big xyt Liquidity Cockpit delivers insight on Systematic Internalisers
15 November 2018
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big xyt
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News
big xyt
Press Release
Analytics platform introduces new dashboard to interrogate SI component volumes
London, Frankfurt, 15 November 2018 big xyt, the independent provider of high-volume, smart data and analytics capabilities is pleased to announce further client driven enhancements to its Liquidity Cockpit.
With the introduction of a dedicated dashboard for analysing SI volumes, eligible users have the option to view and compare the reported SI volumes filtered by adjusted conditions, analysed by time, by region or by symbol. This new dynamic visualisation leverages the recent release extending the adjustment of SI volumes and the range of enquiry options and filters available. Users are now able to better understand the component parts of total SI flow reported. Furthermore, this additional value added functionality introduces comparisons to other market activity such as Large In Scale (LIS) trades and the impact of Double Volume Caps on liquidity dispersion over time.
Robin Mess, CEO at big xyt commented, “Since MiFID II we have been working hard with clients and analysts to deliver a range of different dashboards to our Liquidity Cockpit helping to answer essential questions.” Adding, “With our analysis of SI volumes, we have exceeded the expectations of the trading community by offering new views on the liquidity landscape in Europe.”
Mark Montgomery, Director of Strategy & Business Development at big xyt says, “We are excited by the capabilities of the new dashboards as previously hidden trends are emerging over the year since MiFID II was introduced. In particular Systematic Internaliser volumes above and below Large In Scale look very different to the overall SI volume during 2018.”
The highly interactive dashboards deliver an independent, configurable view for analysing key metrics for market share and market quality. Liquidity Cockpit provides the user with rich options for filtering and drill-downs as well as direct access to underlying data and analysis through CSV, and various APIs.
