ensure long only RL is tested
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c293401b22
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@ -27,17 +27,19 @@ def is_mac() -> bool:
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return "Darwin" in machine
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@pytest.mark.parametrize('model, pca, dbscan, float32', [
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('LightGBMRegressor', True, False, True),
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('XGBoostRegressor', False, True, False),
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('XGBoostRFRegressor', False, False, False),
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('CatboostRegressor', False, False, False),
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('ReinforcementLearner', False, True, False),
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('ReinforcementLearner_multiproc', False, False, False),
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('ReinforcementLearner_test_3ac', False, False, False),
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('ReinforcementLearner_test_4ac', False, False, False)
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@pytest.mark.parametrize('model, pca, dbscan, float32, can_short', [
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('LightGBMRegressor', True, False, True, True),
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('XGBoostRegressor', False, True, False, True),
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('XGBoostRFRegressor', False, False, False, True),
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('CatboostRegressor', False, False, False, True),
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('ReinforcementLearner', False, True, False, True),
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('ReinforcementLearner_multiproc', False, False, False, True),
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('ReinforcementLearner_test_3ac', False, False, False, False),
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('ReinforcementLearner_test_3ac', False, False, False, True),
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('ReinforcementLearner_test_4ac', False, False, False, True)
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])
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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32):
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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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dbscan, float32, can_short):
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if is_arm() and model == 'CatboostRegressor':
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pytest.skip("CatBoost is not supported on ARM")
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@ -59,9 +61,6 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
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freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
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if 'test_3ac' in model or 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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if 'ReinforcementLearner' in model:
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model_save_ext = 'zip'
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freqai_conf = make_rl_config(freqai_conf)
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@ -78,6 +77,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.can_short = can_short
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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freqai.dk.set_paths('ADA/BTC', 10000)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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