BUG: Fix annualized stats with weekly/monthly data

Fixes #537

Author: kernc

backtesting/_stats.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, List, Union
+from typing import TYPE_CHECKING, List, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -119,11 +119,16 @@ def _round_timedelta(value, _period=_data_period(index)):
     annual_trading_days = np.nan
     is_datetime_index = isinstance(index, pd.DatetimeIndex)
     if is_datetime_index:
-        day_returns = equity_df['Equity'].resample('D').last().dropna().pct_change()
+        freq_days = cast(pd.Timedelta, _data_period(index)).days
+        have_weekends = index.dayofweek.to_series().between(5, 6).mean() > 2 / 7 * .6
+        annual_trading_days = (
+            52 if freq_days == 7 else
+            12 if freq_days == 31 else
+            1 if freq_days == 365 else
+            (365 if have_weekends else 252))
+        freq = {7: 'W', 31: 'ME', 365: 'YE'}.get(freq_days, 'D')
+        day_returns = equity_df['Equity'].resample(freq).last().dropna().pct_change()
         gmean_day_return = geometric_mean(day_returns)
-        annual_trading_days = float(
-            365 if index.dayofweek.to_series().between(5, 6).mean() > 2/7 * .6 else
-            252)
 
     # Annualized return and risk metrics are computed based on the (mostly correct)
     # assumption that the returns are compounded. See: https://dx.doi.org/10.2139/ssrn.3054517

backtesting/test/_test.py

@@ -1035,6 +1035,11 @@ def next(self):
             bt = Backtest(df, S, cash=100, trade_on_close=True)
         self.assertEqual(bt.run()._trades['ExitPrice'][0], 50)
 
+    def test_stats_annualized(self):
+        stats = Backtest(GOOG.resample('W').agg(OHLCV_AGG), SmaCross).run()
+        self.assertFalse(np.isnan(stats['Return (Ann.) [%]']))
+        self.assertEqual(round(stats['Return (Ann.) [%]']), -3)
+
 
 if __name__ == '__main__':
     warnings.filterwarnings('error')