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https://github.com/freqtrade/freqtrade.git
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use a dictionary to make code more readable
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robcaulk
parent
d3ad5cb722
commit
7b4abd5ef5
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@ -44,8 +44,8 @@ class BaseEnvironment(gym.Env):
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def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
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reward_kwargs: dict = {}, window_size=10, starting_point=True,
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id: str = 'baseenv-1', seed: int = 1, config: dict = {},
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env_info: dict = {}):
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id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
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fee: float = 0.0015):
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"""
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Initializes the training/eval environment.
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:param df: dataframe of features
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@ -67,12 +67,12 @@ class BaseEnvironment(gym.Env):
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if self.config.get('fee', None) is not None:
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self.fee = self.config['fee']
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else:
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self.fee = env_info.get('fee', 0.0015)
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self.fee = fee
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# set here to default 5Ac, but all children envs can override this
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self.actions: Type[Enum] = BaseActions
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self.tensorboard_metrics: dict = {}
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self.live = env_info.get('live', False)
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self.live = live
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if not self.live and self.add_state_info:
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self.add_state_info = False
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logger.warning("add_state_info is not available in backtesting. Deactivating.")
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@ -17,7 +17,6 @@ from stable_baselines3.common.monitor import Monitor
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from stable_baselines3.common.utils import set_random_seed
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from stable_baselines3.common.vec_env import SubprocVecEnv
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from freqtrade.enums import RunMode
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from freqtrade.exceptions import OperationalException
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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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@ -144,24 +143,14 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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env_info = {"live": False}
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if self.data_provider:
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env_info["live"] = self.data_provider.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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env_info["fee"] = self.data_provider._exchange \
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.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
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env_info = self.pack_env_dict()
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self.train_env = self.MyRLEnv(df=train_df,
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prices=prices_train,
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window_size=self.CONV_WIDTH,
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reward_kwargs=self.reward_params,
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config=self.config,
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env_info=env_info)
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**env_info)
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self.eval_env = Monitor(self.MyRLEnv(df=test_df,
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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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config=self.config,
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env_info=env_info))
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**env_info))
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self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=len(train_df),
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best_model_save_path=str(dk.data_path))
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@ -169,6 +158,20 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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actions = self.train_env.get_actions()
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self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
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def pack_env_dict(self) -> Dict[str, Any]:
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"""
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Create dictionary of environment arguments
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"""
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env_info = {"window_size": self.CONV_WIDTH,
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"reward_kwargs": self.reward_params,
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"config": self.config,
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"live": self.live}
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if self.data_provider:
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env_info["fee"] = self.data_provider._exchange \
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.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
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return env_info
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@abstractmethod
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def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
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"""
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@ -390,8 +393,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
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seed: int, train_df: DataFrame, price: DataFrame,
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reward_params: Dict[str, int], window_size: int, monitor: bool = False,
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config: Dict[str, Any] = {}, env_info: Dict[str, Any] = {}) -> Callable:
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monitor: bool = False,
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env_info: Dict[str, Any] = {}) -> Callable:
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"""
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Utility function for multiprocessed env.
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@ -404,9 +407,8 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
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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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config=config, env_info=env_info)
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env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
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**env_info)
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if monitor:
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env = Monitor(env)
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return env
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@ -5,7 +5,6 @@ from pandas import DataFrame
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.vec_env import SubprocVecEnv
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from freqtrade.enums import RunMode
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
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@ -35,23 +34,20 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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env_info = {"live": False}
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if self.data_provider:
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env_info["live"] = self.data_provider.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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env_info["fee"] = self.data_provider._exchange \
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.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
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env_info = self.pack_env_dict()
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env_id = "train_env"
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self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
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self.reward_params, self.CONV_WIDTH, monitor=True,
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config=self.config, env_info=env_info) for i
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self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
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train_df, prices_train,
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monitor=True,
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env_info=env_info) for i
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in range(self.max_threads)])
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eval_env_id = 'eval_env'
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self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
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test_df, prices_test,
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self.reward_params, self.CONV_WIDTH, monitor=True,
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config=self.config, env_info=env_info) for i
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monitor=True,
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env_info=env_info) for i
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in range(self.max_threads)])
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self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=len(train_df),
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