High-Frequency Trading Need High-Intelligence Computing

By Suresh Aswani, Senior Manager, Solutions Marketing, Hewlett Packard Enterprise

April 4, 2016

In the time it takes the average person to sneeze, today’s high-frequency trader can execute over 10,000 trades.

Securities trading has evolved from the frenzied “open outcry” system to the quiet automation of high-frequency trading (HFT), where supercomputers execute hundreds of trades each millisecond based on financial algorithms.

Naturally, high-frequency traders are very concerned with transaction speed, and in the early days of HFT, simply executing orders as quickly as possible was enough to compete. But the propagation of high performance computing has made fast processing achievable for nearly every trader. In addition to speed, quality Big Data analytics and tools for insight-based trading have become equally important differentiators in this fast-paced world of finance.

In other words, these days it’s more about high-intelligence trading.

Whether we dub them high-frequency or high-intelligence, today’s electronic traders face the arduous task of delivering critical financial returns in less time than ever before, with the added complexity of having to sift through more data to do so.

In addition to continually increasing transaction speed, traders aim to uncover new ways to extract value from an enormous volume of financial data. The ones that succeed will be able to react in real-time to changing market conditions, predict the success of trading strategies and capitalize on data-driven decision-making.

Firms rely on Big Data algorithms to rapidly fire off trade orders without human intervention every millisecond, and a key challenge for traders is putting checks in place to monitor those algorithms and ensure they continue to function properly. Becoming a victim of “runaway data” could have catastrophic consequences for an HFT firm.

The rapidly growing volume of financial data also poses a significant hurdle for regulators, who have been forced to adapt their procedures and expand computing power in order to properly track and monitor trades that happen thousands of times per second.

Regulators have had to intervene to set rules for high-frequency trading that protect financial markets, and reduce the incidence of “flash crash” types of events similar to the one that occurred in 2010.

The challenge for traders is not only keeping pace with changing regulations, but also developing a compliance strategy that addresses how easily regulators can now mine and search their data, revealing non-compliant or suspicious activity more readily. Proper information governance procedures such as risk analytics and low-latency trade checks can help traders achieve compliance and address changing regulatory requirements.

Pinpointing which data should be retained for compliance purposes is key. Software solutions can be put in place to ensure that data is organized, easily discoverable and accessed readily by both internal users and outside regulators. Compliance officers should also aim to continually gain deeper insight about the many data types that might be part of an audit or regulatory check.

Legacy IT infrastructures will be insufficient in scaling to support the growing volumes of data generated daily from high-frequency trading. High performance computing solutions that provide a mix of speed, intelligence and storage are available to help traders accelerate trade speed while also meeting regulatory requirements. Soft bundles of hardware and software can be built onto legacy infrastructures to boost cost savings.

 

Robust computing infrastructures capable of high-speed, high-intelligence trade transactions are a make-or-break for the HFT community. Firms who are able to keep pace with computing needs in this new era of finance will be able to inspire investor confidence, reap greater financial returns and meet ever-changing compliance regulations.

To learn more about the advantages of high-intelligence computing, I invite you to follow me on Twitter at @sureshaswani2.

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