Best trading technology for FX – Quod Financial

While transaction cost analysis (TCA) tools and other data collection solutions have garnered a lot of attention in recent years, there remains a significant disconnect between the amount of available data and how market participants can utilise that data to improve trade execution in a meaningful way.

“There’s a deep problem with TCA,” points out Medan Gabbay, chief revenue officer at Quod Financial. “If you give a trader a TCA report, what do they do with it? How do they know what will provide them with a better execution outcome for a EUR/CHF trade at 2:00pm on a Thursday? The TCA might tell them a given trade is good or bad, but they are limited with what they can do with that information.”

Gabbay continues: “Don’t get me wrong, providing market participants with any form of TCA has undoubtedly been a positive development for the industry, but the real opportunity is to interpret the content of a cost analysis report into actionable insight and concrete solutions.”

With Quod’s Execution Management System (EMS) having more than 400 configurable parameters on the Smart Order Router and leveraging a powerful TCA package, Gabbay says traders can use Quod’s solutions to capitalise on automated feedback and know-how to better execute orders and achieve the best results in real time.

Through machine learning agents and techniques, he says, traders can improve execution quality by unlocking the capacity to interpret all possible combinations between real-time market data and potential outcomes. He continues, “in the context of TCA, machine learning-driven feedback and real-time visualisation can better equip trading desks with actionable advice on how the output of a TCA report could be influenced practically within their trading application”.

Implementing machine learning into a trading desk’s workflow is what Quod Financial has had in its sights from its inception more than a decade ago. The company is keen to assuage fears that automating data collection and trading workflows to improve performance in real time is not an attempt to replace traders, but rather to better equip them with the right tools and methodologies to trade more efficiently. 

For example, they are currently testing solutions based on a traffic-light system, whereby the system provides traders with recommendations on better execution opportunities in line with current market conditions. Based on these suggestions, it is up to the trader to give the green light and approve a recommendation or not.

“We’re aware traders might be hesitant to have automated decisions made on their behalf,” says Gabbay. “A big part of what we’re doing is to ensure people don’t feel they’re out of control, which is why we’re presenting this as recommendations to the traders. We’re not enforcing anything. And that’s important. It’s about giving the trader information and the opportunity to engage in data-driven automated trading.” 

“Having an EMS recommending trading strategies for a particular market in real time is what we fundamentally believe is the future of trading in the FX market,” highlights Gabbay. “And the impact this can have on trading is massive.”

While the impact on such technology could be revolutionary, its adoption within the FX industry is somewhat more challenging. With its client base predominantly coming from the sell side, with increased interest from the buy-side community, Quod is conscious that switching trading technology can be rather slow with all the required testing, integration and configuration. This is why Quod has built an interim solution that can be plugged into any trading interface currently used on the sell side without the need to replace their platform.

Quod’s EMS has been available since 2008 and machine learning enhancements have started their first production use in 2021, with wider availability over 2022.

“This technology and application of machine learning is a major leap forward compared with any of our competitors. It provides such a significant edge that market participants will have no choice than to use these types of products in the future. Machine learning’s impact on e-trading will be similar to the impact e-trading had on voice. It is important to both embrace and rapidly adopt these technologies,” Gabbay says.

He is confident the recent strategic partnership with Refinitiv, now a London Stock Exchange Group business, where the company is providing a fully managed order management solution for sell-side clients, will garner more traction for its technology.

“We are now the official sell-side technology partner for Refinitiv, tasked with enhancing their FX and equity trading capabilities with machine learning automation,” says Gabbay. “They deal with thousands of sell-side institutions, so this provides us with a great opportunity to offer our products on a grander scale.” 

Quod Financial was named Best trading technology for FX at the 2021 FX Markets e-FX Awards.

Sponsored content