program: run_sweep.py
method: bayes
project: price-forecasting
name: Level-2 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_primary:
      values: [True, False]
      distribution: categorical
    expanding_window_meta_labeling:
      values: [True, False]
      distribution: categorical
    n_features_to_select:
      values: [10, 20, 30]
      distribution: categorical
    dimensionality_reduction:
      value: True
    sliding_window_size_primary: 
      values: [180, 280, 380]
      distribution: categorical
    sliding_window_size_meta_labeling: 
      values: [180, 280, 380]
      distribution: categorical
    retrain_every: 
      values: [10, 20, 30]
      distribution: categorical
    scaler:
      value: 'minmax'
    method: 
      value: 'classification'
    no_of_classes:
      values: ['two', 'three-balanced', 'three-imbalanced']
      distribution: categorical
    forecasting_horizon: 
      value: 1
    load_non_target_asset: 
      values: [True, False]
      distribution: categorical
    log_returns: 
      value: True
    index_column: 
      value: 'int'
    primary_models: 
      value: ["LR_two_class", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
    meta_labeling_models: 
      values: ["LR_two_class", "LDA", "KNN", "CART", "NB", "AB", "RF"]
      distribution: categorical
    own_features:
      values: [['single_mom', 'date_days'], [], ['level_1', 'date_days'], ['date_days', 'level_2']]
      distribution: categorical
    other_features:
      values: [[], ['level_1']]
      distribution: categorical