feat(Data): added various data loading config options, walk forward method draft (#9)

* feat(Eval): added format_data_for_backtest()

* feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO

* feat(Core): added walk forward method of training/testing

* fix(Model): remove the unnecessary softmax activation from the keras models

* feat(Core): added walk_forward_train_test()

utils/evaluate.py

@@ -1,6 +1,8 @@
 from math import sqrt
 from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
 from sklearn.metrics import confusion_matrix
+import pandas as pd
+
 def print_regression_metrics(y_true, y_pred):
     rmse = sqrt(mean_squared_error(y_true, y_pred))
     print("RMSE: %.2f" % rmse)
@@ -10,4 +12,13 @@ def print_regression_metrics(y_true, y_pred):
 
 def print_classification_metrics(y_true, y_pred):
     print("Accuracy: %.2f" % accuracy_score(y_true, y_pred))
-    print("Confusion Matrix: \n", confusion_matrix(y_true, y_pred))
+    print("Confusion Matrix: \n", confusion_matrix(y_true, y_pred))
+
+def format_data_for_backtest(aggregated_data: pd.DataFrame, returns_col: str, only_test_data: pd.DataFrame, preds) -> pd.DataFrame:
+    backtest_data = aggregated_data.iloc[-only_test_data.shape[0]:].copy()[returns_col]
+    assert backtest_data.shape[0] == only_test_data.shape[0]
+    backtest_data = backtest_data.reset_index(drop=True)    
+    return pd.concat([backtest_data, pd.Series(preds)], axis='columns')
+
+
+# def backtest()