mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-04 14:47:49 +00:00
Mark Aron Szulyovszky
71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
from .types import RawConfig, Config
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def get_default_ensemble_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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"XGB_two_class",
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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=30,
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sliding_window_size=380,
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retrain_every=10,
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scaler="minmax", # 'normalize' 'minmax' 'standardize'
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assets=["daily_crypto"],
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target_asset="BTC_USD",
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other_assets=["daily_etf"],
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exogenous_data=["daily_glassnode"],
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load_non_target_asset=True,
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own_features=["level_2", "date_days", "lags_up_to_5"],
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other_features=["level_2", "lags_up_to_5"],
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exogenous_features=["z_score"],
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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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labeling="two_class",
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forecasting_horizon=100,
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)
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def get_lightweight_ensemble_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=30,
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sliding_window_size=3800,
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retrain_every=1000,
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scaler="minmax", # 'normalize' 'minmax' 'standardize'
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assets=["fivemin_crypto"],
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target_asset="BTC_USD",
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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=["level_1"],
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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="cusum_fixed",
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labeling="two_class",
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forecasting_horizon=50,
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)
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