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Kalshi data is being standardized by BMLL for quant teams, enabling backtests on Fed, CPI and GDP events through Snowflake, SFTP and Data Lab.
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This is a Mr. Informer briefing on Quant Teams Get Standardized Access to Kalshi Historical Data — a detailed, automation-assisted summary of reporting from ReadWrite. Below you'll find the original reporting summarized in our own words, followed by editorial context on why this matters, technical background, and key takeaways. For full quotes, sourcing, and original detail, read the complete report at the source linked at the bottom of this article.
Why this matters
Standardizing access to prediction market data allows quantitative research teams to rigorously backtest strategies involving macroeconomic indicators like the Fed, CPI, and GDP. In the broader context of financial technology, this kind of data integration bridges niche alternative datasets with mainstream institutional workflows. Readers should take away that prediction market events are increasingly becoming a formal asset class for algorithmic trading models.
Technical context
The partnership leverages BMLL to standardize Kalshi data, which is then delivered to quantitative teams through established enterprise pipelines including Snowflake, SFTP, and Data Lab. This infrastructure enables quants to run historical backtests on event-driven data without manually parsing fragmented source formats. By routing the data through these standard channels, firms can seamlessly incorporate macroeconomic prediction metrics into their existing analytical frameworks.
Key takeaways
- Kalshi historical data is now being standardized for quantitative trading teams.
- The integration allows quants to run backtests on macroeconomic events like the Fed, CPI, and GDP.
- Data delivery is supported through enterprise channels such as Snowflake, SFTP, and Data Lab.
- BMLL is managing the standardization process for the Kalshi datasets.
Read the full original report at ReadWrite →