QuantHouse and BMLL have partnered to support buy-side understanding of market behaviour and testing of investment strategies.
Through the partnership, QuantHouse’s real-time data services will be combined with BMLL’s historical order book data. The harmonised data will be available in one format across all venues and asset classes, accelerating research processes, time to insights and time to market, the firms stated.
Speaking to Global Trading, Paul Humphrey, CEO of BMLL, said: “Clients now have the opportunity to pull in both [real-time and historical data], leapfrog any data engineering and infrastructure costs, and move straight into a research production environment. Ultimately this helps them backtest and improve trading strategies, reduce time to insights and generate alpha more predictably.”
BMLL aims to democratise access to historical order book data, Humphrey explained, and is doing so through data set expansion and partnering with other industry participants to expand global reach.
He continued: ”The data engineering required to map historical and real-time data sets and make these available in one harmonised format is significant, complex and cost-prohibitive for many firms. What we have done here is remove barriers to entry.”
This applies both to smaller firms without the resources to undertake these projects themselves, but also larger buy-side firms wanting to reduce the time quants spend on data curation and cleansing.
Jason Hoang, CEO of global trading and market data at Iress, QuantHouse’s parent company, commented: “Clients will benefit from immediate and flexible access to full-depth, real-time and historical order book data, enabling them to carry out research, backtest their strategies and ultimately improve their performance. [It] will be especially valuable to those using machine learning and AI in their strategies.”
In a press release, Humphrey concluded: “Across the industry as sophistication levels increase, the demand for superior quality historical market data is intensifying. Market participants need easy access to global, ready-to-use data to improve their own products and strategies, gain a deeper understanding of liquidity dynamics and generate alpha more predictably, without the burden of data engineering and infrastructure on their P&L.”
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