feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

* 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

mycodemod/replace_functions.py

@@ -22,7 +22,7 @@ class ReplaceFunctionCommand(VisitorBasedCodemodCommand):
 
     def leave_FunctionDef(self, original_node: cst.FunctionDef, updated_node: cst.FunctionDef) -> cst.FunctionDef:
         functions_docstring = updated_node.get_docstring()
-        docstring_should_be = '"""No docstring here yet."""'
+        docstring_should_be = ''
         if functions_docstring is not None:
             docstring_should_be = '"""\n{}\n\n"""'.format(functions_docstring)