BMLL and Kalshi have formed a strategic partnership that folds Kalshi’s historical prediction-market data into BMLL’s standardized capital-markets data environment, giving quantitative research teams, macro funds, and systematic hedge funds a structured path into Kalshi’s market data for the first time.

BMLL describes itself as an independent provider of harmonized, continually engineered historical Level 3, Level 2, and Level 1 data and analytics for capital markets. Under the deal, BMLL will normalize Kalshi’s historical order book into the same unified schema it already uses for CME Event Contracts, according to the announcement on Markets Media.

That standardization is meant to eliminate the manual work of pulling piecemeal data from disparate APIs, a process the companies say has consumed years of engineering time that could otherwise go toward strategy modeling. Kalshi’s contracts trade between 1¢ and 99¢ and, because they represent financially committed capital, the announcement frames those prices as well-calibrated real-world probabilities rather than raw sentiment.

Why BMLL and Kalshi Say Institutions Need This

Paul Humphrey, CEO 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.”

He added that normalizing Kalshi’s dataset to the CME Event Contracts schema removes the burden of data engineering, letting quant teams bypass complex API parsing and access macro signals directly through Snowflake, SFTP, or the BMLL Data Lab.

Andy Ross, Head of Institutional at Kalshi, said institutional participants increasingly need better ways to price and manage event-driven risk directly, rather than relying solely on proxy assets, and want to understand how prediction-market prices can inform their view of traditional financial markets.

What the Data Is Built For

The normalized feed is designed to let researchers backtest and calibrate models around Federal Reserve rate decisions, CPI releases, and GDP prints. The companies also point to uses in generating cross-asset alpha, hedging regulatory risk across portfolios, and building proprietary prediction indices and forward curves, with an eye toward emerging products like Multivariate Events and Perpetual Futures.

Kalshi remains a CFTC-regulated Designated Contract Market, and the partnership positions its event-contract data as a peer dataset to CME’s rather than a separate, experimental category. For institutional data teams, that shared schema is the practical payoff: one less pipeline to build before prediction-market prices can sit alongside the rest of a macro research stack.