mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-05 15:17:46 +00:00
Mark Aron Szulyovszky
b1c04afb13
* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
56 lines
1.4 KiB
YAML
56 lines
1.4 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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value: 380
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n_features_to_select:
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value: 50
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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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value: ["LDA", "KNN", "SVC", "CART", "NB", "AB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
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meta_labeling_models:
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values: [["LDA"], ["XGB_two_class"], ["LR_two_class"], ["LGBM"], ["LGBM", "LR_two_class"], ["XGB_two_class", "LDA"], ["XGB_two_class", "LR_two_class"]]
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distribution: categorical
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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: ['standard_scaling']
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