remove backtesting from generate_optimizer

freqtrade/optimize/hyperopt/hyperopt.py

@@ -20,7 +20,6 @@ from freqtrade.constants import FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
 from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import file_dump_json, plural
-from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.optimize.hyperopt.hyperopt_logger import logging_mp_handle, logging_mp_setup
 from freqtrade.optimize.hyperopt.hyperopt_optimizer import HyperOptimizer
 from freqtrade.optimize.hyperopt.hyperopt_output import HyperoptOutput
@@ -149,7 +148,7 @@ class Hyperopt:
             )
 
     def run_optimizer_parallel(
-        self, parallel: Parallel, backtesting: Backtesting, asked: list[list]
+        self, parallel: Parallel, asked: list[list]
     ) -> list[dict[str, Any]]:
         """Start optimizer in a parallel way"""
 
@@ -162,7 +161,7 @@ class Hyperopt:
             return self.hyperopter.generate_optimizer(*args, **kwargs)
 
         return parallel(
-            delayed(wrap_non_picklable_objects(optimizer_wrapper))(backtesting, v) for v in asked
+            delayed(wrap_non_picklable_objects(optimizer_wrapper))(v) for v in asked
         )
 
     def _set_random_state(self, random_state: int | None) -> int:
@@ -289,7 +288,7 @@ class Hyperopt:
                             n_points=1, dimensions=self.hyperopter.o_dimensions
                         )
                         f_val0 = self.hyperopter.generate_optimizer(
-                            self.hyperopter.backtesting, asked[0].params
+                            asked[0].params
                         )
                         self.opt.tell(asked[0], [f_val0["loss"]])
                         self.evaluate_result(f_val0, 1, is_random[0])
@@ -309,7 +308,7 @@ class Hyperopt:
 
                         f_val = self.run_optimizer_parallel(
                             parallel,
-                            self.hyperopter.backtesting,
+                            # self.hyperopter.backtesting,
                             [asked1.params for asked1 in asked],
                         )
                         f_val_loss = [v["loss"] for v in f_val]

freqtrade/optimize/hyperopt/hyperopt_optimizer.py

@@ -267,9 +267,7 @@ class HyperOptimizer:
                 # noinspection PyProtectedMember
                 attr.value = params_dict[attr_name]
 
-    def generate_optimizer(
+    def generate_optimizer(self, raw_params: dict[str, Any]) -> dict[str, Any]:
-        self, backtesting: Backtesting, raw_params: dict[str, Any]
-    ) -> dict[str, Any]:
         """
         Used Optimize function.
         Called once per epoch to optimize whatever is configured.
@@ -281,26 +279,30 @@ class HyperOptimizer:
 
         # Apply parameters
         if HyperoptTools.has_space(self.config, "buy"):
-            self.assign_params(backtesting, params_dict, "buy")
+            self.assign_params(self.backtesting, params_dict, "buy")
 
         if HyperoptTools.has_space(self.config, "sell"):
-            self.assign_params(backtesting, params_dict, "sell")
+            self.assign_params(self.backtesting, params_dict, "sell")
 
         if HyperoptTools.has_space(self.config, "protection"):
-            self.assign_params(backtesting, params_dict, "protection")
+            self.assign_params(self.backtesting, params_dict, "protection")
 
         if HyperoptTools.has_space(self.config, "roi"):
-            backtesting.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params_dict)
+            self.backtesting.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(
+                params_dict
+            )
 
         if HyperoptTools.has_space(self.config, "stoploss"):
-            backtesting.strategy.stoploss = params_dict["stoploss"]
+            self.backtesting.strategy.stoploss = params_dict["stoploss"]
 
         if HyperoptTools.has_space(self.config, "trailing"):
             d = self.custom_hyperopt.generate_trailing_params(params_dict)
-            backtesting.strategy.trailing_stop = d["trailing_stop"]
+            self.backtesting.strategy.trailing_stop = d["trailing_stop"]
-            backtesting.strategy.trailing_stop_positive = d["trailing_stop_positive"]
+            self.backtesting.strategy.trailing_stop_positive = d["trailing_stop_positive"]
-            backtesting.strategy.trailing_stop_positive_offset = d["trailing_stop_positive_offset"]
+            self.backtesting.strategy.trailing_stop_positive_offset = d[
-            backtesting.strategy.trailing_only_offset_is_reached = d[
+                "trailing_stop_positive_offset"
+            ]
+            self.backtesting.strategy.trailing_only_offset_is_reached = d[
                 "trailing_only_offset_is_reached"
             ]
 
@@ -319,7 +321,7 @@ class HyperOptimizer:
 
             self.config.update({"max_open_trades": updated_max_open_trades})
 
-            backtesting.strategy.max_open_trades = updated_max_open_trades
+            self.backtesting.strategy.max_open_trades = updated_max_open_trades
 
         with self.data_pickle_file.open("rb") as f:
             processed = load(f, mmap_mode="r")
@@ -327,7 +329,7 @@ class HyperOptimizer:
             # Data is not yet analyzed, rerun populate_indicators.
             processed = self.advise_and_trim(processed)
 
-        bt_results = backtesting.backtest(
+        bt_results = self.backtesting.backtest(
             processed=processed, start_date=self.min_date, end_date=self.max_date
         )
         backtest_end_time = datetime.now(timezone.utc)

tests/optimize/test_hyperopt.py

@@ -606,7 +606,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
     hyperopt.hyperopter.max_date = dt_utc(2017, 12, 13)
     hyperopt.hyperopter.init_spaces()
     generate_optimizer_value = hyperopt.hyperopter.generate_optimizer(
-        hyperopt.hyperopter.backtesting, optimizer_param
+        optimizer_param
     )
     assert generate_optimizer_value == response_expected