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()

utils/helpers.py

@@ -0,0 +1,6 @@
+import pandas as pd
+import numpy as np
+
+def get_first_valid_return_index(series: pd.Series) -> int:
+    first_nonzero_return = np.where(np.logical_and(series != 0, np.logical_not(np.isnan(series))))[0][0]
+    return first_nonzero_return