* fix, feat: Fixed inference processing data. Add transformation attribute.
* feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop).
* feat: Truncated models over time and transformations over time. Fixed some typing aswell.
* fix: Fixed a number of out of array problems.
* feat: Inference now works!
* fix(Steps): runtime error not checking for None
* fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every
* fix(CI): disable ray memory monitoring
* refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start
* feat(Inference): added index_from parameter
* fix(Tests): walk_forward test
* refactor(Pipeline): only predict one asset
* refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py)
* fix(Evaluation): adjust transaction costs
* fix(Config): adjusted retrain_every
Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
* feat(Reporting): added vectorbt-based backtest
* fix(Reporting): added transaction costs
* feat(Reporting): added ability to rebalance only every n days
* feat(Dependencies): added pytorch
* fix(Dependencies): added pytorch-lightning
* feat(CI): added portfolio reporting step
* feat(Reporting): save weights as well
* fix(Reporting): start with less cash
* fix(Evaluation): correlation test should work on a per asset level, not per model level
* fix(Evaluation): correlations series initalized correctly
* fix(Reporting): don't name the run after the incorrectly supposed model_type
* fix(Reporting): put back send_report_to_wandb() into its original place
* fix(CI): sending reports again in comment
* 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
* 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()
Mark Aron Szulyovszky