQMKfGi/dpYFAPt9PjZengUAihetRuJ+BQKZwPQrXioFArfHSTWJ6gUDKSzeJQXCBQPkoXI/CZoFAamZmZmZrgUBuvHSTGFiBQNZNYhBYNYFArhxaZDsQgUCexCCwcvSAQBoEVg4t2IBAWA4tsp3FgECbmZmZmcOAQJUYBFYOy4BA+35qvHTKgEAoMQisHMKAQGy8dJMYxoBAEC2yne/AgEDrJjEIrLiAQPB8PzVeuYBAdmiR7Xy+gEBv5/up8ZGAQGPl0CLbVYBAVbgehesXgEAtMQisHNx/QNt46SYxdH9A6HoUrkcxf0C1R+F6FOp+QGBkO99PtX5A7iYxCKxgfkDASQwCKwF+QBiDwMqh631ANgisHFrifUDi+X5qvOZ9QOqlm8Qg4n1Agz81XrrnfUCGarx0k+x9QFIMAiuHEn5AhT81XroxfkACf2q8dE1+QBLXo3A9gH5AnUOLbOfDfkCy8dJNYvx+QIiVQ4tsDX9A6HoUrkcLf0BIYOXQIhd/QPjSTWIQEH9ArsZLN4nzfkDDdJMYBMZ+QAtWDi2yk35AMFyPwvVgfkBLi2zn+xV+QNB2vp8a4X1AlsL1KFy3fUC6ne+nxn99QOulm8QgPH1A0Ha+nxrLfECmGi/dJGR8QKYaL90kBHxAKtv5fmrMe0C7yHa+n5J7QK7GSzeJY3tA7vup8dI/e0DSoUW28+96QPjSTWIQ3npAm0OLbOflekCY7Xw/NfZ6QH2+nxovB3tAaBBYObQoe0D40k1iEDJ7QL3z/dR4J3tAPolBYOXSekDdo3A9Co96QP5T46WbinpAxZ8aL908ekDoehSuR9l5QKlFtvP9enlAuZ3vp8b1eECGarx0k1p4QOQkBoGVqXdAi+tRuB4Ld0AE1XjpJgt3QDOyne+nznZAhWq8dJNadkC6yHa+nx52QBiDwMqhB3ZA/lPjpZsUdkAOrBxaZC92QHloke18S3ZATLbz/dRwdkDXItv5fnx2QDxeukkMKnZAx8qhRbYzdkCqcD0K11F2QJSXbhKDXHZAiJVDi2xHdkA+iUFg5RJ2QKCZmZmZGXZALTEIrBwEdkA3CKwcWuJ1QGBkO99P1XVAHgRWDi3EdUCTl24Sg2h1QGBkO99P/XRAR2Dl0CKxdEDaeOkmMWJ0QAUAAAAAGHRAWeOlm8Soc0C78/3UeEVzQI5BYOXQ0nJAgBSuR+FkckDg+X5qvAJyQE3hehSu03FAhpVDi2zVcUBLtvP91LZxQK/x0k1icHFA83w/NV5IcUDiJAaBlStxQBDXo3A9InFAHNnO91NJcUCPQWDl0IhxQF45tMh21nFAo++nxksBckACqvHSTQ5yQBZYObTIMnJAisDKoUV6ckAMgZVDi8pyQKpwPQrXA3NA2k1iEFg9c0CzHFpkO2dzQLMcWmQ7X3NAlZduEoNSc0CM61G4HklzQNpNYhBYSXNABgAAAAAsc0CcQ4ts5x9zQAtWDi2yA3NAiJVDi2wNc0Bo5dAi2ylzQLZH4XoUUHNAWbgehet3c0APrBxaZKNzQBhYObTItnNAlZduEoPKc0BsO99PjeVzQOqlm8Qg4nNAisDKoUXUc0Aag8DKobFzQAXVeOkmaXNA5CQGgZU3c0Bb46WbxB5zQEqLbOf7HXNA+NJNYhAsc0DqpZvEIEpzQBICK4cWgXNACCuHFtnEc0AtMQisHAR0QGo730+NRXRAIlpkO9+hdEA/tMh2vuF0QKPvp8ZLLXVA7fup8dJ3dUCRbOf7qc91QPn91Hjp+nVAmkOLbOcJdkC78/3UeCl2QCsxCKwcSHZA/n5qvHRLdkDFyqFFtl92QEZg5dAiX3ZAM90kBoFTdkBoO99PjRF2QA7Xo3A93HVAa5HtfD/BdUAfWmQ73591QLWd76fGd3VAb+f7qfFGdUD9fmq8dCt1QJpuEoPA9HRAZzvfT425dEBw5/up8Xx0QIbAyqFFPnRAuMh2vp8CdEAxsp3vp9xzQI5BYOXQxnNAzXa+nxqxc0AC1XjpJqVzQLVyaJHttHNAWg4tsp3Lc0DWItv5fvJzQNujcD0KFXRAUDeJQWBxdEC2ne+nxq90QBeDwMqh2XRAbZHtfD8JdUDVItv5fi51QKLvp8ZLU3VA0vdT46VndUCwHFpkO4l1QBrZzvdTvXVAI4XrUbj8dUCkGi/dJBx2QJKXbhKDLnZANzMzMzNLdkASLbKd72l2QEDfT42XmnZACoGVQ4vEdkAGK4cW2fZ2QEM1XrpJLndAIFpkO99dd0DNdr6fGm13QIjAyqFFgndA/FPjpZuqd0D4/dR46c53QG68dJMY6ndAdD0K16P2d0C7HoXrUQ54QLrz/dR4IXhA26NwPQoteECTwvUoXC94QNL3U+OlU3hAsEfhehR2eED8fmq8dJV4QEwMAiuHqHhAU42XbhLFeEC7HoXrUeh4QNDMzMzMAHlAw8qhRbb9eEBej8L1KPB4QMlLN4lB6nhAeukmMQjieED7fmq8dNt4QAqsHFpk0XhA8afGSzfReEANAiuHFrt4QJ7vp8ZLtXhAsp3vp8a/eED91HjpJuF4QB2F61G4EHlA7qfGSzdLeUC+yqFFtn15QJ0aL90ksnlAxXa+nxr3eUBSDi2ynUN6QDBeukkMjHpA7dJNYhC+ekAS2c73U+l6QAgCK4cWB3tAIAaBlUMTe0CVmZmZmQN7QPIoXI/C73pASDeJQWDVekB+lUOLbKt6QCuyne+nhHpAqvHSTWJSekAVrkfheiJ6QHa+nxov/XlAvXSTGAToeUAQWDm0yOh5QIBqvHST9HlA7VG4HoX9eUDQ91PjpQN6QNv5fmq8+nlAhhbZzvfpeUCIQWDl0Nh5QL7KoUW203lAtfP91HjVeUDgehSuR9N5QM73U+Ol0XlASAwCK4fSeUCSGARWDu15QEW28/3UEnpAzvdT46U1ekAGgZVDi1Z6QH5qvHSTiHpANolBYOWwekC7dJMYBNx6QH0/NV66D3tASgwCK4cwe0ASWDm0yEh7QAVWDi2yRXtAw/UoXI9Ke0DYo3A9Cl97QP2p8dJNgntAE4PAyqGre0AA1XjpJtF7QEWLbOf75XtAPd9PjZf2e0BzaJHtfAl8QHjpJjEIIHxAsp3vp8ZDfEBlZmZmZl58QOpRuB6FeXxAKocW2c5tfEA9NV66SWB8QIZBYOXQVHxANYlBYOVkfEBOuB6F63F8QFhkO99PhXxAMjMzMzOhfECCwMqhRbB8QLgehetRtnxAUrgeheu9fEBT46WbxNh8QKjGSzeJ6XxAAAAAAAD4fEBNN4lBYPN8QFKNl24S7XxAOolBYOXcfECWGARWDtF8QPgoXI/C1XxAdWiR7XzffECwR+F6FPB8QLjz/dR4CX1A7VG4HoU9fUD7fmq8dHt9QPYoXI/CsX1AexSuR+HqfUDeJAaBlSt+QFXjpZvEYn5Aa7x0kxiMfkByPQrXo6x+QGu8dJMY0n5ASOF6FK7zfkCzne+nxv9+QAaBlUOL+H5AqMZLN4ntfkCGFtnO999+QPh+arx0335AQots5/v9fkCjcD0K1xl/QB1aZDvfP39AppvEILBof0CKQWDl0JR/QIYW2c73039A/tR46SYMgEDufD81XkCAQE5iEFg5c4BA+VPjpZudgED7fmq8dLiAQKvx0k1i2IBAdpMYBFb2gEC48/3UeA6BQPKnxks3IIFANjMzMzMkgUAqMQisHDWBQOalm8QgKoFA0fdT46UbgUDKdr6fGhCBQHRoke18B4FAQmDl0CIDgUDz0k1iEAGBQGQQWDm0B4FAFq5H4XoWgUCnm8QgsC6BQOWlm8QgPIFAHlpkO99YgUBkEFg5tHqBQMCfGi/dmoFAU7geheu3gUAKrBxaZMuBQBauR+F62oFAMgisHFrqgUAk2/l+aveBQMP1KFyPA4JADQIrhxYNgkACK4cW2RWCQB+F61G4IIJA3Pl+arwogkBfukkMAjCCQLbIdr6fOYJALrKd76dGgkAk2/l+akqCQF2PwvUoT4JAVeOlm8RTgkDufD81XlyCQGU730+NZoJAu0kMAitpgkDufD81XnGCQLJyaJHtd4JAlRgEVg6BgkACAAAAAIuCQMhLN4lBloJAq/HSTWKogkCIFtnO972CQGHl0CLb14JAbhKDwMrqgkBXObTIdgKDQArXo3A9FINAcmiR7Xwug0AFgZVDi0SDQJDtfD81TINAOt9PjZdGg0C9nxov3T6DQIUW2c73MINAkRgEVg4Xg0CP7Xw/Nf2CQJNDi2zn4oJAULgehevCgkBUDi2ynaOCQJZuEoPAgIJASAwCK4dsgkDFSzeJQUmCQG49CtejF4JApJvEILDogUBCi2zn+8GBQDe0yHa+l4FAhkFg5dBngUCqHFpkO0KBQHbpJjEIFYFAjcL1KFz1gEBjZmZmZsiAQH6VQ4tsuYBAMDMzMzO0gEAg2/l+aq+AQJrvp8ZLqYBAQIts5/uqgEBGDAIrh62AQPvUeOkmtIBA5fup8dK5gEA3tMh2vruAQK5yaJHtxoBAQGDl0CLQgEAh2/l+atGAQDsK16NwzYBAGS/dJAbHgEDSTWIQWL+AQP//////voBAKocW2c6+gEDQItv5fsSAQDrfT42XzYBAMAisHFrggEBpvHSTGPCAQNN46SYxBIFAFgRWDi0ogUBq5/up8VOBQD9g5dAifoFASAwCK4eWgUCicD0K16mBQHbpJjEIuoFAEq5H4XrDgUAkMQisHMCBQOClm8QgvIFAFQRWDi2zgUB3FK5H4aOBQH/AyqFFlYFAahKDwMqHgUCN7Xw/NYeBQB7b+X5qiIFAhmzn+6mJgUDZJAaBlYuBQASsHFpklIFAgxbZzvecgUCWmZmZmaiBQHk/NV66sIFAYmZmZmazgUDbT42XbruBQNbO91PjzoFAG4XrUbjggUAiBoGVQ/eBQM9NYhBY6IFAUg4tsp3XgUBMjZduEs2BQEThehSuxYFAcpMYBFa1gUAuCKwcWqSBQBKuR+F6g4FAVmQ7309VgUBVObTIdiWBQOT7qfHS8oBAIgaBlUPfgECIbOf7qc+AQHo/NV66qIBAZ7x0kxiFgECm8dJNYl6AQJJDi2znNIBA8P3UeOksgECSQ4ts5xyAQLufGi/dCYBACC2yne/xf0C4nxov3dR/QCayne+nsH9AWuXQItujf0AQ2c73U4t/QLDz/dR4ZX9Adj81XrpDf0AAgZVDi/x+QEU3iUFgyX5AKN0kBoGZfkA+tvP91Hh+QGgSg8DKVX5APrbz/dQsfkA+tvP91CR+QFsQWDm0Nn5Aaj0K16NcfkDOeOkmMXJ+QKPx0k1ifn5AKt0kBoF5fkCCFtnO94t+QEC28/3UiH5AqnJoke2EfkBLuB6F651+QLv1KFyPqn5AdT81XrqhfkDe+6nx0qN+QJ2bxCCwqH5AzqNwPQqrfkDQzvdT47d+QD228/3UpH5A41G4HoWLfkAytMh2vnl+QKud76fGM35Ag2zn+6nZfUAWhetRuHh9QPbUeOkmE31AskkMAiu/fEBqaJHtfIV8QFoQWDm0XHxAQAwCK4dQfEAWsHJokVV8QB5cj8L1cHxANTVeukmsfEDCzMzMzPp8QJZFtvP9Gn1AW2ZmZmZKfUCiHFpkO5N9QOr91Hjpvn1AHTEIrBzsfUBFYhBYORB+QGCR7Xw/JX5A0/l+arwqfkBdO99PjR9+QMB2vp8aEX5Aa2iR7Xw5fkC+SzeJQWB+QDUK16NwbX5AWzvfT42bfkCAQWDl0MB+QEaNl24S0X5A2HoUrkfrfkBVukkMAv1+QDu28/3UCH9A6NJNYhAUf0BOObTIdhZ/QI5uEoPADn9AVuXQItsPf0Btvp8aL+l+QNvQItv5qH5AVeXQIttrfkAehxbZzil+QF68dJMY1n1AVhBYObSafUCGGARWDll9QA0v3SQGLX1Am/HSTWL4fEDNJAaBlbd8QNomMQislHxATrpJDAKNfEC2SzeJQZp8QABYObTImnxAYmiR7Xy5fEAzi2zn+9t8QPrWo3A9BH1AKrTIdr4pfUDVpZvEIFZ9QHbrUbgeh31AMjVeukmofUAuCtejcNN9QILtfD81EH5AUxBYObRefkA9N4lBYKd+QIMYBFYO435AJolBYOU0f0BAuB6F64F/QFAQWDm0zH9AmUW28/0LgECpne+nxi+AQPXUeOkmTIBAvCCwcmhXgEBGYhBYOWuAQI5Di2zneYBA26WbxCCHgEBEN4lBYI6AQH7rUbgelYBAyiLb+X6ggEA9i2zn+6yAQGS8dJMYuYBA7yhcj8L1gEAuXrpJDEKBQHY/NV66eYFAC4PAyqG0gUDop8ZLN/aBQFSPwvUoM4JANzVeuklxgkCLGARWDq+CQDPfT42X6YJAuMqhRbYng0CFl24SgzGDQMpNYhBYL4NAuvUoXI81g0CwHoXrUT+DQJgaL90kSYNAb76fGi9Tg0BeZmZmZluDQEM3iUFgYINAJt0kBoFlg0AArBxaZGSDQOrSTWIQXINANgrXo3BMg0AGLbKd7zKDQAqDwMqhFYNAUDm0yHb/gkDq0k1iEOSCQK3Idr6fx4JAEgRWDi2mgkD7KocW2Y+CQGO8dJMYeoJA9tR46SZpgkDLTWIQWEmCQPoqhxbZOYJAUDm0yHYwgkBODi2ynR2CQGk9CtejE4JARDeJQWAQgkBeZmZmZhaCQKzIdr6fE4JAAKwcWmQVgkBeZmZmZh+CQO4oXI/CPoJA41G4HoVTgkDXT42XbmOCQOImMQiscYJAcekmMQh/gkCqne+nxouCQNL5fmq8l4JASrgeheuhgkCLGARWDqqCQMki2/l+rYJA5nw/NV6ygkDnp8ZLN7mCQBIv3SQGu4JAtp8aL93FgkCFl24Sg8uCQFJkO99P0IJAFoXrUbjWgkBraJHtfOCCQMOhRbbz6YJArch2vp/3gkCxHoXrUQSDQNYkBoGVEoNAkG4Sg8Aqg0De0CLb+UaDQEDhehSuXoNAnXA9Ctdsg0Ao3SQGgWuDQOinxks3Z4NAqp3vp8Zrg0CySQwCK2yDQMF2vp8abINA/oCVQ4tfg0BqaJHtfEyDQE4OLbKdNYNAgUFg5dAlg0DjUbgehRiDQJtwPQrXF4NAvCCwcmgag0Buvp8aLxmDQG6+nxovGYNAjkOLbOcYg0C5yqFFtiuDQGXn+6nxQYNA10+Nl25Mg0CK7Xw/NVaDQJbvp8ZLYoNAeGq8dJNxg0BTZDvfT26DQIRs5/upaINA5FG4HoVig0BeZmZmZl2DQONRuB6FVINA8FPjpZs+g0BKuB6F6yuDQM546SYxIINADa5H4XoMg0AytMh2vu2CQHmVQ4ts34JACFg5tMjPgkB/FtnO97iCQPSp8dJNnoJAd2q8dJN7gkBODi2ynWKCQIRs5/upP4JAwna+nxoZgkDyfmq8dPmBQA/ZzvdT7YFAbZMYBFbhgUDG91PjpdmBQEq4HoXr3oFAyk1iEFjhgUBoPQrXo+WBQEM3iUFg74FAskkMAisDgkBqaJHtfBeCQKZH4XoUMIJAEy/dJAY5gkA2CtejcDKCQI0YBFYOLIJA/4CVQ4smgkDaehSuRyWCQNLO91PjJoJAm0W28/0dgkD/gJVDixmCQGbn+6nxEoJA6tJNYhDigUABrBxaZLGBQMtNYhBYi4FA1SQGgZVkgUCDbOf7qTiBQBEEVg4tDoFAoMZLN4nqgEC/SzeJQdCAQPFT46WbsYBAp0fhehSWgEDnfD81XpyAQFi6SQwCooBAHdv5fmqtgEBjke18P7SAQL8gsHJovIBAiJduEoPGgECl8dJNYs6AQKLGSzeJzIBALjMzMzPKgEC99Shcj76AQJbEILByroBAXRBYObSrgEBMuB6F66aAQCrdJAaBpIBAbWiR7XybgECOGARWDoyAQKLGSzeJeIBA7v3UeOlrgEBGN4lBYF6AQPR+arx0V4BACC2yne9fgEAvXrpJDFyAQMr3U+OlWoBAIjEIrBxUgEDW+X5qvFGAQGKR7Xw/UoBA39Ai2/lSgECuyHa+n1CAQHc/NV66SYBAahKDwMo8gEB+wMqhRSyAQPnUeOkmGoBA+/////8DgEDAdr6fGst/QNP5fmq8kH9ABQIrhxZnf0ALg8DKoTN/QOqnxks3+X5AJYcW2c7BfkAahetRuJ5+QLVJDAIrh35AvfUoXI9+fkATBFYOLYh+QGoSg8DKwX5A6nw/NV4Of0CbGi/dJFJ/QG5oke18sX9AWo/C9SgCgECrR+F6FCyAQDJeukkMVYBA2fl+arx4gEBDtvP91JGAQOX7qfHS2oBAyaFFtvMVgUDVo3A9Ck6BQHfpJjEIfoFA0U1iEFiugUBzkxgEVvOBQAJWDi2yPIJALd0kBoGDgkDnJjEIrLqCQIDAyqFF/IJA4aWbxCABg0A430+NlwqDQLLz/dR4AoNA36WbxCAHg0BgO99Pje+CQC4zMzMz0IJAQrbz/dSVgkACgZVDi26CQOHQItv5P4JAq3Joke0YgkCdRbbz/fiBQIrC9Shc0YFAaz0K16OmgUBIYhBYOXCBQCIxCKwcNoFAUQ4tsp3vgEBwkxgEVsmAQIZs5/upnYBABwIrhxaJgEAe2/l+ameAQNPO91PjSYBA6Hw/NV47gEB3PzVeujyAQODQItv5QoBASo2XbhJZgECM7Xw/NXSAQB7b+X5qh4BA2CQGgZWTgEBNuB6F65yAQE/jpZvEoIBARzeJQWCjgEB4PzVeupuAQOL7qfHSlIBAxHa+nxqWgECXxCCwcqGAQO/91HjpsoBAsvP91HjGgEDzU+Olm8qAQLZJDAIrv4BA/1UOLbK/gEAiMQisHMGAQIMW2c73yIBApvHSTWLGgEBykxgEVrqAQBcv3SQGoYBAxXa+nxp4gEC0HoXrUVSAQBqF61G4OIBAJVyPwvUzgEDK91PjpSuAQBhaZDvfL4BAaOf7qfEygEDlJjEIrEKAQLqfGi/dW4BAFARWDi2ZgECim8QgsN6AQHtqvHSTJYFASDeJQWBigUACgZVDi5mBQKBwPQrX1YFABtejcD0FgkBKYhBYOSWCQLLz/dR4Q4JA66fGSzdjgkA/i2zn+2mCQLZJDAIrcoJAQ+F6FK5ogkDh0CLb+WuCQNKjcD0Kd4JAvvUoXI97gkAIAiuHFoWCQNtPjZdunIJAmxov3SSogkB+wMqhRaWCQJ9wPQrXq4JA3qWbxCC8gkD1fmq8dNqCQK6d76fG74JAsvP91HjygkBTObTIdvGCQGA730+N24JAQ+F6FK7BgkBaukkMAqmCQM5NYhBYj4JA66fGSzd/gkBYj8L1KGeCQH2VQ4tsWYJAIQaBlUNbgkBc5dAi22qCQLLz/dR4g4JA1vl+arylgkCXxCCwcsiCQC8zMzMz7YJA1/l+arwgg0DNItv5fj6DQCmyne+nYINAlZmZmZlvg0Drp8ZLN3SDQABWDi2yeINAbT0K16N1g0BOuB6F63qDQLqfGi/dfINADFg5tMiHg0CLwvUoXIyDQGSR7Xw/mINAKt0kBoGjg0CCFtnO97aDQI4YBFYOzINAUQ4tsp3rg0Ctne+nxhCEQKGbxCCwJ4RAMF66SQw7hEDCSzeJQTWEQMJLN4lBLYRA4vup8dIhhECune+nxhmEQP4qhxbZC4RAnkW28/39g0CeRbbz/eODQBYv3SQGzYNA81PjpZuSg0BaukkMAl+DQAxYObTIPYNAup8aL90Sg0B6arx0k+SCQP4qhxbZvIJAWrpJDAKXgkCnHFpkO3CCQBywcmiRRoJA+/////8agkB+wMqhRSOCQN6lm8QgNoJAyMzMzMxKgkAIAiuHFmuCQK6d76fGjYJA5lG4HoWigkDepZvEIL6CQP4qhxbZ1YJADoPAyqHpgkDyKFyPwvqCQMIgsHJoAYNAcpMYBFYFg0CAwMqhRQaDQN56FK5HA4NAdekmMQgCg0CTbhKDwASDQF4QWDm0CYNAzSLb+X4Qg0CDFtnO9x2DQJNuEoPAKYNAOQrXo3Axg0CN7Xw/NSqDQJrvp8ZLKYNAgutRuB4qg0BjZmZmZiKDQE2Nl24SH4NA2iQGgZUeg0Cune+nxhyDQPIoXI/CG4NAaxKDwMoeg0B7arx0ky6DQGJmZmZmRYNAaef7qfFfg0BMjZduEn+DQGnn+6nxoINAPDVeuknEg0BE4XoUrtuDQM4i2/l++4NAtR6F61ERhEDeehSuRx6EQA6DwMqhKIRACi2yne8vhEBLjZduEiyEQAcCK4cWIYRAoZvEILAXhEC7yqFFtgWEQLqfGi/d/INA7dJNYhD5g0C4dJMYBOaDQHYUrkfhyYNA+/////+rg0C6nxov3Y+DQHK+nxovc4NAFQRWDi1Wg0DaJAaBlTuDQCQxCKwcJ4NAgutRuB4Sg0Cjm8QgsPWCQJ5FtvP97YJAunSTGATtgkCTQ4ts5+iCQJZuEoPA6IJA4aWbxCDygkBe5dAi2/KCQIdBYOXQ+oJAdukmMQgEg0B8arx0kwODQOClm8QgAoNAE9nO91P6gkDL91PjpfmCQJ5FtvP9/oJAEtnO91MQg0DGoUW28xmDQP9VDi2yHINAZ+f7qfEhg0AsCKwcWhiDQJWZmZmZGINAlZmZmZkQg0BrPQrXowWDQO791Hjp/YJA8lPjpZvrgkD41HjpJsWCQMoi2/l+m4JAL166SQyKgkAGAiuHFmeCQI9Di2znVoJATLgehes9gkArCKwcWiuCQKpyaJHtGIJAosZLN4kBgkDi+6nx0uuBQEY3iUFg2oFALjMzMzPVgUBaukkMAs+BQJFDi2zn14FA9n5qvHTVgUDTo3A9CsuBQLqfGi/dw4FAsMh2vp+9gUA7NV66Sb2BQFzl0CLbw4FA7tJNYhDIgUCJl24Sg9SBQI3tfD813IFAZJHtfD/hgUDh0CLb+e+BQDffT42XAIJA66fGSzcMgkC4dJMYBB2CQEpiEFg5IoJAtkkMAisjgkAKLbKd7yyCQIVBYOXQKIJA4vup8dIlgkC2SQwCKyCCQHYUrkfhH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\",\"dtype\":\"float64\",\"shape\":[2148]},\"Volume\":[22351900,11428600,9137200,7631300,4598900,3551000,3109000,2601000,2461400,4573700,7566900,2578800,2926700,2495300,2032900,4353800,3926000,5419900,5361900,4637800,4741000,5319700,3618000,3794400,4272100,4566300,3536600,8473000,15273500,6885900,7570000,6517900,7494100,6697400,7064600,5540300,5241300,5838600,9893000,52263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Application\",\"version\":\"1.0.3\"}};\n", + " var render_items = [{\"docid\":\"536e0b46-a1a4-46e5-9478-0764e5d88d2c\",\"roots\":{\"1428\":\"fcf916c1-cb40-4c87-aa78-a1707487f06e\"}}];\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", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1428" + } + }, + "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 severely limits our losses.\n", + "\n", + "For other strategies of the sort, and other reusable utilities in general, see the\n", + "[_lib_ module reference](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)." + ] + } + ], + "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": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/examples/Strategies Library.py b/doc/examples/Strategies Library.py new file mode 100644 index 0000000..f1581dd --- /dev/null +++ b/doc/examples/Strategies Library.py @@ -0,0 +1,101 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.3' +# jupytext_version: 0.8.6 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# Library of Composable Base Strategies +# ====================== +# +# This tutorial will show how to reuse composable base strategies that are part of this software distribution. +# It is assumed you're already familiar with +# [basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.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 Start User Guide.html), +# but we'll rewrite it as a vectorized signal strategy and add trailing stop-loss. +# +# We'll again use a helper moving average function. +# In practice, one can use functions from any indicator library, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib), +# [Tulipy](https://tulipindicators.org), +# PyAlgoTrade, ... + +from backtesting.test import SMA + +# _Backtesting.py_ package includes +# [_lib_](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html) +# module that contains various reusable utilities for developing strategies. +# Some of those utilities are composable base strategies one 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 = 20 + + def init(self): + # In init() and in next() it is important to call the + # super method to properly initialize all the 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) + + # Taking a first difference (`.diff()`) of a boolean + # series results in +1, 0, and -1 values. In our signal, + # as expected by SignalStrategy, +1 means buy, + # -1 means sell, and 0 means to hold whatever current + # position and wait. See the docs. + signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0) + + # Set the signal vector using the method provided + # by SignalStrategy + self.set_signal(signal) + + # Set trailing stop-loss to 4x ATR + # using the method provided by TrailingStrategy + self.set_trailing_sl(4) +# - + +# 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() +# - + +# Notice how managing risk with a trailing stop-loss severely limits our losses. +# +# For other strategies of the sort, and other reusable utilities in general, see the +# [_lib_ module reference](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html). diff --git a/doc/ipython_config.py b/doc/ipython_config.py new file mode 100644 index 0000000..3cdf513 --- /dev/null +++ b/doc/ipython_config.py @@ -0,0 +1,9 @@ +# 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 diff --git a/doc/pdoc_template/config.mako b/doc/pdoc_template/config.mako new file mode 100644 index 0000000..d079365 --- /dev/null +++ b/doc/pdoc_template/config.mako @@ -0,0 +1,14 @@ +<%! + html_lang = 'en' + show_inherited_members = False + extract_module_toc_into_sidebar = True + list_class_variables_in_index = True + + + 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) +%> diff --git a/doc/pdoc_template/credits.mako b/doc/pdoc_template/credits.mako new file mode 100644 index 0000000..110cf40 --- /dev/null +++ b/doc/pdoc_template/credits.mako @@ -0,0 +1,10 @@ +<%! +from backtesting import __version__ +%> +

+ backtesting ${__version__} + 卐 +

+ + + diff --git a/doc/pdoc_template/head.mako b/doc/pdoc_template/head.mako new file mode 100644 index 0000000..754d768 --- /dev/null +++ b/doc/pdoc_template/head.mako @@ -0,0 +1,28 @@ +<%! + from pdoc.html_helpers import minify_css +%> +<%def name="homelink()" filter="minify_css"> + .homelink { + display: block; + font-size: 2em; + font-weight: bold; + color: #555; + background: #f6f6f6; + text-align: center; + padding: .5em 0; + } + .homelink:hover { + color: inherit; + } + .homelink img { + display: block; + max-width:40%; + max-height: 5em; + margin: auto; + margin-bottom: .3em; + } + + + + + diff --git a/doc/pdoc_template/logo.mako b/doc/pdoc_template/logo.mako new file mode 100644 index 0000000..00a4ba7 --- /dev/null +++ b/doc/pdoc_template/logo.mako @@ -0,0 +1,5 @@ +
+ + Backtesting.py + +
diff --git a/logo.png b/logo.png new file mode 100644 index 0000000..8490324 Binary files /dev/null and b/logo.png differ diff --git a/logo.py b/logo.py new file mode 100644 index 0000000..f47c956 --- /dev/null +++ b/logo.py @@ -0,0 +1,29 @@ +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) diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..dae70dc --- /dev/null +++ b/setup.py @@ -0,0 +1,112 @@ +import os +import sys + +if sys.version_info < (3, 4): + sys.exit('ERROR: Backtesting.py requires Python 3.4+') + + +def _discover_tests(): + import unittest + return unittest.defaultTestLoader.discover('backtesting.test', + pattern='*test*.py', + top_level_dir='.') + + +if __name__ == '__main__': + from setuptools import setup + setup( + name='Backtesting', + description="Backtest trading strategies in Python", + license='AGPL-3.0', + url="https://github.com/kernc/backtesting.py", + long_description=open(os.path.join(os.path.dirname(__file__), 'README.md')).read(), + long_description_content_type='text/markdown', + setup_requires=[ + 'setuptools_git', + 'setuptools_scm', + ], + use_scm_version={ + 'write_to': os.path.join('backtesting', '_version.py'), + }, + install_requires=[ + 'typing ; python_version < "3.5"', + 'numpy', + 'pandas', + 'bokeh >= 0.12.15', + ], + extras_require={ + 'doc': [ + 'pdoc3', + 'jupytext >= 0.7.0', + 'nbconvert', + ], + }, + test_suite="setup._discover_tests", + python_requires='>=3.4', + author='Zach Lûster', + classifiers=[ + 'Intended Audience :: Financial and Insurance Industry', + 'Intended Audience :: Science/Research', + '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', + 'candles', + 'candlestick', + 'cboe', + 'chart', + 'cme', + 'commodities', + 'crash', + 'crypto', + 'currency', + 'drawdown', + 'equity', + 'ethereum', + 'exchange', + 'finance', + 'financial', + 'forex', + 'fund', + 'futures', + 'fx', + 'fxpro', + 'gold', + 'heiken', + 'historical', + 'indicator', + 'invest', + 'investing', + 'investment', + 'macd', + 'market', + 'mechanical', + 'money', + 'oanda', + 'ohlc', + 'ohlcv', + 'order', + 'profit', + 'quant', + 'quantitative', + 'silver', + 'stocks', + 'strategy', + 'ticker', + 'trader', + 'trading', + 'tradingview', + 'usd', + ), + )