persist a single training environment.
This commit is contained in:
robcaulk
parent
5d4e5e69fe
commit
f95602f6bd
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@ -61,7 +61,7 @@
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"train_period_days": 10,
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"backtest_period_days": 2,
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"identifier": "unique-id",
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"data_kitchen_thread_count": 4,
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"data_kitchen_thread_count": 2,
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"feature_parameters": {
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"include_corr_pairlist": [
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"BTC/USDT",
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@ -7,7 +7,7 @@ import numpy as np
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from gym import spaces
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from gym.utils import seeding
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from pandas import DataFrame
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import pandas as pd
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logger = logging.getLogger(__name__)
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@ -47,6 +47,9 @@ class Base5ActionRLEnv(gym.Env):
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self.id = id
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self.seed(seed)
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self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
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def reset_env(self, df, prices, window_size, reward_kwargs, starting_point=True):
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self.df = df
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self.signal_features = self.df
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self.prices = prices
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@ -178,10 +181,15 @@ class Base5ActionRLEnv(gym.Env):
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return observation, step_reward, self._done, info
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def _get_observation(self):
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features_and_state = self.signal_features[(
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features_window = self.signal_features[(
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self._current_tick - self.window_size):self._current_tick]
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features_and_state = DataFrame(np.zeros((len(features_window), 2)),
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columns=['current_profit_pct', 'position'],
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index=features_window.index)
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features_and_state['current_profit_pct'] = self.get_unrealized_profit()
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features_and_state['position'] = self._position.value
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features_and_state = pd.concat([features_window, features_and_state], axis=1)
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return features_and_state
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def get_unrealized_profit(self):
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@ -8,9 +8,10 @@ from pandas import DataFrame
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from abc import abstractmethod
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
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from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions
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from freqtrade.persistence import Trade
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import torch.multiprocessing
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from stable_baselines3.common.monitor import Monitor
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import torch as th
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logger = logging.getLogger(__name__)
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@ -26,6 +27,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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super().__init__(config=kwargs['config'])
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th.set_num_threads(self.freqai_info.get('data_kitchen_thread_count', 4))
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self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
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self.train_env: Base5ActionRLEnv = None
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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@ -65,15 +67,37 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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)
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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model = self.fit_rl(data_dictionary, pair, dk, prices_train, prices_test)
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self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test)
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model = self.fit_rl(data_dictionary, dk)
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logger.info(f"--------------------done training {pair}--------------------")
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return model
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def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
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"""
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User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
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leaving this will default to Base5ActEnv
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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# environments
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if not self.train_env:
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self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params)
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self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
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window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params), ".")
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else:
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self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.train_env.reset()
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self.eval_env.reset()
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@abstractmethod
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def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
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prices_train: DataFrame, prices_test: DataFrame):
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def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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"""
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Agent customizations and abstract Reinforcement Learning customizations
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go in here. Abstract method, so this function must be overridden by
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@ -193,66 +217,39 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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return
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class MyRLEnv(Base3ActionRLEnv):
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class MyRLEnv(Base5ActionRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env. Here the user
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Adds 5 actions.
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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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def calculate_reward(self, action):
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if self._current_tick == self._end_tick:
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self._done = True
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if self._last_trade_tick is None:
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return 0.
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self.update_portfolio_log_returns(action)
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# close long
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if action == Actions.Long_sell.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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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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if action == Actions.Long_sell.value and self._position == Positions.Long:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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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)) * 2)
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trade_type = None
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if self.is_tradesignal(action): # exclude 3 case not trade
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# Update position
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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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# close short
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if action == Actions.Short_buy.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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Action: Long, position: Neutral -> Open Long
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Action: Long, position: Short -> Close Short and Open Long
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if action == Actions.Short_buy.value and self._position == Positions.Short:
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if self.close_trade_profit[-1] > self.profit_aim * self.rr:
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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)) * 2)
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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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return 0.
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@ -3,9 +3,7 @@ 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.common.monitor import Monitor
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from typing import Callable
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from stable_baselines3 import PPO
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@ -16,7 +14,6 @@ from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Posi
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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import gym
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from pandas import DataFrame
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logger = logging.getLogger(__name__)
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@ -48,26 +45,15 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
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User created Reinforcement Learning Model prediction model.
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"""
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def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
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prices_train: DataFrame, prices_test: DataFrame):
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def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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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 = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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env_id = "train_env"
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num_cpu = int(dk.thread_count / 2)
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train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
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self.reward_params, self.CONV_WIDTH) for i in range(num_cpu)])
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eval_env_id = 'eval_env'
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eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
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self.reward_params, self.CONV_WIDTH, monitor=True) for i in
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range(num_cpu)])
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path = dk.data_path
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eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
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eval_callback = EvalCallback(self.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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@ -75,7 +61,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
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policy_kwargs = dict(activation_fn=th.nn.ReLU,
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net_arch=[512, 512, 512])
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model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
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model = PPO('MlpPolicy', self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=f"{path}/ppo/tensorboard/",
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**self.freqai_info['model_training_parameters']
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)
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@ -87,10 +73,37 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
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best_model = PPO.load(dk.data_path / "best_model")
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print('Training finished!')
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eval_env.close()
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return best_model
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def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
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"""
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User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
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leaving this will default to Base5ActEnv
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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# environments
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if not self.train_env:
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env_id = "train_env"
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num_cpu = int(self.freqai_info["data_kitchen_thread_count"] / 2)
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self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
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self.reward_params, self.CONV_WIDTH) for i
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in range(num_cpu)])
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eval_env_id = 'eval_env'
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self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
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self.reward_params, self.CONV_WIDTH, monitor=True) for i
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in range(num_cpu)])
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else:
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self.train_env.env_method('reset_env', train_df, prices_train,
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self.CONV_WIDTH, self.reward_params)
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self.eval_env.env_method('reset_env', train_df, prices_train,
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self.CONV_WIDTH, self.reward_params)
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self.train_env.env_method('reset')
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self.eval_env.env_method('reset')
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class MyRLEnv(Base3ActionRLEnv):
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"""
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@ -9,8 +9,7 @@ from freqtrade.freqai.RL.TDQNagent import TDQN
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from stable_baselines3 import DQN
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from stable_baselines3.common.buffers import ReplayBuffer
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import numpy as np
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from pandas import DataFrame
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import gc
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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@ -21,24 +20,15 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
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User created Reinforcement Learning Model prediction model.
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"""
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def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
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prices_train: DataFrame, prices_test: DataFrame):
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def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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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 = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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# environments
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train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params)
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eval = MyRLEnv(df=test_df, prices=prices_test,
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window_size=self.CONV_WIDTH, reward_kwargs=self.reward_params)
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eval_env = Monitor(eval, ".")
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eval_env.reset()
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path = dk.data_path
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eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
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eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
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log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
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deterministic=True, render=False)
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@ -46,7 +36,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
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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 = TDQN('TMultiInputPolicy', train_env,
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model = TDQN('TMultiInputPolicy', self.train_env,
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tensorboard_log=f"{path}/tdqn/tensorboard/",
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policy_kwargs=policy_kwargs,
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replay_buffer_class=ReplayBuffer,
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@ -58,12 +48,33 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
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callback=eval_callback
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)
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del model
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best_model = DQN.load(dk.data_path / "best_model")
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print('Training finished!')
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gc.collect()
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return best_model
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def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
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"""
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User overrides this as shown here if they are using a custom MyRLEnv
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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# environments
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if not self.train_env:
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self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params)
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self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
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window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params), ".")
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else:
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self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.train_env.reset()
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self.eval_env.reset()
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# User can inherit and customize 5 action environment
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class MyRLEnv(Base5ActionRLEnv):
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@ -4,8 +4,8 @@ import torch as th
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import numpy as np
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import gym
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from typing import Callable
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from stable_baselines3.common.callbacks import (
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EvalCallback, StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold)
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from stable_baselines3.common.callbacks import EvalCallback
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# EvalCallback , StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
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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 stable_baselines3.common.utils import set_random_seed
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@ -15,7 +15,6 @@ from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcement
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from freqtrade.freqai.RL.TDQNagent import TDQN
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from stable_baselines3.common.buffers import ReplayBuffer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from pandas import DataFrame
|
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|
||||
logger = logging.getLogger(__name__)
|
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|
||||
|
|
@ -47,46 +46,23 @@ class ReinforcementLearningTDQN_multiproc(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):
|
||||
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)
|
||||
|
||||
env_id = "train_env"
|
||||
num_cpu = int(dk.thread_count / 2)
|
||||
train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
|
||||
self.reward_params, self.CONV_WIDTH) for i in range(num_cpu)])
|
||||
|
||||
eval_env_id = 'eval_env'
|
||||
eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
|
||||
self.reward_params, self.CONV_WIDTH, monitor=True) for i in
|
||||
range(num_cpu)])
|
||||
|
||||
path = dk.data_path
|
||||
stop_train_callback = StopTrainingOnNoModelImprovement(
|
||||
max_no_improvement_evals=5,
|
||||
min_evals=10,
|
||||
verbose=2
|
||||
)
|
||||
callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2)
|
||||
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=True,
|
||||
callback_after_eval=stop_train_callback,
|
||||
callback_on_new_best=callback_on_best,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
eval_callback = EvalCallback(self.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=[512, 512, 512])
|
||||
|
||||
model = TDQN('TMultiInputPolicy', train_env,
|
||||
model = TDQN('TMultiInputPolicy', self.train_env,
|
||||
policy_kwargs=policy_kwargs,
|
||||
tensorboard_log=f"{path}/tdqn/tensorboard/",
|
||||
replay_buffer_class=ReplayBuffer,
|
||||
|
|
@ -100,12 +76,40 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
|
|||
|
||||
best_model = DQN.load(dk.data_path / "best_model.zip")
|
||||
print('Training finished!')
|
||||
eval_env.close()
|
||||
|
||||
return best_model
|
||||
|
||||
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
|
||||
"""
|
||||
User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
|
||||
leaving this will default to Base5ActEnv
|
||||
"""
|
||||
train_df = data_dictionary["train_features"]
|
||||
test_df = data_dictionary["test_features"]
|
||||
|
||||
# environments
|
||||
if not self.train_env:
|
||||
env_id = "train_env"
|
||||
num_cpu = int(self.freqai_info["data_kitchen_thread_count"] / 2)
|
||||
self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
|
||||
self.reward_params, self.CONV_WIDTH) for i
|
||||
in range(num_cpu)])
|
||||
|
||||
eval_env_id = 'eval_env'
|
||||
self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
|
||||
self.reward_params, self.CONV_WIDTH, monitor=True) for i
|
||||
in range(num_cpu)])
|
||||
else:
|
||||
self.train_env.env_method('reset_env', train_df, prices_train,
|
||||
self.CONV_WIDTH, self.reward_params)
|
||||
self.eval_env.env_method('reset_env', train_df, prices_train,
|
||||
self.CONV_WIDTH, self.reward_params)
|
||||
self.train_env.env_method('reset')
|
||||
self.eval_env.env_method('reset')
|
||||
|
||||
# User can inherit and customize 5 action environment
|
||||
|
||||
|
||||
class MyRLEnv(Base5ActionRLEnv):
|
||||
"""
|
||||
User can override any function in BaseRLEnv and gym.Env. Here the user
|
||||
|
|
|
|||
Loading…
Reference in New Issue