Main image of article Gig Economy's Next Target: Trading Algorithms
This article was originally published on eFinancialCareers The crowdsourcing model for developing trading algorithms has gained traction on the buy side and is picking up momentum. Just as Uber and Airbnb have disrupted their respective industries, it is only a matter of time before “the gig economy” makes an impact on the sell side as well. Tom Ducrot, the founder of venture-capital firm Fides+Ratio and a former executive director at Morgan Stanley, moderated a panel at the Battle of the Quants conference in New York that included four of the biggest players in the algo crowdsourcing space: Quantopian, Quantiacs, WorldQuant Challenge and CloudQuant. These firms are in effect crowdsourced quantitative hedge funds – rather than employ in-house developers, they host competitions and invite freelance developers to write trading algorithms, which are then back-tested. They put real money behind the best of the bunch, and the trading algo development gig workers get a cut of the profits. The way they tell it, this model is already starting to upend the asset management space and is likely to grow in significance. If you’re a great coder who would never be caught dead wearing a suit and tie or working at a bank or hedge fund, this could be your chance to get in the quantitative finance game – without changing out of your bathrobe or leaving the house.

Printer Repairman by Day, Trading Algo Developer by Night 

Jonathan Larkin, the CIO of Quantopian, whose resume also includes J.P. Morgan, Millennium Partners, Nomura, BlueCrest and Hudson Bay, said he believes the addressable market of people with STEM-based backgrounds is around 25 million globally. To date, the firm has attracted 140,000 people to its website who have used the research and data environment to back-test their trading strategies. A mere 25 have had their strategies licensed however: a success rate of just 0.02 percent. “We don’t profile people when they come on board, before we license the strategies we get to know them and do full background checks,” Larkin said. “We’ve licensed strategies from 25 people living on five continents, and the common denominator is their technical experience in a modeling field, typically not financial services, but everything from academia to the oil and gas industry and someone whose day job is calibrating printer jets. “If we license a strategy then we can act as a consultant, and our only revenue is as an asset manager operating strategies from our community,” he said. “It’s our intention to invest significant capital in these – right now it’s a maximum of $10 million per algorithm but we’re trying to ramp up to $50 million by the end of the year. “We pay people a fixed percentage of P&L just like I used to do in a former life working at a multi-manager hedge fund.” Larkin omitted to mention what that percentage is.

The Gig Economy Plus Data Science Plus Algorithmic Trading Equals Crowdsourced Hedge Funds

Martin Froehler, the CEO of Quantiacs, formerly with IdeaLab Research, identified two big trends that are converging: first, the decentralization of labor spurred by companies like Uber and Airbnb; and second, data science becoming more mainstream to the point that it is now frequently taught in universities. His firm hosts Python coding competitions and offers machine-learning libraries. “Crowdsourced algorithmic trading is at the intersection of these two friends,” Froehler said. “Quants get to retain their intellectual property, and the only risk that they run is that they don’t manage money, but if they do manage money, then there is unlimited upside.” For more on how remote contractors are changing how trading algorithms are coded, as well as how data science is influencing the evolution of trading strategies, check out the article on eFinancialCareers