mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
9488e92597
* feature(MetaLabeling): added hacky prototype * fix(MetaLabeling): drop index until first valid X & y * fix(MetaLabeling): transform both X & y before feature selection * fix(MetaLabeling): got feature selection to work * fix(MetaLabeling): correct values for meta_y * feat(MetaLabeling): created predictions multiplied by bet sizes * feat(Pipeline): print out averaged result * fix(Evaluation): correctly deal with non-discretized data * fix(Pipeline): use the right column names * refactor(Pipeline): move out meta-labeling * refactor(Pipeline): complete refactoring * feat(CI): post results to PR * fix(Pipeline): use the correct filename * chore(Config): removed now redundant feature_selection flag * feat(Models): added SVC * fix(Pipeline): accidentally switched two return values * feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file * fix(Pipeline): wrong function name * fix(Sweep): yaml + run_sweep * fix(Sweep): typo in name * fix(Reporting): only save averaged results * feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging * feat(Reporting): print out sharpe improvement in meta-labeling step * fix(Sweep): adjusted config, defaulted to good defaults * fix(Sweep): adjusted sweep
59 lines
1.5 KiB
YAML
59 lines
1.5 KiB
YAML
program: run_sweep.py
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method: grid
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project: price-forecasting
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name: Exogenous data / data transformation
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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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meta_labeling_lvl_1:
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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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values: [['daily_glassnode'], []]
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distribution: categorical
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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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n_features_to_select:
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value: 30
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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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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_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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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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exogenous_features:
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values: [[], ['fracdiff'], ['standard_scaling']]
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distribution: categorical
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