Remove flooring on ratios & add Kelly Criterion (#640)

* Remove flooring on ratios and adding Kelly Criterion

* fix build

Author: t2o2

README.md

@@ -79,6 +79,7 @@ Avg. Trade Duration          32 days 00:00:00
 Profit Factor                            2.13
 Expectancy [%]                           6.91
 SQN                                      1.78
+Kelly Criterion                        0.6134
 _strategy              SmaCross(n1=10, n2=20)
 _equity_curve                          Equ...
 _trades                       Size  EntryB...

backtesting/_stats.py

@@ -117,11 +117,11 @@ def _round_timedelta(value, _period=_data_period(index)):
 
     # 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
+    s.loc['Sharpe Ratio'] = (s.loc['Return (Ann.) [%]'] - risk_free_rate) / (s.loc['Volatility (Ann.) [%]'] or np.nan)  # 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
+    s.loc['Sortino Ratio'] = (annualized_return - risk_free_rate) / (np.sqrt(np.mean(day_returns.clip(-np.inf, 0)**2)) * np.sqrt(annual_trading_days))  # 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['Calmar Ratio'] = annualized_return / (-max_dd or np.nan)
     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())
@@ -137,6 +137,8 @@ def _round_timedelta(value, _period=_data_period(index)):
     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)
+    win_prob = (pl > 0).sum() / n_trades
+    s.loc['Kelly Criterion'] = win_prob - (1 - win_prob) / (pl[pl > 0].mean() / pl[pl < 0].mean())  # noqa: E501
 
     s.loc['_strategy'] = strategy_instance
     s.loc['_equity_curve'] = equity_df

backtesting/backtesting.py

@@ -1134,6 +1134,7 @@ def run(self, **kwargs) -> pd.Series:
             Profit Factor                         2.08802
             Expectancy [%]                        8.79171
             SQN                                  0.916893
+            Kelly Criterion                        0.6134
             _strategy                            SmaCross
             _equity_curve                           Eq...
             _trades                       Size  EntryB...

backtesting/test/_test.py

@@ -276,6 +276,7 @@ def test_compute_stats(self):
                 'Return [%]': 414.2298999999996,
                 'Volatility (Ann.) [%]': 36.49390889140787,
                 'SQN': 1.0766187356697705,
+                'Kelly Criterion': 0.7875234266909678,
                 'Sharpe Ratio': 0.5803778344714113,
                 'Sortino Ratio': 1.0847880675854096,
                 'Start': pd.Timestamp('2004-08-19 00:00:00'),