In this issue:

• hacking hope

• if it feels good you’re too late

• the geometry of volatility scaling

Friends,

Jared Dillian has this great essay, The Reason We Do Everything, from earlier in the year that opens:

Norm MacDonald, one of the funniest people to ever walk the planet, was also a degenerate gambler, busting out a number of times and going to zero. His therapist told him that he gambled in order to escape reality. Norm said, “That’s why everyone does everything.”

You should read the essay, but today I just want to zoom in on something Norm said. I watched the recent documentary Judd Apatow did on him (Netflix). Apparently, Norm was a heavy and eventually reformed gambler. Judd pulls the following quote from Norm’s 2016 memoir Based on a True Story:

Most people would think it’s the wins that keep the gambler going, but any gambler knows this is not true… When the red dice hit the green felt… and you’re declared the winner… you feel relief. Relief is all, and relief is fine, but hardly what a man would give the whole rest of his life to gain. It has to be something else… It is a particular moment, a magic moment that occurs after the placing of a bet and before the result of that bet. It is after the red dice are thrown but before they lie still on the green felt… As long as the red dice are in the air, the gambler has hope. And hope is a wonderful thing to be addicted to.

At the seat of any good comedian’s observational power is a rare balance of sensitivity with cold logic. Norm is able to feel and diagnose simultaneously (his struggle to quit is a notch for philosopher David Hume’s idea that we are slaves to passion).

As you gaze at the horizon in 2026, dotted with inescapable metaphorical gambling billboards, I urge you to remember that their pitches are hacking an otherwise adaptive impulse just like Frosted Flakes hacks our predisposition to the muted sweetness of pre-industrial fruit. The impulse that’s being hacked is our urge to grow.

Celebrating short-term wins when you don’t have an advantage is like falling in love with the good cop during an interrogation. You are being manipulated. In the gambling sense, by chance herself. She’s an abusive partner who gives up the milkshake just enough for you to stay.

We need growth so nature wraps it in hope. Take the motion to gamble as a sign that you crave risk. Risk is good because it precedes growth. Risk involves failure. Failure leads to learning. Learning changes behavior.

Don’t waste the urge towards risk on a loop where the lessons of failure fail to compound towards durable knowledge that you can actually build upon.

Gambling addiction has always seemed to me like the “insanity of doing the same thing over and over again and expecting different results”. Norm humanizes why we are susceptible to it without taking the stupid low road that it has to do with innumeracy or anything like that. But as it increasingly gets hawked as something more noble (ahem, prediction), they are trying to bring it even closer to what constitutes growth and learning. I’m not against gambling any more than I’m not against freedom to indulge in most conventional “vices”. But big tobacco doesn’t even try to put Vitamin C in their cigarettes.

[Ha, ha I’m lying. They actually did.]

Money Angle

I was chatting with a friend this week who I’m so happy for it makes me almost teary-eyed. He turned down an unbelievable job to take a chance on building an HFT startup a few years ago. It’s going VERY well. And it’s fun. And they’re hiring and all the things. I couldn’t help but say, “Man, you’re just in one of those sweet parts of the curve now”. The work fits like a glove and to quote Eddie Murphy “the ends are meeting like a muthfckr”.

It’s HFT with Sharpe so high you don’t bother with numbers, I just call it Charizard. I have to waste my breath because this is as cheap as an option gets, but “are you raising money?”

Of course not.

They raised once at the start and that’s it. Self-financing from here. Interestingly, they raised from VCs, not allocators. This unusual arrangement combined with the mathematical visibility of their edge, which doesn’t exist in long-term stock-picking, just had me thinking again about the nature of investing.

From The Paradox Of Provable Alpha, we know that the only place to mine for alpha among managers is among those with longer-term horizons. The regular investment firms are not as capacity constrained, are an underdog to have any edge at all, and happy to work for a small take on other people’s money. The managers that look like traders, with high turnover and sample sizes, who consistently crush, don’t want your money.

An advantage in long-run investing on the basis of pure analysis is very hard and even if you’re good you there’s no stats that will ever affirm you to be as good as you think. Active managers either think they charge too little, but since they can’t flaunt HFT stats, settle for what the market will bear, or the managers know they are way overpaid. Nobody thinks the fee is exactly right because the industry is some mix of a economic “lemon” problem and a circus exhibition that has little to do with serious investing and more with spectacle. (Pick your favorite negative alpha manager that finds a way to stay relevant and maintain assets).

Alpha in long-term investing is scarce for even more fundamental reasons. The nature of advantage in a zero-sum game (the warm drift of beta is a public good, but alpha is fixed…no winners without losers).

You have probably seen some version of the right/wrong vs consensus/contrarian 2×2 before. If you haven’t, you should sit under a tree with it for a while. It’s liberating in its proscriptiveness.

A looming annunciation of this problem comes from an interview VC Josh Wolfe had nearly a decade ago on Invest Like the Best.

He noticed that every one of his fund’s investments that became a giant winner was highly contentious at the start. Typically, the stake had just one passionate champion for it on the investment committee.

[Do not confuse this with all highly contentious ideas will be winners. It’s a statement conditional on the investment paying off huge already.]

In light of the 2×2, this is not surprising. If the investment makes obvious sense, it will be consensus and even overbid.

I’ve seen an analogous phenomenon in trading, but even in mock trading. I’ve talked about it a lot before, but when I watch a room of people mock trade, I can quickly tell quite a bit about the person from their action or lack of action (in the case of lack of action, I have to probe a bit, but the lack of action comes from a fairly small number of common reasons which are as telling as taking action).

[If I ever host an event like that again, maybe I’ll consider report cards, where I inform the individual what I learned about them and they can tell me if it matched their horoscope.]

But, one consistent trait that I think is good out of the box is the ability to act before all the info is obvious.

If you need to wait until the evidence is convincing, you’re too late. In fact, at that time you may want to be doing the opposite.

This is a ruthless aspect of markets because it defies the requirements of regular decision-making. In your daily life, you’re not in a race to buy the best phone or lunch before it’s gone. You can usually hold out for a bit more info, improve decision quality, and not have your patience used against you.

The ability to act before you have all the info is useful in other zero-sum games as well. There’s an unsavory connotation to say someone has great political instincts because the object is usually someone who seems impure. The left-coded VC who moved right sharply after Covid. Perhaps they were honestly disillusioned with their team. Perhaps it was just good for business. Either way, the begrudging respect one might hold for a winning political trade echoes the lauded financial agnosticism of great traders. Stan has no problem flipping a yard from long to short at a speed that obviously outpaces any kind of formal research process.

VC to me is an interesting solution to long-term investing because its very structure acknowledges the futility of analysis on forecasting what is going to pay-off. Instead, create a marketing machine that both stabilizes assets and makes founders, the lifeblood of your upside, want to come to you. Then diversify the lotto tickets. As long as you’re drawing from a sample that truly has no mean, then surviving practically ensures thriving.

Yes, the surviving is hard, but has surprisingly little to do with returns.

Related

The Efficient Venture Markets Hypothesis (9 min read)

Abraham Thomas doesn’t publish too often but I am always sure to read them. This one opens:

Venture capital is deeply weird these days. Consider these phenomena…

Thomas’ description of VC circa 2026 strikes me as a triumph of venture creating demand for its services by becoming a part of the “market portfolio” (in the CAPM sense of the word). It is now as much beta as SPX members, which elevates it above the bucket of crabs that is zero-sum competition for outperformance. VC funds are essential allocations to those trapped in the asset-managment-industrial complex should seem paradoxical if their scale ensures beta-level returns. But this article makes it make sense.

Money Angle For Masochists

Trigonometry and Vol

I had Claude make me a custom chalkboard app for making math and investing tutorials Khan Academy-style. For fun, I asked Claude to use it to teach me something. Claude knows me better than my mother, so naturally it chose to talk about volatility.

It created this geometric intuition for volatility. Watch the video and then if you feel like visiting trigonometry, below you can read a full treatment of what cosines have to do with correlation and volatility estimates! → Using Geometry to See Why Volatility Scales With √t

I’ve mentioned before that I’m playtesting a game called Market-Maker that I started making just over a year ago. The current form of it didn’t come together until a) I attended the Arbor Bootcamp last November and got inspiration for one of the core mechanics and b) vibe-coding got so good that it could build a fully digital version of the game that can be played on the internet.

I’m still iterating on it (slowly, this is a hobby project), but when I was playing with a trader friend he understood immediately how to price a straddle that expired in round 5 (it’s the lesson of the video).

Although sometimes that sqrt(round) trick isn’t quite right. The reason, to put it in the context of the geometry article, is that there are card interactions that would make the “angle something other than 90 degrees” and for sigma itself to change. Your cards are private information. Looking forward to getting to play this with you. I think it will be quite the mix of fun and educational (but the goal for this is really fun).

So succinct it’s genius

Corey’s comment is outstanding framing.

From My Actual Life

Self-care = Metallica at the Sphere

Stay groovy

☮️

Moontower Weekly Recap

• So you’re interested in trading…

• are the options market’s odds any good?

In this issue:

• Introducing slate

• I-bonds vs TIPs

• Staring at an option quote

Friends,

I’ve mentioned a few times that I’m experimenting with bringing education and practical investing topics to IG.

One of the experiments I plan to do Khan Academy-style short-form where I show my work on a “chalkboard” and speed it up for socials.

Our little 10-year-old artist has one of these USB Huion drawing tablets lying around.

I can use that to draw up the explainers, but on what software? I checked out some popular recs like Xournal+ and Openboard but found neither of them immediately delightful and just another thing I’d need to get used to. I told Muse I wasn’t pleased with those recs.

I call an audible. I had very low expectations of what I was about to try. I tell Muse:

You know I have this Huion pen tablet thing and you understand what I’m trying to do I have a crazy idea can you make me a prompt to give to Claude Code to ask it about the feasibility of it being able to make me my own very lightweight software tableau that can do what I want and is tuned for math and investing so make me a me a prompt and let’s see what happens

It gives me the following hand-off to Claude Code:

I made it small so it fits here easily and in case you really care to look but like people recounting what happened at each round of a poker hand, almost nobody cares. The point is simply that you should be in the habit of asking the AI to write prompts for you when the problem you are trying to solve is unoriginal.

The fact that there are many drawing apps means my issue is unoriginal. So let’s see if Claude Code, using Muse’s prompt, can one-shot a web-based drawing app.

Voila:

drawing_board.html

• To use it, just download the file, press Command + O (or Ctrl + O on Windows) in a web browser and choose it from the file explorer.

That tool was the absolute first shot.

Seeing how cool and responsive it was, it iterated on it several times, adding way more features, including the ability to add functions to the physical pens buttons.

It’s now a drawing tool made for math and investing concepts to be easily screenshotted or recorded formatted for IG stories, YouTube etc. This is truly personalized software.

I deployed it here: slate.moontowermeta.com, give it a shot!! It’s also dynamic for mobile.

Oh, one more thing. I told Muse to make a 3-minute video demonstrating the features and narrating everything it was doing. Pure one shot. Might be more insane than the app itself:

Money Angle

Lucky timing.

I nearly bottom-ticked the TIPs market with my trade last week. I explained in that article that my estimate for the trade’s 1-year expected return is about 8.50% with ~7.50% volatility. That suggests a weekly vol of ~1%

It’s already up 2% in one week or nearly 2 standard deviations. We’ll see if the yield holds as a high. The last time I bought TIPs, I also wrote about it in October 2023 and that high in yields held until just recently.

Anyway, a friend (and mentor!) who read my recent TIPs article reached out to say I should remind readers that I-bonds might be a better choice if their income is under certain thresholds because of the education tax exclusion. I-bonds, like TIPs, are state and local tax-exempt, but you can also avoid federal tax when used for qualified higher-education expenses.

There are income limits:

I bought them for all the members of my household back in November 2021, when they paid 7.12%. I wrote up the details in I-Bonds For You.

Five other differences worth knowing:

1. Price risk. I bonds have no market-price swings. TIPS can lose value if real yields rise, creating a loss if you sell before maturity.

2. Liquidity. I bonds are locked for one year. Redeem before five years and you sacrifice the latest three months’ interest. TIPS can be sold in the market before maturity.

3. Tax timing. I bonds let you defer federal tax until redemption or maturity. In a taxable account, TIPS generate annual taxes on both coupons and inflation adjustments even though you haven’t received the adjusted principal yet.

4. Purchase limits. I bonds generally cap you at $10,000 per person per calendar year. TIPS accommodate much larger investments.

5. How you get paid. I bonds adjust their interest rate and compound internally. TIPS coupon rates remain fixed, but the principal adjusts. They pay cash coupons every six months.

TIPs don’t have limits on how much you can buy, but the other major benefit is a yield you can lock in. I-bonds usually have a paltry fixed rate portion. Since you buy them directly from the Treasury at par, you are locking in a lower fixed return vs TIPs at current pricing. The higher TIPS yield can outweigh the I-bond’s friendlier features such as the potential tax exclusion and the lack of “phantom taxes”.

Money Angle For Masochists

Taking a small punt on gold upside before the end of the year. You can follow the thread here.

Kris

@KrisAbdelmessih

If GLD bottomed this am and you think the bank hacking narrative has legs it’s an interesting time to look at metals upside. The vols screen cheap cross-sectionally and have low percentile call skew (x axis is our "buy vol" algo score), y axis is skew percentile

@KrisAbdelmessih · 2026-10-23 · View on X

Point of sale: GC Dec 4260/4270 call spread

Even though I mentioned GLD, I actually executed in GC, the Comex futures gold options. GLD references gold in London vaults, and Comex delivery is in NY. But that difference has nothing to do with it.

Trading futures options has 1/3 the all-in cost (IBKR and exchange fees) of the equivalent risk in equity options. 250 GLD spreads = 25 GC spreads. The cost difference was about $100 instead of $300.

For educational purposes, I thought I’d share some observations from the point of sale. I targeted the 4260/4270 call spread. It’s a tight spread, similar to if you just wanted a binary bet on the futures expiring above 4265 or ~7% higher by late November.

I bid 1.40 for 25 spreads and didn’t get any contracts until the futures drifted lower to about 4006.70. That’s the first candle in this pic showing GC futures chart until the end of the day. I also superimposed the market in the call spread on the chart.

The market was 1.40 / 1.50 with Bid size (15), ask size (771)

I had been staring at the quote for a while before I even got a fill. After getting hit on 10 lots, I turned on a screen recorder for the next 2.5 hours (until 4pm ET, though the futures continue to trade for another hour)

Keep in mind this is a $10-wide spread with a .01 delta. $10 in gold is about 25 bps.

What follows is what’s running through my mind when I stare at the quote while watching the futures tick. I stepped away for long stretches and the other thing to keep in mind, based on what the recording revealed:

The quote never changed. Not a single market “event”. HFT people refer to any modification in a quote as a market event.

1. The offer never budged.

2. The bid was not joined.

3. Nothing traded.

4. The offer size stayed constant. No additions or subtractions.

My thoughts:

• Taking a partial fill of only 10 out of 25 is bizarre. I’d think the only counterparty to this is a market-maker. Who wakes up looking to trade a 25 bps wide vertical naturally? So I expect when I get hit it’s a market-maker, but then they only sell 10 out of the 25? What is this, a market-maker for ants?! And then when a partial trades, how does the rest of the bid not get swept by the chipper algo which surely knows this is a resting non-market maker starting to get hit.

• The futures dipped a few dollars below where I was filled. If the first seller liked selling at the higher futures price, don’t they like selling the spread even more now? I know the spread is only .01 delta, but… actually, let’s get into that for a moment.

• In the recording window, the futures rallied to 4015, or about $9 higher than when I got a partial fill. At .01 delta, $10 is 10 cents, which is one full tick in this spread, the gap between 1.40 and 1.50. Paying 1.50 with futures at 4015 is the same price as paying 1.40 with futures at 4005. The market wouldn’t fill me at 1.40 at 4005, but offered an equivalent price at 4015. Furthermore, my bid wasn’t joined with more size and the offer didn’t pull.

Anyway, if you untangle the chain of reasoning here, it can help you think more about how options get quoted. When option deltas are low, there are minimum increment problems in edge capture. For example, if an option has .01 delta, you could sell it with the futures at any price over a $10 range and you’d be booking something between a penny and a tenth of a penny of edge but at the tenth-of-a-penny price it’s obviously worse (you’re 10x less profitable if you extrapolate across your business), but you also can’t turn the market over and be bid there for a negative a tenth a penny of edge.

If you flip this logic to the customer side, if you keep track of all the market “events” you can likely deduce when a market on a low delta option will tick to the next price level. If you can buy a call synchronously with the last tick in the future before the market turned, that would be the best possible fill.

If you are a beginner, just understanding that buying a call for X vs 4015 in the futures is a better buy than buying it for X against 4006 in the futures is a step toward understanding the concept of volatility (the second buy is a worse IV than the first by:

change in underlying * delta / vega

From My Actual Life

Another cool AI capability.

A friend recorded this game with a tripod camera and sent me the video.

I gave it to Claude with instructions to extract rebounding/steal/scoring highlights for #17 on blue.

Not bad!

(Except it used a lot of my max plan tokens 🤑)

I’ve been a longtime fan of Nelly and finally got to see him perform. Last weekend we went to his show at the beautiful Mountain Winery in Saratoga (our first time there!)

Earlier this year I mentioned how I binged Nelly & Ashanti: We Belong Together in 2 nights. I’m openly jealous of how good he looks at age 51.

My favorite Nelly songs come from his earlier years.

E.I., Batter Up, and of course this banger I used to watch countless choreography videos of.

Stay groovy

☮️

Moontower Weekly Recap

• like morning sex on doomsday

• the “rewind” tail hedge

In this issue:

• AI thoughts

• When smarts are as cheap as sunshine

• THE option-101 course for the internet

Friends,

I’ve been burying you with investment material, including an analysis of the TIPs + oil portfolio I strapped on this week. There’s also a tail option piece to it that I’ll write about this coming week. Remember I share what I do for educational purposes only, and while the pieces are detailed, they also make for great seeds to put in an LLM for you to augment your learning by discussing the risk and execution with your favorite bot.

This is the epitome of random, but after I wrote my piece, I called 2 friends who run commodity funds before I executed the trades I laid out. Both of them told me that, independently, a couple of their trader friends who only reach out when something gets interesting raised the idea on the same day. I don’t independently know the people who reached out (one of them is both famous- he was played in a movie- and anonymous, so I know of this individual).

Both managers took it as a sign to look a bit closer.

Anyway, instead of burying you more ahead of more investing posts, I’ll use today’s letter to share some useful, interesting or fun hits that grabbed my attention.

In AI

Noah Tye

@noahlt

Lately I’ve been learning math with ChatGPT (Astra) as my tutor, using this document scanning camera and a vibecoded tool that pastes the captured image right into the chat.

This way, I just work problems on paper and ChatGPT gives feedback. It’s really nice.

@noahlt · Oct 9, 2026 · View on X

I feel like I’m getting a big hug when my readers tagged me in the replies to this amazing post by Noah because this is the exact kind of wishlist item I wanted in Socrates 2026: how to use highlights

Noah hacked this together for ~$100:

Why the experience of AI feels so different between people

This puts some numbers on it.

Andrej Karpathy

@karpathy

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

– Napkin math somewhere around 6B people (~75% of the population) have barely come…

Andrej Karpathy @karpathy

Judging by my tl there is a growing gap in understanding of AI capability.

The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is

@karpathy · Oct 11, 2026 · View on X

I’ve said it in a number of posts this year, but I’m in the camp that you won’t recognize knowledge work as you’ve come to know it soon enough. I dislike fear as a marketing tactic, but I also think this is true. And if you feel FOMO, you are right to be rethinking how much of your work will be automated and what you would do to move up the value-creation chain in the role.

I’d be very concerned about any well-paying job that looks like a computer jockey, particularly if you can’t immediately name what makes you uniquely special at it.

This doesn’t even account for how much work will be outright commoditized or immediately copied. My friend Fonz was over this week and playtesting a game I’m developing called Market Maker.

If it’s any good, it will be instantly copied. It’s a discouraging thought, but there’s no point in lingering on such a helpless feeling. Instead, you have to identify and orient towards what stays scarce and therefore valuable in the future: trust and distribution. Plus costly signals, like spending time with people in person and all the things that come with that.

[And of course essential attributes like courage, reliability, and self-efficacy or discipline or whatever you want to call it. That thing where, when you need to get something done, you do it excellently and instill confidence amongst other high performers. In a way, I think this is just the plainest advice to give to a young adult. Point to someone who’s like that as a model for quality and tell the young adult that you must become someone who would have a similar reputation. Stick to your word and be accountable. This is what betting on yourself looks like.]

Money Angle

TIPS: Safer Than They Seem | 6 min read

Elm Wealth’s most recent post is, like mine, harping on TIPs.

Key quotes:

This illustrates an important point: while the inflation-adjusted market price of long-term TIPS can fluctuate significantly, they’re remarkably stable when measured using what really matters: the long-term spending they can support…

A boat tied to a floating dock rises and falls with every tide. If you measure it against the seabed it never stops moving, but measure it against the dock, where you’ll step aboard, and it barely moves at all. Long-term TIPS are like that boat: volatile in today’s dollars, but remarkably steady measured against the real spending they’ll support.

Seen this way, most of the confusion about TIPS disappears. They do exactly what they were designed to do. And as the safest asset going, their real yield is the natural benchmark against which to judge the stock market’s expected real return when you decide how much to invest in stocks.

“Moontower, how do the options look”?

Speaking of automation earlier…the original post I’m quote-tweeting below is an example of how the future is going to increasingly be bots reading for you, and of course bots writing for bots. Considering I write a letter for humans, this does not bode well, and I expect a bear market in reading probably for the rest of my life. I have no plans to stop writing, but I suspect it becomes an increasingly niche thing to do, like being an upholsterer. I have no quarrels with that outcome, but intend to be adaptable to whatever medium helps people learn and ultimately flourish.

Kris

@KrisAbdelmessih

For investing applications route the letters directly to pipeline to MCPs for your data or market analytics and have the summaries intersect with the quantitative.

@citrini is bullish abc, moontower how do the options look?

Brent Beshore @BrentBeshore

As of a month ago, I started having AI sort my inbox every 5 minutes, including collecting all the newsletters and summarizing them into a master newsletter every morning.

Been fantastic for me. Not great for the newsletter business.

@KrisAbdelmessih · Oct 15, 2026 · View on X

Oil

If you’re interested in oil markets, you should learn from Dr. Ilia. Here’s just one of his recent tweets:

Dr. Ilia Bouchouev

@IliaBouchouev

Our first paper under @CarnegieMellon Commodities Quant Lab is on market participants and positioning, explaining who is who in oil derivatives (free download):
<a class="tweet-url" href="https://www.cmu.edu/mscf/commodities-quant-lab/research/hedging-pressure-and-behavioral-trading.html">cmu.edu/mscf/commoditi…</a>
It is an extension of the earlier video lecture on this topic:

@IliaBouchouev · Oct 13, 2026 · View on X

Money Angle For Masochists

It recently came to my attention that fellow contributor to the upcoming How I Invest Volume 2, Drew Dickson, wrote the following in his essay:

Drew is very nice, ridiculous, if we care about the meaning of words actually, to be so glazy. I’ve thought a bit about why I can be regarded in this way when I know that I’m no smarter than anyone else on that list.

My thinking patterns just seem different and those differences are probably seen as some variant perception when they’re really just the output of doing a niche job like trading options. The job rewired my brain. It’s going to sound ridiculous, but I’ve had so many readers ask me how to adopt a similar way of seeing. Well, I don’t know, trade ten of millions of options contracts for 20 years and nothing else and see what happens. You might not like what happens. If you had a rosy outlook before, say goodbye to that shit. Paranoia is your new mind loop. But then you know you’re paranoid, so you have to tell yourself that’s an illusion, and before you know it, you are in mental exile in that triangle phantom zone in Superman II.

Ok let me be serious for a beat here.

I’ve said it repeatedly. To understand options requires understanding arbitrage pricing theory, which means crossing the “bridge of asses” in the field of investing. The underpinning of that theory makes concepts like opportunity cost, value over replacement, the idea of an equilibrium, higher-order effects (ie greeks) and a humbling appreciation for counterfactuals explicit and visceral. Notice how central these concepts are to decision-making generally. Jeff Yass minces no words on the topic. He doesn’t think a person can have sound judgement and decision-making skills without an understanding of option theory. Of course this is a maximalist claim, but the point is that being soaked in this stuff will make you think differently. And those differences are what I think are being, for better or worse, perceived as “smart”.

[I know many an option wizard whose decision-making leads them to disaster in relationships, addictions, or any number of objectionable choices. Are they “smart”? In a pointy sense, yes. But when we talk about smart in the general sense, photographic memory ranks far below “I am getting what I want out of life.” A lot of god-given horsepower is a party trick to be fetishized by Mensa members who can’t wait to advertise their membership in said club. You all get a yellow star sticker. But in case you haven’t noticed, the thing you hang your hat on is now commoditized by electricity, and if we’re getting to the root, it’s sunshine and black rocks. “Smart” is great, but we have plenty of it in isolation. For a short window in the 20th century, a nerd might have been able to have a great life with just ore and trading it for wheat, brick, and lumber at the port of “STEM job”. But the island is flooding with ore with each passing day.]

What does this all mean?

If you learn option theory, I can’t promise it will make you smarter, but it will make you think differently in ways that make people think you’re smart. That doesn’t hurt (unless you’re trying to hustle someone).

But I have a better pitch.

It’s fun! It’s a bicycle for the mind. It’s a puzzle.

Whatever draws you to it, you should know about THIS:

Kris

@KrisAbdelmessih

This will become the Options 101 reference for the Internet.

Totally free.

Stoked to be a contributor.

Agustin Lebron @AgustinLebron3

Excited to announce Options 101!

It's a site that provides free, no-ads, no-shill education on financial options (calls + puts).

The first course is one I'm now teaching this fall at Charles University in Prague. Lecture notes, videos, assignments.

https://t.co/rzTLlbxBoz

@KrisAbdelmessih · Oct 5, 2026 · View on X

I’m putting together a bunch of the 201 materials which will come out next year but the 101 materials are starting to be available, including Agustin’s lecture videos and slides at Charles University in Prague.

Some background

The course teaches options the way professional trading firms train new people. Agustin trained as an electrical engineer, joined Jane Street as a trader in 2008, and by 2011 was running all EMEA options trading out of London. He later wrote the excellent Laws of Trading.

The content is nearly identical to the intern and new-hire training they ran for years at Jane Street. And since Jane was born from SIG, I’m not surprised to see the overlap in how this material was covered in my own training.

Why you should care: options and option-like payoffs show up all over finance and daily life, the math ties together probability, calculus, optimization, and economics, you may need to value stock options you get as compensation, and it can become a career.

Learning goals. By the end, students should be able to:

• Explain what options are and how they’re used

• Relate options to each other and find value bounds with arbitrage arguments

• Price vanilla options with both closed-form and numerical methods

• Manage the risks of an options portfolio

• Build and use volatility models of option curves and surfaces

Non-goals
The course explicitly won’t teach profitable trading strategies. The slide says anyone hoping for “how to make money trading” should take a different course.

Prerequisites

• Basic single-variable calculus

• Basic Python programming

• No prior experience with markets or trading is expected

Format
One lecture a week plus one supervision session a week.

Syllabus

1. What Is an Option? Contracts, payoffs, exercise rights

2. The Value of Optionality: Uncertainty, distributions, time value

3. Put-Call Parity and Synthetic Equivalence: Synthetic positions, static arbitrage

4. Pricing by Replication: Binomial hedges, cash, fair price

5. Black-Scholes: Dynamic hedging and the pricing argument

6. Implied Volatility: Turning option prices into vol quotes

7. Delta and Directional Hedging: Hedge ratios, changing exposure

8. Gamma, Theta, Vega, and P&L: Convexity, decay, volatility risk

9. Volatility Curves and Surfaces: Smiles, skew, strike, expiry

10. No-Arbitrage and Distributions: Bounds, convexity, implied probabilities

11. Financial Time and Variance-Time: Trading time, calendar time, events

12. Stochastic Volatility Models: Random volatility, jumps, calibration

Random but related

My mother recently called to tell me she’s taking an options course at a local college in NJ. Her sister (my aunt) is trading the “wheel” strategy and has encouraged her to learn about options. My mother will never trade as she’s very skittish about anything that looks like gambling, but she enjoys math and puzzles and is always wanting to make sure her brain health is ok so she’s using options as her latest challenge. Plus she knows someone who can help when she’s stuck.

Separately, my uncle, my mother’s brother, has actually been a retail options trader for over 20 years (he was on an fx desk in the late 80s or early 90s) but has been a self-employed tax accountant and trader since.

If you’re wondering whether we talk shop at gatherings…nope. I say my boilerplate “don’t bet more than you are comfortable losing” as I’m in violent agreement with Victor Haghani when he says you can’t change minds.

From My Actual Life

Just a reminder that you can follow on Instagram @kris_to_the_max where I post educational IG stories. I made a couple real estate-related ones this week, including: You are about to incinerate $188,000

And something of a manifesto for my presence on IG: what’s the point

Stay groovy

☮️

Moontower Weekly Recap

• you can’t change minds

• 2 risk premiums that work together

In this issue:

• the Farmer’s Almanac for Investing
• the “iron condor destroyer” tool

Friends,

Just a few fun things up here since the bulk of today’s letter is heavy on investing stuff.

I Was the Wedding Planner for the Guns N’ Roses “November Rain” Ceremony and Reception | 4 min read

Oh McSweeneys. I knew your writers must be jaded hipster millennials.

“Uh, no”

And here’s a clip I’m very, very late to, but making up for lost time by replaying it 20x in a row.

I just love how the announcer who gets caught off guard is so quick to come up with the most charitable interpretation of the question. Also that voice?! He was engineered in a lab to do that job.

Money Angle

Exponential Wealth: Centuries of Stock and Bond Returns free PDF · 380 pp.

In 1976, Roger Ibbotson and Rex Sinquefield published “Stocks, Bonds, Bills, and Inflation” (SBBI), the first long-run total-return history of the major asset classes. Economists had talked about the equity risk premium for years but as Ibbotson puts it, “SBBI gave us a measure of it.” The data became the standard reference for historical return assumptions, from asset allocation to cost-of-capital work.

This is the 50th-anniversary update, rebuilt from CRSP after Morningstar discontinued the original indices, with the dataset now spanning a full century, 1926–2025.

It recaps what stocks, bonds, bills, and inflation have delivered, then widens out to global markets since 1900, the US before 1926, commodity futures since 1871, bubbles and crashes since 1792, and a forecast to 2050.

It’s a free, encyclopedic reference I recommend adding to your favorite LLM’s investing project folder. It’s like the Farmer’s Almanac for money.

It’s also a great reference for methodologies since the studies entail constructing price series and indices, measuring their statistical features, and addressing pitfalls such as survivorship bias, float vs full-cap weighting, shifting size definitions, overlapping observations, and effective sample sizes.

A sprinkling of fun facts:

• $1 in US large caps in 1926 → $14,751 by end of 2025; $814 after inflation. Long Treasuries: $117 nominal, under $8 real. T-bills: $25 nominal, under $2 real.
• Real compound returns: stocks 6.9%, long Treasuries 1.9%, bills 0.3%.
• About a third of the century was spent below a prior real high: 1929–WWII, 1966–82 (16 years of zero real growth), 2000–2012.
• A 50% three-year run-up was followed by another 50% run-up ~3x as often as by a full reversal (144 vs 50 of 394 episodes, 1792–2024). Crashes were followed by full recovery even more reliably.
• Top 25 companies are ~50% of US market cap, a level last seen in the 1930s. Tech is ~40% of cap.
• This is a non-obvious logical bit: Index total return is attainable by any investor but not all investors, for example dividend reinvestment can’t scale to everyone. Reminds me of these fallacy of composition peculiarities like “paradox of thrift” or treating government finances as if they are a household.
• Investors had the least cash when expected returns are highest.
• Micro cap: 16.5% arithmetic, 11.1% geometric, σ = 37%. Mid cap matches micro on compound return with σ = 24%. Post-1976, micro had the lowest compound return of any size bucket.
• October 1987 was a 1-in-10¹²⁸ event under lognormal i.i.d. The chapter links fat tails to the persistent apparent overpricing of OTM S&P puts.

The book closes with a forecast for 2026–2050, using the same approach Ibbotson and Sinquefield used in 1976. It has 3 steps:

1. They anchor interest rates and inflation to today’s Treasury yield curve.
2. Resample historical risk premiums.
3. Simulate thousands of possible paths.

How did this method fare from 1976 to today?

Pretty well, actually. Although the composition of the return wasn’t quite what they expected.

From 1976 to 2025, the forecast called for a median of ~13.3% a year, but stocks only returned ~11.9%. However, it assumed ~6.7% inflation (it was the 70s after all) when actual inflation was only a bit more than half at 3.6%. Which means nominal returns fell short of the forecast while real returns beat it (~8.1% vs ~6.2%).

At this time, inflation and rates are closer to their 100-year averages, so the yield curve isn’t pushing the forecast much in either direction. What does it say about 2026–2050?

• On raw US history: 9.5% nominal, 7.0% real per year.
• The authors think that bakes in an unusually fortunate US century, so they pull the mean toward the global historical experience.
• Preferred forecast: 8.0% nominal, 5.6% real, implying an equity risk premium of about 4.7% over cash. In their words, a premium “very close to what it always has been.”

Seems rosy. We’re not allowed to be this optimistic. Let’s get a second opinion. This one comes from the market itself as translated by Elm Wealth. Their capital markets assumptions, out this week, are implied from current bond yields and valuations (a cyclically adjusted earnings yield) instead of historical premiums.

[Whether CAPE is a more or less reliable forecasting tool is up for debate, but any attempt to forecast returns feels about as ill-fitting as using a vacuum to pull out a splinter. I’m in the camp that it’s a pointless exercise, while vols are more predictable and therefore a sounder basis for “know nothing” sizing, which I need because I know nothing.]

Elm reports as of September 30:

• US stocks: 2.92% real, 5.30% nominal
• Non-US stocks: 5.78% real, 8.15% nominal
• 10-year TIPS: 2.91% real
• US equity risk premium over TIPS: 0.01%

Where do Elm and the authors agree?

• Both take the rate and inflation pieces from market yields.
• Both lean away from projecting the US past forward. Ibbotson adjusts toward global history.

Where do they depart from each other?

• The equity premium. History says ~4.7% over cash. Today’s valuations say roughly zero over TIPS. Measured against the same TIPS yield, Ibbotson’s forecast is still ~2.7 points above it.
• Ibbotson does address valuation. The chapter flags P/E expansion as inflating the historical record and tests stripping it out, but that actually lowered the forecast less than the global adjustment did. In other words, using global historical valuation was more conservative.
• There’s some timing mismatch. Ibbotson uses the end-2025 curve while Elm’s publishing 9 months later when we know 10-year real yields are up over a full 100 bps.

Caveats both sides attach

• Elm: the risk premium is a poor predictor of near-term direction; momentum is positive and its risk indicator reads low.
• Ibbotson: a forecast is the center of a wide distribution, “not a guarantee.”

A lazy man’s framing

Ibbotson forecasts 5.7% real equity returns for a couple of decades. The CAPE method is close to 0. Split the difference, and you are close to the current 2.90% 10-year TIPS yield you can lock in now (for reference, Treasuries returned 1.9% real over the last 100 years.)

The market in general doesn’t appear to be especially frothy, but the risk-reward in bonds has gotten far more attractive with the latest surge in yields. If AI turns out to be deflationary and hurts employment at the same time that housing rolls over because the cost to own and finance has skyrocketed, then bonds will have looked like insurance policies with ex-ante positive carry. In English, a pretty sweet deal.

But I get it, it’s no spaceship outta the underclass.

It’s funny, when you learn about investing, you associate greed with bullishness. Turns out the textbooks have it exactly backwards in the context of our modern economic pathology psychology.

Money Angle For Masochists

Workflows are Moontower’s screeners organized around what you’re trying to do: sell premium (Income), buy protection (Defensive), finance that protection with your upside (Collars).

We just released a new one to help directional traders scan across the market for the best payoff for a given move: Verticals

About the Verticals Workflow

It starts from a simple need:

How do I compare payoffs across tickers in volatility-adjusted terms?

You don’t want to screen across names for the best bang-for-your-buck on a 10% rally because 10% means something very different in SPY vs MU or TSLA.

Instead we use standard deviations computed from the name’s own surface. Pick a move, say +1 SD by the November expiry, and ask the same question of every name: what’s the cheapest vertical that pays in full if the stock gets there?

The Verticals workflow returns the answer in a grid based on the watchlist you care about.

Let’s see how it works (and learn some option math in the process).

Two knobs

You set two things:

1. An expiry. The picker lists every expiry any name in your list carries, with a count of how many names list it. Every row in the grid is the same maturity, so you’re comparing like with like.
2. A signed move. ±0.5, ±1 or ±1.5 SD. Positive means call spreads, negative means put spreads.

For each name, the grid shows the tightest vertical that is fully in the money at that move. Tightest means adjacent listed strikes. Fully in the money means if the stock lands exactly on the target at expiry, you collect the whole strike width.

Finding the breakpoint

The target strike for a z-SD move is:

Then pick strikes:

• Calls: the short strike is the closest listed strike at or below K_z. The long strike is the next one down.
• Puts: the short strike is the closest listed strike at or above K_z. The long strike is the next one up.

Both rules keep the whole spread inside the target, so a stock that gets to the breakpoint maximizes the max spread value (ie it pays the width of the strikes.)

Marking the spread

The obvious price is the spread’s mid, but it’s far too noisy if each leg is 40 cents wide and the spread is worth 30 cents.

Collecting an accurate mark for a spread is a bit of an art, combining curve fitting and option pricing. In the app, the Price you see for the spread is not mid but a Moontower fair value which lives within the bid-ask.

Reading the grid

The headline number is Payout:

A 1-point spread priced at $0.20 pays 4 to 1 if the stock gets to the target.

Payout is the first column your eye goes to, but we include several columns to judge how much to trust a mark.

Filtering for confident markets

• Spread bid / ask: the spread’s own market.
• Mkt width: the legged market, both legs’ bid/ask widths added together. It’s what you’d pay to cross both legs.
• Mkt / strike width: Mkt width as a share of the strike width. A 20-cent-wide market on a $1 vertical might be hard to execute nearfair value.
• Leg width / vol: the wider leg’s bid/ask expressed in vol points, as a share of its IV. It puts a 20-cent market on a $400 stock and a 20-cent market on a $30 stock on the same footing.
• Mid: Compare with Price. When these two sit close together, the market and the model agree. When they’re far apart, the Moontower fair value is probably more reliable.
• Liq: the name’s liquidity tier, which we provide in all our filters. It’sa function of how wide the markets as a percent of vol allowing us to compare across names.

The grid also drops names whose markets are too wide to mark at all. They’re counted in the “N names excluded” line under the grid, so you can see what got left out.

A few screens to start with

• Tight markets only. Mkt / strike width under 20%, Liq High, sorted by Payout. This is the honest version of the leaderboard.
• Where the market and the model agree. Add the Mid column and keep rows where Price is within a few cents of it.
• Cheap upside: +1 SD, Call skew %tile low, sorted by Payout.

Everything filters, groups and sorts like the other workflows, and your choices persist between visits.

This tool will let you look at the market and answer where the cheapest put spread or call spread to bet on a .5 or 1 standard deviation move by some expiry date.

You can also just reverse sort if you’re looking for spreads to sell. Knowing how much people love to sell options, I should probably rebrand this as the iron condor destroyer (an iron condor is a package of 2 OTM vertical spreads typically marketed as a less risky way to sell a strangle).

Stay groovy

☮️

Moontower Weekly Recap

• vibey vs discrete trades
• “how i invest my money”

In this issue:

• academic acceleration
• wrapping up Kelly
• option topics from the RH Summit
• game and food recs

Friends,

One of my close friends runs an advisory for HS students applying to college. He wrote this guest post years ago: Moneyballing College Admissions.

He lives in the Bay Area and told me he’s seeing more parents complaining about the lack of acceleration options in the public school. He said outside CA it’s becoming far more common for high schoolers to take AP Calculus BC before senior year. My 8th graders’ goal is to take it by junior year, giving himself a chance to take Multivariate Calculus by senior year at a local college. This is already offered in some high schools across the nation, including some on the peninsula. Just this week, I talked to someone whose 9th grader was in Calc BC. That only sounds crazy if you haven’t been paying attention to what I’ve been sharing about Math Academy students (my 8th grader says his 5th grade little bro is already doing the same stuff his “advanced” math class is doing.)

In our local school district, we are finally seeing the pendulum swing the other way on math instruction. A few years ago, they got rid of “tracking” in 6th grade, forcing all kids, regardless of their interest/aptitude, into the same class. Well, the seeds of change are obvious in this survey I just filled out, as I don’t think we could have even had this conversation 5 years ago:

That #7 offers choices besides “supportive” and “very supportive” tells me these 2 articles are as important as I think they are.

Smarter Than Yesterday (6 min read)

Pamela Hobart argues “the academic acceleration community needs a viable brand”. Co-sign. Terrific post. I’ve had many of the same thoughts, especially as my kid’s schoolwork will leave me wondering, as Pamela has, “what are we even doing here?”

Everyone Wants a Child Like Eileen Gu (10 min read)

”almost nobody today wants the childhood that produced her.”

Violet Gordeljevic spitting truth in this one. I’ll share a few excerpts that resonated with me. You should read it to see what lands or doesn’t for you.

On Discipline and Exceptionalism

• “So when people say she sounds and talks so disciplined, and that she knows her own mind—well, yes. That is what a decade of being asked to do your best looks like from the outside.”
• “Nobody arrives at exceptional by accident. It doesn’t turn up later as a nice surprise because the child was left alone and bored for long enough.”

On Passion and Competence

• “Passion doesn’t usually come first and then get supported. Usually exposure comes first, then competence (from actually having gone through practice), and passion tends to arrive somewhere after that, because human beings mostly love the things they are good at.”
• “What is important to understand is that she could not have fallen in love with skiing, or Mandarin, or Olympiad maths, if nobody had ever put her in front of these things…A child who is never introduced to numbers will not reveal unusual mathematical ability. A child who never sits at an instrument will not discover she has an ear.”

On Modern Parenting and “Full Schedules”

• “We didn’t lighten the load at all. We drive our children to more things than any generation in history. We just stopped asking them to become good at any of it.”
• “If we are honest, the schedule is full but the demand is zero. It’s honestly like we have swapped skill for entertainment.”
• “Underneath every version of this conversation sits the same line: I don’t need my child to be exceptional, I just want them to be happy. … But being the parent who stretches a child—who asks for one more go, who expects them to learn to write something properly or figure out that math equation, who holds the line at six when she wants to quit ballet—doing this is harder and considerably less pleasant than being her friend.”

On Warmth, Adversity, and Long-Term Value

• “Protecting a child from effort is not the same as protecting a child.”
• “What separates people with more demanding childhoods from those with easy, low demand ones, isn’t how much or even what was asked. It’s rather whether the asking sat inside enough warmth to be survivable.”
• “The choice was never between pressure and love. It was always meant to be both.”

And then my 2 favorite:

• “I have met a great many people who regret being allowed to quit”
• “The thing is, children are not good judges of what they will be glad to be good at at thirty.”

Money Angle

Wrapping up Kelly

I know I’ve published a few articles on the Kelly Criterion recently, but if you prefer other mediums:

🤳🏼 This is the IG story version

If you prefer a more in-depth lesson, the lecture version with Matt Zeigler dropped this week:

📊Accompanying Kelly Slides

Robinhood Summit 2026

I was in Houston this week for the Robinhood Summit to help with 2 sessions.

• A “trading lab” class where Mat Cashman and I gave a lesson on buying options.
• I was also invited to play the role of “trader” on a panel featuring a few of the data providers on RH’s new agentic trading platform. You can think of it like an app store, where users can subscribe to have vetted providers’ data accessible within RH’s trading agent.

Since this panel was on the “main stage” the recording is available here:

Money Angle For Masochists

2 examples from the Summit.

Mat Cashman and I did a teach-in session to a large audience of conference-goers who use options. Here’s one idea from that session.

What does optionality actually buy you?

The simplest way to understand the value of buying an option for directional reasons is to benchmark it to the counterfactual: how do I perform relative to “if I just bought the stock?”

If you buy a call, you do better in the large up move OR the large down move vs just buying the stock. You have more upside leverage, and if the stock craters, you only lose your premium. The tradeoff is that the option strategy underperforms owning the shares on intermediate-sized moves. That’s why we say owning an option is “long volatility” even if someone resists thinking in such admittedly abstract terms.

The chart subtracts the P/L of 100 shares from the P/L of ~2.2 calls on the 1-year 110 strike, at expiry. The calls cost $2,194 and carry the same delta as shares that cost $10,000 (100 shares of a $100 stock).

The shares win between roughly $78 and $137. The gap is widest at $110, where the calls expire worthless and the shares are up $1,000, a $3,200 difference. Below $78, the calls lose $2,194 at most while the shares keep falling. Above $110 the calls behave like 217 shares instead of 100.

If you buy a call and lose your premium, you can’t evaluate if this is a good or bad outcome without considering the counterfactual.

The INTC calls and a possible Micron sympathy trade

This was one of the examples I discussed in the live stream session where we talked about combining sources of data to reason through trade ideas.

1. The print. On Sep 28th, there was notable size buying in INTC October 16 130 calls.
2. An odd expiry. It was a lot of premium spent on high gamma & theta calls which expire a week ahead of Intel’s October 23 earnings call. Why spend that much premium on a short-dated bet that rolls off before the stock’s own catalyst?
3. The sympathy hypothesis. Micron was reporting on the day I was talking after the close, Sep 30th. Maybe the buyer was using INTC as a semis proxy, owning short-dated calls to catch a sympathy move off Micron’s print. It’s one possible story, not something I can confirm.
4. Did the seller make a mistake? A seller may think “these don’t capture earnings, so they don’t deserve a premium,” or INTC shouldn’t move much in the quiet blackout period before an earnings report, ignoring possible spillover effects from MU’s bellwether report and guidance.
5. The next question. Is Micron earnings even a big deal? Look at Micron’s earnings straddle. If the implied move is small, the market isn’t expecting Micron to move much. Then the sympathy thesis loses most of its appeal, since there’s less distance for INTC to get dragged along. Turn out MU earnings were priced cheaply compared to the last few years.
6. Then look at what INTC vol already costs. Implied vol was middle-of-the-road but high cross-sectionally (ie relative to other stocks in the current market regime). Call skew was rich, though not extreme.
7. Put it together. Rich vol plus a firm call skew, the primary market for MU volatility saying earnings are going to be a non-event, and a possible story for putting the INTC buyer “on a hand” adds up to making me more inclined to want to fade the INTC call buyer. Possible trades are selling INTC straddles or, if you already own the stock, writing calls against it.
8. You don’t have to sell the 130s to fade the 130 buyer. Heavy buying in one strike lifts vol across that whole expiry. Strikes near each other move together, so the bid in the 130 calls shows up in the 125s, the 135s, and the at-the-money options too. Selling the straddle, or whatever structure fits your book, still takes the other side of this buyer. Don’t anchor on the exact strike that printed. Put-call parity ensures the entire surface gets richer.

Finally, an analogy I didn’t make at the conference, but notice how much like poker the chain of thought is. The cards you can see (ie the flop) are the measurables like IV rank or call skew. The quantity of options purchased is the bet size. The expiry is like thinking about the position they bet from (ie “under the gun” or right after the button to last position or “dealer”). One of the market-makers’ advantages is that they keep tabs on notable flow, like a poker bot which examines online players’ hand histories and tendencies.

From My Actual Life

Once the RH Summit ended, I was rewarded with some nice personal moments. I took Mat Cashman to Camaraderie in the Heights area of Houston for an exceptional meal by super chef Shawn Gawle.

Shawn is a friend of mine. He lived with Yinh and I for a couple of months when he first moved to SF to be the pastry chef at Saison. If you are in Houston, do not miss Camaraderie.

Then on Friday, Yinh and I celebrated our 17th wedding anniversary. (We’ve been together for 23 years in total).

We both played hooky and went hiking up in Fairfax (Marin County) before an exceptional dinner at Vin in San Rafael. It’s both casual (we were still in hiking clothes) and worth going out of your way for. Unbelievable eating week. Might as well. I have a colonoscopy later this week, so I need to follow some dietary restrictions for the next few days.

We also did a bit of shopping in downtown San Rafael. Picked up some used vinyl at Red Devil and also went to one of the best game shops I’ve ever been in:

This Gamescape has a shared lineage with the classic one on Divisadero in SF but the San Rafael one is much larger. Their game wall is immense and ordered from least to most complex.

I picked up these 2:

Acquire is one of my favorite games and while I have it already, this newer edition has irresistible plastic buildings. I must support this.

Brass Birmingham has been at or near the top of the BGG rankings for nearly a decade, which is Nigel Richards’ level of domination. My favorite boardgame is the first edition of Brass released in 2007, but supposedly the 2018 Brass Birmingham is much better. Time to find out.

Stay groovy

☮️

In this issue:

• soulcraft
• grab a coffee and derive this with me

Friends,

My second favorite thing after a piece of art, writing, music, movie, and sport that I love is watching someone else gush over things they love. It’s why the Lost in Vegas guys are so endearing, especially when the channel first started. You were watching hip-hop heads not only discover but unpeel the layers in Tool’s music.

One of my dad friends recently bought a 1979 Jeep to restore with his son and asked if my son Zak (13) would like to help. Zak’s “hell yea” beat the sound of my last syllable when I asked him. He started watching YouTube, and we landed on a channel where these obsessed details comb the U.S. looking for “barn finds” to clean for the owners. For free! It’s free because the channel has almost 2 million subscribers clamoring to watch an impossible cleaning.

I watched a few videos. I get it. Renewal of beauty, just like the objects of renewal never goes out of style. The process is as timelessly rewarding as the result.

Caitlin’s tweet is an appeal to our spirit of obsession and craft.

Which brings me to a luxurious reading experience:

I like ‘em thick (Adam Mastroianni)

This essay not only feels good but is an important lens on the fast-approaching experiment of whether infinite electric monkeys will pound out Shakespeare.

Normally I might apologize for extensive excerpting but they are the point. I can do no better than this.

It opens:

I owe an apology to every English teacher I ever had. I always assumed that so-called “great” literature was a hoax, a punishment inflicted upon adolescents for the crime of being young. These books did not have anything special about them, and covering up that fact was simply a make-work exercise for former English majors, a sort of “jobs for snobs” program.

I was wrong about this. There is such thing as greatness. More specifically, there is such thing as thickness. Great works of fiction—for that matter, great works of any art—unfurl in response to your attention. The more time you spend with them, the more you get out of them. That kind of responsiveness is so addicting that it can lead people to do crazy things, like try to teach literature to high schoolers.

But thickness is tricky, because rewarding the careful reader often means repelling the casual one. And this is where I would like an apology in return from my English teachers, because while this might have been obvious to them, they never made it obvious to me.

I was presented with art and literature as if it was self-explanatory, and that everything wonderful about it was plainly visible from the outside. But those works were much more like dark, winding caves with treasure stashed inside of them. My teachers were like, “Right, well, into the cave you go!” and I was like “But there’s nothing in there” and they were like “Entering the cave is 30% of your grade” and so I took a few steps into the darkness and I was like “Just as I suspected: an empty cave” and then I came trudging back out and pretended that I saw something.

It goes on to describe spectacular examples of “thickness”. You won’t want to miss the The Garden of Earthly Delights painting and its unintended invitation to hear “butt music”.

Adam presents 4 qualities that make something thick with examples from a children’s book, the “ears” in Hamlet, and Penn (of Penn and Teller fame) eating fire.

There’s an amazing takedown of what passes for popular non-fiction.:

Reading a book like this feels like wandering through a Potemkin village. Touch any of the ideas, and they tip over.

He contrasts gilded examples of non-fiction with solid gold:

Thickness comes from surfacing a few facts well, and in such a way that you realize the existence of entire universes of additional facts that could be known….[Jane Jacobs book] is pointing out a fact that millions of people observe every day, but almost none of them notice.

He addresses an easily anticipated objection, which you’ll be familiar with if you have read any of deBoer on “poptimism”:

If you allow for the existence of secret treasures that can only be accessed with effort and analysis, then you empower the elitists and the snobs. “Buddy, don’t even talk to me until you’ve been in the cave!”

But look around. The snobs are in full retreat. We have swung the pendulum so far toward poptimism, toward the blinkered idea that all art is equal because all humans are equal, toward the ethos that guilty pleasures are simply pleasures, that I’m not sure if we can ever swing it back.

And finally, Adam addresses the elephant:

Erasing the line between the thick and the thin has left us defenseless against slop at the exact moment of its onslaught. Everyone can sense there’s something amiss with the prose that comes out of the machines, but we lack the language to talk about it, and so we’ve converged on the idea that slop simply means using too many em dashes, bullet points, and line breaks.

What separates substance from slop is thickness. Slop holds no secrets; it signifies nothing. Under scrutiny, it evaporates. All it can offer is bottomlessness—sure, there’s nothing on, but at least there are infinite channels!

That’s why I’m neither surprised nor dismayed when studies find that people prefer AI art to human art. Of course they do! In the short term, thinness prevails. When people are making snap judgments, they want pretty flowers, poems that rhyme, pleasing pablum, the simulacrum of thought. But none of these last…

About once a week, I get a pitch from some AI startup that wants to automate some part of my writing. The most recent one says it’s “built for credible thinkers who have a book’s worth of ideas but not the time it typically takes to write one”.

I’m sorry, but if you’re building or using a tool like this, then you’ve got slop for brains. There is no such thing as having a “book’s worth of ideas” that are all ready to go except for the small matter of choosing the right words and putting them in the right order.

I know exactly the feeling that these slop-trepeneurs are preying on, because I feel it all the time: I’ve got these thoughts in my head, and boy oh boy they’re good ones, all-timers, really, and it’s so annoying that I have to spend all this time making the words sound good, when the ideas behind the words are already so good!

But this is an illusion. The ideas are not already good. They need to be thickened. I understand why it’s tempting to force a machine do the hard part for you, but it can’t, and the hard part is the only part worth doing anyway.

Money Angle

People seemed to like the post I wrote 2 weeks ago teaching readers how to compute Kelly optimal bet sizes in their heads. A lot of people reached out saying that despite learning Kelly in the past, this treatment not only made it clearer but also helped them appreciate how its approach informs risk-taking in wider contexts.

Panoptica graciously asked me to republish it under their own banner:

After this post you will be sizing bets in your head (Panoptica)

Just to put a bow on it, I’ll restate what I think are the most crucial lessons without dwelling on the formula:

1. Even educated people are terrible at sizing bets. It’s not because it’s so complex, but I guess it’s like squatting. It seems like you should just know how to do it, but it’s actually something you need to learn the mechanics of.
2. Overbetting is incinerating money. This is something that’s hard to appreciate until you see the math. The reason you size smaller is because of the asymmetry of being wrong on your edge. If you underbet, you slow your growth rate but slow risk even faster. At least you’re exchanging lower returns for a better risk/reward. But if you overbet, you lower your growth rate AND increase your risk even faster than you reduce your reward. Both the numerator and denominator of your risk/reward move in the wrong directions!

If interested, Matt does this neat meta series “Notes on Notes”. It’s a short chat about how and why a particular article comes together in the first place. You can watch it here:

via Matt Zeigler@CultishCreative

A short yet broad-ranging talk with @KrisAbdelmessih on @Panoptica_ai for his @EpsilonTheory: Unplugged essay, “After this post you will be sizing bets in your head” Kelly bets, using AI to learn math, creativity… NEW Notes on Notes!

To round out this Kelly sprint, I have 2 more bits that are again overtures to curious learners who may find math intimidating.

1) Slides

The first is a condensed slide version, which I hope makes this accessible. It was born out of teaching Kelly to my 8th grader at breakfast this past Tuesday. “Zak, I wanna see if I can teach you something neat in 5 minutes.” He rolled his eyes, but at least he humored me while scarfing down his cereal.

📊Moontower Kelly Slides

2) An empowering derivation

The derivation of the Kelly formula is so fun because as it rolls downhill, a number of concepts we talk about in this letter stick to it, so when we get to the end it feels like something grand, but it’s so damn compact.

Another teaching experiment. Let’s see if I can narrate the derivation in a way so that as you follow along it never feels “hard”. I want to prove that this is fun to do and while I don’t expect to convert everyone, I do think there’s a bunch of you who’d like to be able to learn this but feel blocked because you have gaps in your foundations or can’t remember HS math, or just lack some confidence.

Screw all that, I’ll lay my jacket over the puddles so you can see that it’s not so bad out here. Just come along.

Money Angle For Masochists

Deriving Kelly From Scratch

Stating the question

You have a bet. You win with probability p and lose with probability q = 1 − p. If you win, you get paid B times what you risked. If you lose, you lose what you risked. B is also just a return. So if you double your money on a bet, B=1=100% return.

You’re going to bet the same fraction f of your bankroll every time and let it ride.

What’s the f that yields the highest compounded return?

Step 1: What one bet does to your wealth

If your wealth is W and you bet the fraction f:

Win: W → W(1 + Bf) Lose: W → W(1 − f)

Example:

Your starting wealth is $100 and you bet 50% of it on a coin flip. Remember B =1 because when you win you make 100%. When you lose you always lose f which is your bet size.

Win: 100 → 100(1 + 1*.50) = $150 Lose: 100 → 100(1 − .50) = $50

You keep the part you didn’t bet no matter what. A win adds B times your stake. A loss takes your stake away.

Step 2: What many bets do to your wealth

Play n bets. You win h of them and lose the other n − h. Each bet multiplies whatever the last one left you, so the multipliers stack:

Wₙ = W₀(1 + Bf)ʰ(1 − f)ⁿ⁻ʰ

The order of wins and losses doesn’t matter. Only the count does.

Example:

I bet 50% of my wealth each turn on the coin. I play 4 times and I win 3 of them.

100(1+1*.5)3(1-.5)1= 168.75

Order doesn’t matter. If I lost 50% on the first flip, then won 3 in a row:

Start: 100 Lose (×0.5): 50 Win (×1.5): 75 Win (×1.5): 112.50 Win (×1.5): 168.75

Step 3: Turn total growth into a growth rate

Wₙ is where you end up. We want a per-bet growth rate.

You already know how to do this. If an investment grew by a factor of (1 + 8%)¹⁰ over 10 years, you take the 10th root to get the CAGR back. Same move here: take the nth root of this equation: Wₙ = W₀(1 + Bf)ʰ(1 − f)ⁿ⁻ʰ

Note that W₀ can be divided out, just as if your starting identity was 150 = 100(1+8%)¹⁰ and you turned that into 1.5 = 1.08¹⁰ before taking the 10th root to get to annual growth rate.

This equation took our simple total growth equation and turned it into a growth rate equation:

Step 4: Let probabilities take over

We can clean up our new growth equation with several handy notation substitutions.

Ps and Qs

h/n is the share of bets won

(n-h)/n is the share of bets lost

Over a long run, the fraction of bets you win settles down to your win probability:

h/n → p and (n − h)/n → q

G

Wₙ/W₀ is just a wealth multiple. If you made 200% on your investment portfolio over a decade, your wealth multiple is 3 because you started with $1 and ended up with $3.

We’ll call that wealth multiple G (for “gross multiple”)

So the per-bet growth factor becomes:

G = (1 + Bf)ᵖ(1 − f)ᑫ

G = 1.05 means your bankroll grows 5% per bet on a compounded basis. In traditional investing, we’d substitute the word “year” for “bet”. Each flip is like a year.

Our job is to find the f that makes G (the gross multiple of our wealth) as big as possible.

Step 5: Take the log to make the math easy

Maximizing G means taking its derivative with respect to f and setting it to zero.

It’s 2026, you don’t need to know how to actually differentiate an equation. You just have to awaken that part of your brain that knows:

a) a derivative is the slope of a function at a given point on a curve

b) when the slope of a curve is 0 this is a maximum or minimum

c) intuitively, we can reason that this is a growth curve is a hill with no bumps. Its slope starts positive and only ever gets smaller as you bet more, so it can hit zero exactly once, and when it does, you’re at the top. (the jargon version: this growth curve is concave: it bends downward everywhere from the max, with no inflection points)

We are still here:

G = (1 + Bf)ᵖ(1 − f)ᑫ

But G is a product of powers, which is nasty to differentiate.

But remember from simple pleasures, logs fix this. They are the inverse of exponentiation, allowing them to turn multiplication into addition!

Push the cobwebs away to recall that this works:

log(100) = log(10 × 10) = log(10) + log(10) = 1 + 1 = 2

That’s not all logs do.

They pull exponents down in front: log(xᵖ) = p · log(x).

We can prep that equation for differentiation by taking the log of both sides and using both of those sexy log features:

log(G) = p · log(1 + Bf) + q · log(1 − f)

Maximizing log(G) gives the same f as maximizing G, because log always rises when its input rises.

There’s another little bonus.

Log(G) is the log return per bet, the same quantity as ln(S₁/S₀).

It re-expresses a compounded, multiplicative growth factor as a continuously compounded rate, and rates add cleanly.

Step 6: Differentiate and set to zero

Once you know this is a derivative problem because you are trying to maximize then we can rely on crutches (Claude) for thing that’s hard to remember.

Namely that:

• the derivative of log(x) is 1/x.
• If there’s something inside the log, also multiply by the derivative of that inside piece (the chain rule)

So we have our equation:

log(G) = p · log(1 + Bf) + q · log(1 − f)

then we differentiate our terms to be added with respect to f:

• p · log(1 + Bf) → pB/(1 + Bf) (the inside, 1 + Bf, has derivative B)
• q · log(1 − f) → −q/(1 − f) (the inside, 1 − f, has derivative −1)

Set the sum equal to zero, which is where the growth curve is flat at its peak:

pB/(1 + Bf) − q/(1 − f) = 0

Step 7: Solve for f

Move the second term across and cross-multiply:

pB(1 − f) = q(1 + Bf)

pB − pBf = q + qBf

pB − q = pBf + qBf

pB − q = Bf(p + q)

But don’t turn your pattern recognition noggin off!

Since p + q = 1, this collapses to:

f = (pB − q) / B

f = p − q / B

Can you reproduce this right now on a blank sheet of paper?* Probably not. But go through it once the way you used to trace when you learned the motions for drawing comic book characters or flowers or in my case TMNT. After a single reproduction by hand, I assure you some neural pathways will re-open that have had construction signs in front of them for years.

And to tie this back to the beginning, that is the joy of thickness.

[Get your mind back over here, this is a family letter.]

*You will be able to reproduce it on your own after 2 or 3 attempts. I don’t know why the motion of the pencil is a 10x better instructor than reading it a bunch of times (generation effect maybe?). But this is one of those learning principles I take seriously and impress on my kids.

The world will seduce you with ease where you’d be better served by friction. It is a 21st-century skill to have a point of view on the difference.

In this issue:

• for when the world slows down
• family investing webinar
• futures premium cell

Friends,

I’m in Vegas with friends to see GnR this weekend. 36 hours of noise bookended by 1-hour flights of peaceful purification in the form of flight reading.

Most of what I read on the regular is work-related and instrumental. I save appreciative reading for when the world slows down. Flights are a reliable example of such a time.

Here is both a short article and a long one for when you’re in the mood to actually read every word.

The Real Meaning of the “Road Less Taken” | 2 min read

I love this message and agree with the author about it being the one we need. It’s also the message I closed last year’s trading bootcamp talk with, so I’m biased.

Letter from Wendell: How a poet and farmer and a guitarist and teacher connected across time and space | 36 min read

A friend of my wife who reads my stuff sent her this to pass along to me. There is no summarizing an article like this. It would completely miss the point. That’s why I recommend reading it when you are in the mood to read every word. The friend has a good read on me (or my aspirational self anyway).

Money Angle

This summer I scheduled a lab to help kids in the Investment Beginnings Class buy their first portfolios.

I scheduled the lab during market hours so I could help with the execution. Between vacations and camps, a lot of kids were unable to make the lab, so it turned into rolling office hours. I ended up doing 8 sessions, sometimes just with one student on Zoom (one of the kids called from sleepaway camp!)

Parents sat in on some of these. Word spread, so I ended up getting a bunch of students who didn’t come to the 5-week course, but the parents are friends, so I put together a super abbreviated version of the course.

Actually, it wasn’t really a review because that’s hard to have people engage with if they never went through the course in the first place. It evolved into a spiel that overlapped with key ideas from the course. I’ve done it a bunch of times now so it’s more like a 2-hour seminar.

The whole experience makes me wanna try something.

Let’s do a “family investing webinar” where a kid and their parent(s)/guardian(s) join a Zoom call that I host.

2 hours in one shot is too long. We’ll break it up into 2 1-hour sessions.

There won’t be any charge for it, but I will limit it to paid subs to this newsletter.

Money Angle For Masochists

First, a heads-up that Thursday’s using tick data for spot-vol correlations was enough masochism that I was told to back off:

On to today’s masochism…

The futures-premium cell

Back in my SIG days, every trader had a cell on their spreadsheet showing the SPUs (the name for SPX futures back in the day; it’s derived from the September symbol) premium or discount to the “cash”.

A little background

The cash is the SPX index price based on its components’ spot prices. You can compute the index value from the stock bids, and that would be the “SPX cash bid”. You can do this for the offer as well. The average of the bid and offer is SPX mid-market.

Futures contracts, based on no-arbitrage pricing, have a fair value based on what you gain or give up by owning the future instead of the cash basket. Since you save the interest on the cash it would cost to own the basket but forgo any dividends you would have received that offset the drop in the shares when they are paid the futures are valued as

SPX + (interest – dividends)

The interest and dividends are estimated from today until the futures’ expiry date.

Assume:

SPX cash mid market: 7,800

RFR = 3.5%

annual dividend yield =1.5%

t = 1 year

The 1-year future fair value is approximately 7800e^{(3.5% – 1.5%)*1} = 7957.57

The basis between the future and the cash index was known as the EFP (“exchange for physical”). In our example, it equals 157.57

If the future was trading 7977.57, we’d say “the futures are trading over”. In this case, it would be 20 points or about 25 bps rich as a percent of the cash index.

This is an arbitrageable difference as a trading firm can sell the future, buy all the components, and book a theoretical profit that will become a real profit if their carry assumptions hold true. As a market-maker on the floor, I was not involved in index arbitrage this directly (although most of my clerking experience was much closer to these strategies).

The futures are more liquid than the cash index so the basket price lags. If systematic bullish news hits the tape, you cross the tiny bid/ask on the futures. In fact, the ES or e-mini future actually leads the big SPUs, so ES was used for computing the premium or discount.

All of the market-makers had a cell on the spreadsheet on their handheld tablets that showed the futures’ premium/discount to fair value (FV = cash index + EFP). Those premiums or discounts were typically small, 10 bps or less, as the market bounced around. But huge news could catapult the futures 300 bps before much of the basket could blink. As you can imagine, index arbs get very uneven fills on the baskets they try to execute to close the gap. That’s because everyone making markets has not only pulled their stock offers, but lifting resting customer offers, often several levels through the NBBO that was posted a split second ago.

You can beta-weight the “amount over” or premium the futures are trading to estimate a new fair value for the stock. If the stock you trade has a 1.2 beta, then you might think its new fair value is 3.6% higher than the pre-news price if the futures are trading at a 3% premium. Of course, beta is just a statistical quantity so you have a confidence interval around the beta, which is pretty much life as a market maker. Futures are trading 300 bps over, what’s your 2-way on XYZ stock? Maybe I’m “2% over” bid, offered “4% over”. Then, you’d need to be quick to remember what bids and offers on the option chain are resting that you should race to lift (all the calls should increase by their delta * your beta-weighted change in fair value) and hit (all the puts will decrease by the same factor and this is not even considering the effect of gamma).

Today, all the quotes being streamed by traders will get pulled while they simultaneously blast all the resting orders in a flash, but this process happened a bit more at human speed 30 years ago (although you still weren’t gonna beat the floor…the competition for the resting orders would be market-maker on market-maker).

I’m mostly sharing this because it’s fun for the stragglers who read this far into the masochists section, but it’s worth mentioning the context that made me think of it.

This phenomenon sometimes leads to anti-data or an interpretation of data that is exactly the opposite of the typical inference.

Why?

If a contract or security trades on the offer, the assumption is generally that “paper” (trader language for customers) is buying and the market-maker is selling. But in the example above, the market-makers are the aggressors because they know the market is much higher than last sale. They are lifting calls and hitting puts, so your read on flow sentiment is exactly the opposite of a normal market condition.

Just another example of reality having a surprising amount of detail.

Moontower Weekly Recap

• additional thoughts on the risk-reward of option collars
• simple pleasures
• using tick data for spot-vol correlations