Flash Boys: A Wall Street Revolt by Michael Lewis Read Free Book Online Page A

directly by human beings and more directly by machines. The machines were overseen by people, of course, but few of them knew how the machines worked. He knew that RBC’s machines—not the computers themselves, but the instructions to run them—were third-rate, but he had assumed it was because the company’s new electronic trading unit was bumbling and inept. As he interviewed people from the major banks on Wall Street, he came to realize that they had more in common with RBC than he had supposed. “I’d always been a trader,” he said. “And as a trader you’re kind of inside a bubble. You’re just watching your screens all day. Now I stepped back and for the first time started to watch other traders.” He had a good friend who traded stocks at a big-time hedge fund in Stamford, Connecticut, called SAC Capital. SAC Capital was famous (and soon to be infamous) for being one step ahead of the U.S. stock market. If anyone was going to know something about the market that Brad didn’t know, he figured, it would be them. One spring morning he took the train up to Stamford and spent the day watching his friend trade. Right away he saw that, even though his friend was using technology given to him by Goldman Sachs and Morgan Stanley and the other big firms, he was experiencing exactly the same problem as RBC: The market on his screens was no longer the market. His friend would hit a button to buy or sell a stock and the market would move away from him. “When I see this guy trading and he was getting screwed—I now see that it isn’t just me. My frustration is the market’s frustration. And I was like, Whoa, this is serious .”
    Brad’s problem wasn’t just Brad’s problem. What people saw when they looked at the U.S. stock market—the numbers on the screens of the professional traders, the ticker tape running across the bottom of the CNBC screen—was an illusion. “That’s when I realized the markets are rigged. And I knew it had to do with the technology. That the answer lay beneath the surface of the technology. I had absolutely no idea where. But that’s when the lightbulb went off that the only way I’m going to find out what’s going on is if I go beneath the surface.”
    THERE WAS NO way he, Brad Katsuyama, was going to go below the surface of the technology. People always assumed, because he was an Asian male, that he must be a computer wizard. He couldn’t (or wouldn’t) program his own VCR. What he had was an ability to distinguish between computer people who didn’t actually know what they were talking about and those who did. The very best example of the latter, he thought, was Rob Park.
    Park, a fellow Canadian, was a legend at RBC. In college in the late 1990s he’d become entranced by what was then a novel idea: to teach a machine to behave like a very smart trader. “The thing that interested me was taking a trader’s thought process and replicating it,” Park said. He and Brad had worked together at RBC only briefly, back in 2004, before he left to start his own business, but they had hit it off. Rob took an interest in the way Brad thought when he traded. Rob then turned those thoughts into code. The result was RBC’s most popular trading algorithm. Here’s how it worked: Say the trader wanted to buy 100,000 shares in General Motors. The algo scanned the market; it saw that there were only 100 shares offered. No smart trader seeking to buy 100,000 shares would tip his desire for a mere 100 shares. The market was too thin. But what was the point at which the trader should buy GM stock? The algorithm Rob built had a trigger point: It only bought stock if the amount on offer was greater than the historical average of the amount offered. That is, if the market was thick. “The decisions he makes make sense,” Brad said of Rob. “He puts an incredible amount of thought into them. And since he puts so much thought into his decisions, he’s capable of explaining those decisions to