* feat(Labeling): purge overlapping events, sort dataframe at loading time
* fix(Linter): ran
* refactor(Labeling): moved purge_overlapping_events one abstraction level higher
* fix(Data): renamed class
* fix(Data): corrected parameter name
* fix(Config): parameters
* fix(Data): fixed path
* fix(Data): uncommented required code
* feat(EventFilters): use vol based CUSUM
* fix(Config): only retrain every 2000 samples
* fix(Config): filter out even more events
* fix(Inference): added remove_overlapping_events
* refactor(Types): simplified type hierarchy
* 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