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drift/sweep_dynamic_features.yaml
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YAML

program: run_sweep.py
method: grid
project: price-forecasting
name: Meta labelling
metric:
goal: maximize
name: sharpe
parameters:
dynamic_feature_selection:
values: [True, False]
distribution: 'categorical'
meta_labeling_lvl_1:
value: True
assets:
value: ['daily_crypto']
other_assets:
value: ['daily_etf']
exogenous_data:
value: ['daily_glassnode']
expanding_window_level1:
values: [False, True]
distribution: 'categorical'
expanding_window_level2:
value: True
sliding_window_size_level1:
value: 380
sliding_window_size_level2:
value: 380
n_features_to_select:
values: [30, 50, 70, 80]
distribution: 'categorical'
dimensionality_reduction:
value: True
retrain_every:
value: 20
scaler:
value: 'minmax'
method:
value: 'classification'
no_of_classes:
value: 'two'
forecasting_horizon:
value: 1
load_non_target_asset:
value: True
log_returns:
value: True
index_column:
value: 'int'
level_1_models:
value: ["LDA", "KNN", "SVC", "CART", "NB", "AB", "RF", "XGB_two_class", "StaticMom"]
level_2_model:
values: ["LDA", "XGB_two_class"]
distribution: categorical
own_features:
value: ['date_days', 'level_2', 'lags_up_to_5']
other_features:
value: ['level_2', 'lags_up_to_5']
exogenous_features:
value: ['standard_scaling']