* 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
* refactor(Evaluate): print out accuracy, f1, etc. for the final & meta predictions, separated out evaluation step
* fix(Linter): ran
* fix(Tests): syntax change
* fix(Inference): runs now again
* fix(Linter): ran
* 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
* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction
* fix(Evaluate): print results
* fix(Evaluate): make sure we have numerical stability in returns
* fix(Inference): only output and print stats in training mode
* fix(Evaluate): don't add miniscule amount to result
* 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
Mark Aron Szulyovszky