program: run_pipeline.py
method: grid
project: price-forecasting
name: Finding best hyperparameters for price prediction
# early_terminate:
#   type: hyperband
#   min_iter: 2000
# metric:
#   goal: maximize
#   name: sharpe
parameters:
    path : 
      value: 'data/'
    sliding_window_size: 
      values: [50, 90, 130, 160, 180, 280, 380, 500]
      distribution: categorical
    retrain_every: 
      values: [7, 14, 30, 60, 100]
      distribution: categorical
    scaler:
      values: ['minmax', 'normalize', 'minmax', 'standardize', 'none']
      distribution: categorical
    include_original_data_in_ensemble:
      value: True
    method: 
      value: 'classification'
    forecasting_horizon: 
      values: [1,2,3,4,5,6,7,8,9,10]
      distribution: categorical
    load_other_assets: 
      value: False
    log_returns: 
      value: True
    own_features: 
      value: []
    other_features: 
      value: []
    index_column: 
      value: 'int'
    level_1_models: 
      value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF"]
      distribution: constant
    level_2_models: 
      value: ['Ensemble_CART']
      distribution: constant