title: A Jane Street Alum Teaches Trading - Party at the Moontower
author: Editor
content_type: article
publication: Party at the Moontower
published: 2024-07-30T00:00:00
source_url: https://moontowermeta.com/a-jane-street-alum-teaches-trading/
word_count: 10164
Finance and internet genius Patrick McKenzie started a podcast called Complex Systems during his sabbatical. His very first interview is instant canon for trader education:
🎙️* How the Smart Money teaches trading with Ricki Heicklen* (
100 min)
Patrick and Ricki discuss real problems in trading, how trading is taught, and pedagogical game design.
Ricki is a former Jane Street trader who now runs trader bootcamps. Like “real estate seminar”, “trader bootcamp” is a word sequence you should mute. This is an exception. I’m not stepping out on any limbs when I say SIG (my alma mater) and Jane Street are tops for trader education. This isn’t surprising since several of JS’s early employees were SIG defectors (I clerked directly for some who became key players at Jane and some of its later offshoots).
Predictably there’s significant overlap with this material and Moontower educational content. This is a great opportunity for me to share excerpts from Ricki’s interview with my own commentary and links to where I have covered similar ideas.
So let’s jump in…(Ricki’s quotes are in* italics…*all emphasis mine)
Contents
hide
The demographic of who is drawn to trading has varied throughout history. What is the profile of someone who goes into trading today?
I went to Princeton University, an Ivy League school; I studied computer science with a focus on theoretical computer science. I had an internship at Jane Street the summer going into my senior year in college.
I was then hired to work at Jane Street full time as a quantitative trader and started working there immediately after graduating. This is certainly true of the median employee – went to a fancy college or university, for New York traders, mostly coming from the United States, which is where I was.
Often it’s people who have experience spending a long time thinking about math problems or puzzles, but not necessarily a lot of life experience under their belt. Certainly not a lot of professional training and often less background than you might expect in economics and finance specifically – rather, a general comfort with concepts around probability, expected value, and math puzzles writ large.
[Kris: This was still reasonably true 20 years ago but because the skillsets and competition for talent has merged with tech giants, the technical floor is much higher today. Trading firms always hired from top schools but now that list is probably even shorter and the accomplishments of new hires even more exemplary. There were many MIT folks in my cohort but there was still plenty of humanities majors from good schools. Today math competitions are going to grab more attention than being an intellectually well-rounded athlete]
On the focus on competition in finance recruiting vs software
Patrick:
The part about competitions is interesting. One of my theories is that there’s some selection effect for people who have that competitive mindset and want to play games about these sorts of things, but I generally tend to think that strong performance in games, particularly in competitive environments, probably predicts performance in real life.
At least in the tech industry, we don’t back-propagate that into our decisions for advertising at places like the International Math Olympiad. Is finance more rational than we are on this?
Ricki:
I don’t personally know how it is that software goes about recruiting as well, but I think that part of what quantitative trading firms are often trying to do is they are trying to recruit people who have the raw intelligence, that when combined with training that those firms are capable of providing in-house will turn into good trading skills.
I think with software development, there is both more opportunity for people to get good at software development external to the places that are hiring for it, and therefore more ease at measuring what somebody’s skill in that domain is than there is for trading.
Right now, the state of the world is if you want to learn how to be a good trader, basically your only option is to go to a good trading firm. It is very hard to find good materials for learning how to trade, learning how to have the kind of intuitions and heuristics about a market that a trader ought to develop, in any context outside of a firm.
There are a couple of reasons for this. One is that the firms are incentivized to not spread that information too far and wide, and another is that trading skills are not skills that you can easily pick up from reading a book or from consuming a YouTube lecture series. They’re way easier to learn through actually being immersed in the environment and doing it yourself.
That means that you’re going to need a really good ratio of teachers to students in order to properly transmit trading knowledge to those students. This is just not going to be that widely available when you’re bottlenecked on how many good traders there are, and when those traders will benefit a lot more from full time trading than they will from teaching those skills to a few dozen other people.
[Kris: Hence why “trader bootcamps” are a mute term with few exceptions]
“I’m going to impart a bit of information upon you to get you ready for understanding the US equities markets…” – if you only had one sentence, what is that?
The number one sentence for purposes of trading, in general, is to think about adverse selection. Adverse selection is the concept that, conditional on getting to do a trade with someone, your trade might be worse than you’d previously thought it would be – that the world that you are looking at is one that has lots of different models that will explain different systems, and you can make predictions of what those models would output for numbers. But as soon as you are putting an order into a market, you need to think about the profitability of your trade, if it gets traded with, versus if it doesn’t. If it doesn’t, it profits zero.
So the fact of somebody else’s willingness to trade with you should adjust your model, and therefore you should calculate the profitability of the order that you submit based on limiting yourself to worlds in which those trades do happen, i.e. worlds in which the trade that you want to do is worse than it otherwise would have appeared.
[Kris: This is why backtesting is so hard. Simply assuming your slippage is X bps makes assumptions about liquidity that act as if your orders don’t leak info.]
Patrick explains adverse selection in crowdfunding
If a company has decided to raise money on a crowdfunding market, it has been passed on by the people who have made it their life’s work to find profitable investments in venture capital. And it has also been passed on by rich people in tech who can easily write $250,000 angel checks. There is a reason why it is rational for them to get $1,000 checks. [*Patrick says that reason explicitly*: Better investors, who write the larger checks, have passed on the opportunity to invest.]
Therefore, I hear people that there is some notion of “equitable access to growth opportunities in the market,” but I don’t think that public crowdfunding will actually give the general public opportunities to tap into growth markets on an equal footing with VC because, bluntly, a level playing field is one in which professionals destroy amateurs.
[Kris: This is so blindingly obvious to anyone that has been in an adversarial environment and yet we find that meme of “maybe it’ll work for us” ready for the next stove-touching FOMO donkey]
The limit of the adverse selection argument
If adverse selection were as powerful a principle as I’m claiming that it is, shouldn’t nobody ever trade with one another, especially in zero-sum environments?
I think the answer that I give to that is, you need a story for why – despite the fact that this trade is available to you, i. e. despite the fact that there’s somebody on the other side of the trade who wants to do it with you, and nobody else has taken your side of the trade yet – it is still worthwhile for you to do it.
There are a lot of different explanations for why this might be. One explanation is, “nobody else has had the opportunity to do it yet.” You are actually the first person to get there. That might be true for those early-stage VCs – honestly, I didn’t even fully follow all the different players in the ecosystem you just mentioned, I might get some of those details wrong. That might be true for the people who get the first opportunity to invest.
As there are more and more people who had that opportunity and turned it down, the phenomenon of adverse selection should be a larger and larger factor in your weighing against whether you choose to invest. But there could be other reasons.
Patrick adds:
An interesting difference between the private markets and the public markets is that – to do a gross generalization, and I know you can come up with all the ways that this is not true – everyone gets access to an incoming order at approximately the same time, where in VC-land, the thing that you want most is differentiated deal flow, which means when someone has an idea for their new company, they think of you and pitch you on the opportunity of investing first. At that point, you are essentially a person who has exclusive rights to take this trade or pass on it.
[Kris: This is my oft-repreated idea of self-awareness. Are you the first call? Is your money better than green (does a cap table see you as a strategic investor)? Where are you in the pecking order and why? When I was at the fund one of my advantages is I could trade large blocks which earned me flow even if I wasn’t as fast a a market-maker. The point was I understood AND communicated to the brokers why they should show me trades]
How Ricki’s Intro To Trading Bootcamp opens
I find that the best way to learn trading is by doing it. On day one, the first class that I have people participating in my trading bootcamp go through is a class where you walk in the door and immediately you start trading.
What does that look like? I have an order book that I’ve written out on a board. I’ve seeded it with a couple orders of my own that have a huge spread between them, and I’ve written up a contract on that board that will resolve to a specific number.
I like to keep this as far away from actual knowledge of finance and the economy as possible, so my first contract will be, “What is the sum of the number of siblings that each person in this room has?”
This is a nice market for purposes of illustrating trading concepts, because each person in the room has some amount of private information, i.e. the number of siblings they have, has some rough sense of how many siblings on average somebody in the world might have, and then can whittle that down to, what about people primarily from the united states, what about people from the socioeconomic backgrounds that we assess other people in this classroom to most likely be in.
The Tighten or Trade’ Constraint
We go around and play Tighten or Trade, a game in which each person on their turn needs to either tighten the spread by improving the best bid, in that case increasing it, or improving the best offer, decreasing it – or they need to trade with one of the existing orders in the book.
This is an artificial constraint to ensure that trading happens in an environment that is zero-sum and therefore you should be paranoid about approaching if there weren’t such constraints forcing you to do trades with one another.
I’ve found empirically when teaching this class that people don’t necessarily have that paranoia on day one, of avoiding trading in zero sum environments, but in order to make sure that it happens, putting this constraint of requiring people to tighten their trade is a way of guaranteeing that trades will occur.
Simulating news and pulling your quotes
While trading is still open, I have each person in the room come up and write the number of siblings that they have on the board one by one so that we are calculating the sum of the number of siblings that we have, combined, in real time. Trading is still open and people are continuing to trade, but they’re updating their models as each new number gets written.
And this allows me to say things to people like, “Hey, somebody just wrote a seven up on the board. This presumably updates your value for the true sum of the number of siblings in this room upwards. What is the first thing you want to do?”
Usually the first thing that people in the room do is go, “whoa,” and then they look at the trades that they’ve already done and try to figure out how much money they gained or lost as a result of that. For this, I chastise them. I say the absolute first thing you want to do is going to be orienting toward the order book. You want to be staying out and clearing all the orders that you have that might be stale, even if you don’t remember whether or not those orders are good, even if you don’t remember what side of the order book they’re on.
The first thing that happens is, you have new information that markets are different from what you thought, and the fastest thing for you to do to protect yourself is to be out on the stale orders of yours that are now stale to new information.
The next thing you want to do is to go in the direction of the new information’s indication. And you want to be doing this to approximately the right order of magnitude.
In terms of how much you move the price, if you see a seven, that’s a big surprise relative to a one, which might be your expectation of how many siblings someone has, such that if the spread had been one wide or two wide, you should be happy lifting the offer even if you weren’t paying attention beforehand to what your model says the exact sum should be.
The conservative way to approach things is anytime there’s new information and your model shifts, you should be extra paranoid about the orders that you have that are still posted to the book, and you should be rushing to clear those orders, or to say out on your orders so that they’re removed from the order book, in particular because of the concern we talked about earlier of adverse selection.
If you still have orders on the book, let’s say those orders are good to the fair value, i.e. you would be happy for them to be traded with, people are not necessarily going to trade with them because they don’t want to do bad trades.
But let’s say your orders are stale in the direction of being bad. Somebody is going to come in, see that, and trade with it, if they can do that faster than you can clear your order. It is more efficient for you to clear your orders, than for you to recalculate what you think the new fair value is based on having now added in that person’s seven siblings, subtracted them out from the number that you multiply by your expectation of the average number of siblings, make sure you’ve counted how many people have already written, come up with a new number and decide if your order looks good to that number.
A lot of the thing that I’m trying to convey in the trading curriculum as a whole is that, to be a good trader, you don’t necessarily need to be the person to get the exact right number after many minutes of painstaking equations and double checking every single odd constant that gets added to the end of that equation.
You need to be the fastest, you need to be going in the correct direction, and you need to have some sense of approximately how much you think the price of this asset should move, or how much you think the price of this stock should move. Those things need to come first because if you are the first trader, it is possible that you will get a good trade. If you are the 10th trader, it is way less likely because someone of the first nine traders was able to do the overwhelming majority of the good trades and take them out from under you.
You are looking to maximize in dollar terms to make as many dollars as possible, and in order to do that you need to be fast. You need to be fast because, if you are going slower, it is more likely that your model will have mistakes conditional on getting filled, even though your model now feels like it is so much more well thought out and more likely to be correct, if you weren’t then also conditioning on that fill.
Kris: Classic mock trading from tighten or trade to teaching people to yell “out” to cancel bids/offers that are stale when news comes out.
See:
Mock Trading Options With Market MakersYou Can Mock Trade With A Deck Of CardsMock TradingPointing to Figgie*[Trade or Tighten’
Patrick makes an observation that if anything is an understatement:
I like as a pedagogical approach that this allows people to infer some of the lessons without simply being told some of the lessons.
One of the ways adverse selection manifested in StockSlam was that in the final round it was possible to brute force the exact fair value of color if you had quick mental math. Which means a player needs to recognize 2 things:
That it is possible to know fair value in the last round
That if you haven’t figure out fair value and someone trades with your bid or offer you should feel sad.
Many people discover this listen after getting picked off when they realize what happened. Ricki has an analogous situation in her game:
Every time someone goes up and puts a number on the board, you learn a little bit more, but you also have an implication that there is less new information that’s going to come after me, and that’s another lesson that is easier for bright people to pick up on themselves after actually doing it than it is to say, “by the way, every time you get more information, there’s less information in the world that you don’t already know.”