BMLL and Kalshi Partner to Standardize Prediction Market Data for Quants
Historical Kalshi data now accessible through BMLL's unified capital markets schema.
1Strategic data partnership
BMLL integrates Kalshi's historical prediction market data into its standardized capital markets environment, offering structured access to quant teams and hedge funds. This marks the first time Kalshi's data becomes available in a normalized format comparable to CME Event Contracts. The partnership eliminates years of potential engineering work by providing pre-processed data through established channels like Snowflake and SFTP.
2Data standardization benefits
BMLL will normalize Kalshi's order book data using the same schema applied to CME Event Contracts, removing the need for manual API data collection. This standardization saves quant teams from building custom pipelines, allowing direct focus on strategy modeling. Kalshi's contracts trade between 1¢-99¢, representing financially committed capital rather than raw sentiment indicators.
3Institutional demand drivers
BMLL's CEO cites urgent demand from systematic hedge funds for high-fidelity prediction market data to support macro research. Kalshi's institutional lead notes growing need to price event-driven risk directly, beyond proxy assets. The partnership positions Kalshi's data as peer to CME's, not an experimental category, enhancing its credibility for institutional use.
4Research applications
The normalized feed enables backtesting models for Fed rate decisions, CPI releases, and GDP data. Institutions can use it for cross-asset alpha generation, regulatory risk hedging, and building proprietary prediction indices. Emerging products like Multivariate Events and Perpetual Futures are specifically targeted for future development.
5Technical implementation
Data becomes accessible through BMLL's Data Lab, Snowflake, or SFTP, bypassing complex API parsing. The unified schema means prediction market prices can integrate directly with existing macro research stacks. As a CFTC-regulated market, Kalshi's data gains institutional-grade treatment comparable to traditional derivatives exchanges.
6Market context
Prediction markets have historically faced adoption barriers due to data fragmentation. This partnership mirrors the institutionalization path seen in cryptocurrency markets, where standardized data feeds preceded broader acceptance. Kalshi's contracts now join the ecosystem of event-driven financial instruments with professional-grade infrastructure.
7Operational impact
Quant teams gain direct access to calibrated probability signals rather than sentiment indicators. The 1¢-99¢ pricing structure reflects actual capital commitments, providing what partners describe as 'real-world probabilities' for event outcomes. This differs fundamentally from social media sentiment or survey-based prediction tools.
8Strategic implications
The partnership signals prediction markets' maturation as a data source for traditional finance. By aligning with BMLL's institutional data infrastructure, Kalshi moves beyond retail speculation toward systematic trading applications. This could accelerate adoption of event contracts as a distinct asset class within macro portfolios.