Function compute_stats

backtesting/lib.py:174–204

(Re-)compute strategy performance metrics. `stats` is the statistics series as returned by `backtesting.backtesting.Backtest.run()`. `data` is OHLC data as passed to the `backtesting.backtesting.Backtest` the `stats` were obtained in. `trades` can be a dataframe subset of `stat

(
        *,
        stats: pd.Series,
        data: pd.DataFrame,
        trades: pd.DataFrame = None,
        risk_free_rate: float = 0.)

def compute_stats(
        *,
        stats: pd.Series,
        data: pd.DataFrame,
        trades: pd.DataFrame = None,
        risk_free_rate: float = 0.) -> pd.Series:
    """
    (Re-)compute strategy performance metrics.

    `stats` is the statistics series as returned by `backtesting.backtesting.Backtest.run()`.
    `data` is OHLC data as passed to the `backtesting.backtesting.Backtest`
    the `stats` were obtained in.
    `trades` can be a dataframe subset of `stats._trades` (e.g. only long trades).
    You can also tune `risk_free_rate`, used in calculation of Sharpe and Sortino ratios.

        >>> stats = Backtest(GOOG, MyStrategy).run()
        >>> only_long_trades = stats._trades[stats._trades.Size > 0]
        >>> long_stats = compute_stats(stats=stats, trades=only_long_trades,
        ...                            data=GOOG, risk_free_rate=.02)
    """
    equity = stats._equity_curve.Equity
    if trades is None:
        trades = stats._trades
    else:
        # XXX: Is this buggy?
        equity = equity.copy()
        equity[:] = stats._equity_curve.Equity.iloc[0]
        for t in trades.itertuples(index=False):
            equity.iloc[t.EntryBar:] += t.PnL
    return _compute_stats(trades=trades, equity=equity.values, ohlc_data=data,
                          risk_free_rate=risk_free_rate, strategy_instance=stats._strategy)


def resample_apply(rule: str,