284 lines
12 KiB
Python
284 lines
12 KiB
Python
import logging
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from typing import Any, Dict, Tuple
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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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from pandas import DataFrame
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from abc import abstractmethod
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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 Base5ActionRLEnv, Actions, Positions
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from freqtrade.persistence import Trade
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import torch.multiprocessing
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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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import torch as th
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from typing import Callable
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from stable_baselines3.common.utils import set_random_seed
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import gym
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logger = logging.getLogger(__name__)
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torch.multiprocessing.set_sharing_strategy('file_system')
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class BaseReinforcementLearningModel(IFreqaiModel):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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def __init__(self, **kwargs):
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super().__init__(config=kwargs['config'])
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th.set_num_threads(self.freqai_info['rl_config'].get('thread_count', 4))
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self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
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self.train_env: Base5ActionRLEnv = None
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self.eval_env: Base5ActionRLEnv = None
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self.eval_callback: EvalCallback = None
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mod = __import__('stable_baselines3', fromlist=[
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self.freqai_info['rl_config']['model_type']])
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self.MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
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self.policy_type = self.freqai_info['rl_config']['policy_type']
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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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_dataframe: 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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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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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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dk.fit_labels() # 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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# optional additional 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)}' " features"
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)
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk)
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model = self.fit_rl(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, prices_train, prices_test, dk):
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"""
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User overrides this as shown here if they are using a custom MyRLEnv
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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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eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
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# environments
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if not self.train_env:
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self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params)
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self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
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window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params), ".")
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self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=eval_freq,
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best_model_save_path=dk.data_path)
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else:
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self.train_env.reset()
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self.eval_env.reset()
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self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.eval_env.reset_env(test_df, prices_test, self.CONV_WIDTH, self.reward_params)
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self.eval_callback.__init__(self.eval_env, deterministic=True,
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render=False, eval_freq=eval_freq,
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best_model_save_path=dk.data_path)
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@abstractmethod
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def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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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):
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open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True))
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market_side = 0.5
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current_profit = 0
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for trade in open_trades:
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if trade.pair == pair:
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current_value = trade.open_trade_value
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openrate = trade.open_rate
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if 'long' in trade.enter_tag:
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market_side = 1
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else:
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market_side = 0
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current_profit = current_value / openrate - 1
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total_profit = 0
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closed_trades = Trade.get_trades(
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trade_filter=[Trade.is_open.is_(False), Trade.pair == pair])
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for trade in closed_trades:
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total_profit += trade.close_profit
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return market_side, current_profit, total_profit
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def predict(
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self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
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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_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, dk.training_features_list, training_filter=False
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)
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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, filtered_dataframe)
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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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output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
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def _predict(window):
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market_side, current_profit, total_profit = self.get_state_info(dk.pair)
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observations = dataframe.iloc[window.index]
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observations['current_profit'] = current_profit
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observations['position'] = market_side
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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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coin = pair.split('/')[0]
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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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ohlc_list = [f'%-{coin}raw_open_{tf}', f'%-{coin}raw_low_{tf}',
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f'%-{coin}raw_high_{tf}', f'%-{coin}raw_close_{tf}']
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rename_dict = {f'%-{coin}raw_open_{tf}': 'open', f'%-{coin}raw_low_{tf}': 'low',
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f'%-{coin}raw_high_{tf}': ' high', f'%-{coin}raw_close_{tf}': 'close'}
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prices_train = train_df.filter(ohlc_list, axis=1)
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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(ohlc_list, 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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return prices_train, prices_test
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# TODO take care of this appendage. Right now it needs to be called because FreqAI enforces it.
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# But FreqaiRL needs more objects passed to fit() (like DK) and we dont want to go refactor
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# all the other existing fit() functions to include dk argument. For now we instantiate and
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# leave it.
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def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
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return
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def make_env(env_id: str, rank: int, seed: int, train_df, price,
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reward_params, window_size, monitor=False) -> 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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: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, window_size=window_size,
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reward_kwargs=reward_params, id=env_id, seed=seed + rank)
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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)
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return _init
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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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Adds 5 actions.
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"""
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def calculate_reward(self, action):
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if self._last_trade_tick is None:
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return 0.
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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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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(current_price) - np.log(last_trade_price))
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if action == Actions.Long_exit.value and self._position == Positions.Long:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float((np.log(current_price) - np.log(last_trade_price)) * 2)
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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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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(last_trade_price) - np.log(current_price))
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if action == Actions.Short_exit.value and self._position == Positions.Short:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float((np.log(last_trade_price) - np.log(current_price)) * 2)
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return 0.
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