- Gives users greater flexibility in how they access and use BMLL data across equities, futures and options
- Accelerates time to insight across use cases such as TCA, market surveillance, trade and execution analysis, backtesting, stress testing and strategy development
- Enables existing Databricks customers to rapidly access BMLL high-quality data with minimal integration effort or storage costs.
BMLL, the leading independent provider of historical market data and analytics across global equity, ETFs, futures and options, today announced that the BMLL Historical Data is now available via Databricks. This move furthers BMLL’s commitment to delivering content wherever customers need it, with initial users including some of the world’s largest investment management firms.
Driven by customer demand and BMLL’s Client Product Advisory Board (CPAB), the collaboration reflects BMLL’s ongoing strategy to give customers greater choice in how they discover, access and evaluate datasets. Alongside Databricks, BMLL data and analytics are available via a broad range of delivery mechanisms, including API, SFTP, S3 and more.
By launching on Databricks, BMLL enables a wider range of market participants to accelerate time to insight and extract value faster. A series of marketplace notebooks, created by the BMLL team of expert quantitative analysts, allows Databricks users to easily and quickly discover and evaluate the BMLL product suite - with minimal integration effort or the costs associated with data storage. Use cases include execution analysis, research and backtesting, market structure analysis, TCA, market surveillance and risk.
Paul Humphrey, Chief Executive Officer of BMLL, said: “Both the sophistication of the market and the demand for high-quality historical market data continue to grow. This is why firms rely on our robust engineering and normalisation processes to make effective use of our granular data, and we are excited to make BMLL datasets available on the Databricks platform. We essentially meet our customers where they need us to be - within their existing workflows. They now have additional choice and flexibility in how they access our data to carry out faster and more efficient analysis, at scale.”
