BMLL Technologies has struck a partnership with Kalshi that brings the prediction-market venue’s historical order-book records onto BMLL’s institutional data platform.
Announced on 17 September 2026 from London and New York, the deal folds Kalshi’s CFTC-regulated event-contract history into the same high-fidelity, multi-asset coverage that BMLL already supplies for equities, ETFs, futures and options.
The move targets quantitative researchers, macro desks and systematic hedge funds that want event-driven probability signals inside existing research stacks rather than as a separate, messy feed.
Prediction markets have grown quickly as institutions look for ways to price risks that sit outside conventional instruments.
Until now, teams often had to stitch together incomplete files from multiple APIs, spend months on parsers and quality checks, or fall back on noisy sentiment scraped from social media and news.
BMLL’s answer is to recast Kalshi’s full historical book into the identical schema it already uses for CME Event Contracts.
Once that mapping is done, a single model can run across both venues without custom engineering.
Because Kalshi operates as a designated contract market, its prices reflect committed capital rather than opinion polls.
Quotes between one and 99 cents map directly onto implied probabilities. Even thinner contracts still produce usable midpoints around major policy events.
Researchers can therefore treat the series as calibrated macro signals for Federal Reserve decisions, inflation prints and GDP releases, then compare those paths with rates, currencies and equities already sitting in the same warehouse.
Delivery follows BMLL’s usual routes: Snowflake, SFTP and the BMLL Data Lab.
That placement lets firms back-test strategies, build proprietary probability indices, hedge regulatory exposure and prepare for newer contract types such as multivariate events and perpetual futures without leaving their current infrastructure.
Paul Humphrey, BMLL’s chief executive, said clients had been pressing for clean historical prediction-market data and that assembling it internally had been slow and expensive.
Normalising Kalshi to the CME schema, he argued, removes that bottleneck so teams can start work immediately.
Andy Ross, Kalshi’s head of institutional, added that firms want to manage event risk directly instead of relying only on proxy assets, and that putting the archive into a standardised research environment lets them test how prediction prices inform traditional markets.
The partnership sits against a backdrop of rising institutional activity on Kalshi and BMLL’s own expansion after its 2025 acquisition by Nordic Capital. For quant shops the practical result is simpler: one more venue of order-book history, already cleaned and aligned, ready for the same tools they use everywhere else.