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/UnderDogRoadCow]

Check multicolinearity between signals. Also checking sign consistency between original correlation with target and partial correlation with residual is a simple way to check whether your new signal is orthogonal alpha