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drift/sweep_level_2.yaml
T
2022-01-03 13:57:36 +01:00

66 lines
1.8 KiB
YAML

program: run_sweep.py
method: bayes
project: price-forecasting
name: Level-2 models
metric:
goal: maximize
name: sharpe
parameters:
assets:
value: ['daily_crypto']
other_assets:
value: ['daily_etf']
exogenous_data:
value: ['daily_glassnode']
expanding_window_level1:
values: [True, False]
distribution: categorical
expanding_window_level2:
values: [True, False]
distribution: categorical
feature_selection:
value: True
n_features_to_select:
values: [10, 20, 30]
distribution: categorical
dimensionality_reduction:
value: True
sliding_window_size_level1:
values: [180, 280, 380]
distribution: categorical
sliding_window_size_level2:
values: [180, 280, 380]
distribution: categorical
retrain_every:
values: [10, 20, 30]
distribution: categorical
scaler:
value: 'minmax'
include_original_data_in_ensemble:
values: [True, False]
distribution: categorical
method:
value: 'classification'
no_of_classes:
values: ['two', 'three-balanced', 'three-imbalanced']
distribution: categorical
forecasting_horizon:
value: 1
load_non_target_asset:
values: [True, False]
distribution: categorical
log_returns:
value: True
index_column:
value: 'int'
level_1_models:
value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
level_2_model:
values: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "Ensemble_Average"]
distribution: categorical
own_features:
values: [['single_mom', 'date_days'], [], ['level_1', 'date_days'], ['date_days', 'level_2']]
distribution: categorical
other_features:
values: [[], ['level_1']]
distribution: categorical