Strategic collaboration integrates Kalshi’s regulated event contract data into BMLL’s world-class coverage, supporting macro-level research and event-driven trading workflows

BMLL, the leading independent provider of harmonised, continually engineered, historical Level 3, 2 and 1 data and analytics for Capital Markets, announced a strategic partnership with Kalshi, the CFTC-regulated, next-generation financial exchange. The collaboration integrates Kalshi’s historical prediction market data into BMLL’s global, high-fidelity data coverage.

The partnership has been established to address growing institutional interest from quantitative research teams, macro and systematic hedge funds seeking to seamlessly integrate prediction market signals into their core investment workflows for macro-level research and event-driven trading.

Prediction Markets Data Challenges

Prediction markets are evolving rapidly, as institutions increasingly need better ways to price, manage, and trade risks that fall outside traditional asset classes, while also gaining access to better signals for decision-making.

Traditionally, quantitative researchers have navigated a highly fragmented prediction market landscape, forcing data teams to manually pull piecemeal, unstructured data from disparate APIs. Sourcing and standardising this data often requires immense engineering overhead, consuming years of development time that would otherwise be spent on actual strategy modelling. Equally, sourcing high-fidelity historical book data directly from exchanges is challenging, forcing quants to rely on noisy, unstructured natural language processing (NLP) sentiment feeds (such as social media scraping) or web news NLP.

Responding to Market Demand for Standardised, Institutional-Grade Prediction Data

The market demands a single, consolidated, and standardised data feed that eliminates the heavy engineering overhead of gathering fragmented data from disparate APIs. This will enable institutional quants to instantly operationalise high-fidelity prediction market signals for systematic research and event-driven trading.

BMLL directly eliminates this technical barrier by normalising Kalshi’s historical order book into the same unified schema as CME Event Contracts. This standardisation allows systematic firms to skip complex, bespoke data engineering and perform immediate, cross-asset macro research from day one.

In addition, as a CFTC-regulated Designated Contract Market, Kalshi’s contracts represent financially committed capital, meaning contract prices (ranging from 1¢ to 99¢) directly translate to real-world, well-calibrated probabilities. Even on contracts with lower liquidity, the quote midpoint provides a highly calibrated macro probability signal for major policy announcements and key macroeconomic releases.

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.

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, added: “Institutional participants increasingly need better ways to price and manage event-driven risk directly, rather than relying solely on proxy assets. They also want to understand how prediction-market prices can inform their view of traditional financial markets. By bringing Kalshi’s historical market data into BMLL’s normalized 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.”

Using this normalised historical data, quantitative researchers can backtest and calibrate systematic models around critical macroeconomic developments, such as Federal Reserve interest rate decisions, CPI releases, and GDP prints. The data also enables firms to generate cross-asset alpha, hedge regulatory risks across active portfolios, and construct proprietary, institutional-grade prediction indices and forward curves for macro-level analysis. Furthermore, this standardised feed allows researchers to evaluate and prepare for emerging prediction products, including Multivariate Events (MVEs) and Perpetual Futures.

Source: BMLL