“I think of everything as a bet” - Ex-SIG Quant Trader Andrew Courtney

My Substack: https://ethankho.substack.com/ Spotify: https://open.spotify.com/show/0hQrPxi... Apple Podcasts: https://podcasts.apple.com/us/podcast... Former Susquehanna International Group (SIG) International ETFs Head Trader Andrew Courtney breaks down the reality of being a quant trader and market maker at one of the world's elite proprietary trading firms. He reveals what trading floors actually look like—multiple monitors covered with flashing numbers, signals, and price movements that traders analyze all day with zero lunch breaks and constant attention on market microstructure. Andrew explains how SIG's legendary poker training culture shapes traders' ability to think probabilistically, make decisions under uncertainty, and justify every bet both quantitatively and qualitatively. He shares candid insights about who should (and shouldn't) pursue trading careers, the transition from floor trading to electronic markets, and how the tight-knit network at prop trading firms differs dramatically from consulting or investment banking paths. Andrew now runs Kalshinomics, a prediction markets analytics tool, and writes The Whirligig Bear on Substack where he analyzes opportunities in Kalshi, Polymarket, and emerging prediction market platforms. He goes deep on finding edge in prediction markets—from identifying inefficient markets with liquidity incentives to using ChatGPT and AI tools for handicapping obscure Grammy categories. We also talk about... The real day-to-day of quant trading and market making at SIG: staring at screens all day, monitoring signals, and staying alert for when markets go off the rails Why SIG's poker training program—playing for hours daily, turning over cards after every hand, and defending each decision quantitatively—builds world-class traders How thinking in bets becomes second nature and why Andrew now frames every decision (like private school vs public school) as an expected value calculation The cultural differences between floor trading (loud voices, physical presence in the pit) versus upstairs electronic trading (surrounded by sharp peers and data) Why prop trading careers build narrow, dense networks compared to consulting or investment banking, and what that means for long-term career optionality Finding edge in prediction markets: liquidity incentives, identifying who you're trading against, and why some markets are wildly inefficient Using AI and LLMs for forecasting: prompting ChatGPT as a "super forecaster," the limitations of current models, and why the LLM layer was the weakest part of his Grammy bet Trading strategy and bet sizing: when to use Kelly criterion, how to scale into positions, and Bayesian updating based on how the market reacts to your trades Why hyped markets (meme coins, viral events) often present contrarian opportunities and how to identify which side casual money is on The insider trading debate in prediction markets and why Andrew thinks it's corrosive to incentives, trust, and long-term market quality Whether prediction markets are good for society: the value of probabilistic news context versus the risk of casino-ification and degenerate gambling Career advice for aspiring traders: evaluating if you can handle constant screen time, limited networks, and high-variance outcomes How to apply expected value thinking to everyday life: insurance decisions, risk tolerance, and when not to over-optimize (don't EV calculate marriage) Why Kalshinomics focuses on analytics and custom interfaces for serious traders rather than trying to be the "Bloomberg Terminal" of prediction markets Lessons from SIG on decision-making, probability, and building systems that extract signal from noise in high-frequency, high-stakes environments 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