BUG: Buy&Hold duration should match trading duration

Fixes #327

Author: kernc

backtesting/_stats.py

@@ -5,7 +5,7 @@
 import numpy as np
 import pandas as pd
 
-from ._util import _data_period
+from ._util import _data_period, _indicator_warmup_nbars
 
 if TYPE_CHECKING:
     from .backtesting import Strategy, Trade
@@ -111,8 +111,9 @@ def _round_timedelta(value, _period=_data_period(index)):
     if commissions:
         s.loc['Commissions [$]'] = commissions
     s.loc['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100
+    first_trading_bar = _indicator_warmup_nbars(strategy_instance)
     c = ohlc_data.Close.values
-    s.loc['Buy & Hold Return [%]'] = (c[-1] - c[0]) / c[0] * 100  # long-only return
+    s.loc['Buy & Hold Return [%]'] = (c[-1] - c[first_trading_bar]) / c[first_trading_bar] * 100  # long-only return
 
     gmean_day_return: float = 0
     day_returns = np.array(np.nan)

backtesting/_util.py

@@ -42,6 +42,20 @@ def _data_period(index) -> Union[pd.Timedelta, Number]:
     return values.diff().dropna().median()
 
 
+def _strategy_indicators(strategy):
+    return {attr: indicator
+            for attr, indicator in strategy.__dict__.items()
+            if isinstance(indicator, _Indicator)}.items()
+
+
+def _indicator_warmup_nbars(strategy):
+    if strategy is None:
+        return 0
+    nbars = max((np.isnan(indicator.astype(float)).argmin(axis=-1).max()
+                 for _, indicator in _strategy_indicators(strategy)), default=0)
+    return nbars
+
+
 class _Array(np.ndarray):
     """
     ndarray extended to supply .name and other arbitrary properties

backtesting/backtesting.py

@@ -34,7 +34,7 @@ def _tqdm(seq, **_):
 
 from ._plotting import plot  # noqa: I001
 from ._stats import compute_stats
-from ._util import _as_str, _Indicator, _Data, try_
+from ._util import _as_str, _Indicator, _Data, _indicator_warmup_nbars, _strategy_indicators, try_
 
 __pdoc__ = {
     'Strategy.__init__': False,
@@ -1290,14 +1290,11 @@ def run(self, **kwargs) -> pd.Series:
         data._update()  # Strategy.init might have changed/added to data.df
 
         # Indicators used in Strategy.next()
-        indicator_attrs = {attr: indicator
-                           for attr, indicator in strategy.__dict__.items()
-                           if isinstance(indicator, _Indicator)}.items()
+        indicator_attrs = _strategy_indicators(strategy)
 
         # Skip first few candles where indicators are still "warming up"
         # +1 to have at least two entries available
-        start = 1 + max((np.isnan(indicator.astype(float)).argmin(axis=-1).max()
-                         for _, indicator in indicator_attrs), default=0)
+        start = 1 + _indicator_warmup_nbars(strategy)
 
         # Disable "invalid value encountered in ..." warnings. Comparison
         # np.nan >= 3 is not invalid; it's False.

backtesting/test/_test.py

@@ -291,7 +291,7 @@ def test_compute_stats(self):
                 'Avg. Trade Duration': pd.Timedelta('46 days 00:00:00'),
                 'Avg. Trade [%]': 2.531715975158555,
                 'Best Trade [%]': 53.59595229490424,
-                'Buy & Hold Return [%]': 703.4582419772772,
+                'Buy & Hold Return [%]': 522.0601851851852,
                 'Calmar Ratio': 0.4414380935608377,
                 'Duration': pd.Timedelta('3116 days 00:00:00'),
                 'End': pd.Timestamp('2013-03-01 00:00:00'),