add test_models folder

tests/freqai/test_models/ReinforcementLearner_test_4ac.py

@@ -0,0 +1,104 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict
+
+import numpy as np
+import torch as th
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner_test_4ac(BaseReinforcementLearningModel):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+
+        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=[128, 128])
+
+        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
+            model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
+                                    tensorboard_log=Path(
+                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
+                                    **self.freqai_info['model_training_parameters']
+                                    )
+        else:
+            logger.info('Continual training activated - starting training from previously '
+                        'trained agent.')
+            model = self.dd.model_dictionary[dk.pair]
+            model.set_env(self.train_env)
+
+        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(Base4ActionRLEnv):
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+        """
+
+        def calculate_reward(self, action):
+
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            rew = np.sign(pnl) * (pnl + 1)
+            factor = 100
+
+            # reward agent for entering trades
+            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                    and self._position == Positions.Neutral):
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (self._position in (Positions.Short, Positions.Long) and
+                    action == Actions.Neutral.value):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.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)
+                return float(rew * factor)
+
+            # close short
+            if action == Actions.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)
+                return float(rew * factor)
+
+            return 0.