Industry Urged to Beef Up Algo Testing Procedures01.17.2013
Trading technology is undergoing a facelift as the industry seeks to plug holes that resulted in the Knight Capital snafu and other fiascos.
A more rigorous approach to software development is a sine qua non for algorithmic trading, as the Knight episode demonstrates, which saw the U.S. market maker lose $440 million in just 15 minutes last August due to a faulty algorithm.
With the advent of drag-and-drop software development, traders can easily and quickly create algorithms and launch them into production, which creates risks.
“The reason black swan events like the ‘flash crash’ create such turmoil is there is no way to test for them, because they have never happened,” said James McInness, chief executive of Cyborg Trading, a financial technology firm.
In the aftermath of the May 2010 flash crash, when the Dow Jones Industrial Average index plunged 1,000 points, almost 9%, only to recover within minutes, the U.S. Securities and Exchange Commission and the exchanges teamed up to devise new protections to keep computer trading errors from spreading too rapidly or inflicting unacceptable harm on the overall market.
The exchanges reformed their rules for breaking trades, instituted single-stock circuit breakers, updated market-wide circuit breakers and implemented limit up/limit down mechanisms.
One important area of focus is testing and industry preparedness.
“Market participants could contribute to the reduction of trading errors by employing certain best practices, such as rigorous testing of software code prior to deployment, and robust testing environments using real-time order flow and market data,” said Robert Gasser, president and chief executive of agency brokerage ITG, at a hearing on computerized trading held last month by the Senate Banking Committee.
In the past, industry-wide system changes have utilized a testing methodology that tested for system design integrity.
A more robust testing environment would assume breakdowns by all testing participants to visualize the impact on a system’s integrity.
Cyborg Trading’s Algorithm Development Kit (ADK), part of its Cloud Trader platform, provides a customizable simulation environment in which the functionality, correctness and performance of algorithms can be tested before they are launched into production.
“Our simulator provides firms the tools to create any market event they can imagine, and then stress test the robustness of their strategies against it,” McInness said. “Its agent-based design allows for truly sophisticated simulations.”
As a “local sandbox test environment,” ADK provides a dedicated local development machine in order to test functionality throughout the development process, including debugging tools which provide full-code transparency.
The simulations performed using Cyborg’s ADK include authentic market dynamics, including realistic fills and order queue.
“We spent a lot of time looking at others’ simulation environments,” said McInness at Cyborg Trading. “When we went to build our own, we had the choice of either hooking into a market data feed or simulating market data with real people or artificial agents.”
Cyborg chose the latter approach because a simulated environment can recreate the market’s reaction to a trading strategy, something that would be impossible to do with actual market data.
“In low latency trading, small market dynamics can make a big difference,” McInness said. “Those market dynamics can be controlled and captured in a simulator.”
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