In the high desert plain of New Mexico, Roger Hunter monitors automated trades on hog futures and currency pairs.
Roger Hunter in his home office.
Photographer: David Paul Morris/Bloomberg
Four computer screens display a dizzying array of price charts and program codes in the office of his single-story, thatched adobe home in the town of Las Cruces. Out back, where scrub brush stretches into the arid plain between the nearby mountains and the Rio Grande, is a 50-foot-tall wireless Internet tower.
The 66-year-old former math professor turned DIY quant apologizes if he appears out of sorts. As chief technology officer of a two-man startup called QTS Capital Management, Hunter just pulled an all-nighter fixing a systems glitch.
It’s a far cry from Wall Street, but Hunter wouldn’t have it any other way.
“You are away from the hubbub and frantic activity of New York and therefore can be much calmer and more thoughtful,” Hunter said one recent morning, grinding hand-roasted coffee beans. His Kiwi accent is still thick, three decades after decamping to New Mexico to teach abstract algebraic theory. “This is particularly true when developing code and exploring new strategies.”
Hunter in the foothills of the Organ Mountains.
Photographer: David Paul Morris/Bloomberg
Even as technology reshapes industry after industry, the huge leaps in computing power are transforming modern finance in ways that few have ever imagined and giving tech-savvy, DIY types like Hunter the tools to compete on a shoestring. Armed with little more than open-source software and an Internet connection, this growing cadre of like-minded startups has razed virtually every barrier to entry in the 40-year-old world of quantitative investing -- where mathematicians code software to profit from price patterns.
And that’s leaving the status quo of once-unassailable titans like Renaissance Technologies and D.E. Shaw exposed to ever more threats. The rise of these upstarts comes as the proliferation of machine-based strategies has made it harder for traditional players to succeed. In January, Martin Taylor of Nevsky Capital closed his 15-year-old hedge fund, lamenting the distorting influence of computer traders.
“Technological edge is harder to come by because the more egalitarian these tools have become, the more difficult it is to come up with something truly new,” said Andrew Lo, finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm.
Alone, no do-it-yourself quant can match the power, the influence or the money of the industry’s stalwarts. But together, there’s little doubt the DIY crowd is changing the face of financial markets and raising fundamental questions about the industry’s future.
As an ever-increasing pool of quant traders vie for an edge, the “alpha” in finance-speak, some envision a world where so many algorithms are unleashed on electronic markets that old-fashioned research is rendered obsolete and sudden shocks -- such as August’s meltdown in the U.S. stock market -- become all too frequent.
When Hunter arrived in Las Cruces to teach at New Mexico State University after completing his Ph.D. in mathematics from the Australian National University, he never imagined falling in love with the American Southwest, let alone becoming a quant.
An inveterate tinkerer and autodidact with a passion of new technology, Hunter began writing code on the side and developed an early version of software still used by the Federal Reserve to handle advanced math formulas. In the 1990s, he quit teaching and later began dabbling in automated strategies. Hunter teamed up with his partner Ernie Chan after stumbling on one of his investing books, launching QTS as a full-fledged quant fund two years ago.
It’s difficult to know precisely how many startups there are. And in an industry that analysts estimate has ballooned to over $1 trillion in the past year, no good numbers exist. But it’s clear their ranks are swelling.
Membership to Quantopian, a Boston-based firm which started in 2011 to provide coders the tools and software they need to build quantitative strategies, has gone from 1,570 to more than 66,000 today. That figure more than doubled in the past year alone and record numbers joined in January, fueled by the turmoil in global financial markets.
In Manhattan, the elite hedge-fund set still rules, having built up the status and the reputation that comes with years of outsize returns. Point72 Asset Management’s quant business in midtown, with its large glass conference rooms and white walls adorned with founder Steve Cohen’s personal art collection, looks and feels nothing like a startup.
And according to Ross Garon, the head of that business, it has little to fear. Even as the open-source movement gains momentum, the biggest firms still have the best technology and brightest minds (not to mention the most money), to stave off any threat posed by smaller shops, which have yet to prove their staying power.
“The democratization of tools doesn’t necessarily mean there’s the democratization of good judgment of what to research,” said Garon, who runs Cubist Systematic Strategies at Point72, which oversees about $11 billion.
Despite those disadvantages, Hunter and Chan, who works from Niagara-on-the-Lake in southern Ontario, have held their own. QTS returned 12 percent last year, easily outstripping the U.S. stock market and the average for hedge funds globally. Since 2014, QTS assets have grown more than fivefold to $22 million.
To keep costs low, QTS uses a service called AlgoSeek to rent tick data for futures, pulling in “astronomical” amounts of information for $500 a month. The firm employs part-time contractors and uses services like Amazon Web Servers to work out complicated models. Hunter himself wrote the code that QTS’s options trade on, which would otherwise cost nearly $50,000.
“There’s a threat they’re missing,” Dan Dunn, who oversees product management and membership at Quantopian, said of traditional quants. “The reality is there are brilliant people all over the world who they have never seen or heard of until they show up and eat their lunch.”
Yet in many ways, the quant industry has become a victim of its own success. Easy adoption is leveling the playing field and making it harder to score easy profits, said David McLean, a finance professor at DePaul University. He cites research showing that three years after an academic paper on an automated strategy is published, returns based on that strategy are cut by more than half.
JPMorgan’s Marko Kolanovic pointed to the perils of a quant trade grown crowded in the events of August, when U.S. stocks plummeted 11 percent in six days. Many blamed China and the Federal Reserve. Kolanovic told clients automatic selling by “price insensitive” quants made everything worse.
Where Kolanovic sees danger, Hunter senses opportunity. QTS is considering developing code to profit from distortions that manifest in managed futures when too many quants trade the same strategies.
“We’ve thought about trying to take advantage of it, certainly if the algorithm is clearly affecting the market,” Hunter said.
Doing it yourself has never been easier. Many open-source coding languages like R and Python, which are building blocks for critical number crunching, are posted for free on online libraries. Boutique services like Estimize crowd-source earnings estimates.
“There’s so much data, so much open-sourced software and computing power available,” said Emanuel Derman, director of Columbia University’s financial engineering program and the former head of quantitative risk strategy at Goldman Sachs Group Inc. “You can get up to ground level in no time at all.”
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On any given day, Hunter is testing 10 different models while executing eight strategies for clients. He often stares at his screens for hours, tweaking programs and e-mailing QTS’s brokers and data providers. His time horizon is short -- each trade takes at most a few days and is executed via a data center in New Jersey. Soon, Hunter is thinking about starting another office in his native New Zealand, so he can travel there more and continue to work.
Outside, it’s quiet. The only sound is the faint hum of a truck harvesting pecans, shaking the trunk so the nuts fall into a barrel. On warmer days, it’s not unusual to find Hunter stepping out to sip tea with his wife Sally.
It’s a life he would never dream of giving up for a bigger fund in Manhattan, especially when he has everything he needs right at home.
The big players, “they’re obviously doing well, but it’s different,” Hunter said. “I’m in my little office and I can choose to do whatever I want to do. You don’t even need to be in a certain time zone, it doesn’t matter.”