Tag Archives: HFT

The Anne Hathaway Guide to Stocks and Shares

Depending on how you look at it, this is either a harbinger of the emergent-model AI Singularity or a demonstration of the specious voodoo underpinnings of the automated financial markets… possibly both, if you’re a real pessimist [via Kottke.org].

A couple weeks ago, Huffington Post blogger Dan Mirvish noted a funny trend: when Anne Hathaway was in the news, Warren Buffett’s Berkshire Hathaway’s shares went up. He pointed to six dates going back to 2008 to show the correlation. Mirvish then suggested a mechanism to explain the trend: “automated, robotic trading programming are picking up the same chatter on the Internet about ‘Hathaway’ as the IMDb’s StarMeter, and they’re applying it to the stock market.”


Companies are trying to “correlate everything against everything,” [Bates] explained, and if they find something that they think will work time and again, they’ll try it out. The interesting, thing, though, is that it’s all statistics, removed from the real world. It’s not as if a hedge fund’s computers would spit the trading strategy as a sentence: “When Hathway news increases, buy Berkshire Hathaway.” In fact, traders won’t always know why their algorithms are doing what they’re doing. They just see that it’s found some correlation and it’s betting on Buffett’s company.

Now, generally the correlations are between some statistical indicator and a stock or industry. “Let’s say a new instrument comes to an exchange, you might suddenly notice that that an instrument moves in conjunction with the insurance sector,” Bates posited. But it’s thought that some hedge funds are testing strategies out to mine news and social media datasets for other types of correlations.

Crazy, right? Well, irrational on one level, perhaps, but those trading algos are big (bad) business: remember the guy who was accused of stealing some algo code from Goldman Sachs? Eight year stretch [via BoingBoing].

The Flash Crash and the trouble with transparency

A report at Ars Technica compares the computerised financial markets to a vast and infernally complex piece of multi-threaded software running on hardware that was never designed to cope with it (or vice versa), before telling us what I suspect most of us have already guessed: it’s a gigantic house of electronic cards. But ironically enough, part of the problem stems from the very transparency that the shift to electronic trading was supposed to bring with it:

Unlike the market of an earlier era, where humans executed trades by talking to (and shouting at) one another, the electronic communication networks (ECNs) that emerged in the late 70s logged every detail of every trade for later auditing. No more “he said, she said” when resolving a dispute or ferreting out fraud—just go to the tape. But then came the flood.

After a solid decade of moving almost all trading activity onto electronic systems (the NYSE floor is just there for show at this point), the market generates so much data that it’s nearly impossible for a mere governmental agency like the SEC to analyze. There are literally tens of thousands of quotes per second in hundreds of thousands of symbols across multiple electronic exchanges—the SEC would need the brain and computer power of the NSA to even begin to do a credible job of crunching this many numbers for a credible post mortem.


The amount of data isn’t just a problem for regulators. Much of the report details how the systems of the market participants were themselves overwhelmed in real-time with the sudden surge of digital information. Processing began to slow, queues filled, backlogs developed, and machines were eventually pulled offline as the humans intervened and tried to sort out possible data integrity issues.

Beyond the challenges of reconstructing events, the traders also use some subset of the data firehose that the market’s machines throw off today as input to train the algorithms that will run the market tomorrow. So at some point, we’ll wake up and realize that it’s really turtles machines all the way down. Put that in your bong and smoke it, Keanu.

Ouch. And it gets worse, too; go read the whole thing. I think the best way to sum it up in layman’s terms is that we’ve turned the financial markets into something a little like one of those “game of life” software ecosystems… which would be quite a fascinating idea if it weren’t for the fact that unexpected interactions within that ecosystem can affect meatspace in a pretty serious way.

The more I learn about derivatives and futures and all that “clever” quant stuff, the more I think it’s a bunch of hubristic mathematical voodoo bullshit that we’d do well to get shot of sooner rather than later; the only people it really seems to benefit are the wankers who thought it all up in the first place.

Get up to speed on high-frequency trading

New York Stock Exchange buildingRemember that story we ran a few weeks back about the alleged theft of the Goldman-Sachs automated trading code?

Well, thanks to said case, Goldman-Sachs and the high-frequency automatic trading (HFT) practices that they dominate are increasingly sliding into the spotlight of Congressional scrutiny, so Ars Technica have knocked up a brief guide to what it’s all about. If you thought “the markets” were those guys in suits shouting at each other on the trading room floor, think again. [image by Coffee Maker]

If you look under the hood of the markets in 2009, you’ll find that the trading floor has been replaced by electronic networks; the frantic, hand-signaling traders have been replaced by computer systems; and all of moves in the trader’s dance—a thousand little tricks and techniques (some legal, some questionable, and some outright illegal) for taking regular advantage of speed, location, and information to generate profits—are executed hundreds of times per second, billions of times per day. And the whole enterprise is mainly powered by the same hardware from Intel, AMD, and NVIDIA, that Ars readers use for gaming.


Only about three percent of the trading volume on the NYSE is actually carried out by means of traditional “open outcry” trading, where flesh-and-blood humans gather to buy and sell securities. The other 97 percent of NYSE trades are executed via electronic communication networks (ECNs), which, over the past ten years, have rapidly replaced trading floors as the main global venue for buying and selling every asset, derivative, and contract. So the ECNs are the markets in 2009, and those pit traders who pose for the cameras are mainly there for the cameras.

In other words, Josephine Average Stock-Trader is going head to head with supercomputers every time she dips a toe into the game. The ECN algorithms specialise in making millions of tiny trades, each making fractions of pennies of profit – small beer when considered in isolation, but big profits when scaled up to the sheer volume of transactions that these systems can handle.

It’s like a vast virtual ecosystem of predatory code-critters; go find out more about it. Know thy enemy, and all that.