105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
import gc
|
|
import logging
|
|
from typing import Any, Dict # , Tuple
|
|
|
|
import numpy as np
|
|
# import numpy.typing as npt
|
|
import torch as th
|
|
from stable_baselines3 import PPO
|
|
from stable_baselines3.common.callbacks import EvalCallback
|
|
from stable_baselines3.common.monitor import Monitor
|
|
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
from freqtrade.freqai.RL.Base3ActionRLEnv import Actions, Base3ActionRLEnv, Positions
|
|
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class ReinforcementLearningPPO(BaseReinforcementLearningModel):
|
|
"""
|
|
User created Reinforcement Learning Model prediction model.
|
|
"""
|
|
|
|
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
|
|
|
|
train_df = data_dictionary["train_features"]
|
|
test_df = data_dictionary["test_features"]
|
|
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
|
|
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
|
|
|
|
path = dk.data_path
|
|
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
|
|
log_path=f"{path}/ppo/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 = PPO('MlpPolicy', self.train_env, policy_kwargs=policy_kwargs,
|
|
tensorboard_log=f"{path}/ppo/tensorboard/",
|
|
**self.freqai_info['model_training_parameters']
|
|
)
|
|
|
|
model.learn(
|
|
total_timesteps=int(total_timesteps),
|
|
callback=eval_callback
|
|
)
|
|
|
|
del model
|
|
best_model = PPO.load(dk.data_path / "best_model")
|
|
|
|
print('Training finished!')
|
|
gc.collect()
|
|
|
|
return best_model
|
|
|
|
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
|
|
"""
|
|
User overrides this as shown here if they are using a custom MyRLEnv
|
|
"""
|
|
train_df = data_dictionary["train_features"]
|
|
test_df = data_dictionary["test_features"]
|
|
|
|
# environments
|
|
if not self.train_env:
|
|
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
|
|
reward_kwargs=self.reward_params)
|
|
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
|
|
window_size=self.CONV_WIDTH,
|
|
reward_kwargs=self.reward_params), ".")
|
|
else:
|
|
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
|
|
self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
|
|
self.train_env.reset()
|
|
self.eval_env.reset()
|
|
|
|
|
|
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.
|