improve typing, improve docstrings, ensure global tests pass

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -19,7 +19,15 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
     """
 
     def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
-
+        """
+        User customizable fit method
+        :params:
+        data_dictionary: dict = common data dictionary containing all train/test
+            features/labels/weights.
+        dk: FreqaiDatakitchen = data kitchen for current pair.
+        :returns:
+        model: Any = trained model to be used for inference in dry/live/backtesting
+        """
         train_df = data_dictionary["train_features"]
         total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
 
@@ -59,7 +67,15 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         """
 
         def calculate_reward(self, action):
-
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+            :params:
+            action: int = The action made by the agent for the current candle.
+            :returns:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
                 return -2

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -6,7 +6,7 @@ from typing import Any, Dict  # , Tuple
 import torch as th
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.vec_env import SubprocVecEnv
-
+from pandas import DataFrame
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel,
                                                                 make_env)
@@ -55,11 +55,18 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
 
         return model
 
-    def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk):
+    def set_train_and_eval_environments(self, data_dictionary: Dict[str, Any],
+                                        prices_train: DataFrame, prices_test: DataFrame,
+                                        dk: FreqaiDataKitchen):
         """
-        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.
+        User can override this if they are using a custom MyRLEnv
+        :params:
+        data_dictionary: dict = common data dictionary containing train and test
+            features/labels/weights.
+        prices_train/test: DataFrame = dataframe comprised of the prices to be used in
+            the environment during training
+        or testing
+        dk: FreqaiDataKitchen = the datakitchen for the current pair
         """
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
@@ -79,4 +86,4 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
                                        in range(num_cpu)])
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
-                                          best_model_save_path=dk.data_path)
+                                          best_model_save_path=str(dk.data_path))