Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice
Andrew Courtney (@andrewcourt1) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (@Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there.
"I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that."
SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x.
We cover:
- Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand
- The "one eye on the market, always" attention tax that destroys most people's careers
- How to find edge in prediction markets by asking: who am I actually trading against?
- Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades
- The insider trading debate in prediction markets — & why it's "socially corrosive"
- Floor trading vs. upstairs quant: why the transition saved his career
- 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading
- Why he doesn't have collision insurance on his car (& the EV math behind it)
Thank you so much @andrewcourt1 for coming on the pod!
Timestamps:
00:00 Intro
05:00 Floor trading vs. electronic trading
06:28 What makes an upstairs trader
10:16 Poker as trader training
13:00 Thinking in bets as a mental framework
15:11 Decision trees in real life
16:40 Where prediction markets actually have edge
19:00 Why the LLM forecasting layer falls short
19:40 Liquidity incentives and trading low-volume markets
22:00 Limiting downside even when the model is wrong
24:32 Executing in illiquid markets
25:44 Fair value vs. directional conviction
27:11 Bayesian updating when liquidity responds
28:40 Fading hype and crowded narratives
31:07 Longshot bias vs. fanbase bias
34:20 How to judge whether you really have edge
36:40 Building analytics tools for prediction markets
38:20 The temporary edge for smart amateurs
40:35 Where prediction markets fit best
41:20 Markets that shouldn’t exist
43:20 Why insider trading corrodes incentives
46:52 Are prediction markets a net good or bad
50:47 Minimizing degeneracy and maximizing signal
53:32 A simple EV mindset anyone can use