reinforce training with state info, reinforce prediction with state info, restructure config to accommodate all parameters from any user imported model type. Set 5Act to default env on TDQN. Clean example config.

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -6,6 +6,7 @@ import gym
 import numpy as np
 from gym import spaces
 from gym.utils import seeding
+from pandas import DataFrame
 
 logger = logging.getLogger(__name__)
 
@@ -39,7 +40,8 @@ class Base5ActionRLEnv(gym.Env):
     """
     metadata = {'render.modes': ['human']}
 
-    def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True,
+    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):
         assert df.ndim == 2
 
@@ -56,7 +58,7 @@ class Base5ActionRLEnv(gym.Env):
         self.fee = 0.0015
 
         # # spaces
-        self.shape = (window_size, self.signal_features.shape[1])
+        self.shape = (window_size, self.signal_features.shape[1] + 2)
         self.action_space = spaces.Discrete(len(Actions))
         self.observation_space = spaces.Box(
             low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
@@ -161,19 +163,26 @@ class Base5ActionRLEnv(gym.Env):
             self._done = True
 
         self._position_history.append(self._position)
-        observation = self._get_observation()
+
         info = dict(
             tick=self._current_tick,
             total_reward=self.total_reward,
             total_profit=self._total_profit,
             position=self._position.value
         )
+
+        observation = self._get_observation()
+
         self._update_history(info)
 
         return observation, step_reward, self._done, info
 
     def _get_observation(self):
-        return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
+        features_and_state = self.signal_features[(
+            self._current_tick - self.window_size):self._current_tick]
+        features_and_state['current_profit_pct'] = self.get_unrealized_profit()
+        features_and_state['position'] = self._position.value
+        return features_and_state
 
     def get_unrealized_profit(self):