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Fix Issue 1251: covariance calc for insufficient data points #1253

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Merged
merged 2 commits into from
Mar 26, 2025
Merged

Fix Issue 1251: covariance calc for insufficient data points #1253

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a05394c
Fix Issue 1251: covariance calc for insufficient data points
jensnesten Mar 23, 2025
18af117
Minor update
kernc Mar 26, 2025
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14 changes: 9 additions & 5 deletions backtesting/_stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,11 +154,15 @@ def _round_timedelta(value, _period=_data_period(index)):
s.loc['Calmar Ratio'] = annualized_return / (-max_dd or np.nan)
equity_log_returns = np.log(equity[1:] / equity[:-1])
market_log_returns = np.log(c[1:] / c[:-1])
cov_matrix = np.cov(equity_log_returns, market_log_returns)
beta = cov_matrix[0, 1] / cov_matrix[1, 1]
# Jensen CAPM Alpha: can be strongly positive when beta is negative and B&H Return is large
s.loc['Alpha [%]'] = s.loc['Return [%]'] - risk_free_rate * 100 - beta * (s.loc['Buy & Hold Return [%]'] - risk_free_rate * 100) # noqa: E501
s.loc['Beta'] = beta
if len(equity_log_returns) > 1 and len(market_log_returns) > 1:
cov_matrix = np.cov(equity_log_returns, market_log_returns)
beta = cov_matrix[0, 1] / cov_matrix[1, 1]
# Jensen CAPM Alpha: can be strongly positive when beta is negative and B&H Return is large
s.loc['Alpha [%]'] = s.loc['Return [%]'] - risk_free_rate * 100 - beta * (s.loc['Buy & Hold Return [%]'] - risk_free_rate * 100) # noqa: E501
s.loc['Beta'] = beta
else:
s.loc['Alpha [%]'] = np.nan
s.loc['Beta'] = np.nan
s.loc['Max. Drawdown [%]'] = max_dd * 100
s.loc['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100
s.loc['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max())
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