mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-14 12:13:57 +00:00
511 lines
20 KiB
Python
511 lines
20 KiB
Python
import copy
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import importlib
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import logging
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from abc import abstractmethod
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
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import gymnasium as gym
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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import torch as th
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import torch.multiprocessing
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from pandas import DataFrame
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from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
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from sb3_contrib.common.maskable.utils import is_masking_supported
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from stable_baselines3.common.monitor import Monitor
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from stable_baselines3.common.utils import set_random_seed
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from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
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from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
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from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
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from freqtrade.persistence import Trade
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logger = logging.getLogger(__name__)
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torch.multiprocessing.set_sharing_strategy("file_system")
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SB3_MODELS = ["PPO", "A2C", "DQN"]
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SB3_CONTRIB_MODELS = ["TRPO", "ARS", "RecurrentPPO", "MaskablePPO", "QRDQN"]
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class BaseReinforcementLearningModel(IFreqaiModel):
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"""
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User created Reinforcement Learning Model prediction class
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"""
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def __init__(self, **kwargs) -> None:
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super().__init__(config=kwargs["config"])
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self.max_threads = min(
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self.freqai_info["rl_config"].get("cpu_count", 1),
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max(int(self.max_system_threads / 2), 1),
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)
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th.set_num_threads(self.max_threads)
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self.reward_params = self.freqai_info["rl_config"]["model_reward_parameters"]
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self.train_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
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self.eval_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
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self.eval_callback: Optional[MaskableEvalCallback] = None
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self.model_type = self.freqai_info["rl_config"]["model_type"]
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self.rl_config = self.freqai_info["rl_config"]
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self.df_raw: DataFrame = DataFrame()
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self.continual_learning = self.freqai_info.get("continual_learning", False)
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if self.model_type in SB3_MODELS:
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import_str = "stable_baselines3"
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elif self.model_type in SB3_CONTRIB_MODELS:
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import_str = "sb3_contrib"
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else:
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raise OperationalException(
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f"{self.model_type} not available in stable_baselines3 or "
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f"sb3_contrib. please choose one of {SB3_MODELS} or "
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f"{SB3_CONTRIB_MODELS}"
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)
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mod = importlib.import_module(import_str, self.model_type)
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self.MODELCLASS = getattr(mod, self.model_type)
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self.policy_type = self.freqai_info["rl_config"]["policy_type"]
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self.unset_outlier_removal()
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self.net_arch = self.rl_config.get("net_arch", [128, 128])
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self.dd.model_type = import_str
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self.tensorboard_callback: TensorboardCallback = TensorboardCallback(
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verbose=1, actions=BaseActions
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)
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def unset_outlier_removal(self):
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"""
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If user has activated any function that may remove training points, this
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function will set them to false and warn them
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"""
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if self.ft_params.get("use_SVM_to_remove_outliers", False):
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self.ft_params.update({"use_SVM_to_remove_outliers": False})
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logger.warning("User tried to use SVM with RL. Deactivating SVM.")
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if self.ft_params.get("use_DBSCAN_to_remove_outliers", False):
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self.ft_params.update({"use_DBSCAN_to_remove_outliers": False})
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logger.warning("User tried to use DBSCAN with RL. Deactivating DBSCAN.")
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if self.ft_params.get("DI_threshold", False):
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self.ft_params.update({"DI_threshold": False})
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logger.warning("User tried to use DI_threshold with RL. Deactivating DI_threshold.")
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if self.freqai_info["data_split_parameters"].get("shuffle", False):
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self.freqai_info["data_split_parameters"].update({"shuffle": False})
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logger.warning("User tried to shuffle training data. Setting shuffle to False")
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def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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for storing, saving, loading, and analyzing the data.
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:param unfiltered_df: Full dataframe for the current training period
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:param metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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logger.info(f"--------------------Starting training {pair} --------------------")
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_df,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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dd: Dict[str, Any] = dk.make_train_test_datasets(features_filtered, labels_filtered)
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self.df_raw = copy.deepcopy(dd["train_features"])
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dk.fit_labels() # FIXME useless for now, but just satiating append methods
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# normalize all data based on train_dataset only
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prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
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dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
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(dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
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dk.feature_pipeline.fit_transform(
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dd["train_features"], dd["train_labels"], dd["train_weights"]
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)
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)
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if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
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(dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
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dk.feature_pipeline.transform(
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dd["test_features"], dd["test_labels"], dd["test_weights"]
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)
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)
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logger.info(
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f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
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f' features and {len(dd["train_features"])} data points'
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)
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self.set_train_and_eval_environments(dd, prices_train, prices_test, dk)
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model = self.fit(dd, dk)
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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def set_train_and_eval_environments(
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self,
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data_dictionary: Dict[str, DataFrame],
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prices_train: DataFrame,
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prices_test: DataFrame,
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dk: FreqaiDataKitchen,
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):
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"""
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User can override this if they are using a custom MyRLEnv
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:param data_dictionary: dict = common data dictionary containing train and test
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features/labels/weights.
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:param prices_train/test: DataFrame = dataframe comprised of the prices to be used in the
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environment during training or testing
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:param dk: FreqaiDataKitchen = the datakitchen for the current pair
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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env_info = self.pack_env_dict(dk.pair)
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self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
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self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **env_info))
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self.eval_callback = MaskableEvalCallback(
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self.eval_env,
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deterministic=True,
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render=False,
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eval_freq=len(train_df),
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best_model_save_path=str(dk.data_path),
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use_masking=(self.model_type == "MaskablePPO" and is_masking_supported(self.eval_env)),
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)
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actions = self.train_env.get_actions()
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self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
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def pack_env_dict(self, pair: str) -> Dict[str, Any]:
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"""
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Create dictionary of environment arguments
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"""
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env_info = {
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"window_size": self.CONV_WIDTH,
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"reward_kwargs": self.reward_params,
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"config": self.config,
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"live": self.live,
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"can_short": self.can_short,
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"pair": pair,
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"df_raw": self.df_raw,
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}
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if self.data_provider:
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env_info["fee"] = self.data_provider._exchange.get_fee( # type: ignore
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symbol=self.data_provider.current_whitelist()[0]
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)
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return env_info
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@abstractmethod
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def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
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"""
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Agent customizations and abstract Reinforcement Learning customizations
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go in here. Abstract method, so this function must be overridden by
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user class.
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"""
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return
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def get_state_info(self, pair: str) -> Tuple[float, float, int]:
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"""
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State info during dry/live (not backtesting) which is fed back
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into the model.
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:param pair: str = COIN/STAKE to get the environment information for
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:return:
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:market_side: float = representing short, long, or neutral for
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pair
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:current_profit: float = unrealized profit of the current trade
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:trade_duration: int = the number of candles that the trade has
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been open for
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"""
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open_trades = Trade.get_trades_proxy(is_open=True)
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market_side = 0.5
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current_profit: float = 0
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trade_duration = 0
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for trade in open_trades:
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if trade.pair == pair:
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if self.data_provider._exchange is None: # type: ignore
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logger.error("No exchange available.")
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return 0, 0, 0
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else:
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current_rate = self.data_provider._exchange.get_rate( # type: ignore
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pair, refresh=False, side="exit", is_short=trade.is_short
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)
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now = datetime.now(timezone.utc).timestamp()
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trade_duration = int((now - trade.open_date_utc.timestamp()) / self.base_tf_seconds)
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current_profit = trade.calc_profit_ratio(current_rate)
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if trade.is_short:
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market_side = 0
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else:
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market_side = 1
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return market_side, current_profit, int(trade_duration)
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def predict(
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self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
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) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
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"""
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Filter the prediction features data and predict with it.
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:param unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:pred_df: dataframe containing the predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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dk.find_features(unfiltered_df)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_df, dk.training_features_list, training_filter=False
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)
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dk.data_dictionary["prediction_features"] = self.drop_ohlc_from_df(filtered_dataframe, dk)
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dk.data_dictionary["prediction_features"], _, _ = dk.feature_pipeline.transform(
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dk.data_dictionary["prediction_features"], outlier_check=True
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)
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pred_df = self.rl_model_predict(dk.data_dictionary["prediction_features"], dk, self.model)
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pred_df.fillna(0, inplace=True)
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return (pred_df, dk.do_predict)
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def rl_model_predict(
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self, dataframe: DataFrame, dk: FreqaiDataKitchen, model: Any
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) -> DataFrame:
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"""
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A helper function to make predictions in the Reinforcement learning module.
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:param dataframe: DataFrame = the dataframe of features to make the predictions on
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:param dk: FreqaiDatakitchen = data kitchen for the current pair
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:param model: Any = the trained model used to inference the features.
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"""
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output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
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def _predict(window):
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observations = dataframe.iloc[window.index]
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if self.live and self.rl_config.get("add_state_info", False):
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market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
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observations["current_profit_pct"] = current_profit
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observations["position"] = market_side
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observations["trade_duration"] = trade_duration
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res, _ = model.predict(observations, deterministic=True)
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return res
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output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
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return output
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def build_ohlc_price_dataframes(
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self, data_dictionary: dict, pair: str, dk: FreqaiDataKitchen
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) -> Tuple[DataFrame, DataFrame]:
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"""
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Builds the train prices and test prices for the environment.
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"""
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pair = pair.replace(":", "")
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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# price data for model training and evaluation
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tf = self.config["timeframe"]
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rename_dict = {
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"%-raw_open": "open",
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"%-raw_low": "low",
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"%-raw_high": " high",
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"%-raw_close": "close",
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}
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rename_dict_old = {
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f"%-{pair}raw_open_{tf}": "open",
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f"%-{pair}raw_low_{tf}": "low",
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f"%-{pair}raw_high_{tf}": " high",
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f"%-{pair}raw_close_{tf}": "close",
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}
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prices_train = train_df.filter(rename_dict.keys(), axis=1)
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prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
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if prices_train.empty or not prices_train_old.empty:
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if not prices_train_old.empty:
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prices_train = prices_train_old
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rename_dict = rename_dict_old
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logger.warning(
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"Reinforcement learning module didn't find the correct raw prices "
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"assigned in feature_engineering_standard(). "
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"Please assign them with:\n"
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'dataframe["%-raw_close"] = dataframe["close"]\n'
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'dataframe["%-raw_open"] = dataframe["open"]\n'
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'dataframe["%-raw_high"] = dataframe["high"]\n'
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'dataframe["%-raw_low"] = dataframe["low"]\n'
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"inside `feature_engineering_standard()"
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)
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elif prices_train.empty:
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raise OperationalException(
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"No prices found, please follow log warning "
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"instructions to correct the strategy."
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)
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prices_train.rename(columns=rename_dict, inplace=True)
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prices_train.reset_index(drop=True)
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prices_test = test_df.filter(rename_dict.keys(), axis=1)
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prices_test.rename(columns=rename_dict, inplace=True)
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prices_test.reset_index(drop=True)
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train_df = self.drop_ohlc_from_df(train_df, dk)
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test_df = self.drop_ohlc_from_df(test_df, dk)
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return prices_train, prices_test
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def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
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"""
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Given a dataframe, drop the ohlc data
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"""
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drop_list = ["%-raw_open", "%-raw_low", "%-raw_high", "%-raw_close"]
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if self.rl_config["drop_ohlc_from_features"]:
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df.drop(drop_list, axis=1, inplace=True)
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feature_list = dk.training_features_list
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dk.training_features_list = [e for e in feature_list if e not in drop_list]
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return df
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def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
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"""
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Can be used by user if they are trying to limit_ram_usage *and*
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perform continual learning.
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For now, this is unused.
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"""
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exists = Path(dk.data_path / f"{dk.model_filename}_model").is_file()
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if exists:
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model = self.MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
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else:
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logger.info("No model file on disk to continue learning from.")
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return model
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def _on_stop(self):
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"""
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Hook called on bot shutdown. Close SubprocVecEnv subprocesses for clean shutdown.
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"""
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if self.train_env:
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self.train_env.close()
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if self.eval_env:
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self.eval_env.close()
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# Nested class which can be overridden by user to customize further
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class MyRLEnv(Base5ActionRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env. Here the user
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sets a custom reward based on profit and trade duration.
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"""
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def calculate_reward(self, action: int) -> float: # noqa: C901
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"""
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An example reward function. This is the one function that users will likely
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wish to inject their own creativity into.
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Warning!
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This is function is a showcase of functionality designed to show as many possible
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environment control features as possible. It is also designed to run quickly
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on small computers. This is a benchmark, it is *not* for live production.
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:param action: int = The action made by the agent for the current candle.
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:return:
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float = the reward to give to the agent for current step (used for optimization
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of weights in NN)
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"""
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# first, penalize if the action is not valid
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if not self._is_valid(action):
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return -2
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pnl = self.get_unrealized_profit()
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factor = 100.0
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# you can use feature values from dataframe
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rsi_now = self.raw_features[
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f"%-rsi-period-10_shift-1_{self.pair}_{self.config['timeframe']}"
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].iloc[self._current_tick]
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# reward agent for entering trades
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if (
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action in (Actions.Long_enter.value, Actions.Short_enter.value)
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and self._position == Positions.Neutral
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):
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if rsi_now < 40:
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factor = 40 / rsi_now
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else:
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factor = 1
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return 25 * factor
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# discourage agent from not entering trades
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if action == Actions.Neutral.value and self._position == Positions.Neutral:
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return -1
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max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
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if self._last_trade_tick:
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trade_duration = self._current_tick - self._last_trade_tick
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else:
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trade_duration = 0
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if trade_duration <= max_trade_duration:
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factor *= 1.5
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elif trade_duration > max_trade_duration:
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factor *= 0.5
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# discourage sitting in position
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if (
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self._position in (Positions.Short, Positions.Long)
|
|
and action == Actions.Neutral.value
|
|
):
|
|
return -1 * trade_duration / max_trade_duration
|
|
|
|
# close long
|
|
if action == Actions.Long_exit.value and self._position == Positions.Long:
|
|
if pnl > self.profit_aim * self.rr:
|
|
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
|
|
return float(pnl * factor)
|
|
|
|
# close short
|
|
if action == Actions.Short_exit.value and self._position == Positions.Short:
|
|
if pnl > self.profit_aim * self.rr:
|
|
factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
|
|
return float(pnl * factor)
|
|
|
|
return 0.0
|
|
|
|
|
|
def make_env(
|
|
MyRLEnv: Type[BaseEnvironment],
|
|
env_id: str,
|
|
rank: int,
|
|
seed: int,
|
|
train_df: DataFrame,
|
|
price: DataFrame,
|
|
env_info: Dict[str, Any] = {},
|
|
) -> Callable:
|
|
"""
|
|
Utility function for multiprocessed env.
|
|
|
|
:param env_id: (str) the environment ID
|
|
:param num_env: (int) the number of environment you wish to have in subprocesses
|
|
:param seed: (int) the initial seed for RNG
|
|
:param rank: (int) index of the subprocess
|
|
:param env_info: (dict) all required arguments to instantiate the environment.
|
|
:return: (Callable)
|
|
"""
|
|
|
|
def _init() -> gym.Env:
|
|
env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank, **env_info)
|
|
|
|
return env
|
|
|
|
set_random_seed(seed)
|
|
return _init
|