mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 21:57:46 +00:00
Mark Aron Szulyovszky
9488e92597
* feature(MetaLabeling): added hacky prototype * fix(MetaLabeling): drop index until first valid X & y * fix(MetaLabeling): transform both X & y before feature selection * fix(MetaLabeling): got feature selection to work * fix(MetaLabeling): correct values for meta_y * feat(MetaLabeling): created predictions multiplied by bet sizes * feat(Pipeline): print out averaged result * fix(Evaluation): correctly deal with non-discretized data * fix(Pipeline): use the right column names * refactor(Pipeline): move out meta-labeling * refactor(Pipeline): complete refactoring * feat(CI): post results to PR * fix(Pipeline): use the correct filename * chore(Config): removed now redundant feature_selection flag * feat(Models): added SVC * fix(Pipeline): accidentally switched two return values * feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file * fix(Pipeline): wrong function name * fix(Sweep): yaml + run_sweep * fix(Sweep): typo in name * fix(Reporting): only save averaged results * feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging * feat(Reporting): print out sharpe improvement in meta-labeling step * fix(Sweep): adjusted config, defaulted to good defaults * fix(Sweep): adjusted sweep
129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
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def get_default_level_1_daily_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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meta_labeling_lvl_1 = True,
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dimensionality_reduction = True,
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n_features_to_select = 30,
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expanding_window_level1 = False,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 380,
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sliding_window_size_level2 = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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)
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data_config = dict(
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assets = ['daily_crypto'],
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other_assets = [],
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exogenous_data = [],
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load_non_target_asset= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days'],
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other_features = ['single_mom'],
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exogenous_features = ['standard_scaling'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced',
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narrow_format = False,
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)
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regression_models = ["Lasso"]
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classification_models = ["KNN"]
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_model = None
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)
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return model_config, training_config, data_config
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def get_default_level_2_hourly_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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meta_labeling_lvl_1 = True,
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dimensionality_reduction = True,
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n_features_to_select = 30,
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expanding_window_level1 = True,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 2480,
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sliding_window_size_level2 = 1,
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retrain_every = 100,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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)
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data_config = dict(
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assets = ['hourly_crypto'],
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other_assets = [],
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exogenous_data = [],
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load_non_target_asset= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days', 'lags_up_to_5'],
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other_features = ['level_2'],
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exogenous_features = ['standard_scaling'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced',
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narrow_format = False,
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)
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regression_models = ["Lasso", "KNN", "RF"]
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regression_ensemble_model = 'KNN'
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classification_models = ["LDA", "KNN", "CART", "RF", "StaticMom"]
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classification_ensemble_model = 'Ensemble_Average'
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_model = regression_ensemble_model if data_config['method'] == 'regression' else classification_ensemble_model
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)
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return model_config, training_config, data_config
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def get_default_level_2_daily_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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meta_labeling_lvl_1 = True,
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dimensionality_reduction = True,
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n_features_to_select = 30,
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expanding_window_level1 = False,
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expanding_window_level2 = True,
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sliding_window_size_level1 = 380,
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sliding_window_size_level2 = 240,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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)
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data_config = dict(
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assets = ['daily_crypto'],
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other_assets = ['daily_etf'],
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exogenous_data = ['daily_glassnode'],
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load_non_target_asset= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days', 'lags_up_to_5'],
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other_features = ['level_2', 'lags_up_to_5'],
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exogenous_features = ['standard_scaling'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'two',
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narrow_format = False,
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)
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regression_models = ["Lasso", "KNN", "RF"]
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regression_ensemble_model = 'KNN'
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classification_models = ['SVC', 'LDA', 'KNN', 'CART', 'NB', 'AB', 'RF', 'StaticMom']
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classification_ensemble_model = 'LDA'
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_model = regression_ensemble_model if data_config['method'] == 'regression' else classification_ensemble_model
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)
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return model_config, training_config, data_config
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