Convert hyperoptloss resolver to static loader

freqtrade/optimize/hyperopt.py

@@ -66,7 +66,7 @@ class Hyperopt:
 
         self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
 
-        self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
+        self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
 
         self.trials_file = (self.config['user_data_dir'] /

freqtrade/resolvers/hyperopt_resolver.py

@@ -77,9 +77,9 @@ class HyperOptLossResolver(IResolver):
     """
     This class contains all the logic to load custom hyperopt loss class
     """
-    __slots__ = ['hyperoptloss']
 
-    def __init__(self, config: Dict) -> None:
+    @staticmethod
+    def load_hyperoptloss(config: Dict) -> IHyperOptLoss:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary
@@ -89,20 +89,21 @@ class HyperOptLossResolver(IResolver):
         # default hyperopt loss
         hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
 
-        self.hyperoptloss = self._load_hyperoptloss(
+        hyperoptloss = HyperOptLossResolver._load_hyperoptloss(
             hyperoptloss_name, config, extra_dir=config.get('hyperopt_path'))
 
         # Assign ticker_interval to be used in hyperopt
-        self.hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
+        hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
 
-        if not hasattr(self.hyperoptloss, 'hyperopt_loss_function'):
+        if not hasattr(hyperoptloss, 'hyperopt_loss_function'):
             raise OperationalException(
                 f"Found HyperoptLoss class {hyperoptloss_name} does not "
                 "implement `hyperopt_loss_function`.")
+        return hyperoptloss
 
-    def _load_hyperoptloss(
+    @staticmethod
-            self, hyper_loss_name: str, config: Dict,
+    def _load_hyperoptloss(hyper_loss_name: str, config: Dict,
-            extra_dir: Optional[str] = None) -> IHyperOptLoss:
+                           extra_dir: Optional[str] = None) -> IHyperOptLoss:
         """
         Search and loads the specified hyperopt loss class.
         :param hyper_loss_name: name of the module to import
@@ -112,11 +113,12 @@ class HyperOptLossResolver(IResolver):
         """
         current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
 
-        abs_paths = self.build_search_paths(config, current_path=current_path,
+        abs_paths = IResolver.build_search_paths(config, current_path=current_path,
-                                            user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
+                                                 user_subdir=USERPATH_HYPEROPTS,
+                                                 extra_dir=extra_dir)
 
-        hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
+        hyperoptloss = IResolver._load_object(paths=abs_paths, object_type=IHyperOptLoss,
-                                         object_name=hyper_loss_name)
+                                              object_name=hyper_loss_name)
         if hyperoptloss:
             return hyperoptloss
 

tests/optimize/test_hyperopt.py

@@ -198,7 +198,7 @@ def test_hyperoptlossresolver(mocker, default_conf, caplog) -> None:
         'freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver._load_hyperoptloss',
         MagicMock(return_value=hl)
     )
-    x = HyperOptLossResolver(default_conf).hyperoptloss
+    x = HyperOptLossResolver.load_hyperoptloss(default_conf)
     assert hasattr(x, "hyperopt_loss_function")
 
 
@@ -206,7 +206,7 @@ def test_hyperoptlossresolver_wrongname(mocker, default_conf, caplog) -> None:
     default_conf.update({'hyperopt_loss': "NonExistingLossClass"})
 
     with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'):
-        HyperOptLossResolver(default_conf).hyperopt
+        HyperOptLossResolver.load_hyperoptloss(default_conf)
 
 
 def test_start_not_installed(mocker, default_conf, caplog, import_fails) -> None:
@@ -286,7 +286,7 @@ def test_start_filelock(mocker, default_conf, caplog) -> None:
 
 
 def test_loss_calculation_prefer_correct_trade_count(default_conf, hyperopt_results) -> None:
-    hl = HyperOptLossResolver(default_conf).hyperoptloss
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
     correct = hl.hyperopt_loss_function(hyperopt_results, 600)
     over = hl.hyperopt_loss_function(hyperopt_results, 600 + 100)
     under = hl.hyperopt_loss_function(hyperopt_results, 600 - 100)
@@ -298,7 +298,7 @@ def test_loss_calculation_prefer_shorter_trades(default_conf, hyperopt_results)
     resultsb = hyperopt_results.copy()
     resultsb.loc[1, 'trade_duration'] = 20
 
-    hl = HyperOptLossResolver(default_conf).hyperoptloss
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
     longer = hl.hyperopt_loss_function(hyperopt_results, 100)
     shorter = hl.hyperopt_loss_function(resultsb, 100)
     assert shorter < longer
@@ -310,7 +310,7 @@ def test_loss_calculation_has_limited_profit(default_conf, hyperopt_results) ->
     results_under = hyperopt_results.copy()
     results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
 
-    hl = HyperOptLossResolver(default_conf).hyperoptloss
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
     correct = hl.hyperopt_loss_function(hyperopt_results, 600)
     over = hl.hyperopt_loss_function(results_over, 600)
     under = hl.hyperopt_loss_function(results_under, 600)
@@ -325,7 +325,7 @@ def test_sharpe_loss_prefers_higher_profits(default_conf, hyperopt_results) -> N
     results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
 
     default_conf.update({'hyperopt_loss': 'SharpeHyperOptLoss'})
-    hl = HyperOptLossResolver(default_conf).hyperoptloss
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
     correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
                                         datetime(2019, 1, 1), datetime(2019, 5, 1))
     over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
@@ -343,7 +343,7 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
     results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
 
     default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'})
-    hl = HyperOptLossResolver(default_conf).hyperoptloss
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
     correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
                                         datetime(2019, 1, 1), datetime(2019, 5, 1))
     over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),