Abacus Group, which provides hosted IT services to hedge and private equity funds, has launched StorageBurst, an on-demand service that offers secure and redundant data storage off-site.
Chicago-based Vertex Analytics has unveiled ECHO, combining realtime and historic futures tick data with complex event processing (CEP) technology, delivered on either a client-installed or hosted service.
The scale of data in financial services is, although significant, dramatically smaller than it is for online consumer companies like Google or Amazon. For financial services, petabytes are huge, but for those firms they are a rounding error. Nevertheless the need for real-time throughput makes financial services data a unique challenge and there are tremendous learnings and benefits to be had from the big data world not only in terms of how we manage data at enterprise level but also in how we are able to present and manipulate that data through next generation desktops, such as Eikon.
Lime Brokerage has tapped OneMarketData to provide a hosted, on-demand analytics for real-time and historical market data covering major U.S. equities and derivatives markets. The offering - powered by OneMarketData's OneTick tick database and complex event processing product - will deliver an on-demand cloud service that enables quantitative research, rapid design and back-testing of algorithmic trading strategies using deep history across a wide range of markets.
NYSE Technologies is rolling out its Market Data Analytics Lab (MDAL), providing hosted access to its historical trade and quote (TAQ) data, with the ability to perform analytics on the hosted data.
Time series refers to data that has an associative time sequence, a natural ordering to its content. There are many industries where time series data is common including telecom for call data records, health care for medical monitoring and broadly across finance. The Time Series Wiki page provides an academic dissertation of time series drawing a distinction to other types of data.
By Melinda Wilson, Sybase, An SAP Company www.sybase.com
“The last few years have been the most volatile for all of recorded history ... 10 of the biggest 20 daily up-swings and 11 of the largest 20 daily drops since the beginning of 1980 to the end of last month have occurred in just the last three years.” - Andrew Lo, professor of finance at the MIT Sloan School of Management, The New York Times, Sept. 11, 2011
Trading firms are charting a course toward better handling the financial industry’s growing volumes of market data, messages and unstructured data, all the while keeping watch for new opportunities and market manipulation. The right technology can help firms monitor and correlate data streams in real time, but the wrong technology - or doing nothing at all - could run them aground.
The International Securities Exchange, in partnership with Hanweck Associates, has launched the ISE Premium Hosted Database, a hosted database of historical tick data with full OPRA coverage - quotes and trades from all U.S. options exchanges - plus U.S. equities level one data, pre-computed implied volatilities and Greeks, full corporate action histories, and ISE Open/Close trade data. More than six years of data is available - some 600 TB so far.
Trading Technologies International, which develops trading systems for the derivatives markets, has partnered with MathWorks, developer of the Matlab data analysis and numerical computation package, to link the two systems so that data intensive quantitative trading algorithms can access realtime pricing and submit orders directly to markets.
OneMarketData has been building technology to process big data since way before the term even existed. Its customers actually need to process "deep data" - reliable and accurate data instances down to a granular level, stored over long periods of time. We spoke to OMD's director of solutions Louis Lovas about deep data, and what trading firms need from it.
Q: What big data problems for the financial markets does OneMarketData address with OneTick?
A: There has been an explosion of hype surrounding big data that has obviously led to confusion rather than clarity. The term big data has largely been associated with loosely-structured content, originating from web search companies and social media.
