274 lines
12 KiB
Python
274 lines
12 KiB
Python
import logging
|
|
from typing import Any, Dict, Tuple
|
|
|
|
import numpy as np
|
|
import numpy.typing as npt
|
|
import pandas as pd
|
|
import torch as th
|
|
from pandas import DataFrame
|
|
from stable_baselines3 import PPO
|
|
from stable_baselines3.common.buffers import ReplayBuffer
|
|
from stable_baselines3.common.callbacks import EvalCallback
|
|
from stable_baselines3.common.monitor import Monitor
|
|
from stable_baselines3.common.vec_env import SubprocVecEnv
|
|
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
|
from freqtrade.freqai.prediction_models.RL.RLPrediction_agent_TDQN import TDQN
|
|
from freqtrade.freqai.prediction_models.RL.RLPrediction_env_TDQN_5ac import DEnv
|
|
#from freqtrade.freqai.prediction_models.RL.RLPrediction_env_TDQN_3ac import DEnv
|
|
from freqtrade.persistence import Trade
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
class ReinforcementLearningModel(IFreqaiModel):
|
|
"""
|
|
User created Reinforcement Learning Model prediction model.
|
|
"""
|
|
|
|
def train(
|
|
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
|
) -> Any:
|
|
"""
|
|
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
|
|
for storing, saving, loading, and analyzing the data.
|
|
:param unfiltered_dataframe: Full dataframe for the current training period
|
|
:param metadata: pair metadata from strategy.
|
|
:returns:
|
|
:model: Trained model which can be used to inference (self.predict)
|
|
"""
|
|
|
|
logger.info("--------------------Starting training " f"{pair} --------------------")
|
|
|
|
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
|
features_filtered, labels_filtered = dk.filter_features(
|
|
unfiltered_dataframe,
|
|
dk.training_features_list,
|
|
dk.label_list,
|
|
training_filter=True,
|
|
)
|
|
|
|
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
|
|
features_filtered, labels_filtered)
|
|
dk.fit_labels() # useless for now, but just satiating append methods
|
|
|
|
# normalize all data based on train_dataset only
|
|
data_dictionary = dk.normalize_data(data_dictionary)
|
|
|
|
# optional additional data cleaning/analysis
|
|
self.data_cleaning_train(dk)
|
|
|
|
logger.info(
|
|
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
|
)
|
|
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
|
|
|
model = self.fit(data_dictionary, pair)
|
|
|
|
if pair not in self.dd.historic_predictions:
|
|
self.set_initial_historic_predictions(
|
|
data_dictionary['train_features'], model, dk, pair)
|
|
|
|
self.dd.save_historic_predictions_to_disk()
|
|
|
|
logger.info(f"--------------------done training {pair}--------------------")
|
|
|
|
return model
|
|
|
|
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
|
|
|
|
# train_df = data_dictionary["train_features"]
|
|
# # train_labels = data_dictionary["train_labels"]
|
|
# test_df = data_dictionary["test_features"]
|
|
# # test_labels = data_dictionary["test_labels"]
|
|
# # sep = '/'
|
|
# # coin = pair.split(sep, 1)[0]
|
|
# # price = train_df[f"%-{coin}raw_price_{self.config['timeframe']}"]
|
|
# # price.reset_index(inplace=True, drop=True)
|
|
# # price = price.to_frame()
|
|
# price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
|
|
# price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(test_df.index))
|
|
# #train_env = GymAnytrading(train_df, price, self.CONV_WIDTH)
|
|
# agent_params = self.freqai_info['model_training_parameters']
|
|
# reward_params = self.freqai_info['model_reward_parameters']
|
|
# train_env = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
|
|
# #eval_env = DEnv(df=test_df, prices=price_test, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
|
|
# #env_instance = SubprocVecEnv([DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)])
|
|
# #train_env.reset()
|
|
# #eval_env.reset()
|
|
# # model
|
|
# #policy_kwargs = dict(net_arch=[512, 512, 512])
|
|
# policy_kwargs = dict(activation_fn=th.nn.Tanh,
|
|
# net_arch=[256, 256, 256])
|
|
# agent = RLPrediction_agent(train_env)
|
|
# #eval_agent = RLPrediction_agent(eval_env)
|
|
|
|
# # PPO
|
|
# model_name = 'ppo'
|
|
# model = agent.get_model(model_name, model_kwargs=agent_params, policy_kwargs=policy_kwargs)
|
|
# trained_model = agent.train_model(model=model,
|
|
# tb_log_name=model_name,
|
|
# model_kwargs=agent_params,
|
|
# train_df=train_df,
|
|
# test_df=test_df,
|
|
# price=price,
|
|
# price_test=price_test,
|
|
# window_size=self.CONV_WIDTH)
|
|
# # best_model = eval_agent.train_model(model=model,
|
|
# # tb_log_name=model_name,
|
|
# # model_kwargs=agent_params,
|
|
# # eval=eval_env)
|
|
# # TDQN
|
|
# # model_name = 'TDQN'
|
|
# # model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log='./tensorboard_log/',
|
|
# # learning_rate=agent_params["learning_rate"], gamma=0.9,
|
|
# # target_update_interval=5000, buffer_size=50000,
|
|
# # exploration_initial_eps=1, exploration_final_eps=0.1,
|
|
# # replay_buffer_class=ReplayBuffer
|
|
# # )
|
|
# # trained_model = agent.train_model(model=model,
|
|
# # tb_log_name=model_name,
|
|
# # model_kwargs=agent_params)
|
|
# #model.learn(
|
|
# # total_timesteps=5000,
|
|
# # callback=callback
|
|
# # )
|
|
|
|
agent_params = self.freqai_info['model_training_parameters']
|
|
reward_params = self.freqai_info['model_reward_parameters']
|
|
train_df = data_dictionary["train_features"]
|
|
test_df = data_dictionary["test_features"]
|
|
eval_freq = agent_params["eval_cycles"] * len(test_df)
|
|
total_timesteps = agent_params["train_cycles"] * len(train_df)
|
|
|
|
# price data for model training and evaluation
|
|
price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
|
|
price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(test_df.index))
|
|
|
|
# environments
|
|
train_env = DEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
|
|
eval = DEnv(df=test_df, prices=price_test, window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
|
|
eval_env = Monitor(eval, ".")
|
|
eval_env.reset()
|
|
|
|
# this should be in config - TODO
|
|
agent_type = 'tdqn'
|
|
|
|
path = self.dk.data_path
|
|
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
|
|
log_path=f"{path}/{agent_type}/logs/", eval_freq=int(eval_freq),
|
|
deterministic=True, render=False)
|
|
|
|
# model arch
|
|
policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
|
net_arch=[256, 256, 128])
|
|
|
|
if agent_type == 'tdqn':
|
|
model = TDQN('TMultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/{agent_type}/tensorboard/",
|
|
learning_rate=0.00025, gamma=0.9,
|
|
target_update_interval=5000, buffer_size=50000,
|
|
exploration_initial_eps=1, exploration_final_eps=0.1,
|
|
replay_buffer_class=ReplayBuffer
|
|
)
|
|
elif agent_type == 'ppo':
|
|
model = PPO('MultiInputPolicy', train_env, policy_kwargs=policy_kwargs, tensorboard_log=f"{path}/{agent_type}/tensorboard/",
|
|
learning_rate=0.00025, gamma=0.9
|
|
)
|
|
|
|
model.learn(
|
|
total_timesteps=int(total_timesteps),
|
|
callback=eval_callback
|
|
)
|
|
|
|
print('Training finished!')
|
|
|
|
return model
|
|
|
|
|
|
|
|
def get_state_info(self, pair):
|
|
open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True))
|
|
market_side = 0.5
|
|
current_profit = 0
|
|
for trade in open_trades:
|
|
if trade.pair == pair:
|
|
current_value = trade.open_trade_value
|
|
openrate = trade.open_rate
|
|
if 'long' in trade.enter_tag:
|
|
market_side = 1
|
|
else:
|
|
market_side = 0
|
|
current_profit = current_value / openrate -1
|
|
|
|
total_profit = 0
|
|
closed_trades = Trade.get_trades(trade_filter=[Trade.is_open.is_(False), Trade.pair == pair])
|
|
for trade in closed_trades:
|
|
total_profit += trade.close_profit
|
|
|
|
return market_side, current_profit, total_profit
|
|
|
|
|
|
def predict(
|
|
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
|
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
|
"""
|
|
Filter the prediction features data and predict with it.
|
|
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
|
:return:
|
|
:pred_df: dataframe containing the predictions
|
|
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
|
data (NaNs) or felt uncertain about data (PCA and DI index)
|
|
"""
|
|
|
|
dk.find_features(unfiltered_dataframe)
|
|
filtered_dataframe, _ = dk.filter_features(
|
|
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
|
)
|
|
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
|
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
|
|
|
# optional additional data cleaning/analysis
|
|
self.data_cleaning_predict(dk, filtered_dataframe)
|
|
|
|
pred_df = self.rl_model_predict(dk.data_dictionary["prediction_features"], dk, self.model)
|
|
pred_df.fillna(0, inplace=True)
|
|
|
|
return (pred_df, dk.do_predict)
|
|
|
|
def rl_model_predict(self, dataframe: DataFrame,
|
|
dk: FreqaiDataKitchen, model: Any) -> DataFrame:
|
|
|
|
output = pd.DataFrame(np.full((len(dataframe), 1), 2), columns=dk.label_list)
|
|
|
|
def _predict(window):
|
|
observations = dataframe.iloc[window.index]
|
|
res, _ = model.predict(observations, deterministic=True)
|
|
return res
|
|
|
|
output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
|
|
|
|
return output
|
|
|
|
def set_initial_historic_predictions(
|
|
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
|
|
) -> None:
|
|
|
|
pred_df = self.rl_model_predict(df, dk, model)
|
|
pred_df.fillna(0, inplace=True)
|
|
self.dd.historic_predictions[pair] = pred_df
|
|
hist_preds_df = self.dd.historic_predictions[pair]
|
|
|
|
for label in hist_preds_df.columns:
|
|
if hist_preds_df[label].dtype == object:
|
|
continue
|
|
hist_preds_df[f'{label}_mean'] = 0
|
|
hist_preds_df[f'{label}_std'] = 0
|
|
|
|
hist_preds_df['do_predict'] = 0
|
|
|
|
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
|
|
hist_preds_df['DI_values'] = 0
|
|
|
|
for return_str in dk.data['extra_returns_per_train']:
|
|
hist_preds_df[return_str] = 0
|