How Algorithmic Trading Works

Editor's note: After our story on the odder types of robot traders, we planned to step back and explain standard high-frequency algorithmic trading strategies. As it turned out, Joe Flood, author of the critically acclaimed book, The Fires: How a Computer Formula, Big Ideas, and the Best of Intentions Burned Down New York City-and Determined the Future of Cities, had beaten us to it. His feature for the magazine ai5000, a trade journal for investors, offers a pithy and readable explanation of just how robot traders work. ai5000 has generously allowed us to excerpt from Flood's feature here.

It's been a tough year for high-frequency trading. It started last July, when a former Goldman Sachs computer programmer was arrested for allegedly stealing proprietary high-frequency computer code. Few people had any idea what high-frequency trading really involved, but this was the summer of discontent for the recently profitable, but publicly reviled, Goldman "Vampire Squid" Sachs. A few weeks later, The New York Times ran a cover story that credited the trading technique with being able "to master the stock market, peek at investors' orders, and, critics say, even subtly manipulate share prices," to the tune of "$21 billion in profits," during the financial cratering of 2008 (the real number seems somewhere in the $2 to $8 billion range).

Then, this past May 6, saw a trillion dollars in stock market value spontaneously evaporate, only to mysteriously re-condense moments later in what the media dubbed the "Flash Crash," even though flash trading had nothing to do with it. Months later, regulators and traders still aren't sure what happened, but most of the inquiries (and uproar) have focused on high-frequency trading.

"We lump every crime in the world onto high-frequency trading," says Professor Bernard Donefer of New York University's Stern School of Business, who ran electronic trading systems for Fidelity Investments before moving into academia. "It's my opinion that, in fact, the big problem with high-frequency trading is that there is no such thing. There are a whole series of techniques and strategies that use low-latency technology [i.e., super-fast computers and electronic networks], and what you have to do is examine each one of those strategies and techniques and see what it does."

Algorithmic Trading:

During World War II, one of the military's biggest problems was prioritizing the infinite combinations of soldiers, weapons, supplies, and replacement parts that needed to be shipped to the front lines--until the arrival of a young Air Force officer named George Dantzig. In his first year at Berkeley, Dantzig had solved what he thought were homework assignments but were, in fact, two of the great unsolved problems in statistics. Over time, the details of the story changed and it was misattributed to a handful of famous mathematicians (and a recondite janitor in the movie Good Will Hunting) but Dantzig was the source. Almost 70 years later, variations on the "Simplex Method" that Dantzig devised for military supply-line problems are used to slice and dice large trades, spread them out over different exchanges, and either execute them with lightning speed or space them out over time, all in the hopes of minimizing price-slippage. While technically all quant strategies and forms of high-frequency trading employ algorithms, this is the sub-specialty most commonly labeled "algorithmic trading."

Impact and Upside: Like automated marketmaking, algorithmic trading helps provide liquidity and lowers spreads and commissions. It also affects how markets function: drop a large rock into a bucket of water, and there's a good chance the water will slosh around and spill over the sides; drop pebbles into the bucket and you can add just as much rock without any spills.

Downside: Algorithmic trading can hide the identity of large buyers and sellers to prevent speculators from guessing the overall size of the trade and getting in front of it. The speculators, in turn, use a series of baits and cancellations to test the market, looking for patterns and weaknesses. The result is a lot of complicated, high-speed, game-theoretic duels and micro-volatility, all of it in the name of hiding information. As Gene Fama will tell you, a transparent market is an efficient market.

Read the rest of the story at ai5000.

About the Author

Joe Flood is the editor at large of ai5000, and the author of The Fires, a story about computer modeling, urban planning and New York City's 1970s fire epidemic and fiscal crisis. The Fires has won the Bronx Recognizes It's Own (BRIO) Award and been named an Amazon.com Book of the Month .

More comfortable online than out partying, post-Millennials are safer, physically, than adolescents have ever been. But they’re on the brink of a mental-health crisis.

One day last summer, around noon, I called Athena, a 13-year-old who lives in Houston, Texas. She answered her phone—she’s had an iPhone since she was 11—sounding as if she’d just woken up. We chatted about her favorite songs and TV shows, and I asked her what she likes to do with her friends. “We go to the mall,” she said. “Do your parents drop you off?,” I asked, recalling my own middle-school days, in the 1980s, when I’d enjoy a few parent-free hours shopping with my friends. “No—I go with my family,” she replied. “We’ll go with my mom and brothers and walk a little behind them. I just have to tell my mom where we’re going. I have to check in every hour or every 30 minutes.”

Those mall trips are infrequent—about once a month. More often, Athena and her friends spend time together on their phones, unchaperoned. Unlike the teens of my generation, who might have spent an evening tying up the family landline with gossip, they talk on Snapchat, the smartphone app that allows users to send pictures and videos that quickly disappear. They make sure to keep up their Snapstreaks, which show how many days in a row they have Snapchatted with each other. Sometimes they save screenshots of particularly ridiculous pictures of friends. “It’s good blackmail,” Athena said. (Because she’s a minor, I’m not using her real name.) She told me she’d spent most of the summer hanging out alone in her room with her phone. That’s just the way her generation is, she said. “We didn’t have a choice to know any life without iPads or iPhones. I think we like our phones more than we like actual people.”