BMLL, Tradefeedr Open Year-Long Pilot for AI-Ready Trading Analytics

BMLL Technologies and Tradefeedr have announced a data partnership that the two companies say will extend Tradefeedr's transaction cost analysis capabilities from foreign exchange into equities and futures markets.

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The deal pairs BMLL's historical order book datasets, covering Level 3, 2 and 1 data across global equities, ETFs, futures and US equity options, with Tradefeedr's API-based analytics network, which the company says connects more than 100 institutional clients including banks, asset managers and trading platforms.

Outputs, the firms said, will be delivered through Tradefeedr's existing client network and legal framework via a single unified API.

Filling the Multi-Asset Data Gap

Tradefeedr built its business primarily around FX pre- and post-trade analytics. Moving into equities and futures has long required access to granular historical market microstructure data, which BMLL says it can now supply through its harmonized datasets.

"Tradefeedr has built a strong distribution model for execution Execution Execution is the process during which a client submits an order to the brokerage, which consequently executes it resulting in an open position in a given asset. The execution of the order occurs only when it is filled. There is typically a time delay between the placement of the order and the execution which is called latency.In the retail FX space, reliable brokers always strive to deliver best execution to their clients in order to maintain a solid business relationship with them. This is a co Execution is the process during which a client submits an order to the brokerage, which consequently executes it resulting in an open position in a given asset. The execution of the order occurs only when it is filled. There is typically a time delay between the placement of the order and the execution which is called latency.In the retail FX space, reliable brokers always strive to deliver best execution to their clients in order to maintain a solid business relationship with them. This is a co Read this Term analytics but the sourcing of high quality market data has always been a challenge until now," Paul Humphrey, Chief Executive Officer of BMLL, pointed to data sourcing as the key barrier this arrangement is meant to address.

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"This partnership brings BMLL's harmonized historical order book datasets into that workflow to support more consistent benchmarking across futures and equities."

BMLL has been expanding its data partnerships at a steady pace. In February 2026, the company joined forces with Features Analytics to build surveillance benchmarking products using BMLL's order book records.

Earlier, in September 2025, BMLL launched its Trades Plus dataset, described by the company as its first product built directly from client feedback, combining trade records with proprietary classifications.

AI Ambitions Drive the Case for Cleaner Data

Both companies frame the deal around the broader push toward front-office AI adoption, arguing that execution data fragmented across asset classes, brokers and platforms is limiting firms' ability to feed consistent analytics and AI tools. They say a standardized, enriched data layer delivered through a common API would form what they describe as a foundation for the next wave of execution analytics innovation.

"Clients want multi-asset execution analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Read this Term that are consistent, scalable and easy to operationalize," Balraj Bassi, Chief Executive Officer at Tradefeedr, added.

"Access to harmonized historical order book datasets from BMLL gives us the foundation to expand our TCA 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."

The partnership is being enabled through Tradefeedr's participation in the BMLL Activate: Data Credits Program, an initiative that BMLL says allows qualified partners to build and validate new products using its data, with a path toward longer-term deployment.

Open Pilot Invites Market Participants to Shape the Product

Rather than launching a finished product, the two firms are opening a year-long pilot and inviting market participants to help define it. Participants, according to the announcement, will work alongside BMLL and Tradefeedr to set metrics, stress-test data quality, develop AI-ready context layers and feed back on benchmarks and reporting outputs, all delivered within Tradefeedr's existing network and contractual framework.

Tradefeedr built its institutional base by bringing major sell-side firms onto its FX analytics platform. Goldman Sachs, UBS and XTX Markets joined the platform in 2019 as its first market maker clients. The firm has also recently expanded through data alliances with sell-side and buy-side institutions across its advisory board.

BMLL was acquired by Nordic Capital in October 2025, in a deal made alongside minority shareholder Optiver, following a $21 million funding round that Optiver led the previous year. In July 2025, BMLL also partnered with ETF data provider Ultumus on a deal the firms said helped reduce ETF spreads by 16% in initial tests, pointing to a pattern of commercial validation pilots the company has been using to bring new asset class capabilities to market.

A previous partnership with Exegy, announced in March 2025, targeted the US equity options market through a similar integrated data model.

The equities and futures pilot does not have a stated timeline for full commercial launch.