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Mark Aron SzulyovszkyandGitHub
442915f847
* feat(Data): create DataSource, DataCollection, added hourly crypto data * fix(Data): hourly data format, loading & config
60 lines
1.5 KiB
YAML
60 lines
1.5 KiB
YAML
program: run_sweep.py
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method: bayes
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project: price-forecasting
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name: Level-1 models
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metric:
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goal: maximize
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name: sharpe
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parameters:
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path :
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value: 'data/'
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expanding_window_level1:
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values: [True, False]
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distribution: categorical
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expanding_window_level2:
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value: False
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feature_selection:
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value: True
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n_features_to_select:
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values: [10, 20, 30]
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distribution: categorical
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dimensionality_reduction:
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value: True
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sliding_window_size_level1:
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values: [180, 280, 380, 480, 580]
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distribution: categorical
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sliding_window_size_level2:
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value: 1
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retrain_every:
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values: [10, 20, 30]
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distribution: categorical
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scaler:
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value: 'minmax'
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include_original_data_in_ensemble:
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value: False
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method:
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value: 'classification'
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no_of_classes:
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values: ['two', 'three-balanced', 'three-imbalanced']
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distribution: categorical
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forecasting_horizon:
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value: 1
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load_non_target_asset:
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values: [True, False]
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distribution: categorical
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log_returns:
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value: True
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index_column:
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value: 'int'
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level_1_models:
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values: [["LR"], ["LDA"], ["KNN"], ["CART"], ["NB"], ["AB"], ["RF"]]
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distribution: categorical
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level_2_model:
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value: None
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own_features:
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values: [['single_mom', 'date_days'], [], ['level_1', 'date_days'], ['date_days', 'level_2']]
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distribution: categorical
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other_features:
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values: [[], ['level_1'], ['level_2']]
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distribution: categorical
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