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

QuantHouse Launches API Addressing MiFID II Needs

Subscribe to our newsletter

Trading solutions and market data services provider QuantHouse has launched an algorithmic trading stress-testing service, in API form, geared toward handling the increase in volume of market information and activity expected once MiFID II regulation takes effect in 2018, according to Stephane Leroy, business co-founder and chief revenue officer at the company.

“The solution enables clients to build stress testing scenarios from two times their highest volume up to ten times peak volume,” says Leroy. “This ensures clients can not only match the MiFID II required thresholds, but through further customisation and using QuantHouses’s flexible API-enabled framework, customers can test and optimise their infrastructure beyond the needs of MiFID II.”

QuantHouse is hosting the new service at an Interxion data centre in London, which improves access for users, according to Leroy. “Firms benefit from being one cross-connect away from being able to meet their algo stress-testing regulatory requirements under MiFID II,” he says.

QuantFeed, the QuantHouse quantitative model testing technology, has been honed for quant traders’ use, which is the basis for the new service, he explains. “Existing capabilities such as storage and back testing in a lab environment have been enhanced with specific features that allow client to stress test processes that mirror specific market conditions,” says Leroy. “This enables customers to test their model against specific market activity peaks and ensures that firms can meet MiFID II requirements.”

MiFID II requires demonstration of capacity and capability to handle market volatility, and QuantHouse’s new API meets those specific requirements, adds Leroy.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Navigating the Build vs Buy Dilemma: Cloud Strategies for Accelerating Quantitative Research

For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the...

BLOG

Seven 2026 RegTech Outlooks for Compliance, Reporting and Financial Crime

As 2026 gets underway, RegTechs are positioning for a shift in regulatory emphasis from refits, rewrites and attestations to demonstrable evidence. Across the jurisdictions supervisors are shifting from consultation and rulemaking into validation and testing whether firms have operationalised reforms through governance, high-quality data, defensible controls and credible evidence. The seven RegTechs that follow have...

EVENT

AI in Data Management Summit New York City

Following the success of the 15th Data Management Summit NYC, A-Team Group are excited to announce our new event: AI in Data Management Summit NYC!

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...