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']