reduce freqai testing time by reducing retrain frequency and number of features
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robcaulk
parent
44b042ba51
commit
8855e36f57
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@ -27,10 +27,9 @@ def freqai_conf(default_conf, tmpdir):
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"timerange": "20180110-20180115",
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"freqai": {
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"enabled": True,
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"startup_candles": 10000,
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"purge_old_models": True,
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"train_period_days": 2,
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"backtest_period_days": 2,
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"backtest_period_days": 10,
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"live_retrain_hours": 0,
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"expiration_hours": 1,
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"identifier": "uniqe-id100",
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@ -192,13 +192,13 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
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@pytest.mark.parametrize(
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"model, num_files, strat",
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[
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("LightGBMRegressor", 6, "freqai_test_strat"),
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("XGBoostRegressor", 6, "freqai_test_strat"),
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("CatboostRegressor", 6, "freqai_test_strat"),
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("ReinforcementLearner", 7, "freqai_rl_test_strat"),
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("XGBoostClassifier", 6, "freqai_test_classifier"),
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("LightGBMClassifier", 6, "freqai_test_classifier"),
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("CatboostClassifier", 6, "freqai_test_classifier")
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("LightGBMRegressor", 2, "freqai_test_strat"),
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("XGBoostRegressor", 2, "freqai_test_strat"),
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("CatboostRegressor", 2, "freqai_test_strat"),
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("ReinforcementLearner", 3, "freqai_rl_test_strat"),
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("XGBoostClassifier", 2, "freqai_test_classifier"),
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("LightGBMClassifier", 2, "freqai_test_classifier"),
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("CatboostClassifier", 2, "freqai_test_classifier")
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],
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)
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def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
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@ -305,7 +305,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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freqai.start_backtesting(df, metadata, freqai.dk)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 6
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assert len(model_folders) == 2
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# without deleting the existing folder structure, re-run
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@ -333,7 +333,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
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prediction_files = [x for x in path.iterdir() if x.is_file()]
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assert len(prediction_files) == 5
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assert len(prediction_files) == 1
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shutil.rmtree(Path(freqai.dk.full_path))
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@ -1,57 +1,19 @@
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import logging
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from pathlib import Path
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from typing import Any, Dict
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import numpy as np
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import torch as th
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
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from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
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logger = logging.getLogger(__name__)
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class ReinforcementLearner_test_4ac(BaseReinforcementLearningModel):
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class ReinforcementLearner_test_4ac(ReinforcementLearner):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
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train_df = data_dictionary["train_features"]
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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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policy_kwargs = dict(activation_fn=th.nn.ReLU,
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net_arch=[64, 64])
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if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(
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dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
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**self.freqai_info['model_training_parameters']
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)
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else:
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logger.info('Continual training activated - starting training from previously '
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'trained agent.')
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model = self.dd.model_dictionary[dk.pair]
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model.set_env(self.train_env)
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model.learn(
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total_timesteps=int(total_timesteps),
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callback=self.eval_callback
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)
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if Path(dk.data_path / "best_model.zip").is_file():
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logger.info('Callback found a best model.')
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best_model = self.MODELCLASS.load(dk.data_path / "best_model")
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return best_model
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logger.info('Couldnt find best model, using final model instead.')
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return model
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class MyRLEnv(Base4ActionRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env. Here the user
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@ -19,19 +19,6 @@ class freqai_rl_test_strat(IStrategy):
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minimal_roi = {"0": 0.1, "240": -1}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"prediction": {"prediction": {"color": "blue"}},
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"target_roi": {
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"target_roi": {"color": "brown"},
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},
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"do_predict": {
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"do_predict": {"color": "brown"},
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},
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},
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}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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@ -50,10 +37,7 @@ class freqai_rl_test_strat(IStrategy):
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t = int(t)
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informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
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# FIXME: add these outside the user strategy?
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# The following columns are necessary for RL models.
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informative[f"%-{pair}raw_close"] = informative["close"]
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informative[f"%-{pair}raw_open"] = informative["open"]
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@ -79,9 +63,6 @@ class freqai_rl_test_strat(IStrategy):
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# For RL, there are no direct targets to set. This is filler (neutral)
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# until the agent sends an action.
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df["&-action"] = 0
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