init

user_data/hyperopts/sample_hyperopt_loss.py

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+from datetime import datetime
+from math import exp
+
+from pandas import DataFrame
+
+from freqtrade.constants import Config
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+# Define some constants:
+
+# set TARGET_TRADES to suit your number concurrent trades so its realistic
+# to the number of days
+TARGET_TRADES = 600
+# This is assumed to be expected avg profit * expected trade count.
+# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
+# self.expected_max_profit = 3.85
+# Check that the reported Σ% values do not exceed this!
+# Note, this is ratio. 3.85 stated above means 385Σ%.
+EXPECTED_MAX_PROFIT = 3.0
+
+# max average trade duration in minutes
+# if eval ends with higher value, we consider it a failed eval
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+
+class SampleHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    This is intended to give you some inspiration for your own loss function.
+
+    The Function needs to return a number (float) - which becomes smaller for better backtest
+    results.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        config: Config,
+        processed: dict[str, DataFrame],
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for better results
+        """
+        total_profit = results["profit_ratio"].sum()
+        trade_duration = results["trade_duration"].mean()
+
+        trade_loss = 1 - 0.25 * exp(-((trade_count - TARGET_TRADES) ** 2) / 10**5.8)
+        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result