156 lines
5.6 KiB
Python
156 lines
5.6 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.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.RL.BaseRLEnv import BaseRLEnv, Actions, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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logger = logging.getLogger(__name__)
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class ReinforcementLearningPPO(BaseReinforcementLearningModel):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
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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(
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len(test_df.index))
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# environments
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train_env = MyRLEnv(df=train_df, prices=price, window_size=self.CONV_WIDTH,
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reward_kwargs=reward_params)
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eval = MyRLEnv(df=test_df, prices=price_test,
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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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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}/ppo/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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model = PPO('MultiInputPolicy', train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=f"{path}/ppo/tensorboard/", 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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class MyRLEnv(BaseRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env
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"""
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def step(self, action):
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self._done = False
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self._current_tick += 1
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if self._current_tick == self._end_tick:
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self._done = True
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self.update_portfolio_log_returns(action)
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self._update_profit(action)
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step_reward = self._calculate_reward(action)
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self.total_reward += step_reward
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trade_type = None
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if self.is_tradesignal(action):
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"""
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Action: Neutral, position: Long -> Close Long
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Action: Neutral, position: Short -> Close Short
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Action: Long, position: Neutral -> Open Long
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Action: Long, position: Short -> Close Short and Open Long
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Action: Short, position: Neutral -> Open Short
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Action: Short, position: Long -> Close Long and Open Short
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"""
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if action == Actions.Neutral.value:
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self._position = Positions.Neutral
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trade_type = "neutral"
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elif action == Actions.Long.value:
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self._position = Positions.Long
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trade_type = "long"
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elif action == Actions.Short.value:
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self._position = Positions.Short
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trade_type = "short"
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else:
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print("case not defined")
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# Update last trade tick
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self._last_trade_tick = self._current_tick
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if trade_type is not None:
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self.trade_history.append(
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{'price': self.current_price(), 'index': self._current_tick,
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'type': trade_type})
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if self._total_profit < 0.2:
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self._done = True
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self._position_history.append(self._position)
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observation = self._get_observation()
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info = dict(
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tick=self._current_tick,
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total_reward=self.total_reward,
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total_profit=self._total_profit,
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position=self._position.value
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)
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self._update_history(info)
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return observation, step_reward, self._done, info
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def calculate_reward(self, action):
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if self._last_trade_tick is None:
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return 0.
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# close long
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if (action == Actions.Short.value or
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action == Actions.Neutral.value) and self._position == Positions.Long:
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last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(current_price) - np.log(last_trade_price))
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# close short
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if (action == Actions.Long.value or
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action == Actions.Neutral.value) and self._position == Positions.Short:
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last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
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return float(np.log(last_trade_price) - np.log(current_price))
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return 0.
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