stable/freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py

136 lines
5.8 KiB
Python

import logging
from typing import Any, Dict # Optional
import torch as th
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.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
import numpy as np
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
"""
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
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)
# environments
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=reward_params)
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/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])
model = TDQN('TMultiInputPolicy', train_env,
policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/tdqn/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
)
model.learn(
total_timesteps=int(total_timesteps),
callback=eval_callback
)
best_model = DQN.load(dk.data_path / "best_model")
print('Training finished!')
return best_model
class MyRLEnv(Base3ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env
"""
def calculate_reward(self, action):
if self._last_trade_tick is None:
return 0.
# close long
if (action == Actions.Short.value or
action == Actions.Neutral.value) and self._position == Positions.Long:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(current_price) - np.log(last_trade_price))
# close short
if (action == Actions.Long.value or
action == Actions.Neutral.value) and self._position == Positions.Short:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(last_trade_price) - np.log(current_price))
return 0.
# User can inherit and customize 5 action environment
# class MyRLEnv(Base5ActionRLEnv):
# """
# User can override any function in BaseRLEnv and gym.Env. Here the user
# Adds 5 actions.
# """
# def calculate_reward(self, action):
# if self._last_trade_tick is None:
# return 0.
# # close long
# if action == Actions.Long_sell.value and self._position == Positions.Long:
# last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
# return float(np.log(current_price) - np.log(last_trade_price))
# if action == Actions.Long_sell.value and self._position == Positions.Long:
# if self.close_trade_profit[-1] > self.profit_aim * self.rr:
# last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
# return float((np.log(current_price) - np.log(last_trade_price)) * 2)
# # close short
# if action == Actions.Short_buy.value and self._position == Positions.Short:
# last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
# return float(np.log(last_trade_price) - np.log(current_price))
# if action == Actions.Short_buy.value and self._position == Positions.Short:
# if self.close_trade_profit[-1] > self.profit_aim * self.rr:
# last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
# return float((np.log(last_trade_price) - np.log(current_price)) * 2)
# return 0.