Refractor(Main Pipeline): Refractored the two main steps and the data processing. (#156)

* refr: Took out main primary and secondary loops and data processing.

* feat: Tidied the code up.

* feat: Saving models and results now works in a type safe way.

* fix: There was error in the saving function.

* chore: Took out some remaining comments.

* fix: Fixed the previous data checking process.

* feat: Fixed model selection method. I will continue the inference after we merged.

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>

utils/encapsulation.py

@@ -0,0 +1,38 @@
+import pandas as pd
+from models.base import Model
+
+# | Reporting
+# |
+
+
+class Single_Model:
+    def __init__(self, model_name:str, model_over_time:list[Model]):
+        self.model_name: str = model_name
+        self.model_over_time: list[Model] = model_over_time
+
+
+class Training_Step: 
+    def __init__(self, level:str):
+        self.level:str = level
+        self.base: list[Single_Model] = []
+        self.metalabeling: list[list[Single_Model]] = []
+
+
+class Asset():
+    def __init__(self, ticker:str, primary: Training_Step, secondary: Training_Step):
+        self.name:str = ticker
+        self.primary:Training_Step = primary
+        self.secondary:Training_Step = secondary 
+
+
+class Reporting:
+    def __init__(self):
+        self.results:pd.DataFrame = pd.DataFrame() 
+        self.all_predictions:pd.DataFrame = pd.DataFrame() 
+        self.all_probabilities:pd.DataFrame = pd.DataFrame() 
+        self.all_assets:list[Asset] = []
+    
+    def get_results(self)->tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, list[Asset]]:
+        return self.results, self.all_predictions, self.all_probabilities, self.all_assets
+    
+