program: run_sweep.py
method: grid
project: price-forecasting
name: Exogenous data / data transformation
metric:
  goal: maximize
  name: sharpe
parameters:
    meta_labeling_lvl_1:
      value: True
    assets:
      value: ['daily_crypto']
    other_assets:
      value: ['daily_etf']
    exogenous_data:
      values: [['daily_glassnode'], []]
      distribution: categorical
    expanding_window_level1:
      value: True
    expanding_window_level2:
      value: False
    sliding_window_size_level1: 
      value: 380
    sliding_window_size_level2: 
      value: 1
    n_features_to_select:
      value: 30
    dimensionality_reduction:
      value: True
    retrain_every: 
      value: 20
    scaler:
      value: 'minmax'
    method: 
      value: 'classification'
    no_of_classes:
      value: 'three-balanced'
    forecasting_horizon: 
      value: 1
    load_non_target_asset: 
      value: True
    log_returns: 
      value: True
    index_column: 
      value: 'int'
    level_1_models: 
      value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
    level_2_model: 
      value: "Ensemble_Average"
    own_features:
      values: [['date_days', 'level_2', 'lags_up_to_5'], ['date_days', 'level_2', 'fracdiff']]
      distribution: categorical
    other_features:
      values: [['level_2', 'lags_up_to_5'], ['level_2', 'fracdiff']]
      distribution: categorical
    exogenous_features:
      values: [[], ['fracdiff'], ['standard_scaling']]
      distribution: categorical