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

.github/workflows/test.yml

@@ -19,15 +19,19 @@ jobs:
         shell: bash -l {0}
         run: |
           pytest --junit-xml pytest.xml
+      - name: Download data
+        shell: bash -l {0}
+        run: |
+          python run_fetch_data_5min.py
       - name: Run pipeline
         shell: bash -l {0}
         run: |
           export RAY_DISABLE_MEMORY_MONITOR=1
           python run_pipeline.py
-      - name: Run portfolio reporting
-        shell: bash -l {0}
-        run: |
-          python run_portfolio_reporting.py
+#       - name: Run portfolio reporting
+#         shell: bash -l {0}
+#         run: |
+#           python run_portfolio_reporting.py
       - name: Run inference
         shell: bash -l {0}
         run: |