stable/freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
2022-08-24 13:00:55 +02:00

169 lines
6.6 KiB
Python

import logging
from typing import Any, Dict, Optional
import numpy as np
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.BaseRLEnv import BaseRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3.common.buffers import ReplayBuffer
logger = logging.getLogger(__name__)
class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
"""
User created Reinforcement Learning Model prediction model.
"""
def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
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 = MyRLEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH,
reward_kwargs=reward_params)
eval = MyRLEnv(df=test_df, prices=price_test,
window_size=self.CONV_WIDTH, reward_kwargs=reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
path = self.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=Optional(ReplayBuffer)
)
model.learn(
total_timesteps=int(total_timesteps),
callback=eval_callback
)
print('Training finished!')
return model
class MyRLEnv(BaseRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env
"""
def step(self, action):
self._done = False
self._current_tick += 1
if self._current_tick == self._end_tick:
self._done = True
self.update_portfolio_log_returns(action)
self._update_profit(action)
step_reward = self._calculate_reward(action)
self.total_reward += step_reward
trade_type = None
if self.is_tradesignal(action):
"""
Action: Neutral, position: Long -> Close Long
Action: Neutral, position: Short -> Close Short
Action: Long, position: Neutral -> Open Long
Action: Long, position: Short -> Close Short and Open Long
Action: Short, position: Neutral -> Open Short
Action: Short, position: Long -> Close Long and Open Short
"""
if action == Actions.Neutral.value:
self._position = Positions.Neutral
trade_type = "neutral"
elif action == Actions.Long.value:
self._position = Positions.Long
trade_type = "long"
elif action == Actions.Short.value:
self._position = Positions.Short
trade_type = "short"
else:
print("case not defined")
# Update last trade tick
self._last_trade_tick = self._current_tick
if trade_type is not None:
self.trade_history.append(
{'price': self.current_price(), 'index': self._current_tick,
'type': trade_type})
if self._total_profit < 0.2:
self._done = True
self._position_history.append(self._position)
observation = self._get_observation()
info = dict(
tick=self._current_tick,
total_reward=self.total_reward,
total_profit=self._total_profit,
position=self._position.value
)
self._update_history(info)
return observation, step_reward, self._done, info
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.