Aite Group estimates that 12% of all global assets under management (AUM) was driven by quant analysis at the end of 2007, representing US$6.65 trillion. This figure is expected to reach 14% by the end of 2010, representing US$10.86 trillion. Sang Lee, co-founder and managing partner at Aite Group added, âThe application of quantitative analysis is definitely here to stay with adoption rapidly expanding beyond hedge funds and prop trading desks into traditional asset managers.â
VFQR gives financial firms greater flexibility and control over the technology they use for quantitative analysis. The Velocity Analytics Engine is tightly integrated with the industry standard R package for statistical analysis. This open source approach eliminates the need for costly investment in statistical software licenses and training of staff in new programming languages, and allows quant researchers to manage large data volumes using familiar tools. Further, organisations utilising widely adopted open source languages avoid the problem of scarcity of users proficient in expensive proprietary languages. Velocity for Quantitative Research also includes out-of-the-box adapters for Matlab, S-Plus and Excel.
As well as helping quant researchers manage rising data volumes, Velocity reduces the time it takes to develop new strategies and bring them to market by eliminating the need for data translation. It facilitates efficient back testing, featuring a user-friendly data browser and bulk data loader for easy data maintenance. VFQR is fully compatible with all Velocity products and can be easily upgraded from a single box solution to a fully fault tolerant, horizontally scalable enterprise deployment of Velocity. It can be installed on a single box and support multiple users simultaneously.
According to Jeff Hudson, CEO of Vhayu, âOur customers are using Velocity to capture streams of market data, analyse them in real-time, and then store for subsequent analysis. Most of our customers have amassed large stores of tick and reference data. Until now, many customers have used proprietary time series analytic languages to do analysis on large volumes of tick data. At the urging of our customers, we have embraced the open source R language. Now customers can use open source time series language that just about every university graduate knows how to use, instead of licensing costly proprietary languages.â