BMLL, Tradefeedr partner on AI-ready trading data

By Vriti Gothi

BMLL and Tradefeedr have partnered to build an AI-ready analytics layer for equities and futures trading data, as demand grows for standardised datasets capable of supporting automated and AI-driven trading strategies.

The collaboration will extend Tradefeedr’s existing foreign exchange (FX) analytics capabilities into equities and futures, using BMLL’s harmonised historical order book datasets. The combined offering aims to deliver enriched, standardised trading data through a unified API, providing institutional clients with a consistent framework for execution analytics across asset classes.

The initiative comes as market participants increasingly deploy AI across front-office functions, heightening the need for high-quality, structured data. However, execution data remains fragmented across brokers, venues, and asset classes, limiting its usability for benchmarking and downstream analytics. The partnership seeks to address this challenge by integrating BMLL’s Level 1, 2, and 3 historical datasets with Tradefeedr’s analytics APIs and distribution network.

Paul Humphrey, CEO of BMLL, said the collaboration addresses longstanding data sourcing challenges in execution analytics. “Tradefeedr has built a strong distribution model for execution analytics, but the sourcing of high-quality market data has always been a challenge until now. This partnership brings BMLL’s harmonised historical order book datasets into that workflow to support more consistent benchmarking across futures and equities.”

Balraj Bassi, CEO of Tradefeedr, added that clients are seeking scalable, multi-asset analytics solutions that can be operationalised more easily. “Access to harmonised historical order book datasets from BMLL gives us the foundation to expand our transaction cost analysis coverage into equities and futures. We’re inviting market participants to join this pilot to shape what comes next building the analytics delivery stack for the AI era.”

As part of the initiative, the firms will launch a year-long industry pilot, inviting institutional participants to help define performance metrics, validate data quality, and shape AI-ready analytics frameworks. Outputs will be delivered through Tradefeedr’s existing client network, which includes more than 100 institutional users.

The partnership is supported by Tradefeedr’s participation in BMLL’s Activate Data Credits Program, which enables partners to develop and test new products using BMLL datasets before scaling to production.

The move reflects a broader industry shift towards data standardisation and interoperability as firms seek to operationalise AI at scale. By combining historical market data with API-driven analytics delivery, BMLL and Tradefeedr are positioning their joint offering as a foundational layer for next-generation execution analytics in increasingly multi-asset trading environments.