reuse callback, allow user to acces all stable_baselines3 agents via config

freqtrade/freqai/prediction_models/ReinforcementLearner.py

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+import logging
+from typing import Any, Dict  # , Tuple
+
+# import numpy.typing as npt
+import torch as th
+import numpy as np
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner(BaseReinforcementLearningModel):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
+
+        train_df = data_dictionary["train_features"]
+        total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
+
+        policy_kwargs = dict(activation_fn=th.nn.ReLU,
+                             net_arch=[256, 256, 128])
+
+        model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
+                                tensorboard_log=Path(dk.data_path / "tensorboard"),
+                                **self.freqai_info['model_training_parameters']
+                                )
+
+        model.learn(
+            total_timesteps=int(total_timesteps),
+            callback=self.eval_callback
+        )
+
+        if Path(dk.data_path / "best_model.zip").is_file():
+            logger.info('Callback found a best model.')
+            best_model = self.MODELCLASS.load(dk.data_path / "best_model")
+            return best_model
+
+        logger.info('Couldnt find best model, using final model instead.')
+
+        return model
+
+
+class MyRLEnv(Base5ActionRLEnv):
+    """
+    User can modify any part of the environment by overriding base
+    functions
+    """
+    def calculate_reward(self, action):
+
+        if self._last_trade_tick is None:
+            return 0.
+
+        # close long
+        if action == Actions.Long_exit.value and self._position == Positions.Long:
+            last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
+            current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
+            return float(np.log(current_price) - np.log(last_trade_price))
+
+        if action == Actions.Long_exit.value and self._position == Positions.Long:
+            if self.close_trade_profit[-1] > self.profit_aim * self.rr:
+                last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
+                current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
+                return float((np.log(current_price) - np.log(last_trade_price)) * 2)
+
+        # close short
+        if action == Actions.Short_exit.value and self._position == Positions.Short:
+            last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
+            current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
+            return float(np.log(last_trade_price) - np.log(current_price))
+
+        if action == Actions.Short_exit.value and self._position == Positions.Short:
+            if self.close_trade_profit[-1] > self.profit_aim * self.rr:
+                last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
+                current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
+                return float((np.log(last_trade_price) - np.log(current_price)) * 2)
+
+        return 0.