initial push of sortino, work not done, still need own tests

freqtrade/optimize/hyperopt_loss_sortino.py

@@ -0,0 +1,49 @@
+"""
+SortinoHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+from datetime import datetime
+
+from pandas import DataFrame
+import numpy as np
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sharpe Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sharpe Ratio calculation.
+        """
+        total_profit = results["profit_percent"]
+        days_period = (max_date - min_date).days
+
+        # adding slippage of 0.1% per trade
+        total_profit = total_profit - 0.0005
+        expected_returns_mean = total_profit.sum() / days_period
+
+        results['downside_returns'] = 0
+        results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
+        down_stdev = np.std(results['downside_returns'])
+
+        if np.std(total_profit) != 0.0:
+            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+        else:
+            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+            sortino_ratio = -20.
+
+        # print(expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio