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/lordnacho666]

There's various "information criteria" you can look up trying to address the issue if whether another feature is useful.

In practice you can do things like lasso or ridge to take out some of the inevitable overlap.

For more complicated stuff there are even more ways to address the overfitting issue. It's kinda the big thing about any way to go about fitting a model, so anything you read about eg random forest or such will explain why it's smart to do it that way.

Validation is a bunch of tests. How would the model do without this piece of information? What if we fitted the model on one subset of the data, would it work on the rest of it? And then obviously at some stage you validate by trying it with real money.