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A “Futurehunter” Examines the Dangers of Stock Trading at the Speed of Light [Excerpt]

Highly automated systems for buying and selling promise big returns for the fastest traders, but such systems cannot always be controlled
futurist,Intel,finance



From "Humanity in the Machine: What Comes after Greed? (Volume 1)," by Brian David Johnson. Published by York House Press, Ltd.

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In this excerpt from Humanity in the Machine: What Comes after Greed? futurist Brian David Johnson describes his job as a “futurehunter” and his fascination with both algorithmic trading and high-frequency trading (HFT). Both methods use computer programs to automate and accelerate the execution of certain financial transactions. Yet heavy reliance on such technology in the finance world can be dangerous—the Flash Crash of 2010 cited by Johnson stands out as a prominent example.

From Humanity in the Machine: What Comes after Greed? (Volume 1), by Brian David Johnson. Published by York House Press, Ltd. Excerpted with permission from the publisher. All Rights Reserved. Copyright © Brian David Johnson, 2013.

I go into the black box
As a futurist, a big part of my job is having a clear and actionable understanding of the future. But the other part of what I do is to travel the world talking to people about that future and looking for examples of how that future could be happening today. The science fiction writer William Gibson wrote, “The future is already here—it’s just not very evenly distributed.” This part of my work is futurehunting: searching for examples of the future happening today so that we can understand the human, cultural, social, and other impacts it could have.

Because of futurehunting, I was fascinated with HFT and algorithmic trading. As we enter the age of Big Data and a time when we are living in a world surrounded by increasingly more intelligent computers, the sometimes murky and frantic world of HFT seems like a perfect futurehunting example of the future we are moving toward. HFT is an instance of the future playing itself out today. The Flash Crash of 2010 was the dark side of it.

Now that we have an understanding for how the technology works, let’s dig into the world of algo trading. What makes it tick? What do the practitioners have to say about it, and what are its broader implications?

When people talk about this world of financial machines, they often refer to something called the “black box.” This is a term used in high tech to describe a system that is a complete mystery. No one knows how it works except the people who designed it, and they won’t tell you about it. Black boxes worry people because the person who built and uses the black box has all the power.

This mystery is especially dangerous when the black box starts to malfunction, like it did during the Flash Crash of 2010, and people get hurt.

The rise and stall of algorithmic trading
“I want to talk to you about algorithmic trading,” I said into the phone. “I’m interested in high-frequency trading because it’s a kind of secret life of data.” “The peak of all this algo trading has happened,” Alan Wexelblat said quickly and with an easy assurance. “It’s over.”

Alan is a designer. Starting in 2008 at Lime Brokerage, he designed interfaces for the software for high frequency trading. He not only had a front-row seat to the rise of algorithmic trading, he was in the game. Lime Brokerage is a sophisticated technological brokerage firm known for its high-performance, low-latency infrastructure (meaning they have really fast data connections) and market-neutral best execution delivery (meaning they are unbiased as to the markets they choose to trade and do business in). They don’t trade their own funds. For this reason they were touted as neutral because they don’t do in-house trading. Lime won the “Biggest Innovators on Wall St” award from Security Technology Monitor and “Most Cutting-Edge IT Initiative” award from American Financial Technology.

Alan is a good guy. He’s married with two boys that are 13 and 10. Like most dads, he’s worried his sons are growing up too fast. Alan talks with the speed of an automatic weapon, and most of the time when he’s talking about algos, he’s deadly serious.

“The first time I was really introduced to the world of algos and high-frequency trading was when I came to Lime in 2008 and was talking with Tony (Anthony Amicangioli) the CTO at the time. He used a white board to outline his vision for ‘Brokerage 2.0.’ His idea was to make an equities brokerage built from the ground up around the idea of HFT. He wanted automation to replace nearly everything that floor traders traditionally did. That was the fall of 2008.”

“What did you think when you understood what he was talking about?” I asked. “What did it feel like to hear about HFT for the first time?”

“To me the big idea, the real thing, never was HFT itself. I thought the really big idea was always about what you could do if you assumed that HFT was just table stakes…what if that was just the beginning.”

The Flash Crash of 2010 may have brought algorithms and high frequency trading to the public’s attention, but it had caught the eye of a few reporters a little earlier. On Friday, July 24, 2009, nearly a year after Alan had seen it for the first time, high frequency trading hit the media mainstream.

Charles Duhigg wrote a piece for the New York Times that described nothing good with the technology of the practices of HFT. Duhigg wrote, “Powerful computers housed right next to the machines that drive marketplaces like the New York Stock Exchange enable high-frequency traders to transmit millions of orders at lightning speed and, their detractors contend, reap billions at everyone else’s expense. These systems are so fast they can outsmart or outrun other investors, humans and computers alike. After growing in the shadows for years, they are generating lots of talk.”

On August 9, 2010, just three months after the flash crash, the New York Stock Exchange (NYSE) opened its Mahwah, N.J., data center.

“While the change won’t be noticeable to stock traders or investors, it’s a significant moment for the growing market for low-latency trading,” wrote Rich Miller for [the] Data Center Knowledge [Web site]. He continued, “The new data center features colocation space for trading firms seeking high-speed access to the matching engines. NYSE Euronext says it has sold out all the available colocation space in its first phase at Mahwah—reported to be at east two 20,000 square-foot pods.”

Staff reporter for the Wall Street Journal Scott Patterson explained the effect of Mahwah on the world of trading in his 2012 book “Dark Pools,” writing, “The (New York Stock) exchange was all but dead. Mahwah was the new floor, a powerful confluence of capitalism and state-of- the art computer technology. While tourists snapped photos of the exchange’s marble façade on the corner of Wall and Broad, the real trading was taking place 30 miles away in Mahwah’s vast air-conditioned floors of computer servers.”

Why did trading firms need to be co-located with the new hub of the NYSE? Because in the world of algo trading, the speed of light matters. There are few businesses in which the speed of light really matters, but in the world of high frequency trading, pushing up against the speed of light is an integral part of the entire industry. The thing that allows the algos to make so many trades so quickly is that they are hooked directly into the massive servers that now run the world’s trading markets. Like the NYSE’s Mahwah data center, trading no longer takes place as it had for most of the last two centuries. Gone are the days of frantic traders yelling ”Buy!” and ”Sell!” on the floor. Today, all trading happens via computer code in a server. It all happens in a black box.

Like the NYSE, most markets give some trading firms direct fiber optic access into the trading servers. In a fiber optic cable, data (the old ”Buy” and ”Sell” yells of the traders) travel via light. Light can only go so fast (also known as the speed of light). When you want to make a trade faster than your competitor, where you put your server matters, because the speed of light matters.

Let’s pretend you and I are competing to buy a stock on the NYSE at the cheapest price. You and I both send our “Buy” order at the same time. Whoever buys the stock first gets it at a lower price than the person who comes in second, because you and I are buying so much stock that the value of the stock will go up when either of us buys it. Your server is based in Chicago, and my server is cozied up next to the NYSE servers in New Jersey. When we hit “Buy” at the same time, I win, because both of our “Buy” commands travel at the speed of light but my “Buy” command has less distance to cross, so I get there first. I win. You lose. When you’re dealing with the speed of light (also known as latency), distance matters.

Technically what I’ve just told you is wrong, but it’s wrong at a micro level. Rarely does one order sweep out the liquidity—that’s one reason bids and asks are often in lots of 100. What happens is that I get it for a penny less than you do. This is assuming we’re talking generic highly liquid stocks. A hundred pennies means I paid a buck more on that trade. That doesn’t seem like a lot, but if I’m trying to move 100,000 shares that’s a thousand trades and I’m “losing” a buck on each which means it cost me a thousand dollars more to move my shares than I anticipated.

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