ENH: Extract strategy performance method compute_stats (#281)

crazy25000 · kernc · web-flow · commit 77f4c46b8216 · 2021-08-03T02:59:59.000+02:00
* ENH: Extract methods _compute_stats and _compute_drawdown_duration_peaks from Backtest

* ENH: Move compute stats methods to new file, _stats.py

* TST: Update unit tests for compute_drawdown_duration_peaks

* TST: Remove ignore type for CI test failure

* REF: Remove broker dependency from compute_stats, update sharpe ratio to use risk free rate

* REF: Update self._results to account for compute_stats change, fix typo

* Update backtesting/backtesting.py

Co-authored-by: kernc &lt;kerncece@gmail.com&gt;

* Update backtesting/backtesting.py

Co-authored-by: kernc &lt;kerncece@gmail.com&gt;

* REF: Add risk_free_rate to Sortino Ratio

* ENH: Add compute_stats to lib, provide public method

* REF: Extract params to reduce line length

* REF: Use strategy broker to calculate equity

* REF: Use example from test

* Update, make more idempotent, add doc, test

Co-authored-by: kernc &lt;kerncece@gmail.com&gt;
diff --git a/backtesting/_stats.py b/backtesting/_stats.py
@@ -0,0 +1,153 @@
+from typing import List, TYPE_CHECKING, Union
+
+import numpy as np
+import pandas as pd
+
+from ._util import _data_period
+
+if TYPE_CHECKING:
+    from .backtesting import Strategy, Trade
+
+
+def compute_drawdown_duration_peaks(dd: pd.Series):
+    iloc = np.unique(np.r_[(dd == 0).values.nonzero()[0], len(dd) - 1])
+    iloc = pd.Series(iloc, index=dd.index[iloc])
+    df = iloc.to_frame('iloc').assign(prev=iloc.shift())
+    df = df[df['iloc'] > df['prev'] + 1].astype(int)
+
+    # If no drawdown since no trade, avoid below for pandas sake and return nan series
+    if not len(df):
+        return (dd.replace(0, np.nan),) * 2
+
+    df['duration'] = df['iloc'].map(dd.index.__getitem__) - df['prev'].map(dd.index.__getitem__)
+    df['peak_dd'] = df.apply(lambda row: dd.iloc[row['prev']:row['iloc'] + 1].max(), axis=1)
+    df = df.reindex(dd.index)
+    return df['duration'], df['peak_dd']
+
+
+def geometric_mean(returns: pd.Series) -> float:
+    returns = returns.fillna(0) + 1
+    if np.any(returns <= 0):
+        return 0
+    return np.exp(np.log(returns).sum() / (len(returns) or np.nan)) - 1
+
+
+def compute_stats(
+        trades: Union[List['Trade'], pd.DataFrame],
+        equity: np.ndarray,
+        ohlc_data: pd.DataFrame,
+        strategy_instance: 'Strategy',
+        risk_free_rate: float = 0,
+) -> pd.Series:
+    assert -1 < risk_free_rate < 1
+
+    index = ohlc_data.index
+    dd = 1 - equity / np.maximum.accumulate(equity)
+    dd_dur, dd_peaks = compute_drawdown_duration_peaks(pd.Series(dd, index=index))
+
+    equity_df = pd.DataFrame({
+        'Equity': equity,
+        'DrawdownPct': dd,
+        'DrawdownDuration': dd_dur},
+        index=index)
+
+    if isinstance(trades, pd.DataFrame):
+        trades_df = trades
+    else:
+        # Came straight from Backtest.run()
+        trades_df = pd.DataFrame({
+            'Size': [t.size for t in trades],
+            'EntryBar': [t.entry_bar for t in trades],
+            'ExitBar': [t.exit_bar for t in trades],
+            'EntryPrice': [t.entry_price for t in trades],
+            'ExitPrice': [t.exit_price for t in trades],
+            'PnL': [t.pl for t in trades],
+            'ReturnPct': [t.pl_pct for t in trades],
+            'EntryTime': [t.entry_time for t in trades],
+            'ExitTime': [t.exit_time for t in trades],
+        })
+        trades_df['Duration'] = trades_df['ExitTime'] - trades_df['EntryTime']
+    del trades
+
+    pl = trades_df['PnL']
+    returns = trades_df['ReturnPct']
+    durations = trades_df['Duration']
+
+    def _round_timedelta(value, _period=_data_period(index)):
+        if not isinstance(value, pd.Timedelta):
+            return value
+        resolution = getattr(_period, 'resolution_string', None) or _period.resolution
+        return value.ceil(resolution)
+
+    s = pd.Series(dtype=object)
+    s.loc['Start'] = index[0]
+    s.loc['End'] = index[-1]
+    s.loc['Duration'] = s.End - s.Start
+
+    have_position = np.repeat(0, len(index))
+    for t in trades_df.itertuples(index=False):
+        have_position[t.EntryBar:t.ExitBar + 1] = 1
+
+    s.loc['Exposure Time [%]'] = have_position.mean() * 100  # In "n bars" time, not index time
+    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
+
+    gmean_day_return: float = 0
+    day_returns = np.array(np.nan)
+    annual_trading_days = np.nan
+    if isinstance(index, pd.DatetimeIndex):
+        day_returns = equity_df['Equity'].resample('D').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
+    # Our annualized return matches `empyrical.annual_return(day_returns)` whereas
+    # our risk doesn't; they use the simpler approach below.
+    annualized_return = (1 + gmean_day_return)**annual_trading_days - 1
+    s.loc['Return (Ann.) [%]'] = annualized_return * 100
+    s.loc['Volatility (Ann.) [%]'] = np.sqrt((day_returns.var(ddof=int(bool(day_returns.shape))) + (1 + gmean_day_return)**2)**annual_trading_days - (1 + gmean_day_return)**(2*annual_trading_days)) * 100  # noqa: E501
+    # s.loc['Return (Ann.) [%]'] = gmean_day_return * annual_trading_days * 100
+    # s.loc['Risk (Ann.) [%]'] = day_returns.std(ddof=1) * np.sqrt(annual_trading_days) * 100
+
+    # Our Sharpe mismatches `empyrical.sharpe_ratio()` because they use arithmetic mean return
+    # and simple standard deviation
+    s.loc['Sharpe Ratio'] = np.clip((s.loc['Return (Ann.) [%]'] - risk_free_rate) / (s.loc['Volatility (Ann.) [%]'] or np.nan), 0, np.inf)  # noqa: E501
+    # Our Sortino mismatches `empyrical.sortino_ratio()` because they use arithmetic mean return
+    s.loc['Sortino Ratio'] = np.clip((annualized_return - risk_free_rate) / (np.sqrt(np.mean(day_returns.clip(-np.inf, 0)**2)) * np.sqrt(annual_trading_days)), 0, np.inf)  # noqa: E501
+    max_dd = -np.nan_to_num(dd.max())
+    s.loc['Calmar Ratio'] = np.clip(annualized_return / (-max_dd or np.nan), 0, np.inf)
+    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())
+    s.loc['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean())
+    s.loc['# Trades'] = n_trades = len(trades_df)
+    s.loc['Win Rate [%]'] = np.nan if not n_trades else (pl > 0).sum() / n_trades * 100  # noqa: E501
+    s.loc['Best Trade [%]'] = returns.max() * 100
+    s.loc['Worst Trade [%]'] = returns.min() * 100
+    mean_return = geometric_mean(returns)
+    s.loc['Avg. Trade [%]'] = mean_return * 100
+    s.loc['Max. Trade Duration'] = _round_timedelta(durations.max())
+    s.loc['Avg. Trade Duration'] = _round_timedelta(durations.mean())
+    s.loc['Profit Factor'] = returns[returns > 0].sum() / (abs(returns[returns < 0].sum()) or np.nan)  # noqa: E501
+    s.loc['Expectancy [%]'] = returns.mean() * 100
+    s.loc['SQN'] = np.sqrt(n_trades) * pl.mean() / (pl.std() or np.nan)
+
+    s.loc['_strategy'] = strategy_instance
+    s.loc['_equity_curve'] = equity_df
+    s.loc['_trades'] = trades_df
+
+    s = _Stats(s)
+    return s
+
+
+class _Stats(pd.Series):
+    def __repr__(self):
+        # Prevent expansion due to _equity and _trades dfs
+        with pd.option_context('max_colwidth', 20):
+            return super().__repr__()
diff --git a/backtesting/backtesting.py b/backtesting/backtesting.py
@@ -29,7 +29,8 @@ def _tqdm(seq, **_):
         return seq
 
 from ._plotting import plot
-from ._util import _as_str, _Indicator, _Data, _data_period, try_
+from ._stats import compute_stats
+from ._util import _as_str, _Indicator, _Data, try_
 
 __pdoc__ = {
     'Strategy.__init__': False,
@@ -1089,7 +1090,7 @@ def __init__(self,
             exclusive_orders=exclusive_orders, index=data.index,
         )
         self._strategy = strategy
-        self._results = None
+        self._results: Optional[pd.Series] = None
 
     def run(self, **kwargs) -> pd.Series:
         """
@@ -1180,7 +1181,15 @@ def run(self, **kwargs) -> pd.Series:
             # for future `indicator._opts['data'].index` calls to work
             data._set_length(len(self._data))
 
-            self._results = self._compute_stats(broker, strategy)
+            equity = pd.Series(broker._equity).bfill().fillna(broker._cash).values
+            self._results = compute_stats(
+                trades=broker.closed_trades,
+                equity=equity,
+                ohlc_data=self._data,
+                risk_free_rate=0.0,
+                strategy_instance=strategy,
+            )
+
         return self._results
 
     def optimize(self, *,
@@ -1491,134 +1500,6 @@ def _mp_task(backtest_uuid, batch_index):
 
     _mp_backtests: Dict[float, Tuple['Backtest', List, Callable]] = {}
 
-    @staticmethod
-    def _compute_drawdown_duration_peaks(dd: pd.Series):
-        iloc = np.unique(np.r_[(dd == 0).values.nonzero()[0], len(dd) - 1])
-        iloc = pd.Series(iloc, index=dd.index[iloc])
-        df = iloc.to_frame('iloc').assign(prev=iloc.shift())
-        df = df[df['iloc'] > df['prev'] + 1].astype(int)
-        # If no drawdown since no trade, avoid below for pandas sake and return nan series
-        if not len(df):
-            return (dd.replace(0, np.nan),) * 2
-        df['duration'] = df['iloc'].map(dd.index.__getitem__) - df['prev'].map(dd.index.__getitem__)
-        df['peak_dd'] = df.apply(lambda row: dd.iloc[row['prev']:row['iloc'] + 1].max(), axis=1)
-        df = df.reindex(dd.index)
-        return df['duration'], df['peak_dd']
-
-    def _compute_stats(self, broker: _Broker, strategy: Strategy) -> pd.Series:
-        data = self._data
-        index = data.index
-
-        equity = pd.Series(broker._equity).bfill().fillna(broker._cash).values
-        dd = 1 - equity / np.maximum.accumulate(equity)
-        dd_dur, dd_peaks = self._compute_drawdown_duration_peaks(pd.Series(dd, index=index))
-
-        equity_df = pd.DataFrame({
-            'Equity': equity,
-            'DrawdownPct': dd,
-            'DrawdownDuration': dd_dur},
-            index=index)
-
-        trades = broker.closed_trades
-        trades_df = pd.DataFrame({
-            'Size': [t.size for t in trades],
-            'EntryBar': [t.entry_bar for t in trades],
-            'ExitBar': [t.exit_bar for t in trades],
-            'EntryPrice': [t.entry_price for t in trades],
-            'ExitPrice': [t.exit_price for t in trades],
-            'PnL': [t.pl for t in trades],
-            'ReturnPct': [t.pl_pct for t in trades],
-            'EntryTime': [t.entry_time for t in trades],
-            'ExitTime': [t.exit_time for t in trades],
-        })
-        trades_df['Duration'] = trades_df['ExitTime'] - trades_df['EntryTime']
-
-        pl = trades_df['PnL']
-        returns = trades_df['ReturnPct']
-        durations = trades_df['Duration']
-
-        def _round_timedelta(value, _period=_data_period(index)):
-            if not isinstance(value, pd.Timedelta):
-                return value
-            resolution = getattr(_period, 'resolution_string', None) or _period.resolution
-            return value.ceil(resolution)
-
-        s = pd.Series(dtype=object)
-        s.loc['Start'] = index[0]
-        s.loc['End'] = index[-1]
-        s.loc['Duration'] = s.End - s.Start
-
-        have_position = np.repeat(0, len(index))
-        for t in trades:
-            have_position[t.entry_bar:t.exit_bar + 1] = 1  # type: ignore
-
-        s.loc['Exposure Time [%]'] = have_position.mean() * 100  # In "n bars" time, not index time
-        s.loc['Equity Final [$]'] = equity[-1]
-        s.loc['Equity Peak [$]'] = equity.max()
-        s.loc['Return [%]'] = (equity[-1] - equity[0]) / equity[0] * 100
-        c = data.Close.values
-        s.loc['Buy & Hold Return [%]'] = (c[-1] - c[0]) / c[0] * 100  # long-only return
-
-        def geometric_mean(returns):
-            returns = returns.fillna(0) + 1
-            return (0 if np.any(returns <= 0) else
-                    np.exp(np.log(returns).sum() / (len(returns) or np.nan)) - 1)
-
-        day_returns = gmean_day_return = np.array(np.nan)
-        annual_trading_days = np.nan
-        if isinstance(index, pd.DatetimeIndex):
-            day_returns = equity_df['Equity'].resample('D').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
-        # Our annualized return matches `empyrical.annual_return(day_returns)` whereas
-        # our risk doesn't; they use the simpler approach below.
-        annualized_return = (1 + gmean_day_return)**annual_trading_days - 1
-        s.loc['Return (Ann.) [%]'] = annualized_return * 100
-        s.loc['Volatility (Ann.) [%]'] = np.sqrt((day_returns.var(ddof=int(bool(day_returns.shape))) + (1 + gmean_day_return)**2)**annual_trading_days - (1 + gmean_day_return)**(2*annual_trading_days)) * 100  # noqa: E501
-        # s.loc['Return (Ann.) [%]'] = gmean_day_return * annual_trading_days * 100
-        # s.loc['Risk (Ann.) [%]'] = day_returns.std(ddof=1) * np.sqrt(annual_trading_days) * 100
-
-        # Our Sharpe mismatches `empyrical.sharpe_ratio()` because they use arithmetic mean return
-        # and simple standard deviation
-        s.loc['Sharpe Ratio'] = np.clip(s.loc['Return (Ann.) [%]'] / (s.loc['Volatility (Ann.) [%]'] or np.nan), 0, np.inf)  # noqa: E501
-        # Our Sortino mismatches `empyrical.sortino_ratio()` because they use arithmetic mean return
-        s.loc['Sortino Ratio'] = np.clip(annualized_return / (np.sqrt(np.mean(day_returns.clip(-np.inf, 0)**2)) * np.sqrt(annual_trading_days)), 0, np.inf)  # noqa: E501
-        max_dd = -np.nan_to_num(dd.max())
-        s.loc['Calmar Ratio'] = np.clip(annualized_return / (-max_dd or np.nan), 0, np.inf)
-        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())
-        s.loc['Avg. Drawdown Duration'] = _round_timedelta(dd_dur.mean())
-        s.loc['# Trades'] = n_trades = len(trades)
-        s.loc['Win Rate [%]'] = np.nan if not n_trades else (pl > 0).sum() / n_trades * 100  # noqa: E501
-        s.loc['Best Trade [%]'] = returns.max() * 100
-        s.loc['Worst Trade [%]'] = returns.min() * 100
-        mean_return = geometric_mean(returns)
-        s.loc['Avg. Trade [%]'] = mean_return * 100
-        s.loc['Max. Trade Duration'] = _round_timedelta(durations.max())
-        s.loc['Avg. Trade Duration'] = _round_timedelta(durations.mean())
-        s.loc['Profit Factor'] = returns[returns > 0].sum() / (abs(returns[returns < 0].sum()) or np.nan)  # noqa: E501
-        s.loc['Expectancy [%]'] = returns.mean() * 100
-        s.loc['SQN'] = np.sqrt(n_trades) * pl.mean() / (pl.std() or np.nan)
-
-        s.loc['_strategy'] = strategy
-        s.loc['_equity_curve'] = equity_df
-        s.loc['_trades'] = trades_df
-
-        s = Backtest._Stats(s)
-        return s
-
-    class _Stats(pd.Series):
-        def __repr__(self):
-            # Prevent expansion due to _equity and _trades dfs
-            with pd.option_context('max_colwidth', 20):
-                return super().__repr__()
-
     def plot(self, *, results: pd.Series = None, filename=None, plot_width=None,
              plot_equity=True, plot_return=False, plot_pl=True,
              plot_volume=True, plot_drawdown=False,
diff --git a/backtesting/lib.py b/backtesting/lib.py
@@ -22,6 +22,7 @@
 
 from .backtesting import Strategy
 from ._plotting import plot_heatmaps as _plot_heatmaps
+from ._stats import compute_stats as _compute_stats
 from ._util import _Array, _as_str
 
 __pdoc__ = {}
@@ -164,6 +165,38 @@ def quantile(series: Sequence, quantile: Union[None, float] = None):
     return np.nanpercentile(series, quantile * 100)
 
 
+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 `Backtest.run()`.
+    `data` is OHLC data as passed to the `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, ohlc_data=data,
+                          risk_free_rate=risk_free_rate, strategy_instance=stats._strategy)
+
+
 def resample_apply(rule: str,
                    func: Optional[Callable[..., Sequence]],
                    series: Union[pd.Series, pd.DataFrame, _Array],
diff --git a/backtesting/test/_test.py b/backtesting/test/_test.py
