add ability to integrate state info or not, and prevent state info integration during backtesting

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -2,9 +2,7 @@ import logging
 from enum import Enum
 
 import numpy as np
-import pandas as pd
 from gym import spaces
-from pandas import DataFrame
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -145,19 +143,6 @@ class Base5ActionRLEnv(BaseEnvironment):
 
         return observation, step_reward, self._done, info
 
-    def _get_observation(self):
-        features_window = self.signal_features[(
-            self._current_tick - self.window_size):self._current_tick]
-        features_and_state = DataFrame(np.zeros((len(features_window), 3)),
-                                       columns=['current_profit_pct', 'position', 'trade_duration'],
-                                       index=features_window.index)
-
-        features_and_state['current_profit_pct'] = self.get_unrealized_profit()
-        features_and_state['position'] = self._position.value
-        features_and_state['trade_duration'] = self.get_trade_duration()
-        features_and_state = pd.concat([features_window, features_and_state], axis=1)
-        return features_and_state
-
     def get_trade_duration(self):
         if self._last_trade_tick is None:
             return 0

freqtrade/freqai/RL/BaseEnvironment.py

@@ -35,6 +35,7 @@ class BaseEnvironment(gym.Env):
                  id: str = 'baseenv-1', seed: int = 1, config: dict = {}):
 
         self.rl_config = config['freqai']['rl_config']
+        self.add_state_info = self.rl_config.get('add_state_info', False)
         self.id = id
         self.seed(seed)
         self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
@@ -58,7 +59,11 @@ class BaseEnvironment(gym.Env):
         self.fee = 0.0015
 
         # # spaces
-        self.shape = (window_size, self.signal_features.shape[1] + 3)
+        if self.add_state_info:
+            self.total_features = self.signal_features.shape[1] + 3
+        else:
+            self.total_features = self.signal_features.shape[1]
+        self.shape = (window_size, self.total_features)
         self.set_action_space()
         self.observation_space = spaces.Box(
             low=-1, high=1, shape=self.shape, dtype=np.float32)
@@ -126,15 +131,20 @@ class BaseEnvironment(gym.Env):
         """
         features_window = self.signal_features[(
             self._current_tick - self.window_size):self._current_tick]
-        features_and_state = DataFrame(np.zeros((len(features_window), 3)),
-                                       columns=['current_profit_pct', 'position', 'trade_duration'],
-                                       index=features_window.index)
+        if self.add_state_info:
+            features_and_state = DataFrame(np.zeros((len(features_window), 3)),
+                                           columns=['current_profit_pct',
+                                                    'position',
+                                                    'trade_duration'],
+                                           index=features_window.index)
 
-        features_and_state['current_profit_pct'] = self.get_unrealized_profit()
-        features_and_state['position'] = self._position.value
-        features_and_state['trade_duration'] = self.get_trade_duration()
-        features_and_state = pd.concat([features_window, features_and_state], axis=1)
-        return features_and_state
+            features_and_state['current_profit_pct'] = self.get_unrealized_profit()
+            features_and_state['position'] = self._position.value
+            features_and_state['trade_duration'] = self.get_trade_duration()
+            features_and_state = pd.concat([features_window, features_and_state], axis=1)
+            return features_and_state
+        else:
+            return features_window
 
     def get_trade_duration(self):
         """

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -234,11 +234,12 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
 
         def _predict(window):
-            market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
             observations = dataframe.iloc[window.index]
-            observations['current_profit_pct'] = current_profit
-            observations['position'] = market_side
-            observations['trade_duration'] = trade_duration
+            if self.live:  # self.guard_state_info_if_backtest():
+                market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
+                observations['current_profit_pct'] = current_profit
+                observations['position'] = market_side
+                observations['trade_duration'] = trade_duration
             res, _ = model.predict(observations, deterministic=True)
             return res
 
@@ -246,6 +247,17 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
         return output
 
+    # def guard_state_info_if_backtest(self):
+    #     """
+    #     Ensure that backtesting mode doesnt try to use state information.
+    #     """
+    #     if self.rl_config('add_state_info', False) and not self.live:
+    #         logger.warning('Backtesting with state info is currently unavailable '
+    #                        'turning it off.')
+    #         self.rl_config['add_state_info'] = False
+
+    #     return not self.rl_config['add_state_info']
+
     def build_ohlc_price_dataframes(self, data_dictionary: dict,
                                     pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame,
                                                                                DataFrame]: