BMLL, provider of harmonised historical Level 3, 2, and 1 data and analytics for global equity, ETF, and futures markets, has partnered with data solutions provider INQDATA, to offer financial market firms access to comprehensive and accurate historical market data directly within their kdb+ environment, thus increasing performance and reducing infrastructure costs by eliminating the need for additional data science resources.
“What continues to amaze me in this industry is that firms compete heavily to hire quantitative talent, only to have them spend 80% of their time cleaning unsuitable data. This has to change,” states Paul Humphrey, CEO of BMLL, in conversation with TradingTech Insight. “Considering the expense and investment in this talent, we should be drawing the very best from them. It’s like commissioning a painting from an artist and then asking them to redecorate the studio first; it just doesn’t make sense. There’s a lot of useless data occupying financial services firms. Instead of using a kdb+ estate to store raw data and having engineers try to make something of it, we can now provide best-in-class historical data in a format that suits the engine our customers are working on, so they can perform all the analysis they want right out of the gate.”
BMLL’s datasets capture full order book data across more than 100 trading venues, providing consistent granularity at Levels 3, 2, and 1. This data is utilised by banks, brokers, asset managers, hedge funds, global exchange groups, and academic institutions to gain insights into market behaviour.
INQDATA, a cloud-based data solutions provider, simplifies the ingestion, processing, storage, and management of analytic-ready market data. Its high-performance environment, powered by KX’s kdb+ high-performance time-series database, query language and analytics engine used extensively in financial markets, ensures rapid access to cleansed, real-time, and historical datasets.
The collaboration between BMLL and INQDATA enables data scientists and application developers to efficiently access and explore granular historical market data and analytics derived from Level 3 data. By ingesting BMLL data directly into their existing kdb+ estate, the integration allows users to better leverage their kdb+ environment to enhance their trading strategies, test new markets quickly, understand execution costs, and improve the development of quantitative models.
“For years, many in the marketplace have relied on a kdb+ estate to manage their real-time data and provide market analytics. kdb+ has been a popular installation due to its excellent handling of real-time data,” says Humphrey. “However, it’s not the best for just storing historical data. INQDATA addresses this by converting our data into a format that integrates seamlessly with kdb+ estates, allowing users to utilise the data immediately. And that aligns with our commitment to making our data available in the format clients prefer, a principle that also led to our recently announced partnership with Snowflake.”
The integration aims to democratise access to data and analytics, allowing market participants to utilise BMLL’s API library and quantitative analysis tools within their environment. By reducing the burden of data engineering and infrastructure, users can focus on conducting comprehensive analyses to improve trading outcomes.
“Strategy testing and optimisation are clear use cases,” says Humphrey. “Backtesting strategies to explore various what-if scenarios can be done easily and quickly within an existing kdb+ environment, essentially transforming historical data into pre-trade data to guide your algorithms.”
By combining BMLL’s data curation and analytics with INQDATA’s data management capabilities, the partnership offers market participants reliable historical market data without the need for extensive reformatting. The scalable cloud architecture provided by both companies supports large-scale quantitative and market microstructure analyses.
“Customers come to us for the uniqueness and the heavy lifting we’ve done on our content. It’s crucial for us to remain agnostic regarding how clients want their data. This approach is yet another delivery mechanism, providing data to clients in the format they prefer.”
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