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Algo Development 2.0 Looks to Open Source, Cloud & Big Data
Traders Magazine Online News, July 11, 2018
In the next five to 10 years, don’t be shocked if hedge funds begin to outsource their algorithms to a team of quants coding in Python using the cloud and open source tools.
That was one of the future trends discussed on an Algorithmic Trading 2.0 panel at the Intelligent Trading Technology’s Summit hosted by A-Team Group last month in New York.
Banks and hedge funds that are hiring teams of quants internally may consider outsourcing the development of algorithms to the global community of quants, said Jared Broad, CEO of QuantConnect on Algorithmic Trading 2.0.
This may seem counterintuitive since Wall Street guards its intellectual property under lock and key. But with advances in cloud computing and open-source technologies, could crowdsourcing of algorithms be the next step in a sharing economy?
Open Source
While the financial services industry was an early adopter of open source software going back to the Linux operating system in 1991 and the FIX Protocol in the late 1990s, financial firms may have restrictions on contributing code back to the wider open source community.
“When it comes to trading algorithms there is a secret sauce embedded there that I don’t think people ever want to open source,” said Bill Harts, senior advisor to the Modern Markets Initiative, who moderated the panel. Harts, who has been an early adopter of algorithmic trading at Citi, Goldman Sachs and Bank of America, said: “That’s how they make money. Where do you draw the line?” asked Harts.
The case brought against Sergei Aleynikov, a former Goldman Sachs computer programmer who was prosecuted twice for allegedly copying confidential computer source code from his employer, illustrates the grey area of mixing open source with proprietary code. Aleynikov admitted downloading some source code, but maintained his intent was to collect open source software that is not proprietary to his employer. The FBI, which arrested him, said that he was going to use the code to implement a high frequency trading strategy at a new employer. Currently, Aleynikov is appealing a third conviction by a New York state appeals court, reported The New York Times in “A Former Goldman Employee’s Long, Strange Legal Odyssey.”
However, Broad argues that the financial industry is 10 years behind other industries in terms of utilizing open source tools and crowdsourcing.
“Global banks and funds will often invest millions of dollars building their own proprietary technology because of a perceived notion that it will give them an edge in the markets,” he said in a follow up interview. For the vast majority of organizations, it’s simply a heavy technical burden and an additional cost to carry that makes them increasingly inefficient,” he said.
On the panel experts discussed market and technology trends that are impacting algorithmic development including regulations, access to cloud computing for backtesting, demand for transparency, and compliance with global testing standards.
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