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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
f762ceed2a
* feat(FeatureExtraction): added fractionally differentiated returns to remove lagged returns * fix(Sweep): config * fix(Sweep): name * fix(Sweep): grid * feat(Config): separated sliding_window_size_level1 & sliding_window_size_level2 * feat(Dependencies): added ray, now using it to parallel process feature extraction * fix(Dependencies): added pip explicitly * fix(Dependencies): removed ray from root * fix(Models): average model was probably not taking the right timestamp to average * feat(Config): separated expanding_window_level1 & expanding_window_level2 * fix(Config): set n_features_to_select to the optimal 30
54 lines
1.3 KiB
YAML
54 lines
1.3 KiB
YAML
program: run_sweep.py
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method: grid
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project: price-forecasting
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name: Fractional differentiation / number of features
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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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value: True
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expanding_window_level2:
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value: False
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sliding_window_size_level1:
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value: 380
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sliding_window_size_level2:
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value: 1
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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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retrain_every:
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value: 20
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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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value: 'three-balanced'
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forecasting_horizon:
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value: 1
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load_other_assets:
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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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level_1_models:
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value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
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level_2_model:
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value: "Ensemble_Average"
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own_features:
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values: [['date_days', 'level_2', 'lags_up_to_5'], ['date_days', 'level_2', 'fracdiff']]
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distribution: categorical
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other_features:
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values: [['level_2', 'lags_up_to_5'], ['level_2', 'fracdiff']]
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distribution: categorical
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