why poker is used to train traders

This video is the best articulation of why poker is used to train traders.

From the description:

Jerrod Ankenman, professional poker player, co-author of The Mathematics of Poker, and Quantitative Researcher at Susquehanna, explores the connections between poker and trading. Jerrod, who began his career playing poker and went back to earn his PhD, explains how concepts like probability, expected value, risk management, and game theory apply in both the card room and financial markets. Poker and trading both demand strong mathematical thinking, disciplined decision-making, and the ability to manage uncertainty under pressure. Jerrod shares lessons from his poker career (which includes a WSOP bracelet win) that translate directly into quantitative finance and trading strategy.

If you are a trader this video despite not being technical is alpha. It tells you where to look if you pay attention.

A few ideas I’ll re-emphasize:

SIG treats poker as a structured way to train probabilistic thinking. Jerrod structures the flow of the video as a parallel between 3 concepts in poker and their analogs in trading.

Ante
Decision practice
Interpreting outcomes

You’ve heard this before — both poker and trading require making decisions with incomplete information. But a more subtle point is about speed. The goal in both is the same:

“Make the decision now that’s as close as possible to what you’d decide if you had infinite time and information.”

Both poker and trading have an information structure of what’s private and what’s public. But also what’s behavioral. Examples of the trading version of these ideas:

Private info: proprietary models, internal data, trader knowledge sharing.

Public info: news, filings, Fed releases.

Behavioral info: order flow and price action. In other words, what others are doing.

This last point gets a lot of emphasis and maps strongly to poker because at its core, it recognizes that trading is an adaptive, adversarial game not a physics problem. It’s an evolving pattern recognition and categorization problem.

You can’t model every opponent individually, or every trade uniquely, because you shrink the statistical power of your findings. You must intelligently group similar situations into profiles. Familiar poker examples: “tight-aggressive regulars,” “recreational loose players”. In trading, profiles can account for who (ie retail), periodicity (time of day, time of month, and so forth), or why (index rebalance, hedging mandates).

The art is balancing taxonomy and specificity to have enough data to be statistically meaningful, but close enough to be relevant.

A few other powerful ideas:

The big risk isn’t volatility.

It’s being wrong about your edge. The market wiggles are the nature of the game, that’s not a risk.

[I’ll take a moment to repeat myself — if you blow out because the market moved, you were committing malpractice. Being aware that the market will do things amounts to no more than a toddler understanding of risk. Volatility shouldn’t keep you awake at night. That the exchange might cancel your trades or that they may ban orders should.

From Investopedia on the response to the Hunt Brothers’ silver squeeze:

Federal commodities regulators introduced special rules to prevent any more long position contracts from being written or sold for silver futures. This move stopped the Hunts from increasing their positions by temporarily suspending the fundamental rules of the commodities market. With longs frozen and shorts free to pile in, the price of silver began to slide.

From my floor days, I can tell you there’s lore about who knew the valuable bit of info that you were only going to be allowed to do opening trades on the short side. Exchanges were run by the traders and brokers before they went public. This is the weird black or grey swan stuff that bosses worry about.

A company going bankrupt? That’s a line item in portfolio_shock_analysis.xls, not something that makes you cry in public to your investors.]

Back to Jerrod. A big risk is being wrong about your edge. It’s a risk because edge hides behind low signal-to-noise. This is one of the great teachings of poker. Short-term results are noise. He explains that in Limit Hold’em, even a high edge hand has only .02 big bets worth of expectancy vs a standard deviation of 2.5 bets.

[Kris: In investing language, a .008 Sharpe for one trial. The SP500 has a daily expectancy of about 3 bps and 100 bps standard deviation for a daily Sharpe of .03. The poker hand has almost 4x the noise of the daily SP500 return.]

Since poker teaches that you will make the right decision and still lose money, it trains you to emotionally decouple decision quality from result quality.

This is a ceaselessly profound concept. Not because it’s so clever, but because of how it resists internalization. It’s easy to understand, it’s hard to apply the understanding to how we receive the world. Fooled by randomness might be a tired title, but it’s never been stronger as we are bombarded with data.

The risk of being wrong about your edge is insidious because the relative efficiency of markets means it’s hard to make excess returns, but it’s also hard to lose too much doing sensible things. The problem is when sensible things aren’t adding value beyond randomness, but you think they are. You’re wasting your life tossing coins.

[Unless you like action for the sake of action. In that case, you’re understimulated. Go take a risk in the name of actual growth or something.]

The link between speed and skill

Jerrod notes that you don’t have time to “go to the lab” mid-hand or mid-trade. Edge requires building mental shortcuts and intuitions that perform well under time pressure.

This feels easiest to imagine in the world of sports. I’ve heard elite athletes talk about how the big jump from say college to pros is “just” the speed of the game. It’s not that they are doing new things, it’s that they must be able to do the same things faster without losing precision.

I remember reading a profile many moons ago about Jason Kidd who was known for his passing and court vision. I got the sense that he could see a split second into the future. Being able to make and execute a decision just a tiny bit faster compounds into outlier results over time. The long-term ROI on having your intuition slightly better tuned works to disproportionate effect.

This echoes. We play the mock trading games and some people are just a tad faster every time. Maybe when new info hits the game they refresh their market quickly, not necessarily making a perfect 2-way, but it’s biased in the direction of the asymmetry. Ricki Heicklen discussed this with Patrick McKenzie. If you are trading “the sum of the siblings in the room” and someone’s count is revealed, do you Bayesian update in the right direction and in a proportionally coherent way? When you see someone do this consistently, you know they’re clocking differently than the others (and I’m excluding the clueless whose updates are logically incoherent to how they processed a similar situation in the opposite direction).

In competitive scenarios, if you can debug your thinking in the moment, you’re too slow. When making a market for a broker, you need to hear the order, intention, what’s not said and how the trade looks vs the framing of the option chain in seconds (and this of course assumes your tools are already designed with this workflow in mind, showing you the info you need when you pull up the ticker). This is obviously not happening if you need to step through expected value trees. There’s no substitute for reps if you need to decide faster than the speed of system 2 axon to dendrite sex.

Feedback loops to build that intuition

Jerrod is blunt. The best way to learn in poker and trading is post-mortem discussion. Go over the tape with your team. Chat scenarios. A great feature of trading is if you love it, you want to talk about it, so this doesn’t feel like work.

When I was at Parallax, I used to carpool with 3 other traders. Shout out to Steve, Ben, and Rob — I still wish we livestreamed those rides. The commute in the morning was your typical sports or current events banter (ok fine, gossip). But the ride home was all play-by-play of trading scenarios from the day. What happened, what would you have done there, etc?

While Jerrod treats discussion as the primary way to learn (I agree — trading is an apprentice craft) he does acknowledge a role for books.

[Even though I recommend some books, they are more for describing the overall epistemic landscape or inspiration. 99.9% of what I know about trading comes from discussion or experience. I either learned how to price or think about something from someone else or after discovering first-hand a new way to lose].

He mentions that most poker books are wrong. He offers a strategy for figuring out which ones are good. But he also encourages reading the bad books because it reveals how your opponents might think. That bit reminded me of an old post I wrote:

Twitter Reminds Me Of The Trading Pits

[Random: I was hanging out with a trader from my cohort who now runs education for a big prop firm in Chicago. He was re-learning poker because a lot of the stuff we learned 25 years ago is now considered wrong. I’m not surprised, since 1 year of poker information in the online, poker-on-ESPN, poker-celebrity-giving-TED-talks era likely generated a decade worth of info from 20th-century poker.]

In sum, SIG is using poker to build the same mental circuitry that trading relies on in an enclosed, fast-feedback petri dish. It’s speedrunning experiential learning in a low-signal environment so the requirements of a successful trading career seem less alien. If trading were as easy as “just study and you’ll get good grades”, motivation and time would be sufficient ingredients. With trading and poker, you could have infinite time but if you don’t know how to learn, you’re pushing on a string.

Get new posts by email.

Run it on your own portfolio.