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Mark Aron SzulyovszkyandGitHub
5c4a5b0cf1
* refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classes later) * fix(Training): removed mistakenly left in `method` parameter
55 lines
1.4 KiB
YAML
55 lines
1.4 KiB
YAML
program: run_sweep.py
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method: grid
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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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primary_models_meta_labeling:
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value: True
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assets:
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value: ['daily_crypto']
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other_assets:
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value: ['daily_etf']
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exogenous_data:
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value: ['daily_glassnode']
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expanding_window_primary:
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values: [True, False]
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distribution: categorical
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expanding_window_meta_labeling:
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value: False
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n_features_to_select:
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value: 50
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dimensionality_reduction:
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value: True
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sliding_window_size_primary:
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value: 380
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sliding_window_size_meta_labeling:
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value: 380
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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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no_of_classes:
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value: 'two'
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forecasting_horizon:
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value: 1
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load_non_target_asset:
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value: True
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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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primary_models:
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values: [['LogisticRegression_two_class'], ['SVC'], ['LDA'], ['KNN'], ['CART'], ['MNB'], ['NB'], ['AB'], ['RFC'], ['XGB_two_class'], ['LGBM']]
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distribution: categorical
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meta_labeling_models:
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value: ["LGBM", "LogisticRegression_two_class"]
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own_features:
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value: ['date_days', 'level_2', 'lags_up_to_5']
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other_features:
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value: ['level_2', 'lags_up_to_5']
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exogenous_features:
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value: ['z_score'] |