Jane Street's trading floor can be a peculiar place
High frequency trading's quantitative approach can make markets seem like a game. At Jane Street, the gamification was once extreme.
Going Infinite, the Michael Lewis book on Sam Bankman-Fried claims that while SBF worked for Jane Street between 2013 and 2016, the firm used some interesting sounds to make the environment more fun.
When SBF was there, Lewis says the Jane Street trading floor was "one big room with lots of weird sounds." Each corresponded to a different issue that needed to be addressed. Some were obvious pop culture soundbites, such as Homer Simpson's "D'oh!" or the Mario 1-Up sound. Others were more niche, including "you must construct additional pylons," a very early meme from the 1998 video game StarCraft.
The noises were pervasive. One female phone interviewee said that she believed the trader she spoke to was playing video games because the noises were so loud.
What makes a good Jane Street trader?
Lewis says, "a great trader at Jane Street was not a great trader unless he could explain why he was a great trader." An anonymous Jane Street alumni said that leaders ask themselves "why are you great and how do we replicate you?"
For example SBF noticed a continuous lag in pricing between certain South Korean and Japanese stocks. Acting upon this would make money, but it wouldn't be enough for Jane Street. He instead had to discover the root of the anomaly: a bank trader would execute South Korean trade orders before logging off, pushing Japanese trades to their Japanese coworkers.
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Lewis says it's all about "meta level questions" at Jane Street. The process matters more than the result, and the more data you have, the better.
A case in point is SBF's well covered 2016 election model; he was able to accurately predict voting results in the Trump/Clinton election well ahead of time, but made the wrong trading decisions with the data, causing what was the biggest single loss in the firm's history. Rather than be scolded for the loss, he was praised for the model.
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