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drift/config/sweep_primary_models.yaml
T
2022-01-26 23:22:43 +01:00

49 lines
1.3 KiB
YAML

program: run_sweep.py
method: grid
project: price-forecasting
name: Level-1 models
metric:
goal: maximize
name: sharpe
parameters:
primary_models_meta_labeling:
value: True
assets:
value: ['daily_crypto']
other_assets:
value: ['daily_etf']
exogenous_data:
value: ['daily_glassnode']
expanding_window_base:
values: [True, False]
distribution: categorical
expanding_window_meta_labeling:
value: False
n_features_to_select:
value: 50
dimensionality_reduction:
value: True
sliding_window_size_base:
value: 380
sliding_window_size_meta_labeling:
value: 380
retrain_every:
values: [10, 20, 30]
distribution: categorical
scaler:
value: 'minmax'
no_of_classes:
value: 'two'
load_non_target_asset:
value: True
primary_models:
values: [['LogisticRegression_two_class'], ['SVC'], ['LDA'], ['KNN'], ['CART'], ['MNB'], ['NB'], ['AB'], ['RFC'], ['XGB_two_class'], ['LGBM']]
distribution: categorical
meta_labeling_models:
value: ["LGBM", "LogisticRegression_two_class"]
own_features:
value: ['date_days', 'level_2', 'lags_up_to_5']
other_features:
value: ['level_2', 'lags_up_to_5']
exogenous_features:
value: ['z_score']