Allow loading custom hyperopt loss functions

user_data/hyperopts/sample_hyperopt.py

@@ -1,17 +1,31 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 
+from functools import reduce
+from math import exp
+from typing import Any, Callable, Dict, List
+from datetime import datetime
+
+import numpy as np# noqa F401
 import talib.abstract as ta
 from pandas import DataFrame
-from typing import Dict, Any, Callable, List
-from functools import reduce
-
-import numpy
 from skopt.space import Categorical, Dimension, Integer, Real
 
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
 
-class_name = 'SampleHyperOpts'
+# 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
 
 
 # This class is a sample. Feel free to customize it.
@@ -28,6 +42,21 @@ class SampleHyperOpts(IHyperOpt):
     roi_space, generate_roi_table, stoploss_space
     """
 
+    @staticmethod
+    def hyperopt_loss_custom(results: DataFrame, trade_count: int,
+                             min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for more optimal results
+        """
+        total_profit = results.profit_percent.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
+
     @staticmethod
     def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe['adx'] = ta.ADX(dataframe)