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/backtesting.py

@@ -1127,6 +1127,7 @@ 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...
@@ -1298,7 +1299,7 @@ def constraint(_):
                             "of strategy parameters and returns a bool whether "
                             "the combination of parameters is admissible or not")
 
-        if return_optimization and method != 'skopt':
+        if return_optimization and method not in ('skopt', 'skopt_gp'):
             raise ValueError("return_optimization=True only valid if method='skopt'")
 
         def _tuple(x):
@@ -1398,7 +1399,7 @@ def _optimize_skopt() -> Union[pd.Series,
                                        Tuple[pd.Series, pd.Series],
                                        Tuple[pd.Series, pd.Series, dict]]:
             try:
-                from skopt import forest_minimize
+                from skopt import forest_minimize, gp_minimize
                 from skopt.space import Integer, Real, Categorical
                 from skopt.utils import use_named_args
                 from skopt.callbacks import DeltaXStopper
@@ -1453,17 +1454,29 @@ def objective_function(**params):
                 warnings.filterwarnings(
                     'ignore', 'The objective has been evaluated at this point before.')
 
-                res = forest_minimize(
-                    func=objective_function,
-                    dimensions=dimensions,
-                    n_calls=max_tries,
-                    base_estimator=ExtraTreesRegressor(n_estimators=20, min_samples_leaf=2),
-                    acq_func='LCB',
-                    kappa=3,
-                    n_initial_points=min(max_tries, 20 + 3 * len(kwargs)),
-                    initial_point_generator='lhs',  # 'sobel' requires n_initial_points ~ 2**N
-                    callback=DeltaXStopper(9e-7),
-                    random_state=random_state)
+                if method == 'skopt_gp':
+                    res = gp_minimize(
+                        func=objective_function,
+                        dimensions=dimensions,
+                        n_calls=max_tries,
+                        n_jobs=-1,
+                        acq_func='gp_hedge',
+                        n_initial_points=min(max_tries, 20 + 3 * len(kwargs)),
+                        initial_point_generator='random',  # 'sobel' requires n_initial_points ~ 2**N
+                        callback=DeltaXStopper(9e-7),
+                        random_state=random_state)
+                else:
+                    res = forest_minimize(
+                        func=objective_function,
+                        dimensions=dimensions,
+                        n_calls=max_tries,
+                        base_estimator=ExtraTreesRegressor(n_estimators=20, min_samples_leaf=2),
+                        acq_func='LCB',
+                        kappa=3,
+                        n_initial_points=min(max_tries, 20 + 3 * len(kwargs)),
+                        initial_point_generator='lhs',  # 'sobel' requires n_initial_points ~ 2**N
+                        callback=DeltaXStopper(9e-7),
+                        random_state=random_state)
 
             stats = self.run(**dict(zip(kwargs.keys(), res.x)))
             output = [stats]
@@ -1486,7 +1499,7 @@ def objective_function(**params):
 
         if method == 'grid':
             output = _optimize_grid()
-        elif method == 'skopt':
+        elif method in ('skopt', 'skopt_gp'):
             output = _optimize_skopt()
         else:
             raise ValueError(f"Method should be 'grid' or 'skopt', not {method!r}")

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'),