Jason Rand discusses how democratising access to quantitative execution analytics and data visualisations leads to better trading outcomes for participants
First published by The TRADE News
Over recent years, buy- and sell-side collaboration has driven algorithmic development, resulting in innovative ways of provisioning liquidity in a rapidly evolving landscape. The recent shift towards more dynamic, learning-based algorithmic models has positively contributed to the buy-side achieving trading objectives which ultimately have yielded better outcomes for all participants.
Berenberg’s Data Science and Algorithmic Engineering teams draw on a multitude of sources, including partnering with BMLL, leveraging their Level 3 data to layer market event datasets with client trade data by linking unique order IDs via matching engine timestamps. Level 3 data draws insight into cancellations, amends, quote submissions, order queue details and unfilled orders. This allows Berenberg’s TIA to deliver a robust A|B testing environment across 5 years of market and trade data to efficiently optimise algorithmic performance.
The partnership between Berenberg and BMLL is about democratising access to Europe’s most comprehensive analytics engines and largest data lakes. Overlaying millions of child orders on top of billions of market data points no longer requires an army of quantitative researchers or data engineers; it’s encapsulated in TIA.
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