Add sharpe ratio as loss function

freqtrade/optimize/hyperopt.py

@@ -23,7 +23,7 @@ from freqtrade.configuration import Arguments
 from freqtrade.data.history import load_data, get_timeframe
 from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
-from freqtrade.optimize.hyperopt_loss import hyperopt_loss_legacy
+from freqtrade.optimize.hyperopt_loss import hyperopt_loss_legacy, hyperopt_loss_sharpe
 
 
 logger = logging.getLogger(__name__)
@@ -74,6 +74,8 @@ class Hyperopt(Backtesting):
         # Assign loss function
         if self.config.get('loss_function', 'legacy') == 'legacy':
             self.calculate_loss = hyperopt_loss_legacy
+        elif self.config.get('loss_function', 'sharpe') == 'sharpe':
+            self.calculate_loss = hyperopt_loss_sharpe
         elif (self.config['loss_function'] == 'custom' and
               hasattr(self.custom_hyperopt, 'hyperopt_loss_custom')):
             self.calculate_loss = self.custom_hyperopt.hyperopt_loss_custom  # type: ignore

freqtrade/optimize/hyperopt_loss.py

@@ -1,4 +1,7 @@
+from datetime import datetime
 from math import exp
+
+import numpy as np
 from pandas import DataFrame
 
 # Define some constants:
@@ -35,3 +38,27 @@ def hyperopt_loss_legacy(results: DataFrame, trade_count: int,
     duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
     result = trade_loss + profit_loss + duration_loss
     return result
+
+
+def hyperopt_loss_sharpe(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:
+        sharp_ratio = 1.
+
+    # print(expected_yearly_return, np.std(total_profit), sharp_ratio)
+
+    # Negate sharp-ratio so lower is better (??)
+    return -sharp_ratio