ON THE THIRD FLOOR of Citigroup’s Manhattan headquarters, at the far end of a trading floor overlooking the Hudson River, Young Kang, Citi’s global head of algorithmic products, leans over a terminal and monitors the progress of a canny and powerful beast named Dagger. Bred and trained in secret by Citi’s financial engineers, Dagger can stalk through more than 20 markets, public and otherwise—hunting for anomalies, buying and selling, prowling through mountains of historical data—all at the behest of Citi’s clients. Amid the trading-floor din, Dagger fulfills its duties in flickering silence, with a speed and acuity no human can match.
“It’s self-learning,” Kang says. “The numbers keep updating, the strategy keeps adjusting itself. It gets smarter.”
And it makes a lot of money. Algorithms like Dagger can exploit the smallest inefficiencies in the market. They can parse trades in millionths of a second. Some species can detect other algos embarking on predictable trading strategies, and ruthlessly adjust their techniques. They’re growing ever more complex, subtle, and sophisticated. And as they become more popular, they’re creating some serious headaches for regulators.