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/evaluate.py

@@ -49,7 +49,7 @@ def evaluate_predictions(
         return len(series[series != 0])
     no_of_samples = count_non_zero(df.y_pred)
     scorecard['no_of_samples'] = no_of_samples
-    sharpe = sharpe_ratio(df.result)
+    sharpe = sharpe_ratio(df.result + 1e-20)
     scorecard['sharpe'] = sharpe
     benchmark_sharpe = sharpe_ratio(df.forward_returns)
     scorecard['benchmark_sharpe'] = benchmark_sharpe

utils/parallel.py

@@ -0,0 +1,15 @@
+from tqdm import tqdm
+import ray
+
+def parallel_compute_with_bar(computations) -> list:
+
+    def to_iterator(obj_ids):
+        while obj_ids:
+            done, obj_ids = ray.wait(obj_ids)
+            yield ray.get(done[0])
+
+    ret = []
+    for x in tqdm(to_iterator(computations), total=len(computations)):
+        ret.append(x)
+
+    return ret

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