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()

environment.yml

@@ -0,0 +1,20 @@
+name: quant
+channels:
+  - johnsnowlabs
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.9
+  - seaborn
+  - scikit-learn-intelex
+  - ipython
+  - scipy
+  - scikit-learn
+  - numba
+  - pytorch
+  - matplotlib
+  - numpy
+  - quantstats
+  - pytorch-lightning
+  - pytest
+prefix: /usr/local/anaconda3/envs/quant