Add 3ac test

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Emre 2022-12-16 22:31:44 +03:00
parent 7727f31507
commit a8c9aa01fb
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2 changed files with 68 additions and 2 deletions

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@ -34,6 +34,7 @@ def is_mac() -> bool:
('CatboostRegressor', False, False, False), ('CatboostRegressor', False, False, False),
('ReinforcementLearner', False, True, False), ('ReinforcementLearner', False, True, False),
('ReinforcementLearner_multiproc', False, False, False), ('ReinforcementLearner_multiproc', False, False, False),
('ReinforcementLearner_test_3ac', False, False, False),
('ReinforcementLearner_test_4ac', False, False, False) ('ReinforcementLearner_test_4ac', False, False, False)
]) ])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32): def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
@ -58,7 +59,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True}) freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True}) freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_4ac' in model: if 'test_3ac' in model or 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
if 'ReinforcementLearner' in model: if 'ReinforcementLearner' in model:
@ -68,7 +69,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True}) freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True}) freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_4ac' in model: if 'test_3ac' in model or 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
strategy = get_patched_freqai_strategy(mocker, freqai_conf) strategy = get_patched_freqai_strategy(mocker, freqai_conf)

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@ -0,0 +1,65 @@
import logging
import numpy as np
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.Base3ActionRLEnv import Actions, Base3ActionRLEnv, Positions
logger = logging.getLogger(__name__)
class ReinforcementLearner_test_3ac(ReinforcementLearner):
"""
User created Reinforcement Learning Model prediction model.
"""
class MyRLEnv(Base3ActionRLEnv):
"""
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: int) -> float:
# 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.Buy.value, Actions.Sell.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 # type: ignore
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
or (action == Actions.Sell.value and self._position == Positions.Short)
or (action == Actions.Buy.value and self._position == Positions.Long)
):
return -1 * trade_duration / max_trade_duration
# close position
if (action == Actions.Buy.value and self._position == Positions.Short) or (
action == Actions.Sell.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)
return 0.