* 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
* feat(Transformations): removed feature-selection pre-processing step completely
* fix(Core): removed unnecessary `original_X`
* fix(Transformations): use the X_expanding_window to transform subsequent data
* fix(RFE): should check for model correctly
* fix(Config): only re-train the model every 40 timestamp
* fix(MetaLabeling): pass in the correct X to meta-labeling step
* fix(Transformation): PCA should at least keep as many features as sliding_window_size
* feat(Transformations): cache transformations across the same asset
* fix(Tests): missing preloaded_transformations arg
* chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference()
* fix(WalkForward): use Dataframes to call Transformation.fit_transform()
* feat(WalkForward): restored option for models to recieve unscaled data
* fix(Transformations): output DataFrame as expected
* fix(Tests): missing new property
* feat(Models): added lightGBM, moved other models to separate files
* feat(Models): added non-working statsmodel wrapper
* fix(Models): added work-in-progress comment to StatsModels
Mark Aron Szulyovszky