Quants Demand Faster Analytics

Terry Flanagan

System providers embed advanced mathematical programming into products.

Quants, traders and risk managers are seeking faster, ever more powerful market data analytics as demands related to data management, analytics and risk management intensify.

That’s leading analytics providers to pack more punch into their systems, including advanced statistical programming.

Sybase, for example, has embedded support for the R statistical programming language within Sybase RAP—The Trading Edition, an analytics server for financial services.

“Pre-trade analytics, such as compliance checking, risk analysis, trade P/L calculations, etc. drive the need for speed in market data analytics,” Neil McGovern, director of financial services product management at Sybase, told Markets Media.

The combination of Sybase RAP – The Trading Edition and R enables faster algorithm development and extensive back-testing on historical data, providing users with a more powerful approach to analyzing larger and much more complex data, said McGovern.

Fincad earlier this year launched F3 Toolbox for use with MATLAB, which gives MATLAB users access to Fincad’s F3 analytics library; MATLAB is a mathematical programming language that’s used by 2,300 financial firms to develop quantitative trading strategies.

F3 Toolbox enables banks to value and customize any new portfolio or trade without writing a single line of software code, Fincad said. The technology enables efficient scenario analysis and portfolio-level risk calculations, and also enables users with enterprise systems to re-use code created in MATLAB, simplifying integration into C#, C++, or Java-based systems.

Sybase has refined its data management systems to support trading apps. Sybase RAP interacts with Sybase ESP—a complex-event processing system.

“Sybase RAP and Sybase ESP are integrated,” said McGovern. “They pass data between them to ensure that RAP has the latest market data, enriched and cleansed by ESP if required. ESP can also access data in RAP (either the disk or in-memory based storage) if required for more complex decisions.”

The integration of R, a programming language used for advanced statistical modeling, will further this goal.

“By doing pre-processing on the RAP server, users can download the relevant subset of data and use it in their applications without having to download all the data and do the pre-processing in R, resulting in faster algorithm development,” McGovern said.  “Additionally, they can also access large historical datasets in RAP for algorithm development and more accurate back-testing.”

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