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

This commit is contained in:
robcaulk 2022-08-25 19:05:51 +02:00
parent 05ccebf9a1
commit 3199eb453b
5 changed files with 125 additions and 123 deletions
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1
config_examples/config_freqai-rl.example.json
View File

@ -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",

129
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -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,
reward_kwargs=self.reward_params, config=self.config)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
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(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):
@ -232,6 +232,72 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return prices_train, prices_test
def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
"""
Can be used by user if they are trying to limit_ram_usage *and*
perform continual learning.
For now, this is unused.
"""
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.')
return model
# Nested class which can be overridden by user to customize further
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.
"""
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.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)
return float(rew * 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)
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
@ -240,7 +306,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
return
def make_env(env_id: str, rank: int, seed: int, train_df: DataFrame, price: DataFrame,
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:
"""
@ -252,6 +319,7 @@ def make_env(env_id: str, rank: int, seed: int, train_df: DataFrame, price: Data
:param rank: (int) index of the subprocess
:return: (Callable)
"""
def _init() -> gym.Env:
env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
@ -261,54 +329,3 @@ def make_env(env_id: str, rank: int, seed: int, train_df: DataFrame, price: Data
return env
set_random_seed(seed)
return _init
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.
"""
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.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)
return float(rew * 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)
return float(rew * factor)
return 0.

9
freqtrade/freqai/data_drawer.py
View File

@ -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):
"""
@ -423,8 +424,8 @@ class FreqaiDataDrawer:
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
)
# if self.live:
self.model_dictionary[coin] = model
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)
self.save_drawer_to_disk()
@ -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"]:

102
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -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 stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
# from pandas import DataFrame
# from stable_baselines3.common.callbacks import EvalCallback
# from stable_baselines3.common.monitor import Monitor
import numpy as np
logger = logging.getLogger(__name__)
@ -53,71 +53,53 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
return model
def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
prices_train: DataFrame, prices_test: DataFrame,
dk: FreqaiDataKitchen):
class MyRLEnv(BaseReinforcementLearningModel.MyRLEnv):
"""
User can override this if they are using a custom MyRLEnv
User can override any function in BaseRLEnv and gym.Env. Here the user
sets a custom reward based on profit and trade duration.
"""
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))
def calculate_reward(self, action):
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
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.
"""
pnl = self.get_unrealized_profit()
rew = np.sign(pnl) * (pnl + 1)
factor = 100
def calculate_reward(self, action):
# 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
# first, penalize if the action is not valid
if not self._is_valid(action):
return -2
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
pnl = self.get_unrealized_profit()
rew = np.sign(pnl) * (pnl + 1)
factor = 100
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# 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
# discourage sitting in position
if self._position in (Positions.Short, Positions.Long) and \
action == Actions.Neutral.value:
return -1 * trade_duration / max_trade_duration
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
trade_duration = self._current_tick - self._last_trade_tick
# 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)
return float(rew * factor)
if trade_duration <= max_trade_duration:
factor *= 1.5
elif trade_duration > max_trade_duration:
factor *= 0.5
# 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)
return float(rew * factor)
# 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.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)
return float(rew * 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)
return float(rew * factor)
return 0.
return 0.

7
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -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)])