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

utils/resample.py

@@ -0,0 +1,9 @@
+import pandas as pd
+
+def resample_ohlc(df, period):
+    output = pd.DataFrame()
+    output['open'] = df.open.resample(period).first()
+    output['high'] = df.high.resample(period).max()
+    output['low'] = df.low.resample(period).min()
+    output['close'] = df.close.resample(period).last()
+    return output