The Evolution and Revolution of the Trading Nerds

Looking for job security as a trader? “Learn to work well with big data.” That’s the assessment from David Leinweber, co-founder for Innovative Financial Technology and author of the book Nerds on Wall Street.

Leinweber provided the opening keynote address at TradeTech, a two-day conference in New York this week that was aimed at the Institutional Equity Trading market and the impact technology has on market structure and the people who work in that sector. 

“In the financial markets, big data comes in two flavors, structured and unstructured,” said Leinweber. “The structured is market data, which has become bigger and so fast that people are hard pressed to keep up with them. Even the machines people use are hard pressed to keep up with them.”

That’s why he believes one way to look at this is job security, but a different kind of job security than what we had in the past.

“Thirty years ago, if you wanted to hire a trader, you looked for people who played sports, and they tended to be tall so they stood out on the trading floor, but today you want the quants, the nerds, the geeks,” said Leinweber. “Today, the people who thrive are the people who work well with big data and with machines.”

Some of the skills required today include something he calls collaborative intelligence, which he says is a new role for traders. Being able to exploit information and understand their complexities, while having strong language and conceptual abilities, is crucial.  

When stuff is happening in micro-sections, says Leinweber, you need what he calls “algo jockeys” to make sure the machines are working properly. He likens it to putting your car on automatic pilot. The computer is able to control certain things like speed, but you still have to step in and make those quick turns.   

In the markets, with the machines streaming huge amounts of data, problems occur such as the flash crash, which happened when trading algorithms ran amok and the prices on some stocks dropped to near zero.

What the machines needed was human participation, who could see what the algorithms couldn’t see and that the overwhelming flow of information had created a logjam and a situation similar to what happens when you stream a movie over the internet. Every now and then the picture goes a little whacky.

“All computer systems have inherent capacities,” notes Leinweber, “and we have to keep remembering that, especially with the buy side trying to keep up with the market sprinters. Algo trading systems have begun to look like an arms race.” 

This, says Leinweber, is where an “algo jockey” can step in “whenever there’s unusual activity, where new patterns emerge that were not seen before.” This is also when the machines go awry. The algorithms may have misinterpreted an event. In fact, Leinweber believes the flash crash was caused by robot high frequency trading programs misinterpreting the data. “Instead of usual markets, the robot traders saw buy high and sell low and reacted by saying let’s get out of here,” said Leinweber.

Another area that is more amendable for humans is in the area of “unstructured big data” which includes everything on the web, mainstream media news, blogs and social media. There is software that pulls out certain words, but humans can still do a better job of analyzing this data.