mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 21:57:46 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
53 lines
1.4 KiB
YAML
53 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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directional_models_meta:
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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_base:
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value: True
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expanding_window_meta:
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value: True
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sliding_window_size_base:
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value: 380
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sliding_window_size_meta:
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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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no_of_classes:
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value: 'two'
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load_non_target_asset:
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value: True
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directional_models:
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distribution: categorical
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values:
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- ["LDA", "LogisticRegression_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LogisticRegression_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
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meta_models:
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value: ["LGBM", "LogisticRegression_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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