BMLL integrates Kalshi order book data for institutional backtesting
BMLL, a London-based data provider, has integrated Kalshi's historical order book data into its institutional data platform. The normalized data is delivered via Snowflake, SFTP, and Data Lab in BMLL's CME Event Contracts schema. Quantitative and macro research teams at hedge funds can now backtest Kalshi prediction market prices using the same infrastructure they use for traditional exchange data. The partnership targets growing demand from systematic funds for tools to price and manage non-traditional risks tied to event contracts.
Kalshi now has two institutional data distribution deals live within a week, after the Stork arrangement announced days earlier. BMLL's schema normalization matters because quant funds will not rebuild their models around a bespoke format; they need Kalshi prices in the same pipes as CME futures. That reduces friction for systematic strategies to treat event contracts as a standard asset class rather than an exotic overlay.
For BMLL, prediction markets deepen its coverage moat against competitors like Exegy and QuantHouse who lack comparable partnerships. The Snowflake and SFTP channels suggest these desks want cloud-native access, not old-school file drops. Faster normalization means faster adoption: the first macro fund to generate alpha from Kalshi spreads will pull peer assets in its wake. But if latency or data quality lags rival feeds from Polymarket or ForecastEx, BMLL's schema investment becomes a sunk cost rather than a platform lock-in.