Through the collaboration, QuantHouse will supply its low-latency and historical market data to Quod Financial for use within backtesting and to refine its AI-driven trading algorithms, enabling trading firms to carry out real-time, highly accurate transaction cost analysis (TCA) at the point of execution.

QuantHouse’s Head of EMEA Sales and Business Development, Rob Kirby, said: “In today’s trading environment, financial institutions are actively exploring and adopting the latest advances in AI and machine learning (ML) to enhance trading outcomes. The key challenge with deploying these latest innovations into the trading environment is that AI-driven algorithms can only be as accurate as the data on which AI systems are trained.

“QuantHouse’s high-quality, extensive historical data is now being used to train QuodFinancial’s AI/ML models to adapt to, and even anticipate market movements. Traders no longer need to adjust their TCA assumptions or trading strategies manually when unexpected market events happen. They can now analyse the cost associated with each trade, optimise trading strategies and ultimately improve trade executions right at the point where it is needed most: as part of the trade execution.

“We are very excited about this collaboration and look forward to working together to empower market participants with the right data for AI-driven trading optimisation.”

Quod Financial’s Chief Revenue Officer, Medan Gabbay, said: “In financial services, the performance of your technology is defined by the quality and speed of the data that powers your systems. This has never been more true or more important than now, as we go through a transition of data automation and AI/ML. QuantHouse has proven to be an exceptional partner in this data journey.”