diff --git a/README.md b/README.md index 53971af3..7bd33bab 100644 --- a/README.md +++ b/README.md @@ -33,9 +33,9 @@ from backtesting.test import SMA, GOOG class SmaCross(Strategy): def init(self): - Close = self.data.Close - self.ma1 = self.I(SMA, Close, 10) - self.ma2 = self.I(SMA, Close, 20) + price = self.data.Close + self.ma1 = self.I(SMA, price, 10) + self.ma2 = self.I(SMA, price, 20) def next(self): if crossover(self.ma1, self.ma2): @@ -44,8 +44,8 @@ class SmaCross(Strategy): self.sell() -bt = Backtest(GOOG, SmaCross, - cash=10000, commission=.002) +bt = Backtest(GOOG, SmaCross, commission=.002, + exclusive_orders=True) bt.run() bt.plot() ``` @@ -56,30 +56,33 @@ Results in: Start 2004-08-19 00:00:00 End 2013-03-01 00:00:00 Duration 3116 days 00:00:00 -Exposure [%] 94.29 -Equity Final [$] 69665.12 -Equity Peak [$] 69722.15 -Return [%] 596.65 +Exposure Time [%] 94.27 +Equity Final [$] 68935.12 +Equity Peak [$] 68991.22 +Return [%] 589.35 Buy & Hold Return [%] 703.46 -Max. Drawdown [%] -33.61 -Avg. Drawdown [%] -5.68 -Max. Drawdown Duration 689 days 00:00:00 +Max. Drawdown [%] -33.08 +Avg. Drawdown [%] -5.58 +Max. Drawdown Duration 688 days 00:00:00 Avg. Drawdown Duration 41 days 00:00:00 # Trades 93 Win Rate [%] 53.76 -Best Trade [%] 56.98 -Worst Trade [%] -17.03 -Avg. Trade [%] 2.44 +Best Trade [%] 57.12 +Worst Trade [%] -16.63 +Avg. Trade [%] 1.96 Max. Trade Duration 121 days 00:00:00 Avg. Trade Duration 32 days 00:00:00 -Expectancy [%] 6.92 -SQN 1.77 -Sharpe Ratio 0.22 -Sortino Ratio 0.54 -Calmar Ratio 0.07 -_strategy SmaCross +Profit Factor 2.13 +Expectancy [%] 6.91 +SQN 1.78 +Sharpe Ratio 0.18 +Sortino Ratio 0.44 +Calmar Ratio 0.06 +_strategy SmaCross(n1=10, n2=20) +_equity_curve Equ... +_trades Size EntryB... ``` -[![plot of trading simulation](https://i.imgur.com/q6OSQD8.png)](https://kernc.github.io/backtesting.py/#example) +[![plot of trading simulation](https://i.imgur.com/xRFNHfg.png)](https://kernc.github.io/backtesting.py/#example) Find more usage examples in the [documentation]. diff --git a/backtesting/__init__.py b/backtesting/__init__.py index da8824eb..a808cbba 100644 --- a/backtesting/__init__.py +++ b/backtesting/__init__.py @@ -1,6 +1,11 @@ """ # Backtesting.py Documentation +.. warning:: v0.2.0 breaking changes + Version 0.2.0 introduced some **breaking API changes**. For quick ways to + migrate existing 0.1.x code, see the implementing + [pull request](https://github.com/kernc/backtesting.py/pull/47/). + ## Manuals * [**Quick Start User Guide**](../examples/Quick Start User Guide.html) @@ -11,7 +16,7 @@ * [Multiple Time Frames](../examples/Multiple Time Frames.html) * [Parameter Heatmap](../examples/Parameter Heatmap.html) -These tutorials are also available as live notebooks: +These tutorials are also available to test as live Jupyter notebooks: [![Binder](https://mybinder.org/badge_logo.svg)][binder] [binder]: \ @@ -22,6 +27,11 @@ * (contributions welcome) +## FAQ + +Potentially outdated answers to popular questions can be found on the +[issue tracker](https://github.com/kernc/backtesting.py/issues?q=label%3Aquestion). + ## License This software is licensed under the terms of [AGPL 3.0]{: rel=license}, @@ -39,6 +49,6 @@ except ImportError: pass # Package not installed -from .backtesting import Backtest, Strategy, Orders, Position # noqa: F401 +from .backtesting import Backtest, Strategy # noqa: F401 from . import lib # noqa: F401 from ._plotting import set_bokeh_output # noqa: F401 diff --git a/backtesting/_plotting.py b/backtesting/_plotting.py index e063a35a..32b40781 100644 --- a/backtesting/_plotting.py +++ b/backtesting/_plotting.py @@ -4,6 +4,7 @@ import warnings from itertools import cycle, combinations from functools import partial +from typing import List import numpy as np import pandas as pd @@ -31,8 +32,7 @@ from bokeh.palettes import Category10 from bokeh.transform import factor_cmap -from backtesting._util import _data_period, _as_list - +from backtesting._util import _data_period, _as_list, _Indicator with open(os.path.join(os.path.dirname(__file__), 'autoscale_cb.js'), encoding='utf-8') as _f: @@ -85,11 +85,73 @@ def lightness(color, lightness=.94): return color.to_rgb() -def plot(*, results, df, indicators, filename='', plot_width=None, +_MAX_CANDLES = 10000 + + +def _maybe_resample_data(resample_rule, df, indicators, equity_data, trades): + if isinstance(resample_rule, str): + freq = resample_rule + else: + if len(df) < _MAX_CANDLES: + return df, indicators, equity_data, trades + + from_index = dict(day=-2, hour=-6, minute=1, second=0, millisecond=0, + microsecond=0, nanosecond=0)[df.index.resolution] + FREQS = ('1T', '5T', '10T', '15T', '30T', '1H', '2H', '4H', '8H', '1D', '1W', '1M') + freq = next((f for f in FREQS[from_index:] + if len(df.resample(f)) <= _MAX_CANDLES), FREQS[-1]) + warnings.warn("Data contains too many candlesticks to plot; downsampling to {!r}. " + "See `Backtest.plot(resample=...)`".format(freq)) + + from .lib import OHLCV_AGG, TRADES_AGG, _EQUITY_AGG + df = df.resample(freq, label='right').agg(OHLCV_AGG).dropna() + + indicators = [_Indicator(i.df.resample(freq, label='right').mean() + .dropna().reindex(df.index).values.T, + **dict(i._opts, name=i.name, + # HACK: override `data` for its index + data=pd.Series(np.nan, index=df.index))) + for i in indicators] + assert not indicators or indicators[0].df.index.equals(df.index) + + equity_data = equity_data.resample(freq, label='right').agg(_EQUITY_AGG).dropna(how='all') + assert equity_data.index.equals(df.index) + + def _weighted_returns(s, trades=trades): + df = trades.loc[s.index] + return ((df['Size'].abs() * df['ReturnPct']) / df['Size'].abs().sum()).sum() + + def _group_trades(column): + def f(s, new_index=df.index.astype(np.int64), bars=trades[column]): + if s.size: + # Via int64 because on pandas recently broken datetime + mean_time = int(bars.loc[s.index].view('i8').mean()) + new_bar_idx = new_index.get_loc(mean_time, method='nearest') + return new_bar_idx + return f + + if len(trades): # Avoid pandas "resampling on Int64 index" error + trades = trades.assign(count=1).resample(freq, on='ExitTime', label='right').agg(dict( + TRADES_AGG, + ReturnPct=_weighted_returns, + count='sum', + EntryBar=_group_trades('EntryTime'), + ExitBar=_group_trades('ExitTime'), + )).dropna() + + return df, indicators, equity_data, trades + + +def plot(*, results: pd.Series, + df: pd.DataFrame, + indicators: List[_Indicator], + filename='', plot_width=None, plot_equity=True, plot_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): + smooth_equity=False, relative_equity=True, + superimpose=True, resample=True, + reverse_indicators=True, + show_legend=True, open_browser=True): """ Like much of GUI code everywhere, this is a mess. """ @@ -101,39 +163,33 @@ def plot(*, results, df, indicators, filename='', plot_width=None, _bokeh_reset(filename) COLORS = [BEAR_COLOR, BULL_COLOR] + BAR_WIDTH = .8 - 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 + assert df.index.equals(results['_equity_curve'].index) + equity_data = results['_equity_curve'].copy(deep=False) + trades = results['_trades'] - # If all Volume is NaN, don't plot volume plot_volume = plot_volume and not df.Volume.isnull().all() + is_datetime_index = df.index.is_all_dates - # OHLC vbar width in msec. - # +1 will work in case of non-datetime index where vbar width should just be =1 - bar_width = 1 + dict(day=86400, - hour=3600, - minute=60, - second=1).get(time_resolution, 0) * 1000 * .85 + from .lib import OHLCV_AGG + # ohlc df may contain many columns. We're only interested in, and pass on to Bokeh, these + df = df[list(OHLCV_AGG.keys())].copy(deep=False) + # Limit data to max_candles if is_datetime_index: - # 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(bar_width / 2, unit='ms') + df, indicators, equity_data, trades = _maybe_resample_data( + resample, df, indicators, equity_data, trades) - if omit_missing: - bar_width = .8 - df = df.reset_index(drop=True) - trade_data = trade_data.reset_index(drop=True) - index = df.index + df.index.name = None # Provides source name @index + df['datetime'] = df.index # Save original, maybe datetime index + df = df.reset_index(drop=True) + equity_data = equity_data.reset_index(drop=True) + index = df.index new_bokeh_figure = partial( _figure, - x_axis_type='datetime' if is_datetime_index and not omit_missing else 'linear', + x_axis_type='linear', plot_width=plot_width, plot_height=400, tools="xpan,xwheel_zoom,box_zoom,undo,redo,reset,crosshair,save", @@ -150,21 +206,22 @@ def plot(*, results, df, indicators, filename='', plot_width=None, 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), + index=trades['ExitBar'], + datetime=trades['ExitTime'], + exit_price=trades['ExitPrice'], + size=trades['Size'], + returns_positive=(trades['ReturnPct'] > 0).astype(int).astype(str), )) inc_cmap = factor_cmap('inc', COLORS, ['0', '1']) - cmap = factor_cmap('returns_pos', COLORS, ['0', '1']) + cmap = factor_cmap('returns_positive', COLORS, ['0', '1']) colors_darker = [lightness(BEAR_COLOR, .35), lightness(BULL_COLOR, .35)] - trades_cmap = factor_cmap('returns_pos', colors_darker, ['0', '1']) + trades_cmap = factor_cmap('returns_positive', colors_darker, ['0', '1']) - if is_datetime_index and omit_missing: + if is_datetime_index: fig_ohlc.xaxis.formatter = FuncTickFormatter( args=dict(axis=fig_ohlc.xaxis[0], formatter=DatetimeTickFormatter(days=['%d %b', '%a %d'], @@ -178,7 +235,7 @@ def plot(*, results, df, indicators, filename='', plot_width=None, ''') NBSP = ' ' * 4 - ohlc_extreme_values = df[['High', 'Low']].copy(False) + ohlc_extreme_values = df[['High', 'Low']].copy(deep=False) ohlc_tooltips = [ ('x, y', NBSP.join(('$index', '$y{0,0.0[0000]}'))), @@ -216,36 +273,34 @@ def set_tooltips(fig, tooltips=(), vline=True, renderers=(), show_arrow=True): 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 + equity = equity_data['Equity'].copy() + dd_end = equity_data['DrawdownDuration'].idxmax() + if np.isnan(dd_end): dd_start = dd_end = equity.index[0] - timedelta = 0 else: - dd_end = argmax - 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 + dd_start = equity[:dd_end].idxmax() + # If DD not extending into the future, get exact point of intersection with equity + if dd_end != equity.index[-1]: + dd_end = np.interp(equity[dd_start], + (equity[dd_end - 1], equity[dd_end]), + (dd_end - 1, dd_end)) if smooth_equity: - 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) + interest_points = pd.Index([ + # Beginning and end + equity.index[0], equity.index[-1], + # Peak equity and peak DD + equity.idxmax(), equity_data['DrawdownPct'].idxmax(), + # Include max dd end points. Otherwise the MaxDD line looks amiss. + dd_start, int(dd_end), min(int(dd_end + 1), equity.size - 1), + ]) + select = pd.Index(trades['ExitBar']) | interest_points + select = select.unique().dropna() + equity = equity.iloc[select].reindex(equity.index) equity.interpolate(inplace=True) + assert equity.index.equals(equity_data.index) + if relative_equity: equity /= equity.iloc[0] @@ -287,14 +342,15 @@ def _plot_equity_section(): color='blue', size=8) if not plot_drawdown: - drawdown = trade_data['Drawdown'] + drawdown = equity_data['DrawdownPct'] argmax = drawdown.idxmax() fig.scatter(argmax, equity[argmax], legend_label='Max Drawdown (-{:.1f}%)'.format(100 * drawdown[argmax]), color='red', size=8) - fig.line([dd_start, dd_end], equity[dd_start], + dd_timedelta_label = df['datetime'].iloc[int(round(dd_end))] - df['datetime'].iloc[dd_start] + fig.line([dd_start, dd_end], equity.iloc[dd_start], line_color='red', line_width=2, - legend_label='Max Dd Dur. ({})'.format(timedelta) + legend_label='Max Dd Dur. ({})'.format(dd_timedelta_label) .replace(' 00:00:00', '') .replace('(0 days ', '(')) @@ -303,7 +359,7 @@ def _plot_equity_section(): def _plot_drawdown_section(): """Drawdown section""" fig = new_indicator_figure(y_axis_label="Drawdown") - drawdown = trade_data['Drawdown'] + drawdown = equity_data['DrawdownPct'] argmax = drawdown.idxmax() source.add(drawdown, 'drawdown') r = fig.line('index', 'drawdown', source=source, line_width=1.3) @@ -319,20 +375,26 @@ def _plot_pl_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 + returns_long = np.where(trades['Size'] > 0, trades['ReturnPct'], np.nan) + returns_short = np.where(trades['Size'] < 0, trades['ReturnPct'], np.nan) + size = trades['Size'].abs() + size = np.interp(size, (size.min(), size.max()), (8, 20)) trade_source.add(returns_long, 'returns_long') trade_source.add(returns_short, 'returns_short') - MARKER_SIZE = 13 + trade_source.add(size, 'marker_size') + if 'count' in trades: + trade_source.add(trades['count'], 'count') r1 = fig.scatter('index', 'returns_long', source=trade_source, fill_color=cmap, - marker='triangle', line_color='black', size=MARKER_SIZE) + 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]) + marker='inverted_triangle', line_color='black', size='marker_size') + tooltips = [("Size", "@size{0,0}")] + if 'count' in trades: + tooltips.append(("Count", "@count{0,0}")) + set_tooltips(fig, tooltips + [("P/L", "@returns_long{+0.[000]%}")], + vline=False, renderers=[r1]) + set_tooltips(fig, tooltips + [("P/L", "@returns_short{+0.[000]%}")], + vline=False, renderers=[r2]) fig.yaxis.formatter = NumeralTickFormatter(format="0.[00]%") return fig @@ -342,15 +404,16 @@ def _plot_volume_section(): fig.xaxis.formatter = fig_ohlc.xaxis[0].formatter fig.xaxis.visible = True fig_ohlc.xaxis.visible = False # Show only Volume's xaxis - r = fig.vbar('index', bar_width, 'Volume', source=source, color=inc_cmap) + r = fig.vbar('index', BAR_WIDTH, 'Volume', source=source, color=inc_cmap) set_tooltips(fig, [('Volume', '@Volume{0.00 a}')], renderers=[r]) fig.yaxis.formatter = NumeralTickFormatter(format="0 a") return fig def _plot_superimposed_ohlc(): """Superimposed, downsampled vbars""" + time_resolution = pd.DatetimeIndex(df['datetime']).resolution resample_rule = (superimpose if isinstance(superimpose, str) else - dict(day='W', + dict(day='M', hour='D', minute='H', second='T', @@ -362,13 +425,13 @@ def _plot_superimposed_ohlc(): 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')) + df2 = (df.assign(_width=1).set_index('datetime') + .resample(resample_rule, label='left') + .agg(dict(OHLCV_AGG, _width='count'))) # Check if resampling was downsampling; error on upsampling - orig_freq = _data_period(orig_df) - resample_freq = _data_period(df2) + orig_freq = _data_period(df['datetime']) + resample_freq = _data_period(df2.index) if resample_freq < orig_freq: raise ValueError('Invalid value for `superimpose`: Upsampling not supported.') if resample_freq == orig_freq: @@ -376,48 +439,33 @@ def _plot_superimposed_ohlc(): 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 = df2['_width'].cumsum().shift(1).fillna(0) + df2.index += df2['_width'] / 2 - .5 + df2['_width'] -= .1 # Candles don't touch + + df2['inc'] = (df2.Close >= df2.Open).astype(int).astype(str) df2.index.name = None source2 = ColumnDataSource(df2) fig_ohlc.segment('index', 'High', 'index', 'Low', source=source2, color='#bbbbbb') colors_lighter = [lightness(BEAR_COLOR, .92), lightness(BULL_COLOR, .92)] - fig_ohlc.vbar('index', width2, 'Open', 'Close', source=source2, line_color=None, + fig_ohlc.vbar('index', '_width', 'Open', 'Close', source=source2, line_color=None, fill_color=factor_cmap('inc', colors_lighter, ['0', '1'])) def _plot_ohlc(): """Main OHLC bars""" fig_ohlc.segment('index', 'High', 'index', 'Low', source=source, color="black") - r = fig_ohlc.vbar('index', bar_width, 'Open', 'Close', source=source, + r = fig_ohlc.vbar('index', BAR_WIDTH, 'Open', 'Close', source=source, line_color="black", fill_color=inc_cmap) return r def _plot_ohlc_trades(): """Trade 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') + trade_source.add(trades[['EntryBar', 'ExitBar']].values.tolist(), 'position_lines_xs') + trade_source.add(trades[['EntryPrice', 'ExitPrice']].values.tolist(), 'position_lines_ys') fig_ohlc.multi_line(xs='position_lines_xs', ys='position_lines_ys', source=trade_source, line_color=trades_cmap, - legend_label='Trades ({})'.format(len(trade_data)), + legend_label='Trades ({})'.format(len(trades)), line_width=8, line_alpha=1, line_dash='dotted') def _plot_indicators(): @@ -441,6 +489,7 @@ def __eq__(self, other): return self is other ohlc_colors = colorgen() + indicator_figs = [] for i, value in enumerate(indicators): value = np.atleast_2d(value) @@ -456,7 +505,7 @@ def __eq__(self, other): fig = fig_ohlc else: fig = new_indicator_figure() - figs_below_ohlc.append(fig) + indicator_figs.append(fig) tooltips = [] colors = value._opts['color'] colors = colors and cycle(_as_list(colors)) or ( @@ -476,7 +525,7 @@ def __eq__(self, other): 'index', source_name, source=source, legend_label=legend_label, color=color, line_color='black', fill_alpha=.8, - marker='circle', radius=bar_width / 2 * 1.5) + marker='circle', radius=BAR_WIDTH / 2 * 1.5) else: fig.line( 'index', source_name, source=source, @@ -487,7 +536,7 @@ def __eq__(self, other): r = fig.scatter( 'index', source_name, source=source, legend_label=LegendStr(legend_label), color=color, - marker='circle', radius=bar_width / 2 * .9) + marker='circle', radius=BAR_WIDTH / 2 * .9) else: r = fig.line( 'index', source_name, source=source, @@ -509,6 +558,7 @@ def __eq__(self, other): # have the legend only contain text without the glyph if len(value) == 1: fig.legend.glyph_width = 0 + return indicator_figs # Construct figure ... @@ -530,7 +580,10 @@ def __eq__(self, other): ohlc_bars = _plot_ohlc() _plot_ohlc_trades() - _plot_indicators() + indicator_figs = _plot_indicators() + if reverse_indicators: + indicator_figs = indicator_figs[::-1] + figs_below_ohlc.extend(indicator_figs) set_tooltips(fig_ohlc, ohlc_tooltips, vline=True, renderers=[ohlc_bars]) diff --git a/backtesting/_util.py b/backtesting/_util.py index d460a04e..65e75a94 100644 --- a/backtesting/_util.py +++ b/backtesting/_util.py @@ -1,3 +1,4 @@ +import warnings from typing import Sequence from numbers import Number @@ -33,9 +34,9 @@ def _as_list(value): return [value] -def _data_period(df): +def _data_period(index): """Return data index period as pd.Timedelta""" - values = df.index[-100:].to_series() + values = pd.Series(index[-100:]) return values.diff().median() @@ -44,12 +45,10 @@ 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): + def __new__(cls, array, *, name=None, **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): @@ -70,7 +69,20 @@ def __float__(self): return super().__float__() def to_series(self): - return pd.Series(self, index=self._opts['data'].index, name=self.name) + warnings.warn("`.to_series()` is deprecated. For pd.Series conversion, use accessor `.s`") + return self.s + + @property + def s(self) -> pd.Series: + values = np.atleast_2d(self) + return pd.Series(values[0], index=self._opts['data'].index, name=self.name) + + @property + def df(self) -> pd.DataFrame: + values = np.atleast_2d(np.asarray(self)) + df = pd.DataFrame(values.T, index=self._opts['data'].index, + columns=[self.name] * len(values)) + return df class _Indicator(_Array): @@ -84,15 +96,13 @@ class _Data: and the returned "series" are _not_ `pd.Series` but `np.ndarray` for performance reasons. """ - def __init__(self, df): + def __init__(self, df: pd.DataFrame): + self.__df = 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() + self.__arrays = None + self._update() def __getitem__(self, item): return self.__get_array(item) @@ -107,9 +117,27 @@ def _set_length(self, i): self.__i = i self.__cache.clear() + def _update(self): + self.__arrays = {col: _Array(arr, data=self) + for col, arr in self.__df.items()} + # Leave index as Series because pd.Timestamp nicer API to work with + self.__arrays['__index'] = self.__df.index.copy() + + def __repr__(self): + i = min(self.__i, len(self.__df) - 1) + return ''.format(i, self.__arrays['__index'][i], + ', '.join('{}={}'.format(k, v) + for k, v in self.__df.iloc[i].items())) + def __len__(self): return self.__i + @property + def df(self) -> pd.DataFrame: + return (self.__df.iloc[:self.__i] + if self.__i < len(self.__df) + else self.__df) + @property def pip(self): if self.__pip is None: @@ -117,7 +145,7 @@ def pip(self): for s in self.__arrays['Close'].astype(str)]) return self.__pip - def __get_array(self, key): + def __get_array(self, key) -> _Array: arr = self.__cache.get(key) if arr is None: arr = self.__cache[key] = self.__arrays[key][:self.__i] @@ -144,8 +172,8 @@ def Volume(self): return self.__get_array('Volume') @property - def index(self): - return self.__get_array('__index') + def index(self) -> pd.DatetimeIndex: + return self.__get_array('__index') # type: ignore # Make pickling in Backtest.optimize() work with our catch-all __getattr__ def __getstate__(self): diff --git a/backtesting/autoscale_cb.js b/backtesting/autoscale_cb.js index 09f75b16..da888ecf 100644 --- a/backtesting/autoscale_cb.js +++ b/backtesting/autoscale_cb.js @@ -1,40 +1,13 @@ -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 (!window._bt_scale_range) { + window._bt_scale_range = function (range, min, max, pad) { + "use strict"; if (min !== Infinity && max !== -Infinity) { - const pad = (max - min) * .03; + pad = pad ? (max - min) * .03 : 0; range.start = min - pad; range.end = max + pad; - } + } else console.error('backtesting: scale range error:', min, max, range); }; +} clearTimeout(window._bt_autoscale_timeout); @@ -45,20 +18,18 @@ window._bt_autoscale_timeout = setTimeout(function () { * @variable ohlc_range `fig_ohlc.y_range`. * @variable volume_range `fig_volume.y_range`. */ + "use strict"; + + let i = Math.max(Math.floor(cb_obj.start), 0), + j = Math.min(Math.ceil(cb_obj.end), source.data['ohlc_high'].length); - let 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); + let max = Math.max.apply(null, source.data['ohlc_high'].slice(i, j)), + min = Math.min.apply(null, source.data['ohlc_low'].slice(i, j)); + _bt_scale_range(ohlc_range, min, max, true); - _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 + if (volume_range) { + max = Math.max.apply(null, source.data['Volume'].slice(i, j)); + _bt_scale_range(volume_range, 0, max * 1.03, false); + } }, 50); diff --git a/backtesting/backtesting.py b/backtesting/backtesting.py index 85a4aa76..aad39547 100644 --- a/backtesting/backtesting.py +++ b/backtesting/backtesting.py @@ -1,19 +1,26 @@ """ -Core backtesting data structures. -Objects from this module can be imported from the top-level +Core framework data structures. +Objects from this module can also be imported from the top-level module directly, e.g. from backtesting import Backtest, Strategy + +.. warning:: v0.2.0 breaking changes + Version 0.2.0 introduced some **breaking API changes**. For quick ways to + migrate existing 0.1.x code, see the implementing + [pull request](https://github.com/kernc/backtesting.py/pull/47/). """ import multiprocessing as mp import os +import sys import warnings from abc import abstractmethod, ABCMeta from concurrent.futures import ProcessPoolExecutor, as_completed +from copy import copy from functools import partial from itertools import repeat, product, chain from numbers import Number -from typing import Callable, Union, Sequence, Tuple, Type +from typing import Callable, Union, List, Optional, Sequence, Tuple, Type import numpy as np import pandas as pd @@ -28,17 +35,14 @@ def _tqdm(seq, **_): from ._plotting import plot from ._util import _as_str, _Indicator, _Data, _data_period, try_ - __pdoc__ = { 'Strategy.__init__': False, - 'Orders.__init__': False, + 'Order.__init__': False, 'Position.__init__': False, + 'Trade.__init__': False, } -_MARKET_PRICE = 'market' - - class Strategy(metaclass=ABCMeta): """ A trading strategy base class. Extend this class and @@ -151,7 +155,7 @@ def init(): value = _Indicator(value, name=name, plot=plot, overlay=overlay, color=color, scatter=scatter, - # lib.resample_apply() uses this: + # _Indicator.s Series accessor uses this: data=self.data) self._indicators.append(value) return value @@ -187,35 +191,35 @@ def next(self): 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. + def buy(self, *, + size: float = 1 - sys.float_info.epsilon, + limit: float = None, + stop: float = None, + sl: float = None, + tp: float = None): """ - self._broker.buy(price and float(price), - sl and float(sl), - tp and float(tp)) + Place a new short order. For explanation of parameters, see `Order` and its properties. - def sell(self, price=None, *, sl=None, tp=None): + See also `Strategy.sell()`. + """ + assert 0 < size < 1 or round(size) == size, \ + "size must be a positive fraction of equity, or a positive whole number of units" + return self._broker.new_order(size, limit, stop, sl, tp) + + def sell(self, *, + size: float = 1 - sys.float_info.epsilon, + limit: float = None, + stop: float = None, + sl: float = None, + tp: float = 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. + Place a new short order. For explanation of parameters, see `Order` and its properties. + + See also `Strategy.buy()`. """ - self._broker.sell(price and float(price), - sl and float(sl), - tp and float(tp)) + assert 0 < size < 1 or round(size) == size, \ + "size must be a positive fraction of equity, or a positive whole number of units" + return self._broker.new_order(-size, limit, stop, sl, tp) @property def equity(self): @@ -245,8 +249,9 @@ def data(self) -> _Data: the last array value (e.g. `data.Close[-1]`) is always the _most recent_ value. * If you need data arrays (e.g. `data.Close`) to be indexed - Pandas series, you can call their `.to_series()` method - (e.g. `data.Close.to_series()`). + **Pandas series**, you can call their `.s` accessor + (e.g. `data.Close.s`). If you need the whole of data + as a **DataFrame**, use `.df` accessor (i.e. `data.df`). """ return self._data @@ -256,196 +261,395 @@ def position(self) -> 'Position': return self._broker.position @property - def orders(self) -> 'Orders': - """Instance of `backtesting.backtesting.Orders`.""" - return self._broker.orders + def orders(self) -> '_Orders[Order]': + """List of orders (see `Order`) waiting for execution.""" + return _Orders(self._broker.orders) + + @property + def trades(self) -> 'Tuple[Trade]': + """List of active trades (see `Trade`).""" + return tuple(self._broker.trades) + + @property + def closed_trades(self) -> 'Tuple[Trade]': + """List of settled trades (see `Trade`).""" + return tuple(self._broker.closed_trades) -class Orders: +class _Orders(tuple): + """ + TODO: remove this class. Only for deprecation. """ - Orders waiting for execution, available as - `backtesting.backtesting.Strategy.orders` within + def cancel(self): + """Cancel all non-contingent (i.e. SL/TP) orders.""" + for order in self: + if not order.is_contingent: + order.cancel() + + def __getattr__(self, item): + # TODO: Warn on deprecations from the previous version. Remove in the next. + removed_attrs = ('entry', 'set_entry', 'is_long', 'is_short', + 'sl', 'tp', 'set_sl', 'set_tp') + if item in removed_attrs: + raise AttributeError('Strategy.orders.{} were removed in Backtesting 0.2.0. ' + 'Use `Order` API instead. See docs.' + .format('/.'.join(removed_attrs))) + raise AttributeError("'tuple' object has no attribute {!r}".format(item)) + + +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. - 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`. + if self.position: + ... # we have a position, either long or short """ - def __init__(self, broker): - self._broker = broker - self._entry = self._sl = self._tp = self._close = self._is_long = None + def __init__(self, broker: '_Broker'): + self.__broker = broker - 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 + def __bool__(self): + return self.size != 0 + + @property + def size(self): + """Position size in units of asset. Negative if position is short.""" + return sum(trade.size for trade in self.__broker.trades) + + @property + def pl(self): + """Profit (positive) or loss (negative) of the current position in cash units.""" + return sum(trade.pl for trade in self.__broker.trades) + + @property + def pl_pct(self): + """Profit (positive) or loss (negative) of the current position in percent.""" + weights = np.abs([trade.size for trade in self.__broker.trades]) + weights = weights / weights.sum() + pl_pcts = np.array([trade.pl_pct for trade in self.__broker.trades]) + return (pl_pcts * weights).sum() @property def is_long(self): - """True if the waiting entry order is long.""" - return self._is_long + """True if the position is long (position size is positive).""" + return self.size > 0 @property def is_short(self): - """True if the waiting entry order is short.""" - return not self._is_long + """True if the position is short (position size is negative).""" + return self.size < 0 + + def close(self, portion: float = 1.): + """ + Close portion of position by closing `portion` of each active trade. See `Trade.close`. + """ + for trade in self.__broker.trades: + trade.close(portion) + + def __repr__(self): + return ''.format(self.size, len(self.__broker.trades)) + + +class _OutOfMoneyError(Exception): + pass + + +class Order: + """ + Place new orders through `Strategy.buy()` and `Strategy.sell()`. + Query existing orders through `Strategy.orders`. + + When an order is executed or [filled], it results in a `Trade`. + + If you wish to modify aspects of a placed but not yet filled order, + cancel it and place a new one instead. + + All placed orders are [Good 'Til Canceled]. + + [filled]: https://www.investopedia.com/terms/f/fill.asp + [Good 'Til Canceled]: https://www.investopedia.com/terms/g/gtc.asp + """ + def __init__(self, broker: '_Broker', + size: float, + limit_price: float = None, + stop_price: float = None, + sl_price: float = None, + tp_price: float = None, + parent_trade: 'Trade' = None): + self.__broker = broker + self.__size = size + self.__limit_price = limit_price + self.__stop_price = stop_price + self.__sl_price = sl_price + self.__tp_price = tp_price + self.__parent_trade = parent_trade + + def _replace(self, **kwargs): + for k, v in kwargs.items(): + setattr(self, '_{}__{}'.format(self.__class__.__qualname__, k), v) + return self + + def __repr__(self): + return ''.format(', '.join('{}={}'.format(param, round(value, 5)) + for param, value in ( + ('size', self.__size), + ('limit', self.__limit_price), + ('stop', self.__stop_price), + ('sl', self.__sl_price), + ('tp', self.__tp_price), + ('contingent', self.is_contingent), + ) if value is not None)) + + def cancel(self): + """Cancel the order.""" + self.__broker.orders.remove(self) + trade = self.__parent_trade + if trade: + if self is trade._sl_order: + trade._replace(sl_order=None) + elif self is trade._tp_order: + trade._replace(tp_order=None) + else: + assert False + + # Fields getters @property - def entry(self): - """Price at which to enter the position if hit.""" - return self._entry + def size(self): + """ + Order size (negative for short orders). + + If size is a value between 0 and 1, it is interpreted as a fraction of current + available liquidity (cash plus `Position.pl` minus used margin). + A value greater than or equal to 1 indicates an absolute number of units. + """ + return self.__size + + @property + def limit(self) -> float: + """ + Order limit price for [limit orders], or None for [market orders], + which are filled at next available price. + + [limit orders]: https://www.investopedia.com/terms/l/limitorder.asp + [market orders]: https://www.investopedia.com/terms/m/marketorder.asp + """ + return self.__limit_price + + @property + def stop(self): + """ + Order stop price for [stop-limit/stop-market][_] order, + otherwise None if no stop was set, or the stop price has already been hit. + + [_]: https://www.investopedia.com/terms/s/stoporder.asp + """ + return self.__stop_price @property def sl(self): - """Stop-loss (stop-limit) price at which to exit the position if hit.""" - return self._sl + """ + A stop-loss price at which, if set, a new contingent stop-market order + will be placed upon the `Trade` following this order's execution. + See also `Trade.sl`. + """ + return self.__sl_price @property def tp(self): - """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) + """ + A take-profit price at which, if set, a new contingent limit order + will be placed upon the `Trade` following this order's execution. + See also `Trade.tp`. + """ + return self.__tp_price - def cancel(self): - """Cancel all implied orders.""" - self._entry = self._sl = self._tp = self._close = self._is_long = None + @property + def parent_trade(self): + return self.__parent_trade - def __bool__(self): - return bool(self._entry or self._sl or self._tp or self._close) + __pdoc__['Order.parent_trade'] = False - 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__ + # Extra properties + @property + def is_long(self): + """True if the order is long (order size is positive).""" + return self.__size > 0 -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. + @property + def is_short(self): + """True if the order is short (order size is negative).""" + return self.__size < 0 - if self.position: - ... # we have a position, either long or short + @property + def is_contingent(self): + """ + True for [contingent] orders, i.e. [OCO] stop-loss and take-profit bracket orders + placed upon an active trade. Remaining contingent orders are canceled when + their parent `Trade` is closed. + + You can modify contingent orders through `Trade.sl` and `Trade.tp`. + + [contingent]: https://www.investopedia.com/terms/c/contingentorder.asp + [OCO]: https://www.investopedia.com/terms/o/oco.asp + """ + return bool(self.__parent_trade) + + +class Trade: """ - def __init__(self, broker): - self._broker = broker + When an `Order` is filled, it results in an active `Trade`. + Find active trades in `Strategy.trades` and closed, settled trades in `Strategy.closed_trades`. + """ + def __init__(self, broker: '_Broker', size: int, entry_price: float, entry_bar): + self.__broker = broker + self.__size = size + self.__entry_price = entry_price + self.__entry_bar = entry_bar + self.__exit_price = None + self.__exit_bar = None + self.__sl_order = None # type: Optional[Order] + self.__tp_order = None # type: Optional[Order] - def __bool__(self): - return self.size != 0 + def __repr__(self): + return ''.format( + self.__size, self.__entry_bar, self.__exit_bar or '', + self.__entry_price, self.__exit_price or '', self.pl) + + def _replace(self, **kwargs): + for k, v in kwargs.items(): + setattr(self, '_{}__{}'.format(self.__class__.__qualname__, k), v) + return self + + def _copy(self, **kwargs): + return copy(self)._replace(**kwargs) + + def close(self, portion: float = 1.): + """Place new `Order` to close `portion` of the trade at next market price.""" + assert 0 < portion <= 1, "portion must be a fraction between 0 and 1" + # TODO: check this insert + self.__broker.orders.insert(0, Order(self.__broker, -round(self.size * portion), + parent_trade=self)) + + # Fields getters @property def size(self): - """Position size in units of asset. Negative if position is short.""" - return self._broker._position + """Trade size (volume; negative for short trades).""" + return self.__size @property - def open_price(self): - """Price at which the position was opened.""" - return self._broker._position_open_price + def entry_price(self) -> float: + """Trade entry price.""" + return self.__entry_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 + def exit_price(self) -> float: + """Trade exit price (or None if the trade is still active).""" + return self.__exit_price @property - def pl(self): - """Profit (positive) or loss (negative) of current position.""" - return self._pl(self._broker._data.Close[-1]) + def entry_bar(self) -> int: + """Candlestick bar index of when the trade was entered.""" + return self.__entry_bar @property - def pl_pct(self): + def exit_bar(self) -> int: """ - Profit (positive) or loss (negative) of current position, - in percent of position open price. + Candlestick bar index of when the trade was exited + (or None if the trade is still active). """ - return self.pl / (self.open_price * abs(self.size) or 1) + return self.__exit_bar + + @property + def _sl_order(self): + return self.__sl_order + + @property + def _tp_order(self): + return self.__tp_order + + # Extra properties @property def is_long(self): - """True if the position is long (position size is positive).""" + """True if the trade is long (trade 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 + """True if the trade is short (trade size is negative).""" + return not self.is_long + + @property + def pl(self): + """Trade profit (positive) or loss (negative) in cash units.""" + price = self.__exit_price or self.__broker.last_price + return self.__size * (price - self.__entry_price) - def close(self): - """Close the position at current market price.""" - self._broker.close() + @property + def pl_pct(self): + """Trade profit (positive) or loss (negative) in percent.""" + price = self.__exit_price or self.__broker.last_price + return np.sign(self.__size) * (price / self.__entry_price - 1) - def __repr__(self): - return '' % self.size + @property + def value(self): + """Trade total value in cash (volume × price).""" + price = self.__exit_price or self.__broker.last_price + return abs(self.__size) * price + # SL/TP management API -class _OutOfMoneyError(Exception): - pass + @property + def sl(self): + """ + Stop-loss price at which to close the trade. + + This variable is writable. By assigning it a new price value, + you create or modify the existing SL order. + By assigning it `None`, you cancel it. + """ + return self.__sl_order and self.__sl_order.stop + + @property + def tp(self): + """ + Take-profit price at which to close the trade. + + This property is writable. By assigning it a new price value, + you create or modify the existing TP order. + By assigning it `None`, you cancel it. + """ + return self.__tp_order and self.__tp_order.limit + + @sl.setter + def sl(self, price): + self.__set_contingent('sl', price) + + @tp.setter + def tp(self, price): + self.__set_contingent('tp', price) + + def __set_contingent(self, type, price): + assert type in ('sl', 'tp') + assert price is None or 0 < price < np.inf + attr = '_{}__{}_order'.format(self.__class__.__qualname__, type) + order = getattr(self, attr) # type: Order + if order: + order.cancel() + if price: + kwargs = dict(stop=price) if type == 'sl' else dict(limit=price) + order = self.__broker.new_order(-self.size, trade=self, **kwargs) + setattr(self, attr, order) 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): + def __init__(self, *, data, cash, commission, margin, + trade_on_close, hedging, exclusive_orders, index): 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) @@ -454,128 +658,264 @@ def __init__(self, *, data, cash, commission, margin, trade_on_close, length): 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._hedging = hedging + self._exclusive_orders = exclusive_orders + + self._equity = np.tile(np.nan, len(index)) + self.orders = [] # type: List[Order] + self.trades = [] # type: List[Trade] self.position = Position(self) - self.orders = Orders(self) + self.closed_trades = [] # type: List[Trade] 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), "For long orders should be: SL ({}) < BUY PRICE ({}) < TP ({})".format(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), "For short orders should be: TP ({}) < BUY PRICE ({}) < SL ({})".format(tp, price or self.last_close, sl) # noqa: E501 - self.orders._update(price, sl, tp, is_long=False) + return ''.format( + self._cash, self.position.pl, len(self.trades)) + + def new_order(self, + size: float = None, + limit: float = None, + stop: float = None, + sl: float = None, + tp: float = None, + *, + trade: Trade = None): + """ + Argument size indicates whether the order is long or short + """ + size = float(size) + stop = stop and float(stop) + limit = limit and float(limit) + sl = sl and float(sl) + tp = tp and float(tp) + + is_long = size > 0 + + if is_long: + # TODO: check stop as well + assert (sl or -np.inf) <= (limit or self.last_price) <= (tp or np.inf), \ + "Long orders require: SL ({}) < LIMIT ({}) < TP ({})".format( + sl, limit or self.last_price, tp) + else: + assert (tp or -np.inf) <= (limit or self.last_price) <= (sl or np.inf), \ + "Short orders require: TP ({}) < LIMIT ({}) < SL ({})".format( + tp, limit or self.last_price, sl) + + order = Order(self, size, limit, stop, sl, tp, trade) + # Put the new order in the order queue, + # inserting SL/TP/trade-closing orders in-front + if trade: + self.orders.insert(0, order) + else: + # If exclusive orders (each new order auto-closes previous orders/position), + # cancel all non-contingent orders and close all open trades beforehand + if self._exclusive_orders: + for o in self.orders: + if not o.is_contingent: + o.cancel() + for t in self.trades: + t.close() - def close(self): - self.orders.cancel() - self.orders._close = True + self.orders.append(order) - 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 + return order @property - def last_close(self): + def last_price(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 - self._position_open_price = 0 - self._position_open_i = None - @property def equity(self): - return self._cash + self.position.pl + return self._cash + sum(trade.pl for trade in self.trades) + + @property + def margin_available(self): + # From https://github.com/QuantConnect/Lean/pull/3768 + margin_used = sum(trade.value / self._leverage for trade in self.trades) + return max(0, self.equity - margin_used) def next(self): - 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() + i = self._i = len(self._data) - 1 + self._process_orders() # Log account equity for the equity curve equity = self.equity - self.log.equity[i] = equity + self._equity[i] = equity - # Hovever, if negative, set all to 0 and stop the simulation - if equity < 0: - self._close_position() + # If equity is negative, set all to 0 and stop the simulation + if equity <= 0: + assert self.margin_available <= 0 + for trade in self.trades: + self._close_trade(trade, self._data.Close[-1], i) self._cash = 0 - self.log.equity[i:] = 0 + self._equity[i:] = 0 raise _OutOfMoneyError + def _process_orders(self): + data = self._data + open, high, low = data.Open[-1], data.High[-1], data.Low[-1] + prev_close = data.Close[-2] + + # Process orders + for order in list(self.orders): # type: Order + + # Related SL/TP order was already removed + if order not in self.orders: + continue + + # Check if stop condition was hit + stop_price = order.stop + if stop_price: + is_stop_hit = ((high > stop_price) if order.is_long else (low < stop_price)) + if not is_stop_hit: + continue + + # > When the stop price is reached, a stop order becomes a market/limit order. + # https://www.sec.gov/fast-answers/answersstopordhtm.html + order._replace(stop_price=None) + + # Determine purchase price. + # Check if limit order can be filled. + if order.limit: + is_limit_hit = low < order.limit if order.is_long else high > order.limit + # When stop and limit are hit within the same bar, we pessimistically + # assume limit was hit before the stop (i.e. "before it counts") + is_limit_hit_before_stop = (is_limit_hit and + (order.limit < (stop_price or -np.inf) + if order.is_long + else order.limit > (stop_price or np.inf))) + if not is_limit_hit or is_limit_hit_before_stop: + continue + + # stop_price, if set, was hit within this bar + price = (min(open, order.limit, stop_price or np.inf) + if order.is_long else + max(open, order.limit, stop_price or -np.inf)) + else: + # Market-if-touched / market order + price = prev_close if self._trade_on_close else open + price = (max(price, stop_price or -np.inf) + if order.is_long else + min(price, stop_price or np.inf)) + + # Determine entry/exit bar index + is_market_order = not order.limit and not stop_price + time_index = (self._i - 1) if is_market_order and self._trade_on_close else self._i + + # If order is a SL/TP order, it should close an existing trade it was contingent upon + if order.parent_trade: + trade = order.parent_trade + # If this trade isn't already closed (e.g. on multiple `trade.close(.5)` calls) + if trade in self.trades: + self._reduce_trade(trade, price, order.size, time_index) + assert order.size != -trade.size or trade not in self.trades + if order in (trade._sl_order, + trade._tp_order): + assert order.size == -trade.size + assert order not in self.orders # Removed when trade was closed + else: + # It's a trade.close() order, now done + assert abs(trade.size) >= abs(order.size) >= 1 + self.orders.remove(order) + continue + + # Else this is a stand-alone trade + + # Adjust price to include commission (or bid-ask spread). + # In long positions, the adjusted price is a fraction higher, and vice versa. + adjusted_price = price * (1 + (np.sign(order.size) * self._commission)) + + # If order size was specified proportionally, + # precompute true size in units, accounting for margin and spread/commissions + size = order.size + if -1 < size < 1: + size = np.sign(size) * int((self.margin_available * self._leverage * abs(size)) + // adjusted_price) + # Not enough cash/margin even for a single unit + if not size: + self.orders.remove(order) + continue + assert size == round(size) + need_size = int(size) + + if not self._hedging: + # Fill position by FIFO closing/reducing existing opposite-facing trades. + # Existing trades are closed at unadjusted price, because the adjustment + # was already made when buying. + for trade in list(self.trades): + if trade.is_long == order.is_long: + continue + assert np.sign(trade.size) + np.sign(order.size) == 0 + + # Order size greater than this opposite-directed existing trade, + # so it will be closed completely + if abs(need_size) >= abs(trade.size): + self._close_trade(trade, price, time_index) + need_size += trade.size + else: + # The existing trade is larger than the new order, + # so it will only be closed partially + self._reduce_trade(trade, price, need_size, time_index) + need_size = 0 + break + + # If we don't have enough liquidity to cover for the order, cancel it + if abs(need_size) * adjusted_price > self.margin_available * self._leverage: + self.orders.remove(order) + continue + + # Open a new trade + if need_size: + self._open_trade(adjusted_price, need_size, order.sl, order.tp, time_index) + + # Order processed + self.orders.remove(order) + + def _reduce_trade(self, trade: Trade, price: float, size: float, time_index: int): + assert np.sign(trade.size) != np.sign(size) + assert abs(trade.size) >= abs(size) + + size_left = trade.size + size + if not size_left: + close_trade = trade + else: + # Reduce existing trade ... + trade._replace(size=size_left) + if trade._sl_order: + trade._sl_order._replace(size=-trade.size) + if trade._tp_order: + trade._tp_order._replace(size=-trade.size) + + # ... by closing a reduced copy of it + close_trade = trade._copy(size=-size, sl_order=None, tp_order=None) + self.trades.append(close_trade) + + self._close_trade(close_trade, price, time_index) + + def _close_trade(self, trade: Trade, price: float, time_index: int): + self.trades.remove(trade) + if trade._sl_order: + self.orders.remove(trade._sl_order) + if trade._tp_order: + self.orders.remove(trade._tp_order) + + self.closed_trades.append(trade._replace(exit_price=price, exit_bar=time_index)) + self._cash += trade.pl + + def _open_trade(self, price: float, size: int, sl: float, tp: float, time_index: int): + trade = Trade(self, size, price, time_index) + self.trades.append(trade) + # Create SL/TP (bracket) orders. + # Make sure SL order is created first so it gets adversarially processed before TP order + # in case of an ambiguous tie (both hit within a single bar). + # Note, sl/tp orders are inserted at the front of the list, thus order reversed. + if tp: + trade.tp = tp + if sl: + trade.sl = sl + class Backtest: """ @@ -594,7 +934,9 @@ def __init__(self, cash: float = 10000, commission: float = .0, margin: float = 1., - trade_on_close=False + trade_on_close=False, + hedging=False, + exclusive_orders=False ): """ Initialize a backtest. Requires data and a strategy to test. @@ -625,9 +967,15 @@ def __init__(self, To run the backtest using e.g. 50:1 leverge that your broker allows, set margin to `0.02` (1 / leverage). - If `trade_on_close` is `True`, market orders will be executed + If `trade_on_close` is `True`, market orders will be filled with respect to the current bar's closing price instead of the next bar's open. + + If `hedging` is `True`, allow trades in both directions simultaneously. + If `False`, the opposite-facing orders first close existing trades in + a [FIFO] manner. + + [FIFO]: https://www.investopedia.com/terms/n/nfa-compliance-rule-2-43b.asp """ if not (isinstance(strategy, type) and issubclass(strategy, Strategy)): @@ -668,13 +1016,15 @@ def __init__(self, stacklevel=2) data = data.sort_index() if not data.index.is_all_dates: - warnings.warn('Data index is not datetime. Assuming simple periods.', + warnings.warn('Data index is not datetime. Assuming simple periods, ' + 'but `pd.DateTimeIndex` is advised.', 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) + trade_on_close=trade_on_close, hedging=hedging, + exclusive_orders=exclusive_orders, index=data.index, ) self._strategy = strategy self._results = None @@ -684,12 +1034,44 @@ def run(self, **kwargs) -> pd.Series: Run the backtest. Returns `pd.Series` with results and statistics. Keyword arguments are interpreted as strategy parameters. + + >>> Backtest(GOOG, SmaCross).run() + Start 2004-08-19 00:00:00 + End 2013-03-01 00:00:00 + Duration 3116 days 00:00:00 + Exposure Time [%] 93.9944 + Equity Final [$] 51959.9 + Equity Peak [$] 75787.4 + Return [%] 419.599 + Buy & Hold Return [%] 703.458 + Max. Drawdown [%] -47.9801 + Avg. Drawdown [%] -5.92585 + Max. Drawdown Duration 584 days 00:00:00 + Avg. Drawdown Duration 41 days 00:00:00 + # Trades 65 + Win Rate [%] 46.1538 + Best Trade [%] 53.596 + Worst Trade [%] -18.3989 + Avg. Trade [%] 2.35371 + Max. Trade Duration 183 days 00:00:00 + Avg. Trade Duration 46 days 00:00:00 + Profit Factor 2.08802 + Expectancy [%] 8.79171 + SQN 0.916893 + Sharpe Ratio 0.179141 + Sortino Ratio 0.55887 + Calmar Ratio 0.049056 + _strategy SmaCross + _equity_curve Eq... + _trades Size EntryB... + dtype: object """ - data = _Data(self._data) + data = _Data(self._data.copy(deep=False)) broker = self._broker(data=data) # type: _Broker strategy = self._strategy(broker, data, kwargs) # type: Strategy strategy.init() + data._update() # Strategy.init might have changed/added to data.df # Indicators used in Strategy.next() indicator_attrs = {attr: indicator @@ -721,6 +1103,10 @@ def run(self, **kwargs) -> pd.Series: # Next tick, a moment before bar close strategy.next() + # Set data back to full length + # for future `indicator._opts['data'].index` calls to work + data._set_length(len(self._data)) + self._results = self._compute_stats(broker, strategy) return self._results @@ -810,7 +1196,7 @@ def __getattr__(self, item): raise ValueError('No admissible parameter combinations to test') if len(param_combos) > 300: - warnings.warn('Searching best of {} configurations.'.format(len(param_combos)), + warnings.warn('Searching for best of {} configurations.'.format(len(param_combos)), stacklevel=2) heatmap = pd.Series(np.nan, @@ -896,38 +1282,52 @@ def _compute_drawdown_duration_peaks(dd: pd.Series): def _compute_stats(self, broker: _Broker, strategy: Strategy) -> pd.Series: data = self._data + index = data.index - 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) + equity = pd.Series(broker._equity).bfill().fillna(broker._cash).values + dd = 1 - equity / np.maximum.accumulate(equity) dd_dur, dd_peaks = self._compute_drawdown_duration_peaks(pd.Series(dd, index=data.index)) - df['Drawdown Duration'] = dd_dur.values - dd_dur = df['Drawdown Duration'] - df.index = data.index - - def _round_timedelta(value, _period=_data_period(df)): + equity_df = pd.DataFrame({ + 'Equity': equity, + 'DrawdownPct': dd, + 'DrawdownDuration': dd_dur}, + index=index) + + trades = broker.closed_trades + trades_df = pd.DataFrame({ + 'Size': [t.size for t in trades], + 'EntryBar': [t.entry_bar for t in trades], + 'ExitBar': [t.exit_bar for t in trades], + 'EntryPrice': [t.entry_price for t in trades], + 'ExitPrice': [t.exit_price for t in trades], + 'PnL': [t.pl for t in trades], + 'ReturnPct': [t.pl_pct for t in trades], + }) + trades_df['EntryTime'] = trades_df['EntryBar'].apply(index.__getitem__) + trades_df['ExitTime'] = trades_df['ExitBar'].apply(index.__getitem__) + trades_df['Duration'] = trades_df['ExitTime'] - trades_df['EntryTime'] + + pl = trades_df['PnL'] + returns = trades_df['ReturnPct'] + durations = trades_df['Duration'] + + def _round_timedelta(value, _period=_data_period(index)): if not isinstance(value, pd.Timedelta): return value resolution = getattr(_period, 'resolution_string', None) or _period.resolution return value.ceil(resolution) s = pd.Series(dtype=object) - s.loc['Start'] = df.index[0] - s.loc['End'] = df.index[-1] + s.loc['Start'] = index[0] + s.loc['End'] = index[-1] s.loc['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.loc['Exposure [%]'] = np.nan_to_num(durations.sum() / (s.loc['Duration'] or np.nan) * 100) + + have_position = np.repeat(0, len(index)) + for t in trades: + have_position[t.entry_bar:t.exit_bar + 1] = 1 + + s.loc['Exposure Time [%]'] = have_position.mean() * 100 # In "n bars" time, not index time s.loc['Equity Final [$]'] = equity[-1] s.loc['Equity Peak [$]'] = equity.max() s.loc['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100 @@ -937,32 +1337,41 @@ def _round_timedelta(value, _period=_data_period(df)): s.loc['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100 s.loc['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max()) s.loc['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean()) - s.loc['# Trades'] = n_trades = pl.count() + s.loc['# Trades'] = n_trades = len(trades) s.loc['Win Rate [%]'] = win_rate = np.nan if not n_trades else (pl > 0).sum() / n_trades * 100 # noqa: E501 s.loc['Best Trade [%]'] = returns.max() * 100 s.loc['Worst Trade [%]'] = returns.min() * 100 - mean_return = returns.mean() + mean_return = np.exp(np.log(1 + returns).sum() / (len(returns) or np.nan)) - 1 s.loc['Avg. Trade [%]'] = mean_return * 100 s.loc['Max. Trade Duration'] = _round_timedelta(durations.max()) s.loc['Avg. Trade Duration'] = _round_timedelta(durations.mean()) s.loc['Profit Factor'] = returns[returns > 0].sum() / (abs(returns[returns < 0].sum()) or np.nan) # noqa: E501 s.loc['Expectancy [%]'] = ((returns[returns > 0].mean() * win_rate - returns[returns < 0].mean() * (100 - win_rate))) - pl = pl.dropna() - s.loc['SQN'] = np.sqrt(n_trades) * pl.mean() / pl.std() + s.loc['SQN'] = np.sqrt(n_trades) * pl.mean() / (pl.std() or np.nan) s.loc['Sharpe Ratio'] = mean_return / (returns.std() or np.nan) s.loc['Sortino Ratio'] = mean_return / (returns[returns < 0].std() or np.nan) s.loc['Calmar Ratio'] = mean_return / ((-max_dd / 100) or np.nan) s.loc['_strategy'] = strategy - s._trade_data = df # Private API + s.loc['_equity_curve'] = equity_df + s.loc['_trades'] = trades_df + + s = Backtest._Stats(s) return s + class _Stats(pd.Series): + def __repr__(self): + # Prevent expansion due to _equity and _trades dfs + with pd.option_context('max_colwidth', 20): + return super().__repr__() + def plot(self, *, results: pd.Series = None, filename=None, plot_width=None, 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, + superimpose: Union[bool, str] = True, + resample=True, reverse_indicators=False, show_legend=True, open_browser=True): """ Plot the progression of the last backtest run. @@ -994,25 +1403,40 @@ def plot(self, *, results: pd.Series = None, filename=None, plot_width=None, a separate drawdown graph section. If `smooth_equity` is `True`, the equity graph will be - interpolated between points of cash-only positions, + interpolated between fixed points at trade closing times, unaffected by any interim asset volatility. If `relative_equity` is `True`, scale and label equity graph axis with return percent, not absolute cash-equivalent values. - If `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'`. + If `superimpose` is `True`, superimpose larger-timeframe candlesticks + over the original candlestick chart. Default downsampling rule is: + monthly for daily data, daily for hourly data, hourly for minute data, + and minute for (sub-)second data. + `superimpose` can also be a valid [Pandas offset string], + such as `'5T'` or `'5min'`, in which case this frequency will be + used to superimpose. Note, this only works for data with a datetime index. + If `resample` is `True`, the OHLC data is resampled in a way that + makes the upper number of candles for Bokeh to plot limited to 10_000. + This may, in situations of overabundant data, + improve plot's interactive performance and avoid browser's + `Javascript Error: Maximum call stack size exceeded` or similar. + Equity & dropdown curves and individual trades data is, + likewise, [reasonably _aggregated_][TRADES_AGG]. + `resample` can also be a [Pandas offset string], + such as `'5T'` or `'5min'`, in which case this frequency will be + used to resample, overriding above numeric limitation. + Note, all this only works for data with a datetime index. + + If `reverse_indicators` is `True`, the indicators below the OHLC chart + are plotted in reverse order of declaration. + [Pandas offset string]: \ - http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases + https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects + + [TRADES_AGG]: lib.html#backtesting.lib.TRADES_AGG If `show_legend` is `True`, the resulting plot graphs will contain labeled legends. @@ -1034,10 +1458,11 @@ def plot(self, *, results: pd.Series = None, filename=None, plot_width=None, 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, + resample=resample, + reverse_indicators=reverse_indicators, show_legend=show_legend, open_browser=open_browser) diff --git a/backtesting/lib.py b/backtesting/lib.py index ff2d91e2..13aad2b7 100644 --- a/backtesting/lib.py +++ b/backtesting/lib.py @@ -22,7 +22,7 @@ from .backtesting import Strategy from ._plotting import plot_heatmaps as _plot_heatmaps -from ._util import _Array, _Indicator, _as_str +from ._util import _Array, _as_str __pdoc__ = {} @@ -40,6 +40,31 @@ df.resample('4H', label='right').agg(OHLCV_AGG) """ +TRADES_AGG = OrderedDict(( + ('Size', 'sum'), + ('EntryBar', 'first'), + ('ExitBar', 'last'), + ('EntryPrice', 'mean'), + ('ExitPrice', 'mean'), + ('PnL', 'sum'), + ('ReturnPct', 'mean'), + ('EntryTime', 'first'), + ('ExitTime', 'last'), + ('Duration', 'sum'), +)) +"""Dictionary of rules for aggregating resampled trades data, +e.g. + + stats['_trades'].resample('1D', on='ExitTime', + label='right').agg(TRADES_AGG) +""" + +_EQUITY_AGG = { + 'Equity': 'mean', + 'DrawdownPct': 'max', + 'DrawdownDuration': 'max', +} + def barssince(condition: Sequence[bool], default=np.inf) -> int: """ @@ -150,7 +175,7 @@ def resample_apply(rule: str, a time frame to resample `series` to. [Pandas offset string]: \ - http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases +http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases `func` is the indicator function to apply on the resampled series. @@ -177,13 +202,13 @@ def init(self): self.sma = resample_apply( 'D', SMA, self.data.Close, 10, plot=False) - This short snippet is roughly equivalent to: + The above short snippet is roughly equivalent to: class System(Strategy): def init(self): # Strategy exposes `self.data` as raw NumPy arrays. # Let's convert closing prices back to pandas Series. - close = self.data.Close.to_series() + close = self.data.Close.s # Resample to daily resolution. Aggregate groups # using their last value (i.e. closing price at the end @@ -213,9 +238,8 @@ def func(x, *_, **__): assert isinstance(series, _Array), \ 'resample_apply() takes either a `pd.Series`, `pd.DataFrame`, ' \ 'or a `Strategy.data.*` array' - series = series.to_series() + series = series.s - series = series.copy() # XXX: pandas 1.0.1 bug https://github.com/pandas-dev/pandas/issues/31710 # noqa: E501 resampled = series.resample(rule, label='right').agg(agg).dropna() resampled.name = _as_str(series) + '[' + rule + ']' @@ -280,36 +304,51 @@ def init(self): __pdoc__['SignalStrategy.__init__'] = False - def set_signal(self, entry: Sequence[int], exit: Optional[Sequence[bool]] = None, + def set_signal(self, entry_size: Sequence[float], + exit_portion: Sequence[float] = 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`. + Set entry/exit signal vectors (arrays). + + A long entry signal is considered present wherever `entry_size` + is greater than zero, and a short signal wherever `entry_size` + is less than zero, following `backtesting.backtesting.Order.size` semantics. + + If `exit_portion` is provided, a nonzero value closes portion the position + (see `backtesting.backtesting.Trade.close()`) in the respective direction + (positive values close long trades, negative short). If `plot` is `True`, the signal entry/exit indicators are plotted when `backtesting.backtesting.Backtest.plot` is called. """ - self.__entry_signal = _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) + self.__entry_signal = self.I( + lambda: pd.Series(entry_size, dtype=float).replace(0, np.nan), + name='entry size', plot=plot, overlay=False, scatter=True, color='black') + + if exit_portion is not None: + self.__exit_signal = self.I( + lambda: pd.Series(exit_portion, dtype=float).replace(0, np.nan), + name='exit portion', plot=plot, overlay=False, scatter=True, color='black') def next(self): super().next() - if self.position and self.__exit_signal[-1]: - self.position.close() + exit_portion = self.__exit_signal[-1] + if exit_portion > 0: + for trade in self.trades: + if trade.is_long: + trade.close(exit_portion) + elif exit_portion < 0: + for trade in self.trades: + if trade.is_short: + trade.close(-exit_portion) - signal = self.__entry_signal[-1] - - if signal > 0: - self.buy() - elif signal < 0: - self.sell() + entry_size = self.__entry_signal[-1] + if entry_size > 0: + self.buy(size=entry_size) + elif entry_size < 0: + self.sell(size=-entry_size) class TrailingStrategy(Strategy): @@ -352,12 +391,13 @@ def set_trailing_sl(self, n_atr: float = 6): 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) + for trade in self.trades: + if trade.is_long: + trade.sl = max(trade.sl or -np.inf, + 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) + trade.sl = min(trade.sl or np.inf, + self.data.Close[-1] + self.__atr[-1] * self.__n_atr) # NOTE: Don't put anything below this __all__ list diff --git a/backtesting/test/_test.py b/backtesting/test/_test.py index 3cb8d794..1bd17bc1 100644 --- a/backtesting/test/_test.py +++ b/backtesting/test/_test.py @@ -60,8 +60,10 @@ def init(self): def next(self): if crossover(self.sma1, self.sma2): + self.position.close() self.buy() elif crossover(self.sma2, self.sma1): + self.position.close() self.sell() @@ -154,47 +156,55 @@ def next(self, FIVE_DAYS=pd.Timedelta('3 days')): 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 + self.orders.cancel() # cancels only non-contingent 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 + + n_orders = len(self.orders) + self.sell(size=.21, limit=price, stop=price, sl=sl, tp=tp) + assert len(self.orders) == n_orders + 1 + + order = self.orders[-1] + assert order.limit == price + assert order.stop == price + assert order.size == -.21 + assert order.sl == sl + assert order.tp == tp + assert not order.is_contingent elif self.position: - assert not self.orders.entry assert not self.position.is_long - assert not not self.position.is_short - assert self.position.open_price + assert self.position.is_short 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: + + trade = self.trades[0] + if self.data.index[-1] - self.data.index[trade.entry_bar] > FIVE_DAYS: + assert not trade.is_long + assert trade.is_short + assert trade.size < 0 + assert trade.entry_bar > 0 + assert trade.exit_bar is None + assert trade.entry_price > 0 + assert trade.exit_price is None + assert trade.pl / 1 + assert trade.pl_pct / 1 + assert trade.value > 0 + assert trade.sl + assert trade.tp + # Close multiple times + self.position.close(.5) + self.position.close(.5) + self.position.close(.5) + self.position.close() self.position.close() bt = Backtest(GOOG, Assertive) @@ -241,36 +251,42 @@ def test_compute_stats(self): # NOTE: These values are also used on the website! '# Trades': 65, 'Avg. Drawdown Duration': pd.Timedelta('41 days 00:00:00'), - 'Avg. Drawdown [%]': -6.087158560194047, + 'Avg. Drawdown [%]': -5.925851581948801, 'Avg. Trade Duration': pd.Timedelta('46 days 00:00:00'), - 'Avg. Trade [%]': 3.0404430275631444, - 'Best Trade [%]': 54.05363186670138, + 'Avg. Trade [%]': 2.3537113951143773, + 'Best Trade [%]': 53.59595229490424, 'Buy & Hold Return [%]': 703.4582419772772, - 'Calmar Ratio': 0.0631443286380662, + 'Calmar Ratio': 0.049055964204885415, '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, + 'Equity Final [$]': 51959.94999999997, + 'Equity Peak [$]': 75787.44, + 'Expectancy [%]': 8.791710931051735, + 'Exposure Time [%]': 93.99441340782123, 'Max. Drawdown Duration': pd.Timedelta('584 days 00:00:00'), - 'Max. Drawdown [%]': -48.15069053929621, + 'Max. Drawdown [%]': -47.98012705007589, 'Max. Trade Duration': pd.Timedelta('183 days 00:00:00'), - 'Profit Factor': 2.060450149412349, - 'Return [%]': 426.2429346696951, - 'SQN': 0.91553210127173, - 'Sharpe Ratio': 0.23169782960690408, - 'Sortino Ratio': 0.7096713270577958, + 'Profit Factor': 2.0880175388920286, + 'Return [%]': 419.59949999999964, + 'SQN': 0.916892986080858, + 'Sharpe Ratio': 0.17914126763602636, + 'Sortino Ratio': 0.5588698138148217, 'Start': pd.Timestamp('2004-08-19 00:00:00'), 'Win Rate [%]': 46.15384615384615, - 'Worst Trade [%]': -18.85561318387153, + 'Worst Trade [%]': -18.39887353835481, }).sort_index() ) - self.assertTrue( - stats._trade_data.columns.equals( - pd.Index(['Equity', 'Exit Entry', 'Exit Position', - 'Entry Price', 'Exit Price', 'P/L', 'Returns', - 'Drawdown', 'Drawdown Duration']))) + + self.assertSequenceEqual( + sorted(stats['_equity_curve'].columns), + sorted(['Equity', 'DrawdownPct', 'DrawdownDuration'])) + + self.assertEqual(len(stats['_trades']), 65) + + self.assertSequenceEqual( + sorted(stats['_trades'].columns), + sorted(['Size', 'EntryBar', 'ExitBar', 'EntryPrice', 'ExitPrice', + 'PnL', 'ReturnPct', 'EntryTime', 'ExitTime', 'Duration'])) def test_compute_stats_bordercase(self): @@ -308,11 +324,21 @@ def next(self): stats = Backtest(GOOG.iloc[:100], strategy).run() self.assertFalse(np.isnan(stats['Equity Final [$]'])) - self.assertFalse(stats._trade_data['Equity'].isnull().any()) + self.assertFalse(stats['_equity_curve']['Equity'].isnull().any()) self.assertEqual(stats['_strategy'].__class__, strategy) class TestStrategy(TestCase): + def _Backtest(self, strategy_coroutine, **kwargs): + class S(Strategy): + def init(self): + self.step = strategy_coroutine(self) + + def next(self): + try_(self.step.__next__, None, StopIteration) + + return Backtest(SHORT_DATA, S, **kwargs) + def test_position(self): def coroutine(self): yield self.buy() @@ -323,8 +349,6 @@ def coroutine(self): assert self.position.size > 0 assert self.position.pl assert self.position.pl_pct - assert self.position.open_price > 0 - assert self.position.open_time yield self.position.close() @@ -334,17 +358,31 @@ def coroutine(self): assert not self.position.size assert not self.position.pl assert not self.position.pl_pct - assert not self.position.open_price - assert not self.position.open_time - class S(Strategy): - def init(self): - self.step = coroutine(self) + self._Backtest(coroutine).run() - def next(self): - try_(self.step.__next__, None, StopIteration) + def test_broker_hedging(self): + def coroutine(self): + yield self.buy(size=2) + + assert len(self.trades) == 1 + yield self.sell(size=1) + + assert len(self.trades) == 2 + + self._Backtest(coroutine, hedging=True).run() + + def test_broker_exclusive_orders(self): + def coroutine(self): + yield self.buy(size=2) - Backtest(SHORT_DATA, S).run() + assert len(self.trades) == 1 + yield self.sell(size=3) + + assert len(self.trades) == 1 + assert self.trades[0].size == -3 + + self._Backtest(coroutine, exclusive_orders=True).run() class TestOptimize(TestCase): @@ -363,8 +401,8 @@ def test_optimize(self): 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()) + self.assertDictEqual(res.filter(regex='^[^_]').fillna(-1).to_dict(), + res2.filter(regex='^[^_]').fillna(-1).to_dict()) res3, heatmap = bt.optimize(**OPT_PARAMS, return_heatmap=True, constraint=lambda d: d.slow > 2 * d.fast) @@ -374,7 +412,6 @@ def test_optimize(self): with _tempfile() as f: bt.plot(filename=f, open_browser=False) - @unittest.skipIf(not hasattr(_Array, 'df'), 'v2 branch is not merged') def test_nowrite_df(self): # Test we don't write into passed data df by default. # Important for copy-on-write in Backtest.optimize() @@ -441,9 +478,10 @@ def test_params(self): plot_pl=False, plot_drawdown=True, superimpose=False, - omit_missing=False, + resample='1W', smooth_equity=False, relative_equity=False, + reverse_indicators=True, show_legend=False).items(): with self.subTest(param=p[0]): bt.plot(**dict([p]), filename=f, open_browser=False) @@ -491,6 +529,50 @@ def ok(x): # Give browser time to open before tempfile is removed time.sleep(5) + def test_wellknown(self): + class S(Strategy): + def init(self): + pass + + def next(self): + date = self.data.index[-1] + if date == pd.Timestamp('Thu 19 Oct 2006'): + self.buy(stop=484, limit=466, size=100) + elif date == pd.Timestamp('Thu 30 Oct 2007'): + self.position.close() + elif date == pd.Timestamp('Tue 11 Nov 2008'): + self.sell(stop=self.data.Low, + limit=324.90, # High from 14 Nov + size=200) + + bt = Backtest(GOOG, S, margin=.1) + stats = bt.run() + trades = stats['_trades'] + + self.assertAlmostEqual(stats['Equity Peak [$]'], 46961) + self.assertEqual(stats['Equity Final [$]'], 0) + self.assertEqual(len(trades), 2) + assert trades[['EntryTime', 'ExitTime']].equals( + pd.DataFrame(dict(EntryTime=pd.to_datetime(['2006-11-01', '2008-11-14']), + ExitTime=pd.to_datetime(['2007-10-31', '2009-09-21'])))) + assert trades['PnL'].round().equals(pd.Series([23469., -34420.])) + + with _tempfile() as f: + bt.plot(filename=f, plot_drawdown=True, smooth_equity=False) + # Give browser time to open before tempfile is removed + time.sleep(1) + + def test_resample(self): + bt = Backtest(GOOG, SmaCross) + bt.run() + import backtesting._plotting + with _tempfile() as f,\ + patch.object(backtesting._plotting, '_MAX_CANDLES', 10),\ + self.assertWarns(UserWarning): + bt.plot(filename=f, resample=True) + # Give browser time to open before tempfile is removed + time.sleep(1) + def test_indicator_color(self): class S(Strategy): def init(self): @@ -584,12 +666,12 @@ def test_plot_heatmaps(self): def test_SignalStrategy(self): class S(SignalStrategy): def init(self): - sma = self.data.Close.to_series().rolling(10).mean() + sma = self.data.Close.s.rolling(10).mean() self.set_signal(self.data.Close > sma, self.data.Close < sma) stats = Backtest(GOOG, S).run() - self.assertGreater(stats['# Trades'], 1000) + self.assertEqual(stats['# Trades'], 1180) def test_TrailingStrategy(self): class S(TrailingStrategy): @@ -597,7 +679,7 @@ def init(self): super().init() self.set_atr_periods(40) self.set_trailing_sl(3) - self.sma = self.I(lambda: self.data.Close.to_series().rolling(10).mean()) + self.sma = self.I(lambda: self.data.Close.s.rolling(10).mean()) def next(self): super().next() @@ -605,7 +687,7 @@ def next(self): self.buy() stats = Backtest(GOOG, S).run() - self.assertGreater(stats['# Trades'], 6) + self.assertEqual(stats['# Trades'], 50) class TestUtil(TestCase): @@ -627,6 +709,22 @@ class Class: for s in ('Open', 'High', 'Low', 'Close', 'Volume'): self.assertEqual(_as_str(_Array([1], name=s)), s[0]) + def test_pandas_accessors(self): + class S(Strategy): + def init(self): + close, index = self.data.Close, self.data.index + assert close.s.equals(pd.Series(close, index=index)) + assert self.data.df['Close'].equals(pd.Series(close, index=index)) + self.data.df['new_key'] = 2 * close + + def next(self): + close, index = self.data.Close, self.data.index + assert close.s.equals(pd.Series(close, index=index)) + assert self.data.df['Close'].equals(pd.Series(close, index=index)) + assert self.data.df['new_key'].equals(pd.Series(self.data.new_key, index=index)) + + Backtest(GOOG.iloc[:20], S).run() + @unittest.skipUnless( os.path.isdir(os.path.join(os.path.dirname(__file__), diff --git a/doc/examples/Multiple Time Frames.ipynb b/doc/examples/Multiple Time Frames.ipynb index 071bdc32..a0f40c84 100644 --- a/doc/examples/Multiple Time Frames.ipynb +++ b/doc/examples/Multiple Time Frames.ipynb @@ -8,10 +8,10 @@ "============\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", + "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", + "[basic usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html).\n", "\n", "We will put to the test this long-only, supposed\n", "[400%-a-year trading strategy](http://jbmarwood.com/stock-trading-strategy-300/),\n", @@ -19,19 +19,16 @@ "[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", + "In practice, one should use functions from an indicator library, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n", "[Tulipy](https://tulipindicators.org),\n", - "PyAlgoTrade, ..." + "but among us, let's introduce the two indicators we'll be using." ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -75,20 +72,12 @@ "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", "
\n", - " \n", + " \n", " Loading BokehJS ...\n", "
" ] @@ -107,7 +96,7 @@ "\n", " var force = true;\n", "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", @@ -184,6 +173,7 @@ " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", @@ -274,76 +264,103 @@ "\n", " function run_callbacks() {\n", " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", - " function load_libs(js_urls, callback) {\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n", + "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", - " var 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", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " if (url in hashes) {\n", + " element.crossOrigin = \"anonymous\";\n", + " element.integrity = \"sha384-\" + hashes[url];\n", + " }\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", " }\n", - " };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", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", " }\n", "\n", - " 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 js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n", + " var css_urls = [];\n", + " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - " \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", " ];\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", + " if (root.Bokeh !== undefined || force === true) {\n", + " \n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", @@ -358,17 +375,17 @@ " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " console.debug(\"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", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "\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));" + "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) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -440,28 +457,31 @@ "Start 2004-08-19 00:00:00\n", "End 2013-03-01 00:00:00\n", "Duration 3116 days 00:00:00\n", - "Exposure [%] 2.59949\n", - "Equity Final [$] 10032.7\n", - "Equity Peak [$] 11012.5\n", - "Return [%] 0.32725\n", - "Buy & Hold Return [%] 703.458\n", - "Max. Drawdown [%] -10.1606\n", - "Avg. Drawdown [%] -4.78424\n", - "Max. Drawdown Duration 163 days 00:00:00\n", - "Avg. Drawdown Duration 84 days 00:00:00\n", + "Exposure Time [%] 2.79\n", + "Equity Final [$] 10017.44\n", + "Equity Peak [$] 10978.38\n", + "Return [%] 0.17\n", + "Buy & Hold Return [%] 703.46\n", + "Max. Drawdown [%] -10.01\n", + "Avg. Drawdown [%] -9.34\n", + "Max. Drawdown Duration 2653 days 00:00:00\n", + "Avg. Drawdown Duration 1410 days 00:00:00\n", "# Trades 4\n", - "Win Rate [%] 25\n", - "Best Trade [%] 9.51454\n", - "Worst Trade [%] -4.46166\n", - "Avg. Trade [%] 0.210738\n", + "Win Rate [%] 25.00\n", + "Best Trade [%] 9.69\n", + "Worst Trade [%] -4.46\n", + "Avg. Trade [%] 0.08\n", "Max. Trade Duration 35 days 00:00:00\n", "Avg. Trade Duration 21 days 00:00:00\n", - "Expectancy [%] 4.54653\n", - "SQN 0.0261005\n", - "Sharpe Ratio 0.0333333\n", - "Sortino Ratio 0.140578\n", - "Calmar Ratio 0.0207407\n", + "Profit Factor 1.11\n", + "Expectancy [%] 4.61\n", + "SQN 0.01\n", + "Sharpe Ratio 0.01\n", + "Sortino Ratio 0.06\n", + "Calmar Ratio 0.01\n", "_strategy System\n", + "_equity_curve Equ...\n", + "_trades Size EntryBa...\n", "dtype: object" ] }, @@ -491,42 +511,60 @@ "scrolled": false }, "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5e5f09a4f9c5400da75aab3cf7d6a683", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 250 ms, sys: 61.7 ms, total: 312 ms\n", - "Wall time: 4.09 s\n" + "\r", + "CPU times: user 187 ms, sys: 36.2 ms, total: 223 ms\n", + "Wall time: 4.05 s\n" ] }, { "data": { "text/plain": [ - "Start 2004-08-19 00:00:00\n", - "End 2013-03-01 00:00:00\n", - "Duration 3116 days 00:00:00\n", - "Exposure [%] 20.5071\n", - "Equity Final [$] 23090.8\n", - "Equity Peak [$] 23686.3\n", - "Return [%] 130.908\n", - "Buy & Hold Return [%] 703.458\n", - "Max. Drawdown [%] -19.2731\n", - "Avg. Drawdown [%] -3.6514\n", - "Max. Drawdown Duration 778 days 00:00:00\n", - "Avg. Drawdown Duration 95 days 00:00:00\n", - "# Trades 22\n", - "Win Rate [%] 63.6364\n", - "Best Trade [%] 25.0905\n", - "Worst Trade [%] -6.21969\n", - "Avg. Trade [%] 3.83033\n", - "Max. Trade Duration 63 days 00:00:00\n", - "Avg. Trade Duration 30 days 00:00:00\n", - "Expectancy [%] 5.88815\n", - "SQN 2.39317\n", - "Sharpe Ratio 0.484418\n", - "Sortino Ratio 1.69146\n", - "Calmar Ratio 0.19874\n", - "_strategy System(d_rsi=30,level=60,w_rsi=10)\n", + "Start 2004-08-19 00:00:00\n", + "End 2013-03-01 00:00:00\n", + "Duration 3116 days 00:00:00\n", + "Exposure Time [%] 21.55\n", + "Equity Final [$] 22822.75\n", + "Equity Peak [$] 23395.59\n", + "Return [%] 128.23\n", + "Buy & Hold Return [%] 703.46\n", + "Max. Drawdown [%] -18.92\n", + "Avg. Drawdown [%] -3.80\n", + "Max. Drawdown Duration 778 days 00:00:00\n", + "Avg. Drawdown Duration 97 days 00:00:00\n", + "# Trades 22\n", + "Win Rate [%] 63.64\n", + "Best Trade [%] 25.03\n", + "Worst Trade [%] -6.30\n", + "Avg. Trade [%] 3.60\n", + "Max. Trade Duration 63 days 00:00:00\n", + "Avg. Trade Duration 30 days 00:00:00\n", + "Profit Factor 4.75\n", + "Expectancy [%] 5.94\n", + "SQN 2.40\n", + "Sharpe Ratio 0.45\n", + "Sortino Ratio 1.58\n", + "Calmar Ratio 0.19\n", + "_strategy System(d_rsi=30,...\n", + "_equity_curve Equ...\n", + "_trades Size EntryB...\n", "dtype: object" ] }, @@ -559,7 +597,7 @@ "\n", "\n", "\n", - "
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\",\"dtype\":\"float64\",\"shape\":[2148]},\"SMA(C,20)_0\":{\"__ndarray__\":\"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LJq1ytRiFRAsmrXK1GIVEAe9bIJLEBVQB71sgksQFVAHvWyCSxAVUAe9bIJLEBVQB71sgksQFVAy8ongLP/U0DLyieAs/9TQMvKJ4Cz/1NAy8ongLP/U0DLebyaC0FUQMt5vJoLQVRAy3m8mgtBVEDLebyaC0FUQMt5vJoLQVRAhjJWQmKBVECGMlZCYoFUQIYyVkJigVRAhjJWQmKBVECGMlZCYoFUQDt6EmeBkVRAO3oSZ4GRVEA7ehJngZFUQDt6EmeBkVRAO3oSZ4GRVEBjATPv7VVRQGMBM+/tVVFAYwEz7+1VUUBjATPv7VVRQGMBM+/tVVFAPjN4OdC6UUA+M3g50LpRQD4zeDnQulFAPjN4OdC6UUA+M3g50LpRQF4M2/pWLFFAXgzb+lYsUUBeDNv6VixRQF4M2/pWLFFAXgzb+lYsUUDQP2ZlmnVOQNA/ZmWadU5A0D9mZZp1TkDQP2ZlmnVOQNA/ZmWadU5A7xGsEulQT0DvEawS6VBPQO8RrBLpUE9A7xGsEulQT0DvEawS6VBPQNJuK4GvO1BA0m4rga87UEDSbiuBrztQQNJuK4GvO1BAGoKBRPFcUUAagoFE8VxRQBqCgUTxXFFAGoKBRPFcUUAagoFE8VxRQDuSEcxveVFAO5IRzG95UUA7khHMb3lRQDuSEcxveVFAO5IRzG95UUBeRAPygNNSQF5EA/KA01JAXkQD8oDTUkBeRAPygNNSQF5EA/KA01JAwFkL3tfpUkDAWQve1+lSQMBZC97X6VJAwFkL3tfpUkDAWQve1+lSQCdndC94B1JAJ2d0L3gHUkAnZ3QveAdSQCdndC94B1JAJ2d0L3gHUkC6KC38UdpMQLooLfxR2kxAuigt/FHaTEC6KC38UdpMQLooLfxR2kxA2SqsA9hEUkDZKqwD2ERSQNkqrAPYRFJA2SqsA9hEUkDZKqwD2ERSQBJ3ULhMO1NAEndQuEw7U0ASd1C4TDtTQBJ3ULhMO1NAEndQuEw7U0AZsfuQV3xUQBmx+5BXfFRAGbH7kFd8VEAZsfuQV3xUQBmx+5BXfFRAMv9JDy17VEAy/0kPLXtUQDL/SQ8te1RAMv9JDy17VEAy/0kPLXtUQLd5nPPX0FRAt3mc89fQVEC3eZzz19BUQLd5nPPX0FRASl1b902dVUBKXVv3TZ1VQEpdW/dNnVVASl1b902dVUBKXVv3TZ1VQAfiODgWAFRAB+I4OBYAVEAH4jg4FgBUQAfiODgWAFRAB+I4OBYAVEBTEPQ4YMxSQFMQ9DhgzFJAUxD0OGDMUkBTEPQ4YMxSQFMQ9DhgzFJAuwWeDtGqU0C7BZ4O0apTQLsFng7RqlNAuwWeDtGqU0C7BZ4O0apTQDQf1gObslNANB/WA5uyU0A0H9YDm7JTQDQf1gObslNAAlYbkT9xUUACVhuRP3FRQAJWG5E/cVFAAlYbkT9xUUBpCXPcMJhTQGkJc9wwmFNAaQlz3DCYU0BpCXPcMJhTQGkJc9wwmFNA0OzH9zSdU0DQ7Mf3NJ1TQNDsx/c0nVNA0OzH9zSdU0BWuoVJPgdLQFa6hUk+B0tAVrqFST4HS0BWuoVJPgdLQFa6hUk+B0tApNkFgR9wTkCk2QWBH3BOQKTZBYEfcE5ApNkFgR9wTkCk2QWBH3BOQHWWeX6sXUhAdZZ5fqxdSEB1lnl+rF1IQHWWeX6sXUhAdZZ5fqxdSEBf56sfho9GQF/nqx+Gj0ZAX+erH4aPRkBf56sfho9GQF/nqx+Gj0ZAaTLKRlZER0BpMspGVkRHQGkyykZWREdAaTLKRlZER0Agh22y2kpIQCCHbbLaSkhAIIdtstpKSEAgh22y2kpIQCCHbbLaSkhA0zZZwMxjSEDTNlnAzGNIQNM2WcDMY0hA0zZZwMxjSEDTNlnAzGNIQIFGUMuQCERAgUZQy5AIRECBRlDLkAhEQIFGUMuQCERAgUZQy5AIREBEyz48fFxEQETLPjx8XERARMs+PHxcREBEyz48fFxEQETLPjx8XERA8Enl6c7sR0DwSeXpzuxHQPBJ5enO7EdA8Enl6c7sR0DwSeXpzuxHQDX8Ug8/yEpANfxSDz/ISkA1/FIPP8hKQDX8Ug8/yEpANfxSDz/ISkCS9tnjAoNMQJL22eMCg0xAkvbZ4wKDTECS9tnjAoNMQJL22eMCg0xAkvbZ4wKDTECS9tnjAoNMQJL22eMCg0xAkvbZ4wKDTEDpyYyBantPQOnJjIFqe09A6cmMgWp7T0DpyYyBantPQOnJjIFqe09Anf2NUYS0TECd/Y1RhLRMQJ39jVGEtExAnf2NUYS0TECd/Y1RhLRMQOkg9U7YoklA6SD1TtiiSUDpIPVO2KJJQOkg9U7YoklA6SD1TtiiSUAiN7UdW0xHQCI3tR1bTEdAIje1HVtMR0AiN7UdW0xHQCI3tR1bTEdA2gABHuLURkDaAAEe4tRGQNoAAR7i1EZA2gABHuLURkDaAAEe4tRGQHGF4e8nY0hAcYXh7ydjSEBxheHvJ2NIQHGF4e8nY0hAXRLG544hSEBdEsbnjiFIQF0SxueOIUhAXRLG544hSEBdEsbnjiFIQHAtT753M0lAcC1PvnczSUBwLU++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+36HRUBBdYryjUZJQEF1ivKNRklAQXWK8o1GSUBBdYryjUZJQEF1ivKNRklAA1COacSmSEADUI5pxKZIQANQjmnEpkhAA1COacSmSEADUI5pxKZIQANQjmnEpkhAA1COacSmSEADUI5pxKZIQANQjmnEpkhAwdGfYpbtSUDB0Z9ilu1JQMHRn2KW7UlAwdGfYpbtSUDB0Z9ilu1JQIKLbVI8pExAgottUjykTECCi21SPKRMQIKLbVI8pExAgottUjykTEA4Q2I1a+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/XTlADBJ93L9dOUAMEn3cv105QAwSfdy/XTlADBJ93L9dOUDU46oHEWs6QNTjqgcRazpA1OOqBxFrOkDU46oHEWs6QNTjqgcRazpAdShzd2FPQkB1KHN3YU9CQHUoc3dhT0JAdShzd2FPQkB1KHN3YU9CQOBz8EZ/OUFA4HPwRn85QUDgc/BGfzlBQOBz8EZ/OUFA4HPwRn85QUABAey+qXNKQAEB7L6pc0pAAQHsvqlzSkABAey+qXNKQAEB7L6pc0pAFT18MsDbSkAVPXwywNtKQBU9fDLA20pAFT18MsDbSkAVPXwywNtKQKIQ/5u46U1AohD/m7jpTUCiEP+buOlNQKIQ/5u46U1AohD/m7jpTUCcj6Cigf9MQJyPoKKB/0xAnI+gooH/TECcj6Cigf9MQJyPoKKB/0xAIlnNE9mUTUAiWc0T2ZRNQCJZzRPZlE1AIlnNE9mUTUAiWc0T2ZRNQGq7veZ8jUlAaru95nyNSUBqu73mfI1JQGq7veZ8jUlA2XGv8y8tTUDZca/zLy1NQNlxr/MvLU1A2XGv8y8tTUDZca/zLy1NQK8j0Y61J0tAryPRjrUnS0CvI9GOtSdLQK8j0Y61J0tAryPRjrUnS0DOgU+mtZBLQM6BT6a1kEtAzoFPprWQS0DOgU+mtZBLQM6BT6a1kEtATctVamTWSEBNy1VqZNZIQE3LVWpk1khATctVamTWSEBNy1VqZNZIQGbpbiYbAUdAZuluJhsBR0Bm6W4mGwFHQGbpbiYbAUdAZuluJhsBR0Bm6W4mGwFHQGbpbiYbAUdAZuluJhsBR0Bm6W4mGwFHQKKrHUfhrEdAoqsdR+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+8zw3TECADz7zPDdMQIAPPvM8N0xAgA8+8zw3TECADz7zPDdMQIAPPvM8N0xAA7RTZXS7TkADtFNldLtOQAO0U2V0u05AA7RTZXS7TkADtFNldLtOQJ6w3pOaQU5AnrDek5pBTkCesN6TmkFOQJ6w3pOaQU5AnrDek5pBTkALlRNsyMFOQAuVE2zIwU5AC5UTbMjBTkALlRNsyMFOQAuVE2zIwU5A9gtEN4AtUED2C0Q3gC1QQPYLRDeALVBA9gtEN4AtUED2C0Q3gC1QQL400Thn/UxAvjTROGf9TEC+NNE4Z/1MQL400Thn/UxAvjTROGf9TEBKPImn43ZNQEo8iafjdk1ASjyJp+N2TUBKPImn43ZNQO52fdhlNlBA7nZ92GU2UEDudn3YZTZQQO52fdhlNlBA7nZ92GU2UECmD0UdPI9RQKYPRR08j1FApg9FHTyPUUCmD0UdPI9RQKYPRR08j1FAz09VEUJST0DPT1URQlJPQM9PVRFCUk9Az09VEUJST0DPT1URQlJPQMGh9Rk+cE5AwaH1GT5wTkDBofUZPnBOQMGh9Rk+cE5AwaH1GT5wTkD/g73KjxVPQP+DvcqPFU9A/4O9yo8VT0D/g73KjxVPQP+DvcqPFU9A/4O9yo8VT0D/g73KjxVPQP+DvcqPFU9A/4O9yo8VT0Bd1AKfabFMQF3UAp9psUxAXdQCn2mxTEBd1AKfabFMQF3UAp9psUxAt8VbuTviTkC3xVu5O+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/BzZ2VEBifv8HNnZUQGJ+/wc2dlRAYn7/BzZ2VEBifv8HNnZUQGJ+/wc2dlRAYn7/BzZ2VEBifv8HNnZUQGJ+/wc2dlRAYn7/BzZ2VED8fD9de0pSQPx8P117SlJA/Hw/XXtKUkD8fD9de0pSQPx8P117SlJAC5gYD/HFTkALmBgP8cVOQAuYGA/xxU5AC5gYD/HFTkB/1FViC9xIQH/UVWIL3EhAf9RVYgvcSEB/1FViC9xIQH/UVWIL3EhAH5CXTVXHRUAfkJdNVcdFQB+Ql01Vx0VAH5CXTVXHRUAfkJdNVcdFQNeO0wj7CEZA147TCPsIRkDXjtMI+whGQNeO0wj7CEZA147TCPsIRkDm9UaZrmhGQOb1RpmuaEZA5vVGma5oRkDm9UaZrmhGQHGLxZA4AUhAcYvFkDgBSEBxi8WQOAFIQHGLxZA4AUhAcYvFkDgBSECM/9YjN4FFQIz/1iM3gUVAjP/WIzeBRUCM/9YjN4FFQIz/1iM3gUVAYJC+Z7AyTEBgkL5nsDJMQGCQvmewMkxAYJC+Z7AyTEBgkL5nsDJMQDzGlaAOSU5APMaVoA5JTkA8xpWgDklOQDzGlaAOSU5APMaVoA5JTkAZ+QEeeLNKQBn5AR54s0pAGfkBHnizSkAZ+QEeeLNKQBn5AR54s0pAytPCSnwcS0DK08JKfBxLQMrTwkp8HEtAytPCSnwcS0DK08JKfBxLQMrTwkp8HEtAytPCSnwcS0DK08JKfBxLQMrTwkp8HEtAvbOp0WqJS0C9s6nRaolLQL2zqdFqiUtAvbOp0WqJS0C9s6nRaolLQNyWRhQEVkhA3JZGFARWSEDclkYUBFZIQNyWRhQEVkhA3JZGFARWSEA9MdXBU2BHQD0x1cFTYEdAPTHVwVNgR0A9MdXBU2BHQD0x1cFTYEdAsZIEyUQdRECxkgTJRB1EQLGSBMlEHURAsZIEyUQdRECxkgTJRB1EQCgXwLdd8z9AKBfAt13zP0AoF8C3XfM/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+3EDySHZOQD7cQPJIdk5APtxA8kh2TkA+3EDySHZOQD7cQPJIdk5APtxA8kh2TkA+3EDySHZOQOFEnyjk+01A4USfKOT7TUDhRJ8o5PtNQOFEnyjk+01A4USfKOT7TUBGWSbkvl5QQEZZJuS+XlBARlkm5L5eUEBGWSbkvl5QQEZZJuS+XlBAujsttkzMUEC6Oy22TMxQQLo7LbZMzFBAujsttkzMUEDe2j03+OdOQN7aPTf4505A3to9N/jnTkDe2j03+OdOQN7aPTf4505ADsc+CjiwTEAOxz4KOLBMQA7HPgo4sExADsc+CjiwTEAOxz4KOLBMQMSCjnovIk5AxIKOei8iTkDEgo56LyJOQMSCjnovIk5AxIKOei8iTkDArPNZhvRPQMCs81mG9E9AwKzzWYb0T0DArPNZhvRPQMCs81mG9E9A8ofnI5JWUEDyh+cjklZQQPKH5yOSVlBA8ofnI5JWUECEl1+5gzhMQISXX7mDOExAhJdfuYM4TECEl1+5gzhMQISXX7mDOExA7X8si7NcSkDtfyyLs1xKQO1/LIuzXEpA7X8si7NcSkDtfyyLs1xKQD731Oh2REZAPvfU6HZERkA+99TodkRGQD731Oh2REZAPvfU6HZERkDSJ2xFFwdEQNInbEUXB0RA0idsRRcHREDSJ2xFFwdEQNInbEUXB0RA/EEuEjGGR0D8QS4SMYZHQPxBLhIxhkdA/EEuEjGGR0D8QS4SMYZHQKbKMuq/jElApsoy6r+MSUCmyjLqv4xJQKbKMuq/jElApsoy6r+MSUANzs16R1RHQA3OzXpHVEdADc7NekdUR0ANzs16R1RHQA3OzXpHVEdA3HYwUYKBQUDcdjBRgoFBQNx2MFGCgUFA3HYwUYKBQUDcdjBRgoFBQNx2MFGCgUFA3HYwUYKBQUDcdjBRgoFBQNx2MFGCgUFAkecg3r1rRECR5yDevWtEQJHnIN69a0RAkecg3r1rRECR5yDevWtEQN9Cf0+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+rVFTUAylij6tUVNQJSoe01r30hAlKh7TWvfSECUqHtNa99IQJSoe01r30hAlKh7TWvfSEB1NUKEMqJOQHU1QoQyok5AdTVChDKiTkB1NUKEMqJOQHU1QoQyok5AKxe6DXU3T0ArF7oNdTdPQCsXug11N09AKxe6DXU3T0ArF7oNdTdPQHMa5J8ZAU9AcxrknxkBT0BzGuSfGQFPQHMa5J8ZAU9AcxrknxkBT0Bb0dXIrq1PQFvR1ciurU9AW9HVyK6tT0Bb0dXIrq1PQIjMJOH8blBAiMwk4fxuUECIzCTh/G5QQIjMJOH8blBA1joJzWGgUEDWOgnNYaBQQNY6Cc1hoFBA1joJzWGgUEDWOgnNYaBQQNyNG2UQ50xA3I0bZRDnTEDcjRtlEOdMQNyNG2UQ50xA95QMsdmbR0D3lAyx2ZtHQPeUDLHZm0dA95QMsdmbR0D3lAyx2ZtHQIfkGwTa5UZAh+QbBNrlRkCH5BsE2uVGQIfkGwTa5UZAh+QbBNrlRkD7YsjSYyNJQPtiyNJjI0lA+2LI0mMjSUD7YsjSYyNJQPtiyNJjI0lACYzJ2FZlSkAJjMnYVmVKQAmMydhWZUpACYzJ2FZlSkAJjMnYVmVKQDqSPvHbM0pAOpI+8dszSkA6kj7x2zNKQDqSPvHbM0pAl7GEkoj4SkCXsYSSiPhKQJexhJKI+EpAl7GEkoj4SkCXsYSSiPhKQFJphLIhnExAUmmEsiGcTEBSaYSyIZxMQFJphLIhnExAUmmEsiGcTECt3r90b/1IQK3ev3Rv/UhArd6/dG/9SECt3r90b/1IQK3ev3Rv/UhAe/N8uuuETEB783y664RMQHvzfLrrhExAe/N8uuuETEB783y664RMQIbn9N1ipU5Ahuf03WKlTkCG5/TdYqVOQIbn9N1ipU5Ahuf03WKlTkCcCTOLNGROQJwJM4s0ZE5AnAkzizRkTkCcCTOLNGROQJwJM4s0ZE5AnAkzizRkTkCcCTOLNGROQJwJM4s0ZE5AnAkzizRkTkAL6td41C9LQAvq13jUL0tAC+rXeNQvS0AL6td41C9LQAvq13jUL0tA2Xmb4RJ8RkDZeZvhEnxGQNl5m+ESfEZA2Xmb4RJ8RkDZeZvhEnxGQC92wiX8kElAL3bCJfyQSUAvdsIl/JBJQC92wiX8kElAL3bCJfyQSUDgiVa8/+FGQOCJVrz/4UZA4IlWvP/hRkDgiVa8/+FGQOCJVrz/4UZAA7VONh4/SEADtU42Hj9IQAO1TjYeP0hAA7VONh4/SEADtU42Hj9IQIV5UgeffEdAhXlSB598R0CFeVIHn3xHQIV5UgeffEdAhXlSB598R0A8qm92sBZGQDyqb3awFkZAPKpvdrAWRkA8qm92sBZGQBidWczQLUNAGJ1ZzNAtQ0AYnVnM0C1DQBidWczQLUNAGJ1ZzNAtQ0DIh4GMsRtFQMiHgYyxG0VAyIeBjLEbRUDIh4GMsRtFQMiHgYyxG0VASgr673nqQkBKCvrveepCQEoK+u956kJASgr673nqQkBKCvrveepCQFd3nnYUZURAV3eedhRlREBXd552FGVEQFd3nnYUZURAV3eedhRlREARWTf8TDBGQBFZN/xMMEZAEVk3/EwwRkARWTf8TDBGQLfg6214aEdAt+DrbXhoR0C34OtteGhHQLfg6214aEdAt+DrbXhoR0ACk361/7pFQAKTfrX/ukVAApN+tf+6RUACk361/7pFQAKTfrX/ukVAqFbjacYDTECoVuNpxgNMQKhW42nGA0xAqFbjacYDTECoVuNpxgNMQGxcaiwoPk9AbFxqLCg+T0BsXGosKD5PQGxcaiwoPk9AbFxqLCg+T0Dpf7CHOAFQQOl/sIc4AVBA6X+whzgBUEDpf7CHOAFQQOl/sIc4AVBA+2v7cQ8MUED7a/txDwxQQPtr+3EPDFBA+2v7cQ8MUED7a/txDwxQQNYJmxKM9VFA1gmbEoz1UUDWCZsSjPVRQNYJmxKM9VFA1gmbEoz1UUCxXkwmUwdSQLFeTCZTB1JAsV5MJlMHUkCxXkwmUwdSQLFeTCZTB1JA6I+0R4BXUkDoj7RHgFdSQOiPtEeAV1JA6I+0R4BXUkB3A3dEAERTQHcDd0QARFNAdwN3RABEU0B3A3dEAERTQHcDd0QARFNAoQsUj55oU0ChCxSPnmhTQKELFI+eaFNAoQsUj55oU0ChCxSPnmhTQA1vVQscTFRADW9VCxxMVEANb1ULHExUQA1vVQscTFRADW9VCxxMVED8DTDD2uZUQPwNMMPa5lRA/A0ww9rmVED8DTDD2uZUQPwNMMPa5lRA6i36uw4+VUDqLfq7Dj5VQOot+rsOPlVA6i36uw4+VUDqLfq7Dj5VQCuhR1giU1JAK6FHWCJTUkAroUdYIlNSQCuhR1giU1JAK6FHWCJTUkBaf4Z1wuRJQFp/hnXC5ElAWn+GdcLkSUBaf4Z1wuRJQFp/hnXC5ElAGEDt2IAKSUAYQO3YgApJQBhA7diACklAWT4SaJSqSkBZPhJolKpKQFk+EmiUqkpAWT4SaJSqSkBZPhJolKpKQDfV7MM/ZkdAN9Xswz9mR0A31ezDP2ZHQDfV7MM/ZkdAN9Xswz9mR0CgbLZv0IxFQKBstm/QjEVAoGy2b9CMRUCgbLZv0IxFQPMniEBUdkhA8yeIQFR2SEDzJ4hAVHZIQPMniEBUdkhA8yeIQFR2SEDENX0sjRJMQ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\",\"dtype\":\"float64\",\"shape\":[2148]},\"ohlc_low\":{\"__ndarray__\":\"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9N2QLjIdr6fpHZAoxov3SSQdkBODAIrh3J2QHU9CtejbnZAHy/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+nxqWg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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[2148]},\"SMA(C,50)_2_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Kfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3PzOeUm9XKfc/M55Sb1cp9z8znlJvVyn3P32Y7aVRivc/igZZsSAW+D+LVEiUHg/4P6YL4TM66fc/ConkxlXu9z99BC4qCr33PzzEb6X/bvc/xo722pR09z+jeTDEElj3P380zGtNfPc/sJms7OPq9z8LJcOMUeD3PwfyHCzErPc/SaMyYPHd9z9pyROjD1/4P7IrXVwLfPg/g5HHbsed+D8m6F8tn274P0dD9txqX/g/vJvXc/pD+D/3eFFWNJ/4PwpPGC8+zvg/CvwRIdYk+T/M7vn8WAr5P9O9fiPiL/k/TB5/BCY5+T/ZYXwzkkb5P6Bcg1nJUPk/XhKXfuqh+T9Ik7tqxqv5Py54AZGmw/k/vod7x1GC+T+IbFCWgnf5P2mrwgFVJfg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P3AOB6QlGPg/cA4HpCUY+D9wDgekJRj4P1noDlj1Vvg/hqGZHWVh+D+FcJAGc1j4P01cUqtrUfg/vUXrnvhO+D9MCrdysvH4P8m+79VKBfk/DiAHG1oB+T/N82vhNcz4P1Zy2d4qbPk/Yqz6UvsR+j93Xu6JMU36P4jNiwntufo/DNpv49KV+j9P98WbuDn7P8ormy+ygfo/KcVSGu1V+j/Hd+3IFBD7P9UUjEB4Ufs/ojRfG62H+z8l3sXx/8f7P39/Dulz0vw/RHmqOfzS/D8YoWP9IoX8P2FEZiT+xfw/Dzdr5kz3/D+lQZWQy5z9P/yBNi8KH/4/T8A6hK32/T/IcADzTEz+P7hveaKn5f4/ly/zE4qR/z+VfRtYSjP/Pz+Cphvkkv0/0DxEmclU/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D9bEcDDcBL8P1sRwMNwEvw/WxHAw3AS/D8OJb0cNAP8P3vv0voZJvw/qg4cRClp+z+ort/Aomr7P62Z392INvs/svVvC1QO+z/pY6FKi0L6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j9vIfIKAnP6P28h8goCc/o/byHyCgJz+j/ChoZJ4e/5P5F/RQI2Ffo/avlgnVM++j9A4GDzmkz6P6zfKz4un/o/TMPPA0hi+j9toQoDvzv6PwlpbDM+wfo/gLiKSagO+z8tf50fb+f6P6d9UnEMdvs/EM6GgGyH+z+a52duro/7P+QvDNyqGfs/rL8E8hXS+z/vPNCORN37P5qvcYH0Qvs/Af2dcQqg+j/1Sjh4T5P6P/VKOHhPk/o/9Uo4eE+T+j/1Sjh4T5P6P/VKOHhPk/o/9Uo4eE+T+j/1Sjh4T5P6P/VKOHhPk/o/9Uo4eE+T+j/1Sjh4T5P6P/VKOHhPk/o/vLpOFgMD+z92ySOrZyD7P29qi15SVPs/V+uvSi7e+z8pX7untx78P8drSToPxvs/Ibi1eL2T+z8AuehXf2P7P2VZG1b9Kfs/y8hEPYnn+j/ieClon2b6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/3wTKmTKP+j/fBMqZMo/6P98Eypkyj/o/CMUUtOJu+j+MWbQI3iP6P0mWUTQo7fk/End6rZ17+j9w54AsXVT6P8hw+oIv3/o/lJr7szj2+j/wCgIz+M76P8EuSAImw/o/ZgavZUsp+z/fszNU3iH7PxYHVXJn+fo/n6E9Zrk/+z+tJaJnzHf7P/tmIJ4+Vfs/0YUW4T9w+j9U8m+fbnP6P1Tyb59uc/o/VPJvn25z+j9U8m+fbnP6P9lphOHQefo/QnQhuamf+j9eWu3EWW/6P8pQLvCJU/o/I3udN8JA+j9VRGaRmhT6P5b7/cXgv/k/FWa6OnSY+T/qGHD5vMD5P+oYcPm8wPk/6hhw+bzA+T/qGHD5vMD5P+oYcPm8wPk/sNe7P97L+T+1IcZrXvX5Pyq3tBoSF/o/k2qfjses+j+NHOkMjNz6P0it9xvt2Po/53k3FhQm+z/+iCZQxEL7P2H+0TdpOvs/K6uwGeAi+z9S/yCSIef6P/R0dcdiO/s/9HR1x2I7+z89cJUnEBb7P/eh0oiZnfo/Tt7H0Rx5+j/sCRLb3aP6P+wJEtvdo/o/swTPc/qQ+z82+HjXj2P7P5+NEqqKf/s/bAFXaNbo+z/UlvA60QT8P8UGwZlOgfw/zduOXy44/D94Cm/FtEX9P8oSU9j7Y/0/j/qhiPte/T+wk7Almlb9P/CHVXIEf/0/UD2IU5x5/T+IUcauo4D9P9DNtLOeMP0/s7QJ5p7E/D9b6tYh7VX9PwecW9cejPw/vOjhuhiZ/D+86OG6GJn8P7zo4boYmfw/vOjhuhiZ/D+Nx/pwCrf8P0RdWQyYTP0/U/mMrkyE/T9sFbJdG7b9P0DUax2Wkv0/Minz+aLJ/T9qQkiAFAH+P2UuXpbwEP4/ALfcA+4F/j/OLs0X9dX9PybAnhk+rv0/J2GUCqRQ/j9USr0RV1r+P/oCaP4Sff4/CDGFcbEu/j+J/VMUKVn+P/qNZPk0s/4/jD7UIEaU/j/6Amj+En3+PzhbLoWBc/4/dgsSRcKD/j8qfdafPo7+PyrV89gQqP4/pzW7WJLI/j/x6DR1mLv+P95OEN8DAP8/vx4BRhHe/j9kL8lrnxr/P3SQxaCN6P4/7/CMIA8J/z8nssRtrib/P/QMz7WpRv8/2+m8MBPR/z8tWCEaGhUAQCfwxUzfMQBAjhkfmyEbAEA0Jjtr9DAAQMaCOa/uHgBA44MV3ExLAEC/PxxZNjkAQMWDG5M/pP8/Mqe5f6bq/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P7j8dREgz/4/uPx1ESDP/j+4/HURIM/+P03YQ9DYxP4/t7a1Wmfp/j+i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While the strategy doesn't perform as well as simple buy & hold, it does so with significantly lower exposure (time in market).\n", "\n", - "In conclusion, to test strategies on multiple time frames, you need to pass in data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to the lower time frame, filling in the in-betweens.\n", + "In conclusion, to test strategies on multiple time frames, you need to pass in OHLC data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to the lower time frame, filling in the in-betweens.\n", "Which is what the function [`backtesting.lib.resample_apply()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.resample_apply) does for you." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn more by exploring further\n", + "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", + "or find more framework options in the\n", + "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." + ] } ], "metadata": { - "jupytext_format_version": "1.1", + "jupytext": { + "text_representation": { + "format_version": "1.1" + } + }, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -636,7 +689,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/doc/examples/Multiple Time Frames.py b/doc/examples/Multiple Time Frames.py index 7116046f..667c5e11 100644 --- a/doc/examples/Multiple Time Frames.py +++ b/doc/examples/Multiple Time Frames.py @@ -5,7 +5,7 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.3.0 +# jupytext_version: 1.5.1 # kernelspec: # display_name: Python 3 # language: python @@ -16,10 +16,10 @@ # ============ # # 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 +# 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). +# [basic usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html). # # We will put to the test this long-only, supposed # [400%-a-year trading strategy](http://jbmarwood.com/stock-trading-strategy-300/), @@ -27,11 +27,10 @@ # [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), +# In practice, one should use functions from an indicator library, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib) or # [Tulipy](https://tulipindicators.org), -# PyAlgoTrade, ... +# but among us, let's introduce the two indicators we'll be using. # + import pandas as pd @@ -138,5 +137,10 @@ def next(self): # 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 the lower time frame, filling in the in-betweens. +# In conclusion, to test strategies on multiple time frames, you need to pass in OHLC data in the lowest time frame, then resample it to higher time frames, apply the indicators, then resample back to the lower time frame, filling in the in-betweens. # Which is what the function [`backtesting.lib.resample_apply()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.resample_apply) does for you. + +# Learn more by exploring further +# [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) +# or find more framework options in the +# [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). diff --git a/doc/examples/Parameter Heatmap.ipynb b/doc/examples/Parameter Heatmap.ipynb index 82e19f67..5e6e78d9 100644 --- a/doc/examples/Parameter Heatmap.ipynb +++ b/doc/examples/Parameter Heatmap.ipynb @@ -12,10 +12,9 @@ "[basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html).\n", "\n", "First, let's again import our 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, ..." + "In practice, one should use functions from an indicator library, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n", + "[Tulipy](https://tulipindicators.org)." ] }, { @@ -28,7 +27,7 @@ "text/html": [ "\n", "
\n", - " \n", + " \n", " Loading BokehJS ...\n", "
" ] @@ -47,7 +46,7 @@ "\n", " var force = true;\n", "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", @@ -124,6 +123,7 @@ " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", @@ -214,76 +214,103 @@ "\n", " function run_callbacks() {\n", " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", - " function load_libs(js_urls, callback) {\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n", + "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", - " var 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", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " if (url in hashes) {\n", + " element.crossOrigin = \"anonymous\";\n", + " element.integrity = \"sha384-\" + hashes[url];\n", + " }\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", " }\n", - " };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", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", " }\n", "\n", - " 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 js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n", + " var css_urls = [];\n", + " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - " \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", " ];\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", + " if (root.Bokeh !== undefined || force === true) {\n", + " \n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", @@ -298,17 +325,17 @@ " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " console.debug(\"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", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "\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));" + "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) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -327,15 +354,13 @@ "but we will use four moving averages in total:\n", "two moving averages whose relationship determines a general trend\n", "(we only trade long when the shorter MA is above the longer one, and vice versa),\n", - "and two moving averages whose cross-over with Close prices determine the signal to enter or exit the position." + "and two moving averages whose cross-over with daily _close_ prices determine the signal to enter or exit the position." ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from backtesting import Strategy\n", @@ -399,12 +424,27 @@ "execution_count": 3, "metadata": {}, "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e32d43bab1e94f99a1947e9ffcfefe0e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 585 ms, sys: 102 ms, total: 687 ms\n", - "Wall time: 11.7 s\n" + "\r", + "CPU times: user 316 ms, sys: 45.6 ms, total: 362 ms\n", + "Wall time: 13.2 s\n" ] } ], @@ -440,48 +480,28 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "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", + "n1 n2 n_enter n_exit\n", + "20 40 15 10 8803.91\n", + " 60 15 10 10102.87\n", + " 80 15 10 9864.22\n", + " 100 15 10 11003.22\n", + " 120 15 10 12790.06\n", + " ... \n", + "100 200 25 15 9549.19\n", + " 20 8241.72\n", + " 30 10 9953.07\n", + " 15 9701.12\n", + " 20 7485.95\n", "Length: 486, dtype: float64" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -506,10 +526,10 @@ { "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", + "n1 n2 n_enter n_exit\n", + "40 60 25 15 21014.63\n", + "50 120 20 15 21588.32\n", + "40 160 20 15 22418.30\n", "dtype: float64" ] }, @@ -526,7 +546,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But people have this enormous faculty of vision, used to make judgements on much larger data sets much faster.\n", + "But we use vision to make judgements on 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 in how parameters `n1` and `n2`, on average, affect the outcome." ] @@ -583,140 +603,140 @@ " \n", " \n", " 20\n", - " 8742.77\n", - " 10020.29\n", - " 9877.11\n", - " 11005.88\n", - " 12832.05\n", - " 12115.57\n", - " 13380.55\n", - " 9462.92\n", - " 9993.13\n", + " 8803.91\n", + " 10102.87\n", + " 9864.22\n", + " 11003.22\n", + " 12790.06\n", + " 12125.96\n", + " 13291.88\n", + " 9541.35\n", + " 9981.48\n", " \n", " \n", " 30\n", - " 11685.45\n", - " 11947.24\n", - " 12507.64\n", - " 13247.98\n", - " 12471.19\n", - " 10955.33\n", - " 12672.70\n", - " 11049.38\n", - " 10599.47\n", + " 11672.05\n", + " 11888.46\n", + " 12422.99\n", + " 13201.24\n", + " 12374.63\n", + " 10968.28\n", + " 12595.11\n", + " 11002.74\n", + " 10582.33\n", " \n", " \n", " 40\n", - " NaN\n", - " 14522.63\n", - " 11314.04\n", - " 8852.70\n", - " 11252.55\n", - " 9538.35\n", - " 12615.52\n", - " 10804.31\n", - " 10094.83\n", + " nan\n", + " 14434.70\n", + " 11311.47\n", + " 8879.95\n", + " 11234.82\n", + " 9568.99\n", + " 12591.88\n", + " 10777.57\n", + " 10112.56\n", " \n", " \n", " 50\n", - " NaN\n", - " 9268.08\n", - " 10761.43\n", - " 11780.65\n", - " 11928.56\n", - " 10022.42\n", - " 11639.78\n", - " 10783.37\n", - " 9737.71\n", + " nan\n", + " 9275.58\n", + " 10767.09\n", + " 11796.92\n", + " 11899.20\n", + " 10052.85\n", + " 11595.15\n", + " 10766.80\n", + " 9746.36\n", " \n", " \n", " 60\n", - " NaN\n", - " NaN\n", - " 9901.77\n", - " 11012.14\n", - " 9550.52\n", - " 10839.21\n", - " 11360.96\n", - " 10512.38\n", - " 9653.58\n", + " nan\n", + " nan\n", + " 9935.79\n", + " 11009.34\n", + " 9583.58\n", + " 10806.22\n", + " 11361.59\n", + " 10493.33\n", + " 9655.29\n", " \n", " \n", " 70\n", - " NaN\n", - " NaN\n", - " 11015.24\n", - " 9408.20\n", - " 9828.18\n", - " 10759.51\n", - " 11366.77\n", - " 9972.34\n", - " 8627.18\n", + " nan\n", + " nan\n", + " 11037.58\n", + " 9447.27\n", + " 9835.48\n", + " 10758.90\n", + " 11365.42\n", + " 9992.12\n", + " 8672.41\n", " \n", " \n", " 80\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 10148.83\n", - " 10225.92\n", - " 10272.49\n", - " 11111.84\n", - " 10230.37\n", - " 9560.67\n", + " nan\n", + " nan\n", + " nan\n", + " 10111.50\n", + " 10226.34\n", + " 10266.09\n", + " 11116.05\n", + " 10237.26\n", + " 9539.06\n", " \n", " \n", " 90\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 11175.10\n", - " 11240.16\n", - " 10239.04\n", - " 11171.33\n", - " 10561.55\n", - " 10258.52\n", + " nan\n", + " nan\n", + " nan\n", + " 11176.47\n", + " 11228.66\n", + " 10263.17\n", + " 11183.08\n", + " 10523.47\n", + " 10239.37\n", " \n", " \n", " 100\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 11954.44\n", - " 9413.20\n", - " 11689.47\n", - " 10094.51\n", - " 10564.84\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " 11964.01\n", + " 9432.46\n", + " 11700.69\n", + " 10105.04\n", + " 10540.90\n", " \n", " \n", "\n", "" ], "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 " + "n2 40 60 80 100 120 140 160 180 \\\n", + "n1 \n", + "20 8803.91 10102.87 9864.22 11003.22 12790.06 12125.96 13291.88 9541.35 \n", + "30 11672.05 11888.46 12422.99 13201.24 12374.63 10968.28 12595.11 11002.74 \n", + "40 nan 14434.70 11311.47 8879.95 11234.82 9568.99 12591.88 10777.57 \n", + "50 nan 9275.58 10767.09 11796.92 11899.20 10052.85 11595.15 10766.80 \n", + "60 nan nan 9935.79 11009.34 9583.58 10806.22 11361.59 10493.33 \n", + "70 nan nan 11037.58 9447.27 9835.48 10758.90 11365.42 9992.12 \n", + "80 nan nan nan 10111.50 10226.34 10266.09 11116.05 10237.26 \n", + "90 nan nan nan 11176.47 11228.66 10263.17 11183.08 10523.47 \n", + "100 nan nan nan nan 11964.01 9432.46 11700.69 10105.04 \n", + "\n", + "n2 200 \n", + "n1 \n", + "20 9981.48 \n", + "30 10582.33 \n", + "40 10112.56 \n", + "50 9746.36 \n", + "60 9655.29 \n", + "70 8672.41 \n", + "80 9539.06 \n", + "90 10239.37 \n", + "100 10540.90 " ] }, "execution_count": 6, @@ -746,7 +766,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -755,12 +775,14 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -788,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -800,7 +822,7 @@ "\n", "\n", "\n", - "
\n" + "
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Application\",\"version\":\"2.1.1\"}};\n", + " var render_items = [{\"docid\":\"4da48bb4-11ad-4c76-b484-9a0009e06a07\",\"root_ids\":[\"1252\"],\"roots\":{\"1252\":\"9243431c-880e-4f97-8a64-f12f79e440de\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", @@ -823,13 +845,14 @@ " 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", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", " }\n", " }, 10, root)\n", " }\n", @@ -839,7 +862,7 @@ }, "metadata": { 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\n", "\n" ], "text/plain": [ - "Column(id='2574', ...)" + "Column(id='1252', ...)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -879,10 +902,24 @@ "\n", "plot_heatmaps(heatmap, agg='mean')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn more by exploring further\n", + "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", + "or find more framework options in the\n", + "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." + ] } ], "metadata": { - "jupytext_format_version": "1.1", + "jupytext": { + "text_representation": { + "format_version": "1.1" + } + }, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -898,7 +935,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/doc/examples/Parameter Heatmap.py b/doc/examples/Parameter Heatmap.py index 7e39e09e..c473de49 100644 --- a/doc/examples/Parameter Heatmap.py +++ b/doc/examples/Parameter Heatmap.py @@ -6,7 +6,7 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.3.0 +# jupytext_version: 1.5.1 # kernelspec: # display_name: Python 3 # language: python @@ -21,10 +21,9 @@ # [basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html). # # First, let's again import our 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, ... +# In practice, one should use functions from an indicator library, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib) or +# [Tulipy](https://tulipindicators.org). from backtesting.test import SMA @@ -33,7 +32,7 @@ # but we will use four moving averages in total: # two moving averages whose relationship determines a general trend # (we only trade long when the shorter MA is above the longer one, and vice versa), -# and two moving averages whose cross-over with Close prices determine the signal to enter or exit the position. +# and two moving averages whose cross-over with daily _close_ prices determine the signal to enter or exit the position. # + from backtesting import Strategy @@ -121,7 +120,7 @@ def next(self): heatmap.sort_values().iloc[-3:] -# But people have this enormous faculty of vision, used to make judgements on much larger data sets much faster. +# But we use vision to make judgements on larger data sets much faster. # Let's plot the whole heatmap by projecting it on two chosen dimensions. # Say we're mostly interested in how parameters `n1` and `n2`, on average, affect the outcome. @@ -152,3 +151,9 @@ def next(self): plot_heatmaps(heatmap, agg='mean') +# - + +# Learn more by exploring further +# [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) +# or find more framework options in the +# [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules). diff --git a/doc/examples/Quick Start User Guide.ipynb b/doc/examples/Quick Start User Guide.ipynb index f246c284..97470644 100644 --- a/doc/examples/Quick Start User Guide.ipynb +++ b/doc/examples/Quick Start User Guide.ipynb @@ -7,19 +7,23 @@ "_Backtesting.py_ Quick Start User Guide\n", "=======================\n", "\n", - "This tutorial shows some of the features of _backtesting.py_, yet another Python package for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies.\n", + "This tutorial shows some of the features of *backtesting.py*, a Python framework for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies.\n", "\n", - "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 multi-asset 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", + "_Backtesting.py_ is a small and lightweight, blazing fast backtesting framework that uses state-of-the-art Python structures and procedures (Python 3.5+, Pandas, NumPy, Bokeh). It has a very small and simple API that is easy to remember and quickly shape towards meaningful results. The library _doesn't_ really support stock picking or trading strategies that rely on arbitrage or multi-asset portfolio rebalancing; instead, it works with an individual tradeable asset at a time and is best suited for optimizing position entrance and exit signal strategies, decisions upon values of technical indicators, and it's also a versatile interactive trade visualization and statistics tool.\n", "\n", - "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. The entire API easily fits into memory banks of a single human individual. It's best suited for optimizing position entrance and exit strategies, decisions upon values of technical indicators, and it's also a versatile interactive trading strategy visualization tool.\n", "\n", - "### Data\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", + "_You bring your own data._ Backtesting ingests _all kinds of \n", + "[OHLC](https://en.wikipedia.org/wiki/Open-high-low-close_chart)\n", + "data_ (stocks, forex, futures, crypto, ...) as a\n", + "[pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/10min.html)\n", + "with columns `'Open'`, `'High'`, `'Low'`, `'Close'` and (optionally) `'Volume'`. Such data is widely obtainable (see: \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 frames can have other columns, but these are necessary.\n", + "[findatapy](https://github.com/cuemacro/findatapy)).\n", + "Besides these, your data frames can have _additional columns_ which are accessible in your strategies in a similar manner.\n", + "\n", "DataFrame should ideally be indexed with a _datetime index_ (convert it with [`pd.to_datetime()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)), otherwise a simple range index will do." ] }, @@ -28,22 +32,12 @@ "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jk/Documents/projects/trading/python/notebooks/venv/lib/python3.5/site-packages/backtesting/backtesting.py:26: UserWarning: Using tqdm in Jupyter Notebook mode. Raise an issue if you experience problems.\n", - " warnings.warn('Using tqdm in Jupyter Notebook mode. '\n", - "/home/jk/Documents/projects/trading/python/notebooks/venv/lib/python3.5/site-packages/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", "
\n", - " \n", + " \n", " Loading BokehJS ...\n", "
" ] @@ -62,7 +56,7 @@ "\n", " var force = true;\n", "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", @@ -139,6 +133,7 @@ " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", @@ -229,76 +224,103 @@ "\n", " function run_callbacks() {\n", " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", - " function load_libs(js_urls, callback) {\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n", + "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", - " var 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", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " if (url in hashes) {\n", + " element.crossOrigin = \"anonymous\";\n", + " element.integrity = \"sha384-\" + hashes[url];\n", + " }\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", " }\n", - " };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", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", " }\n", "\n", - " 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 js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n", + " var css_urls = [];\n", + " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - " \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", " ];\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", + " if (root.Bokeh !== undefined || force === true) {\n", + " \n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", @@ -313,17 +335,17 @@ " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " console.debug(\"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", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "\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));" + "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 \"
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Let it be a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover).\n", + "Let's create our first strategy to backtest on these Google data, a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover).\n", "\n", - "_Backtesting.py_ doesn't contain its own set of technical indicators. In practice, the user should probably use functions from their favorite indicator library, such as\n", - "[TA-Lib](https://github.com/mrjbq7/ta-lib),\n", + "_Backtesting.py_ doesn't ship its own set of _technical analysis indicators_. Users favoring TA should probably refer to functions from proven indicator libraries, such as\n", + "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n", "[Tulipy](https://tulipindicators.org),\n", - "PyAlgoTrade, ...\n", - "But for this example, we define a simple helper moving average function. " + "but for this example, we can define a simple helper moving average function ourselves:" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -460,41 +479,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note, this is the exact same helper function as the one used in the project unit tests, so we could just import that instead." - ] - }, - { - "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 its two methods:\n", + "A new strategy needs to extend \n", + "[`Strategy`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy)\n", + "class and override its two abstract methods:\n", "[`init()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.init) and\n", "[`next()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.next).\n", "\n", - "Method `init()` is invoked 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", + "Method `init()` is invoked before the strategy is run. Within it, one ideally precomputes in efficient, vectorized manner whatever indicators and signals the strategy depends on.\n", + "\n", + "Method `next()` is then iteratively called by the\n", + "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", + "instance, once for each data point (data frame row), simulating the incremental availability of each new full candlestick bar.\n", "\n", - "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 / trades _within_ candlesticks — any trade is executed on the next candle's _open_ (or the current candle's _close_, see\n", - "[`Backtest(trade_on_close)`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.__init__).\n", - "If you need to trade within candlesticks (e.g. daytrading), instead begin with _more fine-grained_ (e.g. hourly) data." + "Note, _backtesting.py_ cannot make decisions / trades _within_ candlesticks — any new orders are executed on the next candle's _open_ (or the current candle's _close_ if\n", + "[`trade_on_close=True`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.__init__)).\n", + "If you find yourself wishing to trade within candlesticks (e.g. daytrading), you instead need to begin with more fine-grained (e.g. hourly) data." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "from backtesting import Strategy\n", @@ -502,24 +507,27 @@ "\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", + " # Precompute the two moving averages\n", " self.sma1 = self.I(SMA, self.data.Close, self.n1)\n", " self.sma2 = self.I(SMA, self.data.Close, self.n2)\n", " \n", " def next(self):\n", - " # If sma1 crosses above sma2, buy the asset\n", + " # If sma1 crosses above sma2, close any existing\n", + " # short trades, and buy the asset\n", " if crossover(self.sma1, self.sma2):\n", + " self.position.close()\n", " self.buy()\n", "\n", - " # Else, if sma1 crosses below sma2, sell it\n", + " # Else, if sma1 crosses below sma2, close any existing\n", + " # long trades, and sell the asset\n", " elif crossover(self.sma2, self.sma1):\n", + " self.position.close()\n", " self.sell()" ] }, @@ -530,42 +538,65 @@ "In `init()` as well as in `next()`, the data the strategy is simulated on is available as an instance variable\n", "[`self.data`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data).\n", "\n", - "In `init()`, we compute indicators **indirectly by wrapping them in \n", + "In `init()`, we declare and **compute indicators indirectly by wrapping them in \n", "[`self.I()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.I)**.\n", - "The wrapper is passed a function (here, our `SMA` function) along with any 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", + "The wrapper is passed a function (our `SMA` function) along with any arguments to call it with (our _close_ values and the MA lag). Indicators wrapped in this way will be automatically plotted, and their legend strings will be intelligently inferred.\n", "\n", - "In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we go long; if it did and downwards, we close any open long position and go short. Note, there is no position size to adjust; _Backtesting.py_ assumes maximal possible position. We use\n", + "In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we close the possible short position and go long; if it did and downwards, we close the open long position and go short. Note, we don't adjust order size, so _Backtesting.py_ assumes _maximal possible position_. We use\n", "[`backtesting.lib.crossover()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.crossover)\n", "function instead of writing more obscure and confusing conditions, such as:" ] }, { - "cell_type": "raw", - "metadata": { - "collapsed": true - }, + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ + "%%script echo\n", + "\n", " def next(self):\n", " if (self.sma1[-2] < self.sma2[-2] and\n", - " self.sma1[-1] > self.sma2[-1]):\n", + " self.sma1[-1] > self.sma2[-1]):\n", + " self.position.close()\n", " self.buy()\n", "\n", - " elif (self.sma1[-2] > self.sma2[-2] and\n", + " elif (self.sma1[-2] > self.sma2[-2] and # Ugh!\n", " self.sma1[-1] < self.sma2[-1]):\n", - " self.sell() " + " self.position.close()\n", + " self.sell()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Ugh!\n", + "In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and all declared indicators is adjusted** on each `next()` call so that `array[-1]` (e.g. `self.data.Close[-1]` or `self.sma1[-1]`) always contains the most recent value, `array[-2]` the previous value, etc. (ordinary Python indexing of ascending-sorted 1D arrays).\n", "\n", - "In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and any declared 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", + "**Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are NumPy arrays for performance reasons. If you prefer pandas Series or DataFrame objects, use `Strategy.data..s` or `Strategy.data.df` accessors respectively. You could also construct the series manually, e.g. `pd.Series(self.data.Close, index=self.data.index)`.\n", "\n", - "**Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are **NumPy arrays for performance reasons**. If you need `pandas.Series`, use `.to_series()` method (e.g. `self.data.Close.to_series()`) or construct the series manually (e.g. `pd.Series(self.data.Close, index=self.data.index)`).\n", + "We might avoid `self.position.close()` calls if we primed the\n", + "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", + "instance with `Backtest(..., exclusive_orders=True)`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Backtesting\n", "\n", - "Let's see how our strategy performs on historical Google data. We begin with 10,000 units of cash and set broker's commission to realistic 0.2%." + "Let's see how our strategy performs on historical Google data. The\n", + "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", + "instance is initialized with OHLC data and a strategy _class_ (see API reference for additional options), and we begin with 10,000 units of cash and set broker's commission to realistic 0.2%." ] }, { @@ -579,28 +610,31 @@ "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", + "Exposure Time [%] 94.27\n", + "Equity Final [$] 68935.12\n", + "Equity Peak [$] 68991.22\n", + "Return [%] 589.35\n", "Buy & Hold Return [%] 703.46\n", - "Max. Drawdown [%] -33.61\n", - "Avg. Drawdown [%] -5.68\n", - "Max. Drawdown Duration 689 days 00:00:00\n", + "Max. Drawdown [%] -33.08\n", + "Avg. Drawdown [%] -5.58\n", + "Max. Drawdown Duration 688 days 00:00:00\n", "Avg. Drawdown Duration 41 days 00:00:00\n", "# Trades 93\n", "Win Rate [%] 53.76\n", - "Best Trade [%] 56.98\n", - "Worst Trade [%] -17.03\n", - "Avg. Trade [%] 2.44\n", + "Best Trade [%] 57.12\n", + "Worst Trade [%] -16.63\n", + "Avg. Trade [%] 1.96\n", "Max. Trade Duration 121 days 00:00:00\n", "Avg. Trade Duration 32 days 00:00:00\n", - "Expectancy [%] 6.92\n", - "SQN 1.77\n", - "Sharpe Ratio 0.22\n", - "Sortino Ratio 0.54\n", - "Calmar Ratio 0.07\n", + "Profit Factor 2.13\n", + "Expectancy [%] 6.91\n", + "SQN 1.78\n", + "Sharpe Ratio 0.18\n", + "Sortino Ratio 0.44\n", + "Calmar Ratio 0.06\n", "_strategy SmaCross\n", + "_equity_curve Equ...\n", + "_trades Size EntryB...\n", "dtype: object" ] }, @@ -613,23 +647,19 @@ "from backtesting import Backtest\n", "\n", "bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002)\n", - "bt.run()" + "stats = bt.run()\n", + "stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The\n", - "[`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest)\n", - "instance is initialized with data and a strategy _class_ (see API reference for additional options).\n", - "\n", - "When\n", "[`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run)\n", - "method is called, it returns a pandas Series of simulation 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", + "method returns a pandas Series of simulation results and statistics associated with our strategy. We see that this simple strategy makes almost 600% return in the period of 9 years, with maximum drawdown 33%, and with longest drawdown period spanning almost two years ...\n", "\n", "[`Backtest.plot()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot)\n", - "method provides the same results in a more visual form." + "method provides the same insights in a more visual form." ] }, { @@ -646,7 +676,7 @@ "\n", "\n", "\n", - "
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- 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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Drawdown 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= 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\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"value\":2139},\"y\":{\"value\":6.972214817519012}},\"id\":\"1101\",\"type\":\"Scatter\"},{\"attributes\":{\"label\":{\"value\":\"Final 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mZrd3QCYxCKwc6ndA2vl+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+F6lIGBQK1yaJFtqIFA4KWbxCDHgUAVBFYOLeeBQGsSg8BKCIJALzMzM7MogkCHbOf7KUWCQBLZzvfTYIJAJ4cW2c5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12WcEIAAIAZsJZwQgAAQH8Cl3BCAAAA5VSXcEIAAAB8nphwQgAAwOHwmHBCAACAR0OZcEIAAECtlZlwQgAAgN6MmnBCAABARN+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+YR3JCAAAAFetHckIAAEBG4khyQgAAAKw0SXJCAADAEYdJckIAAIB32UlyQgAAQN0rSnJCAACADiNLckIAAEB0dUtyQgAAANrHS3JCAADAPxpMckIAAIClbExyQgAAwNZjTXJCAACAPLZNckIAAECiCE5yQgAAAAhbTnJCAADAba1OckIAAACfpE9yQgAAwAT3T3JCAACAaklQckIAAEDQm1ByQgAAADbuUHJCAABAZ+VRckIAAADNN1JyQgAAwDKKUnJCAABA/i5TckIAAIAvJlRyQgAAQJV4VHJCAAAA+8pUckIAAMBgHVVyQgAAgMZvVXJCAADA92ZWckIAAIBduVZyQgAAQMMLV3JCAAAAKV5XckIAAMCOsFdyQgAAAMCnWHJCAADAJfpYckIAAICLTFlyQgAAQPGeWXJCAAAAV/FZckIAAECI6FpyQgAAAO46W3JCAADAU41bckIAAIC531tyQgAAgFApXXJCAABAtntdckIAAAAczl1yQgAAwIEgXnJCAADAGGpfckIAAIB+vF9yQgAAQOQOYHJCAAAASmFgckIAAMCvs2ByQgAAAOGqYXJCAADARv1hckIAAICsT2JyQgAAQBKiYnJCAAAAePRickIAAAAPPmRyQgAAwHSQZHJCAACA2uJkckIAAEBANWVyQgAAgHEsZnJCAABA135mckIAAAA90WZyQgAAwKIjZ3JCAACACHZnckIAAMA5bWhyQgAAgJ+/aHJCAABABRJpckIAAABrZGlyQgAAwNC2aXJCAAAAAq5qckIAAMBnAGtyQgAAgM1Sa3JCAABAM6VrckIAAACZ92tyQgAAADBBbXJCAADAlZNtckIAAID75W1yQgAAQGE4bnJCAACAki9vckIAAED4gW9yQgAAAF7Ub3JCAADAwyZwckIAAIApeXByQgAAwFpwcXJCAACAwMJxckIAAEAmFXJyQgAAAIxncnJCAADA8blyckIAAAAjsXNyQgAAwIgDdHJCAACA7lV0ckIAAEBUqHRyQgAAALr6dHJCAABA6/F1ckIAAABRRHZyQgAAwLaWdnJCAACAHOl2ckIAAECCO3dyQgAAgLMyeHJCAABAGYV4ckIAAAB/13hyQgAAwOQpeXJCAACASnx5ckIAAMB7c3pyQgAAgOHFenJCAABARxh7ckIAAACtantyQgAAAES0fHJCAADAqQZ9ckIAAIAPWX1yQgAAQHWrfXJCAAAA2/19ckIAAEAM9X5yQgAAAHJHf3JCAADA15l/ckIAAIA97H9yQgAAQKM+gHJCAACA1DWBckIAAEA6iIFyQgAAAKDagXJCAADABS2CckIAAIBrf4JyQgAAwJx2g3JCAACAAsmDckIAAEBoG4RyQgAAAM5thHJCAADAM8CEckIAAABlt4VyQgAAwMoJhnJCAACAMFyGckIAAECWroZyQgAAAPwAh3JCAABALfiHckIAAACTSohyQgAAwPiciHJCAACAXu+IckIAAEDEQYlyQgAAgPU4inJCAABAW4uKckIAAADB3YpyQgAAwCYwi3JCAACAjIKLckIAAMC9eYxyQgAAgCPMjHJCAABAiR6NckIAAADvcI1yQgAAwFTDjXJCAADA6wyPckIAAIBRX49yQgAAQLexj3JCAAAAHQSQckIAAEBO+5ByQgAAALRNkXJCAADAGaCRckIAAIB/8pFyQgAAQOVEknJCAACAFjyTckIAAEB8jpNyQgAAAOLgk3JCAADARzOUckIAAICthZRyQgAAwN58lXJCAACARM+VckIAAECqIZZyQgAAABB0lnJCAADAdcaWckIAAACnvZdyQgAAwAwQmHJCAACAcmKYckIAAEDYtJhyQgAAAD4HmXJCAAAA1VCackIAAMA6o5pyQgAAgKD1mnJCAABABkibckIAAIA3P5xyQgAAQJ2RnHJCAAAAA+ScckIAAMBoNp1yQgAAgM6InXJCAADA/3+eckIAAIBl0p5yQgAAQMskn3JCAAAAMXefckIAAMCWyZ9yQgAAAMjAoHJCAADALROhckIAAICTZaFyQgAAQPm3oXJCAAAAXwqickIAAECQAaNyQgAAAPZTo3JCAADAW6ajckIAAIDB+KNyQgAAQCdLpHJCAACAWEKlckIAAEC+lKVyQgAAACTnpXJCAADAiTmmckIAAIDvi6ZyQgAAwCCDp3JCAACAhtWnckIAAEDsJ6hyQgAAAFJ6qHJCAADAt8yockIAAADpw6lyQgAAwE4WqnJCAACAtGiqckIAAEAau6pyQgAAAIANq3JCAABAsQSsckIAAAAXV6xyQgAAwHyprHJCAACA4vusckIAAEBITq1yQgAAQN+XrnJCAAAARequckIAAMCqPK9yQgAAgBCPr3JCAADAQYawckIAAICn2LByQgAAQA0rsXJCAAAAc32xckIAAMDYz7FyQgAAAArHsnJCAADAbxmzckIAAIDVa7NyQgAAQDu+s3JCAAAAoRC0ckIAAEDSB7VyQgAAADhatXJCAADAnay1ckIAAIAD/7VyQgAAQGlRtnJCAACAmki3ckIAAEAAm7dyQgAAAGbtt3JCAADAyz+4ckIAAIAxkrhyQgAAwGKJuXJCAACAyNu5ckIAAEAuLrpyQgAAAJSAunJCAADA+dK6ckIAAAAryrtyQgAAwJAcvHJCAACA9m68ckIAAEBcwbxyQgAAAMITvXJCAABA8wq+ckIAAABZXb5yQgAAwL6vvnJCAACAJAK/ckIAAECKVL9yQgAAgLtLwHJCAABAIZ7AckIAAACH8MByQgAAwOxCwXJCAACAUpXBckIAAMCDjMJyQgAAgOnewnJCAABATzHDckIAAAC1g8NyQgAAwBrWw3JCAAAATM3EckIAAMCxH8VyQgAAgBdyxXJCAABAfcTFckIAAADjFsZyQgAAQBQOx3JCAAAAemDHckIAAMDfssdyQgAAQKtXyHJCAACA3E7JckIAAEBCoclyQgAAAKjzyXJCAADADUbKckIAAIBzmMpyQgAAwKSPy3JCAACACuLLckIAAEBwNMxyQgAAANaGzHJCAADAO9nMckIAAABt0M1yQgAAwNIiznJCAACAOHXOckIAAECex85yQgAAAAQaz3JCAABANRHQckIAAACbY9ByQgAAwAC20HJCAACAZgjRckIAAID9UdJyQgAAQGOk0nJCAAAAyfbSckIAAMAuSdNyQgAAgJSb03JCAADAxZLUckIAAIAr5dRyQgAAQJE31XJCAAAA94nVckIAAMBc3NVyQgAAAI7T1nJCAADA8yXXckIAAIBZeNdyQgAAQL/K13JCAAAAJR3YckIAAAC8ZtlyQgAAwCG52XJCAACAhwvackIAAEDtXdpyQgAAgB5V23JCAABAhKfbckIAAADq+dtyQgAAwE9M3HJCAACAtZ7cckIAAMDmld1yQgAAgEzo3XJCAABAsjreckIAAAAYjd5yQgAAwH3f3nJCAAAAr9bfckIAAMAUKeByQgAAgHp74HJCAABA4M3gckIAAABGIOFyQgAAQHcX4nJCAAAA3WnickIAAMBCvOJyQgAAgKgO43JCAABADmHjckIAAEClquRyQgAAAAv95HJCAADAcE/lckIAAIDWoeVyQgAAwAeZ5nJCAACAbevmckIAAEDTPedyQgAAADmQ53JCAADAnuLnckIAAADQ2ehyQgAAwDUs6XJCAACAm37pckIAAEAB0elyQgAAAGcj6nJCAABAmBrrckIAAAD+bOtyQgAAwGO/63JCAACAyRHsckIAAEAvZOxyQgAAgGBb7XJCAABAxq3tckIAAAAsAO5yQgAAwJFS7nJCAACA96TuckIAAMAonO9yQgAAgI7u73JCAABA9EDwckIAAABak/ByQgAAwL/l8HJCAAAA8dzxckIAAMBWL/JyQgAAgLyB8nJCAABAItTyckIAAACIJvNyQgAAQLkd9HJCAAAAH3D0ckIAAMCEwvRyQgAAgOoU9XJCAABAUGf1ckIAAICBXvZyQgAAQOew9nJCAAAATQP3ckIAAMCyVfdyQgAAwEmf+HJCAACAr/H4ckIAAEAVRPlyQgAAAHuW+XJCAADA4Oj5ckIAAAAS4PpyQgAAwHcy+3JCAACA3YT7ckIAAEBD1/tyQgAAAKkp/HJCAABA2iD9ckIAAABAc/1yQgAAwKXF/XJCAACACxj+ckIAAEBxav5yQgAAgKJh/3JCAABACLT/ckIAAABuBgBzQgAAwNNYAHNCAACAOasAc0IAAMBqogFzQgAAgND0AXNCAABANkcCc0IAAACcmQJzQgAAwAHsAnNCAADAmDUEc0IAAID+hwRzQgAAQGTaBHNCAAAAyiwFc0IAAED7IwZzQgAAAGF2BnNCAADAxsgGc0IAAIAsGwdzQgAAQJJtB3NCAACAw2QIc0IAAEAptwhzQgAAAI8JCXNCAADA9FsJc0IAAIBarglzQgAAwIulCnNCAACA8fcKc0IAAEBXSgtzQgAAAL2cC3NCAADAIu8Lc0IAAABU5gxzQgAAwLk4DXNCAACAH4sNc0IAAECF3Q1zQgAAAOsvDnNCAAAAgnkPc0IAAMDnyw9zQgAAgE0eEHNCAABAs3AQc0IAAIDkZxFzQgAAQEq6EXNCAAAAsAwSc0IAAMAVXxJzQgAAgHuxEnNCAADArKgTc0IAAIAS+xNzQgAAQHhNFHNCAAAA3p8Uc0IAAMBD8hRzQgAAAHXpFXNCAADA2jsWc0IAAIBAjhZzQgAAQKbgFnNCAAAADDMXc0IAAEA9KhhzQgAAAKN8GHNCAADACM8Yc0IAAIBuIRlzQgAAQNRzGXNCAACABWsac0IAAEBrvRpzQgAAANEPG3NCAADANmIbc0IAAICctBtzQgAAwM2rHHNCAACAM/4cc0IAAECZUB1zQgAAAP+iHXNCAADAZPUdc0IAAACW7B5zQgAAwPs+H3NCAACAYZEfc0IAAEDH4x9zQgAAAC02IHNCAABAXi0hc0IAAADEfyFzQgAAwCnSIXNCAACAjyQic0IAAED1diJzQgAAQIzAI3NCAAAA8hIkc0IAAMBXZSRzQgAAgL23JHNCAADA7q4lc0IAAIBUASZzQgAAQLpTJ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Application\",\"version\":\"2.1.1\"}};\n", + " var render_items = [{\"docid\":\"badac822-823f-452e-b51c-0d5b63ca8666\",\"root_ids\":[\"1413\"],\"roots\":{\"1413\":\"8409e7f9-6c41-46b9-9325-575f97f2c2dd\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", @@ -669,13 +699,14 @@ " 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", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", " }\n", " }, 10, root)\n", " }\n", @@ -685,7 +716,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "1428" + "id": "1413" } }, "output_type": "display_data" @@ -699,13 +730,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Optimization\n", + "## Optimization\n", "\n", - "We hard-coded the two lag parameters (`n1` and `n2`) into our strategy above. However, the strategy may work better with 15–30 or some other cross-over. **We define the parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)**.\n", + "We hard-coded the two lag parameters (`n1` and `n2`) into our strategy above. However, the strategy may work better with 15–30 or some other cross-over. **We declared the parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)**.\n", "\n", "We optimize the two parameters by calling\n", "[`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize)\n", - "method with each parameter a keyword argument pointing to its pool of values to test. Parameter `n1` is tested for values in range between 5 and 30 and parameter `n2` for values between 10 and 70, respectively. Some combinations of values of the two parameters are invalid, i.e. `n1` should not be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which returns `True` (admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes return over the observed period. We could instead choose to optimize any key from the returned `stats` series." + "method with each parameter a keyword argument pointing to its pool of possible values to test. Parameter `n1` is tested for values in range between 5 and 30 and parameter `n2` for values between 10 and 70, respectively. Some combinations of values of the two parameters are invalid, i.e. `n1` should not be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which takes in the parameters and returns `True` (i.e. admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes return over the observed period. We could instead choose to optimize any other key from the returned `stats` series." ] }, { @@ -716,7 +747,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c50da174c9bf416bb806728e8781b1d8", + "model_id": "7df5af7a17a64c18a41c5c4125f4d9f6", "version_major": 2, "version_minor": 0 }, @@ -732,9 +763,47 @@ "output_type": "stream", "text": [ "\r", - "CPU times: user 175 ms, sys: 59.5 ms, total: 234 ms\n", - "Wall time: 1.33 s\n" + "CPU times: user 156 ms, sys: 52 ms, total: 208 ms\n", + "Wall time: 1.55 s\n" ] + }, + { + "data": { + "text/plain": [ + "Start 2004-08-19 00:00:00\n", + "End 2013-03-01 00:00:00\n", + "Duration 3116 days 00:00:00\n", + "Exposure Time [%] 98.14\n", + "Equity Final [$] 105040.13\n", + "Equity Peak [$] 108327.72\n", + "Return [%] 950.40\n", + "Buy & Hold Return [%] 703.46\n", + "Max. Drawdown [%] -44.00\n", + "Avg. Drawdown [%] -6.14\n", + "Max. Drawdown Duration 690 days 00:00:00\n", + "Avg. Drawdown Duration 43 days 00:00:00\n", + "# Trades 152\n", + "Win Rate [%] 51.32\n", + "Best Trade [%] 61.56\n", + "Worst Trade [%] -19.78\n", + "Avg. Trade [%] 1.52\n", + "Max. Trade Duration 83 days 00:00:00\n", + "Avg. Trade Duration 21 days 00:00:00\n", + "Profit Factor 1.96\n", + "Expectancy [%] 6.00\n", + "SQN 1.51\n", + "Sharpe Ratio 0.15\n", + "Sortino Ratio 0.39\n", + "Calmar Ratio 0.03\n", + "_strategy SmaCross(n1=10,n...\n", + "_equity_curve Eq...\n", + "_trades Size Entry...\n", + "dtype: object" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -743,7 +812,15 @@ "stats = bt.optimize(n1=range(5, 30, 5),\n", " n2=range(10, 70, 5),\n", " maximize='Equity Final [$]',\n", - " constraint=lambda p: p.n1 < p.n2)" + " constraint=lambda param: param.n1 < param.n2)\n", + "stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can look into `stats['_strategy']` to access the Strategy _instance_ and its optimal parameter values (10 and 15)." ] }, { @@ -754,32 +831,7 @@ { "data": { "text/plain": [ - "Start 2004-08-19 00:00:00\n", - "End 2013-03-01 00:00:00\n", - "Duration 3116 days 00:00:00\n", - "Exposure [%] 98.14\n", - "Equity Final [$] 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(n1=10,n2=15)\n", - "dtype: object" + "" ] }, "execution_count": 8, @@ -788,20 +840,15 @@ } ], "source": [ - "stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can look into `stats._strategy` to access the Strategy instance and its optimal parameter values (10 and 15)." + "stats._strategy" ] }, { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -812,7 +859,7 @@ "\n", "\n", "\n", - "
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\",\"dtype\":\"float64\",\"shape\":[2148]},\"SMA(C,15)_0\":{\"__ndarray__\":\"AAAAAAAA+H8AAAAAAAD4fwAAAAAAAPh/AAAAAAAA+H8AAAAAAAD4fwAAAAAAAPh/AAAAAAAA+H8AAAAAAAD4fwAAAAAAAPh/AAAAAAAA+H8AAAAAAAD4fwAAAAAAAPh/AAAAAAAA+H8AAAAAAAD4f3npJjEI7FlA11xImlIBWkDe3d3d3f1ZQBBYObTIBlpAca+5kDQlWkCwK0DuNUdaQOzDZ9gVcFpAmsQgsHKoWkCAB/NE/etaQLwta3VMMFtA8NJNYhCIW0CL3pa1OtZbQGm8dJMYJFxABx7ME/WPXECVbhKDwApdQLM6Jngwf11A935qvHTzXUATIPeaC2leQLm7u7u7215AFnYFyL1GX0AziUFg5bBfQNIGOm2gA2BAJL9Y8oslYECh/o1QRk9gQJbgwTxRf2BADZ9hV4CsYEC7LWt1TOBgQAzmifo3ImFAsjomeDBPYUAiBoGVQ2NhQDb78Bl2jWFA0zHBg3niYUAgIiIiIlJiQA9YObTIrmJAzIWkKRUXY0ApFWcfPotjQGStjgke/GNA9Shcj8J9ZECuALnXXPhkQOkmMQisZGVAM6Xi7MO/ZUAVkqZUnPVlQG1Zq2OCJ2ZArI4JHsxTZkCV/GLJL45mQMqhRbbz1WZA7Xw/NV7qZkClVJx9+ORmQO6nxks30WZAE65H4Xq0ZkApzj58hn1mQM9pA502UGZAi96WtToOZkCZUnH24dNlQCmHFtnOr2VAEq5H4XqkZUA8CtejcL1lQKG3Za2O0WVAnah/I5TpZUAS9W+EMgJmQPZT46Wb/GVAn9MGOm3wZUC1rNUxwdNlQH2x5BdLzmVAwDxR/0bQZUAUIPeaC9llQB73mgtJ22VAwDxR/0b4ZUDeT42XbhJmQK1H4XoUFmZAYzvfT40XZkD68Bl2BSBmQKyOCR7MI2ZA1lxImlIxZkD5fmq8dENmQHz4DLsCXGZAvljyiyV/ZkCrY4IH86xmQBxaZDvf52ZA+cWSXywRZ0D+jVBGb1NnQBp2Bci9hmdAcIQyelumZ0AREREREblnQKAaL90k3mdAqjj78Bn+Z0DCPFH/RhBoQA0CK4cWKWhAMQisHFo8aECpOPvwGVZoQEfhehSub2hAzz58hl15aEDnQtKUintoQE7wYJ6oZ2hA1b8RyuhNaECa4ME8URdoQGu8dJMYDGhAjZduEoMAaEA1F5KmVARoQOe0gU4bCGhAzj58hl0BaECfYVeA3BtoQLqQNKXiPGhA35a1OiZQaEA7tMh2vkdoQAyQe82FPGhAuR6F61EwaEDP91PjpSNoQHMhaUrFIWhAjCW/WPI7aEC+5kLSlGJoQKAaL90kdmhATH6x5BeLaEAW2c73U5toQAXIveZCkmhAMcGDeaKWaEAO5on6N3JoQLOBThvoPGhAQYts5/sZaEDdCGX0tgRoQP3/////52dAhM+wK0DeZ0BmA5020NlnQMS95kLS3GdAT3H24TPMZ0A330+Nl65nQJ/+jVBGh2dAJOrfCGVcZ0AAnTbQaStnQNJ46SYxEGdAEyD3mgvpZkD/ca+5kNRmQOGlm8QgyGZAJlyPwvW4ZkATIPeaC6lmQKZ/I5TRm2ZAIgaBlUOLZkB/ThvotIFmQL+DeaL+bWZAnWFXgNxjZkAuT9S/EWJmQBH1b4QyYmZAFQRWDi1yZkC8WPKLJY9mQO0ZdgXIpWZAB5B7zYXMZkA2tMh2vudmQJdScfbhA2dALZb8YskfZ0BthDJ6Wz5nQCVcj8L1WGdAaqDTBjplZ0ANEREREXFnQPep8dJNimdABR7ME/WvZ0DCBA/mieJnQP0qhxbZLmhAnNMGOm2AaEBFxdmHz8BoQDt8hl0BAmlARsXZh884aUAazBP1b3RpQNtPjZdusmlAsTomeDD3aUBMjZduEkNqQPoboYzejmpArLmQNKXqakDHoUW28z1rQNUVIPeai2tA4V5zIWnSa0Alo7dlrQZsQBTZzvdTI2xAC7sC5F4zbEAzXrpJDFJsQAWsHFpke2xAMewKkHulbEDbJAaBldNsQPSaC0lTGm1AT7gehetZbUD/KocW2Z5tQNokBoGV421A9FPjpZs0bkBKqTj78KFuQOp8PzVeIm9APmDl0CKbb0AFgZVDiwRwQG+EMnpbRnBAAHKvuZCIcEArQO41F7pwQKrx0k1i7HBAO99PjZcacUDVMcGDeUZxQHYFyL3mXnFA698IZfRycUDkF0t+sYRxQDMzMzMzm3FA4XoUrkexcUCEeaL+jbxxQE6pOPvwvXFAHlpkO9+/cUDCg3mi/tFxQHvpJjEI4HFAMd0kBoHpcUDpQtKUivdxQJZDi2zn/3FA7JjgwTwJckDTlIqzDxdyQAnzRP0bJXJArRxaZDs7ckCj/o1QRk9yQPW2rNUxXXJAN9BpA51ickDufD81Xm5yQJ19+Ay7enJA5dAi2/mGckBCGb0ta4lyQNcVIPeaj3JAgsDKoUWackCzgU4b6LByQIBOG+i0uXJANdBpA52+ckDbslbHBL9yQE/UvxHKxHJANtBpA53CckCeqH8jlLlyQD3DrgC5t3JA0gY6baC/ckBHmlJx9r1yQDvfT42XunJAfM2FpCmxckC5HoXrUahyQFTjpZvElHJA5RdLfrF4ckBMxdmHz1hyQCL3mgtJS3JAsbmQNKU+ckBvWatjgjNyQPoMuwLkJnJAFa5H4XoYckBTcfbhMxByQMERyuht/XFAj5duEoPocUDOzMzMzNhxQO81F5KmyHFAnu+nxku/cUA7JngwT7xxQD7DrgC5t3FA8dJNYhC4cUDjXnMhabpxQJIYBFYOuXFAK/nFkl+8cUCQ7Xw/NcZxQHW+nxov0XFAordlrY7lcUDgehSuRwVyQDf78Bl2LXJAXEiaUnFGckAleDBP1FtyQDXQaQOdbnJA2YfPsCuEckCvcmiR7ZhyQDEzMzMzs3JAA8i95kLOckBXZDvfT+1yQLyfGi/dCHNAMewKkHslc0BORm/LWjFzQIWIiIiIQHNAz5SKsw9Tc0B3W9bqmFxzQBpaZDvfW3NAZ7x0kxhkc0BzBci95m5zQAOBlUOLfHNAnGFXgNyDc0DNsCtA7oFzQIHAyqFFdnNAAZ020Glnc0Dy/dR46VJzQJw20GkDSXNAj3vNhaQ9c0AKkHvNhUBzQAdl9LasOXNAJqO3Za1Sc0CtuZA0pXJzQKljggfzmHNAaS4kTanIc0BK8GCeqPNzQB9pSsXZI3RAPArXo3BldEDWFSD3mrN0QOAz7AqQB3VAxJJfLPlldUBD/RuhjMp1QLzmQtKUKnZA49Ai2/mGdkCiKRVnH9J2QJlScfbhL3dAdjBP1L9ld0BecyFpSpl3QArXo3A9yndArY4JHsz3d0Akv1jyiy14QOdtWatjWnhAKkDuNReCeEAMdNpAp6l4QL7KoUW213hAa6DTBjoFeUARrkfheih5QKC3Za2OMXlAHrByaJFBeUCCMnpb1mZ5QK4rQO41g3lAw9mHz7CTeUAypeLsw5t5QIolv1jyp3lA33oUrke1eUABnTbQabt5QG49CtejyHlAV2Q730/ReUCEFtnO99N5QE//Riij03lAYYIH80TVeUBVObTIdtZ5QOFecyFp8nlA44n6N0IJekCi4uzDZxx6QMoT9W+EKnpAwPUoXI8+ekCT/GLJL1Z6QDRCGb0tZ3pAxuhtWatrekD7jVBGb4d6QAbXo3A9qnpABtejcD3OekBw2kCnDQB7QIvC9ShcL3tAE5KmVJxZe0Cune+nxot7QI00peLsr3tASxvotIHae0APPJgn6v97QAI6baDTDnxAfJVDi2wbfEBmdUzwYP57QAvmifo3BnxAQEREREQkfEDVzvdT4yF8QJP8YskvFnxApA102kADfEDk+6nx0tl7QNprLiRNtXtATNS/Ecpse0BZj8L1KBx7QPRT46WbxHpAMl66SQxuekBGUyrOPgR6QI3C9Shcs3lADp9hV4BgeUDytqzVMTl5QEupOPvw4XhA2yQGgZV3eEBv9uEz7BZ4QDrfT42XzndApsZLN4mJd0BecyFpSkl3QI97zYWkAXdAMAisHFrodkCVtTomeNR2QNfO91Pj3XZA0r8RyujFdkC85kLSlMJ2QFtImlJxynZAvljyiyXfdkAfPsOuAOV2QB8+w64A+XZAREREREQEd0CwcmiR7QR3QLdlrY4J5nZAzFodEzzEdkCyne+nxrN2QMovlvxinXZAd3d3d3dzdkASyuhtWUt2QPKLJb9YHnZAIiIiIiIGdkATPJgn6ut1QFWcffgMx3VA4+zDZ9i5dUDmQtKUirt1QIDcay4kyXVAI02pOPv0dUC6AuRecyV2QLoC5F5zXXZAl5mZmZmVdkD1mgtJU852QDNeukkMEndAHveaC0lfd0CsR+F6FKZ3QIdBYOXQ7ndAcK+5kDQ5eEDKE/VvhIJ4QAlJUyrOwnhAv/UoXI/ueEAAnTbQaRN5QOzu7u7uNnlAPDVeuklMeUDSeOkmMYB5QLBWxwQPtnlAunSTGATeeUAKAiuHFvV5QM2wK0DuAXpAfdxrLiQJekBDKKO3ZQF6QGd1TPBg6nlAXeXQItvZeUCejN6Wtcp5QPSaC0lTwnlAMewKkHu1eUDwiyW/WLp5QPWaC0lTsnlAF70ta3WUeUBdcyFpSlF5QGAQWDm0DHlAg+tRuB7ReEAN5on6N5p4QNK/EcroZXhAjFBGb8syeEAiTak4+xR4QBKuR+F6AHhAIZTR27Lyd0AbEzyYJ+Z3QLEPn2FX2HdAAg/mifq/d0CFiIiIiJh3QIZBYOXQgndAlG4Sg8B6d0DKhaQpFXt3QGsSg8DKiXdAuXSTGASad0AtT9S/Ea53QKKbxCCwvndA4tAi2/nKd0DbJAaBldt3QCJNqTj75HdAibMPn2Hvd0DWFSD3mvd3QAArhxbZ/ndASn6x5BcPeECAlUOLbC94QNO/EcroQXhAGi/dJAZdeEBP1L8Rynx4QE2pOPvwiXhAASuHFtmeeEChRbbz/bh4QA4tsp3v23hALPnFkl8IeUCPe82FpC15QFerY4IHV3lAW0iaUnF2eUCwne+nxpN5QLLIdr6funlAn4zelrXaeUBe5dAi2+F5QI57zYWk5XlAO5gn6t/oeUDSBjptoOd5QOzDZ9gV5HlAJXgwT9TPeUD/uNdcSLJ5QAcezBP1k3lAnWFXgNxveUAt3SQGgUl5QL+DeaL+HXlAbFmrY4L7eEDrCpB7zdl4QDLsCpB7pXhA4qWbxCBweEAZvS1rdUx4QEW28/3ULHhANolBYOUMeEBLN4lBYPV3QOHsw2fY3XdA8NJNYhDQd0CcNtBpA7l3QCYxCKwcondAfM2FpCmZd0BqSsXZh5t3QD5R/0Yon3dAr0fhehSad0Bdj8L1KJB3QMRn2BUgk3dA/DdCGb2Zd0CHFtnO95d3QJTR27JWl3dAxwQP5omad0AxCKwcWph3QCPb+X5qnHdAmSfq3wihd0AshxbZzqt3QLy7u7u7u3dAAiuHFtm6d0AfPsOuALF3QAuQe82FqHdA3WsuJE2pd0A8mCfq37h3QNajcD0K13dA0wY6baD3d0CV/GLJLx54QGp1TPBgSnhAFGcfPsNieEC8dJMYBHZ4QGDl0CLbkXhAT9S/EcqseECeYVeA3Md4QBxaZDvf33hA2YfPsCv4eEDu7u7u7hJ5QPtiyS+WLHlASX6x5Bc/eUD04TPsCkx5QO8ZdgXIVXlAICIiIiJeeUA2+/AZdml5QFwBcq+5eHlACgIrhxaReUCBeaL+jbB5QMkT9W+ExnlA1RUg95rfeUDA9Shcj/J5QGaR7Xw/AXpAm334DLsSekCgjN6WtSp6QDUXkqZUaHpAKVyPwvW8ekACK4cW2QZ7QNYxwYN5UntA7zUXkqage0DhwTxR/9p7QPs3Qhm9DXxAj1BGb8tCfEBLDAIrh258QCd4ME/Um3xAn++nxkvLfEAaBFYOLQZ9QCGwcmiRPX1AplScffh4fUCvuZA0pap9QLsC5F5zuX1A/0Yoo7e5fUCSXyz5xcp9QBm9LWt10H1AAw/mifrbfUB/3GsuJPV9QECnDXTaCH5ANBeSplQsfkDlQtKUild+QKVUnH34fH5AD1g5tMiKfkDZh8+wK5h+QJIYBFYOpX5A61G4HoWvfkDLWh0TPLh+QD81XrpJxH5ALbKd76fKfkA6baDTBsp+QGWtjgkewH5A5kLSlIqzfkDF2YfPsKN+QKfGSzeJlX5ARbbz/dR0fkDvp8ZLN1l+QP0boYzePn5A+n5qvHQnfkD0tqzVMRF+QMGDeaL++X1A9G+EMnrbfUB+arx0k8B9QP2NUEZvo31AMTMzMzOPfUAa6LSBTnN9QMfobVmrW31AM166SQxKfUBTVVVVVUl9QMZ2vp8aT31A/v////9TfUDWFSD3mld9QEo3iUFgYX1A1OqY4MGIfUBiO99Pja99QMPZh8+w231AbFmrY4IHfkDSvxHK6Cl+QMA8Uf9GTH5ATKk4+/BZfkBnSsXZh2t+QOIXS36xlH5AwPUoXI+qfkACD+aJ+rd+QBWSplScvX5ADxERERHJfkAgIiIiItp+QNK/Ecro0X5A0zHBg3m+fkAkMQisHJZ+QMFn2BUgc35AtWWtjglWfkBjrY4JHkR+QGRmZmZmJn5A8Islv1gOfkAPEREREfl9QP5GKKO31X1A1lxImlK5fUACVg4tsp19QIUW2c73h31AwDxR/0Z0fUAxelvW6lh9QBQg95oLTX1ADLsC5F47fUC1rNUxwSd9QMRLN4lBEH1AFy/dJAb5fEDKE/VvhNZ8QFSA3GsuwHxABawcWmS/fEBA0pSKs7t8QDa0yHa+r3xAp6qqqqqmfEBVObTIdpZ8QPZ+arx0d3xA9zdCGb1ZfEClOPvwGTp8QO+LJb9YGnxAc3d3d3cHfEA1tMh2vgN8QJXgwTxRC3xAMV66SQwafEAVdgXIvTJ8QCPq3whlTHxA+RuhjN5SfEAlzj58hll8QFyeqH8jYHxAgV0Bcq9lfED6Riijt2l8QOGJ+jdCiXxAZAOdNtChfEDDveZC0rx8QDp8hl0B2nxAvvUoXI/ufEBGfrHkFwN9QCmyne+nDn1AsTomeDATfUAVvS1rdSB9QM7bslbHKH1AvoN5ov41fUAf2/l+akB9QACdNtBpV31Aej81XrptfUC+g3mi/oF9QDW0yHa+h31AuuZC0pSSfUC65kLSlJp9QESaUnH2nX1A70T9G6GgfUBEmlJx9qF9QNB46SYxqH1Azk1iEFitfUBV1uqY4KV9QG1oke18n31AvDxR/0aYfUA9Gb0ta419QO1E/RuhfH1AEgRWDi1qfUDNTWIQWFV9QNIVIPeaT31AXBBYObREfUD3jVBGbzt9QLLXXEiaOn1AS7gehetBfUAmsp3vp0p9QCB4ME/US31AjhgEVg5ZfUAJ5on6N259QN2lm8QgkH1AHZTR27KufUBGYhBYOdh9QCIVZx8+A35A4bSBThtAfkAl+cWSX4B+QAd02kCnrX5A99R46SbdfkCvgU4b6Ah/QEtGb8taLX9A3xdLfrFMf0BtaJHtfGt/QPnUeOkmjX9AZuf7qfGuf0ADHswT9ct/QObDZ9gV2H9Axz58hl3pf0BsEoPAygGAQK25kDSlDIBA2fl+arwSgEAXL90kBheAQDMXkqZUHIBAFgRWDi0ggEAzF5KmVCqAQKY4+/AZOoBAa1mrY4JNgEBbukkMAmGAQP1xr7mQdIBAWyz5xZKDgEBJ8GCeqJOAQMgT9W+EpoBAR8XZh8+6gEB1ME/Uv8mAQBa9LWt12IBAHIXrUbjigEA00GkDne6AQDx8hl0B7IBAPe41F5LmgEBj9Las1d2AQFsBcq+50IBATNS/Ecq+gEBjrY4JHrCAQHz4DLsCooBAi96WtTqQgEACnTbQaX+AQAXzRP0bbYBA9gy7AuRSgEBGmlJx9juAQPfFkl8sJ4BACpB7zYUagEBWObTIdgiAQCEiIiIiBoBAez81XroHgEAAK4cW2QSAQOUmMQis/H9ARn6x5Bfrf0DVQKcNdN5/QFCcffgMy39A+o1QRm/Hf0CmHFpkO8d/QI2mVJx9yH9AWnMhaUrVf0BP46WbxNh/QEkb6LSBzn9AU/KLJb/Af0DvtqzVMb1/QGYuJE2pvH9A5N8IZfTGf0D5jVBGb9t/QDtg5dAi939A4V5zIWkKgEAkMQisHBKAQHdb1uqYHoBA+9R46SYngEAesHJokS2AQGNmZmZmNoBAFEt+seQ7gEAPg8DKoUeAQG+vuZA0XYBAipduEoNygEBQKs4+fIyAQPBE/RuhqIBAunSTGATAgEDkQtKUitWAQN7BPFH/7IBAgXmi/o0GgUB9I5TR2yqBQI+mVJx9TIFALCRNqThtgUAbEzyYJ4qBQCFNqTj7rIFAyaFFtvPZgUA+7jUXkgSCQBa9LWt1LoJAD8robVlTgkA97jUXknyCQC4IrBxamIJAPxm9LWuxgkDDSzeJQdSCQByF61G4+oJA9/AZdgUkg0Ak6t8IZUiDQADkXnMheYNAKUDuNReqg0BmA5020NmDQJBfLPnFBIRAU8cED+YphEDviyW/WFSEQBpaZDvff4RAc76fGi+rhEB8arx0k9KEQLufGi/dCoVAxqFFtvNNhUDdwTxR/4KFQA88mCfqn4VAkfxiyS+qhUBJYhBYOaCFQOK0gU4bmIVAUccED+aFhUDzxZJfLHGFQNFcSJpSW4VAO6cNdNo+hUAN9W+EMiaFQL4gsHJoDYVAXoIH80T/hEA8YOXQIueEQIrC9Shcy4RAZuf7qfGwhECsne+nxp2EQBDZzvdTnYRA3xdLfrGmhEDQMcGDecKEQBqF61G41oRAQ5pScfb9hED/nDbQaSuFQC7Bg3miWIVAs6zVMcF/hUA+pw102pqFQNf5fmq8rIVAJocW2c6zhUBkSsXZh7WFQP+cNtBptYVAsvP91HithUBcLPnFkqmFQONC0pSKq4VA7Rl2Bci1hUB6+Ay7AsSFQC8zMzMzxYVA4F5zIWm+hUAKLbKd77GFQMxpA502oIVAhEFg5dCYhUCH3pa1OoKFQHCTGARWaoVAog102kBLhUBeEFg5tEKFQEob6LSBNIVAi3vNhaQfhUCHl24SgwyFQBQv3SQG7YRAJYcW2c6/hEBAb8taHYWEQOVRuB6FT4RAjKZUnH0QhEC3LWt1TMSDQOCJ+jdCiYNAIHgwT9RJg0DEoUW28xODQJfvp8ZL34JARMXZh8+ygkBAKKO3ZYOCQI6Ksw+fPYJAtALkXnPxgUDQo3A9CqOBQMAED+aJWoFAjdHbslYfgUBMKs4+fPKAQPWp8dJNyIBAYdgVIPekgEDqYJ6of52AQKFUnH34hoBAyT58hl1zgED8uNdcSFqAQFyeqH8jRIBATbgehesrgEAqlvxiyQ2AQNOWtTom/H9A+zltoNPaf0CnyHa+n7Z/QDn9G6GMmn9APgwCK4d2f0BvFK5H4TZ/QOSnxks35X5AFiIiIiKafkA2Gb0tay1+QIU0peLsw31AzofPsCtIfUDPQKcNdP58QAsg95oLtXxAZ6+5kDR1fECgqqqqqip8QELwYJ6o43tAH1yPwvXIe0BCN4lBYJ17QE/yiyW/cHtAORm9LWtle0ALZx8+w157QNuJ+jdCbXtAPCijt2Vpe0A2YOXQIm97QJF9+Ay7dntAc7HkF0uue0BQ1uqY4Ml7QF4DnTbQ2XtAKaXi7MP3e0Daifo3QiF8QBzq3whlVHxAP36x5BdvfECjuZA0pZZ8QFSeqH8jsHxAormQNKWmfEBQSJpScaJ8QEONl24Sn3xAK4lBYOWkfEAXTak4+xB9QELUvxHKeH1AbOkmMQjYfUCE7Xw/NS5+QHUH80T9i35A2vup8dLZfkCSGi/dJCp/QPQqhxbZin9AmRov3SQAgEC8PFH/RkKAQPKaC0lThIBAGoXrUbjQgED+415zIRuBQGOR7Xw/XYFA2k+Nl26kgUDVQKcNdLaBQJWZmZmZz4FAhIiIiIjegUDbT42Xbu6BQCSjt2WtAoJAllJx9uEVgkC8EcrobSOCQJcLSVMqLoJAb6+5kDQhgkBKqTj78AmCQKbx0k1i9oFAhM+wK0DkgUD5YskvltqBQBuF61G43IFAje18PzXegUC1SQwCK9+BQPAoXI/C1YFAd/gMuwLQgUDNTWIQWNWBQFXW6pjgzYFAsmWtjgnCgUDXJAaBlbWBQG4haUrFo4FADcrobVmlgUAC80T9G7GBQJoaL90kwIFAm4zelrXEgUDWJAaBlcGBQHuVQ4tstYFAm9MGOm2ggUDCBA/miZCBQG2vuZA0g4FA1M73U+N3gUCnHFpkO1mBQJK1OiZ4RIFA/1UOLbIzgUAR2c73UymBQIt7zYWkH4FAPGDl0CIXgUAd2/l+agiBQKWqqqqq/oBA6xl2BcjvgEDZlrU6JuSAQCtP1L8R1oBAfzJ6W9bIgEAW6LSBTrmAQLa7u7u7tYBA53w/NV6sgECSJ+rfCJOAQHuVQ4tsc4BAvmfYFSBZgEA7pw102kCAQLoRyuhtJYBAPRm9LWsNgEBQHRM8mNN/QPmcNtBph39AZT0K16NIf0DDPnyGXQF/QE6rY4IHu35ABJ9hV4B8fkDX7MNn2FV+QNFrLiRNIX5AhML1KFznfUA+mlJx9vV9QFaeqH8jGH5AitHbslYzfkDyG6GM3j5+QJF9+Ay7Xn5AHDEIrBxyfkBXEFg5tIh+QPJiyS+WkH5AzaNwPQqTfkDfJjEIrKB+QCl6W9bquH5A/Me95kLOfkDLeOkmMch+QF6R7Xw/tX5AWjvfT42vfkD0Riijt41+QKys1THBZ35AU7pJDAI/fkBHuB6F6wl+QHBb1uqYyH1AoPHSTWJofUD0jVBGbxN9QDR8hl0BwnxAitHbslaLfECmK0DuNVd8QBmU0duyGnxAgSW/WPLve0CyLWt1TLB7QDu28/3UkHtAt/UoXI92e0ApMzMzM1N7QL695kLSLHtAFrByaJENe0AK9W+EMgJ7QN60gU4b9HpAufUoXI/KekCmK0DuNbd6QO5T46WbtHpATjm0yHaGekAsF5KmVFB6QCXdJAaBDXpAmykVZx+meUDkNReSplR5QKYrQO4133hAfs+wK0BieEBW5dAi2y14QHUjlNHb5ndAAZB7zYWAd0DPh8+wKyR3QGkhaUrF5XZA+g7mifrjdkC62YfPsLt2QIpDi2znf3ZAqjomeDBXdkAc6t8IZSR2QAxLfrHk93VA0pa1OiYQdkDCPnyGXSV2QJcpFWcfRnZADui0gU5jdkDJMcGDeT52QFXJL5b8QnZA98e95kJGdkBRukkMAi92QPUqhxbZAnZAdsDKoUXCdUDVM+wKkIt1QLHmQtKURnVAQPBgnqgbdUASzBP1b/x0QPzyRP0b3XRADy/dJAaRdECxdJMYBD50QKYrQO4103NAn38jlNFrc0A8tvP91AxzQPe411xIsnJARtS/Ecp8ckD8DuaJ+lNyQEoqzj58DnJAjEOLbOffcUDxN0IZvb1xQH4W2c73q3FANXyGXQGOcUAXsHJokYVxQCKHFtnOi3FAfhbZzveXcUDKTWIQWK1xQFVzIWlK6XFAT/KLJb8cckD+8kT9G2VyQIOXbhKDiHJACTyYJ+qbckB5wMqhRa5yQG13d3d3z3JAjEOLbOfnckCXRbbz/QBzQE7yiyW/HHNARY2XbhIrc0BWnqh/IyxzQMSwK0DuLXNAGgaBlUM7c0CgY4IH81hzQIy1OiZ4bHNA9nGvuZB4c0CMtTomeHhzQDQ1XrpJeHNAdCOU0dt6c0AU95oLSX9zQHukKRVng3NAE8wT9W+Ec0Aja3VM8IBzQBMTPJgnbnNAy3jpJjF0c0AhhxbZzndzQLeDeaL+iXNA1ghl9LaMc0Aj+cWSX5BzQAAezBP1n3NAnJvEILC2c0ApelvW6sRzQFytjgke4HNAcz81Xrr9c0CvHoXrURx0QPNiyS+WVHRA+1UOLbKhdEAmT9S/EfZ0QHEUrkfhRnVAJd0kBoGBdUDe+6nx0r11QBY+w64A4XVADSD3mgv1dUBcrY4JHgx2QH1dAXKvBXZAES/dJAYJdkB6B/NE/f91QOy2rNUxBXZAUDm0yHYGdkC/SzeJQQB2QO1vhDJ673VAG9v5fmrAdUBXLPnFkod1QMtNYhBYXXVAswLkXnMldUCM0duyVut0QGK8dJMYpHRAE+i0gU5/dEDuKFyPwll0QEM3iUFgRXRAU9bqmOAtdECAQWDl0CJ0QHDpJjEIGHRAafbhM+wOdEAqMzMzMwd0QPkqhxbZ/nNA9kYoo7cVdEA3pw102ix0QD7hehSuR3RARBvotIF6dEA/UyrOPqR0QOVgnqh/23RAK166SQwGdUBmPQrXoyx1QIftfD81VnVAMd9PjZeGdUBhdUzwYLp1QB8VZx8+03VAJU/UvxHydUBtd3d3dx92QCzQaQOdUnZAtjxR/0ZodkAKZx8+w4p2QNlecyFpunZA/h3ME/XjdkCAbOf7qQV3QE+rY4IHL3dA5KfGSzdVd0BU5dAi23V3QKO5kDSlkndADEt+seSjd0Ac6t8IZbR3QHukKRVn13dARv9GKKP7d0D/1qNwPRJ4QDnSlIqzK3hAatpApw1QeECLirMPn2l4QLJ0kxgEcnhAHTEIrByCeEBZEFg5tKB4QPqcNtBps3hArfP91Hi5eECOtTomeLx4QJQ20GkDvXhA60T9G6HIeEBCxdmHz9h4QG4FyL3m3nhAb3d3d3ffeEAdBoGVQ994QOQKkHvN4XhA/4CVQ4vkeEAGLbKd7+t4QMewK0DuAXlAVUiaUnEWeUBGYhBYOSx5QM+jcD0KT3lAc82FpCmFeUBrIWlKxcF5QGYSg8DK9XlANFH/RigfekC8Z9gVIEN6QAkRERERZXpAle+nxkuDekBerY4JHpx6QIGIiIiIqHpAi18s+cWyekAho7dlrbZ6QHU/NV66uXpA45jgwTylekAZPsOuAI16QPfUeOkmdXpAWbpJDAJbekAUdgXIvUZ6QDxg5dAiN3pA1IfPsCsoekAHdNpApxl6QKyd76fGA3pAYkrF2YfzeUAvXrpJDN55QEv/Riijz3lAP/0boYzKeUDt/dR46cp5QH+kKRVnz3lAT8cED+bheUB8eaL+jQR6QOXDZ9gVKHpAYkrF2Yc3ekBcEFg5tDx6QNkIZfS2QHpAJiRNqThHekCGUEZvy1p6QFks+cWSg3pAAfNE/RupekCrK0DuNdd6QKsrQO41+3pARMXZh88ge0AHuwLkXj97QHvcay4kXXtAYa2OCR58e0DGoUW284l7QIfelrU6kntA+RuhjN6ue0BLjZduEst7QP0qhxbZ5ntAqY4JHswHfEBhZmZmZiJ8QPxxr7mQMHxAZi4kTakwfEDUQKcNdDZ8QB6U0duyPnxA1ECnDXROfECeKRVnH2Z8QDK0yHa+d3xAvPUoXI+KfED/gJVDi5x8QFlzIWlKrXxAtALkXnO1fED8KocW2bp8QErUvxHKvHxA/SqHFtm2fEBuIWlKxbF8QDdR/0Yos3xA5gqQe83BfED1N0IZvdV8QEkb6LSB8nxA1rJWxwT/fECet2Wtjgl9QOTfCGX0En1AGj7DrgAlfUD2qfHSTT59QEEoo7dlWX1Ap44JHsx7fUBC4XoUrqN9QGeg0wY60X1Aj0OLbOf/fUDttqzVMSV+QK2BThvoTH5A7W+EMnp3fkAs7AqQe5l+QP85baDTqn5AYrx0kxi4fkDhbVmrY8Z+QBc+w64A3X5AS3H24TP8fkBUj8L1KBR/QLHXXEiaLn9AdIZdAXJLf0C0LWt1TGh/QE0OLbKdj39AXtgVIPeyf0BTj8L1KPB/QKu5kDSlFIBAmTbQaQMxgEAQrkfhek6AQBkTPJgncoBAmTbQaQOXgEAxXrpJDLqAQNIxwYN51IBAM9BpA53ggEC+PFH/RvSAQDmYJ+rf/oBAVIDcay4EgUCcGi/dJAqBQIZBYOXQDIFAR8XZh88WgUDyb4QyeheBQJUn6t8IHYFAkRgEVg4lgUD8jVBGby+BQJBfLPnFNoFA/nGvuZBAgUBxkxgEVkyBQNG/EcroW4FAm++nxktvgUDewTxR/3qBQBIg95oLjYFAgTJ6W9amgUAiBoGVQ7+BQDHsCpB714FAbctaHRPogUAKAiuHFvmBQEXhehSuB4JAVvKLJb8SgkDBrgC51xqCQL+DeaL+I4JAH7ByaJErgkBXZDvfTzGCQDb78Bl2N4JAibMPn2E/gkBcAXKvuUiCQJW1OiZ4VoJATGIQWDlcgkAzXrpJDGSCQGBXgNxraIJAGui0gU5xgkDJ6G1Zq3mCQMnobVmrf4JAwfUoXI+MgkD68Bl2BZ6CQM9pA502soJAFkt+seTDgkBw9uEz7NaCQP2NUEZv54JAylodEzz6gkAqQO41FwyDQJFfLPnFEoNAH/eaC0kTg0Cit2WtjhWDQIqzD59hGYNAz2kDnTYWg0B36SYxCBCDQPrwGXYFCoNAwmfYFSD5gkCqqqqqquiCQPrwGXYF0oJAeVvW6pi+gkBpA5020JeCQDxR/0YobYJAVoDcay5AgkBOG+i0gRSCQJIYBFYO7YFAgzJ6W9bKgUC2Za2OCaaBQKCM3pa1gIFATWIQWDlmgUCyVscED0aBQAU6baDTJoFAfPgMuwIOgUDeCGX0tvKAQFFx9uEz2oBAjsL1KFzBgEAwCKwcWriAQBQg95oLuYBAzMzMzMy2gEBFKKO3ZbeAQAErhxbZuoBAgE4b6LTBgEDLzMzMzMKAQGHJL5b8woBAV2Q730+7gEDVXEiaUruAQC1P1L8RvIBAY62OCR7AgEA+pw102sSAQFgdEzyYz4BAntMGOm3egEDqw2fYFe6AQFmPwvUo+IBAgXmi/o0MgUDP27JWxyCBQDb78Bl2NYFANBeSplRAgUBlZmZmZlCBQL3mQtKUYoFAVccED+Z3gUDXFSD3momBQIMyelvWloFAT42XbhKbgUD0b4QyeqGBQFXHBA/mpYFACwIrhxalgUBPjZduEqWBQEP9G6GMqIFAXiz5xZKjgUCgRbbz/ZyBQFxImlJxmIFAAeRecyGbgUBGUyrOPpqBQKD+jVBGm4FANUIZvS2bgUCEFtnO96GBQJyofyOUsYFA8f3UeOnGgUB5zYWkKduBQCr5xZJf1IFASvBgnqjNgUB+ThvotMmBQAkCK4cWw4FA5G1Zq2O4gUDB2YfPsKuBQOAXS36xloFAJocW2c6BgUBvaJHtfG+BQL/1KFyPXIFAqWOCB/NGgUASrkfhejCBQJeZmZmZBYFAktHbslbbgEDqCpB7zaeAQEUoo7dliYBATGIQWDl6gEACVg4tsmGAQBWSplScR4BAd6L+jVA0gEBzkxgEViCAQL7KoUW2E4BA0U1iEFgDgEATkqZUnOF/QMS95kLSpH9AVx0TPJhrf0A330+NlzJ/QJDR27JWE39AEWcfPsPufkA7fIZdAeZ+QL2DeaL+3X5AZrx0kxi0fkAJuwLkXqN+QFYdEzyYo35AsMh2vp+WfkC7WPKLJX9+QN9ecyFpZn5AGcwT9W9MfkAFkHvNhUR+QO791HjpUn5Ah1BGb8tefkCWNtBpA3V+QAA6baDTjn5AhwkezBOpfkAZ95oLSbN+QN1ecyFptn5APv0boYy6fkDPMcGDea5+QEapOPvwjX5AlAtJUypyfkAgeDBP1GN+QJ3+jVBGQ35APkREREQkfkCXqH8jlPF9QJQLSVMqun1Aj0OLbOeHfUCqcmiR7VR9QOSY4ME8JX1AzzHBg3kCfUBlLiRNqeh8QMxNYhBY6XxAjF8s+cXufEDfQtKUivt8QNOyVscE63xA5MNn2BXkfECE3pa1Ou58QNOyVscEB31AoMZLN4kxfUDub4Qyemd9QAGsHFpkn31AN8OuALnbfUDZlrU6JgB+QM8xwYN5Hn5AnEW28/0wfkAAgZVDi0B+QOqnxks3QX5A7ras1TFRfkDLaQOdNmB+QGRKxdmHi35AVWQ730+1fkCwgU4b6Mx+QGW8dJMY3H5ArZ3vp8bjfkCjfyOU0d9+QMtpA5023H5AQ5pScfbZfkBQDi2yndd+QDP78Bl2xX5AAvNE/RutfkCXNtBpA5F+QHn4DLsCaH5Aup8aL90wfkAJLbKd7/N9QCqW/GLJx31ATw4tsp2PfUDsiyW/WFZ9QL2uALnXNH1AJUDuNRcWfUCgVJx9+AR9QOZ8PzVe7nxAgvo3QhnZfECVffgMu9J8QHWGXQFy23xADyD3mgvxfEDLItv5fgJ9QP5VDi2yIX1AUccED+Y9fUBC4XoUrmd9QJvTBjptnH1AY5HtfD/dfUDPBjptoCN+QLMehetRXH5AXRBYObSgfkDCveZC0uB+QK2d76fGI39A4BdLfrFgf0ASkqZUnJV/QONtWatjyn9AfNxrLiT1f0BEKKO3ZRmAQPThM+wKNoBA25a1OiZQgEBM1L8RymiAQAkCK4cWeYBAQYts5/uHgEADgZVDi5aAQHS+nxovpYBA2fl+arzMgECc76fGS/uAQHei/o1QJoFAP2Dl0CJRgUAE80T9G3+BQDX78Bl2rYFABWX0tqzfgUDEBA/miQqCQHGTGARWNoJAXCz5xZJlgkADOm2g046CQHmGXQFyt4JAFy/dJAbfgkA3baDTBgiDQNMxwYN5NINAjAkezBNBg0DRvxHK6EWDQKNUnH34ToNAL3pb1upWg0DC2YfPsFmDQBJnHz7DVINAIr9Y8otJg0Bp5/up8TaDQMUvlvxiJYNAjHvNhaQZg0B3hl0Bcg2DQBBnHz7DAINAhbMPn2HvgkAJLbKd7+GCQEbwYJ6oz4JAFHYFyL24gkC+rgC515KCQDbfT42XcoJAi8L1KFxXgkBqEoPAyj+CQM0i2/l+MoJAcNpApw0ugkDB2YfPsDGCQMywK0DuNYJAFJKmVJwzgkAQg8DKoTWCQJeZmZmZN4JAcGiR7Xw7gkB2ME/UvzmCQBYEVg4tOoJAy4WkKRVBgkCxD59hV1qCQP6411xIcIJAHrByaJGBgkDnmODBPJGCQE9x9uEznIJAVx0TPJijgkB16SYxCKiCQA7K6G1ZqYJA5Pup8dKvgkDk+6nx0rOCQEk3iUFgu4JAdTBP1L/HgkAra3VM8NSCQH6VQ4ts4YJAd1vW6pjsgkAHHswT9fOCQM6wK0Du+YJAP+41F5IEg0AdzBP1bxiDQIslv1jyKYNAxNmHz7A3g0D2U+Olmz6DQLu7u7u7R4NAd+kmMQhQg0BB0pSKs1eDQKZ/I5TRW4NAvVjyiyVVg0A+7jUXkkyDQHoUrkfhSoNAGXYFyL1Ig0A7Uf9GKEWDQCQxCKwcQoNAgjJ6W9Y8g0At3SQGgTGDQAu7AuReKYNAnWFXgNwjg0BzTPBgniqDQDzDrgC5M4NAQYts5/s1g0CmOPvwGTqDQHNM8GCePoNAICIiIiJOg0DGL5b8YlODQKTGSzeJU4NAoHA9CtdRg0DuGXYFyFGDQE2Nl24SU4NAXuXQIttLg0DUMcGDeUKDQJFfLPnFPoNAc5MYBFY2g0DGveZC0iSDQIBOG+i0EYNAHcwT9W8Ag0CcNtBpA+mCQIPrUbgez4JATo2XbhKvgkAGrBxaZJmCQNQxwYN5fIJAe82FpCljgkDVo3A9CkmCQI7C9ShcNYJAfiOU0dsogkBv9uEz7B6CQEO28/3UEoJAAVYOLbIHgkAdPsOuAP+BQI3C9Shc+YFA79JNYhD0gUBsWatjgveBQKC3Za2O/4FAn0W28/0IgkAXdgXIvQiCQB9pSsXZEYJAbBKDwMobgkDx/dR46SSCQEo3iUFgJYJArI4JHswhgkBrLiRNqR6CQNR46SYxGoJAt2WtjgkAgkBcj8L1KOaBQHTaQKcNxoFA46WbxCCogUAqFWcfPoeBQJB7zYWkY4FAqvHSTWJGgUAJZfS2rDWBQK4AuddcIoFA76fGSzcPgUBqvHSTGPqAQLSBThvo4oBAdJMYBFbQgEDOsCtA7rmAQLmQNKXiooBA91PjpZumgEAVIPeaC6+AQJe1OiZ4toBAPgrXo3C7gEBqA5020L2AQMI8Uf9GuoBA3LJWxwS5gEDR27JWx7SAQPS2rNUxsYBAkaZUnH2mgEC7u7u7u5uAQEYoo7dlk4BAy8zMzMyKgEDTeOkmMYKAQIuXbhKDeoBAFUt+seR1gECMUEZvy2yAQD6nDXTaaIBAlbU6JnhigEBLqTj78F2AQEJEREREXoBAR1Mqzj5YgEBiggfzRFGAQAdl9LasRYBAibMPn2E7gEC2Za2OCTaAQBtaZDvfLYBAnah/I5QjgEBRuB6F6xGAQIn6N0IZ/X9AJqO3Za3Wf0Cr1THBg61/QFEqzj58en9ATo2XbhJHf0AEyL3mQh5/QLQ6JngwA39A3Za1OibsfkAG80T9G+F+QEUoo7dl7X5A6d8IZfQKf0A3tMh2vid/QPup8dJNVn9Ad+kmMQh4f0AG80T9G6V/QGT0tqzV2X9AkxgEVg4FgEBCGb0taxuAQHnpJjEIWoBA8tJNYhCagEDPsCtA7tmAQNqHz7ArEIFASgwCK4dKgUBXgNxrLoaBQGFXgNxruoFAVzm0yHbqgUBO1L8RyhCCQMd2vp8aM4JA0k1iEFhZgkD6N0IZvYOCQHoUrkfhooJA3k+Nl27EgkCyD59hV96CQN1PjZdu1IJAsQ+fYVe6gkDLzMzMzKqCQOvDZ9gVkoJAWo/C9Sh6gkBEb8taHV2CQFWA3GsuPIJAT/9GKKMPgkAgIiIiIuiBQB6wcmiRr4FAfpVDi2xzgUAs3SQGgTmBQALIveZCEoFAWwFyr7nogEAtT9S/EcqAQAfXo3A9roBAxAQP5omqgEBJfrHkF5mAQMrMzMzMlIBA3cE8Uf+EgEDHWh0TPHCAQONC0pSKXYBA9Qy7AuRagEBd5dAi21uAQEsb6LSBZoBAAJ020Gl7gEAfIiIiIoyAQDAzMzMzk4BA8m+EMnqdgECDXQFyr6uAQITPsCtAtoBAvjxR/0a6gEDyb4QyermAQOs1F5KmroBAbvbhM+yqgEBjHz7Drq6AQGH0tqzVt4BADRERERG1gEAIAiuHFrGAQFzl0CLbq4BAgqQpFWeZgEDlJjEIrIqAQGzLWh0TfIBALCRNqThtgEDmbVmrY1yAQNVcSJpSV4BAMuwKkHtVgEBaSJpScVqAQLeQNKXiboBAFgRWDi2SgECD61G4Hq2AQOFecyFpyIBALk/UvxHkgEA3baDTBgKBQEJEREREKoFArEfhehRggUBAGb0ta4uBQGrn+6nxtoFAPMOuALnjgUAOERERERGCQOs1F5KmLoJAHbByaJFBgkAqa3VM8FSCQPFvhDJ6aYJAXOXQIttrgkBD4XoUrnmCQNNcSJpShYJAoCkVZx+QgkCN7Xw/NZaCQLe7u7u7n4JAe2q8dJOogkDkbVmrY7qCQCaHFtnOx4JAIE2pOPvIgkAUBFYOLcaCQAstsp3vv4JAKrKd76fAgkBHxdmHz7iCQOFecyFppoJA14fPsCuigkCyyHa+n5SCQP1GKKO3jYJAgXmi/o2UgkC1Za2OCaKCQCIGgZVDq4JAXLpJDAKxgkDsNReSprSCQEl+seQXt4JApA102kDFgkAnzj58htWCQIzC9Shc7YJAh2zn+6kBg0A1baDTBhyDQNMVIPeaPYNAIL9Y8otPg0D9KocW2WiDQGfn+6nxdoNAOQrXo3B/g0DBSzeJQYaDQPHhM+wKjoNAwgQP5omWg0Ajo7dlraCDQHVb1uqYsINAeyOU0dvEg0C3u7u7u9uDQKTGSzeJ7YNAfGq8dJP+g0AeIiIiIgCEQO9E/Ruh/oNAQbbz/dQAhEBXSJpScQCEQPv//////4NAgKQpFWf/g0DcehSuR/+DQC16W9bq/oNAfcDKoUXig0AJLbKd78ODQBv3mgtJoYNA3qWbxCBug0AelNHbsjiDQD/9G6GMDoNAwJJfLPnngkDEL5b8YtGCQMO95kLSuoJAkyfq3wilgkBfggfzRJOCQEvUvxHKioJAOVH/Rih/gkB8I5TR23KCQNrd3d3dY4JAjqZUnH1ugkAbhetRuHyCQGa8dJMYjIJAUVVVVVWfgkDj+6nx0rOCQOHQItv5wIJArw+fYVfUgkByvp8aL+OCQKaqqqqq8IJA1M73U+P9gkBFxdmHzwSDQG4haUrFCYNAEkt+seQPg0D2YskvlhaDQCSHFtnOG4NAwEs3iUEgg0Ag6t8IZRyDQIFBYOXQGoNARPBgnqgbg0Bxov6NUBiDQPRiyS+WGINAsddcSJoag0AviUFg5R6DQKyBThvoJoNAG02pOPsug0BXnqh/IzyDQMx46SYxRINA5xl2BchPg0Ae6t8IZVyDQKmd76fGZ4NASirOPnx6g0ApelvW6pCDQFCrY4IHq4NAzqNwPQrBg0BtBci95taDQC9CGb0t7YNAl9MGOm36g0CmuZA0pQSEQC7QaQOdCoRAek4b6LQNhEDNvxHK6AmEQGWg0wY6C4RAAPNE/RsRhEDD6G1ZqwWEQM546SYx8oNAXMkvlvzcg0CTUnH24ceDQGwhaUrFqYNAGmlKxdmNg0B3arx0k3aDQIXelrU6XoNAXx8+w65Mg0CO/GLJL0KDQCVrdUzwOINAhgkezBMrg0A+b8taHR+DQCKHFtnOD4NADK5H4Xr6gkAwtMh2vuuCQH+IiIiI7IJAfs+wK0DugkAyUf9GKO+CQH9BYOXQ9oJAd04b6LT7gkB761G4Hv+CQON8PzVeBINALdBpA50Og0Ajsp3vpxKDQHEUrkfhCoNAn38jlNEPg0CQUnH24Q2DQIoYBFYOD4NA+eNecyELg0CD3pa1OgiDQG6+nxovAYNA5MNn2BX0gkDZwTxR/+SCQMNaHRM8zoJAMm2g0wbAgkAfMQisHK6CQN/7qfHSnYJAB1g5tMiCgkDq0k1iEGyCQE2cffgMW4JA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Application\",\"version\":\"2.1.1\"}};\n", + " var render_items = [{\"docid\":\"dbedc8d6-403b-416f-848d-54320ed5aea2\",\"root_ids\":[\"1990\"],\"roots\":{\"1990\":\"f9453a2e-ecd4-48b8-9f15-156eb6945f46\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", @@ -835,13 +882,14 @@ " 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", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", " }\n", " }, 10, root)\n", " }\n", @@ -851,35 +899,441 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "2146" + "id": "1990" } }, "output_type": "display_data" } ], "source": [ - "bt.plot()" + "bt.plot(plot_volume=False, plot_pl=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Strategy optimization managed to up its initial performance _on in-sample data_ by almost 70% and beat \n", + "Strategy optimization managed to up its initial performance _on in-sample data_ by almost 50% and even beat simple\n", "[buy & hold](https://en.wikipedia.org/wiki/Buy_and_hold).\n", - "In real life, however, **always take steps to avoid\n", - "[overfitting](https://en.wikipedia.org/wiki/Overfitting)**\n", - "before putting real money at risk.\n", + "In real life optimization, however, do **take steps to avoid\n", + "[overfitting](https://en.wikipedia.org/wiki/Overfitting)**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trade data\n", "\n", + "In addition to backtest statistics returned by\n", + "[`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run)\n", + "shown above, you can look into _individual trade returns_ and the changing _equity curve_ and _drawdown_ by inspecting the last few, internal keys in the result series." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sortino Ratio 0.39\n", + "Calmar Ratio 0.03\n", + "_strategy SmaCross(n1=10,n2=15)\n", + "_equity_curve Equity DrawdownPct DrawdownDura...\n", + "_trades Size EntryBar ExitBar EntryPrice Exit...\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The columns should be self-explanatory." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + 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" + ], + "text/plain": [ + " Equity DrawdownPct DrawdownDuration\n", + "2004-08-19 10000.00 0.00 NaT\n", + "2004-08-20 10000.00 0.00 NaT\n", + "2004-08-23 10000.00 0.00 NaT\n", + "2004-08-24 10000.00 0.00 NaT\n", + "2004-08-25 10000.00 0.00 NaT\n", + "... ... ... ...\n", + "2013-02-25 103035.53 0.05 NaT\n", + "2013-02-26 102952.33 0.05 NaT\n", + "2013-02-27 104206.83 0.04 NaT\n", + "2013-02-28 104391.43 0.04 NaT\n", + "2013-03-01 105040.13 0.03 533 days\n", + "\n", + "[2148 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats['_equity_curve'] # Contains equity/drawdown curves. DrawdownDuration is only defined at ends of DD periods." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SizeEntryBarExitBarEntryPriceExitPricePnLReturnPctEntryTimeExitTimeDuration
0872060114.65185.236140.560.622004-09-172004-11-1256 days
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3-967175180.35179.13116.980.012004-11-302004-12-066 days
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" + ], + "text/plain": [ + " Size EntryBar ExitBar EntryPrice ExitPrice PnL ReturnPct \\\n", + "0 87 20 60 114.65 185.23 6140.56 0.62 \n", + "1 -87 60 69 184.86 175.80 788.18 0.05 \n", + "2 96 69 71 176.15 180.71 437.61 0.03 \n", + "3 -96 71 75 180.35 179.13 116.98 0.01 \n", + "4 97 75 82 179.49 177.99 -145.33 -0.01 \n", + ".. ... ... ... ... ... ... ... \n", + "147 -121 2056 2085 738.65 687.78 6155.24 0.07 \n", + "148 139 2085 2111 689.16 735.54 6447.44 0.07 \n", + "149 -139 2111 2113 734.07 742.83 -1217.79 -0.01 \n", + "150 136 2113 2121 744.32 735.99 -1132.29 -0.01 \n", + "151 -136 2121 2127 734.52 750.51 -2174.91 -0.02 \n", + "\n", + " EntryTime ExitTime Duration \n", + "0 2004-09-17 2004-11-12 56 days \n", + "1 2004-11-12 2004-11-26 14 days \n", + "2 2004-11-26 2004-11-30 4 days \n", + "3 2004-11-30 2004-12-06 6 days \n", + "4 2004-12-06 2004-12-15 9 days \n", + ".. ... ... ... \n", + "147 2012-10-16 2012-11-29 44 days \n", + "148 2012-11-29 2013-01-08 40 days \n", + "149 2013-01-08 2013-01-10 2 days \n", + "150 2013-01-10 2013-01-23 13 days \n", + "151 2013-01-23 2013-01-31 8 days \n", + "\n", + "[152 rows x 10 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats['_trades'] # Contains individual trade data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "Learn more by exploring further\n", "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", - "or find more program options in the\n", + "or find more framework options in the\n", "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." ] } ], "metadata": { - "jupytext_format_version": "1.1", + "jupytext": { + "text_representation": { + "format_version": "1.1" + } + }, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -895,7 +1349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/doc/examples/Quick Start User Guide.py b/doc/examples/Quick Start User Guide.py index 8c95878c..1a5963fa 100644 --- a/doc/examples/Quick Start User Guide.py +++ b/doc/examples/Quick Start User Guide.py @@ -6,7 +6,7 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.3.0 +# jupytext_version: 1.5.1 # kernelspec: # display_name: Python 3 # language: python @@ -16,37 +16,40 @@ # _Backtesting.py_ Quick Start User Guide # ======================= # -# This tutorial shows some of the features of _backtesting.py_, yet another Python package for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies. +# This tutorial shows some of the features of *backtesting.py*, a Python framework for [backtesting](https://www.investopedia.com/terms/b/backtesting.asp) trading strategies. # -# 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 multi-asset 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. +# _Backtesting.py_ is a small and lightweight, blazing fast backtesting framework that uses state-of-the-art Python structures and procedures (Python 3.5+, Pandas, NumPy, Bokeh). It has a very small and simple API that is easy to remember and quickly shape towards meaningful results. The library _doesn't_ really support stock picking or trading strategies that rely on arbitrage or multi-asset portfolio rebalancing; instead, it works with an individual tradeable asset at a time and is best suited for optimizing position entrance and exit signal strategies, decisions upon values of technical indicators, and it's also a versatile interactive trade visualization and statistics tool. # -# 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. The entire API easily fits into memory banks of a single human individual. It's best suited for optimizing position entrance and exit strategies, decisions upon values of technical indicators, and it's also a versatile interactive trading strategy visualization tool. # -# ### Data +# ## 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. +# _You bring your own data._ Backtesting ingests _all kinds of +# [OHLC](https://en.wikipedia.org/wiki/Open-high-low-close_chart) +# data_ (stocks, forex, futures, crypto, ...) as a +# [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/10min.html) +# with columns `'Open'`, `'High'`, `'Low'`, `'Close'` and (optionally) `'Volume'`. Such data is widely obtainable (see: # [pandas-datareader](https://pandas-datareader.readthedocs.io/en/latest/), # [Quandl](https://www.quandl.com/tools/python), -# [findatapy](https://github.com/cuemacro/findatapy), ...). -# Your data frames can have other columns, but these are necessary. +# [findatapy](https://github.com/cuemacro/findatapy)). +# Besides these, your data frames can have _additional columns_ which are accessible in your strategies in a similar manner. +# # DataFrame should ideally be indexed with a _datetime index_ (convert it with [`pd.to_datetime()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)), otherwise a simple range index will do. # + -# Example OHLC data for Google Inc. +# Example OHLC daily data for Google Inc. from backtesting.test import GOOG GOOG.tail() # - -# ### Strategy +# ## Strategy # -# Let's create our first strategy to backtest on these Google data. Let it be a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover). +# Let's create our first strategy to backtest on these Google data, a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover). # -# _Backtesting.py_ doesn't contain its own set of technical indicators. In practice, the user should probably use functions from their favorite indicator library, such as -# [TA-Lib](https://github.com/mrjbq7/ta-lib), +# _Backtesting.py_ doesn't ship its own set of _technical analysis indicators_. Users favoring TA should probably refer to functions from proven indicator libraries, such as +# [TA-Lib](https://github.com/mrjbq7/ta-lib) or # [Tulipy](https://tulipindicators.org), -# PyAlgoTrade, ... -# But for this example, we define a simple helper moving average function. +# but for this example, we can define a simple helper moving average function ourselves: # + import pandas as pd @@ -62,19 +65,21 @@ def SMA(values, n): # - -# Note, this is the exact same helper function as the one used in the project unit tests, so we could just import that instead. - -from backtesting.test import SMA - -# A custom strategy needs to extend `backtesting.Strategy` class and override its two methods: +# A new strategy needs to extend +# [`Strategy`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy) +# class and override its two abstract methods: # [`init()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.init) and # [`next()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.next). # -# Method `init()` is invoked 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 `init()` is invoked before the strategy is run. Within it, one ideally precomputes in efficient, vectorized manner whatever indicators and signals the strategy depends on. # -# Method `next()` is 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 / trades _within_ candlesticks — any trade is executed on the next candle's _open_ (or the current candle's _close_, see -# [`Backtest(trade_on_close)`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.__init__). -# If you need to trade within candlesticks (e.g. daytrading), instead begin with _more fine-grained_ (e.g. hourly) data. +# Method `next()` is then iteratively called by the +# [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) +# instance, once for each data point (data frame row), simulating the incremental availability of each new full candlestick bar. +# +# Note, _backtesting.py_ cannot make decisions / trades _within_ candlesticks — any new orders are executed on the next candle's _open_ (or the current candle's _close_ if +# [`trade_on_close=True`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.__init__)). +# If you find yourself wishing to trade within candlesticks (e.g. daytrading), you instead need to begin with more fine-grained (e.g. hourly) data. # + from backtesting import Strategy @@ -82,24 +87,27 @@ def SMA(values, n): 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 + # Precompute the two moving averages self.sma1 = self.I(SMA, self.data.Close, self.n1) self.sma2 = self.I(SMA, self.data.Close, self.n2) def next(self): - # If sma1 crosses above sma2, buy the asset + # If sma1 crosses above sma2, close any existing + # short trades, and buy the asset if crossover(self.sma1, self.sma2): + self.position.close() self.buy() - # Else, if sma1 crosses below sma2, sell it + # Else, if sma1 crosses below sma2, close any existing + # long trades, and sell the asset elif crossover(self.sma2, self.sma1): + self.position.close() self.sell() @@ -108,60 +116,65 @@ def next(self): # In `init()` as well as in `next()`, the data the strategy is simulated on is available as an instance variable # [`self.data`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.data). # -# In `init()`, we compute indicators **indirectly by wrapping them in +# In `init()`, we declare and **compute indicators indirectly by wrapping them in # [`self.I()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Strategy.I)**. -# The wrapper is passed a function (here, our `SMA` function) along with any 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. +# The wrapper is passed a function (our `SMA` function) along with any arguments to call it with (our _close_ values and the MA lag). Indicators wrapped in this way will be automatically plotted, and their legend strings will be intelligently inferred. # -# In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we go long; if it did and downwards, we close any open long position and go short. Note, there is no position size to adjust; _Backtesting.py_ assumes maximal possible position. We use +# In `next()`, we simply check if the faster moving average just crossed over the slower one. If it did and upwards, we close the possible short position and go long; if it did and downwards, we close the open long position and go short. Note, we don't adjust order size, so _Backtesting.py_ assumes _maximal possible position_. We use # [`backtesting.lib.crossover()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.crossover) # function instead of writing more obscure and confusing conditions, such as: -# + active="" +# + magic_args="echo" language="script" +# # def next(self): # if (self.sma1[-2] < self.sma2[-2] and -# self.sma1[-1] > self.sma2[-1]): +# self.sma1[-1] > self.sma2[-1]): +# self.position.close() # self.buy() # -# elif (self.sma1[-2] > self.sma2[-2] and +# elif (self.sma1[-2] > self.sma2[-2] and # Ugh! # self.sma1[-1] < self.sma2[-1]): -# self.sell() +# self.position.close() +# self.sell() # - -# Ugh! +# In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and all declared indicators is adjusted** on each `next()` call so that `array[-1]` (e.g. `self.data.Close[-1]` or `self.sma1[-1]`) always contains the most recent value, `array[-2]` the previous value, etc. (ordinary Python indexing of ascending-sorted 1D arrays). # -# In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and any declared 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 prefer pandas Series or DataFrame objects, use `Strategy.data..s` or `Strategy.data.df` accessors respectively. You could also construct the series manually, e.g. `pd.Series(self.data.Close, index=self.data.index)`. # -# **Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are **NumPy arrays for performance reasons**. If you need `pandas.Series`, use `.to_series()` method (e.g. `self.data.Close.to_series()`) or construct the series manually (e.g. `pd.Series(self.data.Close, index=self.data.index)`). +# We might avoid `self.position.close()` calls if we primed the +# [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) +# instance with `Backtest(..., exclusive_orders=True)`. + +# ## Backtesting # -# Let's see how our strategy performs on historical Google data. We begin with 10,000 units of cash and set broker's commission to realistic 0.2%. +# Let's see how our strategy performs on historical Google data. The +# [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) +# instance is initialized with OHLC data and a strategy _class_ (see API reference for additional options), and we begin with 10,000 units of cash and set broker's commission to realistic 0.2%. # + from backtesting import Backtest bt = Backtest(GOOG, SmaCross, cash=10000, commission=.002) -bt.run() +stats = bt.run() +stats # - -# The -# [`Backtest`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest) -# instance is initialized with data and a strategy _class_ (see API reference for additional options). -# -# When # [`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run) -# method is called, it returns a pandas Series of simulation 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 ... +# method returns a pandas Series of simulation results and statistics associated with our strategy. We see that this simple strategy makes almost 600% return in the period of 9 years, with maximum drawdown 33%, and with longest drawdown period spanning almost two years ... # # [`Backtest.plot()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.plot) -# method provides the same results in a more visual form. +# method provides the same insights in a more visual form. bt.plot() -# ### Optimization +# ## Optimization # -# We hard-coded the two lag parameters (`n1` and `n2`) into our strategy above. However, the strategy may work better with 15–30 or some other cross-over. **We define the parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)**. +# We hard-coded the two lag parameters (`n1` and `n2`) into our strategy above. However, the strategy may work better with 15–30 or some other cross-over. **We declared the parameters as optimizable by making them [class variables](https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables)**. # # We optimize the two parameters by calling # [`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize) -# method with each parameter a keyword argument pointing to its pool of values to test. Parameter `n1` is tested for values in range between 5 and 30 and parameter `n2` for values between 10 and 70, respectively. Some combinations of values of the two parameters are invalid, i.e. `n1` should not be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which returns `True` (admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes return over the observed period. We could instead choose to optimize any key from the returned `stats` series. +# method with each parameter a keyword argument pointing to its pool of possible values to test. Parameter `n1` is tested for values in range between 5 and 30 and parameter `n2` for values between 10 and 70, respectively. Some combinations of values of the two parameters are invalid, i.e. `n1` should not be _larger than_ or equal to `n2`. We limit admissible parameter combinations with an _ad hoc_ constraint function, which takes in the parameters and returns `True` (i.e. admissible) whenever `n1` is less than `n2`. Additionally, we search for such parameter combination that maximizes return over the observed period. We could instead choose to optimize any other key from the returned `stats` series. # + # %%time @@ -169,22 +182,36 @@ def next(self): stats = bt.optimize(n1=range(5, 30, 5), n2=range(10, 70, 5), maximize='Equity Final [$]', - constraint=lambda p: p.n1 < p.n2) + constraint=lambda param: param.n1 < param.n2) +stats # - -stats +# We can look into `stats['_strategy']` to access the Strategy _instance_ and its optimal parameter values (10 and 15). -# We can look into `stats._strategy` to access the Strategy instance and its optimal parameter values (10 and 15). +stats._strategy -bt.plot() +bt.plot(plot_volume=False, plot_pl=False) -# Strategy optimization managed to up its initial performance _on in-sample data_ by almost 70% and beat +# Strategy optimization managed to up its initial performance _on in-sample data_ by almost 50% and even beat simple # [buy & hold](https://en.wikipedia.org/wiki/Buy_and_hold). -# In real life, however, **always take steps to avoid -# [overfitting](https://en.wikipedia.org/wiki/Overfitting)** -# before putting real money at risk. +# In real life optimization, however, do **take steps to avoid +# [overfitting](https://en.wikipedia.org/wiki/Overfitting)**. + +# ## Trade data # +# In addition to backtest statistics returned by +# [`Backtest.run()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.run) +# shown above, you can look into _individual trade returns_ and the changing _equity curve_ and _drawdown_ by inspecting the last few, internal keys in the result series. + +stats.tail() + +# The columns should be self-explanatory. + +stats['_equity_curve'] # Contains equity/drawdown curves. DrawdownDuration is only defined at ends of DD periods. + +stats['_trades'] # Contains individual trade data + # Learn more by exploring further # [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) -# or find more program options in the +# or find more framework options in the # [full API reference](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 index 0606858b..b2f62748 100644 --- a/doc/examples/Strategies Library.ipynb +++ b/doc/examples/Strategies Library.ipynb @@ -7,19 +7,15 @@ "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", + "This tutorial will show how to reuse composable base trading strategies that are part of _Backtesting.py_ software distribution.\n", + "It is, henceforth, assumed you're already familiar with\n", + "[basic package usage](https://kernc.github.io/backtesting.py/doc/examples/Quick 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", - "Again, we use our 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, ..." + "Again, we'll use our helper moving average function." ] }, { @@ -27,20 +23,12 @@ "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", "
\n", - " \n", + " \n", " Loading BokehJS ...\n", "
" ] @@ -59,7 +47,7 @@ "\n", " var force = true;\n", "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", @@ -136,6 +124,7 @@ " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", @@ -226,76 +215,103 @@ "\n", " function run_callbacks() {\n", " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", - " function load_libs(js_urls, callback) {\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n", + "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", - " var 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", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " if (url in hashes) {\n", + " element.crossOrigin = \"anonymous\";\n", + " element.integrity = \"sha384-\" + hashes[url];\n", + " }\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", " }\n", - " };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", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", " }\n", "\n", - " 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 js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n", + " var css_urls = [];\n", + " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - " \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", " ];\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", + " if (root.Bokeh !== undefined || force === true) {\n", + " \n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", @@ -310,17 +326,17 @@ " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " console.debug(\"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", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "\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));" + "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) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -334,10 +350,10 @@ "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", + "Part of this software distribution is\n", + "[`backtesting.lib`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)\n", + "module that contains various reusable utilities for strategy development.\n", + "Some of those utilities are composable base strategies we can extend and build upon.\n", "\n", "We import and extend two of those strategies here:\n", "* [`SignalStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.SignalStrategy)\n", @@ -353,9 +369,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -365,38 +379,39 @@ "class SmaCross(SignalStrategy,\n", " TrailingStrategy):\n", " n1 = 10\n", - " n2 = 20\n", + " n2 = 25\n", " \n", " def init(self):\n", " # In init() and in next() it is important to call the\n", - " # super method to properly initialize all the classes\n", + " # super method to properly initialize the parent classes\n", " super().init()\n", " \n", " # Precompute the two moving averages\n", " sma1 = self.I(SMA, self.data.Close, self.n1)\n", " sma2 = self.I(SMA, self.data.Close, self.n2)\n", " \n", - " # 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", + " # Where sma1 crosses sma2 upwards. Diff gives us [-1,0, *1*]\n", " signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0)\n", + " signal = signal.replace(-1, 0) # Upwards/long only\n", " \n", - " # Set the signal vector using the method provided\n", - " # by SignalStrategy\n", - " self.set_signal(signal)\n", + " # Use 95% of available liquidity (at the time) on each order.\n", + " # (Leaving a value of 1. would instead buy a single share.)\n", + " entry_size = signal * .95\n", + " \n", + " # Set order entry sizes using the method provided by \n", + " # `SignalStrategy`. See the docs.\n", + " self.set_signal(entry_size=entry_size)\n", " \n", - " # Set trailing stop-loss to 4x ATR\n", - " # using the method provided by TrailingStrategy\n", - " self.set_trailing_sl(4)" + " # Set trailing stop-loss to 2x ATR using\n", + " # the method provided by `TrailingStrategy`\n", + " self.set_trailing_sl(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note, since the strategies in _lib_ may require their own intialization and next-tick logic, be sure to **always call `super().init()` and `super().next()` in your overridden methods**.\n", + "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." ] @@ -404,7 +419,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -412,28 +429,31 @@ "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 [$] 110092.67\n", - "Return [%] 1000.03\n", + "Exposure Time [%] 46.69\n", + "Equity Final [$] 69390.94\n", + "Equity Peak [$] 71890.64\n", + "Return [%] 593.91\n", "Buy & Hold Return [%] 703.46\n", - "Max. Drawdown [%] -37.68\n", - "Avg. Drawdown [%] -5.52\n", - "Max. Drawdown Duration 689 days 00:00:00\n", + "Max. Drawdown [%] -22.49\n", + "Avg. Drawdown [%] -3.45\n", + "Max. Drawdown Duration 576 days 00:00:00\n", "Avg. Drawdown Duration 39 days 00:00:00\n", - "# Trades 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", + "# Trades 38\n", + "Win Rate [%] 60.53\n", + "Best Trade [%] 51.60\n", + "Worst Trade [%] -7.89\n", + "Avg. Trade [%] 7.16\n", + "Max. Trade Duration 183 days 00:00:00\n", + "Avg. Trade Duration 46 days 00:00:00\n", + "Profit Factor 5.47\n", + "Expectancy [%] 11.86\n", + "SQN 2.25\n", + "Sharpe Ratio 0.45\n", + "Sortino Ratio 3.19\n", + "Calmar Ratio 0.32\n", "_strategy SmaCross\n", + "_equity_curve Equ...\n", + "_trades Size EntryB...\n", "dtype: object" ] }, @@ -450,7 +470,7 @@ "\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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+arx0VHVAzCLb+X6XdUDSo3A9CtZ1QIIW2c73IHZARjeJQWBsdkDGoUW287V2QBywcmiR/XZA3qWbxCBFd0AS2c73U5V3QHpqvHSTxXdAmxov3ST2d0BqEoPAyi14QHk/NV66Y3hAFARWDi2geEB9lUOLbNR4QAKBlUOL7XhAhBbZzvcBeUDNItv5fh15QDSJQWDlNnlAqBxaZDtDeUChcD0K10p5QJvvp8ZLVnlAziLb+X5veUDj0CLb+X15QDi0yHa+j3lAW4/C9SigeUALAiuHFrV5QPZT46WbyHlAhRbZzvfdeUATrkfhevF5QA5YObTIAXpA6CYxCKwJekC7dJMYBBF6QL2fGi/dEnpAbRKDwMoTekBYZDvfTyZ6QC3dJAaBMnpAg+tRuB4zekCdGi/dJEF6QCqyne+nYHpAuHSTGASGekC2SQwCK7d6QLZJDAIr5HpAUg4tsp0Ue0BAtvP91EN7QDO0yHa+aHtAemq8dJOOe0BKYhBYObJ7QHXpJjEIvntAipduEoPHe0A7NV66Sa97QEpiEFg5sHtAuHSTGAS5e0DlJjEIrLp7QEg3iUFgwntAhUFg5dDHe0D61HjpJs17QAOsHFpk23tADoPAyqHAe0CAwMqhRZl7QOhRuB6FYXtATY2XbhIhe0DWzvdT49F6QNBNYhBYgXpAkhgEVg4nekAK16NwPdZ5QM3MzMzMdXlAVOOlm8QSeUA6tMh2vsB4QJduEoPAiHhAdr6fGi9weED5fmq8dD94QICVQ4tsAXhAE65H4XrVd0BCi2zn+6d3QM8i2/l+hXdAQGDl0CJSd0ABK4cW2Rt3QNwkBoGVB3dAne+nxkv5dkCnxks3ie52QLt0kxgE3nZAD1g5tMjSdkBRuB6F6712QJVDi2znrHZAWDm0yHaYdkBGtvP91Jx2QLKd76fGnXZA2c73U+OadkBkO99PjYV2QMh2vp8adXZAU+Olm8RfdkBkO99PjUt2QPhT46WbLnZAF9nO91MldkCyne+nxhR2QD4K16NwIHZA3iQGgZU4dkAZBFYOLUJ2QBKDwMqhS3ZA8/3UeOlcdkDn+6nx0nx2QCYxCKwcqHZALbKd76fedkDdJAaBlRV3QNajcD0KVXdAo3A9CteDd0BmZmZmZrd3QCYxCKwc6ndA2vl+arwfeEB+arx0k0x4QNajcD0KhXhAvp8aL93AeEB36SYxCA15QGwSg8DKSHlAUuOlm8R2eUAOWDm0yJ15QOb7qfHSsXlAgJVDi2zJeUAFgZVDi9B5QLLIdr6f1HlAcGiR7XzMeUA1iUFg5cF5QAWsHFpktHlAPTVeukmreUBLYhBYOaV5QHgUrkfhn3lAth6F61GOeUA1iUFg5Xd5QGVmZmZmX3lAM166SQxFeUBmke18Pyh5QNWjcD0KBXlASjeJQWDPeEDa+X5qvJd4QLXz/dR4bnhAAlYOLbJKeEDw0k1iEC14QG4Sg8DKD3hATWIQWDn6d0CicD0K1+d3QJiZmZmZ3ndALwisHFrSd0BWObTIdsJ3QBCDwMqhvndAyszMzMysd0CSGARWDqV3QL7KoUW2pHdAbj0K16Oqd0A2iUFg5bJ3QOx8PzVevXdAH7ByaJHKd0BzkxgEVtp3QEW28/3U6HdAKocW2c71d0A1XrpJDAt4QF66SQwCGnhALt0kBoEreEDMzMzMzD14QMh2vp8aVnhAqMZLN4lxeECmm8QgsI14QJqZmZmZrXhA+VPjpZvUeEASWDm0yO14QOWlm8QgC3lABCuHFtkgeUAA1XjpJjp5QCKwcmiRXHlAANV46SZ1eUBkEFg5tIh5QGMQWDm0knlA+n5qvHSgeUCq8dJNYqx5QAIrhxbZtXlApXA9CtepeUCIFtnO96F5QM7MzMzMlnlA9P3UeOmKeUD6fmq8dH15QHJoke18anlA2vl+arxSeUBjO99PjTh5QGZmZmZmEnlAvHSTGATneECyne+nxsB4QNEi2/l+m3hAnu+nxkt7eECaxCCwclh4QDIzMzMzOHhAzyLb+X4ceEDPItv5fgB4QHs/NV664XdA8NJNYhDQd0DWo3A9Csd3QEKLbOf7xXdA3CQGgZXAd0BiEFg5tLV3QAIrhxbZrHdA1XjpJjGjd0DGSzeJQZx3QO58PzVekHdAAiuHFtmLd0CZmZmZmY53QCLb+X5qmXdArnJoke2bd0B7PzVeup93QF7l0CLbpHdAuUkMAiukd0DDILByaKV3QBbZzvdTqHdAzMzMzMy0d0Cxne+nxsZ3QMVLN4lB23dAIgaBlUPod0BHDAIrh/t3QDnfT42XFHhAyszMzMwpeEBS46WbxDh4QC3dJAaBU3hAA4GVQ4treECC61G4HoR4QF7l0CLbmHhAdukmMQiseECb76fGS754QJ9FtvP90HhAIgaBlUPjeEBcukkMAvN4QPd+arx0D3lAJTEIrBwqeUCqHFpkO0x5QFqPwvUocHlAJlyPwvWLeUCTQ4ts55t5QOgmMQisrnlA9lPjpZu8eUDFSzeJQcJ5QB2F61G4z3lAHrByaJHheUALLbKd7/B5QMv3U+OlHXpACgIrhxZbekAG16NwPZB6QHO+nxov03pAY2ZmZmYUe0DVo3A9Ck97QJ0aL90ki3tAFi/dJAbFe0BTObTIdu57QGjn+6nxHHxAPots5/tFfEDOTWIQWGx8QKpH4XoUkXxAdhSuR+G3fEBz6SYxCNx8QHPpJjEIAX1A9X5qvHQwfUCl8dJNYmd9QBCuR+F6oX1ALQisHFrZfUDL91Pjpfh9QO3STWIQBH5APWDl0CIhfkAgBoGVQzJ+QHK+nxovQn5A4vup8dJJfkC1SQwCK1R+QK2d76fGWn5Arsh2vp9ofkBukxgEVnF+QK2d76fGe35Ag0Fg5dCDfkB2PzVeupB+QJ1wPQrXln5AFS/dJAagfkAS2c73U6h+QKtyaJHtqH5AQ+F6FK6gfkBCtvP91Jh+QC0IrBxajH5AvfUoXI9vfkB7lUOLbFp+QBUv3SQGNX5Aemq8dJMLfkDSo3A9CuR9QCAGgZVDzn1ACAIrhxa9fUAe2/l+aqt9QMahRbbzl31ApvHSTWKNfUAjMQisHIx9QCIGgZVDjH1AWo/C9SiIfUCaxCCwcop9QHo/NV66jn1Ai8L1KFybfUAKLbKd7619QLZJDAIrwn1Amu+nxkvOfUC1HoXrUdR9QHgUrkfh6X1AzfdT46XzfUAesHJokQB+QLufGi/dIn5AGlpkO988fkDoUbgehVt+QEuNl24Sb35AEK5H4XqIfkCa76fGS6l+QLDIdr6ftH5A66fGSzezfkDQeOkmMaN+QIiXbhKDmX5AgOtRuB6NfkBIN4lBYH5+QCMxCKwcYH5ANrTIdr46fkCWmZmZmRZ+QOhRuB6F/X1A20+Nl27ofUDaJAaBldh9QDjfT42X0X1A1aNwPQrPfUBWObTIdrx9QHJoke18rn1A9Shcj8KVfUDNzMzMzHJ9QJZDi2znTn1AQDVeukkkfUBGtvP91AF9QAqsHFpk4XxAYxBYObTZfEB66SYxCM18QM3MzMzMwHxA0k1iEFiyfEASg8DKoax8QICVQ4tsoHxABYGVQ4uXfECWbhKDwId8QMZLN4lBd3xACwIrhxZlfEASg8DKoU98QHW+nxovQHxAQGDl0CI1fEDGdr6fGi58QFqPwvUoLnxA1aNwPQo6fEBAi2zn+0N8QD5g5dAiTnxAG4XrUbhdfEANWDm0yGt8QOX7qfHSd3xA4qWbxCCEfEDC9Shcj5F8QPUoXI/CnXxAW4/C9SinfECOwvUoXLh8QBtaZDvfx3xAyqFFtvPXfEClm8QgsPJ8QJ4aL90kB31Aez81XroffUAqhxbZzit9QFqPwvUoPH1AJlyPwvVJfUBCi2zn+1N9QBKuR+F6X31AULgehetufUAGrBxaZH19QIUW2c73h31A0E1iEFiQfUDWzvdT44p9QCqyne+njH1A0E1iEFiMfUA630+Nl4t9QKJwPQrXi31AUw4tsp2PfUB4FK5H4Yp9QAbXo3A9i31AZZHtfD+BfUDCILByaHV9QKObxCCwcn1APmDl0CJyfUDCILByaGh9QLLIdr6fYX1APTVeuklgfUBNjZduEl19QDjfT42XV31AMDMzMzNbfUAOWDm0yGd9QFY5tMh2f31AuEkMAiuZfUCTQ4ts5659QIhs5/upy31ATrgehev0fUBNjZduEh5+QBuF61G4Qn5ACy2yne9tfkBTDi2ynZF+QFu6SQwCtH5AyszMzMzZfkBAi2zn+/F+QK1yaJHtDX9AQ7bz/dQxf0DKzMzMzFR/QAgCK4cWcH9AMDMzMzOQf0AbhetRuLh/QKBwPQrX239Ai8L1KFz+f0DESzeJQQqAQGi8dJMYFYBAunSTGAQegECicD0KVyeAQNWjcD2KLYBABqwcWuQ2gEDhpZvEoECAQPd+arx0S4BAxEs3iUFYgEA+YOXQImiAQOYmMQgseIBAY2ZmZuaLgEAH16NwvZ6AQFmPwvWoroBA2Pl+ary8gEB0vp8aL8yAQAoCK4eWzoBAl5mZmZnJgECqHFpkO8SAQHbpJjEIvIBA7afGS7e0gECEFtnOd6+AQEK28/3UrIBAReF6FK6kgECC61G4HpyAQBpaZDvfj4BAWGQ730+BgEA+NV66SXSAQI7C9ShcaYBAqfHSTeJhgECeGi/dpFWAQK1H4XoUR4BA3CQGgRU4gEAaL90khiWAQDi0yHa+EIBAQ7bz/dTzf0BS46WbxON/QGi8dJMY2H9Adb6fGi/Sf0B9arx0k9R/QK5yaJHt139AItv5fmraf0BAYOXQIth/QMVLN4lB1X9Aju18PzXVf0DoUbgehdV/QLUehetR339AOgrXo3Drf0C6nxov3fR/QI4YBFYO839Ak24Sg8D2f0DW+X5qvPV/QGJmZmZm+n9Ath6F69ECgEC+yqFFtg2AQMZ2vp+aHIBAju18P7UmgEDM91PjpTWAQDa0yHa+RYBAr53vp8ZVgEA6Ctej8GiAQMz3U+MlfoBAauf7qXGUgEDM91PjJa2AQM4i2/n+woBA0U1iEFjZgEAiBoGVQ/SAQCZcj8L1C4FAmsQgsHIigUD81HjppjiBQHbpJjGIVoFACgIrh5Z8gUDipZvEIKKBQExiEFg5y4FAJTEIrBzygUAesHJokR2CQKBFtvN9Q4JADlg5tMhjgkAMLbKdb4aCQPzUeOkmqYJAzyLb+f7KgkAKAiuHFuyCQJzvp8bLFoNAjsL1KNxBg0AjBoGVQ2qDQAJWDi0ylYNA7Hw/Nd67g0DqUbgeBeiDQL/KoUU2GYRAqfHSTeJKhEAkBoGVw3mEQBcEVg4tqIRAth6F69HahECicD0K1wWFQH5qvHSTIoVAkO18PzUthUCgRbbz/TGFQFhkO9/PQ4VAOt9PjRdHhUDGdr6fGkOFQOYmMQisPoVAvsqhRbY0hUC1HoXr0SmFQDAzMzOzI4VA4tAi2/kmhUClxks3iSOFQIDAyqFFIYVAUeOlm0QghUBR46WbRByFQHO+nxovGIVAmu+nxksMhUAwMzMzs/uEQNbO91Nj6oRAhkFg5VDjhEB6PzVeuuuEQFy6SQyCAYVAjsL1KFwchUB6FK5H4SuFQBXZzvfTQIVAyaFFtvNYhUDTeOkmMWeFQNN46SYxeoVATWIQWLmFhUAHrBxaZJGFQNjO91NjmYVA4HoUrkenhUClm8QgMLaFQK1H4XqUuYVAbhKDwMq7hUB2vp8aL7uFQM73U+OlvIVAVQ4tsh29hUCMl24Sg6yFQPZT46UbkoVARLbz/dRwhUA4tMh2vlaFQMVLN4nBQYVAh0Fg5VAphUBMYhBYORmFQMvMzMxMBIVAcmiR7fzuhEBXObTI9tGEQFUOLbIds4RAE65H4fqIhECWbhKDwE2EQMZLN4lBG4RAE65H4Xrhg0B+arx0k6eDQILAyqHFaoNAcmiR7Xwxg0ARg8DKIQGDQI7C9ShcvYJAqhxaZLt8gkCWbhKDwEOCQCIGgZXDD4JAVjm0yHbUgUCaxCCwcqCBQCUxCKyccYFAth6F61E7gUD2U+OlGxKBQOKlm8Sg8IBAuEkMAivUgEDN91Pjpa+AQFy6SQyCkYBAYBBYOTR/gEBHDAIrh2SAQASBlUOLRIBAFdnO99MfgEAjBoGVwwCAQEpiEFg5x39Aej81Xrp8f0CyyHa+n01/QCZcj8L1JH9ARuF6FK71fkC28/3UeL5+QNV46SYxhX5A2/l+arwyfkBI4XoUrvF9QDIIrBxas31A8tJNYhBqfUCamZmZmR59QBJYObTIxnxAy6FFtvOOfEAyCKwcWlF8QJVDi2znGXxAJjEIrBz0e0DdJAaBldd7QBBYObTI0ntA9Shcj8K7e0Dz/dR46Z17QNJNYhBYhXtAm++nxkuMe0AWBFYOLZ17QCUxCKwco3tA5fup8dLBe0CWmZmZmeR7QMahRbbzD3xA20+Nl24jfEBgO99PjTp8QMV2vp8aRnxAZrx0kxhRfEDlJjEIrGZ8QOp8PzVec3xAY2ZmZmaBfEAKAiuHFtZ8QBtaZDvfE31AZpHtfD9nfUDWzvdT4619QJIYBFYO/X1AonA9CtdRfkBbj8L1KKt+QO18PzVe9X5ACtejcD1Mf0D2U+Olm7p/QKrx0k1iCYBAVg4tsp04gECoxks3CWiAQClcj8L1lYBAeekmMYjDgEBGtvP91PGAQKAaL90kJ4FA+FPjpZtdgUBWDi2yHY6BQDIzMzOzwoFAVzm0yPbSgUBg5dAi2+KBQJ7vp8ZL7IFABFYOLbLtgUAlBoGVQ/CBQHNoke188IFAAAAAAAD0gUDFILBy6PeBQAisHFpk+4FAFK5H4Xr4gUBYObTI9vWBQIGVQ4vs6oFAU+Olm0TlgUClm8QgMOiBQA9YObTI4YFA/dR46abbgUDSTWIQWM+BQE1iEFg5wIFAqvHSTeK2gUAMAiuHFrOBQHa+nxovsIFAKFyPwvWsgUC18/3UeKaBQJIYBFaOqoFA0k1iEFipgUCsR+F6lKmBQG49CtcjooFAN7TIdj6bgUBOjZdukoWBQBKuR+F6boFAM166SQxbgUDqUbgeBU6BQJrEILDyO4FAfD81XjoogUChRbbz/R6BQCyyne+nHYFA9lPjpZsYgUDFSzeJwRaBQGi8dJMYD4FAbj0K1yP7gEDJoUW2c+SAQMMgsHLo1oBARbbz/VTLgEDgehSux6uAQHs/NV66jIBAcmiR7XxxgEDGSzeJQVyAQMh2vp8aPoBA7Xw/NV4vgEByaJHt/BqAQIts5/upCYBAYxBYObTpf0BQjZduEr9/QPYoXI/Ch39AeukmMQhHf0BANV66SQt/QIbrUbge335AK1yPwvWtfkCzcmiR7Y5+QP6p8dJNfn5A7lG4HoVzfkD10k1iEFd+QGUQWDm0QH5AVuOlm8RXfkDwfD81Xm9+QLBH4XoUen5AZRBYObR2fkDiT42Xbn9+QLsehetRfn5Ae+kmMQiDfkAeWmQ733t+QIBqvHSTcH5Am5mZmZlwfkBu5/up8Wx+QDu0yHa+bn5ACqwcWmRifkBLDAIrh0V+QBODwMqhKX5A3iQGgZXtfUClcD0K16t9QK5H4XoUZX1AkxgEVg4wfUBuEoPAyvl8QPYoXI/CvHxAWjm0yHaQfEAusp3vp1N8QFO4HoXrLnxAvXSTGAQRfEAtsp3vp+B7QIts5/uptXtAgpVDi2yWe0AL16NwPX97QDZeukkMXXtA8KfGSzcbe0DgT42Xbud6QOJ6FK5HvXpAWDm0yHaNekA730+Nl196QJiZmZmZOHpAE65H4Xr+eUBwPQrXo8F5QOb7qfHSbXlAne+nxksZeUAaL90kBu94QAIrhxbZrnhAO99PjZdyeEA6tMh2vi14QPp+arx08HdAzczMzMzHd0DdJAaBlZJ3QHnpJjEIU3dAke18PzUNd0AqhxbZzsN2QJmZmZmZmnZAdr6fGi+BdkCR7Xw/NVZ2QCCwcmiRPXZASQwCK4cndkCWbhKDwBN2QM8i2/l+JHZAy8zMzMwndkA630+Nlyl2QJRDi2znKHZAJTEIrBz3dUAlMQisHM51QH1qvHSTp3VA5fup8dKGdUAphxbZzlR1QJrEILByFXVAdb6fGi/hdEBtEoPAyqR0QNwkBoGVWnRAarx0kxgddED5fmq8dONzQGq8dJMYnnNA7Xw/NV5pc0BDi2zn+zNzQD81XrpJ6XJAdr6fGi+wckAZBFYOLWpyQGHl0CLbM3JAUI2XbhIOckACK4cW2fZxQJqZmZmZ7HFAcmiR7XzqcUC+nxov3fFxQCuHFtnO9HFAFtnO91P1cUD6fmq8dAlyQMDKoUW2F3JAbhKDwMovckDgehSuR1hyQBkv3SQGdHJAcWiR7XyUckCP7Xw/NaVyQOX7qfHSq3JABqwcWmSvckAKAiuHFs1yQILrUbge53JAD4PAyqEIc0CWmZmZmTNzQCqyne+nW3NAfWq8dJNrc0DIoUW283pzQMihRbbzf3NArXJoke2Jc0Cune+nxohzQLl0kxgEgXNAQIts5/trc0BGDAIrh19zQCzdJAaBSXNAQrbz/dRDc0DQTWIQWEtzQKObxCCwYHNAfpVDi2xxc0C2HoXrUYpzQLYehetRs3NAcGiR7XzTc0DSeOkmMexzQDnfT42X+3NAipduEoMFdECIbOf7qQx0QHbpJjEIJnRAkkOLbOdKdEC7nxov3X10QOlRuB6FonRAziLb+X7FdEBaj8L1KPd0QCUxCKwcJnVALd0kBoFIdUBsEoPAyoB1QDsK16NwoHVAZGZmZmbAdUCkm8QgsMR1QMmhRbbz1XVArUfhehTedUDYzvdT49R1QLPIdr6f0HVASQwCK4fHdUD6fmq8dLt1QKwcWmQ7qnVA001iEFiMdUBOYhBYOWh1QPT91HjpJ3VA4E+Nl27vdEDLoUW28850QNNNYhBYs3RAPgrXo3CUdEA1XrpJDHZ0QNV46SYxcHRAd76fGi9gdEAaL90kBlZ0QKJFtvP9SHRA5/up8dJXdEAzMzMzM1l0QJ8aL90kW3RAeOkmMQhodEDMzMzMzG90QE1iEFg5fHRAPjVeukmOdECAlUOLbKp0QPL91Hjp13RAehSuR+EIdUD0KFyPwkZ1QJ4aL90kb3VAIdv5fmqSdUA630+Nl7l1QASBlUOL5HVAULgehesLdkBmke18Py92QPMoXI/CW3ZAl5mZmZmNdkDy/dR46bR2QI/tfD81z3ZAwPUoXI/sdkCRGARWDg13QLp0kxgEKndAUw4tsp1Id0DdT42Xbml3QFqPwvUojHdAiWzn+6mtd0BTDi2yncZ3QEk3iUFg4HdAFgRWDi38d0B26SYxCCB4QByF61G4O3hA2iQGgZVXeEASrkfhem94QMD1KFyPh3hAOt9PjZePeECYmZmZmY54QCqHFtnOjHhAonA9CteaeEBS46WbxKh4QH+VQ4tss3hAHFpkO9+8eECuR+F6FMB4QCyHFtnOznhAQTVeukngeEDhT42Xbu94QD8K16NwAHlA3vl+arwaeUAgWmQ73y95QNDMzMzMRnlAbJHtfD9keUAn2/l+aop5QCWwcmiRo3lATeF6FK65eUBqO99PjdR5QIrrUbge9HlApkW28/0RekDKILByaCd6QOD5fmq8NnpA+yhcj8JDekAqBoGVQ1F6QEZg5dAiZHpA9afGSzdvekCIwMqhRXB6QM1LN4lBc3pAazvfT413ekAiL90kBn56QBMCK4cWfHpAeD0K16N2ekCVl24Sg2x6QLVH4XoUU3pAYmQ73083ekCgmZmZmRV6QHC8dJMY+3lAG9nO91PpeUDT91Pjpd15QPOnxks33XlAaDvfT43jeUBDNV66SfV5QA7Xo3A9C3pA2njpJjEYekDLSzeJQSB6QAhWDi2yMHpAAKrx0k1CekA9tMh2vlh6QDLdJAaBcXpADgIrhxaBekAwsp3vp416QE43iUFgmXpABlYOLbKuekDVTWIQWMp6QIKVQ4ts7HpAyna+nxoae0AbL90kBkF7QAIAAAAAYXtAXo/C9SiDe0BiukkMAp17QMggsHJotHtA3vl+arzEe0AYrkfhetR7QOV6FK5H7HtAlhgEVg74e0AtXI/C9QV8QEaLbOf7EnxAQArXo3AlfEDKSzeJQTR8QGpmZmZmR3xAWg4tsp1gfEAeL90kBnp8QJPC9ShcinxAI4XrUbibfEBFNV66SaN8QPr91HjppHxAyyCwcmimfEBIYOXQIqx8QNYi2/l+r3xAzna+nxqxfEDopZvEILl8QMJ0kxgEw3xAmkOLbOfKfEAFAAAAANd8QIvrUbge8XxAUAwCK4cTfUCDPzVeujl9QCawcmiRUn1A69Ai2/lrfUAQ16NwPYR9QPj91HjpmX1AtXJoke2wfUD20k1iEMZ9QBICK4cW4X1AhWq8dJP+fUDV91PjpR5+QAYAAAAAOn5A1fdT46VPfkCIlUOLbGV+QPJRuB6FhX5AUjeJQWCwfkCNFtnO99J+QM1LN4lB9n5ADqwcWmQdf0Buke18P0R/QCJaZDvfaX9AhpVDi2yIf0BIYOXQIrd/QJjtfD81439AvkkMAqsGgEBSYhBYuRuAQALVeOmmMoBARmDl0CJLgEDJILByaGGAQF5kO99PdYBAPt9PjReHgEAVg8DKoaCAQGuR7Xw/tYBAES2ynW/HgEBi5dAi29aAQLjz/dT434BA9yhcj8LtgEA1MzMzs/uAQA4CK4cWC4FABCuHFlkbgUBu5/upcSmBQIfrUbieOIFAAgAAAIBBgUAaBFYOLUuBQEaLbOd7VYFAZBBYObRfgUAmBoGVQ2eBQITAyqHFbYFASwwCKwd5gUCdxCCw8oaBQLbIdr4fmYFA+lPjpZukgUCRwvUoXLeBQMLKoUW2zYFAzszMzMzhgUBaObTI9vOBQGC6SQyCAoJAIrByaJEQgkCr8dJNYhqCQEE1XrpJI4JAjWzn+6krgkB+PzVeujSCQDUzMzMzPoJAbuf7qfFEgkBmO99PDU2CQIkW2c73U4JApnA9ClddgkAY2c7302iCQHaTGARWcIJAhcDKocV7gkDvfD813oiCQCkxCKwcmoJA6yYxCKyogkCKQWDl0LWCQP2p8dLNwoJAku18PzXTgkDOzMzMzOKCQMp2vp+a64JAgpVDi2zugkC2yHa+n/OCQIkW2c5394JA+35qvHT3gkACAAAAAPSCQHm+nxqv8oJAsEfhepTrgkBmO99PDemCQNH3U+Ol4oJA73w/NV7cgkCClUOL7MeCQJluEoNAq4JAGNnO99OMgkDtUbgehWyCQOfQItt5SoJAksL1KFwlgkCCarx0kwKCQOr7qfFS3IFAlhgEVg67gUBbObTIdpqBQKdwPQpXgYFAVbgehetlgUBnO99PDUyBQHQ9CtejNYFAwp8aL10hgUDtJjEIrAqBQNh46SYx+4BAsnJokW3ngEBmO99PjdiAQI5s5/upx4BA43oUrsfEgEAthxbZzsKAQLJyaJFtvoBAXo/C9Si4gECNbOf7KbWAQCYGgZVDtoBAFq5H4Xq5gEBdj8L1KL+AQLtJDAKrxIBAcT0K16PTgEBWDi2yHeCAQP2p8dLN6oBALIcW2c76gEBEi2znew2BQArXo3C9HoFAJQaBlcMqgUDo+6nxUjSBQCUGgZVDP4FAqMZLN4lIgUCsHFpkO1CBQCcxCKwcVoFAd76fGq9bgUBKDAIrB2aBQPhT46WbdIFAWTm0yPaCgUDufD813o6BQOSlm8QgmYFAAyuHFtmhgUB66SYxiKeBQJhuEoNAqoFANl66SYysgUDZzvdT462BQPup8dJNqoFAdJMYBFakgUAmMQisnKGBQDVeukkMq4FAILByaJG0gUBXObTIdsCBQFc5tMj2uYFALt0kBgG2gUDrUbgeBbWBQHA9Ctcjt4FAeOkmMQizgUBLN4lB4KuBQPl+arx0n4FAmJmZmRmSgUA630+NF4OBQASBlUMLdYFA+qnx0s1jgUBvPQrXo1OBQGZmZmbmOoFA2M73U2MlgUCOwvUo3AmBQOKlm8Sg7IBAA1YOLTLYgED2U+OlG7mAQIps5/upl4BANolBYOV1gEABK4cW2WSAQM8i2/n+U4BAFdnO91M9gEB6FK5HYSWAQML1KFyPCIBAUuOlm8TWf0B1vp8aL6t/QDrfT42XgX9AUw4tsp1Wf0Cm8dJNYjV/QBLZzvdTFX9ANbTIdr7ufkDOTWIQWOR+QLh0kxgE4X5AjhgEVg7hfkAlXI/C9dp+QFU5tMh2vX5AOwrXo3ChfkDa+X5qvJJ+QEW28/3UgH5AjsL1KFxtfkBLN4lBYGV+QANWDi2yZ35A7qfGSzdsfkCrHFpkO4B+QD0K16NwjX5AwMqhRbaUfkADVg4tsph+QGZmZmZmmH5AGgRWDi2KfkB2kxgEVn9+QAMrhxbZaH5AKDEIrBxCfkBOYhBYOQ1+QHNoke18231AsHJoke2zfUAlBoGVQ5h9QJ3vp8ZLin1ARuF6FK56fUAFgZVDi3B9QDAIrBxadX1AeOkmMQhwfUCrHFpkO2p9QGDl0CLbSX1Asp3vp8YufUAzMzMzMyl9QDMzMzMzIn1AxSCwcmgkfUBLN4lBYDB9QMMgsHJoPX1AACuHFtlNfUBQuB6F62V9QMihRbbzhX1ACNejcD2qfUAV2c73U9V9QHOTGARWAH5AqhxaZDstfkBWObTIdlZ+QM33U+OlcH5AIwaBlUOIfkAaWmQ735N+QNJ46SYxlH5A2iQGgZWSfkDi0CLb+ad+QCvdJAaBt35Ay/dT46W+fkDqfD81XsJ+QDOJQWDltH5AVTm0yHaefkADgZVDi4p+QGI730+NaX5AUuOlm8RRfkDA9ShcjzZ+QDa0yHa+IX5AHrByaJEDfkAdhetRuON9QK5yaJHtvn1AoEW28/2afUCSGARWDoN9QJvvp8ZLYn1AbRKDwMpHfUDSTWIQWDt9QM33U+OlLn1AqPHSTWIrfUDFSzeJQSd9QC4IrBxaH31AEq5H4XoefUBLYhBYOTB9QKocWmQ7VX1ANrTIdr58fUDdT42XbrB9QOhRuB6F331AHbByaJEcfkBCtvP91FV+QB7b+X5qkX5AYDvfT43PfkBOuB6F6wN/QBpaZDvfNX9Ai5duEoNff0B9arx0k5p/QDrfT42XzX9AK7Kd76f4f0Dz/dR4aRSAQNajcD0KK4BAgJVDi2xDgEByaJHtfFyAQA4tsp1vdIBA+n5qvPSggEB8PzVeusyAQOXQItt58oBAU+Olm0QXgUDpJjEIrD6BQFPjpZvEYIFAYhBYOTSDgUCyne+nxqeBQD41XrpJy4FAlUOLbGfwgUBBYOXQohOCQANWDi2yOIJAnxov3aRXgkDl0CLb+XmCQEfhehSun4JA6SYxCCzDgkDSTWIQWOaCQO+nxku3B4NANV66SYwng0BkO99PDUaDQD41XrrJRoNABqwcWuQ9g0Bmke18PzSDQOPQItt5KoNA+qnx0k0kg0CyyHa+nxuDQIqXbhKDEYNAr53vp0YDg0AWBFYOrfqCQDa0yHY+74JA9VPjpZvigkAFrBxa5MqCQAWsHFpktoJAMl66SQyjgkDTo3A9io6CQC4IrBzae4JA/P////9rgkA7NV66SV6CQNf5fmq8UYJANrTIdr5HgkCune+nRkSCQGE7308NRIJAc76fGq9GgkCYxCCw8kmCQJwaL92kR4JAcZMYBFZJgkB7arx0E06CQB2wcmgRV4JAf8DKocVagkBKYhBYuV+CQJfEILByZoJA7dJNYpB4gkDpfD81XoaCQHg/NV46j4JAT+Olm8SbgkB+wMqhRaWCQAKBlUMLroJAeD81Xjq3gkBeEFg5NMGCQCvdJAYByoJAvvUoXI/SgkDPTWIQWNuCQGNmZmbm5YJA1s73U+PygkBPuB6FawaDQGi8dJMYFYNAfWq8dJMeg0AlMQisHCGDQC8IrBzaI4NAvZ8aL90qg0A2iUFg5TGDQL7KoUU2OINA79JNYhA5g0CD61G4njuDQJzvp8ZLPoNAZpHtfD9Cg0BG4XoUrkKDQOcmMQisQYNAV2Q7389Ag0ASrkfhekKDQBYEVg6tQoNALN0kBoFCg0DgpZvEoEWDQOynxks3R4NAFy/dJAZBg0CKl24SAz6DQMEgsHJoPYNAju18P7VEg0CMwvUoXESDQH6VQ4vsQINALgisHNo9g0D61HjpJjuDQFIOLbIdQINArp3vp0ZAg0ATBFYOLTyDQC4zMzMzO4NA1M73U2M3g0BXj8L1qC+DQOh8PzXeJoNASWIQWDkcg0A0tMh2vg2DQCwIrBza/YJAMF66SQzogkAiMQisnNCCQGoSg8DKtYJAyvdT46WcgkCCFtnO94KCQKPGSzeJbYJAdhSuR2FggkDrp8ZLt1SCQGjn+6nxS4JA4/up8dI/gkD0U+OlmzCCQM0i2/n+KIJAHtv5fmohgkCa76fGSxiCQE/jpZvEFIJAFARWDi0TgkDVzvdT4wmCQC0IrBzaAoJAqBxaZLsCgkBWZDvfTwOCQCIGgZVDBoJAdL6fGq8GgkBe5dAiWw6CQO2nxks3FYJATo2XbhIJgkCP7Xw/NfWBQAasHFrk3oFAth6F61HIgUDHdr6fmq+BQHs/NV66mYFAvsqhRbaIgUCyyHa+H3eBQH1qvHSTZYFAi5duEoNUgUDup8ZLNz+BQDEzMzOzKYFAeBSuR2EcgUAg2/l+agyBQKXGSzeJ+oBA0E1iEFjqgEAmXI/CddyAQDSJQWBlzoBAkkOLbOe9gEC0HoXrUaqAQMAgsHJopYBAW+XQItumgEC1SQwCK6qAQEY3iUFgrIBAlsQgsPKrgEALWDm0SKmAQCRcj8J1o4BASWIQWDmcgEA6NV66SZSAQFm6SQwCi4BA0HjpJjGHgEBHN4lB4IOAQAtYObTIgIBAJocW2U58gEAiMQisnHaAQPnUeOkmb4BAwks3icFlgEBO46WbRF6AQF87308NVIBAr8h2vh9KgEDEdr6fGkaAQJWZmZkZO4BA4/up8VIvgEBCtvP91ByAQGA7308NDYBAM4lBYOUCgEBmEoPAyux/QKMcWmQ7zX9ACoPAyqGqf0D11HjpJox/QFbl0CLbb39A1U+Nl25Zf0ACAiuHFkh/QJLEILByRn9AWzvfT41Pf0BNDi2ynVx/QLr1KFyPcn9AS+Olm8R+f0A7i2zn+4x/QL0gsHJopH9AsB6F61G8f0AeMQisHNF/QPZ+arx0D4BAfZVDi2w7gEAiMQisnGqAQGA730+Nk4BAAoGVQ4vDgEBkke18v/qAQNXO91NjNIFArp3vp0ZsgUBeEFg5tJmBQGE7308Nx4FA4tAi2/ntgUCAwMqhRRCCQMNLN4lBKIJAqkfhepRCgkCYxCCw8k6CQFHjpZvEYYJAIgaBlUNpgkAXL90kBnmCQOT7qfFSfYJAlpmZmZmKgkC+9ShcD32CQOHQItv5bYJAjxgEVo5UgkAUBFYOrTuCQEG28/3UEoJAwCCwcujfgUDZJAaBla+BQMR2vp8ahoFAdOkmMYhkgUDkJjEILECBQDgK16NwIYFAuZ8aL10GgUByvp8ar/GAQJSZmZmZ2YBANt9PjZfHgEDu/dR46bGAQEY3iUFgqIBAr8h2vp+YgEAq3SQGAZOAQDffT40XhIBADoPAyqF2gEAyXrpJjGuAQK6d76fGaIBA06NwPYpsgEDyKFyPQn2AQL/1KFyPk4BAaef7qfGmgEBnvHSTGK+AQAOBlUMLroBAqhxaZDuwgEA2tMh2PrKAQP8qhxZZsoBAxna+n5qtgEAbhetROKiAQNj5fmo8oYBAW7pJDIKVgEA0iUFgZY2AQIhs5/upgYBA7tJNYpB5gEDmJjEIrHWAQJ5FtvN9eIBA9n5qvPR9gEBGDAIrh4SAQJaZmZkZi4BAlpmZmRmdgEA0iUFgZauAQJnEILDyvIBAMl66SYzNgECVbhKDwOaAQDa0yHa+AIFAkRgEVo4agUDaJAaBFSyBQE2Nl24SQ4FA1KNwPYpfgUCrR+F6lIGBQK1yaJFtqIFA4KWbxCDHgUAVBFYOLeeBQGsSg8BKCIJALzMzM7MogkCHbOf7KUWCQBLZzvfTYIJAJ4cW2c51gkA2tMh2PoSCQPD91HjpioJAy/dT4yWXgkAdsHJokaGCQEc3iUHgrYJAwks3icG0gkCJl24Sg7aCQLQehetRsIJAjxgEVg6vgkB7arx0k6iCQCeHFtlOmoJARgwCK4eVgkCdGi/dpJGCQDoK16PwmYJA6VG4HoWlgkCJbOf7qa6CQIUW2c53uoJAnhov3aTAgkDv0k1iEMWCQMZ2vp8ay4JA1KNwPQrYgkBd5dAi296CQMEgsHLo44JAipduEoPkgkAH16NwveeCQK+d76fG8YJAkRgEVo78gkAU2c73UxCDQCqyne+nIoNAn0W28306g0AyXrpJjFaDQBpaZDtfa4NAq0fhehSCg0AOWDm0SJODQD5g5dAioINAKrKd7yeyg0CO7Xw/NcODQPmp8dJN0YNA935qvPTbg0DqUbgehd6DQCmHFtnO3INApsZLNwndg0BvPQrXo96DQMMgsHJo4YNAGi/dJAblg0D7qfHSzeeDQGZmZmbm7oNAzvdT4yXdg0BJDAIrB82DQOpRuB6FuYNAwvUoXA+gg0AyMzMzM4ODQFlkO99Pa4NAr3JokW1Rg0AmMQisHDeDQOF6FK5HFYNAjZduEgP0gkA9Ctej8NqCQCCwcmiRyoJAbhKDwErEgkDm0CLb+b6CQBoEVg4tuYJAeL6fGq+vgkAisHJokaqCQNmjcD0Ko4JAm5mZmRmYgkB9FK5H4YqCQKZwPQpXkoJArhxaZLudgkCqxks3iaiCQHm+nxovt4JAl0OLbOfHgkBDYOXQotOCQCTb+X7q44JA6iYxCCzzgkDOzMzMzAODQMdLN4lBEoNAgpVDi2wZg0CDwMqhxReDQBBYObTIF4NAMjMzM7MWg0Dup8ZLNxKDQPL91HjpEYNAnhov3SQUg0BAYOXQohaDQL2fGi/dHINAgsDKoUUkg0BdukkMAjCDQPqp8dLNN4NAg+tRuJ5Eg0BovHSTmFSDQOcmMQisYYNARuF6FK5xg0ARg8DKIX2DQEBg5dAijINA1qNwPYqWg0Cmxks3iZ6DQMd2vp+aq4NAukkMAqu6g0BeukkMAsaDQK5H4XoU0INAiUFg5VDcg0AAAAAAAOWDQILAyqFF7INA4XoUrkf6g0C6SQwCq/uDQK5H4XoU9INAikFg5VDqg0BnZmZm5t+DQOSlm8Sgz4NAE4PAyqG7g0BCYOXQoqmDQPX91HhploNAokW2832Hg0BMN4lBYHeDQC/dJAYBaoNAnMQgsHJbg0DLoUW2c0qDQCuHFtlOPINALrKd76cyg0C9dJMYhCSDQDMzMzMzG4NAUI2XbpIVg0ACK4cW2QqDQMQgsHLo+4JAW4/C9Sj0gkBFtvP9VPOCQJvEILDy84JA1qNwPYr8gkDWo3A9CgaDQLKd76fGB4NAehSuR2EOg0DUeOkmMQ6DQAwCK4cWDoNAvp8aL10Jg0BEi2zn+/+CQOJ6FK7H+4JAzczMzEz1gkAK16NwveqCQE5iEFi52oJAHVpkO1/TgkC9dJMYhMSCQOOlm8Sgt4JA9ihcj0KrgkDm0CLb+Z2CQBFYObRIj4JATmIQWLl/gkDwp8ZLt2uCQCUGgZXDT4JAAiuHFlk4gkBKDAIrhyyCQNIi2/l+H4JASeF6FC4WgkAWrkfhegSCQPpT46Wb94FAtJ3vp0brgUD2/dR4ad+BQKZwPQrX14FAIIXrUTjRgUBi5dAi29SBQPlT46Wb1YFAgGq8dJPcgUDxp8ZLt+KBQIpBYOXQ5YFA2KNwPQrogUC6HoXrUe2BQMhLN4nB74FAjpduEoPzgUACAAAAgPqBQI1s5/up/oFAvnSTGAQBgkCS7Xw/tQCCQLFyaJHtBoJA6iYxCCwZgkARWDm0yCqCQHnpJjGIPYJAYOXQIttOgkAVrkfhemCCQArXo3C9fIJAEoPAyqGRgkASg8DKoaaCQFCNl26SuIJAYhBYObTFgkDTTWIQ2NuCQCGwcmiR8oJAyHa+nxoKg0DqUbgehSaDQFPjpZtEQ4NAKFyPwnVdg0DQItv5fn+DQFPjpZtEpINAFK5H4frGg0CWQ4ts5+aDQN9PjZduAYRAbOf7qXEZhEAehetRODKEQDeJQWDlTYRAgsDKoUVnhEAK16NwvXiEQF66SQyCh4RAHoXrUTiZhEAxCKwcWq+EQBov3SSGxIRASgwCKwfWhECKQWDlUOWEQNrO91Nj9YRAx0s3iUEMhUCyne+nxiWFQBFYObRIPYVALrKd7ydKhUAGgZVDC1OFQG3n+6lxYoVAukkMAitxhUD8qfHSTX6FQGZmZmZmj4VAarx0k5imhUC28/3U+LqFQAIrhxbZ0YVAz/dT4yXuhUC1yHa+Hw6GQPhT46WbLIZAL90kBgFIhkASg8DKIWWGQCuHFtnOg4ZAppvEIDCihkBnZmZm5sKGQJDC9Shc3oZAKlyPwvX2hkBANV66yQ2HQDZeukmMIodA7FG4HgU4h0AFVg4tMkqHQE5iEFg5WIdAtch2vp9kh0DWeOkmMW+HQNrO91NjeodAcT0K1yNth0D2KFyPQliHQObQItv5O4dAy6FFtnMgh0BzaJHt/AGHQKJFtvN94oZAmG4Sg8DChkBcj8L1KKKGQGZmZmZmhoZAIbByaJFohkB66SYxiEaGQF2PwvUoJIZAHlpkO9//hUD+1HjpJtuFQNijcD2KuoVALIcW2U6YhUBKDAIrB3aFQC6yne+nUoVASOF6FK4rhUBh5dAiWwCFQHI9Ctej9YRA9f3UeOnwhEBwEoPAyuuEQKVwPQrX5oRAfBSuR2HghEDE9Shcj92EQIkW2c734IRAyna+n5rlhEAl2/l+6umEQAQrhxbZ7IRAgmq8dBPwhEC/dJMYhPKEQIfrUbge/IRA2873U+MIhUDVTWIQ2BGFQN35fmo8HoVAcj0K16MthUA2MzMzs0GFQLFH4XqUV4VA7lG4HgV1hUCObOf7KYqFQCKF61E4noVAjUFg5dC0hUB+FK5H4ceFQCKF61E424VASIts53vqhUB+6SYxiPOFQOZ6FK7H9oVArvHSTWL6hUDiJAaBlQWGQHU9CtejEoZANzMzM7MmhkDT91PjJTiGQBVYObTIS4ZAw58aL91ghkBnEFg5tHKGQB4EVg6tg4ZAPIlBYOWLhkDiJAaBFZWGQAYrhxbZkoZAoMQgsPKOhkCW7Xw/tYiGQPj91HjpgIZA+ihcj0KLhkClGi/dJJ2GQPbSTWIQr4ZAqHA9CtfAhkAvhxbZTtaGQN7O91Pj6IZAw58aL931hkBGYOXQogqHQJkYBFYOE4dA0KFFtnMfh0A5MzMzMy6HQH7pJjGIPIdAEgIrhxZOh0DJ9ShcD1+HQN6jcD0KdodAx8qhRTaNh0ArBoGVQ6qHQA6sHFrkyodAHdnO99Pzh0CRbOf7qReIQLid76dGLYhA69Ai23k/iECPQWDlUE6IQIVqvHQTXohAwnSTGIRwiEBeObTIdoOIQJFs5/upl4hA\",\"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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(719%)\"},\"renderers\":[{\"id\":\"1097\"}]},\"id\":\"1112\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1192\",\"type\":\"BasicTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"1375\"}},\"id\":\"1369\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"axis\":{\"id\":\"1191\"},\"dimension\":1,\"ticker\":null},\"id\":\"1194\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1370\",\"type\":\"UndoTool\"},{\"attributes\":{\"data\":{},\"selected\":{\"id\":\"1130\"},\"selection_policy\":{\"id\":\"1131\"}},\"id\":\"1113\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"x0\":{\"field\":\"index\"},\"x1\":{\"field\":\"index\"},\"y0\":{\"field\":\"High\"},\"y1\":{\"field\":\"Low\"}},\"id\":\"1287\",\"type\":\"Segment\"},{\"attributes\":{},\"id\":\"1371\",\"type\":\"RedoTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"blue\"},\"line_color\":{\"value\":\"blue\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"value\":2147},\"y\":{\"value\":6.939094172000013}},\"id\":\"1114\",\"type\":\"Scatter\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1100\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1372\",\"type\":\"ResetTool\"},{\"attributes\":{\"source\":{\"id\":\"1113\"}},\"id\":\"1117\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"1113\"},\"glyph\":{\"id\":\"1114\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1115\"},\"selection_glyph\":null,\"view\":{\"id\":\"1117\"}},\"id\":\"1116\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"1061\",\"type\":\"PanTool\"},{\"attributes\":{\"active_drag\":{\"id\":\"1246\"},\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":{\"id\":\"1247\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1246\"},{\"id\":\"1247\"},{\"id\":\"1248\"},{\"id\":\"1249\"},{\"id\":\"1250\"},{\"id\":\"1251\"},{\"id\":\"1252\"},{\"id\":\"1253\"},{\"id\":\"1270\"}]},\"id\":\"1255\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1373\",\"type\":\"CrosshairTool\"},{\"attributes\":{\"dimensions\":\"width\",\"maintain_focus\":false},\"id\":\"1062\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"1195\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1374\",\"type\":\"SaveTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"blue\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"blue\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"value\":2147},\"y\":{\"value\":6.939094172000013}},\"id\":\"1115\",\"type\":\"Scatter\"},{\"attributes\":{\"overlay\":{\"id\":\"1069\"}},\"id\":\"1063\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"dimensions\":\"width\",\"maintain_focus\":false},\"id\":\"1196\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1130\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1064\",\"type\":\"UndoTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1203\"}},\"id\":\"1197\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1131\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1391\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1065\",\"type\":\"RedoTool\"},{\"attributes\":{},\"id\":\"1198\",\"type\":\"UndoTool\"},{\"attributes\":{\"label\":{\"value\":\"Final (694%)\"},\"renderers\":[{\"id\":\"1116\"}]},\"id\":\"1132\",\"type\":\"LegendItem\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1375\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"data_source\":{\"id\":\"1037\"},\"glyph\":{\"id\":\"1386\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1387\"},\"selection_glyph\":null,\"view\":{\"id\":\"1389\"}},\"id\":\"1388\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1066\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1199\",\"type\":\"RedoTool\"},{\"attributes\":{\"data\":{},\"selected\":{\"id\":\"1152\"},\"selection_policy\":{\"id\":\"1153\"}},\"id\":\"1133\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1037\"}},\"id\":\"1389\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1067\",\"type\":\"CrosshairTool\"},{\"attributes\":{},\"id\":\"1200\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1392\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1068\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1201\",\"type\":\"CrosshairTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"value\":142},\"y\":{\"value\":0.9206436760000004}},\"id\":\"1134\",\"type\":\"Scatter\"},{\"attributes\":{},\"id\":\"1394\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1152\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1202\",\"type\":\"SaveTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"cyan\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"cyan\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"value\":2113},\"y\":{\"value\":7.189063692000012}},\"id\":\"1096\",\"type\":\"Scatter\"},{\"attributes\":{\"source\":{\"id\":\"1133\"}},\"id\":\"1137\",\"type\":\"CDSView\"},{\"attributes\":{\"active_drag\":{\"id\":\"1061\"},\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":{\"id\":\"1062\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1061\"},{\"id\":\"1062\"},{\"id\":\"1063\"},{\"id\":\"1064\"},{\"id\":\"1065\"},{\"id\":\"1066\"},{\"id\":\"1067\"},{\"id\":\"1068\"},{\"id\":\"1090\"}]},\"id\":\"1070\",\"type\":\"Toolbar\"},{\"attributes\":{\"data_source\":{\"id\":\"1133\"},\"glyph\":{\"id\":\"1134\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1135\"},\"selection_glyph\":null,\"view\":{\"id\":\"1137\"}},\"id\":\"1136\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"border_line_color\":\"#333333\",\"glyph_width\":0,\"items\":[{\"id\":\"1400\"}],\"label_text_font_size\":\"8pt\",\"location\":\"top_left\",\"margin\":0,\"padding\":5,\"spacing\":0},\"id\":\"1399\",\"type\":\"Legend\"},{\"attributes\":{\"active_drag\":{\"id\":\"1195\"},\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":{\"id\":\"1196\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1195\"},{\"id\":\"1196\"},{\"id\":\"1197\"},{\"id\":\"1198\"},{\"id\":\"1199\"},{\"id\":\"1200\"},{\"id\":\"1201\"},{\"id\":\"1202\"},{\"id\":\"1224\"},{\"id\":\"1226\"}]},\"id\":\"1204\",\"type\":\"Toolbar\"},{\"attributes\":{\"label\":{\"value\":\"entry size\"},\"renderers\":[{\"id\":\"1388\"}]},\"id\":\"1400\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"red\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"value\":142},\"y\":{\"value\":0.9206436760000004}},\"id\":\"1135\",\"type\":\"Scatter\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1069\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1203\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1153\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1431\",\"type\":\"Title\"},{\"attributes\":{\"fill_color\":\"#ffffea\",\"line_color\":\"#ffcb66\",\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"equity_dd\"}},\"id\":\"1081\",\"type\":\"Patch\"},{\"attributes\":{\"data_source\":{\"id\":\"1079\"},\"glyph\":{\"id\":\"1081\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1082\"},\"selection_glyph\":null,\"view\":{\"id\":\"1084\"}},\"id\":\"1083\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"Max Drawdown (-22.5%)\"},\"renderers\":[{\"id\":\"1136\"}]},\"id\":\"1154\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_color\":{\"field\":\"returns_positive\",\"transform\":{\"id\":\"1040\"}},\"marker\":{\"value\":\"triangle\"},\"size\":{\"field\":\"marker_size\",\"units\":\"screen\"},\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"returns_long\"}},\"id\":\"1215\",\"type\":\"Scatter\"},{\"attributes\":{\"data_source\":{\"id\":\"1094\"},\"glyph\":{\"id\":\"1095\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1096\"},\"selection_glyph\":null,\"view\":{\"id\":\"1098\"}},\"id\":\"1097\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"args\":{\"ohlc_range\":{\"id\":\"1005\"},\"source\":{\"id\":\"1037\"},\"volume_range\":{\"id\":\"1232\"}},\"code\":\"if (!window._bt_scale_range) {\\n window._bt_scale_range = function (range, min, max, pad) {\\n \\\"use strict\\\";\\n if (min !== Infinity && max !== -Infinity) {\\n pad = pad ? (max - min) * .03 : 0;\\n range.start = min - pad;\\n range.end = max + pad;\\n } else console.error('backtesting: scale range error:', min, max, range);\\n };\\n}\\n\\nclearTimeout(window._bt_autoscale_timeout);\\n\\nwindow._bt_autoscale_timeout = setTimeout(function () {\\n /**\\n * @variable cb_obj `fig_ohlc.x_range`.\\n * @variable source `ColumnDataSource`\\n * @variable ohlc_range `fig_ohlc.y_range`.\\n * @variable volume_range `fig_volume.y_range`.\\n */\\n \\\"use strict\\\";\\n\\n let i = Math.max(Math.floor(cb_obj.start), 0),\\n j = Math.min(Math.ceil(cb_obj.end), source.data['ohlc_high'].length);\\n\\n let max = Math.max.apply(null, source.data['ohlc_high'].slice(i, j)),\\n min = Math.min.apply(null, source.data['ohlc_low'].slice(i, j));\\n _bt_scale_range(ohlc_range, min, max, true);\\n\\n if (volume_range) {\\n max = Math.max.apply(null, source.data['Volume'].slice(i, j));\\n _bt_scale_range(volume_range, 0, max * 1.03, false);\\n }\\n\\n}, 50);\\n\"},\"id\":\"1405\",\"type\":\"CustomJS\"},{\"attributes\":{\"factors\":[\"0\",\"1\"],\"palette\":[\"tomato\",\"lime\"]},\"id\":\"1040\",\"type\":\"CategoricalColorMapper\"},{\"attributes\":{\"source\":{\"id\":\"1094\"}},\"id\":\"1098\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1432\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"source\":{\"id\":\"1038\"}},\"id\":\"1218\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"1038\"},\"glyph\":{\"id\":\"1215\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1216\"},\"selection_glyph\":null,\"view\":{\"id\":\"1218\"}},\"id\":\"1217\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1253\",\"type\":\"SaveTool\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"#ffffea\",\"line_alpha\":0.1,\"line_color\":\"#ffcb66\",\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"equity_dd\"}},\"id\":\"1082\",\"type\":\"Patch\"},{\"attributes\":{\"logo\":null,\"toolbars\":[{\"id\":\"1070\"},{\"id\":\"1204\"},{\"id\":\"1028\"},{\"id\":\"1255\"},{\"id\":\"1376\"}],\"tools\":[{\"id\":\"1061\"},{\"id\":\"1062\"},{\"id\":\"1063\"},{\"id\":\"1064\"},{\"id\":\"1065\"},{\"id\":\"1066\"},{\"id\":\"1067\"},{\"id\":\"1068\"},{\"id\":\"1090\"},{\"id\":\"1195\"},{\"id\":\"1196\"},{\"id\":\"1197\"},{\"id\":\"1198\"},{\"id\":\"1199\"},{\"id\":\"1200\"},{\"id\":\"1201\"},{\"id\":\"1202\"},{\"id\":\"1224\"},{\"id\":\"1226\"},{\"id\":\"1019\"},{\"id\":\"1020\"},{\"id\":\"1021\"},{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"},{\"id\":\"1025\"},{\"id\":\"1026\"},{\"id\":\"1403\"},{\"id\":\"1246\"},{\"id\":\"1247\"},{\"id\":\"1248\"},{\"id\":\"1249\"},{\"id\":\"1250\"},{\"id\":\"1251\"},{\"id\":\"1252\"},{\"id\":\"1253\"},{\"id\":\"1270\"},{\"id\":\"1367\"},{\"id\":\"1368\"},{\"id\":\"1369\"},{\"id\":\"1370\"},{\"id\":\"1371\"},{\"id\":\"1372\"},{\"id\":\"1373\"},{\"id\":\"1374\"},{\"id\":\"1401\"}]},\"id\":\"1474\",\"type\":\"ProxyToolbar\"},{\"attributes\":{\"data\":{\"x\":[810,1205.2940920576862]},\"selected\":{\"id\":\"1176\"},\"selection_policy\":{\"id\":\"1177\"}},\"id\":\"1155\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1079\"}},\"id\":\"1084\",\"type\":\"CDSView\"},{\"attributes\":{\"factors\":[\"0\",\"1\"],\"palette\":[\"rgb(178, 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%b\",\"%a %d\"],\"months\":[\"%m/%Y\",\"%b'%y\"]},\"id\":\"1042\",\"type\":\"DatetimeTickFormatter\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":1.5,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"equity\"}},\"id\":\"1086\",\"type\":\"Line\"},{\"attributes\":{\"line_color\":\"red\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"value\":3.7778795440000024}},\"id\":\"1156\",\"type\":\"Line\"},{\"attributes\":{\"fill_color\":{\"field\":\"returns_positive\",\"transform\":{\"id\":\"1040\"}},\"marker\":{\"value\":\"inverted_triangle\"},\"size\":{\"field\":\"marker_size\",\"units\":\"screen\"},\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"returns_short\"}},\"id\":\"1220\",\"type\":\"Scatter\"},{\"attributes\":{\"data_source\":{\"id\":\"1037\"},\"glyph\":{\"id\":\"1086\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1087\"},\"selection_glyph\":null,\"view\":{\"id\":\"1089\"}},\"id\":\"1088\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"1155\"},\"glyph\":{\"id\":\"1156\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1157\"},\"selection_glyph\":null,\"view\":{\"id\":\"1159\"}},\"id\":\"1158\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"args\":{\"axis\":{\"id\":\"1011\"},\"formatter\":{\"id\":\"1042\"},\"source\":{\"id\":\"1037\"}},\"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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Application\",\"version\":\"2.1.1\"}};\n", + " var render_items = [{\"docid\":\"660da296-3d62-48c3-bdea-8ed684922b5d\",\"root_ids\":[\"1476\"],\"roots\":{\"1476\":\"5eb27549-7526-4ec7-88f1-a3a3a92f4886\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", @@ -473,13 +493,14 @@ " 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", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", " }\n", " }, 10, root)\n", " }\n", @@ -489,7 +510,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "1428" + "id": "1476" } }, "output_type": "display_data" @@ -509,10 +530,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice how managing risk with a trailing stop-loss severely limits our losses.\n", + "Notice how managing risk with a trailing stop-loss secures our gains and limits our losses.\n", "\n", - "For other strategies of the sort, and other reusable utilities in general, see the\n", - "[_lib_ module reference](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)." + "For other strategies of the sort, and other reusable utilities in general, see\n", + "[**_backtesting.lib_ module reference**](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn more by exploring further\n", + "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n", + "or find more framework options in the\n", + "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)." ] } ], @@ -532,7 +563,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/doc/examples/Strategies Library.py b/doc/examples/Strategies Library.py index 29b3e7ad..3625c066 100644 --- a/doc/examples/Strategies Library.py +++ b/doc/examples/Strategies Library.py @@ -5,7 +5,7 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.3.0 +# jupytext_version: 1.5.1 # kernelspec: # display_name: Python 3 # language: python @@ -15,26 +15,22 @@ # 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). +# This tutorial will show how to reuse composable base trading strategies that are part of _Backtesting.py_ software distribution. +# It is, henceforth, assumed you're already familiar with +# [basic package usage](https://kernc.github.io/backtesting.py/doc/examples/Quick 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. # -# Again, we use our 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, ... +# Again, we'll use our helper moving average function. 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. +# Part of this software distribution is +# [`backtesting.lib`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html) +# module that contains various reusable utilities for strategy development. +# Some of those utilities are composable base strategies we can extend and build upon. # # We import and extend two of those strategies here: # * [`SignalStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.SignalStrategy) @@ -54,36 +50,37 @@ class SmaCross(SignalStrategy, TrailingStrategy): n1 = 10 - n2 = 20 + n2 = 25 def init(self): # In init() and in next() it is important to call the - # super method to properly initialize all the classes + # super method to properly initialize the parent classes super().init() # Precompute the two moving averages sma1 = self.I(SMA, self.data.Close, self.n1) sma2 = self.I(SMA, self.data.Close, self.n2) - # 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. + # Where sma1 crosses sma2 upwards. Diff gives us [-1,0, *1*] signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0) + signal = signal.replace(-1, 0) # Upwards/long only - # Set the signal vector using the method provided - # by SignalStrategy - self.set_signal(signal) + # Use 95% of available liquidity (at the time) on each order. + # (Leaving a value of 1. would instead buy a single share.) + entry_size = signal * .95 + + # Set order entry sizes using the method provided by + # `SignalStrategy`. See the docs. + self.set_signal(entry_size=entry_size) - # Set trailing stop-loss to 4x ATR - # using the method provided by TrailingStrategy - self.set_trailing_sl(4) + # Set trailing stop-loss to 2x ATR using + # the method provided by `TrailingStrategy` + self.set_trailing_sl(2) # - -# 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**. +# 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. @@ -97,7 +94,12 @@ def init(self): bt.plot() # - -# Notice how managing risk with a trailing stop-loss severely limits our losses. +# Notice how managing risk with a trailing stop-loss secures our gains and limits our losses. # -# For other strategies of the sort, and other reusable utilities in general, see the -# [_lib_ module reference](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html). +# For other strategies of the sort, and other reusable utilities in general, see +# [**_backtesting.lib_ module reference**](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html). + +# Learn more by exploring further +# [examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials) +# or find more framework options in the +# [full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules).