persist a single training environment.

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -7,7 +7,7 @@ import numpy as np
 from gym import spaces
 from gym.utils import seeding
 from pandas import DataFrame
-
+import pandas as pd
 logger = logging.getLogger(__name__)
 
 
@@ -47,6 +47,9 @@ class Base5ActionRLEnv(gym.Env):
 
         self.id = id
         self.seed(seed)
+        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
+
+    def reset_env(self, df, prices, window_size, reward_kwargs, starting_point=True):
         self.df = df
         self.signal_features = self.df
         self.prices = prices
@@ -178,10 +181,15 @@ class Base5ActionRLEnv(gym.Env):
         return observation, step_reward, self._done, info
 
     def _get_observation(self):
-        features_and_state = self.signal_features[(
+        features_window = self.signal_features[(
             self._current_tick - self.window_size):self._current_tick]
+        features_and_state = DataFrame(np.zeros((len(features_window), 2)),
+                                       columns=['current_profit_pct', 'position'],
+                                       index=features_window.index)
+
         features_and_state['current_profit_pct'] = self.get_unrealized_profit()
         features_and_state['position'] = self._position.value
+        features_and_state = pd.concat([features_window, features_and_state], axis=1)
         return features_and_state
 
     def get_unrealized_profit(self):