Add new loss function based on profit/drawodown ratio per pair

freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_per_pair.py

@@ -0,0 +1,59 @@
+"""
+MaxDrawDownPerPairHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from typing import Any, Dict
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+class MaxDrawDownPerPairHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation calculates the profit/drawdown ratio per pair and
+    returns the worst result as objetive, forcing hyperopt to optimize
+    the parameters for all pairs in the pairlist.
+
+    This way, we prevent one or more pairs with good results from inflating
+    the metrics, while the rest of the pairs with poor results are not
+    represented and therefore not optimized.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(backtest_stats: Dict[str, Any],
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for better results.
+        """
+
+        ##############################################
+        # Configurable parameters
+        ##############################################
+        # Minimum acceptable profit/drawdown per pair
+        min_acceptable_profit_dd = 1.0
+        # Penalty when acceptable minimum are not met
+        penalty = 20
+        ##############################################
+
+        score_per_pair = []
+        for p in backtest_stats["results_per_pair"]:
+            if p["key"] != "TOTAL":
+                profit = p.get("profit_total_abs", 0)
+                drawdown = p.get("max_drawdown_abs", 0)
+
+                if drawdown != 0 and profit != 0:
+                    profit_dd = profit / drawdown
+                else:
+                    profit_dd = profit
+
+                if profit_dd < min_acceptable_profit_dd:
+                    score = profit_dd - penalty
+                else:
+                    score = profit_dd
+
+                score_per_pair.append(score)
+
+        return -min(score_per_pair)
+