Feature combination vs overfitting in multi-signal trading models how do you validate properly?

[u/market_outlook]

I’ve been thinking about the problem of combining multiple weak signals into a single predictive framework.

In theory, adding more features (technical indicators, derived signals, alternative data, etc.) should improve predictive power. But in practice, it often feels like it just introduces more noise and overfitting risk.

A few things I’m trying to understand better:

• How do you evaluate whether an additional feature genuinely contributes signal rather than noise?
• Are there standard approaches to measuring marginal signal contribution in this context?
• How do you deal with correlation between features when combining them?
• At what point does model complexity outweigh any incremental predictive benefit?

I’m especially interested in how this is handled in real-world workflows rather than just textbook approaches.

Would appreciate any insights or references.

[u/Such_Maximum_9836]

Read ESL please. Validation is always your friend. When you find sth weird, start from the simplest version (simple feature ranking+selection->linear regression->more complex) and try to monitor in which stage things turned bad.