Market Data Firm Spots the Tracks of Bizarre Robot Traders

Nanex 2.jpg

Mysterious and possibly nefarious trading algorithms are operating every minute of every day in the nation's stock exchanges.

What they do doesn't show up in Google Finance, let alone in the pages of the Wall Street Journal. No one really knows how they operate or why. But over the past few weeks, Nanex, a data services firm has dragged some of the odder algorithm specimens into the light.

The trading bots visualized in the stock charts in this story aren't doing anything that could be construed to help the market. Unknown entities for unknown reasons are sending thousands of orders a second through the electronic stock exchanges with no intent to actually trade. Often, the buy or sell prices that they are offering are so far from the market price that there's no way they'd ever be part of a trade. The bots sketch out odd patterns with their orders, leaving patterns in the data that are largely invisible to market participants.


Alexis Madrigal:
Explaining Bizarre Bot Trader Behavior

Joe Flood:
How Algorithmic Trading Works
Timothy Lavin:
Monsters in the Market
Alexis Madrigal:
Tech and the Flash Crash

In fact, it's hard to figure out exactly what they're up to or gauge their impact. Are they doing something illicit? If so, what? Or do the patterns emerge spontaneously, a kind of mechanical accident? If so, why? No matter what the answers to these questions turn out to be, we're witnessing a market phenomenon that is not easily explained. And it's really bizarre.

It's thanks to Nanex, the data services firm, that we know what their handiwork looks like at all. In the aftermath of the May 6 "flash crash," which saw the Dow plunge nearly 1,000 points in just a few minutes, the company spent weeks digging into their market recordings, replaying the day's trades and trying to understand what happened. Most stock charts show, at best, detail down to the one-minute scale, but Nanex's data shows much finer slices of time. The company's software engineer Jeffrey Donovan stared and stared at the data. He began to think that he could see odd patterns emerge from the numbers. He had a hunch that if he plotted the action around a stock sequentially at the millisecond range, he'd find something. When he tried it, he was blown away by the pattern. He called it "The Knife." This is what he saw: 


"When I pulled up that first chart, we saw 'the knife,' we said, that's certainly algorithmic and that is weird. We continued to refine our software, honing the algorithms we use to find this stuff," Donovan told me. Now that he knows where and how to look, he could spend all day for weeks just picking out these patterns in the market data. The examples that he posts online are just the ones that look the most interesting, but at any given moment, some kind of bot is making moves like this in the stock exchange.

"We probably get 10 stocks in any 10 minutes where we see something like this," Donovan said. "It's happening all the time."

These odd bots don't really make sense within the normal parameters of the high-frequency trading business. High-frequency traders do employ algorithms to look for patterns in the market and exploit them, but their goal is making winning trades, not simply sending quotes into the financial ether.

Here's the way a stock trade is supposed to work: a buyer says they'll pay some amount for 100 shares of a company, a seller makes an ask for slightly more money, and the two of them usually meet in the middle. Perhaps a middle man (no joke intended) helps match buyer and seller and takes a cut. That's the role that a lot of high-frequency traders play: they help make markets work. Regulatory changes over the past several years have extended their usefulness and provided a nice business model for those that can move quickly to provide options for buyers and sellers.

"Under the maker-taker model, market participants that offer to provide, or make, liquidity by posting an order to buy or sell a certain number of shares at a particular price receive a rebate," explained Michael Peltz in a June feature for Institutional Investor. "Those that execute against that order -- that is, take the liquidity -- have to pay a fee. Exchanges earn the difference between the rebate they pay and the fee they charge. The SEC limits taker fees to 0.30 cents a share; rebates tend to be lower for economic reasons, but for high frequency firms trading millions of shares a day, they can make for a pretty good living."

In a sense, they take nickel-and-diming down an order of magnitude or two. The advantage is that their trades are low-risk: they rarely hold positions for very long and any individual stock, future, or currency can't really sink the boat. High-frequency traders have become a target for all kinds of people, but most of them appear to make their money being a little faster and little smarter than their competitors. And if they are playing by the rules, they improve the quality of markets by minuscule amounts trade after trade after trade.

Presented by

How to Cook Spaghetti Squash (and Why)

Cooking for yourself is one of the surest ways to eat well. Bestselling author Mark Bittman teaches James Hamblin the recipe that everyone is Googling.

Join the Discussion

After you comment, click Post. If you’re not already logged in you will be asked to log in or register.

blog comments powered by Disqus


How to Cook Spaghetti Squash (and Why)

Cooking for yourself is one of the surest ways to eat well.


Before Tinder, a Tree

Looking for your soulmate? Write a letter to the "Bridegroom's Oak" in Germany.


The Health Benefits of Going Outside

People spend too much time indoors. One solution: ecotherapy.


Where High Tech Meets the 1950s

Why did Green Bank, West Virginia, ban wireless signals? For science.


Yes, Quidditch Is Real

How J.K. Rowling's magical sport spread from Hogwarts to college campuses


Would You Live in a Treehouse?

A treehouse can be an ideal office space, vacation rental, and way of reconnecting with your youth.

More in Technology

Just In