mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 22:27:47 +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
105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
from .types import RawConfig, Config
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def get_dev_config() -> RawConfig:
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regression_models = ["Lasso"]
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classification_models = ["LogisticRegression_two_class"]
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return RawConfig(
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directional_models_meta = False,
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dimensionality_reduction = False,
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n_features_to_select = 30,
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expanding_window_base = False,
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expanding_window_meta = False,
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sliding_window_size_base = 380,
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sliding_window_size_meta = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
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assets = ['daily_only_btc'],
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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= True,
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own_features = ['level_2', 'date_days'],
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other_features = ['single_mom'],
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exogenous_features = ['z_score'],
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directional_models = classification_models,
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meta_models = [],
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event_filter = 'none',
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labeling = 'two_class'
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)
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def get_default_ensemble_config() -> RawConfig:
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regression_models = ["Lasso", "KNN", "RFR"]
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classification_models = ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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meta_models = ['LogisticRegression_two_class', 'LGBM']
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return RawConfig(
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directional_models_meta = True,
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dimensionality_reduction = False,
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n_features_to_select = 30,
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expanding_window_base = False,
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expanding_window_meta = True,
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sliding_window_size_base = 380,
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sliding_window_size_meta = 240,
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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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)
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def get_lightweight_ensemble_config() -> RawConfig:
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regression_models = ["Lasso", "KNN"]
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classification_models = ['LogisticRegression_two_class', 'SVC']
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meta_models = ['LogisticRegression_two_class', 'LGBM']
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return RawConfig(
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directional_models_meta = True,
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dimensionality_reduction = True,
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n_features_to_select = 30,
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expanding_window_base = False,
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expanding_window_meta = True,
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sliding_window_size_base = 380,
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sliding_window_size_meta = 240,
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retrain_every = 40,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
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assets = ['daily_crypto_lightweight'],
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target_asset = 'BCH_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' ],
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other_features = ['level_2'],
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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 = 'none',
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labeling = 'two_class'
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)
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