BMLL, Tradefeedr Partner on Analytics for Equities and Futures Data

Why It Matters

By unifying fragmented execution data across asset classes, the partnership enables firms to feed reliable, standardized inputs into AI models, accelerating quantitative strategy development and improving benchmark consistency. This could set a new industry standard for multi‑asset trading analytics.

Key Takeaways

• BMLL provides harmonised historical order book data
• Tradefeedr adds equities and futures to its analytics API
• Year-long pilot invites institutions to shape AI-ready analytics
• Partnership aims to standardise multi‑asset execution data

Pulse Analysis

The push toward artificial intelligence in trading has turned data quality into a strategic differentiator. While AI models promise predictive power, they are only as good as the inputs they receive. Execution data today remains siloed across brokers, platforms, and asset classes, forcing firms to spend significant resources on data cleaning and integration. BMLL’s Level‑3, Level‑2, and Level‑1 historical datasets offer a rare, cloud‑native source of granular order‑book information that can be directly ingested by machine‑learning pipelines, reducing latency and error rates.

Tradefeedr’s strength lies in its unified API layer that already serves more than 100 major financial institutions for FX analytics. By integrating BMLL’s equities and futures data, the partnership creates a single endpoint for multi‑asset execution analytics, simplifying workflow automation for buy‑side and sell‑side firms. The year‑long pilot will let participants co‑design benchmark metrics and AI‑ready context layers, ensuring the resulting APIs address real‑world trading challenges. This collaborative approach accelerates time‑to‑value for firms looking to embed advanced transaction‑cost analysis and market‑impact models into their trading desks.

Beyond immediate product benefits, the alliance signals a broader shift toward data‑centric AI ecosystems in capital markets. With BMLL’s recent backing—$21 million from Optiver in 2024, a $26 million Series B in 2022/23, and a 2025 acquisition by Nordic Capital—the company is positioned to scale its data offerings rapidly. Tradefeedr’s open‑banking‑style API philosophy could become a de‑facto standard for execution analytics, prompting competitors to pursue similar data‑harmonisation strategies. Institutions that adopt these unified, AI‑ready analytics early may gain a measurable edge in strategy optimisation and regulatory compliance.