Virtu launches big data tools for the buyside

Darren Sinden

Leveraging existing data science capabilities is a clever retention tool, particularly if that data analysis can show your customers that you are doing a good job

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Non-bank market maker and high-frequency trading company Virtu Financial has launched a new set of tools specifically aimed at the buy-side.

The two new products which are called Open Intell and Open Python are designed to facilitate big data analytics for Virtu’s customers.

Open Intell, which is powered by Amazon Web Services, will allow users to outsource the retrieval and enrichment of large data sets to Virtu’s data science teams. Where the data can be interrogated analysed using machine learning and artificial intelligence techniques to quickly provide insights and intelligence.

Open Python is described as self serve option for those clients who have their own data engineering abilities once again Open Python is powered by Amazon’s cloud computing ecosystem and works in combination with Virtus’s existing Open Technology API’s.

Virtu’s Open APIs are a source of cleaned and normalized market data that clients can help Virtu clients to backtest investment strategies, measure their execution performance and analyse their trading costs.

Speaking about the new data tools Erin Stanton, Global Head of Analytics Client Services and Coverage at Virtu said that: “Not all organizations have the resources to build-out and maintain entire data science teams—and this is where the concept of outsourcing with an experienced and trusted partner really makes sense ”

She added that “Our global team of data analysis experts can act as an extension of a client’s internal trading desk/TCA group”

TCA or transaction cost analysis is becoming an increasingly important tool for institutional investors and other large scale traders fragmentation in the markets means that those organisations that are managing and handing out large orders want to be sure that they are being executed most efficiently.

Buy-side trading desks are also interested in measuring metrics such as market impact and price volatility during the lifetime of an order or series of orders. To undertake that analysis holistically requires the use of large data sets drawn from a variety of different liquidity sources and execution venues and the in house ability to manage and manipulate that data.

Understanding which venues, algorithms and methodologies work best for the execution of your business and of course identifying those that don’t is an increasingly key part of modern trading.

We recently wrote about the increasing importance of data science to both sides of the business. However, sell-side firms seemed to better organised and have something of first-mover advantage, and they were found to be more likely to invest in these new technologies.

Leveraging existing data science capabilities is a clever retention tool, particularly if that data analysis can show your customers that you are doing a good job for them.

Refinitiv’s annual report into the use of AI and machine learning found that data quality and availability were among the biggest barriers to the wider adoption of these data science techniques. These are exactly the sort of obstacles that Virtu’s initiative is trying to overcome.

Virtu Financial will report Q4 2020 earnings on the 11th of February.

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