About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

ITRS Adds Analytic Framework for Data Integrity to Geneos Suite

Subscribe to our newsletter

ITRS Group is piloting an analytic framework for data integrity that is designed to give investment banks, brokers and exchanges a real-time view across pricing data sourced from multiple market data feeds.

A production version of the Geneos Market Data Monitor (MDM) will be released on 20 February, offering users predefined relative price algorithms – as well as an environment to develop their own algos – that compare the content and latency of feeds from vendors, ensure data integrity and contribution consistency, and flag up events such as data gaps and spikes.

The Geneos MDM module is built on existing ITRS Geneos monitoring technology, including a configurable dashboard for real-time alerts. It includes a library of feed adapters for market data from firms including NYSE, Trading Technologies, GL Trade, Thomson Reuters and Bloomberg, and an open application programming interface based on the open source LUA scripting language that supports rapid algo development and performance.

Ian Salmon, head of business development at ITRS, says regulatory scrutiny is driving the need for robust processes around pricing, particularly derived pricing. These processes need to be auditable, demonstrable and automated, yet allow human intervention and action when necessary. He explains: “Whether an institution is consuming or publishing market data, the integrity, accuracy and consistency of prices in real time is paramount. By using MDM’s tick-by-tick analytics, our users are able to compare the behaviour of a price feed, perhaps a Spot FX currency pair, against custom analytic calculations. When an anomaly is flagged, the Geneos dashboard proactively alerts the appropriate team, allowing it to track, audit and resolve the issue before it has an impact on the market.”

As well as an environment for algo development, the MDM framework includes a toolkit to build custom interfaces to proprietary pricing feeds. The solution can be deployed in-house or provided as a managed solution by ITRS. Salmon concludes: “We are running three pilots of MDM as well as proofs of concept with potential users. Most interest is coming from investment banks and the brokerage community, with specific interest being in checking contributed data from interdealer brokers that create and distribute prices.”

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: The Role of Data Fabric and Data Mesh in Modern Trading Infrastructures

The demands on trading infrastructure are intensifying. Increasing data volumes, the necessity for real-time processing, and stringent regulatory requirements are exposing the limitations of legacy data architectures. In response, firms are re-evaluating their data strategies to improve agility, scalability, and governance. Two architectural models central to this conversation are Data Fabric and Data Mesh. This...

BLOG

Glimpse Markets Partners with Boltzbit to Embed Live-learning AI into Fixed Income Workflows

Glimpse Markets, the buy-side data sharing network focused on the cash bond markets, has partnered with Boltzbit, the deeptech AI company, to embed live-learning, agent-based AI directly into its buy-side bond data-sharing platform, as part of a multi-phase integration programme set to begin in early 2026. Rather than positioning AI as a downstream analytics layer,...

EVENT

Data Management Summit London

Now in its 16th year, the Data Management Summit (DMS) in London brings together the European capital markets enterprise data management community, to explore how data strategy is evolving to drive business outcomes and speed to market in changing times.

GUIDE

The Reference Data Utility Handbook

The potential of a reference data utility model has been discussed for many years, and while early implementations failed to gain traction, the model has now come of age as financial institutions look for new data management models that can solve the challenges of operational cost reduction, improved data quality and regulatory compliance. The multi-tenanted...