mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 13:17:47 +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
57 lines
1.3 KiB
YAML
57 lines
1.3 KiB
YAML
program: run_sweep.py
|
|
method: grid
|
|
project: price-forecasting
|
|
name: Meta labelling
|
|
metric:
|
|
goal: maximize
|
|
name: sharpe
|
|
parameters:
|
|
meta_labeling_lvl_1:
|
|
values: [True, False]
|
|
distribution: categorical
|
|
assets:
|
|
value: ['daily_crypto']
|
|
other_assets:
|
|
value: ['daily_etf']
|
|
exogenous_data:
|
|
value: ['daily_glassnode']
|
|
expanding_window_level1:
|
|
value: False
|
|
expanding_window_level2:
|
|
value: True
|
|
sliding_window_size_level1:
|
|
value: 380
|
|
sliding_window_size_level2:
|
|
value: 380
|
|
n_features_to_select:
|
|
value: 30
|
|
dimensionality_reduction:
|
|
value: True
|
|
retrain_every:
|
|
value: 20
|
|
scaler:
|
|
value: 'minmax'
|
|
method:
|
|
value: 'classification'
|
|
no_of_classes:
|
|
value: 'two'
|
|
forecasting_horizon:
|
|
value: 1
|
|
load_non_target_asset:
|
|
value: True
|
|
log_returns:
|
|
value: True
|
|
index_column:
|
|
value: 'int'
|
|
level_1_models:
|
|
value: ["LDA", "KNN", "SVC", "CART", "NB", "AB", "RF", "StaticMom"]
|
|
level_2_model:
|
|
values: ["LDA", "KNN", "NB", "AB", "RF", "XGB_two_class"]
|
|
distribution: categorical
|
|
own_features:
|
|
value: ['date_days', 'level_2', 'lags_up_to_5']
|
|
other_features:
|
|
value: ['level_2', 'lags_up_to_5']
|
|
exogenous_features:
|
|
value: ['standard_scaling']
|