* 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>
* feat(Models): added StaticAverageModel for average ensembling
* feat(Models): made sure we only pipe in predictions to StaticAverageModel, added StaticNaiveModel as potential baseline
* chore(Models): removed unnecessary commented out code
* feat: initial wandb configured. Sweep parameters aren't configured yet.
* feat: Wandb logs now results.
* feat: gitignore.
* fix: Took out print()
* feat: Changed default value of wandb to False.
* feat: Added wandb to turn of automatically if there is no environment variable to start it (when we push it). Added environment configuration aswell.
* feat: Each assets model is seperated into a run that tracks the results.
* fix: Nonetype error, truncated assets.
* fix: Fixed the logging to wandb.
* fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available
* feat(Utils): added get_first_valid_return_index()
* feat(Ensemble): return models from `run_whole_pipeline`
* feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion,
* chore(Pipeline): remove unnecessary extra ensemble results dataframe
* refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file
* feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio
* feat(CI): added environment.yml file
* chore(Environment): update env.yml
* feat(CI): added testing workflow
* fix(CI): renamed enviroment.yml
* fix(Tests): added missing new parameter to walk_forward_train_test()
Daniel Szemerey