CoreWeave, the AI infrastructure name whose entire business model is buying GPUs faster than the revenue comes in, went out for $3.0 billion of convertible notes. Demand was strong enough that the deal got upsized to $3.5 billion and settled within days.

That’s a coupon the US Treasury itself couldn’t beat.

So, something about this picture is clearly off:

“Who hands a cash-burning AI credit billions of dollars at 1.75%, and why were they fighting each other for allocation?”

The short answer is that nobody in that order book was lending.

The buyers were, overwhelmingly, hedge funds running one of the oldest trades in the business: convertible bond arbitrage. And within hours of the deal pricing, most of them had shorted CoreWeave stock against their allocation.

This might seem a bit strange at first, so today, we’re going to walk through the whole machine using the real CoreWeave deal: what the instrument actually is, how the trade gets put on in dollars, what three months of CRWV chaos did for the people running it, and why the issuer is thrilled to play along.

Nobody Here Is Lending

At root, a convertible bond is two instruments stapled together:

convert ≈ straight bond + embedded call option

For the CoreWeave deal, each $1,000 note converts into 8.3612 shares, which pencils out to a conversion price of about $119.60, a 30% premium over the $92.00 close on the day the deal priced.

Now, priced as straight debt, a 1.75% coupon on this credit is nonsense. A plain six-year bond from a cash-burning AI name would need a coupon several times higher to clear the market.

The spread between that hypothetical coupon and 1.75% is the price of the call option. The buyer accepts a below-market coupon and gets paid in optionality instead (chart below).

There’s a sweetener, too: new converts typically come a few points cheap to theoretical value, because the only way to move billions of dollars of paper in a single evening is to price it at a discount. The arb funds are buying vol below fair.

This explains the last piece of the pattern, the one that confuses equity holders every single time: the stock usually drops the morning a convert is announced.

Every fund that expects an allocation is shorting its delta at the same moment. That drop is the hedge going on, live and in public.

The Trade, In Dollars

Say you took down $1 million face of the CoreWeave 1.75s of 2032 at issue. The position looks like this:

1. Buy $1 million face of the notes at par.

2. Count your shares. At 8.3612 per $1,000, the position controls 8,361 shares of CRWV if converted.

3. Short the delta. The model calls the embedded option something like a 55 delta at pricing, so you short roughly 4,600 shares at $92, about $425K of stock against your $1 million of bonds.

4. Collect the carry. The 1.75% coupon, plus a positive rebate on the short-stock proceeds. During the zero-rate decade this second leg paid nothing; now the short leg itself is a source of yield.

5. Rebalance as the stock moves and the delta moves with it.

At this point you own the convert, you’re flat the stock, and you’re clipping carry from two directions.

And step 5, the boring-sounding one, is where the actual money is.

The Swings Are the Salary

A delta-hedged convert is a long gamma position, and long gamma has a very specific personality:

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This past June 26th, the Nasdaq Closing Cross printed roughly $334 billion of stock in one shot. Nothing else all year came close.

The Closing Cross is Nasdaq’s closing auction where instead of stocks trading continuously the way they do all day, every buy and sell order queued up for the close gets matched at one single price per stock, all at once.

What’s strange is that the date of that $334 billion trade had been public for years.

It happens because FTSE Russell rebuilds its stock indexes every June on a published calendar, an event called the reconstitution, and roughly $11 trillion is managed against Russell indexes. When the lists change, the money has to move. All of it, at the same moment.

Everywhere else in markets, participants go to absurd lengths to hide their orders: slicing them into crumbs, routing them through dark pools, randomizing their timing. But here, the biggest order of the year is announced in advance, sized in advance, and executed at a pre-scheduled second.

So naturally, it raises the question that every trading desk eventually asks:

“If the biggest order of the year is announced in advance, who gets paid for standing in front of it?”

For about thirty years, the answer was basically anyone who bothered.

Today, standing in front of that order is one of the largest-scale, most sophisticated trades in markets: probability-weighted baskets spanning hundreds of events a year, hedged to the dollar, levered 5 to 10x inside the biggest funds on earth.

So we’re going to walk through exactly how it’s run, from scratch and down to the final details, assuming you’ve never touched market structure before. And by the end, you’ll be able to run a baby version of it yourself.

The Most Obedient Buyers on Earth

An index fund is exactly what it sounds like: a fund that copies a list.

The S&P 500 is a list of 500 big American companies, each assigned a weight based on its size. An S&P 500 index fund takes your money and buys all 500 in exactly those proportions.

The manager has zero discretion. If the list changes, the fund changes.

And the manager is graded on a single number: tracking error, which is how far the fund’s return drifts from the index’s return. A fund that returns 9.98% in a year the index returns 10.00% is doing its job.

A fund that beats the index by 2% is just as broken as one that trails it by 2%, because the product being sold is the index itself.

Now, the wrinkle that creates this entire trade: when a new stock joins the index, the index itself incorporates it at the stock’s closing price on the effective date (the date the change officially takes effect).

So a fund that buys at exactly that closing price, whatever that price turns out to be, shows zero tracking error. Buying earlier and cheaper is riskier for the manager: if the stock dips before the close, the fund lags the index and someone has to explain why.

The career-safe move is to buy at the close, at the closing price, every single time.

Conveniently, there’s a venue built for exactly that. At 4:00 PM the exchange takes every order flagged market-on-close (an MOC order says “fill me at whatever the official closing price ends up being”) and matches all of them in one giant batch.

That batch has been swallowing more of the market every year. In Q2 2024, about 9.4% of all the dollar volume traded in American stocks went through closing auctions, a record, and on big rebalance days it runs closer to 20%.

So when an index changes, we know who has to trade (every fund copying the list), which direction, and precisely when: the closing auction on the effective date.

The Free Lunch Years

In the 1990s, the trade this created was almost insultingly simple.

S&P announces that a stock is joining the S&P 500. You buy it that evening. You wait the handful of days until the change takes effect (the median gap between announcement and effective date has been about 4.8 trading days), then you sell to the index funds who are forced to buy from you.

That earned roughly +7% in about a week, on average, for a decade.

None of this was a secret, either. Shleifer and Harris & Gurel documented ~3% announcement-day pops back in 1986, and academics gave it a name: the index effect.

The cleanest accounting of what happened next comes from Greenwood and Sammon’s paper, The Disappearing Index Effect. Additions to the S&P 500 returned, from announcement to effective date and adjusted for the market: +3.4% in the 1980s, +7.3% in the 1990s, +5.1% in the 2000s, and +0.8% in the 2010s, which is statistically indistinguishable from zero.

Deletions were even harsher on the way down: -4.6%, then -16.1%, then -12.4%, then -0.6%.

The free lunch was real for decades, and then it wasn’t (chart below).

So the naive version is dead. Which leaves a puzzle:

“The forced buying didn’t shrink. Indexed money is bigger than it has ever been. So where did the return go?”

Hold that thought.

To answer it, you first need to understand the flow.

The Whole Trade Is Arithmetic

Roughly $16 trillion is benchmarked to the S&P 500, and about $10 trillion of that is passively indexed, per S&P’s own survey of assets. That $10 trillion is the obedient money from earlier: it must buy whatever joins the list.

And the size of that forced buying is computable, in advance, by anyone:

indexed dollars x index weight = dollars to buy

Divide the dollars by the share price and you get shares to buy. Divide the shares by the stock’s ADV (average daily volume, how many shares change hands on a normal day) and you get the number that actually matters: days of ADV.

Days-of-ADV is the unit of violence. A stock where the forced buying equals 8 days of normal trading is a fundamentally different event from one where it equals 0.5 days. The first one has to move the price; the second gets absorbed without a ripple.

There is no forecast anywhere in that arithmetic.

Deletions run the same math in reverse. The catch on deletes is that profiting from one means shorting the stock, which means borrowing shares first (your broker locates shares from a lender, you sell them, and you buy them back later to return them). The borrow is a constraint as names everyone wants to short get expensive to locate, or impossible.

The data you need for all of this is free or nearly free:

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There are well-established paths that are pretty linear:

1. Momentum

   1. Isolate a liquid universe that demonstrates strong investor appetite (e.g., weekly optionable, high average notional volume)

   2. Create a metric for describing each stock’s returns

      1. Combining the Sharpe ratio and beta over a lookback can be used to create a rank-able momentum score, of sorts.

   3. Long the n highest scores, short the n lowest.

2. Relative Value

   1. Long high-quality assets

      1. Large market cap, healthy balance sheet, strong free cash flow

   2. Short poor quality assets

      1. Tiny market cap, debt-laden balance sheet, declining/negative free cash flow

   3. Hedge out the vol

      1. A beta hedge is typically used due to higher volatility in smaller cap names, so you may often end up shorting $2 of the low-quality assets for every $1 of high-quality you long.

And the list can go on, and on, and on.

The combined result is a return profile so stable you can build an entire business on it: lever it, sell it to allocators, pay bonuses off it.

So, there was one question we kept coming back to:

Why isn’t anyone running this playbook in crypto?

The asset class is volatile, the average participant is expressing a hunch, and the perp market lets you short basically anything with a ticker. If there were ever a venue where the multi-strategy model should print, it’s this one.

So today, we’re going to walk through exactly how we built ours: the sleeves, the combination machinery, and live execution across roughly 150 tokens at a time.

These Aren’t Stocks, Kid

When we first started exploring trading crypto systematically, we did what everyone does: long/short momentum.

The universe was liquid USDT-perps with at least $1M in daily volume and a 30-day realized vol of 30% or higher, rebalanced weekly with a one-week hold. Sensible, boring, the same recipe that’s worked in every asset class for decades.

And it worked here too, technically. Long momentum (28-day, risk-adjusted) came in at a weekly Sharpe of 0.71. Short momentum did better, at 1.20.

Net profitable, sure, but nothing you’d want to stake an operation on: a sub-1 Sharpe in an asset class this wild means long stretches underwater waiting for the math to catch up.

The traditional move at this point is to grind. Tweak the lookback, add filters, keep optimizing until the backtest is beautiful and the live results are anything else.

We went the other direction. Instead of perfecting one strategy, we started collecting them.

First came shorts on tokens with increases in circulating supply (unlocks, emissions), the same dilution logic we run in equities: someone is about to sell, so get in front of it.

Now, in crypto, it’s insanely difficult to ascribe explanations to returns as there aren’t any earnings reports or fundamental comparables, and the idiosyncratic context lives on X threads you’d never follow.

So, in order to expand our catalog of possible strategies, we did something we’d never do in equities: pure math.

To keep things at a high level, this mostly looked like:

• Probability based: Under a basic assumption that daily return direction should be about 50/50 up or down, then in theory, if an asset has gone up n times in a row, it is probabilistically unlikely to have that same direction in the next sample.

  • Of course, the issue with this is that it ignores things like autocorrelation, volatility clustering and so on, but remember, the goal was just to expand our catalog of uncorrelated strategies.

  • The downward version of this (if sessions_up > n, short) had a weekly Sharpe of 0.35, while the upward version (if sessions_down > n, buy) had a weekly Sharpe of 1.20.

• Implied Information: If a token has a +/- n standard deviation move, just follow the direction.

  • For instance, if asset A has an annual vol of 30% and it suddenly goes up 30% in a single session, just long it, and vice versa.

  • This produced a weekly Sharpe of 0.96.

Every one of these was generally net profitable, even after fees. And every one of them had a Sharpe we’d never build a business around: the best single sleeve topped out around 1.24.

Alone, none of them justified the infrastructure.

So, we tested running them all at once.

Silo, Silo, Silo.

The first thing worth checking before combining anything is whether the sleeves are actually different bets, or just the same bet five times over.

If every sleeve is secretly long crypto beta, stacking them accomplishes nothing.

So, we ran the correlation matrix on weekly sleeve returns, and the numbers were genuinely better than we expected (chart below):

• Long momentum vs. short momentum: -0.34

• Down-streak buy vs. short momentum: -0.62

• Long momentum vs. up-streak fade: -0.48

Negative numbers are the good stuff. When the momentum shorts are bleeding in a squeeze, the down-streak buys tend to be getting paid, and vice versa. One sleeve’s bad week is routinely another sleeve’s good one.

The quiet star was gap follow (implied information): roughly zero correlation against everything else (-0.14 to +0.10 across the board). It has no opinion on what the rest of the book is doing, which makes it the cleanest diversifier in the stack.

Now, you can’t just run five strategies side by side and call it a portfolio. Two problems show up immediately.

The first is overlap.

At the weekly rebalance, two sleeves will regularly want the same token, sometimes in opposite directions.

A token that’s down 3 days straight is a buy for the streak sleeve and, often enough, a short for the momentum sleeve.

Paying fees to be simultaneously long and short the same token in two different sleeves is a great way to donate to the exchange. So, we wrote a hard rule:

1. Each sleeve builds its basket independently at the rebalance.

2. If two sleeves claim the same ticker, the sleeve with the historically higher Sharpe trades it.

3. The ticker is dropped from the losing sleeve’s basket entirely. The book never holds the same token long in one sleeve and short in another.

The second problem is weighting. Crude equal weighting across components sounds fair until one wild name with triple-digit vol is suddenly steering the whole operation.

So components are weighted by inverse volatility, computed walk-forward on trailing history only (no lookahead), with equal weights early on until a name has enough history to earn a real estimate. The sleepy names get size, the wild ones get trimmed.

With the collision logic and the weighting in place, we ran the combined book across all 61 weekly rebalances (chart below):

• Gross Sharpe: 2.68

• Net, at 30bps per side: 1.97

• Net, at a punitive 50bps per side: 1.50

That comes out to an annualized +70.5% on 26.1% vol, with a max drawdown of -9.9% and a realized beta to BTC of -0.05. Roughly double the best single sleeve, on a book that genuinely does not care which way crypto goes.

To be fair: 61 weeks was a thin sample and the cost model is a flat 30/50bps per side with no funding drag on the shorts.

Can We Actually Trade This?

When we’re running a research experiment, it’s never for a “hmm, that’s interesting”. We do this entirely to actually realize profits, so after quadruple-checking our calcs, we began setting up a live, production version.

As we’ve mentioned before, accessing crypto perps as a U.S. citizen can be a nightmare, but eventually, we set up an account at Bitunix (no affiliation) and integrated their API.

The book holds 150+ individual tokens long and short at any given time, which puts manual execution completely out of reach; nobody is clicking through 150 order tickets every week without eventually fat-fingering a short.

Now, the infrastructure is not sophisticated, and we mean that sincerely:

• At each weekly rebalance we loop through the valid ticker set, take each name’s weight, and submit a limit order for it.

That’s it.

Some of those orders rest unfilled for a few minutes, but since we’re not moving at a huge clip, this has worked fine so far. In general, the fills have landed close enough to the marks that we haven’t felt the need to build anything more clever.

A few notes from official launch:

• Inverse-vol weighting shrinks outliers so that no single blowup hurts you, but also means you don’t get the full kick of just equal-weighting.

• The book is often net short by 30% of notional, despite being “market-neutral”.

  • For instance, if gross risk is $100,000, the net short of the full portfolio is often ~-$30,000.

• Despite being net short, the uncorrelated and dispersed nature of the components results in an average beta of -0.05.

• Realized transaction costs have been 2-5bps; the exchange charges 0.02% for resting limit orders and 0.05% for taker orders.

  • Although we only send limit orders, when the bid/ask moves in the second before execution (orders sent via REST API), it ends up taking liquidity and auto-filling, resulting in the higher fee.

• Over the first 4 rebalancing periods, the net unlevered return has been ~6.4% across the entire book.

Final Thoughts

We’ll keep monitoring this over the coming months; hopefully by then, the real question will become scalability:

How much capital can these perp books absorb before our lazy resting limit orders stop getting filled at prices we like?

After all, we’re not using any leverage at all, but for each liquid token in our book, we can push things all the way to 20x on a cross-margin basis. At that point, we’d have to get actually serious about proper execution, but the upside is enticing.

While this was a great next step in a new-to-us asset class, the bigger lesson was a shift in orientation.

For years we were ultra-focused on making single strategies the absolute best they could possibly be: more models, better features, another round of tuning to squeeze a 1.1 Sharpe toward 1.4.

When switching to a portfolio management view instead, we start asking what a strategy does to everything around it. A 0.35 Sharpe streak fade looks like a waste of a slot until you notice its -0.62 correlation to the short book, at which point it becomes one of the most valuable things on the desk.

Historically, we would’ve just tossed strategies like that in the recycle bin.

Without getting too ahead of ourselves, it makes us wonder what would happen if we looped back through everything we’ve ever run/tested and just put it all together?

• For instance, in 0-DTE index options, what if we overlaid the short volatility strategy with an intraday momentum sleeve, then possibly another long volatility leg? What if we added in 3 more purely quantitative techniques and merged those in too?

  • Again, each leg might not have the 2+ Sharpe we need to feel safe, but if they’re legitimately uncorrelated to each other, what if combining them together actually does?

If we can have a multi-strategy desk in every asset class, running all the time, each balancing the other out… well, you can pretty much see the picture yourself.

As always, thank you for reading, and we hope to see you in the next one.

Traditionally, that’s true as such a profit mechanism naturally lends itself to a “if it was easy, everyone would be doing it” sort of defense.

However, it’s far from the only place where that kind of no-risk edge exists and more importantly, outside of HFT, this kind of edge is “easier”.

Being in the quantitative trading space, it’s largely your job to be at the very minimum, aware of these kinds of trades so that even if you don’t trade it directly, it can give you a baseline idea of where else you might apply it on your own.

So today, we’re going to be showing you first-hand what it takes to capture a specific type of arbitrage you might be loosely familiar with:

The Treasury Basis Trade

Now, we don’t expect everyone to be bond market experts, so to understand this idea, it'll help to walk through an example.

1. You see a 10-year treasury trading at a yield of 4% ($96)

2. At the same time, the CME 10-year treasury future is trading at a yield of ~3.99% ($96.05)

3. So, you short the future and simultaneously buy the bond, collecting a theoretical spread.

   1. Naturally, even with perfect execution, this spread isn’t worth much, so you borrow.

   2. Instead of running this trade with a million bucks, you hit the repo market with the bond you bought acting as collateral, then you lever it up 50-100x.

4. On the futures expiration date, you deliver the bond you bought and close out the trade.

5. Repeat.

With such an attractive risk-profile and relative simplicity, one naturally begs the question:

Could you actually run a version of this yourself?

Well, sort of.

To better answer that, we need to break the trade down into the pieces you’d actually have to assemble yourself.

Strip away the jargon and the professional version is really just three things stacked on top of each other:

1. You own a specific Treasury.

2. You short the matching future against it.

3. You finance the trade so the tiny spread is worth the effort.

The first two, you can do from your couch. The third is where the whole thing lives or dies, so we’ll save it for last.

One Account, Two Markets

The first hurdle is boring but real: you need a single account that can touch both the cash bond market and the futures market.

That rules out most of what a normal person already has. Your TreasuryDirect account can buy the note, but it can’t short a future. Your average stock app can’t do either leg properly.

What you want is a broker that clears futures and gives you access to secondary-market Treasuries, with Interactive Brokers being the obvious pick for a retail setup.

Leg One: Own The Bond

Now, you don’t just buy any 10-year and call it a day.

The CME future doesn’t track “the 10-year” in the abstract. Whoever is short the contract has to deliver a specific bond from an eligible basket at expiration, and they’ll always hand over whichever one is cheapest to deliver (the CTD).

The eligible basket holds a handful of different notes, each with its own price, coupon, and maturity. The contract puts them on a level playing field using a conversion factor for each one, and the short simply runs the math on all of them: buy the bond today, deliver it into the contract, and see which one leaves the most money in your pocket.

The winner of that little contest is the cheapest to deliver.

So if you want your long leg to actually mirror what the future is pricing, you buy the CTD issue itself, not a random note off the run.

Practically, that means pulling up the deliverable basket for the contract you care about (/ZN for the 10-year), identifying the current CTD, and buying that exact CUSIP in the secondary market.

You’re now long the same bond the entire futures market is implicitly quoting.

Leg Two: Short The Future

This part is genuinely a few clicks.

You short one 10-year Treasury future, each contract representing $100,000 in face value. The margin the exchange makes you post is tiny relative to that notional, usually a couple thousand dollars, which is your first taste of the leverage that makes this trade work.

The only real subtlety is how many contracts to short, because it isn’t one future per $100k of bonds.

The bond and the future don’t move dollar-for-dollar, so you size the short to match the dollar duration (DV01). You scale by the CTD’s conversion factor so that a one-basis-point move in yields moves both legs by roughly the same dollar amount:

contracts to short ≈ (face value of your bond / 100,000) × conversion factor

Get that ratio right and the two legs cancel on rate moves. Get it wrong and you’ve accidentally put on a directional bet on yields, which is exactly the thing to avoid.

At this point, congratulations!: you are long the cash bond, short the future, and pocketing that theoretical ~0.05% spread.

Can You Even Get the Data?

That word “theoretical” is doing a lot of work, and the first reason is almost embarrassingly basic: can you even see the number?

You need four things to compute the basis, and three of them are refreshingly public:

• The deliverable basket and its conversion factors come straight from the CME.

• The financing rate you’ll benchmark against is SOFR, sitting on FRED and updated daily.

• The futures prices are on whatever quote feed you already have.

The fourth input is where it gets hard: live, accurate cash Treasury prices.

The cash Treasury market is over-the-counter (OTC), so there’s no consolidated tape like equities have.

The good pricing lives on Bloomberg terminals and dealer runs, while the retail-accessible quotes tend to be wide, stale, or both.

This matters because the entire edge lives in a spread of a few cents. If your cash bond price is off by more than the basis you’re trying to capture, that number you’re calling an edge is just your own data error.

Reading the Signal

So, how do you actually know when the basis is worth putting on?

A couple of terms you’ll bump into here, and they’re simpler than they sound.

The gross basis is the raw price gap between the cash bond and the future, adjusted by that conversion factor.

Net that against the carry you earn from holding and financing the bond, and you get the net basis.

Take the same idea and express it as a rate instead of a price, and you get the implied repo rate: the return you’d lock in by buying the cash bond, delivering it into the future, and collecting the convergence.

Now here’s the whole intuition, and it’s the one thing to walk away with.

Set that implied repo against your actual cost of financing the bond.

→ Implied repo above your financing cost = the trade pays you to carry it.

When the market hands you a higher implied return than it charges you to borrow, you capture the difference, geared up dozens of times. When those two rates cross, the printer quietly turns into a slow bleed.

That single comparison is the entire signal and it’s exactly why this isn’t free money for everyone. The implied repo you can actually capture depends on the repo you can source.

A prime-brokered relative-value fund and a retail account are looking at two different trades, because they finance at two very different rates.

Now, About That Leverage

Here’s where the couch version runs into the wall.

Remember, that spread is tiny. On an unlevered basis, capturing five hundredths of a percent is a rounding error that commissions will happily eat for lunch.

The pros solve this in the repo market. They take the bond they just bought, post it as collateral, borrow back almost all the cash they spent, buy more bonds, and repeat until they’re levered 50 to 100x.

You, sitting at home, might have a bit of trouble doing this.

Repo is an institutional counterparty market, so the minimum sizes are enormous, and nobody is opening a tri-party repo line for $100k. That single door being closed is the real reason this trade “belongs” to funds and doesn’t get spoken about much.

However, not all hope is lost.

Read more

The volume is insane. Fin-Twit is calling for a 5-bagger. You’re up big and feeling like a genius.

Two trading days later, a filing for new shares hits the wire.

The company is selling 7 million new shares into exactly the strength you just bought. By the close, you’ve given back the entire move and then some.

You got diluted.

If you’ve spent any time trading small-caps, you’ve likely lived some version of this. When it happens, most attribute it to bad luck, a rug pull, or a thing that just happens.

What they don’t see is that the whole sequence is one of the most repeatable, predictable, and mechanically extractable edges in the entire market.

So today, we’re going to take it apart from first principles: how dilution actually works, why price always moves the same way after it, and then we’ll walk through a real-world trade example from catalyst to close using the same dilution data we run at Alphanume.

With that covered, let’s get right into it.

They Sold A Little Stock, What’s The Big Deal?

The first stock market officially opened in 1602 and since then companies have devised countless ways of raising more capital to fund and grow their operations. Whether it’s tapping the bond market, issuing commercial paper, taking on a line of credit, whatever.

However, when all of those more respectable avenues of capital are closed, there exists a worst-case, emergency option: Dilution.

In sum, share dilution is basically just creating new shares that didn’t exist before, then selling them on the open market.

This is the absolute last-ditch option for a few reasons:

• It tells the market you’re out of options.

  • Bonds, credit lines, commercial paper, those all require someone on the other side to look at your books and decide you’re good for it. When a company skips all of that and just prints shares to sell, it sends the message that nobody else would give them the money on reasonable terms.

• It punishes the people who already believed in you.

  • Every new share that didn’t exist before makes every existing share worth a little less. Same company, same business, but more slices of the pie. Shareholders who bought in early get quietly watered down to fund a raise they had no say in.

• It signals management thinks the stock is expensive, not cheap.

  • A company buys back its own shares when it thinks they’re undervalued. Selling new shares is the exact opposite trade. When insiders choose to issue stock instead of repurchase it, they’re effectively telling you the current price is a good price to sell at, and they should know better than anyone.

• There’s no obligation to do anything good with the cash.

  • Debt comes with covenants, schedules, and people watching how the money gets spent. Dilution proceeds come with none of that. The company can burn it keeping the lights on for another two quarters and owes no explanation, which is usually exactly what’s happening.

As we’ve covered in our A Junior Quant’s Guide to Event-Driven Trading, this structure is the lifeblood and oxygen for a systematic event-driven trading approach:

That arrow diagram is abstract, so here’s what it looks like on a real name. We’ll go deeper into the data later, but to start:

On June 1st, 2026, AEVEX Corp. (AVEX), an $8b drone maker submitted an S-1 filing indicating that they’d be issuing 5 million new shares after a 50% rally in the prior week.

Not even one week later, 40% of the company’s value was erased:

Now, one textbook example doesn’t make a strategy. So before we go shorting every S-1 that crosses the wire, there’s a wrinkle in how these filings actually work.

The Nuance

To trade this properly, you first have to understand how those new shares actually travel from a niche filing to the tradeable open market.

First, it starts with the registration.

When a company wants to sell new shares to the public, it can’t simply dump them on the tape. It first has to register them with the SEC, and that registration is the document you’re watching for:

• Bigger names with a long reporting history use an S-3, a reusable shelf they sell from over time; the small and micro-cap names often don't qualify, so they file an S-1 instead. Either way, the filing is the company raising its hand and saying, on the record, that more supply is coming.

The moment a dilution filing hits the wire, sophisticated participants know the float is about to expand, and they start selling ahead of it. You’ll often see the initial leg down within minutes of the document posting, well before a single new share has actually changed hands.

However, and it’s the part you have to know:

Not every S-1 is dilutive.

At root, an S-1 is just a registration form.

Plenty of them are completely benign: registering shares for an employee plan, allowing an early investor to resell an existing stake, or covering a routine shelf that may never get used. None of that creates new supply hitting the open market.

If you short every S-1 that crosses the wire on reflex, you’ll get run over by the ones that were never going to dilute anyone. So the filing has to actually be read.

You’re looking for the specifics:

• Are these newly issued shares or existing ones being resold?

• Is the company itself the seller?

• How many shares relative to the existing float?

A company with a billion shares outstanding registering 50,000 of them is inconsequential, but a nano-cap registering 3 million new shares against a tiny existing float is a trade.

Next, once you’ve confirmed it’s genuinely dilutive, there’s a second clock running: the effective date.

A registration alone doesn’t let the company sell anything the instant it’s filed. The SEC has to declare it effective first, and only then can the shares legally be sold into the market.

You can think of the two filings as:

1. “We’re going to do something hugely -EV soon, maybe, probably.” (S-1)

2. “You know that bad thing we mentioned? It’s official now.” (EFFECT notice)

So with dilution, you’re effectively trading a sequence with two distinct punishment points.

The Part Where We Make Money

Alright, now that you have the base foundations, we can finally get into actually putting on a trade.

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“There’s no edge in vanilla options anymore. The surfaces are too clean, the market makers are too good, and you’re not finding anything in 2026 that a desk in Chicago didn’t price five minutes ago.”

If you spend any time around trading discourse, you’ve likely heard some version of this take. It’s confidently delivered, usually by someone who once tried selling weekly strangles and got tagged.

And to be fair, it’s not totally wrong.

SPX options in 2026 really are as close to efficient as it gets. The days of catching obvious mispricings on a Bloomberg terminal at lunch are over, and they have been for a while.

What gets lost in this take though, is that “mispriced” was always the wrong word for it.

At the end of the day, professional options trading isn’t about hunting quotes that are wrong against some objective truth, but rather about taking the public price, comparing it to your own internal price, and putting on a trade if the difference is big enough.

So, it’s less “this option is objectively priced wrong” and more “this option is cheap/rich relative to this specific assumption”.

Unfortunately however, the whole process of pricing your own options around a bespoke view is extremely vague if not totally inaccessible to most non-MM traders.

So today, we want to crack-open that process and show you first-hand how you can intuitively use sophisticated options models to not just price options “better” than what you see on screen, but also how you generate some of these views and assumptions to begin with.

Without further ado, let’s get right into it.

It Goes Way Deeper Than Black-Scholes

To begin, we have to understand why anyone even bothers to use anything other than Black-Scholes in the first place.

An option model, at root, is a story about how the underlying behaves between now and expiration.

Black-Scholes tells the clean version where markets are basically random walks and implied vol tends to stay the same throughout its life.

As a description of how markets actually behave, though, it’s not really true:

• What about stocks with a momentum factor, where the forward returns are more predictable than just a random walk?

• What about those currently undergoing extreme realized volatility, where implied vol is either way too high or way too low?

So, over the last fifty years the industry has constantly innovated to create new models that better reflect reality in ways the textbook Black-Scholes doesn’t.

To see why this matters, here’s the same 90-day 105-strike put on a $100 underlying, priced under five different models with identical observable inputs (5% rate, no dividends, 25% vol):

Two commonly used industry models, Barone-Adesi-Whaley and Merton Jump-Diffusion, are demonstrable standouts compared to the typical Black-Scholes price, and for a good reason:

• Barone-Adesi-Whaley model

  • Put simply, this is the Black-Scholes model, but with a small premium to cover the early-exercise risk in American Options.

    • SPX and index options can use BS since they’re cash-settled and have no early exercise risk, but single stock options have the added risk of being able to be exercised for shares at any time.

• Jump-Diffusion model

  • In sum, this model is used when prices trade discontinuously rather than in a smooth drift like assumed in Black-Scholes. The left-tail risk is also priced higher, so OTM options carry a higher than average premium.

    • If there is an upcoming event (earnings, regulatory ruling, etc.), the price can have 1 day where it abruptly goes in either direction, so this model comes in handy.

So, right off the bat, in professional options trading, the model used is often very case-dependent.

When pricing American options, you might use the BAW model, but when pricing those options knowing that a huge event is on the horizon, you might layer on a Jump-Diffusion model.

Now, this might still seem a bit theoretical, so to really cement the understanding, let’s walk through an actual trading example.

How Do You Make Money With This Stuff?

Okay, it’s Wednesday at 12:00 PM and we’re discretionary vol traders looking for cheap options.

First, we need a name.

Scrolling through a screener, we land on CVNA.

A high-quality short report dropped on it three days ago, we've read the report, we think most of it is fair, and we're roughly aligned with the thesis. The stock is down about 2% on the week and volume is unremarkable.

The options market is pricing things like nothing happened.

That last part is the trade.

The short report is sitting in plain view, and either the market digests it over the next quarter and the stock leaks lower, or the next earnings call poses a tough enough question that the bid evaporates faster than that.

Either way, we think realized vol on a 6-month horizon is going to come in higher than the surface is pricing.

The cleanest way to express this without taking a directional view is the 6-month at-the-money straddle. We don’t need to be right about which way; we just need to be right that something happens.

So, we pull up our Options Pricer, select the Merton jump-diffusion model (we think a large sudden move is likely), and input the metrics as it’s trading now:

Quick clarification on some terms:

• Jump intensity λ: how often jumps happen, in jumps per year on average. λ = 2 means we expect two material discontinuous moves per year, on average. Higher λ means jumps are more frequent.

• Jump mean μ_J: the average size of a jump given that one occurs, expressed in log terms (so roughly a percentage move). μ_J = −0.10 means jumps tend to be downward, around −10% on average. A symmetric view would set μ_J = 0; an upside-biased name (think a biotech awaiting an FDA decision) might set it positive.

• Jump dispersion σ_J: how much variation there is around that mean jump size. σ_J = 0.20 means that when a jump happens, it’s not always exactly −10%; it could be −5% or −25% or +5%, with the spread around the mean governed by this parameter. Larger σ_J means jumps are more variable in magnitude.

So, right off the bat, our fair view of that 6-month call option is a price of ~$14.60.

Now, we check what the option is currently trading at on the open market:

At the time of capture, the 6-month call was trading at around $14.15 against our fair value of $14.60. Not a marked difference, and on its own, not enough to put on a trade.

But there’s a major caveat before we stop here.

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What makes this case notable beyond the existing popularity of the founder, is that multi-strategy funds aren’t supposed to do this.

The entire goal of a multi-strategy approach is to run multiple uncorrelated strategies to create a steady stream of returns in any given regime; long momentum here, short junk bonds there, long some vol over there, whatever.

When news of this first broke out, Eifert, the face of this, gave his full perspective on what happened:

It was shockingly honest and transparent, but for the large swaths of people not familiar with esoteric vol arb strategies, it did sound a bit jargon-y.

The entire story is actually pretty fascinating, so today, we want to peel back a few layers and walk you through what actually happened, in a way that you can intuitively grasp.

We’ll start with a high-level look at what volatility arbitrage even is, go into the strategies the desk ran, then show you exactly where things went wrong.

By the end of this you’ll not only learn more about a fascinating, but under-mentioned corner of quantitative trading, but you’ll also get a clear view on what exactly not to do, should you be in a similar position.

So, with that said, let’s get right into it.

WTH Is “vol arb” Anyway?

The birds-eye view of vol arb is essentially selling expensive derivatives and simultaneously buying cheap derivatives.

We know that sounds vague, so let’s walk through an example.

1. You spot two related instruments priced differently

   1. Two stocks around $100, same industry, similar products, historically similar movement.

   2. A one-month at-the-money option on Stock A implies 40% volatility; the same option on Stock B implies 28%. The market is forecasting far more turbulence for A than for B.

2. You check whether the gap is justified

   1. A difference like that usually exists for a reason: earnings, a lawsuit, a pending FDA decision, a takeover rumor. If a real catalyst explains it, there is no trade. If you check and find nothing, then the gap might be a genuine dislocation.

3. You sell the expensive option and buy the cheap one

   1. Short the Stock A option, collecting the rich 40% premium. Long the Stock B option, paying the cheaper 28%. You are now short expensive volatility and long cheap volatility, betting only that the 12-point gap converges.

4. You hedge out the directional exposure

   1. Selling options leaves you with unwanted exposure to which way the stock goes. You buy or short shares of each name until the position has no directional tilt (delta-neutrality) and re-hedge as prices move. What remains is a clean bet on volatility itself.

5. You collect the spread as the gap closes

   1. With no catalyst to justify the spread, Stock A drifts like a normal stock and its realized volatility comes in well below the 40% you sold. Stock B does roughly what its 28% implied, so the option you bought costs you little.

   2. The expensive side was overpriced and you were short it; the cheap side was fair and you were long it. The spread converges, and the convergence is the profit.

Of course, this was a simplified example, but it’s the core basis of what’s generally referred to as volatility arbitrage.

Eifert’s account is unusually detailed as it names the exact trades they took.

So with that covered, the exact strategies they were running will be a lot easier to understand.

What On Earth Were They Actually Doing?

Term Structure Trades

“long cheap gamma at the front of the curve, short expensive volatility in the belly of the curve, and long again at the back.”

Protection has a term structure, like bonds. Plot implied vol against time-to-expiry and you get a curve: cheap and jumpy at the front, richer and more stable at the back.

QVR was short “the belly”: the two-to-four-month region where structured-product hedging oversupplies volatility, and long the cheaper wings further back.

Being short the belly pays two ways: the rich vol you sold decays, or the market moves and your long front-month gamma fires.

So you’re covered in a quiet market or a volatile market, in theory.

What became a problem was that the belly they were short went, in Eifert’s words, “turbo-bid”, and spiked away from the rest of the curve, driven by inflows into VIX-linked products.

The front-month gamma leg (the hedge) only pays off on movement, so with realized volatility on the floor it sat there doing nothing. The core leg lost, the other was inert, and the hedged trade was just a loss.

Skew

“long the massively over-supplied long-term downside on the back of autocall issuance... short medium-term downside against it, and long short-dated crash puts.”

Skew is the price difference across strikes: downside puts trade richer than upside calls because everyone wants crash protection.

QVR’s skew book had three legs across maturity: long long-dated downside, short medium-term downside, long short-dated crash puts. Long the wings, short the middle. The same shape as the term structure trade, just in a different product.

The leg that needs explaining is the long-dated one, because of why that downside was cheap: autocalls.

• An autocall is a structured product banks sell to yield-hungry investors; you get a fat coupon, say 10%, as long as an index holds a level: above SPX 7000 by Dec 2027 and the deal pays out.

• Below it, the deal rolls another year and tries again. Fall through a deeper barrier, though, say -30%, and you eat the full loss like you owned the index outright. The investor is selling crash protection for yield, which leaves the bank holding a mountain of long-dated downside it has to hedge.

That hedging floods cheap long-dated downside into the options market, which is exactly the leg QVR was buying.

These were different parts of the surface, in different products entirely, but both were short medium-term volatility, and both leaned on autocall-hedging flow staying calm.

When medium-term vol went bid, it hurt both the term structure book and the skew book.

Short Vol, Short Delta

“short volatility (via put spreads on VIX) versus short delta (via ES futures).”

Short vega is short volatility; short delta is short the market. The pairing is historically beautiful: when the market sells off, vol spikes, so one leg pays while the other takes the hit. The hedge depends entirely on vol and direction staying linked.

Then, the Iran-Israel conflict escalated:

Implied vol surged as hedges got bid. But the market didn’t sell off, not really.

Investors panic-bought protection while holding their equities, so volatility rose without the market falling.

Short vega lost on the rising implied vol and short delta lost on a market that drifted up.

Reverse Dispersion

“We started building a reverse dispersion position at all time high spread levels around 17.5 (3-month tenor). That spread went as high as 22.”

Dispersion is a trade around correlation. Index volatility depends on how much single stocks move and how much they move together.

The vol spread, single-name vol over index vol, is wide when correlation is low. QVR shorted that spread at a record-high 17.5, betting correlation would rise and the spread would compress.

It ran to 22 and a new record became a deeper record.

The painful part is what the loss meant:

• A short-spread position is a long-correlation bet, and rising correlation normally travels with cheap index volatility, which would have made the term structure and skew books print.

• Reverse dispersion was supposed to be the diversifier that paid when the others struggled, but instead it lost alongside them.

So, What Actually Went Wrong?

Now, these strategies are clearly very sophisticated, and most of the time, they work well enough to keep these shops in business. Each has a hedge on top of another hedge, where that hedge acts as a hedge against something else.

So, with all this hedging, one can naturally ask how a -28% loss just happens.

Well, it wasn’t a single bad trade gone wrong, but rather the same trade going wrong, sizing up, having it go wrong again, sizing up even more, having it go wrong again, and eventually having investors run out of patience.

Every position QVR held was a convergence bet: some spread had stretched away from its normal level, and QVR was positioned for it to snap back.

That phrase, snap back, is where the trouble started: the assumption that dislocations have to snap back eventually.

When a convergence trade loses money, it often looks like an opportunity for a better entry. A spread at 17.5 was attractive; the same spread at 22 is, on the same logic, even more attractive. So when there’s a losing month, you don’t cut, you just add more.

The bet got bigger as it lost, then it lost again, so the bet got bigger again.

No single month was a disaster, the fund just went down 7-9%, four times in a row.

Just a string of increasingly large bets that spreads would tighten, made into a market where spreads kept widening instead.

It’s entirely possible, even likely, that QVR’s positions will eventually be vindicated, but similar to a martingale strategy, no one has the privilege of infinite time or infinite capital.

So, What’s The Big Picture?

It’s tempting to file this under a risk management failure and move on; Eifert uses the phrase himself. But he is also careful to say it was “a much more nuanced one than just having a stupidly risky trade on and blowing up”.

He saw the correlation across his strategies shifting, attributed it to the right causes, and chose to “hold and increase positions,” waiting for “the reversion that would take us from down 15-20% on the year to up 20%.” He just needed the fund to survive long enough to flip it, and “four months of down 7-9% in a row” is, in his own words, “too much for investors to reasonably handle.”

The trades may yet be right and the spreads may still converge; it just won’t be QVR’s flagship collecting the payoff, as the redemptions came first and “the economics of a small/medium sized hedge fund business” stopped working.

So, if there was only one thing to take away from this, we can sum it up to:

Being right about the destination does not help if you get liquidated before it arrives.

or, put colloquially,

“The market can stay irrational longer than you can stay solvent.”

Interestingly, QVR is not the only desk exposed to this same risk. The same structured-product machine that distorted QVR’s belly is feeding the same suppressed-volatility, short-premium picture into a great many portfolios at once: autocall and QIS issuance, bank dispersion swaps sold to institutions with, as Eifert puts it, “very limited knowledge of the strategy themselves”, covered call ETFs, and so on.

A large share of the volatility world is holding some costume of the same trade, just on different desks, in different tenors, and through different marketing language.

So, if you came here to see how these more opaque corners of the market actually work, hopefully the term structure, the skew book, dispersion, and the rest feel a little less like black boxes now.

As always, thank you for reading, and we’ll see you in the next one.

“The datasets are incredibly interesting, but APIs are too difficult to use.”

Which, honestly, was fair.

The original version of Alphanume was built for people already living inside quant infrastructure stacks: Python environments, cloud jobs, point-in-time backtests.

For long-time systematic traders and developers, that worked fine. However, it created a quiet bottleneck.

Plenty of people understood the market mechanics perfectly well and still couldn’t explore the datasets without engineering around them first.

That kind of operational friction is normal inside institutional quant workflows, but the problem was that it had become the thing standing between the research and the trader.

So, we rebuilt the entire platform from the ground up.

Not just the homepage, not just the docs, but the entire platform.

Every dataset on Alphanume now has a fully interactive dashboard with full access directly in the browser. You can filter, inspect, search, visualize, and explore the datasets immediately without writing a single line of code:

The API still exists, naturally, and nothing about it changed (if you have already built around it, you lost nothing). It’s just not the only door in anymore.

Same datasets, same point-in-time histories, same alpha surface underneath. You just get to it directly now.

If you want to inspect historical SPX 0-DTE strike bands against realized intraday movement, that is a browser tab. Dilutive SEC filings, lifecycle resolution, market cap at filing, event persistence, same thing. Filing-intensity spikes, de-SPAC completions, corporate-default timelines, point-in-time momentum constituents, the volatility regime classifier, the optionable-universe snapshots, and the list goes on.

Now, these are not gimmick datasets dressed up with nicer charts. They’re the same datasets sitting underneath a large part of the research we publish: the strike infrastructure, the dilution and corporate-distress systems, the prediction-market work, the cross-sectional momentum baskets, all of it.

Most of this data does not exist anywhere else in clean, point-in-time form, and that’s why we started this in the first place.

To everyone who has been here through the API-only era, querying endpoints and building around the rough edges: thank you. You were using this platform when using it took real effort, and a lot of what got rebuilt got rebuilt because of what you told us.

We are not slowing down on the part that actually matters. The proprietary data front is where the work continues, and the quantitative trading experiments we run here every week feed that engine directly. Every strategy we test surfaces a question, and the good questions become datasets. That loop is what positions us to keep building some of the most predictive data out there.

And to everyone who opens these posts when they land and gives us your time: we do not take it for granted. Thank you for coming along.

But sometimes, being frank, coming up with ideas is hard.

Sure, every once in a while you pick something up from a market observation, something you read, or a shower thought. But that happens infrequently, and most of those ideas do not pan out anyway.

We’ve been faced with the same problem before, so eventually we decided that if we were going to truly succeed in this business, we needed a way to generate usable ideas in an industrialized, systematic fashion.

Now, the typical school of thought is to just hire a bunch of junior quants that do this full-time, but we wanted something more agile.

In our prior Unironically Using Claude for Quantitative Trading, we established that while these AI tools weren’t great for coming up with novel ideas, they were great for “common-sense” tasks that would traditionally be outsourced to a junior.

That probably still holds, but we couldn’t help ourselves, and decided to try again.

So today, we’ll be taking a good look on how these tools can be guided to generate actually usable alphas, where they fall short, and how edges like these get implemented on a long/short market-neutral desk.

With that being said, let’s get started.

What Are We Even Betting On?

Unlike our first experiment with these models, we have to start from the premise that we don’t already have a pre-formed idea.

So first, we need to think about what actually moves prices to begin with.

In equities, the common school of thought is that stock prices are dictated by earnings and future expectations of those earnings.

This makes sense as there are few other incentives to hold stock other than the earnings it generates or will generate in the future.

So if earnings are the focus, we need three things: historical earnings, their estimates, and their forward expectations. That last one is hard to quantify cleanly, so we’ll use a simple proxy: analyst expectations from the major houses like BofA, Goldman, and Morgan Stanley.

With that as our area to crack, we can stop here and shift to idea generation.

Prompting to Vet, Not Fetch

The naive version of this is to ask a model for alpha ideas and get back a list of named anomalies.

While we don’t need to have an idea that no one else has, if we want to actually get somewhere, it might be good to do something that isn’t already commonly-known and commoditized (e.g., the index rebalancing effect).

So before prompting, we have to intuit a few constraints:

• Mechanism-first reasoning

  • Instead of asking for anomalies, we’ll first ask for plausible reasons a mispricing could exist. If a candidate ends up resembling a known effect, that’s fine; we mainly care about the reasoning process that got it there.

• Realistic guardrails

  • A medium-frequency long/short book with a one-month holding period rules out a large fraction of what an unconstrained model defaults to: no optimistic intraday execution assumptions, no 1-3 day edges that assume you trade at a specific price on a specific day, etc.

• Demanding a failure mode

  • If the model can’t articulate why its suggested edge might not work, it might not be a great one, so we want it to be able to break itself a bit before it brings something back to us.

With those constraints in mind, we can build a prompt that gets us closer to what we need:

Shopping It Around

The goal here is just idea generation and picking out the best of what comes back, so we shopped the same prompt to all the major models: Claude, GPT, Gemini, and even Grok.

For each, we chose the highest thinking setting available to give ourselves the best shot.

Surprisingly, the outputs were pretty good:

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1. A prediction market implies a 20% probability that SPY closes above $X today, offering roughly a 5x payout if it happens.

2. At the same time, the equivalent X-1/X SPY call spread is trading for around $0.20, also offering roughly a 5x payout if SPY closes above $X by expiration.

So naturally, the first reaction is:

“Oh well, the market is efficient and that’s all there is to it.”

Now, it isn’t a totally wrong premise, but it is a bit… lazy.

After all, options and derivatives are not just things to bet on, but rather instruments that can be decomposed on an atomic basis to replicate any view, payoff or hedge you want.

So, there must be some way of combining the two in a +EV fashion, right?

As you’ll see shortly, the answer to that is a little muddy.

So today, we’re going to be taking a look at the nuances and edge cases that come up when you try mashing these derivatives together in a genuinely profitable way.

We’ll see where the market is efficient, where it isn’t, and walk through a few pathways most prediction market traders haven’t considered yet.

Without further ado, let’s get right into it.

What the Options Chain Already Knows

Before going any deeper, it’s important to understand how options are combined to generate implied probabilities.

The simple version is as follows:

1. Implied Prob ≈ Spread Value / Spread Width

   1. Assume you buy a 739 strike call and simultaneously sell a 740 strike call, for a total cost of $0.20.

      1. The distance between strikes (the width) is $1.

      2. The spread costs $0.20 to put on.

      3. $0.20 / $1.00 → 20%

This is less rigorous than other methods like risk-neutral butterflies, but 9 times out of 10 it’s a solid approximation as it’s quite literally the payout odds the market is giving you for that view.

With that covered, let’s bring things back to prediction markets:

Pictured here is a market for whether the SPY ETF will close above a given strike price, K, on date t.

Say we had a view that on that date, the price would indeed be above $735.

The prediction market implies a 63% chance it’s true, paying out about 37%.

Now let’s see what the options market was offering at the same time:

The equivalent options position is a 734/735 debit spread, which pays out the max if SPY closes above $735.

Assuming midpoint pricing for simplicity, this spread would cost about $0.635, offering a payout of ~37%.

This 1:1 pricing is natural as low-latency infrastructure for stock quotes has become so commoditized that almost anyone can keep these two markets in line.

But as mentioned earlier, this is only the jumping-off point.

The Beta Boys Are Cool Again

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It’s a sunny Tuesday in May, your desk has had a slow month, and PnL reviews are coming up.

So, you get an idea.

First, you buy a large financial position tied to the price of electricity at a specific pricing node near San Francisco. The position pays out if prices at that node spike.

Then, you call your scheduling desk and have them submit an order that routes power across a node you know is already congested. You have no intention of actually delivering the power, you just want it on the schedule.

The grid operator’s algorithm sees your scheduled flow, recognizes that the line is now over capacity, and pays you to relieve the congestion by canceling the schedule you never planned to use. Meanwhile, the local price at your node spikes because the grid is now treating your fake demand as a real constraint.

You sell the position into the spike.

An hour later, the schedule is canceled, the price normalizes, and you go back to your coffee.

That trade is called the Death Star. Enron’s West Coast power desk ran it dozens of times in 2000 and 2001 and the transcripts of the traders laughing about it are still public.

If you’re reading this, you probably already know about the typical stocks, bonds, options, and futures.

You may even read publications that cover them with math and models.

What you may not know is that the some of the most lucrative quantitative trading in the United States is not happening in NYC. It’s happening in Houston, Portland, Calgary, and on trading floors most quants have never even heard of.

Electricity trading is one of the most actively traded commodity markets in the world. It has its own derivatives complex, settlement quirks, latency games, and alt-data ecosystem.

So, today, we’re going to be taking a good look at the fascinating overlap between quantitative trading and power markets.

You’ll see not just how they’re so profitable and predictable, but you’ll also walk away with an understanding of an entirely new asset class that you might not even have known existed.

How Does This Stuff Even Work?

Electricity markets are strange because electricity has to be used almost immediately after it’s produced.

You can store small amounts in batteries, but for the most part, the grid works like a giant live balancing act:

• Too little power → blackouts

• Too much power → equipment damage

So every second of the day, grid operators are trying to keep production and consumption in balance.

Pretty simple.

The interesting, and more importantly, tradeable part is that electricity is not priced uniformly across a region.

Power in one part of California can become extremely cheap while power in another part becomes extremely expensive, sometimes within the same few minutes.

This is largely because electricity still has to move through physical transmission lines with genuine limitations.

It helps to imagine a highway during rush hour: if everyone wants to drive into San Francisco at the same time, traffic backs up and the “cost” of moving through the system rises.

If too much power tries to move through a clogged line, the grid operator has to start rerouting flows, turning generators on and off, and changing prices to relieve the congestion.

So, the core question among traders in this market is:

“Can you predict where the grid is going to become stressed before it happens?”

Turns out, you absolutely can.

How Does Anyone Actually Make Money Here?

Most trading in this space happens through something known as an FTR (Financial Transmission Right), which is basically just a contract that pays out based on the difference in price between two locations.

To see this, let’s take two points:

• The Texas Panhandle: A windy region with massive wind farms that generate and send power outward

• Dallas, Texas: A major city with huge power demand for offices and homes.

On a windy day, the price in the Panhandle collapses as the wind farms produce more than the grid can move out. So the price there might be $1.50 per megawatt-hour, while the local price in Dallas might be $2.50, creating a spread of $1.

One day later, when there isn’t much wind at all, the price in the Panhandle might be $2, while the price in Dallas stayed the same, so the spread now is just $0.50.

The value of that spread is what gets traded.

So, the desk starts asking another simple question:

“How large will that price gap become next week?”

Now the operation turns into a forecasting problem and the trading team begins collecting inputs that might influence the spread:

• weather forecasts

• temperature forecasts

• expected wind production

• historical congestion patterns

• scheduled infrastructure outages

• regional demand forecasts

The trading team then feeds those inputs into a model that predicts the average price spread between the Panhandle and Dallas over the next month.

The training data is typically several years of historical spreads at the same two locations, paired with the above features as they were known at the time.

If the model predicts next month’s average spread will be materially larger than what the market is currently implying, the desk buys the contract and profits if that forecast ends up being correct.

Run that across dozens of contract pairs every month, across multiple regional markets, and you start to see why these desks exist.

Not Exactly Free Money

At first glance, the whole thing can look almost too predictable. Electricity has to clear in real time, so once the grid becomes stressed, prices eventually normalize as supply and demand rebalance.

You wouldn’t be totally wrong to think that, after all, just take a look at the spot electricity price in Spain over the past year.

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The objective here is to provide genuinely useful insights, workflows, and ideas that people can actually apply, or at minimum, things they probably haven’t already heard a hundred times before.

To do that well, I want to better understand who’s actually reading this publication, what areas are most valuable, and where I should focus going forward.

So, I put together a short audience survey.

It takes around 3–5 minutes and covers:
• market/research interests
• workflows and tooling
• content preferences
• pricing expectations
• future products/research directions

Every response will be personally read and taken into account. A lot of the future direction of Alphanume Research will likely come directly from what people say here.

Survey link:

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Appreciate all the support so far.

– Earl

We cracked the books, asked around, and did everything else we could to find out.

In sum, it wasn’t predicting.

We found that the accounts were almost all trading the 5-minute crypto up/down markets, running a fair value calculation against a live BTC feed, and picking off stale retail orders on a slow CLOB.

Pretty unsexy, but extremely profitable.

Naturally, this sparked a fire in us to find out who else was making a killing on prediction markets, and exactly how.

It wasn’t long before we found out where.

Today, we’ll be taking another deep dive into prediction markets, but in a corner that not many people know about, with one of the highest ceilings for edge we've seen.

By the end of this, you’ll either be extremely motivated to go after some of these yourself, or intimidated by how seriously people build operations around this stuff.

So, without further ado, let’s get right into it.

What’d They Say Again?

If you’ve spent any time on Polymarket or Kalshi during a major political speech, you've probably seen these and immediately written them off:

Mention Markets

As the name implies, these are essentially markets that resolve based on whether or not a person says a specific phrase during some pre-defined event.

Presidential addresses, earnings calls, livestreams, anything:

Naturally, our first thought was a normal one:

“How could anyone systematically know what someone is going to say?”

Sure, maybe you have insider edge and spoke to the person in question, maybe you built a historical dataset of past talks from that person to get a fair value; but either way, how can you really know something like that?

Well, as we’ve learned time and time again, it all comes down to speed and understanding niche market mechanisms.

Be First, Be Smarter, or Cheat

While we wouldn’t say the average counterparty here is as under-developed as those in the BTC up or down markets, they’re often under-tooled.

A meaningful share of the volume in these contracts is essentially narrative-based. People watch the speech, hear the political coverage, decide Trump is definitely going to mention tariffs because it’s been in the news all week, and place a directional bet hours before the event.

A different share is moved by what can be dubbed as “vibes positioning”. The contract for “tariff” is priced at 95 cents because everyone knows tariffs are the topic. Someone notices that “border” is only at 30 cents and decides that’s a better bet, without any particular reason to think so.

Some popular accounts also run livestreams of their activity and the typical infrastructure looks like this:

In the above demonstration, the trader was making a bet on whether or not the speaker would say “autism”. While watching the livestream, he placed a “yes” bet when the context of the conversation steered in that direction, which was ultimately followed by the speaker saying the target word.

It's not the most advanced setup, but taking the trade around the immediate context was actually pretty smart and the average price of the trade was ~12 cents, resulting in roughly a 9x return on capital, so, not too shabby.

However, there are a few structural problems with this approach.

The biggest problem is the inherent lag of streaming through a third-party service. For most live broadcasts, especially politically sensitive ones, there’s often a deliberate delay before any video reaches the base CDN server.

It’s often a safety mechanism for if something happens that shouldn’t be broadcast (e.g., an act of violence).

Now, mention markets tend to be rather illiquid, so realistically, most participants in that market are using the same livestream to base trades on, making the above approach not totally flawed.

However, not all mention markets are as illiquid. When there’s a major event like a Fed conference that draws $1m+ in volume, retail gets wiped and things get serious.

To see how, we have to introduce you to a little something known as the Bloomberg Squawk Box:

When there’s a market-moving event being broadcast live, the order of who gets the feed first is well established:

1. A person at the venue with a microphone-grade audio feed hears it first.

2. A Bloomberg terminal squawk gets it next, usually within a second.

3. The TV broadcast is anywhere from 4 to 12 seconds behind. Closed captioning on that broadcast adds another 6 to 15 seconds.

4. A major newswire transcript landing in someone’s inbox might be 30 to 60 seconds later.

5. The retail trader refreshing CNN’s website is somewhere in the back of all of this.

For small mention markets like this one where you can maybe put down $500 max, the competition is fairer. When it’s actually market moving, you can be certain that anyone at home with a Bloomberg terminal is doing exactly this.

It isn't a total game over though; a terminal can technically be purchased by anyone for $30,000/yr and there's still edge in the smaller markets where everyone's on the same feed and you can be smart about context. For the larger markets however, you want your feed to be genuinely the fastest possible.

Fortunately, if you aren’t interested in shelling out a down payment for a niche market that might not even exist tomorrow, there is still another way.

Just Read The Rules

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Historically, if you wanted to trade a stock, you’d need to work with a brick-and-mortar broker, have them log it on paper, then wait a week for a confirmation. When you wanted to get an update on the price, you’d either have to make a call or wait for the next day’s newspaper.

But in the ‘90s, the internet made it way easier to trade, so naturally, that’s exactly what people did. A lot.

As more individuals started trading, interesting places like SiliconInvestor and Yahoo Finance message boards became the predecessors to what we know r/wallstreetbets to be today:

However, there was a pretty big problem.

Even on strictly online platforms like E-Trade, the commissions were north of $20 per trade. This meant that for someone placing 30 trades a day, they’d need to make over $150,000 per year just to break-even.

Naturally, tons of people lost their shirts.

This was clearly unacceptable for maintaining faith in U.S. markets, so in response, the regulatory bodies enacted the Pattern Day Trader rule (PDT), to restrict accounts with less than $25,000 from placing more than 3 back-and-forth trades in one day.

This slowed the bleed and things went on as normal. As commissions lowered to even $0 on many platforms, people continued to day trade, often using cash accounts to avoid the limitation.

However, in just one month from now, that limitation is going away. Individuals who were locked out of short selling and levered active trades are now going to get full access at the new minimum of $2,000.

Now, we're not interested in debating whether or not the rule should've gone away, but we are interested in how this might be a genuinely tradeable event.

That was the core research question we set out to answer, and what we found was actually pretty interesting.

So, today, we’ll be cracking our research process open to figure out how to turn this rather minor rule change into an industrialized operation.

With that being said, let’s get right into it.

Who’ll Be Trading What

Okay, so we know that there will be thousands of new people entering the market. What we need to figure out is what they’ll be trading and how.

To figure that out, we have to look at what existing day traders are already doing.

Most day traders generally trade based on signals generated by their visual interpretation of a stock’s chart. We’re not going to argue the merits or demerits of this approach, we just need to have an awareness of it.

Now, we want to at least have some baseline proof that this sort of activity is market-moving as it is now. If there isn’t any meaningful impact from this type of trading now, then we can’t assume that there will be later.

So, we have to first answer the question of:

“When day traders receive a signal, does their combined activity meaningfully show up?”

We set out to find out what the most popular strategies among this segment were, and we found the most common one to be what’s dubbed an “opening range breakout”.

In sum, if the price of a stock is greater than the maximum observed price during the first n-minutes of trading, it represents a buying signal.

With that defined, we expect that once such an event happens, we’d see either a rise in volume, price, or both.

Here’s what we found:

A quick look at a retail favorite like TSLA confirms the basic premise: when the signal fired, there tended to be a noticeable increase in relative volume.

Whether the signal itself ultimately “worked” wasn’t really the important part, but rather that the trigger appeared to consistently coordinate behavior.

Okay, so if we know that at some timestamp t, there will be a substantial increase in buying volume based on signal x, there’s a pretty simple path to monetization:

Front-running the trigger itself.

The signal that kickstarts this is mechanical and thus able to be systematized. After the 15-minute observation window closes at 9:45, we know that the cohort is staring at one of two horizontal lines, waiting for a close above or below.

From here, we can isolate the probability of the signal firing in the next 1–2 minutes as our prediction problem.

To do this, we can build a simple classification model where the features might be: where current price is relative to the ranges, how wide the observation range was, and realized vol of the underlying. The prediction target may be whether or not the trigger ended up happening sometime within the next 5 minutes.

When that probability crosses some threshold, the model can fire buy orders instantly, anticipating that the mechanical crowd will soon be reacting to the perceived buy signal.

Once the trigger actually happens, you just sell your pre-bought shares into that crowd of buyers.

Of course, these strategies are not tied to any core economic rationale, so you’ll still be exposed to market beta and the idiosyncratic risk of the underlying company, but it’s a starting framework on thinking about how to be positioned when you’re expecting certain actors to make certain behaviors at defined times.

But naturally, not everyone entering the market will gravitate toward momentum breakouts and chart patterns. Some will eventually drift toward strategies that actually do have structural economic rationale underneath them, and that’s where things really start to pick up.

A Great (or Terrible) Year for Short Selling

Everyone is familiar with the core actors in the S&P 500 and other major indices, but there are thousands of other tickers that don’t get mentioned and often just continuously underperform until the eventual delisting and/or bankruptcy.

Short selling requires a margin account as you’re borrowing shares, so if traders will now only need $2,000 to participate actively, this is likeliest the next arena to see a boost in flows.

To see what we’re getting at, let’s take a look at one of the most profitable short selling edges that still works today:

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At root, the VIX is a pretty simple product:

• People bid up the price of OTM options → VIX goes up

• People don’t bid as much for OTM options → VIX goes down

Now, of course, you can’t directly trade the index, but you can trade the futures tied to it.

So, if you know that the futures price has to track the price of the index, then in theory, couldn’t you just game this for a profit?

1. Buy VIX futures with lots of leverage

2. Submit a bunch of orders on deep OTM SPX options

   1. For instance, bidding 0.05 for a 20% OTM put that expires in 30 days

3. The index rises, and by proxy, so does the price of the futures

4. Profit.

Well, believe it or not, this process happens on vol trading desks every single month.

Or at least allegedly it does:

Today, we’re going to be taking a deep look at a fascinating market mechanism of how pros actually trade the VIX, with no holds barred.

Only a few hundred people actually know how this stuff works in detail, so by the end of this, we’re confident you’ll have picked up something new.

So, without further ado, let’s get right into it.

Forecasts Are For Suckers

Alright, we don’t want to overwhelm you with formulas and papers, so we’ll start off at a high-level.

VIX futures and options expire monthly, and on settlement morning, the process generally looks like this:

1. 30 minutes before market open, the expiring VIX futures and options stop trading.

2. At market open, the strip of SPX options to be used in the calculation is chosen.

   1. For the strikes that actually traded, the opening price is used. If the strike didn’t actually trade, the midpoint of the bid-ask is used.

3. With those prices, the standard VIX calculation is run to generate the final value.

4. This final value, the VRO, is what the futures and options settle to.

In theory, if you had a large enough position in the futures, you could waste a few hundred dollars by overpaying for nonsensical out-of-the-money SPX options at the open, then by settlement, even if the value is a few cents higher, your leveraged futures position would generate a major profit (each VIX futures point is worth one thousand dollars per contract).

Where this gets interesting is the evidence that this isn’t really theoretical at all.

This Might Actually Be Happening

In the Manipulation in the VIX? paper by Griffin and Shams, a large-scale study of SPX option data was done to see if there were any anomalies in trading that could spot this.

You can draw your own conclusions, but the evidence is pretty damning:

ONLY the options that actually impact settlement explode in volume

The furthest out of the money options tend to have the most sensitivity in the VIX calculation as, in theory, they reflect how much investors are bracing for extreme tail events. Across the sample, it was found that these options specifically received the most abnormal volume at settlement.

Now, this could just be coincidental, so let’s see how active those same options are on any regular trading day:

As demonstrated, these far OTM contracts only receive spikes in volume on the settlement day.

Finally, the biggest culprit comes from this line of the paper:

“The patterns are only present in SPX options and not in nearly identical OEX or SPY options, and only in OTM SPX options that are included in the VIX settlement calculation and not in ITM options that are excluded.”

So at this point you might be thinking the obvious thing:

Well, you can and you can’t.

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And likely, you group it into one of two buckets:

• A venue for insider trading on niche events like Oscar winners or elections.

• A -EV gambling den where most users donate to sophisticated players.

Whichever bucket you identify it with, the one thing you don’t see it as, is a place for building a scalable, systematic business with a high ceiling for profit.

We didn’t either. Not until we took a look at the leaderboard:

The dollar figures here are not the focus, what’s more interesting is the shape of the returns.

Even if you’re a good predictor who’s profitable, your best-case equity curve will look like the NASDAQ 100; good periods, large bad periods, but overall okay.

In order to have a virtually straight line up and high sharpe ratio, like those pictured above, you have to be doing something a little different.

We wanted to figure out exactly what these sophisticated players were doing and how such performance could be possible. By the end, we were actually pretty impressed at just how complex some of these operations are.

So, today, we’ll be showing you the exact mechanics, in detail, of what one of these profitable prediction market operations look like.

Without further ado, let’s get right into it.

Where The Sharpe Actually Comes From

Naturally, our starting point was to see what kinds of markets some of these accounts had in common.

Were they just consistently nailing the price range of oil? The weather in Miami? Whether or not there’d be a fed rate cut?

Turns out, it’s a little bit more strange:

In almost all of the highest performing accounts, they were exclusively trading the up/down 5-minute market in cryptocurrencies.

Before going further, let’s see how that market works:

1. Every 5 minutes, the exchange opens a new binary contract asking whether BTC will be higher or lower at the end of the window.

2. For Kalshi, the reference price is the CF Benchmarks BTC-USD Real-Time Index, which is computed from a basket of major spot exchanges (Coinbase, Kraken, Bitstamp, etc.).

3. The contract opens at the start of the 5-minute window and closes at the end of it (e.g., 8:00-8:05). The starting price is locked in at open, the ending price is locked in at close.

4. YES pays $1 if the close is above the open, NO pays $1 if it isn’t. Contracts trade in cents between 1 and 99, where the price is the implied probability of that outcome.

5. Throughout the 5-minute window, traders can buy and sell YES/NO at whatever the current order book reflects.

6. At settlement, the winning side is paid out in full and the losing side goes to zero.

The way you profit is straightforward: buy contracts for less than their true probability of paying out, or sell them for more.

Pretty simple.

Now, in this particular market, the average counterparty tends to be a bit… underdeveloped.

Making it a target-rich environment for pros:

So, now that you have a grasp on how it works, we can look at how these operations are industrialized.

It’s Not Predicting At All, Really

Before we get into the trade itself, we have to look at where the price data is even coming from.

For Polymarket, it resolves based on a Chainlink Data Streams price report at a specific timestamp right at the close of the window.

The settlement price is not really “the price of Bitcoin”, but rather a specific report from a specific oracle network at a specific timestamp.

So now you have two parallel things happening:

1. The actual underlying BTC market, ticking in real-time across spot exchanges.

2. The Polymarket order book, which is a bunch of people trying to price a binary contract that will eventually settle against the oracle’s print.

In theory, both should be tightly coupled, but they often aren’t.

By the last 60 to 90 seconds of the window, the underlying BTC move is mostly determined. If BTC is up 0.4% with 90 seconds left, YES is going to settle in the money with very high probability.

But remember, these aren’t super liquid and the counterparty isn’t always operating on that same framework:

• There are stale orders sitting on the book from people who placed them thirty seconds ago.

• There are retail traders pricing the contract based on what they think will happen in the remaining 90 seconds.

• There is still a wide bid-ask spread.

So, it’s simple: if fair value is .88 (88% chance it closes higher than open), and the current bid-ask spread is .83-.86, you buy up all of the asks below .88.

Now, “fair value” here is a bit vague. How do you really know the true probability of what Bitcoin is going to do in the next 90 seconds?

Well, generally like this:

A bit of an oversimplification for time, but there are indeed standardizable ways of computing what the fair value of the contract might be at any given time.

However, anyone with a laptop can run that calculation. What actually creates the edge is everything else around it:

Latency

This is where the picture gets ugly for retail.

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But what does insider trading even mean?

• If you’re on the board and trade ahead of something you planned, then yeah, that’s insider trading, no argument.

• But what if you simply did your homework for a minor informational edge and provided value to markets by trading at prices you deemed inaccurate?

• If news hits the Bloomberg Terminal first, but 90% of participants don’t have that feed, is it still insider trading?

At the end of the day, none of this really matters unless you’ve got some market-moving information that would actually make you stop and think before acting.

But, what if you did?

Today, we’re going to show you first-hand what it looks like to aggressively hunt down informational edges and where that line of “insider trading” gets blurred. This won’t be pouring over some random SEC filing either, you’ll see what it looks like when a fund REALLY wants to get their hands on some edge.

So, without further ado, let’s get right into it.

The Low Hanging Fruit

To start off with the obvious, if you knew that Pinterest received 3x the average website traffic over the last 3 weeks, it would at least to some degree reflect the idea that more people are using their services, and ultimately likely to result in a higher earnings figure for the quarter.

Now, if you own a website, you know that while you can see how many visits you got today, I can’t. This is also the case for most websites.

But what if you really wanted to know how many visits a website got?

To see how this gets done, let’s see how some of the largest aggregators in the space do it:

1. Acquire or build a portfolio of basic, but popular browser extensions.

   1. Things like free VPNs, coupon finders, theme changers, ad-blockers. Whatever, as long as it’s installed by a large mass of active users.

2. Embed deep-tracking cookies in these extensions and bury them in the Terms of Service and Privacy Policies

   1. When a user has the extension active, URL visits are logged, along with time on page, click paths, and referrer chains.

      1. After all the user did “agree” to that use of data.

3. Send the data back to your database and aggregate the results.

4. Publish and sell the insights to hedge funds looking for edge.

Controversial, expensive, and morally grey, but technically legal and the potential edge is massive when done at scale. Consent laundering.

But before you get any ideas: this is also exactly what got Jumpshot, a subsidiary of the Avast antivirus software shut down after reporters leaked just how granular the resold clickstream actually was.

That aside, this is a pretty established practice with layers of sophistication, so at this point we consider this data source to be pretty much commoditized and unlikely to be abnormally predictive.

Further, web traffic data is only indicative of performance for a few hundred companies. Most of Dow Chemical’s customers aren’t filling industrial paint orders via the website.

Undeterred, the hunt continues. You know that the real kinds of information are still out there.

The Higher Hanging Fruit

With the view that commoditized web traffic data probably won’t get you there, you get another bright idea:

“What if you just asked people who would know?”

Now, naturally, this crosses the line into actual MNPI (material non-public information) that can land you in trouble. So, you reframe the question:

"What if you just asked research questions to people broadly related to some information?"

And from there, things get interesting.

Before we walk through what that actually looks like in practice, it's worth pausing on the legal frame that determines whether any of this is allowed in the first place:

Mosaic Theory

This is basically the idea that a research analyst can legally combine many individually non-material, public or non-confidential pieces of information to arrive at a conclusion that is itself material, and trade on that conclusion, without it constituting insider trading:

So, in sum:

• Information you gathered, observed, or inferred is yours to use.

• Information that someone breached a duty of confidence to put in your hands isn't, no matter how cleverly you stitch it together.

Now, you’ll see why we needed to make this disclaimer first.

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Every day, dozens of public companies issue dividend payments. Some large, most small, but every day, a check goes out to someone.

Upon knowing this, every market participant gets a bright idea:

“What if I just collected dividend payments and sold the stock right after?”

Not long after, they find out that the price tends to drop by the dividend amount on the ex-dividend date, making the returns net 0. At that point, the search gets shelved, a lesson gets learned, and the trader moves on.

Nevertheless, it does pose an interesting question.

Yes, a dividend is basically just a withdrawal of cash from the company, creating no new economic value. But it is still a tangible market event where you can know the cashflow being distributed to millions of shareholders with pin-point precision.

So, if you have all of this information of who’s getting what and exactly when, you might ask:

“Is there really no way to derive a profitable extraction of this?”

As it turns out, there actually is.

We’re the first to publicly document this approach, so if you’re interested in running these kinds of niche, but profitable strategies, you’re going to want to see this.

Just Forget About The Ex-Date

Okay, so let’s start with what we know.

When a company is paying a dividend, it has an ex-dividend date which is the cutoff point for when you have to own the shares by. For instance, if the ex-dividend date is April 17th, you have to be an owner of the stock on April 16th or sooner.

On the ex-date, the dividend is immediately priced in:

• The exchange marks down existing limit orders

• The options market prices puts higher and calls lower

  • It does this as soon as the dividend is known, as the dividend yield is a direct plug-in to the Black-Scholes model.

Additionally, if you have an open short position before the ex-dividend date, you are entitled to make that payment from your own position.

So, right off the bat, trying to game the actual payment mechanism is a dead-end.

However, after that initial mechanism, the resulting movements in the stock go back to normal; driven by information, new catalysts, and most importantly, investor behavior.

Every single dividend payment and schedule is public, so if we know all of them, we can create a new hypothesis:

• Tax structure effects

  • Do investors react differently to MLP distributions (taxed as tax-deferred cost of capital) versus REIT dividends (taxed as ordinary income)?

• Special, one-off dividends

  • If a company has never paid a dividend but issues a large one-off after a settlement, do investors behave differently than they would with a monthly-paying dividend ETF?

• DRIP re-investment timing

  • There’s a large community of investors in products like SCHD whose strategy is to automatically re-invest dividends as they come in. How long does that re-investment actually take to hit the tape?

Each of these is a fully-formed research question on its own, and we’ll likely come back to the others in future posts. But for today, we’re going to narrow down to the one we think has the cleanest behavioral thesis to test: how investors react to the payment itself, and whether the one-off nature of a dividend changes that reaction.

Despite getting a check, many investors can perceive dividend payments as a net negative event. It can be read as the company having cash and no ideas on how to boost growth, so they just pass it through to shareholders.

This might be especially true for one-off dividends; if the stock hasn’t done anything for years, but investors finally receive some outcome, they may conclude that the position is “complete” and sell it to allocate elsewhere.

So, to test this out, let’s first take a look at what usually happens to price on the payment date:

Before getting into the numbers, a quick note on methodology: we chose the payment date itself rather than the ex-dividend or record date, because that’s when most investors will actually be notified that a dividend even happened.

Outside of hedge funds and sophisticated traders, most long-term holders aren’t tracking the nuances of ex-dividend dates or record dates, but they are aware of the morning notification hitting their account saying a fresh payment has arrived.

That notification is the trigger for the behavioral response we’re trying to isolate.

With that framing in mind, here’s what we found:

From a sample of ~3,000 dividend payments between 2023 and 2026, the price of the stock tends to go down intraday on the payment date, on average.

It isn’t a linear relationship (with yearly payments being an outlier), but given the large sample size in uncorrelated names, it’s worth looking deeper.

So, going back to our original theory, if we think that, in aggregate, a good chunk of holders will get rid of the companies after they actually pay out, we can form a simple strategy:

1. On the scheduled payment date, we submit a short market-on-open order

   1. This allows us to execute as close as possible to the open price that we can reliably backtest on.

2. Near the close of the trading day, we submit a buy market-on-close order.

   1. This lets us execute as close as possible to the official “close” price that you often see.

3. Repeat.

With that defined, let’s see how well this approach held up:

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Now, we’re not going to give the 10,000th macro opinion on this or provide any political views one way or the other, but this is a uniquely interesting event from a quantitative perspective. Some things here haven’t happened before, but some things have, and they are shockingly predictable.

Instead, we’re going to strip this down to what’s actually measurable and focus on the parts of this crisis that aren’t being talked about. By the end of this, you’ll walk away learning something new and you’ll know exactly what to watch going forward.

So, without further ado, let’s dive right in.

What Does The Options Market Think?

This wouldn’t be a good quantitative view at all if we didn’t first check out one of the most informed and sophisticated sources of information:

The Options Market

To begin, we need to get a view on the implied volatility of Crude Oil futures. This is basically a single number that represents how much the market expects prices to move by in one year:

As of writing, the Cboe Crude Oil Volatility Index is at 84.

This means that the market thinks the price of crude oil futures will move up or down by 84% over the course of one year.

For context, the long-run average sits around 30, so the market is pricing in roughly 2.5x normal uncertainty.

Now, most times, this number tends to be a bit overstated. So much so that it’s become a widespread industry practice to sell options that are priced with that number, viewing the options as being rich (overpriced).

It’s basically collecting premiums for things that never happen and the core of a multi-billion dollar industry.

Let’s take a look at how business is doing now:

Pictured above is the performance of a short volatility options strategy, but applied to USO, a liquid Crude Oil ETF.

For context, a short straddle is a relatively simple strategy: if the market thinks the asset will move up or down by 10% (implied vol), but it actually moves by 3% (realized vol), you profit.

So, as you can see, despite the headline-packed year and very-real things happening, it has been a rather profitable year for cooler heads.

Conversely, for those more anxious and actively betting that major things will continue happening, it has been a rather unfortunate year:

Interestingly enough, this “nothing ever happens” approach doesn’t only come up in options markets.

In a study across millions of Polymarket bets, it was found that “No” bets win a surprising 73% of the time:

So, our first takeaway is that even in times of apparent crisis, taking the view of “the worst case scenario won’t come true“ is generally what tends to work out.

With that front covered, we then pulled some more data and that’s when things really got interesting.

Where Things Start to Break

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The claims range from ambitious to genuinely unhinged:

As a result, the general consensus among serious practitioners is that if you’re using LLMs anywhere near a trading workflow, something has gone wrong.

We had a slightly different take.

In most of these cases, the person posting has never actively traded at scale. So while their implementation is almost certainly wrong, that doesn’t necessarily indict the tool itself.

So, we figured:

And that’s exactly what we did, and frankly, what we found was pretty surprising.

So today, we’re going to go on a deep-dive into working with these models the right way. By the end, we promise that you’ll see these being used in a way you haven’t before.

So, without further ado, let’s get right into it.

Prompt Like You Mean It

Okay, first things first, this isn’t 2022 anymore; models like Opus 4.6 and the like are now capable of not just vaguely understanding what you prompt them with, but can now actually do things like work with APIs, execute code, and fully reason out the tasks you give it.

So, instead of going with something like:

“Create a 10 sharpe trading strategy, make no mistakes”

We’re going to task it like we would a real quantitative researcher:

Think about how you’d actually onboard a new analyst. You wouldn’t hand them a Bloomberg terminal and say “find alpha”, but you also wouldn’t hand them a fully written strategy and say “just code it.”

You’d do something in between: give them a defined universe to work with, access to the data they need, a clear objective, and then let them figure out the approach.

With that as the framework, we can start designing the assignment:

1. Using Alphanume’s next-day movers endpoint, we pulled a historical basket of 5 stocks with high likelihoods of large moves in the immediate next trading session.

2. From there, we gave Claude the tools: sector classifications, historical pricing data, volatility metrics, etc.

   1. We showed it what the requests look like, what the response structure is, and let it understand the data it had to work with.

3. Then, we set the objective: “Build a hedged book around these expected movers.”

   1. Not “build a profitable strategy”, not “find the best trade.” Just: here’s a set of names we expect to move, construct a portfolio that captures that move while hedging out as much market and sector risk as possible.

4. And most importantly, we didn’t tell it how to hedge. We didn’t say “use 30-day realized vol to find the closest peer.”
   We added one more constraint: for every pair it constructs, it needs to provide an economic rationale for why the long leg should outperform the short leg over that specific duration.

   1. This is the part that filters out the nonsense. Anyone can match two stocks by sector and call it a pairs trade, but forcing the model to reason about why the trade works, in the context of the specific pair, is what stops this from just becoming a mechanical screen.

And then, we let it cook.

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