feat: implement training and deployment pipeline for LightGBM model on Mac to LXC

- Added comprehensive plans for training a LightGBM model on M4 Mac Mini and deploying it to an LXC container.
- Created scripts for model training, deployment, and a full pipeline execution.
- Enhanced model transfer with error handling and logging for better tracking.
- Introduced profiling for training time analysis and dataset generation optimization.

Made-with: Cursor

scripts/train_model.py

@@ -69,7 +69,7 @@ def _process_index(args: tuple) -> dict | None:
     ind = Indicators(window)
     df_ind = ind.calculate_all()
 
-    if df_ind.isna().any().any():
+    if df_ind.iloc[-1].isna().any():
         return None
 
     signal = ind.get_signal(df_ind)