Recent posts by Mona

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Posts about Quantitative Trading Systems:

How Granularity in Model Monitoring Saves Quants from Costly Mistakes

If you’ve ever been responsible for monitoring models at a quant hedge fund, you know how tricky it can be. One minute everything looks fine; the next, you’re in a full-blown fire drill because of a performance issue you didn’t catch in time. In other cases, you learn way too late that there was something simple you could have changed that would have improved your results significantly, or some specific area that was completely overlooked. But why does this happen? The problem often comes down to granularity—or rather, the lack of it.

The Need for Specialized Monitoring in Quantitative Trading Models

The Need for Specialized Monitoring in Quantitative Trading Models

In the world of automated trading, quantitative (quant) models are at the core of decision-making. These models analyze vast datasets to execute trades at speeds and volumes that far exceed human capability. However, ensuring that these models consistently perform well requires effective monitoring. Traditional approaches to performance management, which focus on broad financial metrics, IT infrastructure, and standard machine learning (ML) monitoring, are often insufficient. Specialized, deep monitoring is necessary to truly understand how these models behave and to maintain their effectiveness over time.