reuse callback, allow user to acces all stable_baselines3 agents via config
This commit is contained in:
robcaulk
@@ -55,7 +55,7 @@
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],
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"freqai": {
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"enabled": true,
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"model_save_type": "stable_baselines_dqn",
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"model_save_type": "stable_baselines",
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"conv_width": 10,
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"purge_old_models": true,
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"train_period_days": 10,
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@@ -85,8 +85,11 @@
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"verbose": 1
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},
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"rl_config": {
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"train_cycles": 15,
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"eval_cycles": 5,
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"train_cycles": 10,
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"eval_cycles": 3,
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"thread_count": 4,
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"model_type": "PPO",
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"policy_type": "MlpPolicy",
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"model_reward_parameters": {
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"rr": 1,
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"profit_aim": 0.02
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@@ -266,59 +266,28 @@ class Base5ActionRLEnv(gym.Env):
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# close long
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if action == Actions.Long_exit.value and self._position == Positions.Long:
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if len(self.close_trade_profit):
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# aim x2 rw
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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(
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self.prices.iloc[self._last_trade_tick].open)
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current_price = self.add_sell_fee(
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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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# less than aim x1 rw
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elif self.close_trade_profit[-1] < self.profit_aim * self.rr:
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last_trade_price = self.add_buy_fee(
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self.prices.iloc[self._last_trade_tick].open
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)
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current_price = self.add_sell_fee(
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self.prices.iloc[self._current_tick].open
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)
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return float(np.log(current_price) - np.log(last_trade_price))
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# # less than RR SL x2 neg rw
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# elif self.close_trade_profit[-1] < (self.profit_aim * -1):
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# last_trade_price = self.add_buy_fee(
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# self.prices.iloc[self._last_trade_tick].open)
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# current_price = self.add_sell_fee(
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# self.prices.iloc[self._current_tick].open)
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# return float((np.log(current_price) - np.log(last_trade_price)) * 2) * -1
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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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if action == Actions.Long_exit.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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# close short
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if action == Actions.Short_exit.value and self._position == Positions.Short:
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if len(self.close_trade_profit):
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# aim x2 rw
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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(
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self.prices.iloc[self._last_trade_tick].open
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)
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current_price = self.add_buy_fee(
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self.prices.iloc[self._current_tick].open
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)
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return float((np.log(last_trade_price) - np.log(current_price)) * 2)
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# less than aim x1 rw
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elif self.close_trade_profit[-1] < self.profit_aim * self.rr:
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last_trade_price = self.add_sell_fee(
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self.prices.iloc[self._last_trade_tick].open
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)
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current_price = self.add_buy_fee(
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self.prices.iloc[self._current_tick].open
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)
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return float(np.log(last_trade_price) - np.log(current_price))
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# # less than RR SL x2 neg rw
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# elif self.close_trade_profit[-1] > self.profit_aim * self.rr:
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# last_trade_price = self.add_sell_fee(
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# self.prices.iloc[self._last_trade_tick].open)
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# current_price = self.add_buy_fee(
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# self.prices.iloc[self._current_tick].open)
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# return float((np.log(last_trade_price) - np.log(current_price)) * 2) * -1
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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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if action == Actions.Short_exit.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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return 0.
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def _update_profit(self, action):
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@@ -11,8 +11,12 @@ from freqtrade.freqai.freqai_interface import IFreqaiModel
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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.callbacks import EvalCallback
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from stable_baselines3.common.monitor import Monitor
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import torch as th
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from typing import Callable
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from stable_baselines3.common.utils import set_random_seed
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import gym
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logger = logging.getLogger(__name__)
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torch.multiprocessing.set_sharing_strategy('file_system')
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@@ -25,9 +29,15 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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def __init__(self, **kwargs):
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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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th.set_num_threads(self.freqai_info['rl_config'].get('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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self.eval_env: Base5ActionRLEnv = None
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self.eval_callback: EvalCallback = None
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mod = __import__('stable_baselines3', fromlist=[
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self.freqai_info['rl_config']['model_type']])
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self.MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
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self.policy_type = self.freqai_info['rl_config']['policy_type']
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def train(
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self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
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@@ -67,7 +77,7 @@ 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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self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test)
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self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk)
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model = self.fit_rl(data_dictionary, dk)
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@@ -75,13 +85,13 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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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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def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk):
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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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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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eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
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# environments
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if not self.train_env:
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@@ -90,11 +100,17 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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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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self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=eval_freq,
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best_model_save_path=dk.data_path)
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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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self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
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self.eval_env.reset_env(test_df, prices_test, self.CONV_WIDTH, self.reward_params)
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self.eval_callback.__init__(self.eval_env, deterministic=True,
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render=False, eval_freq=eval_freq,
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best_model_save_path=dk.data_path)
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@abstractmethod
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def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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@@ -206,16 +222,28 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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# all the other existing fit() functions to include dk argument. For now we instantiate and
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# leave it.
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def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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return
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def make_env(env_id: str, rank: int, seed: int, train_df, price,
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reward_params, window_size, monitor=False) -> Callable:
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"""
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Utility function for multiprocessed env.
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:param env_id: (str) the environment ID
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:param num_env: (int) the number of environment you wish to have in subprocesses
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:param seed: (int) the inital seed for RNG
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:param rank: (int) index of the subprocess
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:return: (Callable)
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"""
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def _init() -> gym.Env:
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env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
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reward_kwargs=reward_params, id=env_id, seed=seed + rank)
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if monitor:
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env = Monitor(env, ".")
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return env
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set_random_seed(seed)
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return _init
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class MyRLEnv(Base5ActionRLEnv):
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"""
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@@ -229,24 +257,24 @@ class MyRLEnv(Base5ActionRLEnv):
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return 0.
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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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if action == Actions.Long_exit.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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if action == Actions.Long_sell.value and self._position == Positions.Long:
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if action == Actions.Long_exit.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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# close short
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if action == Actions.Short_buy.value and self._position == Positions.Short:
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if action == Actions.Short_exit.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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if action == Actions.Short_buy.value and self._position == Positions.Short:
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if action == Actions.Short_exit.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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@@ -471,12 +471,11 @@ class FreqaiDataDrawer:
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elif model_type == 'keras':
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from tensorflow import keras
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model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
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elif model_type == 'stable_baselines_ppo':
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from stable_baselines3.ppo.ppo import PPO
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model = PPO.load(dk.data_path / f"{dk.model_filename}_model")
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elif model_type == 'stable_baselines_dqn':
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from stable_baselines3 import DQN
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model = DQN.load(dk.data_path / f"{dk.model_filename}_model")
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elif model_type == 'stable_baselines':
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mod = __import__('stable_baselines3', fromlist=[
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self.freqai_info['rl_config']['model_type']])
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MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
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model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
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if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
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dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
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82
freqtrade/freqai/prediction_models/ReinforcementLearner.py
Normal file
82
freqtrade/freqai/prediction_models/ReinforcementLearner.py
Normal file
@@ -0,0 +1,82 @@
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import logging
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from typing import Any, Dict # , Tuple
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# import numpy.typing as npt
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import torch as th
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import numpy as np
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from pathlib import Path
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logger = logging.getLogger(__name__)
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class ReinforcementLearner(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_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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train_df = data_dictionary["train_features"]
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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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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 = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(dk.data_path / "tensorboard"),
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**self.freqai_info['model_training_parameters']
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)
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model.learn(
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total_timesteps=int(total_timesteps),
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callback=self.eval_callback
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)
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if Path(dk.data_path / "best_model.zip").is_file():
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logger.info('Callback found a best model.')
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best_model = self.MODELCLASS.load(dk.data_path / "best_model")
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return best_model
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logger.info('Couldnt find best model, using final model instead.')
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return model
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class MyRLEnv(Base5ActionRLEnv):
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"""
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User can modify any part of the environment by overriding base
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functions
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"""
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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.Long_exit.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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if action == Actions.Long_exit.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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# close short
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if action == Actions.Short_exit.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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if action == Actions.Short_exit.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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return 0.
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@@ -1,17 +1,59 @@
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from typing import Any, Dict, List, Optional, Tuple, Type, Union
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import gym
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import torch
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import logging
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import torch as th
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from typing import Any, Dict, List, Optional, Tuple, Type, Union
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from stable_baselines3 import DQN
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from stable_baselines3.common.buffers import ReplayBuffer
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from stable_baselines3.common.policies import BasePolicy
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from stable_baselines3.common.torch_layers import (BaseFeaturesExtractor,
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FlattenExtractor)
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from stable_baselines3.common.type_aliases import GymEnv, Schedule
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from pathlib import Path
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from stable_baselines3.dqn.policies import (CnnPolicy, DQNPolicy, MlpPolicy,
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QNetwork)
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from torch import nn
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import gym
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from stable_baselines3.common.torch_layers import (BaseFeaturesExtractor,
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FlattenExtractor)
|
||||
from stable_baselines3.common.type_aliases import GymEnv, Schedule
|
||||
from stable_baselines3.common.policies import BasePolicy
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
|
||||
"""
|
||||
User can customize agent by defining the class and using it directly.
|
||||
Here the example is "TDQN"
|
||||
"""
|
||||
|
||||
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
|
||||
|
||||
train_df = data_dictionary["train_features"]
|
||||
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
|
||||
|
||||
policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
||||
net_arch=[256, 256, 128])
|
||||
|
||||
# TDQN is a custom agent defined below
|
||||
model = TDQN(self.policy_type, self.train_env,
|
||||
tensorboard_log=Path(dk.data_path / "tensorboard"),
|
||||
policy_kwargs=policy_kwargs,
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
|
||||
model.learn(
|
||||
total_timesteps=int(total_timesteps),
|
||||
callback=self.eval_callback
|
||||
)
|
||||
|
||||
if Path(dk.data_path / "best_model.zip").is_file():
|
||||
logger.info('Callback found a best model.')
|
||||
best_model = self.MODELCLASS.load(dk.data_path / "best_model")
|
||||
return best_model
|
||||
|
||||
logger.info('Couldnt find best model, using final model instead.')
|
||||
|
||||
return model
|
||||
|
||||
# User creates their custom agent and networks as shown below
|
||||
|
||||
|
||||
def create_mlp_(
|
||||
@@ -72,7 +114,7 @@ class TDQNetwork(QNetwork):
|
||||
|
||||
def init_weights(self, m):
|
||||
if type(m) == nn.Linear:
|
||||
torch.nn.init.kaiming_uniform_(m.weight)
|
||||
th.nn.init.kaiming_uniform_(m.weight)
|
||||
|
||||
|
||||
class TDQNPolicy(DQNPolicy):
|
||||
@@ -175,7 +217,7 @@ class TDQN(DQN):
|
||||
exploration_initial_eps: float = 1.0,
|
||||
exploration_final_eps: float = 0.05,
|
||||
max_grad_norm: float = 10,
|
||||
tensorboard_log: Optional[str] = None,
|
||||
tensorboard_log: Optional[Path] = None,
|
||||
create_eval_env: bool = False,
|
||||
policy_kwargs: Optional[Dict[str, Any]] = None,
|
||||
verbose: int = 1,
|
||||
@@ -0,0 +1,84 @@
|
||||
import logging
|
||||
from typing import Any, Dict # , Tuple
|
||||
|
||||
# import numpy.typing as npt
|
||||
import torch as th
|
||||
from stable_baselines3.common.callbacks import EvalCallback
|
||||
from stable_baselines3.common.vec_env import SubprocVecEnv
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel,
|
||||
make_env)
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ReinforcementLearner_multiproc(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"]
|
||||
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
|
||||
|
||||
# model arch
|
||||
policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
||||
net_arch=[512, 512, 512])
|
||||
|
||||
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
|
||||
tensorboard_log=Path(dk.data_path / "tensorboard"),
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
|
||||
model.learn(
|
||||
total_timesteps=int(total_timesteps),
|
||||
callback=self.eval_callback
|
||||
)
|
||||
|
||||
if Path(dk.data_path / "best_model.zip").is_file():
|
||||
logger.info('Callback found a best model.')
|
||||
best_model = self.MODELCLASS.load(dk.data_path / "best_model")
|
||||
return best_model
|
||||
|
||||
logger.info('Couldnt find best model, using final model instead.')
|
||||
|
||||
return model
|
||||
|
||||
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk):
|
||||
"""
|
||||
If user has particular environment configuration needs, they can do that by
|
||||
overriding this function. In the present case, the user wants to setup training
|
||||
environments for multiple workers.
|
||||
"""
|
||||
train_df = data_dictionary["train_features"]
|
||||
test_df = data_dictionary["test_features"]
|
||||
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
|
||||
|
||||
# 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)])
|
||||
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
|
||||
render=False, eval_freq=eval_freq,
|
||||
best_model_save_path=dk.data_path)
|
||||
else:
|
||||
self.train_env.env_method('reset')
|
||||
self.eval_env.env_method('reset')
|
||||
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.eval_callback.__init__(self.eval_env, deterministic=True,
|
||||
render=False, eval_freq=eval_freq,
|
||||
best_model_save_path=dk.data_path)
|
||||
@@ -1,104 +0,0 @@
|
||||
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.
|
||||
@@ -1,132 +0,0 @@
|
||||
import logging
|
||||
from typing import Any, Dict # , Tuple
|
||||
|
||||
import numpy as np
|
||||
# import numpy.typing as npt
|
||||
import torch as th
|
||||
from stable_baselines3.common.monitor import Monitor
|
||||
from typing import Callable
|
||||
from stable_baselines3 import PPO
|
||||
from stable_baselines3.common.callbacks import EvalCallback
|
||||
from stable_baselines3.common.vec_env import SubprocVecEnv
|
||||
from stable_baselines3.common.utils import set_random_seed
|
||||
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
import gym
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def make_env(env_id: str, rank: int, seed: int, train_df, price,
|
||||
reward_params, window_size, monitor=False) -> Callable:
|
||||
"""
|
||||
Utility function for multiprocessed env.
|
||||
|
||||
:param env_id: (str) the environment ID
|
||||
:param num_env: (int) the number of environment you wish to have in subprocesses
|
||||
:param seed: (int) the inital seed for RNG
|
||||
:param rank: (int) index of the subprocess
|
||||
:return: (Callable)
|
||||
"""
|
||||
def _init() -> gym.Env:
|
||||
|
||||
env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
|
||||
reward_kwargs=reward_params, id=env_id, seed=seed + rank)
|
||||
if monitor:
|
||||
env = Monitor(env, ".")
|
||||
return env
|
||||
set_random_seed(seed)
|
||||
return _init
|
||||
|
||||
|
||||
class ReinforcementLearningPPO_multiproc(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=[512, 512, 512])
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
best_model = PPO.load(dk.data_path / "best_model")
|
||||
print('Training finished!')
|
||||
|
||||
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')
|
||||
|
||||
|
||||
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.
|
||||
@@ -1,115 +0,0 @@
|
||||
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 freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, 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
|
||||
import gc
|
||||
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], 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}/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', self.train_env,
|
||||
tensorboard_log=f"{path}/tdqn/tensorboard/",
|
||||
policy_kwargs=policy_kwargs,
|
||||
replay_buffer_class=ReplayBuffer,
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
|
||||
model.learn(
|
||||
total_timesteps=int(total_timesteps),
|
||||
callback=eval_callback
|
||||
)
|
||||
|
||||
del model
|
||||
best_model = DQN.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()
|
||||
|
||||
|
||||
# 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.
|
||||
@@ -1,148 +0,0 @@
|
||||
import logging
|
||||
from typing import Any, Dict # Optional
|
||||
import torch as th
|
||||
import numpy as np
|
||||
import gym
|
||||
from typing import Callable
|
||||
from stable_baselines3.common.callbacks import EvalCallback
|
||||
# EvalCallback , StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
|
||||
from stable_baselines3.common.monitor import Monitor
|
||||
from stable_baselines3.common.vec_env import SubprocVecEnv
|
||||
from stable_baselines3.common.utils import set_random_seed
|
||||
from stable_baselines3 import DQN
|
||||
from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
from freqtrade.freqai.RL.TDQNagent import TDQN
|
||||
from stable_baselines3.common.buffers import ReplayBuffer
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def make_env(env_id: str, rank: int, seed: int, train_df, price,
|
||||
reward_params, window_size, monitor=False) -> Callable:
|
||||
"""
|
||||
Utility function for multiprocessed env.
|
||||
|
||||
:param env_id: (str) the environment ID
|
||||
:param num_env: (int) the number of environment you wish to have in subprocesses
|
||||
:param seed: (int) the inital seed for RNG
|
||||
:param rank: (int) index of the subprocess
|
||||
:return: (Callable)
|
||||
"""
|
||||
def _init() -> gym.Env:
|
||||
|
||||
env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
|
||||
reward_kwargs=reward_params, id=env_id, seed=seed + rank)
|
||||
if monitor:
|
||||
env = Monitor(env, ".")
|
||||
return env
|
||||
set_random_seed(seed)
|
||||
return _init
|
||||
|
||||
|
||||
class ReinforcementLearningTDQN_multiproc(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}/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', self.train_env,
|
||||
policy_kwargs=policy_kwargs,
|
||||
tensorboard_log=f"{path}/tdqn/tensorboard/",
|
||||
replay_buffer_class=ReplayBuffer,
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
|
||||
model.learn(
|
||||
total_timesteps=int(total_timesteps),
|
||||
callback=eval_callback
|
||||
)
|
||||
|
||||
best_model = DQN.load(dk.data_path / "best_model.zip")
|
||||
print('Training finished!')
|
||||
|
||||
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
|
||||
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.
|
||||
Reference in New Issue
Block a user