BMLL’s order book dataset adds futures from CME, Eurex and ICE

  • Full-depth order book data from CME, Eurex and ICE covers equities, fixed income, short-term interest rates, commodities, cryptocurrencies and FX

BMLL announced the expansion of its product offering to include global futures as asset class packages. Futures datasets are huge, complex, and highly interconnected. Gaining meaningful insight from beyond the top of the order book requires significant computational workloads. BMLL’s scalable compute environment simplifies working with this immense data set.

Most futures contracts traded on a single venue typically have comparable and connected contracts on other exchanges. The BMLL data warehouse allows quants to analyze full-depth order book data for all major futures asset classes from the CME, Eurex and ICE venues in a single harmonized format. This opens up the ability to do complex, Level 3 Data analysis at scale across venues and markets, and conduct simulations such as model calibrations and testing of latency curves and market impact to define optimal order size and placement in a fully configurable framework.

Paul Humphrey, CEO, BMLL Technologies, said in a statement: “Packaging our futures product is a natural evolution to our core equities offering. As major trends such as the LIBOR transition and the explosion in retail trading in the futures environment take center stage, the ability to analyze these complex and diverse data sets is vital.”

Elliot Banks, CPO, BMLL Technologies, said in a statement: “The sheer size of the futures market requires a scalable, cloud based platform that can provide the insight and analytics needed for the ever increasing data demands of market participants…With our harmonized data and the BMLL Data Lab, (customers) can now look at multiple years of trading history on CME, Eurex and ICE in a consistent format, and they can quickly and easily test strategies.”

This means that brokers, asset managers, hedge funds, FCMs and ISVs (independent software vendors) can accelerate their speed and quality of output by spending less time gathering, organizing and cleaning data, and more time deriving predictive insights and backtesting strategies to generate alpha more predictably.


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