remove unuse code and fix coding conventions

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -71,10 +71,6 @@ class Base3ActionRLEnv(gym.Env):
         self.history = None
         self.trade_history = []
 
-        self.r_t_change = 0.
-
-        self.returns_report = []
-
     def seed(self, seed: int = 1):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
@@ -101,9 +97,6 @@ class Base3ActionRLEnv(gym.Env):
 
         self._profits = [(self._start_tick, 1)]
         self.close_trade_profit = []
-        self.r_t_change = 0.
-
-        self.returns_report = []
 
         return self._get_observation()
 

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -73,11 +73,6 @@ class Base5ActionRLEnv(gym.Env):
         self.history = None
         self.trade_history = []
 
-        # self.A_t, self.B_t = 0.000639, 0.00001954
-        self.r_t_change = 0.
-
-        self.returns_report = []
-
     def seed(self, seed=None):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
@@ -104,9 +99,6 @@ class Base5ActionRLEnv(gym.Env):
 
         self._profits = [(self._start_tick, 1)]
         self.close_trade_profit = []
-        self.r_t_change = 0.
-
-        self.returns_report = []
 
         return self._get_observation()
 
@@ -178,12 +170,6 @@ class Base5ActionRLEnv(gym.Env):
 
         return observation, step_reward, self._done, info
 
-    # def processState(self, state):
-    #     return state.to_numpy()
-
-    # def convert_mlp_Policy(self, obs_):
-    #     pass
-
     def _get_observation(self):
         return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
 

freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py

@@ -62,7 +62,6 @@ class ReinforcementLearningExample3ac(IStrategy):
 
         coin = pair.split('/')[0]
 
-
         if informative is None:
             informative = self.dp.get_pair_dataframe(pair, tf)
 

freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py

@@ -62,7 +62,6 @@ class ReinforcementLearningExample5ac(IStrategy):
 
         coin = pair.split('/')[0]
 
-
         if informative is None:
             informative = self.dp.get_pair_dataframe(pair, tf)
 

freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py

@@ -81,7 +81,10 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
                              net_arch=[512, 512, 512])
 
         model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
-                    tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=learning_rate, gamma=0.9, verbose=1
+                    tensorboard_log=f"{path}/ppo/tensorboard/",
+                    learning_rate=learning_rate,
+                    gamma=0.9,
+                    verbose=1
                     )
 
         model.learn(

freqtrade/freqai/prediction_models/ReinforcementLearningTDQN_multiproc.py

@@ -4,7 +4,8 @@ import torch as th
 import numpy as np
 import gym
 from typing import Callable
-from stable_baselines3.common.callbacks import EvalCallback, StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
+from stable_baselines3.common.callbacks import (
+    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
@@ -18,6 +19,7 @@ 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:
     """
@@ -39,6 +41,7 @@ def make_env(env_id: str, rank: int, seed: int, train_df, price,
     set_random_seed(seed)
     return _init
 
+
 class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
     """
     User created Reinforcement Learning Model prediction model.
@@ -69,11 +72,22 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
                                   self.CONV_WIDTH, monitor=True) for i in range(num_cpu)])
 
         path = dk.data_path
-        stop_train_callback = StopTrainingOnNoModelImprovement(max_no_improvement_evals=5, min_evals=10, verbose=2)
+        stop_train_callback = StopTrainingOnNoModelImprovement(
+            max_no_improvement_evals=5,
+            min_evals=10,
+            verbose=2
+        )
         callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2)
-        eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
-                                     log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
-                                     deterministic=True, render=True, callback_after_eval=stop_train_callback, callback_on_new_best=callback_on_best, verbose=2)
+        eval_callback = EvalCallback(
+            eval_env, best_model_save_path=f"{path}/",
+            log_path=f"{path}/tdqn/logs/",
+            eval_freq=int(eval_freq),
+            deterministic=True,
+            render=True,
+            callback_after_eval=stop_train_callback,
+            callback_on_new_best=callback_on_best,
+            verbose=2
+        )
         # model arch
         policy_kwargs = dict(activation_fn=th.nn.ReLU,
                              net_arch=[512, 512, 512])