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drift/config/sweep_ensemble.yaml
T

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YAML

program: run_sweep.py
method: grid
project: price-forecasting
name: Meta labelling
metric:
goal: maximize
name: sharpe
parameters:
assets:
value: ['daily_crypto']
other_assets:
value: ['daily_etf']
exogenous_data:
value: ['daily_glassnode']
initial_window_size:
value: 380
distribution: categorical
n_features_to_select:
values: [40, 50, 60]
distribution: categorical
dimensionality_reduction_ratio:
value: 0.5
retrain_every:
value: 20
scaler:
value: 'minmax'
no_of_classes:
value: 'two'
load_non_target_asset:
value: True
directional_models:
distribution: categorical
values:
- ["LDA", "LogisticRegression_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
- ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
- ["LogisticRegression_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
meta_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']