added daily sharpe ratio hyperopt loss method, ty @djacky

freqtrade/commands/cli_options.py

@@ -256,7 +256,7 @@ AVAILABLE_CLI_OPTIONS = {
         help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
         'Different functions can generate completely different results, '
         'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
-        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss.'
+        'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.'
         '(default: `%(default)s`).',
         metavar='NAME',
         default=constants.DEFAULT_HYPEROPT_LOSS,

freqtrade/optimize/hyperopt_loss_sharpe_daily.py

@@ -0,0 +1,69 @@
+"""
+SharpeHyperOptLoss
+
+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 SharpeHyperOptLossDaily(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_yearly_return = total_profit.sum() / days_period
+
+        # if np.std(total_profit) != 0.0:
+        #     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 = -20.0
+
+        # # print(expected_yearly_return, np.std(total_profit), sharp_ratio)
+        # return -sharp_ratio
+
+        sum_daily = (
+            results.resample("D", on="close_time").agg(
+                {"profit_percent": sum, "profit_abs": sum}
+            )
+            * 100.0
+        )
+
+        if np.std(total_profit) != 0.0:
+            sharp_ratio = (
+                sum_daily["profit_percent"].mean()
+                / sum_daily["profit_percent"].std()
+                * np.sqrt(365)
+            )
+        else:
+            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+            sharp_ratio = -20.0
+
+        return -sharp_ratio