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

models/momentum.py

@@ -1,16 +1,15 @@
 from __future__ import annotations
-from models.base import Model
 import numpy as np
+from .base import Model
+from sklearn.base import BaseEstimator, ClassifierMixin
 
-class StaticMomentumModel(Model):
+class StaticMomentumModel(BaseEstimator, ClassifierMixin, Model):
     '''
     Model that uses only one feature: momentum. It's positive if momentum is greater than 0, otherwise it's negative.
     '''
 
-    method = 'classification'
     data_transformation = 'original'
     only_column = 'mom'
-    model_type = 'static'
     predict_window_size = 'single_timestamp'
 
     def __init__(self, allow_short: bool) -> None:
@@ -21,13 +20,10 @@ class StaticMomentumModel(Model):
         # This is a static model, it can' learn anything
         pass
 
-    def predict(self, X) -> tuple[float, np.ndarray]:
+    def predict(self, X) -> np.ndarray:
         negative_class = -1.0 if self.allow_short == True else 0.0
         prediction = 1.0 if X[-1][0] > 0 else negative_class
-        return (prediction, np.array([]))
-
-    def clone(self) -> StaticMomentumModel:
-        return self
-
-    def initialize_network(self, input_dim:int, output_dim:int):
-        pass
+        return np.array(prediction)
+    
+    def predict_proba(self, X) -> np.ndarray:
+        return np.array([])