chore: update freqai to modern typing syntax

freqtrade/freqai/RL/BaseEnvironment.py

@@ -2,7 +2,7 @@ import logging
 import random
 from abc import abstractmethod
 from enum import Enum
-from typing import List, Optional, Type, Union
+from typing import Optional, Union
 
 import gymnasium as gym
 import numpy as np
@@ -89,7 +89,7 @@ class BaseEnvironment(gym.Env):
             self.fee = fee
 
         # set here to default 5Ac, but all children envs can override this
-        self.actions: Type[Enum] = BaseActions
+        self.actions: type[Enum] = BaseActions
         self.tensorboard_metrics: dict = {}
         self.can_short: bool = can_short
         self.live: bool = live
@@ -163,7 +163,7 @@ class BaseEnvironment(gym.Env):
         Unique to the environment action count. Must be inherited.
         """
 
-    def action_masks(self) -> List[bool]:
+    def action_masks(self) -> list[bool]:
         return [self._is_valid(action.value) for action in self.actions]
 
     def seed(self, seed: int = 1):
@@ -375,7 +375,7 @@ class BaseEnvironment(gym.Env):
     def current_price(self) -> float:
         return self.prices.iloc[self._current_tick].open
 
-    def get_actions(self) -> Type[Enum]:
+    def get_actions(self) -> type[Enum]:
         """
         Used by SubprocVecEnv to get actions from
         initialized env for tensorboard callback

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -4,7 +4,7 @@ import logging
 from abc import abstractmethod
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
+from typing import Any, Callable, Optional, Union
 
 import gymnasium as gym
 import numpy as np
@@ -114,7 +114,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             training_filter=True,
         )
 
-        dd: Dict[str, Any] = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        dd: dict[str, Any] = dk.make_train_test_datasets(features_filtered, labels_filtered)
         self.df_raw = copy.deepcopy(dd["train_features"])
         dk.fit_labels()  # FIXME useless for now, but just satiating append methods
 
@@ -151,7 +151,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
     def set_train_and_eval_environments(
         self,
-        data_dictionary: Dict[str, DataFrame],
+        data_dictionary: dict[str, DataFrame],
         prices_train: DataFrame,
         prices_test: DataFrame,
         dk: FreqaiDataKitchen,
@@ -183,7 +183,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         actions = self.train_env.get_actions()
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
 
-    def pack_env_dict(self, pair: str) -> Dict[str, Any]:
+    def pack_env_dict(self, pair: str) -> dict[str, Any]:
         """
         Create dictionary of environment arguments
         """
@@ -204,7 +204,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         return env_info
 
     @abstractmethod
-    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
         """
         Agent customizations and abstract Reinforcement Learning customizations
         go in here. Abstract method, so this function must be overridden by
@@ -212,7 +212,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         """
         return
 
-    def get_state_info(self, pair: str) -> Tuple[float, float, int]:
+    def get_state_info(self, pair: str) -> tuple[float, float, int]:
         """
         State info during dry/live (not backtesting) which is fed back
         into the model.
@@ -250,7 +250,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_dataframe: Full dataframe for the current backtest period.
@@ -303,7 +303,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
     def build_ohlc_price_dataframes(
         self, data_dictionary: dict, pair: str, dk: FreqaiDataKitchen
-    ) -> Tuple[DataFrame, DataFrame]:
+    ) -> tuple[DataFrame, DataFrame]:
         """
         Builds the train prices and test prices for the environment.
         """
@@ -482,13 +482,13 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
 
 def make_env(
-    MyRLEnv: Type[BaseEnvironment],
+    MyRLEnv: type[BaseEnvironment],
     env_id: str,
     rank: int,
     seed: int,
     train_df: DataFrame,
     price: DataFrame,
-    env_info: Dict[str, Any] = {},
+    env_info: dict[str, Any] = {},
 ) -> Callable:
     """
     Utility function for multiprocessed env.

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -1,6 +1,6 @@
 import logging
 from time import time
-from typing import Any, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -86,7 +86,7 @@ class BaseClassifierModel(IFreqaiModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/base_models/BasePyTorchClassifier.py

@@ -1,6 +1,6 @@
 import logging
 from time import time
-from typing import Any, Dict, List, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -44,7 +44,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param dk: dk: The datakitchen object
@@ -100,9 +100,9 @@ class BasePyTorchClassifier(BasePyTorchModel):
 
     def encode_class_names(
         self,
-        data_dictionary: Dict[str, pd.DataFrame],
+        data_dictionary: dict[str, pd.DataFrame],
         dk: FreqaiDataKitchen,
-        class_names: List[str],
+        class_names: list[str],
     ):
         """
         encode class name, str -> int
@@ -119,7 +119,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
             )
 
     @staticmethod
-    def assert_valid_class_names(target_column: pd.Series, class_names: List[str]):
+    def assert_valid_class_names(target_column: pd.Series, class_names: list[str]):
         non_defined_labels = set(target_column) - set(class_names)
         if len(non_defined_labels) != 0:
             raise OperationalException(
@@ -127,7 +127,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
                 f"expecting labels: {class_names}",
             )
 
-    def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
+    def decode_class_names(self, class_ints: torch.Tensor) -> list[str]:
         """
         decode class name, int -> str
         """
@@ -141,14 +141,14 @@ class BasePyTorchClassifier(BasePyTorchModel):
 
     def convert_label_column_to_int(
         self,
-        data_dictionary: Dict[str, pd.DataFrame],
+        data_dictionary: dict[str, pd.DataFrame],
         dk: FreqaiDataKitchen,
-        class_names: List[str],
+        class_names: list[str],
     ):
         self.init_class_names_to_index_mapping(class_names)
         self.encode_class_names(data_dictionary, dk, class_names)
 
-    def get_class_names(self) -> List[str]:
+    def get_class_names(self) -> list[str]:
         if not self.class_names:
             raise ValueError(
                 "self.class_names is empty, "

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -1,6 +1,6 @@
 import logging
 from time import time
-from typing import Any, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -24,7 +24,7 @@ class BasePyTorchRegressor(BasePyTorchModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -1,6 +1,6 @@
 import logging
 from time import time
-from typing import Any, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -88,7 +88,7 @@ class BaseRegressionModel(IFreqaiModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/data_drawer.py

@@ -7,7 +7,7 @@ import threading
 import warnings
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
-from typing import Any, Dict, Tuple, TypedDict
+from typing import Any, TypedDict
 
 import numpy as np
 import pandas as pd
@@ -69,14 +69,14 @@ class FreqaiDataDrawer:
         self.config = config
         self.freqai_info = config.get("freqai", {})
         # dictionary holding all pair metadata necessary to load in from disk
-        self.pair_dict: Dict[str, pair_info] = {}
+        self.pair_dict: dict[str, pair_info] = {}
         # dictionary holding all actively inferenced models in memory given a model filename
-        self.model_dictionary: Dict[str, Any] = {}
+        self.model_dictionary: dict[str, Any] = {}
         # all additional metadata that we want to keep in ram
-        self.meta_data_dictionary: Dict[str, Dict[str, Any]] = {}
+        self.meta_data_dictionary: dict[str, dict[str, Any]] = {}
-        self.model_return_values: Dict[str, DataFrame] = {}
+        self.model_return_values: dict[str, DataFrame] = {}
-        self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
+        self.historic_data: dict[str, dict[str, DataFrame]] = {}
-        self.historic_predictions: Dict[str, DataFrame] = {}
+        self.historic_predictions: dict[str, DataFrame] = {}
         self.full_path = full_path
         self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
         self.historic_predictions_bkp_path = Path(
@@ -87,14 +87,14 @@ class FreqaiDataDrawer:
         self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
         self.load_drawer_from_disk()
         self.load_historic_predictions_from_disk()
-        self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
+        self.metric_tracker: dict[str, dict[str, dict[str, list]]] = {}
         self.load_metric_tracker_from_disk()
-        self.training_queue: Dict[str, int] = {}
+        self.training_queue: dict[str, int] = {}
         self.history_lock = threading.Lock()
         self.save_lock = threading.Lock()
         self.pair_dict_lock = threading.Lock()
         self.metric_tracker_lock = threading.Lock()
-        self.old_DBSCAN_eps: Dict[str, float] = {}
+        self.old_DBSCAN_eps: dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
             "model_filename": "",
             "trained_timestamp": 0,
@@ -228,7 +228,7 @@ class FreqaiDataDrawer:
                     self.pair_dict, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE
                 )
 
-    def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
+    def save_global_metadata_to_disk(self, metadata: dict[str, Any]):
         """
         Save global metadata json to disk
         """
@@ -242,7 +242,7 @@ class FreqaiDataDrawer:
         if isinstance(obj, np.generic):
             return obj.item()
 
-    def get_pair_dict_info(self, pair: str) -> Tuple[str, int]:
+    def get_pair_dict_info(self, pair: str) -> tuple[str, int]:
         """
         Locate and load existing model metadata from persistent storage. If not located,
         create a new one and append the current pair to it and prepare it for its first
@@ -446,7 +446,7 @@ class FreqaiDataDrawer:
 
         pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
 
-        delete_dict: Dict[str, Any] = {}
+        delete_dict: dict[str, Any] = {}
 
         for directory in model_folders:
             result = pattern.match(str(directory.name))
@@ -704,7 +704,7 @@ class FreqaiDataDrawer:
 
     def get_base_and_corr_dataframes(
         self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
-    ) -> Tuple[Dict[Any, Any], Dict[Any, Any]]:
+    ) -> tuple[dict[Any, Any], dict[Any, Any]]:
         """
         Searches through our historic_data in memory and returns the dataframes relevant
         to the present pair.
@@ -713,8 +713,8 @@ class FreqaiDataDrawer:
         :param metadata: dict = strategy furnished pair metadata
         """
         with self.history_lock:
-            corr_dataframes: Dict[Any, Any] = {}
+            corr_dataframes: dict[Any, Any] = {}
-            base_dataframes: Dict[Any, Any] = {}
+            base_dataframes: dict[Any, Any] = {}
             historic_data = self.historic_data
             pairs = self.freqai_info["feature_parameters"].get("include_corr_pairlist", [])
 

freqtrade/freqai/data_kitchen.py

@@ -5,7 +5,7 @@ import random
 import shutil
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Optional
 
 import numpy as np
 import numpy.typing as npt
@@ -64,15 +64,15 @@ class FreqaiDataKitchen:
         live: bool = False,
         pair: str = "",
     ):
-        self.data: Dict[str, Any] = {}
+        self.data: dict[str, Any] = {}
-        self.data_dictionary: Dict[str, DataFrame] = {}
+        self.data_dictionary: dict[str, DataFrame] = {}
         self.config = config
-        self.freqai_config: Dict[str, Any] = config["freqai"]
+        self.freqai_config: dict[str, Any] = config["freqai"]
         self.full_df: DataFrame = DataFrame()
         self.append_df: DataFrame = DataFrame()
         self.data_path = Path()
-        self.label_list: List = []
+        self.label_list: list = []
-        self.training_features_list: List = []
+        self.training_features_list: list = []
         self.model_filename: str = ""
         self.backtesting_results_path = Path()
         self.backtest_predictions_folder: str = "backtesting_predictions"
@@ -104,9 +104,9 @@ class FreqaiDataKitchen:
         else:
             self.thread_count = self.freqai_config["data_kitchen_thread_count"]
         self.train_dates: DataFrame = pd.DataFrame()
-        self.unique_classes: Dict[str, list] = {}
+        self.unique_classes: dict[str, list] = {}
         self.unique_class_list: list = []
-        self.backtest_live_models_data: Dict[str, Any] = {}
+        self.backtest_live_models_data: dict[str, Any] = {}
 
     def set_paths(
         self,
@@ -127,7 +127,7 @@ class FreqaiDataKitchen:
 
     def make_train_test_datasets(
         self, filtered_dataframe: DataFrame, labels: DataFrame
-    ) -> Dict[Any, Any]:
+    ) -> dict[Any, Any]:
         """
         Given the dataframe for the full history for training, split the data into
         training and test data according to user specified parameters in configuration
@@ -213,10 +213,10 @@ class FreqaiDataKitchen:
     def filter_features(
         self,
         unfiltered_df: DataFrame,
-        training_feature_list: List,
+        training_feature_list: list,
-        label_list: List = list(),
+        label_list: list = list(),
         training_filter: bool = True,
-    ) -> Tuple[DataFrame, DataFrame]:
+    ) -> tuple[DataFrame, DataFrame]:
         """
         Filter the unfiltered dataframe to extract the user requested features/labels and properly
         remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
@@ -306,7 +306,7 @@ class FreqaiDataKitchen:
         test_labels: DataFrame,
         train_weights: Any,
         test_weights: Any,
-    ) -> Dict:
+    ) -> dict:
         self.data_dictionary = {
             "train_features": train_df,
             "test_features": test_df,
@@ -321,7 +321,7 @@ class FreqaiDataKitchen:
 
     def split_timerange(
         self, tr: str, train_split: int = 28, bt_split: float = 7
-    ) -> Tuple[list, list]:
+    ) -> tuple[list, list]:
         """
         Function which takes a single time range (tr) and splits it
         into sub timeranges to train and backtest on based on user input
@@ -535,7 +535,7 @@ class FreqaiDataKitchen:
 
     def check_if_new_training_required(
         self, trained_timestamp: int
-    ) -> Tuple[bool, TimeRange, TimeRange]:
+    ) -> tuple[bool, TimeRange, TimeRange]:
         time = datetime.now(tz=timezone.utc).timestamp()
         trained_timerange = TimeRange()
         data_load_timerange = TimeRange()
@@ -603,7 +603,7 @@ class FreqaiDataKitchen:
 
     def extract_corr_pair_columns_from_populated_indicators(
         self, dataframe: DataFrame
-    ) -> Dict[str, DataFrame]:
+    ) -> dict[str, DataFrame]:
         """
         Find the columns of the dataframe corresponding to the corr_pairlist, save them
         in a dictionary to be reused and attached to other pairs.
@@ -612,7 +612,7 @@ class FreqaiDataKitchen:
         :return: corr_dataframes, dictionary of dataframes to be attached
                  to other pairs in same candle.
         """
-        corr_dataframes: Dict[str, DataFrame] = {}
+        corr_dataframes: dict[str, DataFrame] = {}
         pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
 
         for pair in pairs:
@@ -628,7 +628,7 @@ class FreqaiDataKitchen:
         return corr_dataframes
 
     def attach_corr_pair_columns(
-        self, dataframe: DataFrame, corr_dataframes: Dict[str, DataFrame], current_pair: str
+        self, dataframe: DataFrame, corr_dataframes: dict[str, DataFrame], current_pair: str
     ) -> DataFrame:
         """
         Attach the existing corr_pair dataframes to the current pair dataframe before training
@@ -731,7 +731,7 @@ class FreqaiDataKitchen:
         :param is_corr_pairs: bool = whether the pair is a corr pair or not
         :return: dataframe = populated dataframe
         """
-        tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+        tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
 
         for tf in tfs:
             metadata = {"pair": pair, "tf": tf}
@@ -810,8 +810,8 @@ class FreqaiDataKitchen:
                 f"{DOCS_LINK}/freqai-feature-engineering/"
             )
 
-        tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+        tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
-        pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
+        pairs: list[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
 
         for tf in tfs:
             if tf not in base_dataframes:
@@ -828,7 +828,7 @@ class FreqaiDataKitchen:
         else:
             dataframe = base_dataframes[self.config["timeframe"]].copy()
 
-        corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
+        corr_pairs: list[str] = self.freqai_config["feature_parameters"].get(
             "include_corr_pairlist", []
         )
         dataframe = self.populate_features(
@@ -953,7 +953,7 @@ class FreqaiDataKitchen:
         Returns default FreqAI model path
         :param config: Configuration dictionary
         """
-        freqai_config: Dict[str, Any] = config["freqai"]
+        freqai_config: dict[str, Any] = config["freqai"]
         return Path(config["user_data_dir"] / "models" / str(freqai_config.get("identifier")))
 
     def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
@@ -992,7 +992,7 @@ class FreqaiDataKitchen:
         return timerange
 
     # deprecated functions
-    def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
+    def normalize_data(self, data_dictionary: dict) -> dict[Any, Any]:
         """
         Deprecation warning, migration assistance
         """

freqtrade/freqai/freqai_interface.py

@@ -5,7 +5,7 @@ from abc import ABC, abstractmethod
 from collections import deque
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional, Tuple
+from typing import Any, Literal, Optional
 
 import datasieve.transforms as ds
 import numpy as np
@@ -59,11 +59,11 @@ class IFreqaiModel(ABC):
     def __init__(self, config: Config) -> None:
         self.config = config
         self.assert_config(self.config)
-        self.freqai_info: Dict[str, Any] = config["freqai"]
+        self.freqai_info: dict[str, Any] = config["freqai"]
-        self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
+        self.data_split_parameters: dict[str, Any] = config.get("freqai", {}).get(
             "data_split_parameters", {}
         )
-        self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
+        self.model_training_parameters: dict[str, Any] = config.get("freqai", {}).get(
             "model_training_parameters", {}
         )
         self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
@@ -80,14 +80,14 @@ class IFreqaiModel(ABC):
         self.dd.current_candle = self.current_candle
         self.scanning = False
         self.ft_params = self.freqai_info["feature_parameters"]
-        self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
+        self.corr_pairlist: list[str] = self.ft_params.get("include_corr_pairlist", [])
         self.keras: bool = self.freqai_info.get("keras", False)
         if self.keras and self.ft_params.get("DI_threshold", 0):
             self.ft_params["DI_threshold"] = 0
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
 
         self.CONV_WIDTH = self.freqai_info.get("conv_width", 1)
-        self.class_names: List[str] = []  # used in classification subclasses
+        self.class_names: list[str] = []  # used in classification subclasses
         self.pair_it = 0
         self.pair_it_train = 0
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
@@ -99,13 +99,13 @@ class IFreqaiModel(ABC):
         self.base_tf_seconds = timeframe_to_seconds(self.config["timeframe"])
         self.continual_learning = self.freqai_info.get("continual_learning", False)
         self.plot_features = self.ft_params.get("plot_feature_importances", 0)
-        self.corr_dataframes: Dict[str, DataFrame] = {}
+        self.corr_dataframes: dict[str, DataFrame] = {}
         # get_corr_dataframes is controlling the caching of corr_dataframes
         # for improved performance. Careful with this boolean.
         self.get_corr_dataframes: bool = True
-        self._threads: List[threading.Thread] = []
+        self._threads: list[threading.Thread] = []
         self._stop_event = threading.Event()
-        self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
+        self.metadata: dict[str, Any] = self.dd.load_global_metadata_from_disk()
         self.data_provider: Optional[DataProvider] = None
         self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
         self.can_short = True  # overridden in start() with strategy.can_short
@@ -901,7 +901,7 @@ class IFreqaiModel(ABC):
 
         return
 
-    def update_metadata(self, metadata: Dict[str, Any]):
+    def update_metadata(self, metadata: dict[str, Any]):
         """
         Update global metadata and save the updated json file
         :param metadata: new global metadata dict
@@ -954,7 +954,7 @@ class IFreqaiModel(ABC):
         """
 
     @abstractmethod
-    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         Most regressors use the same function names and arguments e.g. user
         can drop in LGBMRegressor in place of CatBoostRegressor and all data
@@ -968,7 +968,7 @@ class IFreqaiModel(ABC):
     @abstractmethod
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, NDArray[np.int_]]:
+    ) -> tuple[DataFrame, NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 from catboost import CatBoostClassifier, Pool
 
@@ -21,7 +21,7 @@ class CatboostClassifier(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 from catboost import CatBoostClassifier, Pool
 
@@ -22,7 +22,7 @@ class CatboostClassifierMultiTarget(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 from catboost import CatBoostRegressor, Pool
 
@@ -21,7 +21,7 @@ class CatboostRegressor(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 from catboost import CatBoostRegressor, Pool
 
@@ -22,7 +22,7 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from lightgbm import LGBMClassifier
 
@@ -20,7 +20,7 @@ class LightGBMClassifier(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from lightgbm import LGBMClassifier
 
@@ -21,7 +21,7 @@ class LightGBMClassifierMultiTarget(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from lightgbm import LGBMRegressor
 
@@ -20,7 +20,7 @@ class LightGBMRegressor(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from lightgbm import LGBMRegressor
 
@@ -21,7 +21,7 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from typing import Any
 
 import torch
 
@@ -52,10 +52,10 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
         super().__init__(**kwargs)
         config = self.freqai_info.get("model_training_parameters", {})
         self.learning_rate: float = config.get("learning_rate", 3e-4)
-        self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
-        self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from typing import Any
 
 import torch
 
@@ -51,10 +51,10 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
         super().__init__(**kwargs)
         config = self.freqai_info.get("model_training_parameters", {})
         self.learning_rate: float = config.get("learning_rate", 3e-4)
-        self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
-        self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -60,10 +60,10 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         super().__init__(**kwargs)
         config = self.freqai_info.get("model_training_parameters", {})
         self.learning_rate: float = config.get("learning_rate", 3e-4)
-        self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
-        self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,
@@ -100,7 +100,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
 
     def predict(
         self, unfiltered_df: pd.DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[pd.DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[pd.DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, Optional
 
 import torch as th
 from stable_baselines3.common.callbacks import ProgressBarCallback
@@ -44,7 +44,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
     take fine-tuned control over the data handling pipeline.
     """
 
-    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
         """
         User customizable fit method
         :param data_dictionary: dict = common data dictionary containing all train/test
@@ -77,7 +77,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             )
             model = self.dd.model_dictionary[dk.pair]
             model.set_env(self.train_env)
-        callbacks: List[Any] = [self.eval_callback, self.tensorboard_callback]
+        callbacks: list[Any] = [self.eval_callback, self.tensorboard_callback]
         progressbar_callback: Optional[ProgressBarCallback] = None
         if self.rl_config.get("progress_bar", False):
             progressbar_callback = ProgressBarCallback()
@@ -101,7 +101,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         return model
 
-    MyRLEnv: Type[BaseEnvironment]
+    MyRLEnv: type[BaseEnvironment]
 
     class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
         """

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from pandas import DataFrame
 from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
@@ -22,7 +22,7 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
 
     def set_train_and_eval_environments(
         self,
-        data_dictionary: Dict[str, Any],
+        data_dictionary: dict[str, Any],
         prices_train: DataFrame,
         prices_test: DataFrame,
         dk: FreqaiDataKitchen,

freqtrade/freqai/prediction_models/SKLearnRandomForestClassifier.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -24,7 +24,7 @@ class SKLearnRandomForestClassifier(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,
@@ -61,7 +61,7 @@ class SKLearnRandomForestClassifier(BaseClassifierModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param  unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/prediction_models/XGBoostClassifier.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -26,7 +26,7 @@ class XGBoostClassifier(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,
@@ -64,7 +64,7 @@ class XGBoostClassifier(BaseClassifierModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, Tuple
+from typing import Any
 
 import numpy as np
 import numpy.typing as npt
@@ -26,7 +26,7 @@ class XGBoostRFClassifier(BaseClassifierModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,
@@ -64,7 +64,7 @@ class XGBoostRFClassifier(BaseClassifierModel):
 
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
         :param  unfiltered_df: Full dataframe for the current backtest period.

freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from xgboost import XGBRFRegressor
 
@@ -21,7 +21,7 @@ class XGBoostRFRegressor(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/XGBoostRegressor.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from xgboost import XGBRegressor
 
@@ -21,7 +21,7 @@ class XGBoostRegressor(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict
+from typing import Any
 
 from xgboost import XGBRegressor
 
@@ -21,7 +21,7 @@ class XGBoostRegressorMultiTarget(BaseRegressionModel):
     top level config.json file.
     """
 
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary holding all data for train, test,

freqtrade/freqai/tensorboard/TensorboardCallback.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Dict, Type, Union
+from typing import Any, Union
 
 from stable_baselines3.common.callbacks import BaseCallback
 from stable_baselines3.common.logger import HParam
@@ -13,10 +13,10 @@ class TensorboardCallback(BaseCallback):
     episodic summary reports.
     """
 
-    def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
+    def __init__(self, verbose=1, actions: type[Enum] = BaseActions):
         super().__init__(verbose)
         self.model: Any = None
-        self.actions: Type[Enum] = actions
+        self.actions: type[Enum] = actions
 
     def _on_training_start(self) -> None:
         hparam_dict = {
@@ -27,7 +27,7 @@ class TensorboardCallback(BaseCallback):
             # "batch_size": self.model.batch_size,
             # "n_steps": self.model.n_steps,
         }
-        metric_dict: Dict[str, Union[float, int]] = {
+        metric_dict: dict[str, Union[float, int]] = {
             "eval/mean_reward": 0,
             "rollout/ep_rew_mean": 0,
             "rollout/ep_len_mean": 0,

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import pandas as pd
 import torch
@@ -25,7 +25,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         criterion: nn.Module,
         device: str,
         data_convertor: PyTorchDataConvertor,
-        model_meta_data: Dict[str, Any] = {},
+        model_meta_data: dict[str, Any] = {},
         window_size: int = 1,
         tb_logger: Any = None,
         **kwargs,
@@ -61,7 +61,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.tb_logger = tb_logger
         self.test_batch_counter = 0
 
-    def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
+    def fit(self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]):
         """
         :param data_dictionary: the dictionary constructed by DataHandler to hold
         all the training and test data/labels.
@@ -102,7 +102,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
     @torch.no_grad()
     def estimate_loss(
         self,
-        data_loader_dictionary: Dict[str, DataLoader],
+        data_loader_dictionary: dict[str, DataLoader],
         split: str,
     ) -> None:
         self.model.eval()
@@ -119,8 +119,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.model.train()
 
     def create_data_loaders_dictionary(
-        self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]
+        self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]
-    ) -> Dict[str, DataLoader]:
+    ) -> dict[str, DataLoader]:
         """
         Converts the input data to PyTorch tensors using a data loader.
         """
@@ -181,7 +181,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         checkpoint = torch.load(path)
         return self.load_from_checkpoint(checkpoint)
 
-    def load_from_checkpoint(self, checkpoint: Dict):
+    def load_from_checkpoint(self, checkpoint: dict):
         """
         when using continual_learning, DataDrawer will load the dictionary
         (containing state dicts and model_meta_data) by calling torch.load(path).
@@ -200,8 +200,8 @@ class PyTorchTransformerTrainer(PyTorchModelTrainer):
     """
 
     def create_data_loaders_dictionary(
-        self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]
+        self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]
-    ) -> Dict[str, DataLoader]:
+    ) -> dict[str, DataLoader]:
         """
         Converts the input data to PyTorch tensors using a data loader.
         """

freqtrade/freqai/torch/PyTorchTrainerInterface.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, List
 
 import pandas as pd
 import torch
@@ -9,7 +8,7 @@ from torch import nn
 
 class PyTorchTrainerInterface(ABC):
     @abstractmethod
-    def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None:
+    def fit(self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]) -> None:
         """
         :param data_dictionary: the dictionary constructed by DataHandler to hold
         all the training and test data/labels.
@@ -41,7 +40,7 @@ class PyTorchTrainerInterface(ABC):
         return self.load_from_checkpoint(checkpoint)
 
     @abstractmethod
-    def load_from_checkpoint(self, checkpoint: Dict) -> nn.Module:
+    def load_from_checkpoint(self, checkpoint: dict) -> nn.Module:
         """
         when using continual_learning, DataDrawer will load the dictionary
         (containing state dicts and model_meta_data) by calling torch.load(path).

freqtrade/freqai/utils.py

@@ -1,7 +1,7 @@
 import logging
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -155,7 +155,7 @@ def plot_feature_importance(
         store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
 
 
-def record_params(config: Dict[str, Any], full_path: Path) -> None:
+def record_params(config: dict[str, Any], full_path: Path) -> None:
     """
     Records run params in the full path for reproducibility
     """