mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
9d47ee942d
* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba * fix(WalkForward): inference mini-batch parallelization * fix(WalkForward): don't use the parallel version of any of the functions * feat(CI): download the data required * fix(Project): 5min_crypto folder added * fix(Evaluate): make sure we have numerical stability in returns * feat(Models): use SKLearn models directly to enable composability * feat(Inference): batched inference now working, added forecasting_horizon * fix(Inference): works again * fix(Inference) * chore(Models): remove unused Ensemble model * fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then * Update test.yml
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
from .types import RawConfig, Config
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def get_dev_config() -> RawConfig:
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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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forecasting_horizon = 100,
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)
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def get_default_ensemble_config() -> RawConfig:
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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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forecasting_horizon = 100,
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)
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def get_lightweight_ensemble_config() -> RawConfig:
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classification_models = ['LogisticRegression_two_class', 'LGBM']
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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 = True,
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expanding_window_meta = True,
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sliding_window_size_base = 3800,
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sliding_window_size_meta = 2400,
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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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