add state/action info to callbacks

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -71,7 +71,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         model.learn(
             total_timesteps=int(total_timesteps),
-            callback=self.eval_callback
+            callback=[self.eval_callback, self.tensorboard_callback]
         )
 
         if Path(dk.data_path / "best_model.zip").is_file():
@@ -88,6 +88,33 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
         """
+        def reset(self):
+
+            # Reset custom info
+            self.custom_info = {}
+            self.custom_info["Invalid"] = 0
+            self.custom_info["Hold"] = 0
+            self.custom_info["Unknown"] = 0
+            self.custom_info["pnl_factor"] = 0
+            self.custom_info["duration_factor"] = 0
+            self.custom_info["reward_exit"] = 0
+            self.custom_info["reward_hold"] = 0
+            for action in Actions:
+                self.custom_info[f"{action.name}"] = 0
+            return super().reset()
+        
+        def step(self, action: int):
+            observation, step_reward, done, info = super().step(action)
+            info = dict(
+                tick=self._current_tick,
+                action=action,
+                total_reward=self.total_reward,
+                total_profit=self._total_profit,
+                position=self._position.value,
+                trade_duration=self.get_trade_duration(),
+                current_profit_pct=self.get_unrealized_profit()
+            )
+            return observation, step_reward, done, info
 
         def calculate_reward(self, action: int) -> float:
             """
@@ -100,17 +127,24 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
+                self.custom_info["Invalid"] += 1
                 return -2
 
             pnl = self.get_unrealized_profit()
             factor = 100.
 
             # reward agent for entering trades
-            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+            if (action ==Actions.Long_enter.value
                     and self._position == Positions.Neutral):
+                self.custom_info[f"{Actions.Long_enter.name}"] += 1
+                return 25
+            if (action == Actions.Short_enter.value
+                    and self._position == Positions.Neutral):
+                self.custom_info[f"{Actions.Short_enter.name}"] += 1
                 return 25
             # discourage agent from not entering trades
             if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                self.custom_info[f"{Actions.Neutral.name}"] += 1
                 return -1
 
             max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
@@ -124,18 +158,22 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             # discourage sitting in position
             if (self._position in (Positions.Short, Positions.Long) and
                     action == Actions.Neutral.value):
+                self.custom_info["Hold"] += 1
                 return -1 * trade_duration / max_trade_duration
 
             # close long
             if action == Actions.Long_exit.value and self._position == Positions.Long:
                 if pnl > self.profit_aim * self.rr:
                     factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                self.custom_info[f"{Actions.Long_exit.name}"] += 1
                 return float(pnl * factor)
 
             # close short
             if action == Actions.Short_exit.value and self._position == Positions.Short:
                 if pnl > self.profit_aim * self.rr:
                     factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                self.custom_info[f"{Actions.Short_exit.name}"] += 1
                 return float(pnl * factor)
-
+            
+            self.custom_info["Unknown"] += 1
             return 0.