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add sb3_contrib models to the available agents. include sb3_contrib in requirements.

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This commit is contained in:
robcaulk 2022-08-21 19:58:36 +02:00
parent 8b3a8234ac
commit d88a0dbf82
3 changed files with 35 additions and 25 deletions
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2
freqtrade/freqai/RL/Base5ActionRLEnv.py
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@ -223,12 +223,10 @@ class Base5ActionRLEnv(gym.Env):
(action == Actions.Neutral.value and self._position == Positions.Long) or
(action == Actions.Short_enter.value and self._position == Positions.Short) or
(action == Actions.Short_enter.value and self._position == Positions.Long) or
# (action == Actions.Short_exit.value and self._position == Positions.Short) or
(action == Actions.Short_exit.value and self._position == Positions.Long) or
(action == Actions.Short_exit.value and self._position == Positions.Neutral) or
(action == Actions.Long_enter.value and self._position == Positions.Long) or
(action == Actions.Long_enter.value and self._position == Positions.Short) or
# (action == Actions.Long_exit.value and self._position == Positions.Long) or
(action == Actions.Long_exit.value and self._position == Positions.Short) or
(action == Actions.Long_exit.value and self._position == Positions.Neutral))

54
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -6,6 +6,7 @@ import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from abc import abstractmethod
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.freqai.RL.Base5ActionRLEnv import Base5ActionRLEnv, Actions, Positions
@ -21,6 +22,9 @@ logger = logging.getLogger(__name__)
torch.multiprocessing.set_sharing_strategy('file_system')
SB3_MODELS = ['PPO', 'A2C', 'DQN', 'TD3', 'SAC']
SB3_CONTRIB_MODELS = ['TRPO', 'ARS']
class BaseReinforcementLearningModel(IFreqaiModel):
"""
@ -34,9 +38,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.train_env: Base5ActionRLEnv = None
self.eval_env: Base5ActionRLEnv = None
self.eval_callback: EvalCallback = None
mod = __import__('stable_baselines3', fromlist=[
self.freqai_info['rl_config']['model_type']])
self.MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
self.model_type = self.freqai_info['rl_config']['model_type']
if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3'
elif self.model_type in SB3_CONTRIB_MODELS:
import_str = 'sb3_contrib'
else:
raise OperationalException(f'{self.model_type} not available in stable_baselines3 or '
f'sb3_contrib. please choose one of {SB3_MODELS} or '
f'{SB3_CONTRIB_MODELS}')
mod = __import__(import_str, fromlist=[
self.model_type])
self.MODELCLASS = getattr(mod, self.model_type)
self.policy_type = self.freqai_info['rl_config']['policy_type']
def train(
@ -137,7 +151,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
current_profit = current_value / openrate - 1
total_profit = 0
closed_trades = Trade.get_trades_proxy(pair = pair, is_open=False)
closed_trades = Trade.get_trades_proxy(pair=pair, is_open=False)
for trade in closed_trades:
total_profit += trade.close_profit
@ -223,6 +237,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
def fit(self, data_dictionary: Dict[str, Any], pair: str = '') -> Any:
return
def make_env(env_id: str, rank: int, seed: int, train_df, price,
reward_params, window_size, monitor=False) -> Callable:
"""
@ -244,6 +259,7 @@ def make_env(env_id: str, rank: int, seed: int, train_df, price,
set_random_seed(seed)
return _init
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user
@ -257,26 +273,20 @@ class MyRLEnv(Base5ActionRLEnv):
# 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)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
factor = 2
return float((np.log(current_price) - np.log(last_trade_price)) * factor)
# 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)
last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
factor = 1
if self.close_trade_profit and self.close_trade_profit[-1] > self.profit_aim * self.rr:
factor = 2
return float(np.log(last_trade_price) - np.log(current_price) * factor)
return 0.

4
requirements-freqai.txt
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@ -9,4 +9,6 @@ lightgbm==3.3.2
torch==1.12.1
stable-baselines3==1.6.0
gym==0.21.0
tensorboard==2.9.1
tensorboard==2.9.1
optuna==2.10.1
sb3-contrib==1.6.0