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31dc847be1
* feat: Added optional import of models. * fix: Models weren't wrapped into abstract class, fixed it. * chore: Deleted leftover comments. * fix: Same merge commit as on remote. * fix: System wasn't putting in RF because there was no differentiation between RF as regressor and RF as classificator. * fix(Models): use the XGBoostModel wrapper Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
61 lines
1.6 KiB
YAML
61 lines
1.6 KiB
YAML
program: run_sweep.py
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method: grid
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project: price-forecasting
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name: Meta labelling
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metric:
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goal: maximize
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name: sharpe
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parameters:
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primary_models_meta_labeling:
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value: True
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assets:
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value: ['daily_crypto']
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other_assets:
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value: ['daily_etf']
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exogenous_data:
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value: ['daily_glassnode']
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expanding_window_primary:
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value: True
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expanding_window_meta_labeling:
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value: True
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sliding_window_size_primary:
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value: 380
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sliding_window_size_meta_labeling:
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values: [250, 300, 380]
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distribution: categorical
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n_features_to_select:
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values: [40, 50, 60]
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distribution: categorical
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dimensionality_reduction:
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value: True
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retrain_every:
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value: 20
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scaler:
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value: 'minmax'
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method:
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value: 'classification'
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no_of_classes:
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value: 'two'
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forecasting_horizon:
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value: 1
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load_non_target_asset:
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value: True
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log_returns:
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value: True
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index_column:
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value: 'int'
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primary_models:
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distribution: categorical
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values:
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- ["LDA", "LR_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LR_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LR_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
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meta_labeling_models:
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value: ["LGBM", "LR_two_class"]
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own_features:
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value: ['date_days', 'level_2', 'lags_up_to_5']
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other_features:
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value: ['level_2', 'lags_up_to_5']
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exogenous_features:
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value: ['z_score']
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