feat(Core): ensemble models, correct forward returns calculation, scaling, only train from when asset returns are available, major bug fixed in walk_forward_train_test (#35)

* fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available

* feat(Utils): added get_first_valid_return_index()

* feat(Ensemble): return models from `run_whole_pipeline`

* feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion,

* chore(Pipeline): remove unnecessary extra ensemble results dataframe

* refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file

* feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio

* feat(CI): added environment.yml file

* chore(Environment): update env.yml

* feat(CI): added testing workflow

* fix(CI): renamed enviroment.yml

* fix(Tests): added missing new parameter to walk_forward_train_test()

tests/test_walk_forward.py

@@ -1,20 +1,21 @@
 import pytest
 import numpy as np
 import pandas as pd
-from utils.walk_forward import walk_forward_train_test
+from training.walk_forward import walk_forward_train_test
 from sklearn.base import BaseEstimator
 
-def __generate_test_data():
+no_of_rows = 100
+
+def __generate_test_data(no_of_rows):
     no_columns = 6
-    no_rows = 100
-    X = [[row] * no_columns for row in range(no_rows)]
+    X = [[row] * no_columns for row in range(no_of_rows)]
     assert X[0][0] == 0
     assert X[1][0] == 1
     assert X[2][0] == 2
     assert X[3][0] == 3
     X = pd.DataFrame(X)
 
-    y = [row+1 for row in range(no_rows)]
+    y = [row+1 for row in range(no_of_rows)]
     assert y[0] == 1
     assert y[1] == 2
     assert y[2] == 3
@@ -24,7 +25,7 @@ def __generate_test_data():
 
 
 def test_walk_forward_train_test():
-    X, y = __generate_test_data()
+    X, y = __generate_test_data(no_of_rows)
 
     window_length = 10
     class StubModel(BaseEstimator):
@@ -38,4 +39,16 @@ def test_walk_forward_train_test():
             return np.array([X[0][0] + 1])
 
     model = StubModel()
-    walk_forward_train_test('test', model, X, y, window_length, 10)
+    scaler = None
+    models, predictions = walk_forward_train_test(
+        model_name='test',
+        model=model,
+        X=X,
+        y=y,
+        target_returns=y,
+        window_size=window_length,
+        retrain_every=10,
+        scaler=scaler)
+    for i in range(window_length, no_of_rows):
+        predictions[i] == y[i]
+