program: run_sweep.py
method: bayes
project: price-forecasting
name: Level-2 models
metric:
  goal: maximize
  name: sharpe
parameters:
    path : 
      value: 'data/'
    expanding_window_level1:
      values: [True, False]
      distribution: categorical
    expanding_window_level2:
      values: [True, False]
      distribution: categorical
    feature_selection:
      value: True
    n_features_to_select:
      values: [10, 20, 30]
      distribution: categorical
    dimensionality_reduction:
      value: True
    sliding_window_size_level1: 
      values: [180, 280, 380]
      distribution: categorical
    sliding_window_size_level2: 
      values: [180, 280, 380]
      distribution: categorical
    retrain_every: 
      values: [10, 20, 30]
      distribution: categorical
    scaler:
      value: 'minmax'
    include_original_data_in_ensemble:
      values: [True, False]
      distribution: categorical
    method: 
      value: 'classification'
    no_of_classes:
      values: ['two', 'three-balanced', 'three-imbalanced']
      distribution: categorical
    forecasting_horizon: 
      value: 1
    load_other_assets: 
      values: [True, False]
      distribution: categorical
    log_returns: 
      value: True
    index_column: 
      value: 'int'
    level_1_models: 
      value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
    level_2_model: 
      values: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "Ensemble_Average"]
      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