program: rnn_sweep.py
method: bayes
project: integer-sequence
name: Finding best hyperparameters for price prediction
early_terminate:
  type: hyperband
  min_iter: 2000
metric:
  goal: maximize
  name: sharpe
parameters:
    path : 'data/'
    sliding_window_size: 
      values: [50, 90, 130, 160, 180, 280, 380, 500]
      distribution: categorical
    retrain_every: 
      values: [7, 14, 30, 60, 100]
    scaler:
      values: ['minmax', 'normalize', 'minmax', 'standardize', 'none']
    include_original_data_in_ensemble:
      values: [True, False]
    method: 
      values: ['classification', 'regression']
    forecasting_horizon: 
      values: [1,2,3,4,5,6,7,8,9,10]