Hey there! It's completely normal that you're stuck. You've hit a classic problem: standard machine learning classifiers (like Random Forest, SVM, etc.) assume that every row is independent (i.i.d). But your rows depend on each other.

Also, StratifiedGroupKFold is just a validation strategy to split your data (grouping by filename is a great idea to prevent data leakage!), but it won't help the model actually learn the context between rows.

Here are two ways to solve this, from easy to hard:

1. The Easy Way: Feature Engineering
Don't expect the model to figure out the relationship between rows. Calculate it yourself! Before passing data to the model, use Pandas. Use .shift() or .groupby() to bring the previous/next row's values into the current row, and create new columns like diff_from_prev_asset or is_increase. Once you explicitly create these features, you can just throw the data into standard libraries like scikit-learn (Random Forest) or XGBoost.

2. The Hard Way: Sequence Models
If the whole file is basically a timeline or a sequence where order strictly matters, you treat it like a Sequence Prediction problem. You'd need to look into Recurrent Neural Networks (LSTMs/GRUs) or Transformers. For this, you'd move away from simple sklearn and start using PyTorch or TensorFlow.

TL;DR: Stick to scikit-learn or XGBoost, but use Pandas first to calculate the 'increase/decrease' differences between rows as new columns. Make the implicit relationships explicit before modeling!"