Big Data and the Future of Trading
Big Data holds Big Implications for the future of trading, including algorithmic trading, strategy development, quantitative analysis sand post-trade analytics.
“A lot of people talk about Big Data and don’t really understand what it is,” said Jamie Oschefski, director of U.S. sales at trading system provider Cyborg Trading. “Big Data comes in different forms and allows for better visibility, faster analytical cycles and improved control over risk and credit exposure.”
Cyborg is providing its clients with technology that works with all asset classes to cope with the magnitude of data they deal with on a daily basis.
“If you look across the 13 U.S. equity exchanges, 9 options exchanges and 50 or so dark pools, there are 40 trillion records created per day which equates to roughly 2 petabytes of data every day,” Oschefski said. “To put that into perspective, that is the same as storing the 12 billion photos on Facebook or roughly 26 years of HD Video.”
Rationalizing, organizing and distributing this data is the ‘Big Data’ problem faced by many firms, and if they don’t align themselves with the right technology to handle it, they will ultimately be left behind.
Effective market data management is about linking disparate data sets under some common thread to tease out an intelligible answer, said Louis Lovas, director of solutions at OneMarketData, provider of the OneTick database.
As a tick database, OneTick is about capture and analysis of data for back testing quantitative models and TCA, both of which are hugely dependent on quality of data.
The need for high quality of data cuts across all aspects of the trade lifecycle. Those trade-related solutions, model back-testing, portfolio mark-to-market and compliance depend on a high-degree of data quality, where accuracy is vital to determining outcomes.
“There are a lot of commercial database products, especially in the Big Data space,” said Lovas. “But when you’re talking about financial market data, where there are huge amounts to collect and analyze, it takes more than the ability to capture and store. It requires turning data into usable content for trade performance analysis or back testing. And most of those database solutions lack this capability.”
Cyborg Trading “employ a significant amount of PhD’s specializing in machine learning, artificial intelligence and data science to keep up with the innovative demands of our clients,” said Oschefski. According to Cyborg’s CEO, James McInnes, “Finance is a science problem now, not a business problem. In finance today, someone with a physics or computer science degree is more qualified than someone with an MBA.”
Cyborg uses a distributed system for a more holistic view of the market activity, which also makes data readily available for intraday analysis.
“Leveraging the cloud to farm out computationally expensive activities is becoming more and more attractive,” Oschefski said. “The latency arms race is being led by only a few firms, and based on the discussions with our clients, the firms that can rationalize and normalize this all this data will ultimately have the long term competitive advantage.”
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