Our big TSLA covered call study

I’m excited to unveil a study Mark Phillips and I have been working on since late 2024. We presented it on an X livestream on Thursday:

The video is 90 minutes and loaded not just with results but education.

We break down:

  • Why covered calls are more than just “income” strategies 📉
  • How volatility and path dependency impact performance 📊
  • The nuances of delta hedging and risk normalization ⚖️
  • How indicators like IV, VRP, and skew perform vs a naive strategy ✅
  • The tradeoffs between indicator accuracy and sample size 🚀

Whether you’re new to options or managing advanced strategies, this deep dive will sharpen your understanding of volatility P&L, trading mechanics, and how even simple strategies have complex outcomes.

🎯 Key Takeaways

  • Covered calls = long delta, short vol
  • Separate volatility P&L from directional P&L to assess strategy mechanics
  • Option backtests involve many design choices—beware of hidden assumptions
  • Writing calls on single names vs indexes brings ironic tradeoffs
  • Volatility pricing is often efficient, especially in liquid names
  • Most 1-month option P&L comes from realized vol, not just theta decay

Written recap

✍🏽Mark did a wrote up a recap including our tables: Dialing in on TSLA covered calls

Options as LEGOs

I never go back and watch interviews that I’ve done, but I did this one with my son, Zak.

And I’m really proud of it because I got to teach…using his spreadsheet!

The objective — teach an option’s concept to Matt as if he’s a kid and demonstrate why it matters for average investors in general AND why it matters to professionals.

Challenge accepted.

By seeing options as Legos, we see that everything can be built out of a few pieces. It explains the BOXX ETF and covered calls. It explains why understanding this one concept you can collapse the zoo of option thingies (straddles, strangles, condors, christmas trees, flies, boxes, jelly rolls) into structures you can re-derive from basic material.

This video starts at square #1 — the definition of calls and puts. Truly suitable for the beginner. Leave your ego at the door…I’ve already accepted that I’m not smarter than a 5th 6th grader.


FYI

I saw some AI tool called manus.im on my feed so I clicked on it.

[Anyone else feel like they’re speed-dating robots these days?]

I gave it one simple prompt:

“How is a covered call similar to a short put?”

I swear I heard it laugh at me before responding with this deck:

shortcuts to get implied vol from a straddle

I just want to say that you people are sick. This is the most viral tweet I can remember sending in recent times.

Because I happened to be helping the 3rd grader with improper fractions recently I saw 1.25 as 5/4 which is immediately recognizable as a square root of 25/16.

Sprinkle in some trader math that condemns you to see the sqrt(251) as 16 and you get an even more compact version:

Step by step:

The real masochism in that thread happens further below…

what the vol spread chart hides

People will message me with charts like this to ask my opinion of a pairs trade:

This is not a pair anyone sent me but the form is the same.

I guess-and-tested a few pairs whose daily return correlation is at least as high as some pairs I’ve been sent. In this case, I picked 2 homebuilders whose correlation over the last 500 calendar days turned out to be .90

[I used the hedge ratio tool from last Thursday to find this pair]

I respond to these messages with a few things that come to mind but I’ll flesh the responses out more in this post. Think of it as a map of data and interpretation pitfalls. There are bits in here for both novices and pros that’ll stimulate a “I never thought of that before”.

The charts above aren’t worthless but they’re very faint beeps on a metal detector during a beach stroll.

Let’s see why…

 

Thoughts as they come to me when I look at the chart…

👁️The first thing my eyeballs say to my brain when looking at a vol spread chart is “why not a ratio chart?”. Over the period, the mean vol spread of LEN to DHI is +.83 and the vol level is about 40%

If vols doubled and the vol spread had the same average I’d be surprised, so a vol ratio strips out the level-dependant assumption that is silently suggested in a spread chart.

The nature of the relationship is of practical importance because it affects how you weight a pair. Any weighting scheme has trade-offs and matching the trade-offs to the bet you are expressing is, well, advisable.

I won’t rehash weighting here but both of these links go into it:

In this case, the spread and ratio seem good enough proxies for one another. A scatterplot rather than a time series would provide a more granular look but given the 30d maturity and no earnings extractions the extra granularity would probably cause us to overemphasize extremes that would probably be smoothed by arduous surface cleaning.

🔁Possible spot/vol artifacts

If LEN price drops relative to DHI I expect its vol will expand more than DHI. To the extent that a widening vol spread simply reflects recent price action we may deem the vol aberration “more justified” or “not an opportunity”.

Whether a widening vol spread being explained by a sell-off is meaningful or not is unavoidably context-dependent. If LEN falls relatively after earnings, I’d expect its vol spread to narrow from a wider reading that we likely sampled before earnings. But this is just an example of “news flash: lazy low-resolution charts are too underpowered for a trading thesis”.

That said, I do believe the idea of spot/vol mattering in terms of how much weight we’d give an aberration even qualitatively is at least relevant enough to pivot data in a few ways to understand the shape (or lack thereof) of a relationship.

Let’s play “charts plus thoughts”:

  • We see a hint of vol/spot effects in the downward-sloping trendline. As LEN price increases relative to DHI its vol premium falls. But the r-square is trash. Weak relationship.
  • This isn’t saying that spot-vol relationship in the stocks vs their vols is weak just that the vol ratio to price ratio relationship is weak. Remember these stocks’ returns are .90 corr. When LEN goes down, it’s vol expands but DHI usually goes down when LEN goes down (and its vol will likely be expanding as well in that case).
  • My background is in commodities where the spot/vol correlations in general are higher than they are in single stocks (but not as strong as equity indices which I’ll address a bit more below). Here’s just LEN IV vs spot

    The grey trend line shows a weak relationship between spot and vol in terms of slope and a correlation that would be around .55-.60…but then I drew those red dashed lines because the story is really:

    LEN is been like a 30% vol stock give or take 5 clicks in a price range of about $165 give or take 30% (ie an annual st dev). It sold off recently with the rest of the world and vol went up to 40% or about 1/3, as the stock stepped from one economic platform down to another.

    As a matter of generalizable perspective:

    All of this feels interesting from the altitude of an investor’s vantage point, but beneath the level of signal from a trading pov. The distinction is useful for any type of money decision — am I getting trader or investor resolution? Is the option trade I’m doing matched to a trader’s dashboard or an investor’s?

     

The vol ratio vs price ratio chart showed a weak relationship. Not unexpected. LEN vols and price ratios are correlated. So instead let’s scatterplot the 1-month change in vol spread (our first chart said it’s a reasonable stand-in for ratio) vs the 1-month relative performance of LEN vs DHI directly. This is not about levels but about motion.

Honey badger, I mean the vol spread, don’t care. For a pair of 30% IV names that are 90 corr, 5%-10% relative performance is significant (can you use the risk-remaining lesson in last Thursday’s post to show that? The learning sticks if you apply it.)

And yet the scatter doesn’t validate the IV spread caring that much.

Let’s do a final chart before chatting.

We saw above that there’s a faint vol ratio to spot ratio relationship in the pair. As LEN goes up relative to DHI its vol ratio declines a touch but there’s a useless amount of noise from our distance.

Straddles are another way to look at vols and spot prices in a combined way. The approximation for a straddle price depends on vol, spot price, and DTE. DTE is constant. If spot and vol were negatively correlated with a -1 beta you’d expect straddle prices to stay constant over many price levels. If vol has a less than a -1 beta to spot then as price increases straddles increase and vice versa. Over large moves I always expect this to be the case.

For smaller moves, the constant straddle might hold as it often does in something like crude oil, a market where traders have a history of speaking in terms of straddles (or “breakevens” in case you ever wondered about Ari’s twitter name) instead of vol. In that market, discussing vol without price level is like owning one shoe.

For single stocks, this effect is going to be less pronounced. I expect straddle prices to rise with price as the spot effect in the straddle formula overwhelms the declining vol. Voila…the LEN – DHI straddle spread vs price ratio. Upward sloping af.

[Additional thought on this chart:

It likes like there are 2 channels which lovingly accommodate the trendline’s horny advance. I didn’t bother, but when you see things like that, it’s usually a clue to color code your dots by date. I don’t mean to abuse the metaphor but there are probably a couple periods of note here.]


🚧Detour on spot-vol correlation in single stocks

The spot-vol correlation in single stocks will vary by sector. The correlation looks fairly small in these homebuilders. We saw that directly in the spot/vol charts, the change in vol ratio vs change in spot ratio, the change in the vol spread vs change in relative performance, and in the strong positive relationship between straddle spread and price ratio suggesting straddles don’t stay constant but are driven by price more than vol.

One reason I could imagine homebuilders in particular having a muted spot vol relationship is they benefit demand side from lower mortgage rates. I’ll put my finger in the air and say they act like a levered 60/40 portfolio. They are diversified if stocks and bonds are anti-correlated.

[In fact, one way to potentially think of their vols is possibly from the quanto lens that one might think of pricing EWJ vol by considering the individual vols of Nikkei vs Yen. The options on EWJ relative to the legs imply a correlation between Japanese equities and FX — which is substantial in an export-led economy.]

Even though spot/vol corrs vary by sector, overall the skew and spot/vol correlations will be smaller than equity indices. Consider 2 reasons that derive from logic not vol surface data which only reflect the logic:

✔️Stock returns are positively skewed

Most companies go bankrupt. A few drive the total return of the indices. Black-scholes’ positive skew lognormal distribution is not a horrible description of companies…pump up the vol and it says, the median return is negative but there’s a long right tail. See Is There Actually An Equity Premium Puzzle?

✔️Average single stock skew must be less than index skew

An equity index is a portfolio of stocks. The vol of a portfolio depends on the vol is positively related to the vol of its constituents and their cross-correlation. If the correlation falls, all else equal, the portfolio vol falls.

Index downside skew must account for both pathways of vols increasing:

  1. The average stock in the basket’s vol increasing
  2. The cross-correlation of the stocks in the basket increasing

Most stocks share a systemic, undiversifiable risk — the possibility of the economy as a whole slowing down. This shared risk gets priced in index skew as an extra kicker over single stock skew because as correlation goes up, index vol increases faster than the increase in single stock vols.

Recast as a trading statement:

If you could sell all the single stock skew at the same level you could buy the index skew you’d be getting “correlation going to 1” for free.


 

What the vol spread chart hides

In traditional investing, we look at charts that prices travelling from the lower left to the upper right. We can’t feel the drawdowns along the green path it draws. Our eyes focus on the endpoints. Looks easy. All the suffering that you are getting paid for is eradicated, nothing but an expectation of profit for showing up remains.

This turns out to be (has been?) a salutory illusion. The chart makes it easy to buy while hiding the source of edge — a time horizon to not care about the dips. The time series of a mean-reverting vol spread does the same thing.

Let’s break down its lies.

🔮Snooping ahead

The mean and standard deviation are known for the sample period only after the sample period is over. If we transport ourselves to a place on the graph where it’s “obvious” to sell we are doing so with the benefit of hindsight. At the time of the trade, we didn’t know we were “there” on the finished chart. This insidious self-deception happens easily when we look at charts, don’t bring it with you when you move towards actually trading.

🧩Implied vol is only part of the puzzle.

The actual p/l of an option trade done to capture a vol discrepancy is a combination of:

  1. vega p/l or change in implied
  2. your realized p/l or what I call the “tug of war between your gamma and theta”. For a 1-month option the tug-of-war is going to have a large influence on your results but the chart’s thesis was IV driven.

     

If the market were strong-form efficient all the money you’d make on mean reversion of the IV spread would be lost on your realized vol p/l if the IV spreads perfectly predicted subsequent stock movements! I’m not saying that they do, I am saying that any focus purely on the IV spread only studying half the p/l drivers.

Discrepancies result from flow. Updating fair value based on flow is an inexact science, but to assume that the full amount of discrepancy is edge is the same as saying the flow has zero information.

🍦Vanillas are the real exotics

Vanilla options are messy. How?

  1. This time series is a constant maturity vol. To replicate it, you’d need to rebalance the options of the 2 expires bracketing 1-month on each trading day.
  2. As the spot prices of the pair move around, your moneyness and greeks change knocking your initial weighting off balance. Contrast this with a variance swap where holding time constant, your gamma is constant over the range of strike prices. The pricing is exotic but the experience is familiar because it ties mechanically back to a familiar calculation — the standard deviation of close-to-close returns. Vanilla pricing is familiar but the experience is exotic.
  3. You were trying to capture a vol discrepancy which means you probably want to delta-hedge to isolate the edge.

     

🩺Clinical vs practical

Maintaining a desired exposure in vanilla options requires rebalancing both the options and deltas. To do this faithfully every day as a taker would cost more than any perceived edge is paying.

This means in practice you must be willing to warehouse and tolerate noise. This means you must trade smaller for a given amount of edge if you want to maintain the same risk/reward of the clinically hedged expression.

In the real world, this type of trading is the domain of entities that can sell on the offer and buy on the bid, allowing them to leg the hard leg for edge (the liquidity of which will also be the limiting reagent on the trade).

If you are not a market-maker, trade the leg you have more conviction in or use the history to enter/exit the leg that fits into your overall portfolio more synergistically.

 

Last word

These charts should be thought of as indicators as opposed to backtests. The allure of these charts is they hint “backtest”. By peeling back their insidious, white lies, we can approach them with the right humility —they are clues. The opening statement in a trial, not the final verdict.

 

Stay groovy

☮️

the 2 vectors of volatility scaling

I fired up Corey Hoffstein’s goated Flirting With Models podcast to hear Scott Phillips discuss “ugly” edges in crypto. This episode came highly recommended in my corner of twitter. It does not disappoint.

But I want to zoom in on one part. Scott says:

If you did what I do perfectly, you’d be up well over Sharpe two, and probably with size. But the way that I do it, our four-year Sharpe is 1.7 with retail-level costs. So cross-sectional momentum is a little bit better than that, but you don’t have the really nice positive skew of trend. And cross-sectional carry is about a Sharpe 1.7 as well, and slightly orthogonal. So you blend the three of them together, and then you’re at Sharpe two easily — and without even good execution…The math holds. Returns scale with the square root of independent bets.

Scott misspeaks here (easy to do in a conversation) but what Scott means is volatility scales with the square root of independent bets (returns scale linearly). This concept underpins one of my most important posts — Understanding Edge. It is the basis of all trading businesses without exception. It’s Day 1 learning at a prop shop. Munger has that “Take a simple idea and take it seriously” advice…This is THE idea Jeff Yass took seriously.

I’m repetitive on log and compounding math for 2 reasons that extend beyond the shock factor of the “lilypads in a pond” puzzle:

a) Investing is a serially repeated game so compound returns are our primary concern

b) Option theory sits atop logreturn math which is just continuous compounding (in fact e, the Euler constant of 2.718, is your growth of $1 if you continuously compound at 100% rate for 1 unit of time. See Using Log Returns And Volatility To Normalize Strike Distances)

We’ve confronted this math many times from different angles:

Compounding is a multiplicative process that describes how returns scale across time. “Volatility drain” reminds us that volatility’s interaction with growth is embedded in that process.

We already understand how compounding scales across time as a function of volatility.

But volatility itself has scaling properties.

That’s what Scott means when he says (or meant to say) it grows by the square root of independent bets. Regular readers have seen me scale a daily return to an annual return by √251 or ~16 many times. But I don’t always remember to say that this assumes no correlation between daily vols (mapping to the “independent bets” language).

In multiple posts, we have seen that volatility is understated in the presence of autocorrelation:

Autocorrelation affects how we scale vol through time per asset.

This is only half the volatility scaling story. It’s one vector.

The second vector is how we scale vol across our portfolio.

This depends not on autocorrelation, but on pair-wise correlation.

Independent bets are simply bets that have no (ie zero) correlation with one another.

It is why you can blend several strategies and end up with a composite Sharpe greater than any of the components.

Let’s look closer.

Let’s consider an equal-weighted portfolio of ETFs:

  • IBIT (BTC)
  • GLD (gold)
  • HYG (high yield credit)
  • IEF (10-year notes)
  • QQQ (Nasdaq stocks)

Method:

  1. I used the hedge ratio tool to fetch the correlation of daily returns for each of the 10 possible pairs (5 choose 2) for the past 16 months.
  2. I chose a reasonable vol for each name and then backed out an expected return by assuming the Sharpe Ratio of each asset is .40. I’m ignoring RFR in SR — I’m just using expected return / vol.
  3. I convert the correlation table into a covariance table to do the matrix multiplication for computing portfolio vol. I explain the math in writing, video, and in a spreadsheet I’ve shared before:
    1. ✍🏽Computing portfolio vol (moontower)
    2. 🔻 Portfolio Vol Spreadsheet (download)
    3. 🎥Portfolio Vol Explainer (Moontower YouTube)

 

Here’s my work for the equal-weighted portfolio:

What to take note of:

  • If we compute the portfolio vol in the “stressed” condition where all the pair-wise correlations are 1.0, then the portfolio vol is equal to the weighted average vol of the components. In this case, the portfolio vol is 20%. There’s no diversification benefit to the portfolio vol or SR!
  • When we use the actual correlations the portfolio vol drops to 12.5%. Since the expected return is unchanged the SR improves from .4 to .64. Massive diversification benefit.
  • The ratio of the portfolio variance to average weighted stock variance is the average weighted pair-wise correlation. The average weighted stock variance is the same as the portfolio variance if all the correlations are 1.0
    • For the option fans: this math is exactly how implied correlation is backed out of the index option market. The ratio of index variance to weighted average stock in the basket’s variance is the implied correlation! The cheaper index vols are relative to stock vols, the more implied diversification benefit there is in the index. This concept sits at the heart of dispersion trading.

A portfolio that equal weights IBIT and IEF sounds a bit comical. Let’s re-do the analysis with an inverse vol-weighted portfolio:

Now IEF has 10x the weight IBIT does. If you multiply each component’s weight by its vol you’ll notice they each have equal risk contribution.

The normal portfolio has a Sharpe of .68 but if all corrs go to 1.0 the SR drops to .40 and the portfolio vol goes from 6% to 10%.

Even if the math isn’t interesting, the principle should be seared into your risk brain. Adding zero or negative correlation assets to a portfolio can improve its risk/reward even if the asset’s individual SR is inferior to the average component. An asset’s stand-alone properties are not as important as how it contributes to the risk/reward of your portfolio just as how an asset’s arithmetic return does not mean much if don’t consider its vol. After all, we care about compounded returns.

These dual concepts, timer-series volatility (influenced by auto-correlation) and cross-sectional volatility (influenced by pair-wise correlation of components) give a fuller picture of how returns AND volatility accumulate through time. You care how both the numerator (return) and denominator (risk) scale.

Application to allocators

Professional allocators recognize that much of the low-hanging fruit of long-term results is sound portfolio construction. Basic hygiene in how you stack investments by understanding their properties in various states of the world is something you have more control over because volatility and correlation are more stable than expected return predictions. Simple optimizers are incredibly sensitive to expected returns — which is a bit self-defeating when you realize this is the metric we know the least about.

To add a bit of practical color. I have a good friend — a physics academic turned investor — who was CIO for the family office of one of the partners of [insert the best quant firm you’ve heard of and it’s probably the one]. He admit something he found embarrassing — a shockingly small number of ideas underpinned the family office’s approach and none of them is a secret. The scaling property of portfolios is one of them. If they sourced 16 investments that generated a high-quality portfolio, in order to double the shape they needed 4x as many additions to that portfolio of similar quality (which was already high) to double the portfolio Sharpe. Pretty much impossible when all the investments are LP stakes in quant funds (due diligence alone is too labor intensive, never mind finding that many great managers).

[I’ll get around to toying with this tool a bit more to tinker my way to seeing how the scaling works — for example going from a 3 stock to 12 stock portfolio where I add names with equal SR but different correlations. I’m down to the wire on writing this post as it is.]

Application to traders

The last part of this video shows how the interaction of return and volatility can inform how you think about sizing risk.

If you prefer text, the heart of the material is captured here:

Understanding Daily vs. Annual Sharpe — and Sizing Risk with Intent

Let’s start with something simple but important.

🗓 Daily SPX Returns

The S&P 500 has a daily mean drift of about 3 basis points, with a standard deviation of about 100 basis points in normal conditions. That gives you a:

Daily Sharpe ≈ 0.03

(This ignores the risk-free rate, which is roughly 1.5 bps/day.)


📆 Annual SPX Returns

Now zoom out.

The S&P’s annual return is roughly 8%, with an annualized volatility of 16%. That gives:

Annual Sharpe = 8 / 16 = 0.50

Here’s the interesting part:

Annual Sharpe (~0.50) ≈ 16× Daily Sharpe (0.03)

Why? Because volatility annualizes by the square root of time:

√251 ≈ 15.8, or roughly 16


🎯 Sharpe Ratio Benchmarks

Let’s anchor a few examples to make this more intuitive:

  • SPX benchmark: 3 bps return / 100 bps vol → Sharpe 0.03
  • Sharpe 16 strategy: 1% return/day, 1% vol/day → Sharpe 1.0 daily
  • Sharpe 2 strategy (annual) =
    → Daily Sharpe ≈ 2 / 16 = 0.125

So:

Sharpe 2 strategy means your daily standard deviation is ~8× your expected return.

That 8-to-1 ratio is a helpful sanity check.


💰 Backing Into Risk Limits

Let’s say you want to size your book around a Sharpe 2 strategy. Here’s one framing:

  • Daily P&L target: $1,000
  • Daily volatility: $8,000
    → Daily Sharpe = 0.125
    → Annual Sharpe = 2.0

Now annualize:

  • Yearly expectancy: $1,000 × 251 ≈ $251,000
  • Annual volatility: $8,000 × √251 ≈ $126,744

What does this imply for annual ranges?

  • +1 SD year: $124,256 – $377,743
  • +2 SD year: ~0 – $500k
  • You’re brushing against zero at 2 SDs

📐 How to Use This for Book Sizing

You don’t just ask how much do I expect to make?

You ask:

  • What kind of daily swings are consistent with my goals and risk tolerance?
  • Can I size positions and set Greek tolerances so that my volatility lands near ~8× my expectancy (for a Sharpe 2 strategy)?
  • Do I have enough emotional and capital runway to survive the downside scenarios?

another example of vol goggles

I saw this on Kalshi back on 4/25:

Do you interpret that as bullish?

Simmer on it a bit.

I’ll come back to it in a sec but before that I want to point out that it reminds me of this tweet:

In both the Kalshi market and the tweet a normal person sees a delta bid.

In both cases, I (and most option traders probably) see a vol bid.

For the tweet, it’s all explained in one-touch.

Why does the Kalshi market look like a vol bid to me?

Look at my gut reflex to the Kalshi quote in this tweet. The answer lies is in the first thing I asked Grok.

I’m exhaustingly repetitive in trying to advocate for seeing the world with a vol lens because at its core it’s a prompt to think about risk and its price.

But ya know, maybe also just ignore it — BTFD and carry on is an American birthright. Anything that keeps you from interpreting information as bullish should be burned for warmth so feel free to print a stack of moontowers like this one and light a match. Don’t worry, I am pathologically unable to take offense anyway.

shorter VRP lookbacks

Good time to re-surface Harel’s gem:

The Realized Volatility Puzzle (9 min read)
Harel Jacobson

This one is a bookmarkable dictionary of various realized volatility measures.

Realized vol computations are on my mind because as we’ve been upgrading our data pipelines in the moontower app we are discussing enhancements to our realized vol infra to leverage the upgrades.

I won’t go into our details here but the recent rally holds a clue as to why many classic measures of realized vol struggle — they are too slow to reflect the present.

This is my custom list in the app as of Friday’s close. I point you to the 30d VRP (“volatility risk premium”) column…all those negative numbers mean the 1-month implied vols are trading at a large discount to the 1-month realized vol. In other words, the options market expects the next month to be much calmer than the Vitamix-on-max-speed market Liberation market of April 2025.

moontower.ai

It’s a bit like looking at VRP after earnings — it’s “low” because they divide a large price move into a volatility that anticipates a more normal environment.

A manual adjustment to our VRP calcs in the app is to look at our vol cone chart. This is TSLA. You can see that the current 30d realized vol (green line is current daily RV readings of various lookback) is way above the 30d IV…but the 1 week IV has vol premium to the 1-week realized vol…in other words, realized vol has crashed (also obvious from the green line):

moontower.ai

Traditional VRP measures struggle both ahead of known events (that’s why pro’s “extract”) and after a period of insanity that the market feels is at least partially resolved. We are working on enhancements to automate the adjustments you should make coming out of high vol periods.

SD from 200d MA column

In the screenshot above I drew a box around the SD from 200d MA column. We added it recently to the Cockpit view.

The definition:

ln(price/200d MA) divided by 6m IV to normalize

It’s not a signal just a useful way to get your bearings after a lot of movement with a meaningful comparison. The ln() is basically the same as “the % difference from the current price to the 200d MA”

We divide by 6m IV which is a stable enough ruler to compare across names. If TSLA and SPY are both 5% lower than their 200d MA, TSLA is much “closer” once you adjust for its volatility.

etf fair value

I replaced my IBIT shares into long calls when BTC vol got crushed this week while selling my VIX futures which held up quite well on the equity rally, a possibility I suggested when I explained that the elevated but also flat term structure of VIX suggested vol was here to stay in 2025. From vol speed round:

As I was looking at IBIT I thought I’d share a habit I picked up from distant past of ETF arbitrage — estimating the prem/discount baked into the ETF price I’m about to trade:

  1. Go to the IShares web page to grab 3 values (highlighted)
Image
  1. Enter them in my spreadsheet to compute the ratio of NAV to BTC price. The cells with the box around them are via the previous close values. I then apply the fair ratio to the live BTC price compute IBIT’s live premium or discount.
Image

hedge ratios

Net of last week’s April expiry the flurry of trading from the last few weeks has left me net long delta. That’s not accidental. I had plenty of room to be a buyer on balance in the turmoil since cutting lots of portfolio delta heading into the election.

[If interested…I rebalanced much of stock delta into t-bills bills then re-deployed into bonds, TIPs and silver in late January.]

Coming out of the April expiry last week, my position gained ES futures, VIX futures, IBIT, with my only outright option position being some May AAPL put spreads.

Back in November I published this video which show how I look at the risk of our household portfolio.

But in the flurry of trading while I’m mentally separating what I’m doing for edge and my total portfolio, I do have some delta bias under the hood to effectively dollar cost average since I de-risked in October.

But I want to keep close tabs on how much I’m adding which means summing positions in a vol or even better beta-aware fashion. NVDA is far more volatile than SPY so just as traders combine all their deltas into SPX or some other benchmark terms I like to also normalize back to SPY equivalent risk.

This is a doorway into hedge ratios and beta-weighting. Beta-weighting is not just vol weighting but includes correlation.

I’ve written extensively about how to do this in:

🔗From CAPM To Hedging (17 min read)

That post steps through derivations and the basics of correlation math but in my personal portfolio spreadsheet I needed to make a little widget where I could just put 2 symbols and get a hedge ratio.

We actually have a calculator in the app but you need to supply the inputs.

However I realized with Excel’s built-in =stockhistory I could not only get the inputs, but easily plot time series and scatterplots to understand the shape of the “idio” risk and variation in the beta over time.

🚀We are going to add this functionality to the moontower.ai app. We already have the data, just need to extend the UI.

In the recent month, I wanted to understand how many SPX delta equivalent I had in VIX futures. I used VXX as a proxy put it into Excel and voila got some idea of how much “less” long SPX I am with the VIX futures incorporated.

So….

I made a video walking through the Excel tool. It’s some of the most practical traders-use-this-everyday-type knowledge. We skip the derivations and jump to how do I actually use this data to size hedge ratios or estimate my book’s beta.

I hope you find it as useful as I think it is. (If not, I’ve got more work to do on the explanation side so let me know!)

Paid subs get the spreadsheet. If you have the stockhistory function in Excel this will work seamlessly for you. It’s addicting to toggle thru pairs so fair warning!

The spreadsheet link below has 2 bonus items not shown in the video:

1) A widget for spitting out the hedge ratio not just running data

2) A little position radar template on a separate tab to keep your gameplan organized


Before getting to the sheet, tomorrow night is Ricki Heicklen’s Trade Gala party in SF. I mean yesterday we learned that Jane Street made $20B in 2024 which doubled what they made in 2023 which shattered their record from the prior year. I think they are the most profitable firm in the world per employee (they have about 3k employees).

I think it was Morgan Housel who once wrote that very few orgs in the world could say their edge is just flat out “we’re smarter than you”. There’s usually some other sauce. He gave the example of RenTec. Jane Street is another that can boast the same. This is a cool opportunity to see how that crowd thinks.

This is a special moontower invite that Ricki sent me. She’s repeatedly acknowledged that anyone she meets that comes out of this community is a breath of fresh air which she poses as a compliment. I’m not one to insult a compliment but it’s a testament to y’all. I don’t control how you act.

Anyway the party starts at 8pm and runs thru 6am…well I’ll let you click on this yourself. This is actually a custom invite for Moontower readers:

Trade Gala Party

A quick description..Trade Gala is a limited-access costume party with markets, a puzzlehunt, and demo-ing a new trading game.

If you want to attend the trading BootCamp the rest of the weekend sign up below. It’s truly ridiculous. If you are a novice it will open your mind to what trading looks like thru the eyes of sharps. It is hard to come by this in the level of detail you’ll see here.

If you are a pro, well, you might want to know you might notice some things that you are up against. There’s a lot to learn for everyone.

trading.camp

It’s view only but you can save a copy for yourself and do what you like from there.

  1. When you click the link it the link will open in your browser.
  2. Download a Copy. (see pic)
For the spreadsheet continue to moontower.substack