News & Insights

Quantlab was reviewed by Nicholas Burgess, an independent quantitative researcher. His assessment explores performance, architecture, and the role of consistency in modern quant infrastructure.

A faster RFQ pipeline does more than accelerate quotes. It aligns pricing and risk within the same market snapshot.

When message rates surge, consistency matters more than speed. Because a fast quote built on stale data is still wrong.

If your yield curve needs “a small tweak”, you don’t have a curve problem. You have a framework problem.

Fast risk is meaningless if the market data behind it is slow. Speed only matters when price and risk share the same moment in time.

If you can’t explain why a curve looks the way it does, you can’t really trust it. In many institutions, bootstrapping still happens inside black boxes. That works — right up until it doesn’t.

We were saddened to learn of the passing of John C. Hull, whose work has shaped modern quantitative finance more than almost any other individual.

Most curve engines fail for reasons that have nothing to do with maths. Curve construction isn’t a mathematical problem. It’s an engineering discipline.

Low-latency pricing only works if your market view is up to date. In fixed income, even millisecond-level curve latency can quietly distort prices, risk metrics and limit PnL. Not because models are slow, but because inputs are stale. Speed without coherence is not an edge.