* feat(HPO): added `run_hpo` script
* fix(Linter): ran
* feat(HPO): removed any reference to sweep (superseeded by optuna)
* fix(HPO): optimize for sharpe
* fix(Config): removed glassnode data, save trials from hpo
* feat(Labelling): added three-balanced method works again
* fix(BetSizing): set the correct class labels
* fix(HPO): powerset should return what's expected, added two new normalization methods
* fix(Linter): ran
* fix(DataLoader): sort the dataframe when fetching data
* fix(Config): only take z-score of other assets
* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba
* fix(WalkForward): inference mini-batch parallelization
* fix(WalkForward): don't use the parallel version of any of the functions
* feat(CI): download the data required
* fix(Project): 5min_crypto folder added
* fix(Evaluate): make sure we have numerical stability in returns
* feat(Models): use SKLearn models directly to enable composability
* feat(Inference): batched inference now working, added forecasting_horizon
* fix(Inference): works again
* fix(Inference)
* chore(Models): remove unused Ensemble model
* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then
* Update test.yml
Mark Aron Szulyovszky