multiproc TDQN with xtra callbacks

freqtrade/freqai/prediction_models/ReinforcementLearningTDQN_multiproc.py

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+import logging
+from typing import Any, Dict  # Optional
+import torch as th
+import numpy as np
+import gym
+from typing import Callable
+from stable_baselines3.common.callbacks import EvalCallback, StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import SubprocVecEnv
+from stable_baselines3.common.utils import set_random_seed
+from stable_baselines3 import DQN
+from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+from freqtrade.freqai.RL.TDQNagent import TDQN
+from stable_baselines3.common.buffers import ReplayBuffer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+def make_env(env_id: str, rank: int, seed: int, train_df, price,
+             reward_params, window_size, monitor=False) -> Callable:
+    """
+    Utility function for multiprocessed env.
+
+    :param env_id: (str) the environment ID
+    :param num_env: (int) the number of environment you wish to have in subprocesses
+    :param seed: (int) the inital seed for RNG
+    :param rank: (int) index of the subprocess
+    :return: (Callable)
+    """
+    def _init() -> gym.Env:
+
+        env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
+                      reward_kwargs=reward_params, id=env_id, seed=seed + rank)
+        if monitor:
+            env = Monitor(env, ".")
+        return env
+    set_random_seed(seed)
+    return _init
+
+class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen):
+
+        agent_params = self.freqai_info['model_training_parameters']
+        reward_params = self.freqai_info['model_reward_parameters']
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+        eval_freq = agent_params["eval_cycles"] * len(test_df)
+        total_timesteps = agent_params["train_cycles"] * len(train_df)
+        learning_rate = agent_params["learning_rate"]
+
+        # price data for model training and evaluation
+        price = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(len(train_df.index))
+        price_test = self.dd.historic_data[pair][f"{self.config['timeframe']}"].tail(
+            len(test_df.index))
+
+        env_id = "train_env"
+        num_cpu = int(dk.thread_count / 2)
+        train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, price, reward_params,
+                                   self.CONV_WIDTH) for i in range(num_cpu)])
+
+        eval_env_id = 'eval_env'
+        eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, price_test, reward_params,
+                                  self.CONV_WIDTH, monitor=True) for i in range(num_cpu)])
+
+        path = dk.data_path
+        stop_train_callback = StopTrainingOnNoModelImprovement(max_no_improvement_evals=5, min_evals=10, verbose=2)
+        callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2)
+        eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
+                                     log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
+                                     deterministic=True, render=True, callback_after_eval=stop_train_callback, callback_on_new_best=callback_on_best, verbose=2)
+        # model arch
+        policy_kwargs = dict(activation_fn=th.nn.ReLU,
+                             net_arch=[512, 512, 512])
+
+        model = TDQN('TMultiInputPolicy', train_env,
+                     policy_kwargs=policy_kwargs,
+                     tensorboard_log=f"{path}/tdqn/tensorboard/",
+                     learning_rate=learning_rate, gamma=0.9,
+                     target_update_interval=5000, buffer_size=50000,
+                     exploration_initial_eps=1, exploration_final_eps=0.1,
+                     replay_buffer_class=ReplayBuffer
+                     )
+
+        model.learn(
+            total_timesteps=int(total_timesteps),
+            callback=eval_callback
+        )
+
+        best_model = DQN.load(dk.data_path / "best_model.zip")
+        print('Training finished!')
+        eval_env.close()
+
+        return best_model
+
+
+class MyRLEnv(Base3ActionRLEnv):
+    """
+    User can override any function in BaseRLEnv and gym.Env
+    """
+
+    def calculate_reward(self, action):
+
+        if self._last_trade_tick is None:
+            return 0.
+
+        # close long
+        if (action == Actions.Short.value or
+                action == Actions.Neutral.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))
+
+        # close short
+        if (action == Actions.Long.value or
+                action == Actions.Neutral.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))
+
+        return 0.
+
+# User can inherit and customize 5 action environment
+# class MyRLEnv(Base5ActionRLEnv):
+#     """
+#     User can override any function in BaseRLEnv and gym.Env. Here the user
+#     Adds 5 actions.
+#     """
+
+#     def calculate_reward(self, action):
+
+#         if self._last_trade_tick is None:
+#             return 0.
+
+#         # close long
+#         if action == Actions.Long_sell.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_sell.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_buy.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_buy.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.