Most investors track performance, very few track where the performance came from Attribution is the missing layer in most trading systems 💱

In traditional quant workflows, nearly 40% of a model’s “alpha” disappears once you separate signal from noise The real issue is the lack of accountability for each predictor’s contribution.

Signals that aren’t monitored decay fast Research from multiple quant firms shows that untracked signals lose statistical validity within 6–10 weeks in fast-moving markets If you don’t measure decay, you inherit it…

Model hygiene matters: – feature drift – overfitting – correlation spikes – unused weights These are solvable issues, but only if they’re measured Ignoring them turns a good model into a liability.

The systems that win long-term will be the ones that can: track signal contribution remove dead predictors reweight based on live evidence and make every component accountable. That accountability loop is where Yiedl is focused 👉 not just performance, but proof of where it came from.