feat(Inference): Inference now runs on the entire pipeline, only train/predict one asset, adjust trading costs (#173)

* fix, feat: Fixed inference processing data. Add transformation attribute.

* feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop).

* feat: Truncated models over time and transformations over time. Fixed some typing aswell.

* fix: Fixed a number of out of array problems.

* feat: Inference now works!

* fix(Steps): runtime error not checking for None

* fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every

* fix(CI): disable ray memory monitoring

* refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start

* feat(Inference): added index_from parameter

* fix(Tests): walk_forward test

* refactor(Pipeline): only predict one asset

* refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py)

* fix(Evaluation): adjust transaction costs

* fix(Config): adjusted retrain_every

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>

config/preprocess.py

@@ -36,6 +36,9 @@ def __preprocess_data_collections_config(data_dict: dict) -> dict:
     for key in keys:
         preset_names = data_dict[key]
         data_dict[key] = flatten([data_collections[preset_name] for preset_name in preset_names])
+    target_asset = next(iter([asset for asset in data_dict['assets'] if asset[1] == data_dict['target_asset']]), None)
+    if target_asset is None: raise Exception('Target asset wasnt found in assets')
+    data_dict['target_asset'] = target_asset
     return data_dict