A Jane Street Alum Teaches Trading

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)

---

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 Makers
• You Can Mock Trade With A Deck Of Cards
• Mock Trading
• Pointing 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:

1. That it is possible to know fair value in the last round
2. 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.”

Why people who have the best model for the world may or may not make the most money from trading.

I actually think in markets like these (ie the siblng market game), where there will be a settlement to the correct value, you’re more likely to make money by having good models than you will in markets like for various stocks in the US equities markets, where a lot of what you’re doing is trying to price things relative to what people in the market will think something’s worth than to a model that takes into account e.g. the earnings reports of a company and figures out what the actual value proposition of the product that they create is.

You are much more interested in what the directions that these prices move within the next few minutes or within the next few days will be. This also exists in a smaller form in the markets that I run that might resolve on the order of half an hour from now, where if you can notice what trends will happen in the next 10 minutes or what trends are already emerging, you can profit by buying and then selling or by selling and then buying a contract that doesn’t actually accumulate a position that you will get paid proportional to at the end, but instead, does what’s called flipping a contract, where by taking both legs of the contract, you can make money on the difference between those two prices.

[Kris: In the StockSlam game sessions we ran there was no private info and the color race was random. However, some players would follow a strategy of hoarding a color because if it won it guaranteed them victory. To be clear the strategy has zero expectancy. However, if you just try to “flip” for edge you probably won’t win — you’ll lose to a a random hoarder. The key is to understand that over the course of many games, the “flipper” who makes positive expectancy trades will win over time even though they never win any single match! In StockSlam, there was no way to have an “investing” edge by having a better model of the world since it was random but there were many relative value scalp opportunities.]

Incentivizing liquidity to overcome the fear of adverse selection

By the point that we have people start writing the number of siblings they have up on the board, we’ve relaxed the constraints of Tighten or Trade, and people are now allowed to clear their orders from the market. 

In fact, it is prudent for them to do so.

As a result, we want to incentivize people to trade and not just have liquidity entirely dry up in the market when there isn’t enough trading. We can do this by adding in naive customer flow: every minute we will flip a coin; if it’s heads, we’ll lift the best offer, i.e. buy from the best offer. If it’s tails, we will sell to the best bid. And this will incentivize people to tighten those spreads because they will be competing with one another for that top position, so that they’ll get to do trades with what is obviously explicitly an intentionally naive customer flow, uninformed trades from the coin flip bot.

[Kris: In StockSlam we used “broker cards” that players drew to simulate random order flow that you’d be happy to trade with on your bid or offer.]

How order types leak info

Market orders are much more likely to come from naive customers. This is because a market order is strictly dominated by a limit order with a really high price.

There is some number of dollars above which you would not want to buy Shmapple stock. If you specify a limit order with a limit of $200,000, that’s strictly better than a market order because any cases where the market order would trade and the limit order wouldn’t are cases where you should probably feel really, really sad, in that above $200,000 range, to have traded.

[Kris: A market order is a bit of code that says I’ll buy shares at ANY price — a statement no human has ever made.]

A market order is more likely to be a naive customer. However, I think where that point is less relevant is that you don’t often have access to the information of something being a market order or the ability to select preferential trading with them. There are many structures, including many auction mechanisms, that will prioritize those market orders in terms of who you trade with: a willingness to buy at a higher price than other market participants should allow you to get some kind of priority in a case where those orders are being aggregated and then executed all at the same time, and this should be a reason to make people who are providing to Order Flow, i.e. market makers, in an auction more inclined toward wanting to do so. 

But I do think this fact about market orders is a really useful fact for purposes of making the decision not to send them, and less useful for you as a market maker, because you’re just not able to take advantage of it in nearly as many cases.

“Fill or kill” order types

A fill or kill order, says “immediately fill this order for me if there’s an order that would take the other side of that trade in the book, or kill this order – don’t leave it up.” This is as distinguished typically from limit orders, which are orders that if you send them into the market, will stay on the order book and other people can come and transact with them later. 

One reason you might want to do this is because of similar to earlier, the concern about adverse selection, in which if you leave an order up, it will get traded with in cases where it is stale to new information that comes out, or cases where other people see that order and judge it to be bad. 

Fill or kill isn’t totally immune to the problem of adverse selection because it’s still true that any order it gets to trade with is an indication that somebody else is happy trading with you, even after conditioning on your choice to trade with them and has therefore put up an order that would trade with this. But it is a lot safer in terms of not having the problem of stale orders out on the market that then get traded against.

Why Ricki’s simulation stocks returns are drawn from a stochastic process rather than use Patrick’s simulation where stock prices were real returns from a time in history (although a user would have no feasible way to identify it)

We explicitly model the movement in stock prices as random walks for some of the stocks that we’re putting on the market instead of using actual historical returns. One reason is because it’s simpler and when you’re in the early phases of teaching somebody about trading, keeping the API, keeping the parameters of the trading game much simpler will do a better job at inculcating those first order lessons than something that also incorporates a bunch of noise that comes from, you know, the various other things that might be happening to Bank of America stock in the background that they won’t be able to anticipate.

It’s also simpler for purposes of reviewing and teaching it and saying, “your model doesn’t need to be taking into account all of these other complicated things, but instead should be interacting with this product in this way.” We are very explicit up front about what algorithms will determine the prices of these stocks as they move forward.

The other reason for this is because part of what we’re trying to teach is about the relationships and in particular the ability to do arbitrage between different markets. One thing we have going is we have three different stocks, call them A, B, and C, that are each determined by a random walk that happens continuously over the two hour trading period. We then have a separate product, ETF, which is worth exactly the sum of stocks A, stocks B, and stocks C, but with different bot behavior happening in that market, such that, as you see the prices move in A, B, and C – and those prices will get moved by sophisticated trading bots that know the true future value of them as determined by just the number output by the random walk function – then you can take that information and go and move the markets in ETF and convert shares of A, B, and C into shares of the ETF and vice versa.

I cannot emphasize this more: keeping things as simple as possible early on is really valuable. That’s why using real life historical returns won’t give you the ability to lever up the impactfulness of those early lessons – and getting people to the point where they understand the first order things actually takes a lot more time than you might expect it to.

You mentioned that class one on day one is teaching you a bit about the laws of adverse selection, and I actually want to correct that. I think class one on day one is just teaching you, “what is the API for interacting with an order book?” It’s still teaching you some lessons as we go – in particular, teaching you lessons about why is it that an exchange might be designed in this way, or what is it that you should be worried about paying attention to; how much magnitude there is in terms of contract size, but it’s also just teaching you, “what does contract size mean when there are two orders out there?” Why is that equivalent to there being one order and then another order at the same price as opposed to meaning, say, “for 35 dollars I’ll buy the package of two shares as opposed to two shares each for $35.” Why does that make sense in the context of the Exchange’s API to this order book? 

(Day two is when we start teaching adverse selection.)

The meaning of arbitrage

I think often one of the sources of confusion is people use the word arbitrage to explain a whole bunch of different things that are not in fact raw arbitrage in the form I’m about to describe. Arbitrage is the act of trading two different products that are essentially the same product, or aggregate to become the same product, in a way that causes you to profit risk free.

What does this mean? Let’s say you have two stocks, A and B, and there is a separate stock, SUM, that is the sum of A and B – that might be an exchange traded fund, an ETF, that is the basket of A and B combined. If you can buy stock A, buy stock B and sell this ETF (the sum of A and B) and make a profit by doing all of those trades, you’ve done arbitrage.

You’ve managed to engage with the market in a way that allows you to end up with no position. You now have no exposure to A, B, or SUM; when there are movements that happen where A increases by a dollar, you profit a dollar off of having a long position in A, but you also lose a dollar off of your short position in SUM.

And as a result, you have not profited or, or lost out. You have not made a profit or loss in the aggregate of the things you’re holding, and therefore it is essentially the same as if you were to successfully create that ETF and cancel out your positions, or equivalently, redeem it and cancel out your positions in the stocks.

A hands-on experience of arbitrage

One of the ways that we try to teach arbitrage specifically is by creating markets that are still based on things in the real world. We have a crosswords contest in which people race doing crosswords that are up on a screen, doing the exact same crossword – we have the fastest time of a member of team green, the fastest time of a member of team orange, the sum of those two fastest times and the difference green minus orange of those two times.

Then after we’ve concluded trading, I’ll ask everybody to take a few minutes to calculate, to come up with a state of the order books that would allow them to do arbitrage. The first mistake that people make here is they say, “Oh, let’s say green is trading for 10, Orange is trading for 14 and some is trading for 26. I could buy the first two and sell some.” 

Why is that wrong? It’s wrong because trading at isn’t one specific number that you could do any transaction at that number. There is a bid and there is an offer. You need to be comparing the offers in green and orange with the bid in the sum – or vice-versa, the bids in green and orange if you want to sell it and the offer in some if you want to be buying it – and figure out are there any sets of trades that you could do that would be profitable, recognizing the need to cross the spreads. You don’t just get to transact at whatever the midpoint is in that market or whatever the last price is in that market. Figuring out which sides of the order book you need to be looking at together to see if you have an arbitrage opportunity is the kind of thing that is conceptually, in theory, trivial, but so, so easy to make a mistake on.

While teaching this class I regularly make a sign error – accidentally think that we’re supposed to go in one direction and not the other. Working through specific, concrete examples is going to get students way closer to not making those errors, or figuring out where those mistakes will crop up, than just reasoning from first principles about what you want to do.

It also helps them write up in spreadsheets that are reading in the electronic markets we have. What cases cause there to be an arbitrage and what don’t? The thing that I then push them to do is not just check if those models make sense from first principles, but, let’s change the stock price of green by one value – how does this push everything going on? Let’s change the offer here by one value. How does that push everything going on? Let’s say there’s this settlement and you’ve taken on these positions. What is your PNL? 

Walking through different examples of how changing the prices that these values either trade at or settle to, and how that changes your profitability, how that changes the payouts of your positions, does a way better job informing people of the directional pushes and the effects that they have than just explaining from first principles why those would be the case.

Real-world concerns with arbitrage

I want to add one more complication, which is anytime you’re engaging with multiple financial products, you are adding additional risk about what might happen with those products. Let’s say you buy shares in an ETF and then the company that is issuing that ETF goes belly up or mismanaged their portfolio or reported a number that wasn’t actually the correct number, but was in fact reporting someone’s error somewhere. There’s so many opportunities for error at each piece in the system, including the ones that you assume are true or think it would be impossible to ever be violated, that adding complications to your portfolio in the interest of succeeding at arbitrage diminishes the guarantee that you are making risk free money. 

You are taking on additional risks the more products you’re interacting with.

So one of the concerns about arbitrage that I teach is this concern about needing to cross spreads in both cases – this is something I sometimes call transaction costs in addition to the kinds of fees you might need to pay for each trade – but another is the risk of just having multiple different positions, both for purposes of your own internal accounting and ways you’re more likely to incur spreadsheet errors, for example, and also in terms of the other sources of risk that come from external factors about why you might think you have a certain position, e.g. that your books don’t necessarily match some of your counterparty’s books for some reason, or those of the exchange you’re interacting with, because there are errors all over the system. 

There are times that one set of trades will get busted or canceled retroactively, and other sets of trades that you’ve done will not – so even though you thought you had a flat position, a flat delta as you mentioned people sometimes refer to it, in fact, you do not, little known to you.

The challenge of teaching position sizing

Position sizing is one of the things that I’ve struggled with teaching the most because I think there’s this intuition of, if a trade is good, you should do that trade for the full size available to you until the point that you’ve moved the market such that it’s no longer good. And again, when I’m teaching things to a first approximation, that’s pretty reasonable. You want to do good trades. One reason to do similarly sized trades across a lot of different markets is that you will get better data about how good your trading is and how much you’re improving.

Another reason is because you will be better diversified to not lose out against noise for purposes of your own portfolio staying positive, separate from your ability to track it for educational purposes or for accounting purposes.

There’s also just the fact that you will be less likely to go down to zero and then no longer be able to make money in the markets if you have size that’s spread more evenly between different places. 

How is it that I teach sizing?

I’m going to be honest with you: a lot of the fundamental lessons about sizing are ones that we don’t get to in the time that we have available to us. 

We’re trying to teach fundamentals that are easy to digest. It’s not that sizing isn’t important – it’s extremely important – it’s that sizing is just a little bit too complicated to be able to successfully teach how to do successfully, especially cause you have two different questions.

One is a question of the impact that your trades will have and how they’ll move the market in terms of optimal sizing if that’s the only opportunity available to you, and another is sizing in terms of the relative values of different parts of your portfolio and how you want to keep it steady in light of like different things changing.

There are two different important things to be teaching on how to size trades reasonably.

One is for a specific given trade, if you were only optimizing the decisions you were making as they pertain to that trade, that was the only opportunity you had, what is the optimal size to do that trade for purposes of like your expected value of its performance? That’s the kind of thing that we’ll often teach through having these liquidity-providing bots or having bots that are acting like naive customers in a market that will trade up a stock a certain amount such that you can give people kind of this formal equation, and we try to keep those as simple as possible of how much it’ll move the market. Then people can figure out based on how much the market in a few of the stocks moves, how much should the market in the exchange traded fund move and therefore how much should they trade it up in order to get it to that point.

Likewise, in terms of arbitrage between the ETF and the stock, you should figure out which of those legs of the trade will be more constrained, which one has less size available for you to take on, and have that be your cap on the size that you take on in those two markets so that you’re not sitting with two trades that would have been good if you could have done them for arbitrary size but now have much more size on one of them than on the other.

That’s as it pertains to a specific trade. In terms of the question of sizing as it pertains to sizing your different positions across lots of different markets in ways that are reasonably balanced with each other, that level of sophistication is often beyond the scope of a two day trading bootcamp, or even when I do the longer version, a 10 day trading bootcamp for high school students.

It will be the kind of thing I will touch on insofar as it pedagogical benefits for people to have lots of different positions that allow them to track performance with less noise getting in the way of the performance of their trades, and i’ll talk a little bit about why diversification is important for purposes of not having a portfolio that can easily drop to zero and take you out of the pool. 

But the question of what exact equations you want to use to result in sizing between different positions based on how much money is to be made in those different markets, and also on the fact that you want to have some balance between them, tends to be one step more complicated than the things that we end up getting around to covering in this curriculum. I think it’s a hard thing to teach and it’s important to teach well, and you shouldn’t start trading with your own money before having a good understanding of it, but it is it is interesting to me that it is harder to teach than some of the other concepts that I do manage to cover, like adverse selection and movements in price or how much you want to trade toward that price.

[Kris: I agree with the difficulty of teaching sizing. In options portfolio context this post offers a practical perspective: Options trading as a widget factory.

Bet sizing for discrete gambles is not intuitive but well-covered:

• Kelly Math Weirdness
• Getting Paid To Flip Million Dollar Coins
• Bet Sizing Is Not Intuitive
• My grandma is $24.05 bid, stop embarrassing yourself ]

Why teach high schoolers about trading

I’m trying to leave them with two different things.

Number one is a sense of, do you like this thing? Do you maybe want to be a quant trader? Would you enjoy thinking about these questions a lot more and going forth and doing them? That’s something that inclines me very far toward the educational end of the spectrum because I’m trying to give them a flavor for what quant trading is. I think a lot of people who are very knowledgeable in domains that range from software engineering to math puzzles to history have just no idea of what it is a quant trader does, and in particular, what kinds of thinking skills and tools a quant trader ought to have, so even getting people to the point of understanding why heuristics are so important for trading, why speed is important for trading, and what kinds of things to be paranoid about or to pay attention to in markets, will get them a lot of the way there in terms of just having this general domain knowledge that can inform whether it’s something they want to dig into more.

My other goal is to teach them about the kinds of heuristics around trading that can inform them whether or not a specific area or trade is one worth then investing a lot more effort into thinking about. 

Adverse selection is a concept that’s trying to teach you to be more paranoid about your trades. Information about naive customer flow is teaching you that despite adverse selection and other attributes of more sophisticated market players doing better than you, it is still worthwhile sometimes to do trades if you can identify the good ones. And, when should you have a story for why it might be good to trade, such that you then invest a lot more time thinking about that trade, and trying to understand whether your story for it is true or not? 

I guess I’ll add a third thing which is that, I think that a lot of the features in financial markets and the things that I’m trying to teach toward crop up in lots of different places in life – adverse selection being one of the biggest examples of this – in ways that people are not necessarily paying attention to, but once you’ve been a certain amount trading-pilled, once you’ve gone through this curriculum, you’ll notice a lot more and be able to incorporate it into your ability to ascertain whether an environment is more cooperative leaning or competitive leaning – whether an environment is high trust or low trust – and how to set up incentives and agreements and contracts so that your environment can either be more safely high trust or more legibly low trust in order to cause people to make choices and do things that will be positive expectancy for them, and ideally, in the world I’m trying to create, positive expectancy for everyone by giving us the tools required to determine what places are low trust, and how to make places high trust so that we can cooperate and not burn the commons on values that might be good for all of us.

Patrick tests Ricki with the same puzzle he gave to Stockfighter players: “There are a hundred traders in this market, you have access to the order book, find out who the insider is. How would you go about doing that?”

[Patrick notes: Ricki’s answer here was delivered in real-time after thinking for approximately 15 seconds. Less than 100 of 50,000 technologists, many of whom strove for several hours, successfully implemented any of the four ideas she came up with on the spot.

Kris: In case you wondered if trading was actually a skill you can learn, Ricki’s answer demonstrates domain knowledge]

Great question, and I love this exercise. It feels to me like a good simulation of what a lot of interacting in real financial markets is like, in that you will have some participants more sophisticated than others, and identifying which ones are which is especially important.

I think the first thing that I would look to is, if I can see the behavior of the different entities with names attached to them – this is not going to be true in many major financial markets in the real world, but might have been in your simulation – I’m going to look at which ones are kind of always moving in the same direction as the one that the earnings report moves the stock in, in advance of it.

I’m going to want to look in particular at ones that do these trades very shortly before the earnings report is released because it is likely that they will want to focus their positions there so that there’s less noise, so that they are less susceptible to effects of noise that happen over the course of 30 days.

But I’ll also just want to be applying a filter of, do they go in the same direction as the stock ends up moving during the 30 day period prior to the earnings release. 

I’m going to want to look, if there are lots of different financial products available in this market, at the highest-leverage ones to figure out where it is that those traders are putting most of their efforts.

So if you see that buying, say, out of the money call options on companies that then move up a bunch.

Patrick McKenzie: This is the classic Matt Levine point. If you’re going to do insider training, don’t do it with very out of the money call options that are expiring this Friday.

Ricki Heicklen: Yep, and nevertheless he keeps gathering more and more examples of people behaving in this way. Of course, he gets to see the examples of the ones who get caught and not the ones who don’t get caught, but…

Patrick McKenzie: We as society are being adverse-selected when we see the results of the legal process. We are finding the dumbest crooks.

Finally, if you have a way of tracking the profits and losses of individual entities, again, if you can track the individual agents’ trades and what their balance is at each point in time, the ones that see the biggest bump following an earnings report and whatever ensuing market volatility takes place on account of that earnings report will likely be the ones that you should be paying more attention to.

A more sophisticated insider trader might make some deliberately bad trades in there in order to throw you off the scent, might go in the opposite direction at certain points in time, but to a first approximation, those are the kinds of signals you’re going to want to be filtering on to find the insider trader in this market.

An idea I learned at SIG with a poker analog — “paying for information”…Ricki doesn’t call it that but the concept is here

The best way to figure out what trades will cause what effects are by doing those trades for a small size and seeing what happens as a result of them. And that will save you so much time of doing so, in particular for things like catching your own mistakes in, let’s say in arbitrage land, where you do what we jokingly call “garbitrage” of going in the exact opposite direction of the arbitrage you intend to perform. 

This is something you will catch better by just trying and failing to do the arbitrage you want, and why you should always turn on your bots with, you know, a one hundredth of the size that you would want them to actually end up trading with.

The usefulness of graphs

I especially like the point there about how useful graphs can be. I think that a mistake that people often make with trading is they will have a giant CSV with tons of data points, and the only thing they’ll do with that is calculate summary statistics, like your t-statistic for how good a certain trade would be, or even read through the tape of like what trades happen in a certain time period, but in a way that human brains are less likely to successfully consume than pictures.

Picture books are so much easier to read than massive tomes of just words straightforwardly, and likewise, seeing charts of this data will be a very quick way of causing you to notice patterns that deviate from the other things you see than trying to read through an entire CSV.

[Kris: Total agreement. I have a special affinity for scatterplots.]

The information game

[Kris: I’m excerpting a huge chunk of this because it’s great. It’s a topic I wrote about in Twitter Reminds Me Of The Trading Pits and discuss in my interview with Corey Hoffstein.]

Let’s say you are a trader who has a lot of information about a specific strategy that you execute day after day. And you’re at dinner talking to somebody externally, and they’re interested in what you’re working on.

Well, you know that you can’t reveal the specific details of the strategy, and you know that you can’t explain any piece of your code. Let’s say you’re talking to somebody external. That might be a roommate of yours who happens to also work in finance. It might be out at a bar late at night. You will often end up revealing information, even if you are not disclosing any of the details, much less the code of the specific strategy in question, just by revealing what things you care about: what areas of the market, what countries, what asset classes, etc, you are thinking about. You might reveal this just by mentioning something about that asset class. You might even reveal it just by knowing more about that asset class when the other person brings up a few things with you.

If they talk to you about stocks and bonds and options and you reveal that, “oh, I actually don’t know much about bonds, that’s not my area of expertise, but I know a lot about options,” they now have a bit of data that there’s more profit to be made in options in expectation than they had previously thought because your firm has you focusing on options.

If they can get a good sense of how many people are in each different desk in a firm, or what desks even exist in that firm – if you have a desk specifically devoted to trading certain kinds of commodities, that will tip them off to the fact that those commodities have money to be made in them.

Sometimes the only thing that a competitor will need is to know where your focus is in order to be able to then take five of their researchers and say, hey, I think there’s money to be made in Brazilian options, or something like that – let’s put some attention into that part of the ecosystem, and now we too will be able to extract that trade.

Patrick McKenzie: I love this thing that there are things which seem very professionally normative and not leaking inside information at all, which allow others to adversarially reconstruct things that very much are proprietary information – like simply asking a question like, Oh, how many people sit with you? Or, how many friends do you have at work? Or, even like, do you feel lonely at work? “No, I’ve got seven buddies!”

That plus your LinkedIn profile could already be enough to leak market-moving information to other people. .

Ricki Heicklen: There are also a lot of these cases of accidental information leakage, in which somebody ends up communicating something to someone else at a different firm, inadvertently, whether through saying how many people sit near them on their desk, or what kinds of products they’re thinking about, or even, reactively to other people’s questions, indicating what they do and don’t know, or what assumptions they make.

One of my favorite examples of this is that the financial industry uses a whole bunch of acronyms. This is because acronyms are more efficient communicators and often clearer ways to express something to someone else. It’s also just because acronyms in general are useful.

Many of these acronyms are overloaded, and there are multiple things they can mean. If somebody external to your firm uses an acronym with you, and you assume it means a certain thing and react as though it means that thing, and they previously thought it meant something else, all of a sudden they know that you think about that concept a bunch, and that that concept is therefore relevant to your work because there is some money to be made in that realm or that asset class or that category of stocks.

This is just so hard to defend against. 

Patrick McKenzie: So, things I’ve seen in real life that have commercially significant consequences without giving away anything. Oh, I don’t even know if it’s possible to not give away anything now. 

Something as simple as a book recommendation in the context of who is doing the recommendation and when they are getting to that book leaks information about e.g. if it is the CTO at a particular firm who is suddenly attempting to bone up on a particular industry and they tweet out what they are reading about, you know, “I’m very into insurance tech right now,” that should move your estimates of whether that firm is institutionally interested in insurance where it wasn’t.

And given the contours of that firm you know, if it’s unexpected that they’re involved in insurance, that is probably useful information to someone somewhere. The classic example of a side channel for this is, planes are very easy to track on the internet for various reasons that get more into aviation than anything else. Some people travel by private planes, and those private planes are registered to them. If a CEO routinely flies to a particular place that only has one interesting business, it is highly likely that there is a deal happening there, and most frequently that deal might be I’m attempting to acquire this interesting business.

[Kris: I recall alt data satellite companies pitching us on this ability to track planes for this reason about 10 years ago…by the time they were pitching a vol trader on this as opposed to a Point 72 long/short pod the opportunity is long gone. Remember, where are you in the pecking order for certain kinds of info?]

And so, people will get an extremely commercially significant bit of information out of something which is one, legally required, two, absolutely anodyne, and three, does not on its surface look like inside information at all, because it isn’t inside information, it’s open source.

Ricki Heicklen: That’s fascinating. I love that example. Another one that might be similarly publicly available is who somebody is following on Twitter.

There are many Twitter accounts that will tweet out stock advice and people working at financial firms will often follow those Twitter accounts – the Twitter accounts they think are more likely to give advice that is a leading indicator of something that will happen in the market.

If you just scroll through somebody’s followers, and you see that there are certain financial advisors, or certain funds, or certain individuals that they’re following, whether those individuals are people who say things that are relevant for markets, like Donald Trump or Joe Biden, or whether they’re people who are giving advice about where to invest, you’ll end up with a competitive advantage because now you know those are places to pay attention to.

Patrick McKenzie: I love Twitter as a product, but there are many, many excellent reasons to not use Twitter. One is that your likes are public – that has caused cancellation of various people for liking tweets that have unpopular opinions. [Kris: not anymore]

One far less obvious threat model is, what is the CEO of this firm thinking about in their private moments on a day-to-day basis?

And one can very easily back solve from that to what the firm might be engaged in. Or, is there a person at another firm who suddenly picks up four followers in a row from a particular firm? Very plausibly, a conversation has happened. There’s just an infinite number of these side channels.

So what can one do about it, aside from not using Twitter and never having friends?

[Ricki poses the common solution — use an alt even if it doesn’t solve all the cases, for example, somebody’s public profile quickly being followed by a number of people from a certain industry.]

Tradeoffs in defending against info leakage

There’s some firm behavior that I think is pretty reasonable that has to do with siloing employees, or taking employees and making sure that they only have access to the amount of information that they need local to the work that they’re doing, and not access to every single trading strategy, or how much money the firm is making, because the best way to keep someone from leaking information is preventing them from having that information in the first place.

Siloing employees comes with a lot of costs. Trends that you might notice between different trading desks, or phenomena you might notice in the market that turn out to be relevant to somebody else’s strategy won’t propagate nearly as easily between different parts of the firm. A mistake that somebody is making is not going to propagate as easily between different parts of the firm.

And two strategies that look like they might be doing different things but are actually doing the same trade and duplicating that trade and therefore in aggregate sizing it too largely, putting on too much size in that trade will not be as easy to detect if you don’t have a bird’s eye view of everything happening, and if you don’t have a lot of eyes on everything happening.

But the benefits of siloing for purposes of information leakage are large, especially the larger your firm gets. First of all, you’re going to have a stronger defense against malicious actors who leave, join another firm, and give their trading strategies, because they’ll only know one or two of the strategies or whatever they were focused on, and not the entire firm’s IP.

Second of all, you are improving how safe your firm is in cases where people leak information inadvertently – they’re not going to be revealing as much information about what different desks are focused on.

[Kris: I must admit the focus on secrecy and hiding your tracks is very reminiscent of my time at SIG.]