mirror of
https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
5482e3fc95
* refactor(Training): remove non-expanding window option * feat(Config): added `start_date` property * fix(Inference): added start_date here as well * fix(Linter): ran
81 lines
2.2 KiB
Python
81 lines
2.2 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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start_date=None,
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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_1"],
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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=True,
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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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def get_minimal_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,
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n_features_to_select=0,
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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=False,
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own_features=[],
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other_features=[],
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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="none",
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event_filter_multiplier=3.5,
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remove_overlapping_events=True,
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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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