use a dictionary to make code more readable

freqtrade/freqai/RL/BaseEnvironment.py

@@ -44,8 +44,8 @@ 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 = {},
+                 id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
-                 env_info: dict = {}):
+                 fee: float = 0.0015):
         """
         Initializes the training/eval environment.
         :param df: dataframe of features
@@ -67,12 +67,12 @@ class BaseEnvironment(gym.Env):
         if self.config.get('fee', None) is not None:
             self.fee = self.config['fee']
         else:
-            self.fee = env_info.get('fee', 0.0015)
+            self.fee = fee
 
         # set here to default 5Ac, but all children envs can override this
         self.actions: Type[Enum] = BaseActions
         self.tensorboard_metrics: dict = {}
-        self.live = env_info.get('live', False)
+        self.live = live
         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.")

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -17,7 +17,6 @@ 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
@@ -144,24 +143,14 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
-        env_info = {"live": False}
+        env_info = self.pack_env_dict()
-        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,
+                                      **env_info)
-                                      reward_kwargs=self.reward_params,
-                                      config=self.config,
-                                      env_info=env_info)
         self.eval_env = Monitor(self.MyRLEnv(df=test_df,
                                              prices=prices_test,
-                                             window_size=self.CONV_WIDTH,
+                                             **env_info))
-                                             reward_kwargs=self.reward_params,
-                                             config=self.config,
-                                             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))
@@ -169,6 +158,20 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         actions = self.train_env.get_actions()
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
 
+    def pack_env_dict(self) -> Dict[str, Any]:
+        """
+        Create dictionary of environment arguments
+        """
+        env_info = {"window_size": self.CONV_WIDTH,
+                    "reward_kwargs": self.reward_params,
+                    "config": self.config,
+                    "live": self.live}
+        if self.data_provider:
+            env_info["fee"] = self.data_provider._exchange \
+                .get_fee(symbol=self.data_provider.current_whitelist()[0])  # type: ignore
+
+        return env_info
+
     @abstractmethod
     def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
         """
@@ -390,8 +393,8 @@ 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,
+             monitor: bool = False,
-             config: Dict[str, Any] = {}, env_info: Dict[str, Any] = {}) -> Callable:
+             env_info: Dict[str, Any] = {}) -> Callable:
     """
     Utility function for multiprocessed env.
 
@@ -404,9 +407,8 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
 
     def _init() -> gym.Env:
 
-        env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
+        env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
-                      reward_kwargs=reward_params, id=env_id, seed=seed + rank,
+                      **env_info)
-                      config=config, env_info=env_info)
         if monitor:
             env = Monitor(env)
         return env

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -5,7 +5,6 @@ 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
@@ -35,23 +34,20 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
-        env_info = {"live": False}
+        env_info = self.pack_env_dict()
-        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.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
-                                        self.reward_params, self.CONV_WIDTH, monitor=True,
+                                        train_df, prices_train,
-                                        config=self.config, env_info=env_info) for i
+                                        monitor=True,
+                                        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,
+                                                monitor=True,
-                                                config=self.config, env_info=env_info) for i
+                                                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),