Minor update

Author: kernc

backtesting/_stats.py

@@ -154,15 +154,14 @@ 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])
+    beta = np.nan
     if len(equity_log_returns) > 1 and len(market_log_returns) > 1:
+        # len == 0 on dummy call `stats_keys = compute_stats(...)` pre optimization
         cov_matrix = np.cov(equity_log_returns, market_log_returns)
         beta = cov_matrix[0, 1] / cov_matrix[1, 1]
-        # Jensen CAPM Alpha: can be strongly positive when beta is negative and B&H Return is large
-        s.loc['Alpha [%]'] = s.loc['Return [%]'] - risk_free_rate * 100 - beta * (s.loc['Buy & Hold Return [%]'] - risk_free_rate * 100)  # noqa: E501
-        s.loc['Beta'] = beta
-    else:
-        s.loc['Alpha [%]'] = np.nan
-        s.loc['Beta'] = np.nan
+    # Jensen CAPM Alpha: can be strongly positive when beta is negative and B&H Return is large
+    s.loc['Alpha [%]'] = s.loc['Return [%]'] - risk_free_rate * 100 - beta * (s.loc['Buy & Hold Return [%]'] - risk_free_rate * 100)  # noqa: E501
+    s.loc['Beta'] = beta
     s.loc['Max. Drawdown [%]'] = max_dd * 100
     s.loc['Avg. Drawdown [%]'] = -dd_peaks.mean() * 100
     s.loc['Max. Drawdown Duration'] = _round_timedelta(dd_dur.max())