wrap strategies with HyperoptStrategy for module lookups with pickle

freqtrade/optimize/hyperopt.py

@@ -11,6 +11,7 @@ import pickle
 import signal
 import sys
 from argparse import Namespace
+from copy import deepcopy
 from functools import reduce
 from math import exp
 from operator import itemgetter
@@ -26,10 +27,25 @@ from freqtrade.arguments import Arguments
 from freqtrade.configuration import Configuration
 from freqtrade.optimize import load_data
 from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.strategy.interface import IStrategy
 
 logger = logging.getLogger(__name__)
 
 
+HyperoptStrategy = None
+
+
+def wrap_strategy(strategy: IStrategy) -> Optional[HyperoptStrategy]:
+    """Wraps a given Strategy instance to HyperoptStrategy"""
+    global HyperoptStrategy
+
+    attr = deepcopy(dict(strategy.__class__.__dict__))
+    # Patch module name to make it compatible with pickle
+    attr['__module__'] = 'freqtrade.optimize.hyperopt'
+    HyperoptStrategy = type('HyperoptStrategy', (IStrategy,), attr)
+    return HyperoptStrategy()
+
+
 class Hyperopt(Backtesting):
     """
     Hyperopt class, this class contains all the logic to run a hyperopt simulation
@@ -39,7 +55,6 @@ class Hyperopt(Backtesting):
     hyperopt.start()
     """
     def __init__(self, config: Dict[str, Any]) -> None:
-
         super().__init__(config)
         # set TARGET_TRADES to suit your number concurrent trades so its realistic
         # to the number of days
@@ -57,6 +72,9 @@ class Hyperopt(Backtesting):
         # check that the reported Σ% values do not exceed this!
         self.expected_max_profit = 3.0
 
+        # Wrap strategy to make it compatible with pickle
+        self.analyze.strategy = wrap_strategy(self.analyze.strategy)
+
         # Configuration and data used by hyperopt
         self.processed: Optional[Dict[str, Any]] = None