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chore: update freqai to modern typing syntax

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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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20
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@@ -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.