feat(Model): added own Model class, SkLearnModel wrapper and StaticMomentumModel (#61)

* feat(Model): added own `Model` class, SkLearnModel wrapper and StaticMomentumModel

* fix(Tests): added missing Model variable

* fix(Tests): added missing clone method()

models/base.py

@@ -0,0 +1,38 @@
+
+from typing import Literal, Optional
+from sklearn.base import clone
+
+class Model:
+
+    # data_format: Literal['dataframe', 'numpy'] 
+    data_scaling: Literal["scaled", "unscaled"]
+    # data_format: Literal["wide", "narrow"]
+    only_column: Optional[str]
+
+    def fit(self, X, y):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def clone(self):
+        pass
+
+
+class SKLearnModel(Model):
+
+    # data_format = 'numpy'
+    data_scaling = 'scaled'
+    only_column = None
+
+    def __init__(self, model):
+        self.model = model
+
+    def fit(self, X, y):
+        self.model.fit(X, y)
+
+    def predict(self, X):
+        return self.model.predict(X)
+    
+    def clone(self):
+        return SKLearnModel(clone(self.model))

models/momentum.py

@@ -0,0 +1,27 @@
+from models.base import Model
+import numpy as np
+
+class StaticMomentumModel(Model):
+    '''
+    Model that uses only one feature: momentum. It's positive if momentum is greater than 0, otherwise it's negative.
+    '''
+
+    data_format = 'dataframe'
+    data_scaling = 'unscaled'
+    only_column = 'mom'
+
+    def __init__(self, allow_short: bool) -> None:
+        super().__init__()
+        self.allow_short = allow_short
+
+    def fit(self, X, y):
+        # This is a static model, it can' learn anything
+        pass
+
+    def predict(self, X):
+        negative_class = -1.0 if self.allow_short == True else 0.0
+        prediction = 1.0 if X[-1][0] > 0 else negative_class
+        return np.array([prediction])
+
+    def clone(self):
+        return self