add PTH ruff selection

freqtrade/freqai/data_drawer.py

@@ -126,7 +126,7 @@ class FreqaiDataDrawer:
         """
         exists = self.global_metadata_path.is_file()
         if exists:
-            with open(self.global_metadata_path, "r") as fp:
+            with self.global_metadata_path.open("r") as fp:
                 metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
                 return metatada_dict
         return {}
@@ -139,7 +139,7 @@ class FreqaiDataDrawer:
         """
         exists = self.pair_dictionary_path.is_file()
         if exists:
-            with open(self.pair_dictionary_path, "r") as fp:
+            with self.pair_dictionary_path.open("r") as fp:
                 self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
         else:
             logger.info("Could not find existing datadrawer, starting from scratch")
@@ -152,7 +152,7 @@ class FreqaiDataDrawer:
         if self.freqai_info.get('write_metrics_to_disk', False):
             exists = self.metric_tracker_path.is_file()
             if exists:
-                with open(self.metric_tracker_path, "r") as fp:
+                with self.metric_tracker_path.open("r") as fp:
                     self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
                 logger.info("Loading existing metric tracker from disk.")
             else:
@@ -166,7 +166,7 @@ class FreqaiDataDrawer:
         exists = self.historic_predictions_path.is_file()
         if exists:
             try:
-                with open(self.historic_predictions_path, "rb") as fp:
+                with self.historic_predictions_path.open("rb") as fp:
                     self.historic_predictions = cloudpickle.load(fp)
                 logger.info(
                     f"Found existing historic predictions at {self.full_path}, but beware "
@@ -176,7 +176,7 @@ class FreqaiDataDrawer:
             except EOFError:
                 logger.warning(
                     'Historical prediction file was corrupted. Trying to load backup file.')
-                with open(self.historic_predictions_bkp_path, "rb") as fp:
+                with self.historic_predictions_bkp_path.open("rb") as fp:
                     self.historic_predictions = cloudpickle.load(fp)
                 logger.warning('FreqAI successfully loaded the backup historical predictions file.')
 
@@ -189,7 +189,7 @@ class FreqaiDataDrawer:
         """
         Save historic predictions pickle to disk
         """
-        with open(self.historic_predictions_path, "wb") as fp:
+        with self.historic_predictions_path.open("wb") as fp:
             cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
 
         # create a backup
@@ -200,16 +200,16 @@ class FreqaiDataDrawer:
         Save metric tracker of all pair metrics collected.
         """
         with self.save_lock:
-            with open(self.metric_tracker_path, 'w') as fp:
+            with self.metric_tracker_path.open('w') as fp:
                 rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
                                number_mode=rapidjson.NM_NATIVE)
 
-    def save_drawer_to_disk(self):
+    def save_drawer_to_disk(self) -> None:
         """
         Save data drawer full of all pair model metadata in present model folder.
         """
         with self.save_lock:
-            with open(self.pair_dictionary_path, 'w') as fp:
+            with self.pair_dictionary_path.open('w') as fp:
                 rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
                                number_mode=rapidjson.NM_NATIVE)
 
@@ -218,7 +218,7 @@ class FreqaiDataDrawer:
         Save global metadata json to disk
         """
         with self.save_lock:
-            with open(self.global_metadata_path, 'w') as fp:
+            with self.global_metadata_path.open('w') as fp:
                 rapidjson.dump(metadata, fp, default=self.np_encoder,
                                number_mode=rapidjson.NM_NATIVE)
 
@@ -424,7 +424,7 @@ class FreqaiDataDrawer:
         dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
         dk.data["label_list"] = dk.label_list
 
-        with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
+        with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
             rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
 
         return
@@ -457,7 +457,7 @@ class FreqaiDataDrawer:
         dk.data["training_features_list"] = dk.training_features_list
         dk.data["label_list"] = dk.label_list
         # store the metadata
-        with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
+        with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
             rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
 
         # save the train data to file so we can check preds for area of applicability later
@@ -471,7 +471,7 @@ class FreqaiDataDrawer:
 
         if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
             cloudpickle.dump(
-                dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
+                dk.pca, (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("wb")
             )
 
         self.model_dictionary[coin] = model
@@ -491,7 +491,7 @@ class FreqaiDataDrawer:
         Load only metadata into datakitchen to increase performance during
         presaved backtesting (prediction file loading).
         """
-        with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
+        with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
             dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
             dk.training_features_list = dk.data["training_features_list"]
             dk.label_list = dk.data["label_list"]
@@ -514,7 +514,7 @@ class FreqaiDataDrawer:
             dk.data = self.meta_data_dictionary[coin]["meta_data"]
             dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
         else:
-            with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
+            with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
                 dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
 
             dk.data_dictionary["train_features"] = pd.read_pickle(
@@ -552,7 +552,7 @@ class FreqaiDataDrawer:
 
         if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
             dk.pca = cloudpickle.load(
-                open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
+                (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("rb")
             )
 
         return model

freqtrade/freqai/utils.py

@@ -211,7 +211,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
         "pairs": config.get('exchange', {}).get('pair_whitelist')
     }
 
-    with open(params_record_path, "w") as handle:
+    with params_record_path.open("w") as handle:
         rapidjson.dump(
             run_params,
             handle,

pyproject.toml

@@ -68,5 +68,5 @@ extend-select = [
   # "DTZ",  # flake8-datetimez
   # "RSE",  # flake8-raise
   # "TCH",  # flake8-type-checking
-  # "PTH",  # flake8-use-pathlib
+  "PTH",  # flake8-use-pathlib
 ]