BMLL partners with Kalshi to expand prediction market data

By Divya Shah

BMLL, a provider of harmonised market data and analytics, has partnered with Kalshi, the CFTC-regulated financial exchange, to integrate Kalshi’s historical prediction market data into its global data platform. 

The collaboration aims to address growing demand from quantitative research teams, systematic hedge funds and macro investors seeking to incorporate prediction of market signals into investment research and event-driven trading strategies. 

As part of the partnership, BMLL will normalise Kalshi’s order-to-book data into the same unified schema used for CME Event Contracts. The standardisation is designed to reduce the complexity of data integration, enabling institutional investors to conduct cross-asset macroeconomic analysis without undertaking extensive data engineering work. 

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. Sourcing and normalising these fragmented datasets has historically been inefficient and resource-heavy for quant teams, slowing down valuable research time and critical development. 

Kalshi’s prediction market contracts, which trade between 1¢ and 99¢, offer probability-based indicators linked to real-world events. As a CFTC-regulated Designated Contract Market, the platform provides market-driven probability signals that can be used to assess expectations around major policy decisions and macroeconomic developments. 

By adding Kalshi to our coverage and normalising its historical dataset to match the CME Event Contracts schema, we are removing the burden of data engineering. This allows quant teams to bypass complex API parsing and immediately unlock predictive macro signals through Snowflake, SFTP, or the BMLL Data Lab.” 

Andy Ross, Head of Institutional at Kalshi, said, “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.” 

The integrated dataset will enable quantitative researchers to backtest and refine models around key macroeconomic events such as Federal Reserve interest rate decisions, inflation data releases, and GDP announcements.  

The data can also be used to support cross-asset trading strategies, assess regulatory risks and develop proprietary prediction indices and forward-looking market indicators. 

In addition, the standardised feed is expected to help firms evaluate emerging prediction market products, including Multivariate Events (MVEs) and Perpetual Futures, as institutional interest in alternative data sources continues to grow.