reduce code for base use-case, ensure multiproc inherits custom env, add ability to limit ram use.

config_examples/config_freqai-rl.example.json

@@ -58,6 +58,7 @@
         "model_save_type": "stable_baselines",
         "conv_width": 4,
         "purge_old_models": true,
+        "limit_ram_usage": false,
         "train_period_days": 5,
         "backtest_period_days": 2,
         "identifier": "unique-id",

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -19,6 +19,7 @@ from typing import Callable
 from datetime import datetime, timezone
 from stable_baselines3.common.utils import set_random_seed
 import gym
+from pathlib import Path
 logger = logging.getLogger(__name__)
 
 torch.multiprocessing.set_sharing_strategy('file_system')
@@ -110,9 +111,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
-        self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
+        self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
                                       reward_kwargs=self.reward_params, config=self.config)
-        self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
+        self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test,
                                 window_size=self.CONV_WIDTH,
                                 reward_kwargs=self.reward_params, config=self.config))
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
@@ -126,7 +127,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         go in here. Abstract method, so this function must be overridden by
         user class.
         """
-
         return
 
     def get_state_info(self, pair: str):
@@ -232,38 +232,22 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
         return prices_train, prices_test
 
-    # TODO take care of this appendage. Right now it needs to be called because FreqAI enforces it.
+    def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
-    # But FreqaiRL needs more objects passed to fit() (like DK) and we dont want to go refactor
-    # all the other existing fit() functions to include dk argument. For now we instantiate and
-    # leave it.
-    def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
-        return
-
-
-def make_env(env_id: str, rank: int, seed: int, train_df: DataFrame, price: DataFrame,
-             reward_params: Dict[str, int], window_size: int, monitor: bool = False,
-             config: Dict[str, Any] = {}) -> Callable:
         """
-    Utility function for multiprocessed env.
+        Can be used by user if they are trying to limit_ram_usage *and*
-
+        perform continual learning.
-    :param env_id: (str) the environment ID
+        For now, this is unused.
-    :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:
+        exists = Path(dk.data_path / f"{dk.model_filename}_model").is_file()
+        if exists:
+            model = self.MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
+        else:
+            logger.info('No model file on disk to continue learning from.')
 
-        env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
+        return model
-                      reward_kwargs=reward_params, id=env_id, seed=seed + rank, config=config)
-        if monitor:
-            env = Monitor(env)
-        return env
-    set_random_seed(seed)
-    return _init
 
-
+    # Nested class which can be overridden by user to customize further
-class MyRLEnv(Base5ActionRLEnv):
+    class MyRLEnv(Base5ActionRLEnv):
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
@@ -296,7 +280,8 @@ class MyRLEnv(Base5ActionRLEnv):
                 factor *= 0.5
 
             # discourage sitting in position
-        if self._position in (Positions.Short, Positions.Long) and action == Actions.Neutral.value:
+            if self._position in (Positions.Short, Positions.Long) and \
+               action == Actions.Neutral.value:
                 return -1 * trade_duration / max_trade_duration
 
             # close long
@@ -312,3 +297,35 @@ class MyRLEnv(Base5ActionRLEnv):
                 return float(rew * factor)
 
             return 0.
+
+    # TODO take care of this appendage. Right now it needs to be called because FreqAI enforces it.
+    # But FreqaiRL needs more objects passed to fit() (like DK) and we dont want to go refactor
+    # all the other existing fit() functions to include dk argument. For now we instantiate and
+    # leave it.
+    def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
+        return
+
+
+def make_env(MyRLEnv: Base5ActionRLEnv, env_id: str, rank: int,
+             seed: int, train_df: DataFrame, price: DataFrame,
+             reward_params: Dict[str, int], window_size: int, monitor: bool = False,
+             config: Dict[str, Any] = {}) -> 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, config=config)
+        if monitor:
+            env = Monitor(env)
+        return env
+    set_random_seed(seed)
+    return _init

freqtrade/freqai/data_drawer.py

@@ -90,6 +90,7 @@ class FreqaiDataDrawer:
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
                 "priority": 1, "first": True, "data_path": "", "extras": {}}
+        self.limit_ram_use = self.freqai_info.get('limit_ram_usage', False)
 
     def load_drawer_from_disk(self):
         """
@@ -423,7 +424,7 @@ class FreqaiDataDrawer:
                 dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
             )
 
-        # if self.live:
+        if not self.limit_ram_use:
             self.model_dictionary[coin] = model
         self.pair_dict[coin]["model_filename"] = dk.model_filename
         self.pair_dict[coin]["data_path"] = str(dk.data_path)
@@ -464,7 +465,7 @@ class FreqaiDataDrawer:
 
         model_type = self.freqai_info.get('model_save_type', 'joblib')
         # try to access model in memory instead of loading object from disk to save time
-        if dk.live and coin in self.model_dictionary:
+        if dk.live and coin in self.model_dictionary and not self.limit_ram_use:
             model = self.model_dictionary[coin]
         elif model_type == 'joblib':
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
@@ -486,7 +487,7 @@ class FreqaiDataDrawer:
             )
 
         # load it into ram if it was loaded from disk
-        if coin not in self.model_dictionary:
+        if coin not in self.model_dictionary and not self.limit_ram_use:
             self.model_dictionary[coin] = model
 
         if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -3,12 +3,12 @@ from typing import Any, Dict
 
 import torch as th
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Positions
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 from pathlib import Path
-from pandas import DataFrame
+# from pandas import DataFrame
-from stable_baselines3.common.callbacks import EvalCallback
+# from stable_baselines3.common.callbacks import EvalCallback
-from stable_baselines3.common.monitor import Monitor
+# from stable_baselines3.common.monitor import Monitor
 import numpy as np
 
 logger = logging.getLogger(__name__)
@@ -53,26 +53,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         return model
 
-    def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
+    class MyRLEnv(BaseReinforcementLearningModel.MyRLEnv):
-                                        prices_train: DataFrame, prices_test: DataFrame,
-                                        dk: FreqaiDataKitchen):
-        """
-        User can override this if they are using a custom MyRLEnv
-        """
-        train_df = data_dictionary["train_features"]
-        test_df = data_dictionary["test_features"]
-
-        self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
-                                 reward_kwargs=self.reward_params, config=self.config)
-        self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
-                                window_size=self.CONV_WIDTH,
-                                reward_kwargs=self.reward_params, config=self.config))
-        self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
-                                          render=False, eval_freq=len(train_df),
-                                          best_model_save_path=str(dk.data_path))
-
-
-class MyRLEnv(Base5ActionRLEnv):
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
@@ -105,7 +86,8 @@ class MyRLEnv(Base5ActionRLEnv):
                 factor *= 0.5
 
             # discourage sitting in position
-        if self._position in (Positions.Short, Positions.Long) and action == Actions.Neutral.value:
+            if self._position in (Positions.Short, Positions.Long) and \
+                    action == Actions.Neutral.value:
                 return -1 * trade_duration / max_trade_duration
 
             # close long

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -34,7 +34,7 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
                                     **self.freqai_info['model_training_parameters']
                                     )
         else:
-            logger.info('Continual training activated - starting training from previously '
+            logger.info('Continual learning activated - starting training from previously '
                         'trained agent.')
             model = self.dd.model_dictionary[dk.pair]
             model.tensorboard_log = Path(dk.data_path / "tensorboard")
@@ -65,13 +65,14 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
 
         env_id = "train_env"
         num_cpu = int(self.freqai_info["rl_config"]["thread_count"] / 2)
-        self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
+        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
                                         self.reward_params, self.CONV_WIDTH,
                                         config=self.config) for i
                                         in range(num_cpu)])
 
         eval_env_id = 'eval_env'
-        self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
+        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+                                                test_df, prices_test,
                                                 self.reward_params, self.CONV_WIDTH, monitor=True,
                                                 config=self.config) for i
                                        in range(num_cpu)])