Real estate people understand the value of accounting losses in service of deferring taxes while an asset’s returns compound.

In the institutional investing world many investors such as endowments are tax-exempt.

Retail investors in public stocks have less places to hide outside of tax-advantaged accounts which are hard to jam lots of assets into in the first place.

The rise of ETFs have come with some relief on the tax side as you decide when to pay taxes because you decide when to sell even as the holdings are rebalanced. Mutual funds can leave you footing a prorated portion of the pool’s taxes regardless of how long you’ve been an investor.

While the ETF advantage is real it’s relatively minor compared to the ability to tax-loss harvest. By owning the individual components of a stock index you can sell losers, rebalance into peer stocks, and accumulate short-term losses to offset long-term capital gains on the subset of names that moon.

I say minor because of the “brain damage” (more effort, slippage, tracking error although if it’s random only matters if you’re managing money for others) and higher management fees associated with TLH. See Alpha Architect’s The Costs and Benefits of Tax-Loss-Harvesting (TLH) Versus an ETF.

Another restraint on TLH enthusiasm is limitation on writing off losses greater than $3,000 per year. Losses are more valuable in an NPV sense if you can use them to offset significant capital gains when diversifying out of a large gain in a concentrated position. With markets where they are, especially the Mag 7 and BTC, this is common high-class problem.

Still, the fintech world with the rise of robo-advisors and software is enabling both retail and advisors to “direct index” making TLH both easier and less costly.

Getting a sense of proportion

Let’s do some simplistic hand-wavey math to get a sense of proportion for how TLH might work if you were simply long a $1mm basket of stocks.

Assuming individual stocks were i.i.d. (“independent and identically distributed”) with expected mean monthly return of 0 and standard deviation of 10% (10% *√12 ~ 35% annual estimate of single stock volatility) then conditional on a stock being down its expected loss is 6.75%

This is symmetrical. Given that a stock is up the average expected return is +6.75%. We are just using arithmetic returns.

In mathematical expectation you expect the portfolio p/l to be 0, with half the stocks up and half down. Thinking about the portfolio as 2 halves, you expect $500k to earn 6.75% or $33,750 and $500k to lose $33,750.

Now suppose you rebalance the losers into a Wario basket of names that have the same exact characteristics as the ones we sold. You have now crystallized $33,750 of short-term tax losses but our exposure is the same. You have gained an asset to offset future tax liabilities.

Just staying simplistic, if all the stocks proceed to go up by 5% over the next 365 days and you sell on day 366 to get LTCG treatment (assume 23.8% — which is just Federal!) then what is your after tax return?

Winning stocks:

$533,750 * 1.05 = $559,728.75 or $59,728.75 total profit on a basis of $500k

Losing Stocks:

$466,250 * 1.05 = $489,562.50 or $23,312.50 profit on a basis of $466,250

resulting in:

+$83,041.25 LTCG (5% on $1mm exposure)

-$33,750 short term losses

= taxable gain of $49,291.25

tax bill = 23.8% x $49,291.25 = $11,731.32

After cutting the check what do you have in your account?

$1,049,291.25 – $11,731.32 = $1,037,559.93 or a 3.75% after-tax return.

 

💡The tax benefit

You’ll notice that if you bought $1mm of an ETF that went up 5% in a year and sold on day 366 your $50,000 profit less 23.8% taxes would net you about the same after-tax return.

So where’s the benefit?

It’s in the optionality of this pool of short term losses that you have control over. You could just let this $1,050,000 portfolio grow and keep the short-term losses as an asset in your back pocket to use against future tax liabilities, some of which are going to be taxed higher than LTCG rate. Alternatively if you need to divest a large chunk of a profitable position to raise cash, you’ll have a large pool of losses to offset the gain.

The real value of TLH emerges when:

• Offsetting Higher Tax-Rate Gains
  • Short-term capital gains (STCG) or ordinary income are taxed at higher rates than LTCG. If your harvested losses offset STCG or ordinary income, you reduce your taxes significantly.
• Perpetual Deferral
  • Step-Up in Basis
    • If you hold the portfolio until death, the heirs may receive a step-up in basis, erasing deferred taxes entirely.
  • Charitable Contributions:
    • Gains on low-basis positions can be avoided by donating appreciated securities to charity.

This toy example is compelling enough to realize it’s important. But there’s also another flavor of TLH on the scene with the potential to generate significant short-term accounting losses while the overall value of the portfolio grows.

TLH on levered long-short portfolios held in a separately managed account (SMA)

Using portfolio margin and a quant framework (this can range fairly basic to factor-intensive), an investor can run the same beta they desired in a typical long-only ETF but generate significant short-term losses by using their stocks as collateral to overlay a long-short portfolio.

This is typically done with an advisor who will in turn be using a sub-advisor whose infrastructure allows them to scale portfolio adjustments across thousands of custom custom portfolios held in SMAs.

I’ve heard some claims of how much more impactful this can be but again it’s critical to sanity check with actual numbers to make sure the sense of proportion is reasonable. From there you start layering common caveats which are easier to handicap in terms of bps per year.

In this case, the sanity checks called for simulation.

A little foreshadowing — if you are in a high tax bracket or trying to work out of a concentrated position with a low cost basis you are going to want to see this.

You get the simulation code, you can run it in your browser and it will even download the full output. I’ll show you a few manipulations for the output so you can get a strong grasp on the mechanics. This is one of those concepts that you can’t unsee once you see it. Multiple bulbs going on at the same time.

(It’s also quite depressing so much time is spent on taxes and the ROI on that time is validated by the math. Both taxes and the time spent on their minimization is deadweight loss. I like markets, I hate structuring and law and tax and basically all the crap that’s probably higher yield to understand. And just going through this exercise depressed me even further because it confirmed how important it is.)

Onwards…

This Jupyter notebook can be run directly in the Google Colab environment.

🔗TLH.ipynb

Open the link and press “play”.

The output will:

Return a summary in the browser of the simulation results

download a CSV to your browser

 

Stepping through the tax-loss harvesting (TLH) simulation

This simulation models a tax-loss harvesting strategy applied to a hypothetical stock portfolio over a 12-month period. The objective is to demonstrate how TLH can potentially reduce taxes by systematically harvesting losses on individual stocks while maintaining the portfolio’s market exposure.

Key Steps and Mechanics of the Simulation

1. Portfolio Setup: $1mm long equity portfolio
   • The portfolio consists of two parts:
     • Long Positions: $1,300,000 allocated across 100 individual stocks.
     • Short Positions: $300,000 allocated across another 100 individual stocks.
   • Each stock in the long portfolio has a starting price of $100 and is equally weighted. Each stock in the short portfolio also has a starting price of $100 and is equally weighted.
2. Return Simulation:
   • Every month, the returns for each stock are randomly and independently generated based on a normal distribution with:
     • Mean Return: .80% per month (approximately 10% annualized compound return).
     • Volatility: 10% per month
3. Monthly Rebalancing for Tax-Loss Harvesting:
   • Harvesting Criteria: At the end of each month, the simulation checks each position to see if it meets the tax-loss harvesting criteria.
   • Long Positions: If the price of a long stock falls below its cost basis ($100), the position is “harvested.”
     • Harvesting Process:
       • The position is closed, and the realized loss is calculated based on the difference between the cost basis and the current price.
       • This loss is recorded as a crystallized loss, and the realized loss amount is added to the cumulative short-term losses for the portfolio.
       • A new stock is bought in its place with a fresh cost basis of $100 using the harvested amount. Since the original position suffered a loss, the proceeds will be less than $13,000 ($100 * 130) worth of shares. Since the new stock is also $100, the share quantity must be less than 130.
   • Short Positions: If the price of a short stock rises above its cost basis ($100), the position is similarly harvested.
     • Harvesting Process:
       • The position is closed, realizing a loss based on the difference between the cost basis and the current price.
       • This loss is recorded as a crystallized loss, and the amount is added to the cumulative short-term losses.
       • A short position in a new name is established to match the notional amount of the covered position. A fresh cost basis of $100, but the short share quantity will necessarily be less than 30 shares.
   • No Harvest for Profitable Positions: Positions that remain above (long) or below (short) their cost basis are not harvested and continue with their updated prices and fixed share quantities into the next month.
4. Tracking Results:
   • For each month, the simulation tracks:
     • Monthly Short-Term Losses: The sum of all realized losses from harvested positions within the month.
     • Cumulative Short-Term Losses: The running total of all realized losses harvested up to that point in the year.
     • Monthly Tax Benefit: Calculated as the monthly short-term losses multiplied by a specified long capital gains tax rate (assumed to be 23.8%) since that’s what they will be used to offset.
     • Cumulative Tax Benefit: The running total of tax savings from all harvested short-term losses over the year.
5. Assumptions:
   • Consistent Cost Basis: Each new position, whether long or short, always has a fresh cost basis of $100, regardless of the prior stock’s price at liquidation.
   • Monthly Frequency: The portfolio is evaluated and rebalanced for TLH at the end of each month, meaning opportunities to harvest losses are considered 12 times over the year.
   • Independent Stock Movements: Each stock’s returns are generated independently of others, with no correlation among stock prices.
   • Equal Allocation and Reinvestment: Both the long and short portfolios are equally allocated across the stocks, and any harvested amount is fully reinvested into a new position with the same initial investment amount.
   • Static Portfolio Size: The portfolio maintains 100 long and 100 short positions, with new stocks replacing harvested ones to keep the portfolio composition stable.
6. Output:
   • At the end of the simulation, the following information is displayed:
     • Detailed Monthly Summary Table: Includes individual stock performance, crystallized losses, and other details for each stock every month.
     • Month-End Summary: Shows monthly and cumulative short-term losses and tax benefits. This provides insights into how the strategy’s tax benefits accumulate over the year.
     • Overall Portfolio Statistics: Total portfolio gain/loss, gross return, accumulated short-term losses, and the final tax benefit as a percentage of the initial portfolio value.

Summary

I address a few of the real-world considerations further below.

But to put the value of this concisely:

Making a $100k capital gain on an investment is not as useful as making $300k with $200k of short-term losses even though the net is the same.

[Notice how you might not have enough capital gains to take advantage of all these short-term losses. Which is why a strategy like this is especially useful for investors sitting on concentrated profits — they can work out out of it much with smaller tax impact. Holdings can be used to collateralize shorts with portfolio margin!]

Real World Considerations

• In practice, a TLH strategy would seek to rebalance into names with similar characteristics (whether by factor, sector etc) to avoid wash-sale rules. In the simulation each stock has the same vol but this proxies using equal-vol weighting in the real world.
• There is a cost of leverage although it is partially offset by the short-stock rebate on shorts.
• Note that as the market rises, the number of positions that are underwater declines. Names you recently rebalanced into will have a better chance to experience loss to harvest than a name that you have been holding for years of a bull market. However the levered version of TLH which includes shorts and longs offers far more opportunity to harvest than a long only portfolio which might have very few losers after several years.
  • As time passes the surface area for loss harvesting stabilizes towards something of a steady state.
• The strategy is meant to maintain a 100% long exposure (130 long vs 30 short) so if volatility increases it is still bad news. But the tax loss harvesting portion can actually benefit from higher volatility so there’s a natural buffer.
• Monthly turnover means slippage costs. Those will increase with volatility.

Wrapping up

I leave it to you to decide how interesting this all might be.

(I find it compelling but still taking it apart.)

I’m not a tax expert. I’m not a simulation expert. Hell, it was a long battle with ChatGPT to get that code to a place that felt right. (It was about 50 iterations of “run code”, “pivot table the CSV data”, “see if the lifecycle of trade/rebalance/accounting made sense”, “tell ChatGPT how the desired behavior of the code diverged from the actual behavior”, “repeat”).

Example of stitched together images of pivot tables to investigate:

• This one allowed me to see crystallized losses by month. You can see how they decline over time. That’s because as the market rises the further stocks are above their cost basis which means less opportunity for harvesting. This would be much more pronounced if there you did not employ shorting.

This table lets you see price paths

 

Position and portfolio values over time

I hope some of you will get your hands dirty with this as well. I want to know what I’m missing or flat-out misunderstanding. Even placing sane error bars around the real-world considerations would be helpful.

When you learn a new language you ascend levels of competence. You start with everyday words and basic grammar with a goal of at least being comprehensible. This will unlock simple conversations, a milestone that enables and encourages regular practice. Like getting through the first month of guitar when you just wrestle with physical finger placement and learning your A, E, G, C, D open chords. The moment you can fluidly switch between the chords you can play the majority of music you hear on the radio. That’s when your learning takes off.

With enough practice in language or music you can think outside your native tongue or improvise in real-time. In skill acquisition terms, you have achieved “unconscious competence”.

Options are the language of risk

You start by mapping basic vocabulary to real-world sensation. “Long call” = “happy if stock go up”. With practice, you develop taste in how to use this vocabulary. In English, “I know how you feel” expresses empathy but you don’t say that to someone who just lost a loved one both because it’s not true (you don’t know how they feel) and because this comment awkwardly makes the moment about you. Well-intentioned commiseration clumsily executed.

Trade structuring can be clumsily executed too. You think something could double in the next 6 months and you…sold puts? If you are right, congratulations on making a tiny bit of cash on direction but opportunity costing yourself a small fortune by expressing the trade with a short vol position when your entire thesis insinuates orgasmic levels of vol!

You touched options without understanding that they are always about vol. Like knowing the words but not how to use them. You’re still stuck at “unconscious incompetence”.

Before you go further it will take 4 minutes to review this idea directly…see Translating to “option surface” language

Wizardry

You have a book of spells. You desire stealth so you look up “cloak of invisibility” and see you need some a salamander tail, a runestone and peroxide to conjure it. Common ingredients in a sorcerer’s home.

In investing you have a sense that a stock is “probably going higher but if I’m wrong I’m really wrong.” That’s a natural language description of a somewhat routine scenario. You need to translate that to code. Option code.

• What instrument is levered to the “probability that a stock goes up”?
  • A call spread (if this is not clear, review a deeper understanding of vertical spreads)
• What instrument is levered to “if down, magnitude is yuuuge”?
  • A far OTM put

So the package of “probably going up, but if down, then down big” can be represented in option language as “long a call spread plus a teeny put”.

That’s the name of the spell.

Now, as a wizard you can adjust the size, ratio, and particular strike to both

a) suit your taste and

b) take “what the option market is giving you”.

What the option market is giving you

This ties right back to the most underappreciated concept in trading….is it bid or offered?

If you believe that an asset’s upcoming moves are distributed so it probably goes up but if not it crashes, you know what spell to cast. That’s half the battle.

Next question:

Is the option market offering you the ingredients for the spell at an attractive price? Or is every wizard trying to cast the same spell and bidding for the same ingredients?

We are going to look at a couple spells and their cost of ingredients.

You’ll learn how to measure price, decompose recipes, and if you read between the lines you’ll notice — even when no trade is indicated you receive a valuable update to your assumptions or an opportunity to express the bet in a way that exploits the price anomaly. There’s a beautiful yin and yang to options. If the cost of a one spell gets expensive there’s a counterspell that gets cheaper with respect to your view!

Let’s get to some data.

Setup

We are going to look at 2 spells that can make sense for betting on “probably going up, but if down, then down big”. An interesting thing about this distribution how its kinda the base case distribution for SPY right? Both history and the options market agree that the SPY proposition looks like “hey you’ll make some money on average and every now and then there will be a big drawdown. also, don’t expect the market to crash up overnight”.

[💡That fact in itself is useful because if you think another market conforms to that distribution you know how the SPY option market prices that spell! You may even adopt a prior that the SPY price for that spell could be an upper-bound or be considered “expensive” if another asset started trading that way. If that’s hard to understand, don’t sweat it, it’s a slightly more advanced inference that a relative value trader might make.]

We are going to price the spells and look at charts.

• Our universe will be: SPY, TLT, GLD, USO and BITO
• Our date range is almost 4 years (Jan 2021 – Nov 15, 2024…except for BITO which start 10/21)
• We are looking at the closest listed expiry to the 90-day maturity (based on what option chains are listed there is about 15% tolerance on DTE around 90 but BITO chains are more sparse so DTE can vary by as much as 30 days or 33% tolerance)
• We are choosing strikes closest to breakpoints that are 1 or 2 standard devs OTM depending on the spell. If we measure strike distance with standard deviation we are using the ATM vol to define the SD. So if 2 assets are both $100 a 1 standard deviation OTM call will be further away (ie a higher strike) in the higher vol asset. This concept is detailed in these series of posts:
  • Breakpoints
  • Ratio’d

There will be lots of little observations along the way so in the spirit of “enough with the rulebook, let’s just play”, we’ll proceed.

Learning the method via SPY

The first spell we’ll examine is a package of:

1-s.d. / 2-s.d. call spread

+

2-s.d. OTM put

measured as a percentage of the spot price.

For example, on 1/12/2021 the details were:

The package cost 1.16% of the spot price on that day.

Exploring the long call spread + long far OTM spell

Let’s move on to charts. I will point out notable features of each chart. I’ll point out the bits of learning in each one.

Chart 1

🌙The red line is the price of the package. It ranges from about 70-150 bps for a 3-month expiry

🌙The blue line is the ATM call as a percentage of spot. If you remember the straddle approximation formula you know that by dividing the spot price out this measure will track implied volatility.

🌙The package is long volatility (you lay out premium for it) so it’s not surprising that its price is correlated with the vol level. You also see that the package price itself varies between something like 20% and 50% of the ATM call value. When vol is low, it’s a higher percentage of the call price. Ponder that for a moment. It means OTM options are a bit “stickier”. Skew as a percentage of ATM vol (aka ”normalized skew”) flattens as vol increases and steepens as vol softens. We’ve talked about before in the context of conditioning skew percentiles on vol levels by using scatterplots to see that skew has a curved relationship to vol.

Chart 2

🌙Same data in scatterplot form. You can see the correlation clearly (r = .76). Red dot is the recent observation (11/15/24). The package price is high compared to the vol level. I don’t like to anthropomorphize markets and say “the vol surface agrees with the statement that the market is likely to go up but if not crash” but the price of that expression or spell is high. The market is not offering it cheaply.

Chart 3

🌙Lots of smoothing — dark red line is the 200d moving avg of the package. The dark pink is the rolling 200d standard deviation of the package price. Dashed pink is the 200d MAD (mean absolute deviation — just another measure of deviation. If it’s close to .80 then the distribution of the stat is close to normal, if it’s less it’s fatter tailed. It costs nothing to add this metric so I tend to do it whenever I look at a standard dev just in case there’s a disparity from .80. The dashed light blue line shows that its stable at .80 even if it’s hard to see on that axis).

🌙The dark blue dashed line is the z-score of the observation vs a 200 day lookback. You can see the spike on 8/5/24

Chart 4

🌙I was just curious so I threw up a scatter of the package price vs the trailing 1-week return. It gets cheaper when the market rallies but that’s just the spot-vol effect. On large moves either way it does look like there’s some modest smile effect where even on a large up move the package is more likely be on the more expensive side. Maybe the most interesting part of the chart is the X-axis itself. You can see the left skew of weekly returns. There are more large up moves than down moves but 2 largest moves are negative and out of bounds compared to the rest of the blob.

Decomposing the spell: the price of the ingredients

Let’s look at the building blocks of the spell to see what’s driving the value of the package.

🌙In 2022, the bulk of the package price was driven by the far OTM put. But you can see some small stretches like October 2022 when equity markets bottomed for the year (lows that haven’t been touched since) where the call spread was actually more expensive than the puts! We’ll chart the ratio of the package to the ATM call price directly to see that the package got cheap relative to the vol — again the skew flattening (shaded region was Oct 2022 lows). In this case, it was the put skew especially that got hammered.

 

[I remember 2022 in vol markets as the year tail and defensive strategies massively underperformed because funds had boughts puts at the end of 2021, a frothy year in from which a sell-off was actually consensus. If a sell-off is stabilizing, and restores order to the force, the skew is not gonna perform.

Markets are biology not physics he shouts into the void. It’s poker all the way down. Notice how I don’t try to create rules from what has happened or play technical analysis. I just look for prices that are anomalous, which doesn’t mean wrong, it just means it deserves attention. From there you have to like think and stuff. Get used to sitting in paradox. There is no closure. Markets are utterly indifferent to your need for coherence.

For what it’s worth a similar setup happened in late 2018 — vols ripped into the Q4 sell-off but skew got crushed because of a large overhang of recycled downside vega from the autocallable structured products. There was an inflection point in the open interest though so a clever cat would realize you could load up on the hated, supplied region of the skew because the market’s outstanding vega profile would flip on a second leg down (ie well supplied to flat).

So what do you do there? One thing that you can do (and it worked) was buy the puts and hedge on a heavy delta in expectation of a rally. If it rallies, you win on the outsize delta plus the puts sliding up the skew curve as they become further OTM. If the market sells off into the abyss where the open interest and market-wide greeks quickly decay you are long vega into an exploding vol market. It was one of the easier risk-reward setups that comes along every now. I know I’m gonna get emails from retail traders asking about how you can plug into this and I’ll just preempt it now. You can’t. It’s an information game the propagates out from the OTC market and the all the risk reshuffling that gets farmed out in chunks to institutional vol desks. At some level it gets “into” the listed market but the scent has to be picked up higher in the chain of issuance to understand the arrows, dependencies, and inflections.]

Exploring the 1 sd collar spell

The next spell is the 1 sd collar (also known as a risk reversal or god help you if you trade commodities, “a fence”)

Long 1-s.d OTM.put

–

1-s.d. OTM call

Notice how this is put “minus” call. that means if the collar trades for a positive value then the put price > call price.

Again we will divide the package price by the spot price to normalize and we are still using 90 day maturity.

Let’s use USO, the oil etf. Instead of pointing out what’s interesting in each of the charts I will cherry-pick questions so you can practice.

USO:

Chart 1

🌙What does it mean that the collar price sometimes goes negative? Do you think you can spot the Ukraine invasion in the chart?

Chart 2

🌙 The collar price can get extreme in favor of the put OR the call at high volatility. Why might that make sense for oil?

Chart 3

🌙The chart starts in January 2021. The puts started the year high relative to the calls declined over the course of the year then flipped in 2022. What’s the story behind the change?

Chart 4

🌙Compared to the SPY chart’s x-axis earlier what do you notice about oil’s weekly returns?

 

The decomposition of the collar into the separate call and put legs will let you see the drivers.

In early 2022, you can see the put leg was relatively stable after peaking at the end of 2021 but the collapse in collar pricing was driven by the calls exploding higher.

🌙Can you see the Oct 7, 2023 Hamas attacks in the chart?

SPY collars

I’ll just point out the animal spirits in the stock market. SPY collars are relatively cheap these days.

The contribution of the legs to the cheapness looks balanced. Puts are a bit low and calls are a bit high.

BITO collars

BITO is an etf that tracks BTC futures. It’s performance is marred for the same reason VXX stinks. Negative roll returns as the prompt future must be rolled into a premium 2nd month in a contango (upward-sloping) futures market.

You can see the effect clearly in the BITO cumulative return chart but it’s still useful to see the point to point changes. Even though the cumulative return since its inception is -35% you can see that in the past 2 months its surged from -60% to -35% mirroring BTC giant rally.

Interestingly, it looks like when they were first listed, the calls were premium to the puts (negative collar prices) before establishing the familiar regime we see in many assets — the puts are premium.

[More story time: In the mid-aughts during the commodity “supercycle” there was a lot of noise about commodities as an asset class, financialization, yada yada. I was on the NYMEX floor when WTI first breached $100. IIRC a trader overpaid for a one-lot in the pit lot to own the memorialized print.

Anyway, call skew in the oil markets got annihilated with financialization. Why? Because to passive investors, calls are free money to be sold. So as something becomes financialized you have some prior about what will happen to the call skew. Something to consider for all those bulled up on IBIT calls these days. I’m not exactly sure what happened in BITO when it got listed, but maybe the first option quoter was still wearing footie pajamas back when commodities became an asset class.

As you can see from the USO charts, the skew in oil is fascinating because barrel prices can crash down like during COVID or up. From a long history of trading oil, owning the call skew has been positive expectancy because hedgers are a steady supply of upside-vol yet the true distribution includes crash-up potential.

This was also true in heating oil and gasoline. Gasoline is a smaller market than crude oil and less liquid. My approach to OTM calls in RBOB — buy em’ when they’re cheap, only sell them closing. Never open on the short side “because they screen high.” Same attitude in nat gas or anything that gets real sloppy on the upside as liquidity evaporates. There’s probably edge in being short because that’s where you’d expect the risk premia to live but the risk/reward and survival imperatives mean only those messing with other people’s money get wide-eyed at that stuff. I guess you could always trade it small but also maybe find other ways to deal with your boredom.]

Spellbooks

I’m not going to show charts for each asset but just summarize just show this self-explanatory spellbook.

You could imagine columns for decomposing the legs and their stats. On and on. You could see how one might want 10 screens.

[We don’t have this in moontower.ai but we’re excited about all the wood we’re gonna chop in 2025 😉]

Actually on the 10 screens bit — a normal human thinks that sounds like a nightmarish work environment. But you have to have some faith that this works just like walking and chewing gum. You get used to it. Plus all the dashboards are the survivors of the evolutionary screen-space tournament. What’s left is a bunch of tables and charts that your eyes easily scan and process into a gestalt. It’s like the market is having conversations all around you but a well-constructed cockpit will simulate the cocktail party effect — you’ll identify the most prominent features of what market parameters are changing today. This is the “science” part. The actions are the art.

Wrapping up

Some repetition and a smattering of observations that might not be obvious to learners

• Using 90 day options means we need to multiply the typical cost of these structures by 4 to annualize
• Assets vary in what a “typical” vol surface looks like. SPY always has a pronounced put skew. Coffee and VIX have inverted skews. Gold is usually a true smile with both far OTM puts and calls trading at premium vols to ATM. The same spell therefore varies in cost across markets.
• Decomposing spells will let you see what legs are driving the cheapness or expensiveness of the package.
• In keeping with the “options as a language” metaphor, volatility metrics like “implied vol” and “skew” can be seen as a abbreviations. Like if the put skew is high, the price of the collar will be high. But when you trade you don’t trade “skew” or “vol” directly but you trade contracts with actual prices. The vol metrics are handy ways to normalize in the same way that P/E is an attempt to normalize for comparison. But you trade actual instruments not metrics. As an option user, you open an option chain but its gibberish. Vol surfaces and metrics help you make sense of “all the numbers” on the screen with curves and historical context. We use the metrics to zero-in on what we want to do, but for execution we shift gears back into prices and strikes not vols and deltas. These spells are like a transformer layer between vol lens thinking and price thinking.
• You have an opinion about the market, but is your opinion bid or offered? The options market can tell you. Remember stock and asset prices in general are low resolution. Options are surgical. Just like parlays, the relative prices between the propositions imply tradeable ideas. Options are the coding language in which ideas are scripted into trades.