458 lines
19 KiB
Python
458 lines
19 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 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 stable_baselines3.common.callbacks import EvalCallback
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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
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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, Positions
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from freqtrade.freqai.RL.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']
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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(self.freqai_info['rl_config'].get(
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'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
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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[SubprocVecEnv, Type[gym.Env]] = gym.Env()
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self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
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self.eval_callback: Optional[EvalCallback] = 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(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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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 = \
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TensorboardCallback(verbose=1, actions=BaseActions)
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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.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(
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self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
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) -> 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("--------------------Starting training " f"{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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data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
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features_filtered, labels_filtered)
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self.df_raw = copy.deepcopy(data_dictionary["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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data_dictionary = dk.normalize_data(data_dictionary)
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# data cleaning/analysis
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self.data_cleaning_train(dk)
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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(data_dictionary["train_features"])} data points'
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)
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self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk)
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model = self.fit(data_dictionary, 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(self, data_dictionary: Dict[str, DataFrame],
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prices_train: DataFrame, prices_test: DataFrame,
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dk: FreqaiDataKitchen):
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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 = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=len(train_df),
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best_model_save_path=str(dk.data_path))
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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 = {"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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if self.data_provider:
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env_info["fee"] = self.data_provider._exchange \
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.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
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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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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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filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dk)
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pred_df = self.rl_model_predict(
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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(self, dataframe: DataFrame,
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dk: FreqaiDataKitchen, model: Any) -> 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(self, data_dictionary: dict,
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pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame,
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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 = {'%-raw_open': 'open', '%-raw_low': 'low',
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'%-raw_high': ' high', '%-raw_close': 'close'}
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rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
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f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
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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('Reinforcement learning module didnt 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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elif prices_train.empty:
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raise OperationalException("No prices found, please follow log warning "
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"instructions to correct the strategy.")
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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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: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.
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# you can use feature values from dataframe
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rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.pair}_"
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f"{self.config['timeframe']}"].iloc[self._current_tick]
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# reward agent for entering trades
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if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
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and self._position == Positions.Neutral):
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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 (self._position in (Positions.Short, Positions.Long) and
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action == Actions.Neutral.value):
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return -1 * trade_duration / max_trade_duration
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# close long
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if action == Actions.Long_exit.value and self._position == Positions.Long:
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if pnl > self.profit_aim * self.rr:
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factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
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return float(pnl * factor)
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# close short
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if action == Actions.Short_exit.value and self._position == Positions.Short:
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if pnl > self.profit_aim * self.rr:
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factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
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return float(pnl * factor)
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return 0.
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def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
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seed: int, train_df: DataFrame, price: DataFrame,
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monitor: bool = False,
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env_info: Dict[str, Any] = {}) -> Callable:
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"""
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Utility function for multiprocessed env.
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:param env_id: (str) the environment ID
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:param num_env: (int) the number of environment you wish to have in subprocesses
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:param seed: (int) the inital seed for RNG
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:param rank: (int) index of the subprocess
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:param env_info: (dict) all required arguments to instantiate the environment.
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:return: (Callable)
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"""
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def _init() -> gym.Env:
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env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
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**env_info)
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if monitor:
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env = Monitor(env)
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return env
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set_random_seed(seed)
|
|
return _init
|