mirror of
https://github.com/freqtrade/freqtrade.git
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274 lines
12 KiB
Python
274 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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import torch as th
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from pandas import DataFrame
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from stable_baselines3 import PPO
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from stable_baselines3.common.buffers import ReplayBuffer
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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.vec_env import SubprocVecEnv
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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.prediction_models.RL.RLPrediction_agent_TDQN import TDQN
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from freqtrade.freqai.prediction_models.RL.RLPrediction_env_TDQN_5ac import DEnv
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#from freqtrade.freqai.prediction_models.RL.RLPrediction_env_TDQN_3ac import DEnv
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from freqtrade.persistence import Trade
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logger = logging.getLogger(__name__)
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class ReinforcementLearningModel(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 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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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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model = self.fit(data_dictionary, pair)
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if pair not in self.dd.historic_predictions:
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self.set_initial_historic_predictions(
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data_dictionary['train_features'], model, dk, pair)
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self.dd.save_historic_predictions_to_disk()
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
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# train_df = data_dictionary["train_features"]
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# # train_labels = data_dictionary["train_labels"]
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# test_df = data_dictionary["test_features"]
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# # test_labels = data_dictionary["test_labels"]
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# # sep = '/'
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# # coin = pair.split(sep, 1)[0]
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# # price = train_df[f"%-{coin}raw_price_{self.config['timeframe']}"]
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# # price.reset_index(inplace=True, drop=True)
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# # price = price.to_frame()
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# price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
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# price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(test_df.index))
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# #train_env = GymAnytrading(train_df, price, self.CONV_WIDTH)
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# agent_params = self.freqai_info['model_training_parameters']
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# reward_params = self.freqai_info['model_reward_parameters']
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# train_env = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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# #eval_env = DEnv(df=test_df, prices=price_test, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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# #env_instance = SubprocVecEnv([DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)])
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# #train_env.reset()
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# #eval_env.reset()
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# # model
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# #policy_kwargs = dict(net_arch=[512, 512, 512])
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# policy_kwargs = dict(activation_fn=th.nn.Tanh,
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# net_arch=[256, 256, 256])
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# agent = RLPrediction_agent(train_env)
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# #eval_agent = RLPrediction_agent(eval_env)
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# # PPO
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# model_name = 'ppo'
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# model = agent.get_model(model_name, model_kwargs=agent_params, policy_kwargs=policy_kwargs)
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# trained_model = agent.train_model(model=model,
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# tb_log_name=model_name,
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# model_kwargs=agent_params,
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# train_df=train_df,
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# test_df=test_df,
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# price=price,
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# price_test=price_test,
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# window_size=self.CONV_WIDTH)
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# # best_model = eval_agent.train_model(model=model,
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# # tb_log_name=model_name,
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# # model_kwargs=agent_params,
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# # eval=eval_env)
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# # TDQN
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# # model_name = 'TDQN'
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# # model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log='./tensorboard_log/',
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# # learning_rate=agent_params["learning_rate"], gamma=0.9,
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# # target_update_interval=5000, buffer_size=50000,
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# # exploration_initial_eps=1, exploration_final_eps=0.1,
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# # replay_buffer_class=ReplayBuffer
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# # )
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# # trained_model = agent.train_model(model=model,
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# # tb_log_name=model_name,
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# # model_kwargs=agent_params)
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# #model.learn(
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# # total_timesteps=5000,
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# # callback=callback
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# # )
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agent_params = self.freqai_info['model_training_parameters']
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reward_params = self.freqai_info['model_reward_parameters']
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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 = agent_params["eval_cycles"] * len(test_df)
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total_timesteps = agent_params["train_cycles"] * len(train_df)
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# price data for model training and evaluation
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price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
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price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(test_df.index))
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# environments
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train_env = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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eval = DEnv(df=test_df, prices=price_test, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
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eval_env = Monitor(eval, ".")
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eval_env.reset()
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# this should be in config - TODO
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agent_type = 'tdqn'
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path = self.dk.data_path
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eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
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log_path=f"{path}/{agent_type}/logs/", eval_freq=int(eval_freq),
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deterministic=True, render=False)
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# model arch
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policy_kwargs = dict(activation_fn=th.nn.ReLU,
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net_arch=[256, 256, 128])
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if agent_type == 'tdqn':
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model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/{agent_type}/tensorboard/",
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learning_rate=0.00025, gamma=0.9,
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target_update_interval=5000, buffer_size=50000,
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exploration_initial_eps=1, exploration_final_eps=0.1,
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replay_buffer_class=ReplayBuffer
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)
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elif agent_type == 'ppo':
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model = PPO('MultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/{agent_type}/tensorboard/",
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learning_rate=0.00025, gamma=0.9
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)
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model.learn(
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total_timesteps=int(total_timesteps),
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callback=eval_callback
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)
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print('Training finished!')
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return model
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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(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(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.full((len(dataframe), 1), 2), columns=dk.label_list)
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def _predict(window):
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observations = dataframe.iloc[window.index]
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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 set_initial_historic_predictions(
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self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
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) -> None:
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pred_df = self.rl_model_predict(df, dk, model)
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pred_df.fillna(0, inplace=True)
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self.dd.historic_predictions[pair] = pred_df
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hist_preds_df = self.dd.historic_predictions[pair]
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for label in hist_preds_df.columns:
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if hist_preds_df[label].dtype == object:
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continue
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hist_preds_df[f'{label}_mean'] = 0
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hist_preds_df[f'{label}_std'] = 0
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hist_preds_df['do_predict'] = 0
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if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
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hist_preds_df['DI_values'] = 0
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for return_str in dk.data['extra_returns_per_train']:
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hist_preds_df[return_str] = 0
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