program: run_sweep.py
method: bayes
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/'
    expanding_window:
      values: [True, False]
      distribution: categorical
    sliding_window_size: 
      values: [180, 280, 380]
      distribution: categorical
    retrain_every: 
      values: [10, 20, 30]
      distribution: categorical
    scaler:
      values: ['minmax', 'none']
      distribution: categorical
    include_original_data_in_ensemble:
      values: False
    method: 
      value: 'classification'
    no_of_classes:
      values: ['two', 'three-balanced', 'three-imbalanced']
      distribution: categorical
    forecasting_horizon: 
      values: 1
      distribution: categorical
    load_other_assets: 
      values: [True, False]
      distribution: categorical
    log_returns: 
      value: True
    index_column: 
      value: 'int'
    level_1_models: 
      values: [["LR"], ["LDA"], ["KNN"], ["CART"], ["NB"], ["AB"], ["RF"]]
      distribution: categorical
    level_2_models: 
      value: []
    own_features:
      values: [['only_mom', 'date_days'], [], ['level_1', 'date_days'], ['level_1', 'date_days', 'level_2']]
      distribution: categorical
    other_features:
      value: []