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/config.py

@@ -15,6 +15,7 @@ def get_dev_config() -> tuple[dict, dict, dict]:
 
     data_config = dict(
         assets = ['daily_only_btc'],
+        target_asset = 'BTC_USD',
         other_assets = [],
         exogenous_data = [],
         load_non_target_asset= True,
@@ -52,12 +53,13 @@ def get_default_ensemble_config() -> tuple[dict, dict, dict]:
         expanding_window_meta_labeling = True,
         sliding_window_size_primary = 380,
         sliding_window_size_meta_labeling = 240,
-        retrain_every = 40,
+        retrain_every = 10,
         scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
     )
 
     data_config = dict(
         assets = ['daily_crypto'],
+        target_asset = 'BTC_USD',
         other_assets = ['daily_etf'],
         exogenous_data = ['daily_glassnode'],
         load_non_target_asset= True,
@@ -103,6 +105,7 @@ def get_lightweight_ensemble_config() -> tuple[dict, dict, dict]:
 
     data_config = dict(
         assets = ['daily_crypto_lightweight'],
+        target_asset = 'BTC_USD',
         other_assets = ['daily_etf'],
         exogenous_data = ['daily_glassnode'],
         load_non_target_asset= True,