* 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
* feat: Added collection of models into a dictionary.
* feat: Models are now saved in a structured way into a dictionary.
* Rename run_model_test.py to run_model_dev.py
* fix(Pipeline): missing variable statement
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
* 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
* feat: Parametricized model selection works now.
* feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute.
* feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file.
* fix: Took out prints and fixed the mismatch of ensemble models when classifing.
* fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem
* fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models`
* feat(Sweep): added `run_sweep.py` shortcut
* feat(Pipeline): skip training a meta model if array is empty
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
Mark Aron Szulyovszky