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chore: update freqai to modern typing syntax

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This commit is contained in:
Matthias
2024-10-04 06:50:31 +02:00
parent f369151e8e
commit 96f737f13e
32 changed files with 148 additions and 149 deletions
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8
freqtrade/freqai/RL/BaseEnvironment.py
View File

@@ -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

20
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@@ -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.

4
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@@ -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.

18
freqtrade/freqai/base_models/BasePyTorchClassifier.py
View File

@@ -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, "

4
freqtrade/freqai/base_models/BasePyTorchRegressor.py
View File

@@ -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.

4
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@@ -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.

32
freqtrade/freqai/data_drawer.py
View File

@@ -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.model_return_values: Dict[str, DataFrame] = {}
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
self.historic_predictions: Dict[str, DataFrame] = {}
self.meta_data_dictionary: dict[str, dict[str, Any]] = {}
self.model_return_values: dict[str, DataFrame] = {}
self.historic_data: dict[str, 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] = {}
base_dataframes: Dict[Any, Any] = {}
corr_dataframes: dict[Any, Any] = {}
base_dataframes: dict[Any, Any] = {}
historic_data = self.historic_data
pairs = self.freqai_info["feature_parameters"].get("include_corr_pairlist", [])

48
freqtrade/freqai/data_kitchen.py
View File

@@ -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_dictionary: Dict[str, DataFrame] = {}
self.data: dict[str, Any] = {}
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.training_features_list: List = []
self.label_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,
label_list: List = list(),
training_feature_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")
pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
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
"""

24
freqtrade/freqai/freqai_interface.py
View File

@@ -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.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
self.freqai_info: dict[str, Any] = config["freqai"]
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.

4
freqtrade/freqai/prediction_models/CatboostClassifier.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/CatboostRegressor.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/LightGBMClassifier.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/LightGBMRegressor.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
View File

@@ -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,

8
freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
View File

@@ -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.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
self.model_kwargs: dict[str, Any] = config.get("model_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,

8
freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
View File

@@ -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.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
self.model_kwargs: dict[str, Any] = config.get("model_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,

10
freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
View File

@@ -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.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
self.model_kwargs: dict[str, Any] = config.get("model_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.

8
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@@ -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]
"""

4
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@@ -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,

6
freqtrade/freqai/prediction_models/SKLearnRandomForestClassifier.py
View File

@@ -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.

6
freqtrade/freqai/prediction_models/XGBoostClassifier.py
View File

@@ -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.

6
freqtrade/freqai/prediction_models/XGBoostRFClassifier.py
View File

@@ -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.

4
freqtrade/freqai/prediction_models/XGBoostRFRegressor.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/XGBoostRegressor.py
View File

@@ -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,

4
freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py
View File

@@ -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,

8
freqtrade/freqai/tensorboard/TensorboardCallback.py
View File

@@ -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,

18
freqtrade/freqai/torch/PyTorchModelTrainer.py
View File

@@ -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]
) -> Dict[str, DataLoader]:
self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]
) -> 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]
) -> Dict[str, DataLoader]:
self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]
) -> dict[str, DataLoader]:
"""
Converts the input data to PyTorch tensors using a data loader.
"""

5
freqtrade/freqai/torch/PyTorchTrainerInterface.py
View File

@@ -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).

4
freqtrade/freqai/utils.py
View File

@@ -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
"""