update sortino calc

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py

@@ -10,7 +10,8 @@ import numpy as np
 from pandas import DataFrame
 
 from freqtrade.optimize.hyperopt import IHyperOptLoss
-
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sortino
 
 class SortinoHyperOptLoss(IHyperOptLoss):
     """
@@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sortino Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        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_ratio']
-        down_stdev = np.std(results['downside_returns'])
-
-        if down_stdev != 0:
-            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
-            sortino_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, down_stdev, sortino_ratio)
         return -sortino_ratio