* feat: Basic scaffolding up for inference process after training.
* feat: Saving and loading models works. Inference works nearly.
* feat: Added inference pipeline.
* feat: Saving model now accoring to date and time; loading models now selects from latest file. Fixed the creation of dictionary of models.
* feat: Added lightweight asset config, but full pipeline.
* feat: Added new naming for dictionary.
* fix: Fixed dictionary naming convention.
* fix: Fixed naming again, now the model structure is good
* fix: Changed the output path and the return values from run_pipeline.
* feat: Added function to make sure folder exists for output models.
Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
* 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
Daniel Szemerey