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[WIP] Support multiple instruments(symbols) backtest #639

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[WIP] Support multiple instruments(symbols) backtest #639

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3cf29e4
support multiple symbols
May 8, 2022
97f08d4
fix some bugs for multiple asset
May 8, 2022
82ace33
fix linter issue
May 8, 2022
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support multiple symbols
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robert1003 committed May 8, 2022
commit 3cf29e4ca7b5ee6c1a57c1ea0dde05a9ac32aa21
12 changes: 10 additions & 2 deletions backtesting/_stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@ def geometric_mean(returns: pd.Series) -> float:
def compute_stats(
trades: Union[List['Trade'], pd.DataFrame],
equity: np.ndarray,
symbols: List[str],
ohlc_data: pd.DataFrame,
strategy_instance: 'Strategy',
risk_free_rate: float = 0,
Expand Down Expand Up @@ -92,8 +93,15 @@ def _round_timedelta(value, _period=_data_period(index)):
s.loc['Equity Final [$]'] = equity[-1]
s.loc['Equity Peak [$]'] = equity.max()
s.loc['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100
c = ohlc_data.Close.values
s.loc['Buy & Hold Return [%]'] = (c[-1] - c[0]) / c[0] * 100 # long-only return

r = []
for symbol in symbols:
c = ohlc_data[f"{symbol}_Close"].values
_r = (c[-1] - c[0]) / c[0] * 100
s.loc[f'Buy & Hold Return for {symbol} [%]'] = _r # long-only return
r.append(_r)
s.loc[f'Avg Buy & Hold Return [%]'] = np.mean(r)


gmean_day_return: float = 0
day_returns = np.array(np.nan)
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