* 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
* refactor(Training): added InferenceResult & TrainedModel types
* refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc.
* fix(Pipeline): getting it to compile
* refactor(WalkForward): separate preprocessing step
* feat(Pipeline): separate out transformations processing step
* refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step
* refactor(WalkForward): moved functions to separate folder
* fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster)
* fix(Tests): and evaluation
* fix(Tests): for realz
* fix(Inference): preloading everything now, renamed primary models to directional models
* fix(BetSizing): was running transformations on the wrong data, oops
* fix(BetSizing): concatenated on the wrong axis accidentally
* fix(Reporting): able to use the new Stats type
* fix(BetSizing): renamed int column names
* fix(Portfolio): name the column properly
* fix(Reporting): rename the correct Series, lol
* fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index
* fix(WalkForward): accidentally using the wrong index
* fix(WalkForward): use the correct indicies to fetch last model/transformations
* fix(CI): changed the name of the results