There’s a lot of money to be made in high-frequency trading for those who can think outside the box. But barriers to entry, which include the need to make major investments in technology infrastructure, make it difficult for outsiders to break into the industry.
CloudQuant, a Chicago-based algorithmic trading startup, lets anyone try their hand at devising their own strategies. And if it works, they’ll pay the creator to put it into action.
“We built this Python-based research platform that we’ve been using for internal quantitative trading since 2011, but this year we created a website that gives anyone access to our research tools,” said CEO Morgan Slade. “There are people all over the world who have great ideas, yet a lot of large firms are recycling old ones.”
Unlike traditional investors, who buy and sell stocks and commodities based on where they think the market is going, quantitative traders build rule-based strategies that automatically get executed when specific conditions are met. Competing firms often race each other by the nanosecond to take advantage of momentary price fluctuations — unless they discover price patterns their competitors haven’t.
A former portfolio manager at Citadel Investment Group, Slade said he managed a team of 35 to 40 researchers with educations at the master’s and Ph.D. level. Even then, the number of strategies his team could execute was limited by staffing, not infrastructure.
By effectively opening up his research department to anyone with an interest in trading, Slade hopes to unlock new efficiencies of scale — and new approaches to familiar challenges, too.
“People aren’t going to do the same things we always do,” he said. “They’ll pursue whatever they think is interesting, as data scientists, as quantitative traders or as engineers.”
After launching its public-facing platform earlier this year, CloudQuant has already funded its first crowdsourced trading strategy to the tune of $15 million. The algorithm’s creator, an academic based in the United Kingdom, will receive 10 percent of the profits made by their strategy, Slade said — and more investments are on the way.
The target audience for CloudQuant are data scientists, developers and former finance professionals. The basic idea, said Slade, is to offer a lucrative side-gig for data-savvy people, regardless of their day jobs.
“We don’t expect them to be expert traders, or know anything about market structures or trade execution,” said Slade. “We have someone who works with them to get that set up, and we provide all the capital, so they don’t have any downside risk.”
Users of the platform have access to all the data professional traders do, Slade said, and they retain the rights to the strategies they try out on the platform. In fact, CloudQuant can’t even see the inner workings of a crowd-generated algorithm unless the user agrees to explore a potential partnership.
Although CloudQuant strategies are coded in Python, the coding skills required to use the platform are minimal. According to Slade, a power user of Microsoft Excel can learn enough Python to devise strategies in a matter of hours. That’s important, he said, because it broadens the potential user base significantly.
Structured as a startup within Kershner Trading Group, the CloudQuant platform team fluctuates in size between four and 10, Slade said. The team also supports internal users within Kershner.
Images via Shutterstock and CloudQuant.