feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference() (#161)

* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference()

* fix(WalkForward): use Dataframes to call Transformation.fit_transform()

* feat(WalkForward): restored option for models to recieve unscaled data

* fix(Transformations): output DataFrame as expected

* fix(Tests): missing new property

models/average.py

@@ -7,7 +7,7 @@ class StaticAverageModel(Model):
     Model that averages .
     '''
 
-    data_scaling = 'unscaled'
+    data_transformation = 'original'
     only_column = 'model_'
     feature_selection = 'off'
     model_type = 'static'

models/base.py

@@ -7,7 +7,7 @@ import numpy as np
 
 class Model(ABC):
 
-    data_scaling: Literal["scaled", "unscaled"]
+    data_transformation: Literal["transformed", "original"]
     feature_selection: Literal["on", "off"]
     # data_format: Literal["wide", "narrow"]
     only_column: Optional[str]

models/momentum.py

@@ -7,7 +7,7 @@ class StaticMomentumModel(Model):
     Model that uses only one feature: momentum. It's positive if momentum is greater than 0, otherwise it's negative.
     '''
 
-    data_scaling = 'unscaled'
+    data_transformation = 'original'
     only_column = 'mom'
     feature_selection = 'off'
     model_type = 'static'

models/naive.py

@@ -7,7 +7,7 @@ class StaticNaiveModel(Model):
     Model that carries the last observation (from returns) to the next one, naively.
     '''
 
-    data_scaling = 'unscaled'
+    data_transformation = 'original'
     only_column = None
     feature_selection = 'off'
     model_type = 'static'

models/neural.py

@@ -7,7 +7,7 @@ import pytorch_lightning as pl
 
 class LightningNeuralNetModel(Model):
 
-    data_scaling = 'scaled'
+    data_transformation = 'transformed'
     only_column = None
     feature_selection = 'off'
     model_type = 'ml'

models/sklearn.py

@@ -6,7 +6,7 @@ from sklearn.base import clone
 
 class SKLearnModel(Model):
 
-    data_scaling = 'scaled'
+    data_transformation = 'transformed'
     only_column = None
     feature_selection = 'on'
     model_type = 'ml'

models/statsmodels.py

@@ -8,7 +8,7 @@ from copy import deepcopy
 class StatsModel(Model):
 
     # This is work in progress
-    data_scaling = 'scaled'
+    data_transformation = 'transformed'
     only_column = None
     feature_selection = 'on'
     model_type = 'ml'

models/xgboost.py

@@ -6,7 +6,7 @@ from sklearn.base import clone
 
 class XGBoostModel(Model):
 
-    data_scaling = 'scaled'
+    data_transformation = 'transformed'
     only_column = None
     feature_selection = 'on'
     model_type = 'ml'