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Add env_info dict to base environment

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Emre 2022-12-14 22:03:05 +03:00
parent 2285ca7d2a
commit 2018da0767
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3 changed files with 25 additions and 19 deletions
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17
freqtrade/freqai/RL/BaseEnvironment.py
View File

@ -11,9 +11,6 @@ from gym import spaces
from gym.utils import seeding
from pandas import DataFrame
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
logger = logging.getLogger(__name__)
@ -48,7 +45,7 @@ class BaseEnvironment(gym.Env):
def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
reward_kwargs: dict = {}, window_size=10, starting_point=True,
id: str = 'baseenv-1', seed: int = 1, config: dict = {},
dp: Optional[DataProvider] = None):
env_info: dict = {}):
"""
Initializes the training/eval environment.
:param df: dataframe of features
@ -59,7 +56,7 @@ class BaseEnvironment(gym.Env):
:param id: string id of the environment (used in backend for multiprocessed env)
:param seed: Sets the seed of the environment higher in the gym.Env object
:param config: Typical user configuration file
:param dp: dataprovider from freqtrade
:param env_info: Environment info dictionary, used to pass live status, fee, etc.
"""
self.config = config
self.rl_config = config['freqai']['rl_config']
@ -71,17 +68,13 @@ class BaseEnvironment(gym.Env):
self.compound_trades = config['stake_amount'] == 'unlimited'
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
elif dp is not None:
self.fee = dp._exchange.get_fee(symbol=dp.current_whitelist()[0]) # type: ignore
else:
self.fee = 0.0015
self.fee = env_info.get('fee', 0.0015)
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.tensorboard_metrics: dict = {}
self.live: bool = False
if dp:
self.live = dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.live = env_info.get('live', False)
if not self.live and self.add_state_info:
self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.")
@ -213,7 +206,7 @@ class BaseEnvironment(gym.Env):
"""
features_window = self.signal_features[(
self._current_tick - self.window_size):self._current_tick]
if self.add_state_info and self.live:
if self.add_state_info:
features_and_state = DataFrame(np.zeros((len(features_window), 3)),
columns=['current_profit_pct',
'position',

16
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -17,6 +17,7 @@ from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.utils import set_random_seed
from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
@ -24,7 +25,6 @@ from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
from freqtrade.persistence import Trade
from freqtrade.data.dataprovider import DataProvider
logger = logging.getLogger(__name__)
@ -144,18 +144,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = {"live": False}
if self.data_provider:
env_info["live"] = self.data_provider.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params,
config=self.config,
dp=self.data_provider)
env_info=env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df,
prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params,
config=self.config,
dp=self.data_provider))
env_info=env_info))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path))
@ -385,7 +391,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
seed: int, train_df: DataFrame, price: DataFrame,
reward_params: Dict[str, int], window_size: int, monitor: bool = False,
config: Dict[str, Any] = {}, dp: DataProvider = None) -> Callable:
config: Dict[str, Any] = {}, env_info: Dict[str, Any] = {}) -> Callable:
"""
Utility function for multiprocessed env.
@ -400,7 +406,7 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
reward_kwargs=reward_params, id=env_id, seed=seed + rank,
config=config, dp=dp)
config=config, env_info=env_info)
if monitor:
env = Monitor(env)
return env

11
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -5,6 +5,7 @@ from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.enums import RunMode
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
@ -34,17 +35,23 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = {"live": False}
if self.data_provider:
env_info["live"] = self.data_provider.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH, monitor=True,
config=self.config, dp=self.data_provider) for i
config=self.config, env_info=env_info) for i
in range(self.max_threads)])
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True,
config=self.config, dp=self.data_provider) for i
config=self.config, env_info=env_info) for i
in range(self.max_threads)])
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),