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refactor(Naming): use new convention, added Ensemble model parameter back, support multiple Meta-Labeling models (#132)

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* refactor(Naming): use `primary_models` & `meta_labeling_models`

* refactor(Naming): using primary * meta_labeling across config and in pipeline

* feat(Pipeline): added back Ensemble models

* fix(Pipeline): compiler error

* fix(Config): typo

* chore(Pipeline): removed unused averaging step

* revert the changes in discretizing

* chore(Pipeline): remove sharpe improvement logging

* fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame

* fix(Pipeline): discard unnecessary ensemble_probabilities

* fix(Pipeline): fixes regarding various meta-labeling ensemble bugs

* fix(Reporting): use the new naming convention

* fix(Reporting): use the right variable

* feat(Sweep): new sweep for ensemble models

* fix(Sweep): config reference

* fix(Config): simplified dev config

* fix(Models): use the faster LR model

* fix(Models): use LGBM in the meta-labeling model for speed

* fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
This commit is contained in:
Mark Aron Szulyovszky
2022-01-09 17:21:06 +01:00
committed by GitHub
parent 22b3167cb9
commit b1c04afb13
20 changed files with 202 additions and 239 deletions
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run_feature_selection.py
+2 -2
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@@ -1,12 +1,12 @@
# #%%
# import pandas as pd
# import pandas_ta as ta
# from config.config import get_default_level_2_daily_config
# from config.config import get_default_ensemble_config
# from config.preprocess import preprocess_config
# from data_loader.load_data import load_data
# # %%
# model_config, training_config, data_config = get_default_level_2_daily_config()
# model_config, training_config, data_config = get_default_ensemble_config()
# model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)
# data_config['target_asset'] = data_config['assets'][0]