Add sharpe ratio hyperopt loss

freqtrade/optimize/default_hyperopt_loss.py

@@ -1,6 +1,7 @@
 """
-IHyperOptLoss interface
-This module defines the interface for the loss-function for hyperopts
+DefaultHyperOptLoss
+This module defines the default HyperoptLoss class which is being used for
+Hyperoptimization.
 """
 
 from math import exp

freqtrade/optimize/hyperopt_loss_sharpe.py

@@ -0,0 +1,42 @@
+"""
+IHyperOptLoss interface
+This module defines the interface for the loss-function for hyperopts
+"""
+
+from datetime import datetime
+
+from pandas import DataFrame
+import numpy as np
+
+from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
+
+
+class SharpeHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the a 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
+        Using 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_yearly_return = total_profit.sum() / days_period
+
+        if (np.std(total_profit) != 0.):
+            sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
+        else:
+            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+            sharp_ratio = 20.
+
+        # print(expected_yearly_return, np.std(total_profit), sharp_ratio)
+        return -sharp_ratio