BMLL has integrated Kalshi’s historical prediction market data into its global, high-fidelity data coverage through a new partnership between the two firms.
The deal is expected to allow systematic hedge funds and quantitative research teams to access normalised, historical prediction market data alongside BMLL’s existing institutional coverage, to subsequentlyincorporate event probabilities directly into macro research and trading workflows.
Paul Humphrey, chief executive of BMLL, said: “Our systematic hedge fund and quantitative clients have shown urgent and active demand for high-fidelity, historical prediction market data to support macro-level research.
“Sourcing and normalising these fragmented datasets has historically been inefficient and resource-heavy for quant teams, slowing down valuable research time and critical development.”
The partnership marks a response to industry demand for a single, consolidated and standardised data feed that removes the engineering overhead of pulling fragmented data from disparate application programming interfaces (APIs).
Specifically, this barrier has previously slowed institutional quants from operationalising prediction market signals for systematic research and event-driven trading.
The integration between BMLL and Kalshi is expected to address these hurdles by normalising Kalshi’s historical order book into the same unified scheme used for CME Event Contracts, allowing systematic firms to bypass bespoke data engineering and run cross-asset macro research from day one.
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Andy Ross, head of institutional at Kalshi, added: “By bringing Kalshi’s historical market data into BMLL’s normalised research environment, firms can compare those signals, test strategies and incorporate event probabilities directly into their macro research and risk-management workflows.
“This is another important step in making prediction markets part of the institutional toolkit.”
The normalised data will enable quantitative researchers to backtest and calibrate systematic models around key macro events, such as Fed rate decisions, CPI releases, GDP prints, as well as generate cross-asset alpha, hedge regulatory risk across portfolios, and build proprietary prediction indices and forward curves.
The standardised feed will also help firms prepare for emerging prediction products, including multivariate events and perpetual futures.