like morning sex on doomsday

Emi and the engineers on my moontower.ai team have been harping on me to download Grokbot. It’s an always-on AI agent that lets you assign multi-step work to persistent AI teammates “staff” on their own cloud computers.

The Cursor team (acquired by SpaceXAI) just feels like they nailed this. You keep hearing about “agents” working around the clock for nerds who like to brag about their AI armies. Grokbot sands down the rough edges to give normal people a chance to plug in to a world of their own assistants.

You download it on your computer (Mac, Windows or Linux) and get the phone app from the App Store or Google Play. Your bots and conversations sync between them, so I start something on my laptop and check on it from my phone.

It’s included with paid Cursor plans and runs on a weekly usage allowance. First-time users get the first month for $15, which renews at $30 a month afterwards. There’s several plans that give you usage and pre-pay discounts.

Let’s dive in.

The setup

The onboarding UX is gentle, fast, and self-explanatory. You name your first bot who will act as the manager bot. I just called it “Chief”.

You can simply chat with Chief and tell it to do asks or spin up more bots. We’ll get to that. Let me take you through my first few hours.

Chief

There’s a “chief” agent that knows my whole world at a high level. I seeded it with 4 sets of sources.

1. “About me” doc

I had Claude generate a description of my businesses and context about my life it knows from what I discuss with it. For example, coaching sports teams or building an ADU on our property.

2. Connecting apps and MCP/APIs

  • Gmail, two accounts: your personal and your work inbox

  • Google Drive

  • Google Calendar

  • Moontower.ai MCP

  • Twitter

  • Notion

  • Linear

  • Stripe

  • Slack

  • Supabase

  • GitHub

  • Had it help me create a Discord bot it can communicate with

3. Knowledge base

I had it read and index all my writing across Substack, WordPress, moontower.ai/blog, and notion.moontowermeta.com. You could have it crawl your PKM (personal knowledge management) system, digital notes, Drive, or even a local file system.

4. A Mission

This summer in hedging is for gardeners, I wrote:

I spent over an hour walking through the local hills while talking to ChatGPT’s voice functionality Thursday evening. It was the easiest way to think through a large automation project. I won’t discuss the project yet but the sense I had while architecting it with ChatGPT was a new one. It was the feeling I know some friends had about 8 months ago and a smaller group of people (but not a tiny group) had 18 months ago and so on. A tiny group had the feeling a decade ago.

I never got around to implementing the project which I called Personal AI OS. In short, the automation project is an operating layer that turns your messy idea dumps and recurring obligations into verified work, so you don’t have to do the project-management overhead yourself. The AI’s within it have roles like orchestrator, chief, data scientist, etc. I had several spec documents related to its construction based on about an hour of back-and-forth with ChatGPT.

As with many things AI, the thing you can imagine just seems to become reality if you procrastinate for 3 months.

[This is simultaneously exhilarating and terrifying, like morning sex on doomsday].

I gave Grokbot the spec documents, so it understood my objectives. Honestly, I don’t think it really needed them since those documents are clunky blueprints to a problem the Cursor wizards have already had their sights on.

Subagents

Then each lane gets its own specialist.

One for writing, one for Moontower ops, one for coding, one for travel, one for household logistics, and one that reads my X lists so I can check X less. They can message each other while each maintains its own memory.

Why split them up?

Every one of these bots has a limited working memory per conversation. You’ve probably seen LLMs inform you when a thread gets long that it’s “compacting”. This specialized focus per agent lets a thread get longer without degrading

Grok itself tells me the bigger reason it works is that the important stuff per agent can be stored in its own files. For example, the writing bot keeps a voice guide, an index of everything I’ve published and a map of ideas I keep coming back to. I don’t know if it’s accurate to say this, but it at least feels like each agent is the equivalent of what Claude calls a “project” in the setup I’ve been using all year.

The cost of subagents is the handoffs. The chief only knows the outline of what the writer is doing, so anything that crosses lanes has to be written down or passed along on purpose. But handing off is part of manual work as well. As it is, if I tell an agent what I want to do on a website, I tell it to make me the prompt to give to my Claude browser extension agent, and it just makes the changes from the prompt.

The next step is to automate even the hand-off. I set up a shared board in Notion called the Agent Handoff Board. It’s one database where my agents (Grokbot, Claude, Meta Muse) pass work to each other. Each gets its own inbox view. The rules are what you’d give a new hire: check the queue a few times a day, claim a row before you start it, log progress in dated lines you never go back and edit, set a “next check” date, and if you need me, flag the row and write the exact question.

Nothing sends an email, files a dispute, pays, buys, or deletes anything because of the board without my OK in chat. The board is for coordination but permission still comes from me.

Grokbot read the page, set up its own recurring check, and started on the first row.

First hours of work

After telling the Chief to spin up these agents, I noticed the Chief starting to delegate. For this post, I asked it what it did for me since onboarding. It asked its direct reports.

This made me laugh, although I’m sure it was sincere:

Ops and X Digest still haven’t sent their rundowns. I’ll add their lines once they do.

Just like human teams, always a couple of slackers.

I hear “Ops” was seen at the dollhouse for the lunch buffet. I thought that went out of style with the trading pits.

Finally:

All six agents have answered, so here’s the whole team’s list. Ops warned that its Stripe numbers are internal business data, so I left them out.

[lightly edited for some privacy stuff]

What the agents did in the first 2 hours

Chief

  • Built the team: the specialist bots plus a shared Notion board where they hand work to each other.

  • Connected Gmail, Calendar, Notion, Slack, Linear, Stripe, Supabase (read-only) and a read-only Discord bot.

  • Wrote a Monday brief and a markets and vol digest, and split the digest into 3 posts sized for Discord.

  • Set up 5 recurring jobs. They pair newsletter ideas with what vol is doing, check classifieds, turn beta feedback into draft Linear tickets, flag community questions nobody has answered, and send a Friday best-of-Discord recap to the writer. None of them post without my OK.

  • Automated classified ad bookings with a Notion tracker and a Stripe checkout link.

Writer

  • Indexed 2,680 documents across Substack, WordPress, Notion and moontower.ai.

  • Mapped 71 recurring ideas across 11 themes, each backed by quotes checked word for word.

  • Fact-checked a reader’s I-bonds pitch

  • Caught two number errors in an IG story before I recorded it.

Ops

  • Found the last product-update email and catalogued new features since the last one

  • Ran a 30-day health check on billing, looking at chargebacks, failed renewals and retries.

  • Traced a bug where the in-app chat gave the same canned answer to a specific user. Diagnosed the source and drafted a reply to the user who reported the issue, and I sent it.

X Digest

  • Read 334 posts across my two focus lists and picked 5, so about 1.5% made the cut.

  • Worked around the fact that it can’t read lists directly by using a public search filter.

Travel

  • Priced holiday flights from three Bay Area airports, then about ten other destinations, plus a second family’s airport on the other coast.

  • Pointed out that moving the dates a few days likely saves more than changing the destination. Nothing was booked.

Household

  • Turned my scattered notes into a status brief for each area of the house.

  • Rebuilt a year of emails on a home office redo and ADU. Figured out who owed the next reply.

Coding

  • Got connected to 15 code repositories. No coding work has been assigned yet.

The first handoff

The first row on the board was a medical bill. A One Medical visit in September got coded as a problem visit instead of the annual physical it was, which turned a free preventive visit into an $800+ bill. Grok pulled the claim data, built the case for why the code should be the physical, drafted what to say to One Medical’s billing office and the fallback if they wouldn’t budge, and lined up the United Healthcare follow-up.

Impressions

In the short time I’ve had Grokbot running, I’ve noticed many proactive updates from each agent. I’m also using it for things I shouldn’t (for example, doing work I have Claude and Muse doing), but I’m trying to get a feel for it and compare its performance. It will take time to feel out where its assistance is most appreciated and how to use it best. And this will have more stakes than time. I’ve already used 25% of my weekly usage!

Because I work with Emi, I get a glimpse (and still cannot even fully “grok”) of how someone who lives on the edge of technology approaches work in 2026.

Emi Gal
@emigal
I’ve been using Grok @bot as my daily driver for a few weeks now and:

– it has replaced 100% of my search-like queries on chatgpt / claude
– it has replaced 100% of my Hermes usage & automations
– it now runs 100% of my Cursor cloud agents, reviews PRs, merges for me

It also…

I’m slower than I’d like to be on the uptake of the tools. I explained this feeling 6 months ago.

I like watching people like Nat use AI because they are plugged into the right layer of abstraction. The layer of recursion where every action is coupled with instructions to learn from the action so next time the action is improved. Infinite loop until you find a limit of perfection that would satisfy Zeno himself.

In contrast, when I use AI, I feel like I’m living during the debut of the automobile and thinking, “Wow, I could drive that to the granary to buy food for my horse.”

I’m going to repost this again:

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

Andrej is telling you that:

The 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.

I took the liberty of making his block of text easier on your eyes:

Look at the .2% number. 20 mm people. That’s less than the number of millionaires in the US alone.

The .2% scaled down to the U.S. is ~700k people.

A bit less than the population of SF. About the same number of Americans who suffer a stroke each year. Or the number of electricians in the US. Joe Walsh has 4x that many monthly listeners on Spotify. A regular season NBA game has twice as many viewers.

I don’t know about the investing part of all this because I’m not convinced this isn’t a wildly deflationary technology, but from the point of view of people’s understanding of change, we are still very early.

But let’s get back to the basics that matter. Consumerism!

Emi told me on Monday he used Grokbot to research and order a TV from Costco. Before the TV shipped, the bot found the same TV at Sam’s Club for $400 less, on its own. It asked him whether he wanted to cancel the Costco order and buy from Sam’s. With his approval, it took care of everything, including the payment.

We spend a lot of time and mental overhead routing things around. Acceleration comes with a lot of unknowns, but I think a major part of any optimistic scenario is having more time to focus on what is irreplaceable about your vocation and the relationships you spend so much time doing logistics to enjoy.

when smarts are as cheap as sunshine

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.

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.

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

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.]

THE options-101 course for the internet

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:

Buy many copies and give them out to the clients or investors in your life! → Pre-order link (Ships Tuesday, Oct 20th).

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

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.

Moontower #332

From the team behind the Arbor Trading Bootcamps which I constantly say are so good they should not exist:

Join us at Metagame, November 6–8 at Lighthaven in Berkeley, for a weekend of games, puzzles, and immersive play. Explore tabletop games and RPGs, discover cryptic clues, and take part in a weekend-long Megagame connecting puzzles, games, and stories across the event.

Moontower readers receive $50 off regular tickets.

Explore Metagame and get tickets →

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.

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

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.

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:

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:

Buy many copies and give them out to the clients or investors in your life! → Pre-order link (Ships Tuesday, Oct 20th).

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

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” — an interview with Victor Haghani

A great Munchie today. As usual, I highlight the parts I want to keepsake and inject my own comments here and there.

LTCM Co-founder Victor Haghani: “Taking Risk Is Always a Negative” | 1 hr 12 min watch

LTCM: right and ruined (01:27)

It’s good to think about investing as being two decisions: what to invest in and how much. And almost all of the focus of financial media, training, education, financial education is on what we should invest in… And at LTCM, we found some really good whats to invest in. But the sizing decision, how much of those good things is the right amount of good things, is equally critical, and is actually an easier decision to get right. But you have to realize how critical it is and that it deserves focus, and enough focus. And I think at LTCM, that’s where the mistake was, was on the sizing. It wasn’t on the selection of the trades, it was on the sizing.

EV ≠ risk-adjusted value (03:00)

Consider a 60/40 coin in your favor, but you must play 20 flips. This is a great “what” to invest in. But how much? Betting everything on every flip maximizes expected wealth, but you almost certainly end up broke.

The strategy that gives you the highest expected wealth at the end is actually to bet all of your money on heads each of the 20 flips. You have almost no chance of coming out of it with any wealth left… People don’t realize that’s the highest expected value thing to do, but it really shows you that expected value and risk-adjusted value are two different things. And it’s this risk-adjusted value that we want to maximize.

Risk-adjusted value is another way to say compounded return. Compounding naturally incorporates risk because the math of compounding is highly sensitive to variance. We’ll get to that later.

How the coin-flip players blew it (05:15)

  • Players came from graduate finance programs and Wall Street PM seats
  • They treated independent flips as dependent: bet heads after a run of tails, bet tails after five heads

Some people just bet a dollar every time. They just figured, “Well, if I bet a dollar, I’ll never go bankrupt”… and they really didn’t maximize the opportunity that they were given. And then other people were too aggressive and actually went bankrupt. They got their $25 down to like $3 and then they just started to YOLO it, out of impatience or whatever.

A good way to play this game is to bet a constant proportion of your bankroll on each flip. Bet on heads every time. Bet as fast as you can, like hit the buttons as fast as you can. And what should that constant fraction be? …between 10 and 20%. Like 20% is Kelly, and maybe you want to be a bit less…

And once we told people that, everybody accepted it. It was beautiful to see. It was like, “Oh gosh, I just wish somebody had told me that before.”

Sizing is non-zero-sum (08:20)

I really like this point:

Getting your sizing right is not a zero-sum activity. For you to be right, nobody needs to be wrong. We can all be correct together. But if you want to beat the market, you can only beat the market by choosing investments that you find that other people have not found and are doing worse than the market. The only way to beat the market is for somebody to underperform the market. It’s zero-sum.

Risk as an internal fee (12:55)

I plan to write a bit more on this idea myself. The “fee” framing Victor uses is fantastically plain, which I love, but I suspect people don’t believe this. I’m working on a post to step readers through the idea. As we’ll see later in this interview, my hopes aren’t high that it will convince anyone. Still, I believe it’s a profoundly worthy idea to flesh out.

There is a cost of risk which is the same as paying a fee. So when you’re paying an investment management fee to somebody, you pay that fee and you see that fee… And so if somebody turns up to most investors and says, “I’ve got this S&P 500 fund and the fee is 75 basis points,” people are like, “Come on, give me a break.” …Taking risk, you should think about taking risk as also paying a fee… The problem is that it’s kind of like you’re paying it to yourself. There’s nobody charging it to your account and it’s going out. It’s an internal accounting metric that you need to have.

For typical degrees of risk aversion for people, I would say you could think of that charge as just being equal to the variance of your portfolio. So if you have a portfolio that has 15% standard deviation… then I’m saying that you should be thinking that the cost of risk that you’re paying is 0.15 squared, is 15% squared, which is 2¼%.

His rule of thumb: cost of risk = γσ²/2.

This formula comes from something called the “Merton Rule” and it’s basically Kelly adapted for non-discrete-style betting, more akin to typical investing contexts in stocks and bonds.

A typical investor (γ ≈ 2) pays σ² while a Kelly bettor (γ = 1) pays σ²/2. Remember prescribed Kelly sizing is optimal for maximizing long-term CAGR, but it’s also very risky.

The Kelly bettor is a bit of a mythical being out there… I think that the typical guy is somewhere between what I described and 50% more risk averse actually.

Concentrated stock math (16:30)

If you have this concentrated stock portfolio that has 30% volatility, then you should be charging yourself, what’s 30% squared? It’s unbelievable. It’s 9%, right? It’s four times as much as 15 squared.

I’m willing to accept that the idiosyncratic risk is 50/50. Of course, there’s somebody on the other side of your hopeful 50/50 bet, and probably that other person is smarter than me anyway. So I don’t want to do it. I don’t want to have D. E. Shaw or Renaissance or whatever on the other side of my trades… Once I recognize that, it’s like, “Wait a second, I’ve got to make an extra 7% on those trades, an extra 7% just to break even and get a higher risk-adjusted return.”

That’s quite a hurdle considering that in a relatively efficient market you’re a coin flip to have bet right.

He talks a lot more about this in the interview so you can check that out on your own.

“How do I become a billionaire” is the wrong question (22:20)

When young people at a VC firm that invited them in to talk about his book…

It’s the right answer to the wrong question. That’s what’s going on here, right? If the question is, “How do I become a billionaire?” The right answer is take a lot of risk. Take a ton of risk… What do I need to do to become a billionaire is the wrong question. That’s the wrong objective.

The right objective for people is that you want to have the highest smooth, risk-adjusted, or we call it sustainable, lifetime spending and giving of money, adjusted for inflation, cost of living… The objective is not whoever dies with the most wealth wins. The objective is not I want to maximize the probability of having enough to retire. It’s not I want to die broke. Well, dying broke is easy. You spend all your money tomorrow and take a pill.

A few of the people said like, “But if I do what you’re saying to do, how am I ever going to become a billionaire?” And it’s like, that’s not the right question.

The only thing in a bear market is risk-aversion lol.

Crystal Ball, humans (27:25)

Victor saw a Taleb tweet:

He tweeted that if you gave people the front page of the Wall Street Journal one day ahead of time, they would all go bust in a year… They would be so overconfident. They would be so greedy. They wouldn’t size the trades right. They wouldn’t recognize that sometimes the employment report is hot and bonds go up.

The Elm team hired an intern to build a program to test this idea.

  • Business-school round: about 100 players staked $50 each, about $5,000 total. Elm paid out about $5,300, so with the next day’s news they eeked out a small profit. About 1/6 went bust, and the hit ratio was 52%. Some players ran 20x leverage.
  • In the web version: about 50k players with $1M of play money. The results were predictably unimpressive.
  • Six macro pros averaged about a 60% hit ratio and +70%, and all six made money:

Most of them were very selective. Like they only bet on six or seven of the 15 days. They didn’t always bet on stocks and bonds… They were just looking for something that they were really confident in and then they bet pretty big.

This reminds me of the “most important” gambling topic.

Crystal Ball, LLMs (34:55)

His prediction for head-to-head play:

Totally different experience because all you want to do is beat the AI. So you lose your mind… It’s like, “How can I beat Grok? I’ve got to at least be able to beat Grok.” Like Claude gets ahead, but it’s like, “Okay, I’ve got to at least get ahead of Grok.”

The paper confirmed his hunch. Head-to-head players used 30x average gross leverage versus 25x solo, and maxed their leverage on 10% of plays versus 6%

All of them are too aggressive and risk-taking… Claude is getting like just over a 60% hit ratio.

This doesn’t mean that they can beat the market, but it does mean that they’re doing better than the average human that comes to play the game… Most of what’s going on right now is people are running AIs to see if they can beat the market, but they’re not really comparing it to the average investor.

Paper companion numbers:

  • Base models ended at Claude $2.59M, ChatGPT $1.47M, Grok $968k, Gemini $492k
  • Stock sizing averaged 7–12x, and the market has moved more than 5% on 23 days since 2000
  • Merton share implies 7.5–15% daily σ; the AIs ran 20–40%
  • Gemini and Grok had positive Sharpe ratios and still lost money from overbetting
  • All four knew Kelly from the coin flip but didn’t connect it to stocks and bonds
  • After reading the book, sizing became far more appropriate and prediction skill stayed the same

Elm’s sizing question (47:40)

The main question we’re asking people is saying like, imagine that stocks have an expected return 4% higher than safe bonds. How much of your wealth do you want in stocks if that were the case? So that answers the risk aversion question and their view of the riskiness of stocks all in one go… And then we say, “Well, if stocks only have a 2% expected return above bonds, what do you want to do then?” And they’re like, “I’m happy to have 37.5% in stocks.”

Notice that the 4% → 75% allocation answer implies γ ≈ 2 at about 16% equity vol. This is the “typical guy” as his σ² rule.

Remember Merton: w = ERP / (γσ²)

ERP is “equity risk premium”

(Just fyi: Elm charges 12 bps; its ETF runs a 75% equity baseline)

You can’t change minds (1:02:45)

I’ve got similar stories up the wazoo:

One thing I’ve learned in doing what we’ve been doing for the last 15 years is that you just don’t change anybody’s mind about anything.

My mother day trades stocks. She’s 92 years old… She’s only been day trading since commissions went to zero. But for 20 years, I’ve been telling her to invest in index funds, that she’s a nonprofessional investor, that she should just own index funds and not worry about it. But every day she’s day trading now, in the morning, in the evening. It’s taking up a couple of hours of every one of her days. She loves it. She thinks she’s doing well. And nothing that I could say, no amount of time I could spend with her, would convince her to not be a day trader and would convince her that she’s not doing well.

And she thinks that I’m intelligent. Like she doesn’t think I’m her stupid son. She thinks that I’m intelligent. She knows I work in finance. She knows that some people listen to me. She knows I wrote a book, right? So she knows that, and she knows that there’s no conflict of interest, that I want to help her. Like, she knows that I don’t benefit in any way from her not day trading. So it’s really hard to change anybody’s mind.

Iron Condor Destroyer

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.

moontower.ai

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).

the farmer’s almanac for investing

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.

Moontower #331

Note of an Instagram Experiment

I’m trying something new that has become much easier with Meta’s personal AI assistant Muse: turning Moontower posts into Instagram Stories and Reels.

Math and personal finance ideas, one frame at a time. Muse handles the heavy lifting of reading the posts and synthesizing the arc. I then edit the frames and add music.

This week so far:

  • Why Godzilla can’t exist (stories)
  • You already trade options. You just call it a car lease. (stories)
  • Selling your 3% mortgage incinerates $188,000 (stories)

The feedback has been encouraging!

Follow @kris_to_the_max.

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.

moontower.ai

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

At the Robinhood Summit last week, Mat Cashman and I hosted a “Trading Lab” emphasizing that options are surgical tools. You only want to reach for them when you have a specific view. While their payoff is highly levered to nailing a particular timing, they are unforgiving to vague forecasts like “I generally want to be long this stock”. If you buy the stock instead, you know your exposure. Your delta is 1.00 and doesn’t change.

If you buy an option, your exposure changes due to time passing alone. So that initial decision thrusts you into a series of future decisions, each one challenging you to tighten your thesis and face yet another bid/ask spread.

To demonstrate an appropriate use of options we stepped through a highly specific example during the lab and even executed the trade live!

I’m going to walk you through it here today.

Setup

The session was on Wednesday, September 30th. Nike (NKE) reports earnings after the close Thursday, October 1st.

Some context:

✔️NKE is down ~75% since its lifetime peak which occurred 5 years ago. While falling short of the peak, it did stage a large rally from late 2022 into 2023, before resuming its downward March which accelerated this year with the stock down >40% YTD at the time of the session.

✔️NKE typically pays about $1.40/yr in dividends or greater than 4% yield at recent stock prices. However, it can no longer fund the dividend from current cash flows. They could fund it from balance sheet assets (ie dip into their savings) to be able to pay it for several more years. I learned this from Brett Caughran’s tweet:

✔️I used moontower.ai’s MCP (which is now included with individual plans 😉) to ask Claude if the options market was anticipating a cut. It turns out the implied dividend about 1 year out is 50% of what NKE has been paying. NKE’s woes are out in the open, the stock and options markets see them.

With such a backdrop, we expect this to be an important earnings call. After all, if a plane is nosediving, we expect the captain to say something. (We know the content of the words will be reassuring, but we’ll listen to the tone of his voice to infer how scared or optimistic we should be.)

Pulling up the option chain

With the above context in mind, we load the October 2nd option chain. This is clean as a market gets around an earnings move since the options expire 24 hours after the earnings call.

During the session, I reason aloud through what I’m looking at.

The stock is ~$36. The 36 straddle, which can be interpreted as the average expected move size, is ~$3 or 8% of the stock price. Earnings are a big deal.

I gracefully elide the question of whether you should be bullish or bearish. If I was privy to such information, I’d be a Market Wizard. But many people have views and wanna bet. I can help you reason through your choices given you have a view. So let’s get specific.

What are you paying for?

We arbitrarily side with the bulls, but the analysis that follows works for bears as well if they substitute different contracts.

If the 36 straddle is at-the-money, meaning the stock is also $36, then the 36 call is half the straddle or $1.50.

If the stock experiences the expected move to the upside those calls will double your money as they will be worth $3 of intrinsic value at expiry. In the language of odds, you get paid 1-1 on an 8% up move.

But if this is our specific bet, can we find an expression of the trade with more octane?

We load up the 38.5/39 call spread. It’s marked for $.12 and if NKE expires $39 or higher, it will be worth $.50.

You risk $.12 to make $.38 so you are getting just over 3-1 odds for the same $39 outcome.

Why does this sound so much better than getting 1-1 if we just buy the 36 call for $1.50?

Because the outright call continues to pay off if the stock climbs more than 8%. You are paying for the extra profit potential if NKE goes up 10, 15, 20 percent. Being vague leads you to pay for more margin of error. But if you’re specific, you can get extra leverage on being right. Of course, this is risky because if you’re wrong most scenarios lead to total loss.

Let’s deal with the risk

The setup of the session was that we have $5,000 of capital.

If we are wrong on the vertical spread and the stock doesn’t clear $39 then we likely lose our entire premium.

We get a 3-to-1 payoff when we are right.

This type of binary outcome is a perfect setup to use Kelly sizing. The nice thingabout Kelly is that it assumes you have a total loss when you are wrong, so the sizing itself is the risk management. You’ve already budgeted for the maximum risk.

We’ll get to the sizing in a second but just for building fluency in thinking probabilistically, let’s acknowledge a few heuristics:

1. We know from the derivation of the straddle approximation, that the straddle is 80% of 1 standard deviation. Therefore, it encompasses ~79% of the distribution assuming normality. Therefore the upper tail, where NKE goes up more than $3, is expected 21% of the time. To get 3-1 odds would require a minimum probability of 25% to be a positive EV bet. But, normality is doing a lot of lifting in that sentence. Earnings are more likely a binary outcome, and while it’s too simplistic to say there’s a 50/50 chance of the stock going up or down 8% (which would make this vertical spread seem irresistibly cheap), it’s not unreasonable to think the probability of that spread hitting is greater than 25% (or for that matter, for the symmetrical downside put spread for those with a bearish view).

In the name of walking you through sizing GIVEN you already have the bull view, we’ll presume you believe there’s a 40% chance the stock pops higher by the amount of the straddle or $3 to $39.

How much should we bet on the vertical spread if we are getting 3-1?

Kelly:

f* = p − q/b

where:

p = probability of winning

q = 1−p or probability of losing

b = the odds you’re getting → what you win divided by what you risk

Plug and chug:

f* = .40 – .60/3 = 20% or $1,000 of the allotted capital

Converting to contracts:

Each option contract is $.12 or $12 once we adjust for the 100 share multipler.

$1,000/$12 ~ 83 contracts

We don’t want to risk overbetting if we overestimate our p (i.e., probability of winning), so let’s size to about half Kelly and only buy 40 contracts.

We actually bought the vertical spreads live during the session.

Postscript

NKE ended up falling after earnings. The equivalent put spread on the downside would have been the 33.50/33 put spread, looking to get paid if NKE fell $3.

NKE closed Friday at $33.90, so the put spread would have expired worthless. At one point, the stock touched $32.09, so the spread would have been deep in-the-money, but because there was still some time value remaining, you wouldn’t have been able to capture its maximum value, although you probably could have flipped out of it and tripled your money, from $.12 to $.36

But it would have been hard to do since, with the stock sitting around $32 you have theta on your side, and you’d probably be thinking, “even if the stock rallies $1 or 3% intraday, I’ll have turned my $.12 into $.50.”

Alas, the stock ripped 6% intraday after bottoming, leaving the put spread worthless!

Notice how tricky this is for everyone. If you were short the spreads, you ended up collecting the full $.12 of premium, but the path would have greyed your hair as it looked like you’d face maximum loss or feel forced to cover only to later watch NKE stage a heroic rally.

Closing words

Options are surgical. I think of them like term life insurance. I have a specific thing I’m trying to mitigate or take advantage. If I buy them and lose, it may or may not have been a good outcome depending on what the counterfactual would have been. The term life analogy is an obvious demonstration of benchmarking to the right outcome.

The more tightly you can wrap an option expression around a terminal value thesis, the easier the risk management is because you can budget for total loss and let sizing do the heavy work.

Thinking probabilistically is a habit of mind. Reasoning between what’s in a price and your own confidence intervals around scenarios will develop not just discipline but it gives you metrics, even if they are finger-in-the-air estimates, to journal or track, allowing you to get a bit more calibrated with each rep.

2 option ideas from the Robinhood Summit 2026

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).

🔨Exercise: Solve for what delta the calls are

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.