mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
from .types import RawConfig
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def get_default_config() -> RawConfig:
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classification_models = [
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"LogisticRegression_two_class",
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"LDA",
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"NB",
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"RFC",
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"LGBM",
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# "StaticMom",
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]
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meta_models = ["LogisticRegression_two_class", "LGBM"]
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return RawConfig(
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dimensionality_reduction_ratio=0.5,
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n_features_to_select=50,
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initial_window_size=3800,
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retrain_every=2000,
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scaler="minmax", # 'normalize' 'minmax' 'standardize' 'robust'
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assets=["fivemin_crypto"],
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target_asset="BTCUSDT",
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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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own_features=["level_2"],
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other_features=["z_score"],
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exogenous_features=[],
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directional_models=classification_models,
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meta_models=meta_models,
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event_filter="cusum_vol",
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event_filter_multiplier=3.5,
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remove_overlapping_events=False,
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labeling="two_class",
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forecasting_horizon=10,
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transaction_costs=0.002,
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save_models=True,
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ensembling_method="voting_soft",
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)
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