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

training/types.py

@@ -19,33 +19,26 @@ class TrainingOutcome:
     stats: Optional[Stats]
     model_over_time: ModelOverTime
 
-@dataclass
-class EnsembleOutcome:
-    weights: WeightsSeries
-    stats: Optional[Stats]
-
 @dataclass
 class BetSizingWithMetaOutcome:
     model_id: str
-    meta_training: list[TrainingOutcome]
+    meta_training: TrainingOutcome
     meta_transformations: TransformationsOverTime
     weights: WeightsSeries
     stats: Optional[Stats]
 
 @dataclass
 class DirectionalTrainingOutcome:
-    training: list[TrainingOutcome]
+    training: TrainingOutcome
     transformations: TransformationsOverTime
     
 @dataclass
 class PipelineOutcome:
     directional_training: DirectionalTrainingOutcome
-    bet_sizing: list[BetSizingWithMetaOutcome]
-    ensemble: EnsembleOutcome
-    secondary_bet_sizing: Optional[BetSizingWithMetaOutcome]
+    bet_sizing: BetSizingWithMetaOutcome
 
     def get_output_weights(self) -> WeightsSeries:
-        return self.secondary_bet_sizing.weights if self.secondary_bet_sizing else self.ensemble.weights
+        return self.bet_sizing.weights
 
-    def get_output_stats(self) -> Optional[Stats]:
-        return self.secondary_bet_sizing.stats if self.secondary_bet_sizing else self.ensemble.stats
+    def get_output_stats(self) -> Stats:
+        return self.bet_sizing.stats