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BUG: Fix pandas insertion error on Windos

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Fixes https://github.com/kernc/backtesting.py/issues/21
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Kernc
2019-10-01 04:44:24 +02:00
parent 5bcaafd6b3
commit a46d72a635
2 changed files with 27 additions and 35 deletions
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60
backtesting/backtesting.py
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@@ -891,46 +891,38 @@ class Backtest:
return value.ceil(resolution)
s = pd.Series()
s['Start'] = df.index[0]
s['End'] = df.index[-1]
# Assigning Timedeltas needs the key to exist beforehand,
# otherwise the value is interpreted as nanosec *int*. See:
# https://github.com/pandas-dev/pandas/issues/22717
s['Duration'] = 0
s['Duration'] = s.End - s.Start
s.loc['Start'] = df.index[0]
s.loc['End'] = df.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['Exposure [%]'] = np.nan_to_num(durations.sum() / (s['Duration'] or np.nan) * 100)
s['Equity Final [$]'] = equity[-1]
s['Equity Peak [$]'] = equity.max()
s['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100
s.loc['Exposure [%]'] = np.nan_to_num(durations.sum() / (s.loc['Duration'] or np.nan) * 100)
s.loc['Equity Final [$]'] = equity[-1]
s.loc['Equity Peak [$]'] = equity.max()
s.loc['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100
c = data.Close.values
s['Buy & Hold Return [%]'] = abs(c[-1] - c[0]) / c[0] * 100 # long OR short
s['Max. Drawdown [%]'] = max_dd = -np.nan_to_num(dd.max()) * 100
s['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100
s['Max. Drawdown Duration'] = 0
s['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max())
s['Avg. Drawdown Duration'] = 0
s['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean())
s['# Trades'] = n_trades = pl.count()
s['Win Rate [%]'] = win_rate = np.nan if not n_trades else (pl > 0).sum() / n_trades * 100
s['Best Trade [%]'] = returns.max() * 100
s['Worst Trade [%]'] = returns.min() * 100
s.loc['Buy & Hold Return [%]'] = abs(c[-1] - c[0]) / c[0] * 100 # long OR short
s.loc['Max. Drawdown [%]'] = max_dd = -np.nan_to_num(dd.max()) * 100
s.loc['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100
s.loc['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max())
s.loc['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean())
s.loc['# Trades'] = n_trades = pl.count()
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()
s['Avg. Trade [%]'] = mean_return * 100
s['Max. Trade Duration'] = 0
s['Max. Trade Duration'] = _round_timedelta(durations.max())
s['Avg. Trade Duration'] = 0
s['Avg. Trade Duration'] = _round_timedelta(durations.mean())
s['Expectancy [%]'] = ((returns[returns > 0].mean() * win_rate -
returns[returns < 0].mean() * (100 - win_rate)))
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['Expectancy [%]'] = ((returns[returns > 0].mean() * win_rate -
returns[returns < 0].mean() * (100 - win_rate)))
pl = pl.dropna()
s['SQN'] = np.sqrt(n_trades) * pl.mean() / pl.std()
s['Sharpe Ratio'] = mean_return / (returns.std() or np.nan)
s['Sortino Ratio'] = mean_return / (returns[returns < 0].std() or np.nan)
s['Calmar Ratio'] = mean_return / ((-max_dd / 100) or np.nan)
s.loc['SQN'] = np.sqrt(n_trades) * pl.mean() / pl.std()
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['_strategy'] = strategy
s.loc['_strategy'] = strategy
s._trade_data = df # Private API
return s

2
setup.py
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@@ -32,7 +32,7 @@ if __name__ == '__main__':
install_requires=[
'typing ; python_version < "3.5"',
'numpy',
'pandas >= 0.21.0, != 0.25.0',
'pandas >= 0.25.0, != 0.25.0',
'bokeh >= 1.1.0',
],
extras_require={