feat(Labeling): purge overlapping events, sort dataframe at loading time (#226)

* feat(Labeling): purge overlapping events, sort dataframe at loading time

* fix(Linter): ran

* refactor(Labeling): moved purge_overlapping_events one abstraction level higher

* fix(Data): renamed class

* fix(Data): corrected parameter name

* fix(Config): parameters

* fix(Data): fixed path

* fix(Data): uncommented required code

* feat(EventFilters): use vol based CUSUM

* fix(Config): only retrain every 2000 samples

* fix(Config): filter out even more events

* fix(Inference): added remove_overlapping_events

* refactor(Types): simplified type hierarchy

config/presets.py

@@ -17,7 +17,7 @@ def get_default_config() -> RawConfig:
         dimensionality_reduction_ratio=0.5,
         n_features_to_select=50,
         sliding_window_size=3800,
-        retrain_every=1000,
+        retrain_every=2000,
         scaler="minmax",  # 'normalize' 'minmax' 'standardize' 'robust'
         assets=["fivemin_crypto"],
         target_asset="BTCUSDT",
@@ -29,9 +29,11 @@ def get_default_config() -> RawConfig:
         exogenous_features=[],
         directional_models=classification_models,
         meta_models=meta_models,
-        event_filter="cusum_fixed",
+        event_filter="cusum_vol",
+        remove_overlapping_events=False,
         labeling="two_class",
-        forecasting_horizon=50,
+        forecasting_horizon=10,
+        transaction_costs=0.002,
         save_models=True,
         ensembling_method="voting_soft",
     )

config/types.py

@@ -26,8 +26,10 @@ class RawConfig(BaseModel):
     other_features: list[str]
     exogenous_features: list[str]
     event_filter: Literal["none", "cusum_vol", "cusum_fixed"]
+    remove_overlapping_events: bool
     labeling: Literal["two_class", "three_class_balanced", "three_class_imbalanced"]
     forecasting_horizon: int
+    transaction_costs: float
     save_models: bool
     ensembling_method: Literal["voting_soft", "stacking"]
 
@@ -49,8 +51,10 @@ class Config:
     other_features: list[tuple[str, FeatureExtractor, list[int]]]
     exogenous_features: list[tuple[str, FeatureExtractor, list[int]]]
     event_filter: EventFilter
+    remove_overlapping_events: bool
     labeling: EventLabeller
     forecasting_horizon: int
+    transaction_costs: float
     no_of_classes: Literal["two", "three-balanced", "three-imbalanced"]
     save_models: bool