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()

README.md

@@ -1,5 +1,5 @@
-# Price prediction models
+# Financial time series prediction models
 
 ## Installation
 
-Make sure you use keras `2.6.0`, otherwise you won't be able to train models.
+Use the conda environment file attached!:)