Part of the challenge in talking about options with people, and frankly with writing this blog, is taking a topic that few people understand and trying to explain it to people who are smart-but-unfamiliar.

Here’s an example. A couple of weeks ago, I had coffee with a buddy of mine who asked if I use leverage. But that wasn’t his real question-behind-the-question. He really wanted to know if I was taking on an insane amount of risk where I could possibly blow up. He works in finance, but not specifically in the options or derivatives realm. He just knows I left my very stable career to trade options full time.

This, of course, led to a situation where I gave him the right answer that was also the wrong one.

“Yes. Of course.”

I saw his eyebrows shoot up. He looked a little panicked and concerned for me. That’s his James-is-going-to-lose-all-his-money face.

See, I was worried about overexplaining, because that’s when people’s eyes usually glaze over.

I didn’t feel like getting into a rambling explanation of how just because you use leverage doesn’t mean you’re taking on leverage risk because it’s all about your notional exposure. Like if the options you’re trading are 10:1 leverage but you only use 1/10 of your funds, you’re not taking on any more risk than trading without leverage.

See? You’re already falling asleep.

Nor did I get into how I don’t trade on margin, because that’s a thing you use for stocks, which I don’t touch, whereas options work on buying power reduction.

Again, you’re falling asleep.

What he really wanted to know is could I lose all my money in 1 big black swan event? Because every story about every trader, hedge fund, or prop firm that got destroyed always comes down to the misuse of leverage, which often gets simplified in people’s minds to simply “leverage.”

For example, I originally thought to write this post the week after the Situational Awareness blowup.

If you’re not familiar, I can explain the Situational Awareness hedge fund implosion in one (run-on) sentence: A 24-year-old former FTX (yes, that one) employee who was fired from OpenAI used a viral essay to raise $45 billion for his hedge fund despite no money management experience and lost 78% of it after making an absurd 4:1 leverage long/short bet on AI that was mathematically guaranteed to fail, which he would have known had he simply asked Claude.

The financial world is wild sometimes.

Now, you should never believe anyone who says they can’t blow up. There is always a way things can go wrong.

But I chose to construct my 0DTE portfolio in a way that makes the single-day blowup impossible:

1. All strategies have defined risk at entry. Meaning they’re hedged off as soon as they’re entered and the max loss is already known and accounted for.

If one of my strats loses by 50 or 1000 points, it makes no difference. Except in the latter case, I end up having an amazing day because…

2. Hedging strategies are an active part of my portfolio. Every day, I have more long options than short options. That includes tail hedging, which makes money on huge up or down moves.

There’s no better feeling than having a great day when the market tanks.

3. I built my portfolio with a risk-first approach. I have 34 different strategies with medium, low, or inverse correlation (they can’t all win or lose on the same day) and they’re sized in a way that inherently prevents a single-day blowup.

The worst realized max loss day was -1.03%, but the worst possible max loss day (taking my most active day and calculating the loss if SPX ended up in the worst possible place) would have been -2.44%. (Whether or not that is ever realistic is up for debate because, in all likelihood, the day would have traded differently and the same trades wouldn’t have opened, probably.)

None of this is to say I can’t lose. Far from it.

But if I do, it would be death by a thousand cuts and live to trade another day, not a South Park aaaand it’s gone moment.

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

Your backtest doesn’t have a brain. It’s a collection of data points, not a sentient being that predicts the future.

That’s not saying it’s worthless. Just that it can’t make actual decisions, especially at the portfolio level.

I run 36 different strategies right now. They all play a different part. Some are short premium. Some are long volatility. Some follow trends. Some are simply hedges.

As a basket of positions, they tend to have low and even inverse correlation with each other, which is exactly what you want in a portfolio. Stuff that doesn’t all just do the same thing, winning and losing on all the same days.

In fact, my portfolio was engineered that way, using backtests of that previous data I mentioned. That’s what good portfolio construction looks like.

But it also creates a new problem. That look of well-thought-out safety hides one thing backtests can’t determine: concentration risk.

Here’s what I mean. You can have multiple strategies with structural similarities, but if they have different signals, they may very well not activate on the same days, usually.

Then all of the sudden, they do.

Let’s take your short put / long market types of plays. You could have one that fires on a trend continuation trigger (if the market goes up, a bet that it keeps going), a mean reversion play (buy the dip), one that’s more of a duration trade to collect premium over a period of time, etc. And potentially multiple versions of each on different, more granular signals.

On their own, the risk parameters look totally reasonable. Put together in a portfolio, it’s a beautiful straight-up-and-to-the-right line on a chart.

Even Claude chimes in saying your risk parameters are totally fine.

Mainly because: they’ve never all fired at the same time and lost at the same time.

So far…

Then some day, they do. And you just set your new all-time max drawdown. (This has happened to every trader at some point, whether they’re big on backtesting or not, myself included. We call it “tuition” but only if you learn from it…)

One of the things that drew me to 0DTE trading was how your concentration risk can’t build up day after day (e.g., selling a 45DTE put every day of the week just means you can eventually have 45 puts get blown out at the same time.)

But it can still happen in 0DTE (or any duration) because it’s impossible to predict which signals could overlap and trigger similar trades in the same direction on the same day. Like too many of any trade around the same time of day, too many hedges where you slowly bleed PL, too many long or short trends that get reversed.

Portfolio-level backtesting and construction (looking at everything together, not just positions individually) is tricky because:

1. You need to do it. Ignoring patterns and known concentration issues is just silly (and a good way to lose all your money). Knowing how you’re most likely to lose is table stakes. But at the same time…

2. It’s next-level survivorship bias. If you make additions to your portfolio strictly based on what your preferred performance metrics are (if you recall, I’m a big Sortino + Ulcer guy), you’re going to fall right into the ‘so far’ trap.

Another way of saying this: If you have a bunch of strategies you created that have always performed well, then of course combining them together is going to look like a killer ensemble. And you can work with the (reasonable) assumption that when a few of them fall apart, the collective whole will still be fine.

But what happens if the thing that makes one of them fall apart makes all of them fall apart at once?

3. Claude is almost no help in this area. The data will always support piling on stuff that worked before. Until it doesn’t. Only then will AI recommend you stop trading something (usually in a condescending tone as if it were obvious all along. You know the one I’m talking about…)

In fact, if you go back and look at any single trader, hedge fund, or investment house that blew up and went out of business, It’s always the same thing: they traded far too big with far too much concentration risk, because their model at the time said it was ok. Because up to that point, it was.

In terms of takeaways, there are two things I like to do:

1. Any time you have potentially stackable positions (like my short puts example, or any other trade type that could accumulate over time or the course of a day with a similar strategy), I like to ask Claude “what’s the most number of these positions that were active at the same time?”

And when it replies, “20, and it made +$42,000!” I like to respond with, “That’s nice, but what would the P/L look like had they all lost?”

2. Backtesting software + Claude made data analysis a million times easier. But what that really does is free up brain space to think about the stuff they can’t do: thinking up not-yet-seen worst-case scenarios and how to avoid them.

Which is the job in trading now.

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

In trading, it’s really, really easy to get data to lie to you.

One great thing about being a trader right now: if you can sift through the bullshit online hucksters (admittedly no easy task), there’s actually a lot of brilliant quants on the internet with sound ideas. It’s never been easier to either learn the basics or dramatically up your game. For free.

And with the help of AI, it’s also easy for the non-mathematician, non-quant to execute those ideas.

Sort of.

Where you can get smacked in the face as a trader is when you follow best practices and methodologies to a T, thinking you’re doing all the right things, yet still have something blow up on you.

Because the real risk didn’t exist anywhere in the data you were using.

This is a big (and not very talked about) challenge for 0DTE traders. There are literally only 4 years of 5-day-a-week data. Which isn’t nothing. But it’s also nowhere near as robust as anyone would like. Meanwhile, people trading commodity futures, for example, can pull 100 years of price action to test and inform their decisions.

Does that mean using a data-driven approach for 0DTE trading is unreliable? Absolutely not.

What it does mean: you can’t use your brain to process all these complex quantitative ideas, then turn your brain off for the simple risks that are staring you straight in the face. Because you’d be trusting patterns that happened in the past to continue in the future. Which we all know, is silly. (Cue the obligatory “past performance does not equal future results” line.)

Portfolio scaling is what I want to talk about today. Taking not just a position, but your entire portfolio, and increasing size to what you feel is an appropriate level of risk…

…and praying you didn’t miss something that blows up in your face later.

I’ll give you a high-level example. I evaluated a strategy recently that looked absolutely amazing on paper (meaning, the backtest results). A short call spread strategy that just never seemed to lose. 95% win rate, solid CAGR, runs in tranches a few times during the day, positive every year, MAR/Sharpe/Sortino all check the boxes.

The problem is (if you’re a trader and saw the win rate, you know what’s coming): the risk-reward sucks.

Risk $920 to make $80 on average. It’s so far out of the money it feels like free money. The classic “pick up pennies in front of a steamroller” trade.

It just never got run over, yet. (That’s a problem, because the data to date would indicate that it’s invincible.)

It lost a few times here and there, but it never had that crushing day when multiple tranches fired and a single market move wiped it out. And certainly not multiple days in a row.

Now, you could argue the strategy is curve fit, but I don’t think it was. Only 2 triggers, the occurrences weren’t abnormally low.

My read is that the data is lying because, well, there’s just not enough of it.

I mention this example because I’ve looked at a slew of different portfolio sizing methodologies. Some of the most sound fundamental concepts of scaling, namely using the Kelly Criterion or sizing based on risk (e.g. Conditional Value at Risk or CVaR), both told me to ratchet this one up. At the same time, they both wanted me to cut back and/or stop trading more consistent performers (ones with reasonable risk/reward profiles) because the backtest metrics weren’t as eye-popping by comparison.

And that’s a consistent theme I’ve seen: some scaling methodologies recommending absolutely idiotic sizing, not just with this strat but with others, too.

Why? Because the data, or rather the results so far, looked amazing to a math equation.

But they just don’t pass the sniff test.

I won’t try to explain Kelly Criterion or CVaR here, but the tldr on why they broke down: both formulas treat your backtested numbers as the whole truth about a strategy’s risk. Neither one could tell the difference between ‘this strategy works’ and ‘this strategy worked so far but it’s Russian roulette.’

So they size up the trades you might be suspicious of, while sizing down anything that’s already taken a dump and proven it can survive it.

The real question: how do you merge the data-driven + a common sense view?

Well, if you’re satisfied that what you’ve built is sized appropriately, you could use the straightforward “double the size of everything when you’re ready to double your risk.” I know how funny that sounds, but if you somehow do suddenly have double the AUM (assets under management, or as non-financial guru humans call it, “money”), you could do exactly that.

But that’s not realistic. Waiting to have twice as much cash before you scale could take a while…

My preference is Priority Queues. More specifically, a queue based on “Correlation-Adjusted Sortino.”

Ok, I’m gonna fess up. 3 months ago, I didn’t know what these were, and I thought I invented fire. Until Claude told me it’s already a thing and has a name. If you’re not getting emotionally deflated by Claude on a daily basis, you’re not even trying.

A Priority Queue primer: Once you commit to proportional scaling, you hit a rounding problem. Let’s say you have a 100K portfolio to keep these numbers easy. At 200K, that’s really simple: double everything as I said above.

But what do you do when you hit 150k? You can’t go from 1 to 1.5 lots on everything. So you have to decide in what order to scale up your positions before you hit that 200k milestone.

The Priority Queue is exactly that: a ranked order list that determines which strategy gets the next additional lot as your money to invest increases.

But how do you determine that order, you ask?

Well, there are a thousand ways to do this. But in my last piece, I talked about how I used the combination of Sortino and Ulcer to determine what makes the cut in terms of portfolio additions.

So I took that concept a step further. I got my ego-deflating friend Claude to build a script that determines ‘marginal Sortino,’ i.e., which position, when given an extra lot, improves portfolio Sortino the most before hitting the next ‘doubled everything’ portfolio AUM level. The calculation accounts for correlation rather than just individual performance: scaling your strongest solo strat first means nothing if it moves in lockstep with five other things you’ve already increased.

If you ramp the ‘best’ individual strats first, you can quickly turn a balanced portfolio into something that’s overweighted with concentrated risk. I make both my portfolio inclusion and scaling decisions based on the impact it has on the portfolio’s risk-adjusted returns, with everything I trade in mind.

Tldr: Be aware of the limitations of your data. Use common sense. Scale based on the effect on the whole portfolio.

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

The (hopefully literal) million-dollar question for options traders: How do you decide what strategies get added to your portfolio and which ones don’t?

It’s not as simple as strong individual strategy performance. (Let’s assume you’ve already picked something that doesn’t totally suck.) Correlation, sequence risk, and capital requirements all have to stack up against everything else you’re already running. Even at the portfolio level, there are dozens of metrics to choose from for optimization. Picking the right ones is its own problem.

I’ll share my own thought process. I want to make as much money as possible, like everyone else. But only after reducing risk as much as I can. Just for fun, I’ll take a dump on 2 of the most overrated metrics, then mention the 2 I prioritize:

Overrated:

• Sharpe. Aka the consensus gold standard of performance metrics for reasons I don’t fully understand.

Formula: (Portfolio Return − Risk-Free Rate) ÷ Standard Deviation of Returns

It measures return per unit of total volatility, which seems reasonable until you realize it penalizes upside and downside volatility equally.

Meaning, Sharpe doesn’t like it when you make too much money and penalizes you for it. You read that right.

• Max Drawdown. Yes, drawdowns matter, and you definitely need to know what the worst-case-so-far outcome is. But optimizing a portfolio around it is dumb.

Formula: (Trough Value − Peak Value) ÷ Peak Value. i.e., the worst peak-to-trough decline in your history.

Meaning, it’s measuring only one moment in time. And ignoring all the other bad periods. So when you “fix” the worst period, you just have a new worst period to try to optimize around.

It’s an endless game of wack-a-mole

My focus:

• Sortino. Aka Sharpe, but for people who like making money.

Formula: (Portfolio Return − Risk-Free Rate) ÷ Downside Deviation

Same concept as Sharpe, but only downside volatility counts against you. Gains are highly encouraged.

• Ulcer Index. Aka “here’s a crazy thought, let’s look at ALL drawdowns, not just the biggest one.”

Formula: Square root of the average of squared drawdown percentages over a period. (Yes, I also have a hard time conceptualizing that one.)

Instead of one bad moment, it captures every bad period, both the depth and duration of all those drawdowns combined.

👉 Here’s the thing: if you optimize for Sortino and Ulcer Index, Sharpe and Max Drawdown will fall into place on their own anyway.

In terms of how I put this into action, it’s simple: for any strategy being considered, Sortino must increase and Ulcer Index must stay the same or decrease. Measured against my entire portfolio, not just the new strategy by itself.

Then I always verify with two independent platforms. My Claude project and my backtesting software (Tradesteward) have to agree. Definitely a measure-twice, add-once type of deal.

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

When I tell people I’m trading professionally full-time now, one question inevitably comes up: “How is AI impacting you?”

I think that’s the go-to opener between Office Dorks™️these days. But when people ask me, the tone feels different. Like they assume AI will blow up the entire trading industry. Billion-dollar institutions will use it to rig the game in a way we can’t, right?

Honestly, it’s the opposite. Remember: the biggest prop firms have already been using machine learning for decades. None of this is new to them.

If anything, AI is a leveling of the playing field. Only if you learn how to *really* use it.

2 tangible examples of stuff I’ve nailed down with Claude Projects in the last two weeks (that kept me from writing more blog posts because trading makes money and writing doesn’t):

1. I automated my hedging strategies. No more game-time decisions, which undoubtedly lead to overpaying for protection you didn’t need.

Many indy traders optimize portfolios by dumping CSVs of their backtests into AI (or software they built with AI).

👉The problem with that approach: Just because Claude knows the results of your trades doesn’t mean it actually knows how the hell you got there.

TradeSteward is my primary software for backtesting and automation. If I use it all damn day, wouldn’t it help if Claude were an expert on it?

So I turned it into a power user. I screen-capped every setting in TradeSteward and had Claude build a Markdown file, then attached it to the project.

Then I took it a step further: I had it build a strategy map, giving it context of every setting that triggers my trades to fire.

In other words, I went beyond simple correlation and made Claude understand how my 40+ strategies cluster and bleed together on bad days. Then we created hedges that reduced those losses, depending on which trades fire.

2. I created a series of “boost” strategies to double down on trades when the probabilities are better.

Think of it like playing blackjack in a casino. When the count is in your favor, increase your bet size.

That may be simple enough for an experienced backtester to do across a few strategies.

But to do them on literally every strategy you have, including detecting which strategies don’t benefit, then re-testing them to make sure they also benefit the full suite together, not just as a single strategy…that’s a lot of goddamn iterations.

Like more than any human can manage. Either from the sheer volume of it or just remembering what the hell you did and didn’t test yet.

This is several months of work completed in a week and a half.

👉Here’s the thing: No matter what field you’re in, you can make your job ten times more efficient by doing the same thing. Whatever software you use to run your day, make Claude an expert in it. Then it’ll solve your problems for you.

And it should go without saying: no, do not blindly follow Claude or any AI to the point where you’re letting it completely make your investment decisions for you.

Disclaimer: This is educational content, not financial advice. I'm not a registered financial advisor. Trading options involves substantial risk and isn't suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can't afford to lose.

Subscribe now

But I’d still appreciate y’all reading. If you’re new, subscribe:

If backtesting is the superpower that takes you from being yolo trader just gambling to a systematic trader who wins consistently, then your kryptonite is overfitting.

Also known as curvefitting. Also known as cherry picking data. Also known as lying to yourself.

A disclaimer before I get into it: no one will ever agree on exactly what constitutes overfitting, and it’s virtually impossible to avoid completely. Traders love to argue over this sh*t.

When you’re backtesting, all you’re really doing is trying to find signals and trade structures in historic market data that consistently make money. For example, if a trade seems to work great when the market trends down in the morning but sucks when the market rips up, you might want to only trade on down mornings.

But then you keep testing. You notice the trade gets even more profitable when volatility is high. And the market trended up the day before between 1.1% and 1.3%. And it’s a Thursday. And the Cubs play a day game.

Your sample size went from 1000 trades to 12. And those 12 won every time!

Yet all you did was find more and more obscure reasons to remove the bad trades from the data. That’s all overfitting really is.

And when you fine-tune strategies to such an extreme degree based on things that happened in the past, your trade can’t adapt to what the market does in the future.

It’s like studying for a test back in school. You could not bother learning the material and just memorize all the answers for the practice test. But then you’d fail the real test when they asked different questions.

The scary part is: you can do this by accident. Finding conditions that improve your trades is the entire point of backtesting. And seeing the results get better and better as you add filters is totally addictive.

Some warning signs of overfitting:

👉Your equity curve is too perfect.

Up-and-to-the-right charts are beautiful. If it never loses or has bad streaks, you’ve simply plucked out all the bad days. That won’t happen in live trading.

👉You have too many signals & filters.

Two or three? That might be legit. But a dozen? No way those results are real.

👉Your reasoning makes no sense.

Trading is complex. You may never know exactly why some trades work.

But “buy calls when VIX is between 14.3 and 14.8 on the second Tuesday after a down Friday in months that start with M” is clearly nonsense.

👉It’s fragile as well. Tiny changes blow it up.

If a trade works at 10:20 but falls apart at 10:25, that’s an issue. Or if your trade works with a 30 delta strike but falls apart with 29 or 31 delta, there’s no edge, just noise.

Real edges are robust; they don’t fall apart with tiny changes like that.

Anyway, that’s your overfitting primer. I’ll get into more specifics next time. Enjoy the weekend.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

An easy way to cut off online trading hucksters at the knees: Ask them for a backtest that proves their work. If they’re frauds, you’ll hear crickets.

If you’re not already a trader, you probably have no idea what the hell a backtest is. (Every time I tell a non-trader I spend most of my day backtesting, they look at me like I have two heads.)

Backtesting is a critical skill for systematic trading, so I’m going to spend a few blog posts on it.

The boring definition: A backtest is a simulation that applies a trading strategy’s rules to historical market data to calculate what the performance would have been if you had executed that strategy during that time period.

In other words, if I had a time machine, what trades would have made me a gazillion dollars?

There’s an obvious flaw in that. As anyone who’s ever read the fine print on any financial prospectus can tell you: “Past performance does not guarantee future results.”

That’s true. But a backtest can definitely tell you what totally sucks and does not work.

It can also tell you what only worked for a short period of time. And how likely you are to blow up.

Before you think “there’s no way I can do this,” keep in mind: Software does the heavy lifting. It takes your strategy inputs (structure, entry signals, timing, sizing), runs them through historical data, simulates execution (fills, commissions, slippage), and spits out performance metrics (returns, drawdowns, Sharpe ratio, etc).

Then it’s just a matter of coming up with ideas. Finding things that work. Throwing out anything that doesn’t. So easy, a former recruiter could do it.

Of course, there are a ton of ways backtesting can go wrong: overfitting, survivorship bias, ignoring transaction costs, insufficient sample sizes, unrealistic execution assumptions, data quality issues, etc. Which, thankfully, gives me an endless number of things to write about in the future…

But if you can find a successful strategy that avoids those types of pitfalls, that’s where you can make money in a reasonably-reliable-and-not-completely-luck-based way. (See obligatory disclaimer below.)

Back to the online hucksters. YouTube, Instagram, and TikTok are flooded with people pumping out trading advice. A lot of it even sounds plausible and is based on trading fundamentals.

Yet the devil is in the details. Financial content creators don’t even have to be good at trading to make money. They can simply charge you for access to their community or just make money off the social engagement. Even if they claim that they made some crazy return for a year or two, and it’s actually true, that doesn’t mean they did it in a way that’s actually repeatable and eliminates blow-up risk.

Anyone who can’t show the receipts isn’t worth listening to. Creating those receipts via backtesting is the real work in systematic trading.

More to come…

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

When I tell people I trade full-time for a living, they usually start asking me what I think about a stock they’re trading. Then they’re a little shocked to find out I’ve never traded a stock in my life. (Long ETF holdings notwithstanding).

Instead, I trade systematic options positions (both 0DTE and longer duration) that have a quantitative signal and have been extensively backtested with historical market data. For example, I have a bullish call butterfly strategy, a bearish call spread strategy, a neutral iron condor strategy, etc.

(I actually trade WAY more than that. As of this writing, I have 30 strategies that could execute on any given day.)

Why do I bother with options instead of just picking stocks?

Because options strategies are assets, too. Just like stocks, gold, Bitcoin, or real estate. Except you can actively determine the outcome.

From an investment perspective, what really makes something an asset?

👉A return profile. What’s a reasonable expected return?

👉Measurable volatility. What kind of swings should you expect, and how much can you stand to lose?

👉Historical performance you can backtest. Can you use data to inform your decisions?

👉Liquidity. The ability to allocate capital at a size that’s right for you. And the ability to get out of the position when you need to.

Sure, NVIDIA and Apple have that, but so does my bullish call butterfly.

The only difference is that stocks are a theoretical ownership share of a company that exists in your brokerage account, whereas option strategies are truly your intellectual property. Mine exist in my head and in the automation software I programmed them into.

With stock, I say ‘theoretical’ ownership because unless you have the bankroll to control the board or angle a hostile takeover, you don’t really own anything. You’re along for the ride.

But with systematic trading, you really do control everything: The trade structure. The quantitative triggers. The position sizing. The risk parameters.

And most importantly, how they all fit together.

Back to the 3 trades I mentioned.

The bullish call butterfly wins when the market trends up.

The bearish call spread strategy wins when the market trends down.

A neutral iron condor strategy wins when the market stays in place.

I’ve been running these strategies for the past year, and the live results very closely match the backtest data that extends to 5/16/2022 (the beginning of SPX daily options) used in this chart.

The return is WAY larger. With 1/3 the drawdown. And it doesn’t matter which direction the market goes.

I’ll take this 0DTE options ensemble over buy and hold $SPY any day. In fact, I do…

(Note: there are pitfalls to overfitting backtest data too perfectly into a portfolio view like this, which I’ll discuss in a future post.)

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

Finally back from the holidays, you guys. You know what I didn’t do with my time off?

Lose $50 million on Christmas Eve following some online trading huckster.

If you haven’t heard about the Captain Condor blow-up yet, here’s a summary: Thousands of retail traders paid some schmuck $5,500 a year (each) to access his private community to copy his trades that had a 100% mathematical guarantee of a catastrophic loss.

Yes, you read that right. Just in time for Christmas, no less. Full story here.

This is a story about both the perils of copytrading a moron and how course-selling, community-building, BS-merch-peddling influencers have no bottom.

First off: the trade itself didn’t even matter. (It was a bunch of iron condors, but could have been anything.) The Martingale betting system, however, guaranteed failure. (Full breakdown of the implosion here.)

The Martingale, if you’re not familiar, is most commonly associated with roulette. It goes like this: When you lose a bet, double your bet size. If you lose again, double it again, again, and again, until you eventually win back your original, single bet.

Your bet sizing looks like this: 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, etc., to infinity.

I hope that sounds insane, because it is. You take on exponentially growing risk just to get back to winning 1.

To be fair, Martingale has a 100% win rate…if you have literally unlimited money.

Without that, it has a 100% blow-up rate. Eventually.

Now, some people don’t believe that. They think the odds are in their favor after a losing streak, because you have to win eventually, right?

And that’s the Gambler’s Fallacy. The belief that after losing 9 coin flips in a row, the 10th just has to be a winner. Because you’re ‘due.’ The odds of 10 heads in a row are so small!

That, of course, is wrong. Every flip has the same odds. The unlikely thing (9 losses in a row) already happened. It’s in the past. The next bet has the same odds as always.

👉Over a long enough time frame, there WILL eventually be a streak of losing trades long enough to lose your entire bankroll running a Martingale. (Assuming you didn’t wise up and stop playing.)

That happened on December 24th. Captain Condor’s followers lost an estimated $50 million. Merry Christmas.

But here’s the bigger story. Yes, Captain Condor allegedly lost, too.

But…

👉He got to walk home with the approx $5.5 million/year he earned from his followers by providing his “community.”

That’s one hell of a risk-free return.

But does this actually surprise anyone? This sort of pay-for-access influencer behavior is everywhere.

Self-help gurus sell $5,000 coaching and $997 courses to “unlock your potential,” while their only successful business is selling those courses.

Fitness gurus and biohacking bros sell supplements and courses so you can be just like them. Even though many of them look the way they do because of PEDs and Photoshop.

The ‘get rich flipping houses’ scammers. The dropshipping experts. The social media growth hackers. It’s an endless cesspool that targets people who are desperate to get ahead.

But the Captain Condor story takes the cake. People didn’t just lose this month’s rent, they lost their life savings.

He even said he’d regroup and try again. Why wouldn’t he?

👉The betting strategy failed. But his pay-for-access business model? That worked to perfection.

In 2026, I’m going to continue writing Zero Days To Give because I love writing and I love trading. I think you should, too.

But it is, and always will be, free.

Disclaimer: This is educational content, not financial advice. I’m not a registered financial advisor. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can’t afford to lose.

Subscribe now

How would you like to make an extra $23k for doing the same thing you’re already doing?

(Yes, that’s a bullsh*t question. But stay with me.)

Same starting capital. Same trades. Same edge.

👉The only difference: compounding frequency.

If you’re a passive investor, you probably have no idea what the hell that is. But don’t feel bad. A lot of active traders don’t know either.

Compounding frequency is the rate at which you rebalance your trading size based on the gains (or losses). It has a material impact on your yearly returns.

But first, let’s discuss the difference between compounding in passive (buy and hold) investing vs. active trading.

(Note: we’re going to use 20% return for all examples. It’s aggressive, but doable, especially in active trading. Yet another post for another day…)

👉In passive investing, your unit size (i.e. shares) stays constant. You compound via asset appreciation (stock prices going up).

Buy 1,000 shares of ABC stock at $100 = $100,000. Hold for 5 years:

• Year 1: Stock goes to $120 (+20%). 1,000 shares = $120,000

• Year 2: Stock goes to $144 (+20%). 1,000 shares = $144,000

• Year 5: Stock goes to $248.83 (+20%). 1,000 shares = $248,832

👉In active trading, you compound via position scaling. You realize gains and reinvest them by trading bigger or more positions.

Here’s the same 1-2-5 year time frame. Same 20% annual returns, except we’re showing what happens when you compound your position sizing daily, monthly, yearly, or not at all. That works out to 0.0794% per trading day.

The $23k I mentioned is the difference between compounding daily and yearly over 5 years.

That is the inherent edge of active trading. And 0DTE options more specifically. Because they expire every day, they make daily compounding possible.

So free money by adjusting your position size more frequently. What’s the catch?

👉Drawdowns destroy the compounding engine.

(Which is why I focused on drawdowns in my last two posts, here and here).

I guess it’s not so much of a catch as it is a (hopefully?) blatantly obvious concept.

You can’t increase your trading size when your account shrinks. That $23k advantage disappears if you take on too much risk and have a crushing month that keeps you from scaling your position sizing.

👉Risk management isn’t just a defensive concept. It’s offense. By protecting your capital from drawdowns, you’re protecting your ability to compound.

Disclaimer: This is educational content, not financial advice. I'm not a registered financial advisor. Trading options involves substantial risk and isn't suitable for everyone. You can lose more than your initial investment. Do your own due diligence and never trade with money you can't afford to lose.

And apologies for the math dump in advance. Everything I’m showing below can be calculated very easily with AI, but it’s good to know how the bacon is made.

What if I told you there was a way to calculate the exact probability of losing all your money? Interested?

👉Sweet. Because today I’m explaining Risk of Ruin.

Risk of Ruin is the probability you’ll lose your entire trading capital before your edge has time to materialize.

Not just a bad day or drawdown (you know what that is because you read my last post).

But the probability you’ll actually go broke. Which can happen even if you have positive expected value if you trade too damn big for your account size.

A simplified Risk of Ruin formula assuming equal payoffs (every trade wins or loses the same amount):

RoR = [(1-p) / p]^(C/R)

• p = win rate

• C = Total capital (in units)

• R = Risk per trade (in units)

Yes, I understand this may look like gibberish to you. But it tells you the probability of going broke given your edge, bankroll, and bet size.

Before I plug in numbers, a key insight: Risk of Ruin changes exponentially with position size. (Cut your position size in half, your risk is cut by far more than half.)

Let’s say you have a simple strategy:

• 55% win rate

• Average win: $100

• Average loss: $100

• Starting capital: $10,000

Scenario 1: Risk $1,000 per trade (10% of capital)

p = 0.55 (win rate)

C = 10 (you can lose 10 units of $1,000)

R = 1 (risking 1 unit per trade)

RoR = [(1 - 0.55) / 0.55]^(10/1)

RoR = 13.74%

That’s a 13.74% chance of going broke, which sucks. Let’s scale down.

Scenario 2: Risk $500 per trade (5% of capital)

p = 0.55 (same win rate)

C = 20 (you can lose 20 units of $500)

R = 1

RoR = [(1 - 0.55) / 0.55]^(20/1)

RoR = 1.89%

Risk of Ruin dropped to 1.89%. Still too high.

Scenario 3: Risk $250 per trade (2.5% of capital)

p = 0.55 (same win rate)

C = 40 (you can lose 40 units of $250)

R = 1

RoR = [(1 - 0.55) / 0.55]^(40/1)

RoR = 0.036%

👉Same edge and win rate. But Risk of Ruin went from 13.74% to 0.036% by cutting your position size from 10% to 2.5%.

Personally, I go lower still. Let’s look at my 28-strategy SPX options portfolio that has variable returns (not just full win or full loss), where I use a more complex generalized formula.

My daily portfolio stats (888 trading days):

• Win rate: 70.5%

• Average winning day: +0.31%

• Average losing day: -0.15%

• Worst single day: -1.44%

Then we use these to calculate the inputs for the generalized formula:

• μ = 0.17% (expected daily return: 70.5% × 0.31% - 29.5% × 0.15%)

• σ = 0.34% (standard deviation of all daily returns)

And that Risk of Ruin formula is: RoR = e^((-2*μ*C)/σ²)

• e = 2.718 (Euler’s number)

• μ = average return

• σ = standard deviation

• C = capital units

Let me show you the calculation for 10% risk:

μ = 0.17%

σ = 0.34%

C = 10 units (can sustain 10 losses of 10% each)

σ² = 0.34² = 0.1156

Exponent = (-2 × 0.17 × 10) / 0.1156 = -29.41

RoR = e^(-29.41)

RoR = 0.00000000000032%

Result: 11 decimal places to see a non-zero digit.

Same formula for 5% risk gives 23 decimal places. At my actual 1.44% risk (worst single day above), it’s 85 decimal places.

👉Tdlr: The math says I ain’t going broke.

(It may raise questions about overfitted backtest data, which I’ll discuss in the future.)

So why doesn’t every trader calculate their Risk of Ruin and eliminate their blow up risk?

No idea. People are weird.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

As for me, I’m thankful you’ve subscribed. Now enjoy your bland bird tomorrow. I’ll be eating steak.

When I tell people that I have less risk in my options portfolio than they do in their buy-and-hold stock portfolio, they look at me like I have two heads.

Then they usually ask what I mean by that, to which I reply, “My drawdowns are a lot less. The S&P 500 had a drawdown of ~19% this year. I target 4-5%.” (Then I yammer about position sizing and non-correlation.)

At which point they look at me like I have THREE heads. And ask “what’s a drawdown?”

This concept is critical. Drawdowns are a fundamental risk metric for any active trader. (That crowd is thinking ‘duh’ as they read this.)

But passive, buy-and-hold investors who never realize profits? It’s not even on their radar. It should be.

First, the boring definition of a drawdown: the peak-to-trough decline in your portfolio value, expressed as a percentage.

Or as I like to say: how much did you get wrecked since your last all-time high?

Why is it important? Because the math behind it is weirder than you may realize.

Let’s say you have a $100k account with a 19% drawdown. What percentage gain do you need to get back to that starting $100k?

If you said 19%...you’re wrong!

Now that you only have $81k, you need a 23.5% gain to get back to even.

Here’s what that looks like:

See that 50% line? You lose half your money, you need to double up just to break even. And past that? You’re screwed.

To be fair, if you’re truly never touching that money for decades, AND you’re not using leverage, you can probably ignore drawdowns.

But almost nobody fits that description. 3 reasons why you should understand drawdowns:

1. If you’re in any of those 2x, 3x, 4x leveraged ETFs, you can absolutely blow up your account.

This year’s 19% S&P 500 drawdown was nothing out of the norm. But here’s what happened in those ETFs:

👉2x leveraged ETF: -38% (need +61% to recover)

👉3x leveraged ETF: -57% (need +133% to recover)

👉4x leveraged ETF: -76% (need +317% to recover)

The kicker: the S&P 500’s up 15% this year. SPYU (4x leveraged) is up 17%. Do the math. 4x leverage should = 60%. That drawdown ate 43% of the gains, and it could have gone much worse with a deeper drawdown.

(Research volatility decay & leveraged ETF rebalancing if you’re playing with leveraged ETFs. You’re welcome.)

2. You might actually need that money someday.

New house. Wedding. Family or health expenses. Starting a new business. Emergency. Any and all.

Converting your investments to cash at the bottom of a bad drawdown (which can happen at any time) means you may never recover. Ask anyone who had to do this in 2008. (We’ll call this Life Risk. Yes, I made that up just now.)

3. If you read my nonsense, part of you wants to take active ownership of your finances.

Maybe to trade full-time. Or retire early. Or prepare for Life Risks. Understanding how much drawdown risk you’re taking is the first step to active investing.

Because the first rule of trading is Don’t Blow Up.

Next up at Zero Days to Give: Risk of Ruin, then Compounding, both of which build on drawdowns.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

Last chance to subscribe 👇

Subscribe now

Last week, I talked about how I made over 10,000 trades this year without pressing any buttons. Not a humblebrag. Just an illustration of what’s possible with systematic, automated trading.

What I didn’t talk about is The Why.

For a more mathematical explanation, ask ChatGPT (or whatever your favorite AI is) about the Law of Large Numbers. My layman’s definition: The more occurrences you have, the closer whatever-it-is-you’re-trying-to-do is to its long-term expected value. In my case, trading.

I’ll illustrate. Flip a coin. It has a 50:50 chance, also known as neutral Expected Value (EV).

If you flip a coin ten times, you might get 5 heads and 5 tails, but you might get 6 and 4. Or 7 and 3. In small sample sizes, anything is possible. Uneven runs like that happen easily.

But the more flips you make, the closer it’ll get to that even 50:50.

Bored? Go ahead and try it. Flip a quarter 100 times. Or 1,000. Or 10,000. The longer you go, the more likely you’ll hit that distribution.

But if you’ve been reading along with my nonsense, you know we don’t look for neutral EV, we look for positive EV trades.

Which, in this example, means we’re flipping an unevenly weighted quarter. Imagine a coin that landed on heads 60% of the time and tails 40% of the time.

And you’re betting on heads, so you’re going to flip that thing as much as possible.

That’s basically the Law of Large Numbers in action. Find a trade with edge. Do it over and over. Eventually, the odds fall in your favor.

The problem is, you’re still dependent on that ONE strategy working. Sure, over the course of a year, the Law of Large Numbers will play out. But day-to-day? Week-to-week? You never know when the wrong side of the coin will hit over and over, even if you have edge. You’ll still have losing streaks. And trading bigger to try and make more money will just make your losing days worse.

And that’s why you need an ensemble approach. You don’t want one trade that has edge. You want as many uncorrelated, positive EV trades as possible.

(The uncorrelated part is important. Because if all your coins flip the same way at the same time, you’re just back to having one big bet. True diversification means the results are independent of each other.)

Building on this analogy, let’s say you can only flip that weighted quarter once per day. But each day you can flip a weighted nickel, a weighted dime, a weighted penny, a weighted peso, etc.

The results of each are independent of each other. But they’re all still weighted to give you positive EV over time.

That’s basically how an uncorrelated trading ensemble works.

And the net result: your daily/weekly/monthly P&L is much smoother. And psychologically, you’re not sweating out every moment, hoping that one trade you made wins.

You just let the math do its thing and go about your day.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

I made over 10,000 trades this year. You probably have two questions:

1. How the F did you make that many? Weren’t you working a full-time job most of the year?

and

2. Why the hell would you do that?

We’ll answer question 2 in the next post when we discuss the Law of Large Numbers and ensemble trading.

The quick answer to question 1: because discretionary trading is dumb. Once you go down the systematic trading rabbit hole, you might as well automate everything.

If that was jibberish to you, I’ll define the terms:

👉Discretionary trading is when you decide, in real-time, what to trade.

How to enter it, size it, when to exit, etc. All based on “gut feel” and experience.

Basically gambling. You read some nonsense on Wall Street Bets or you ‘like’ a stock and do what ‘feels right.’

And probably lose your money. Unless you happen to ride the current bull run and mistake luck for skill.

(Counterpoint: There are professional discretionary traders who are great. But they’re well-trained domain experts in specific niches. They follow systems that can’t be easily coded because of too many variables.)

For regular people, discretionary trading doesn’t work because you’re the weakest link in your own system. You get tired. You have limited focus. You get greedy. You panic. And you never know if you’re lucky or good.

👉Systematic trading is when every trade parameter is predetermined based on backtesting and portfolio analysis.

Entry & exit conditions, size, technical signals, market movement, etc. If the conditions match, the trade fires.

No judgment calls or emotion. No manual overrides. Everything has been planned before the day started. Any audibles would just make things worse.

Systematic trading solves the most common problems:

1. Greed: If you’ve already tested the optimal size and profit targets, there’s no game-time decisions to make.

2. Panic: The opposite problem. You’re not going to win every day, but if you’ve determined your risk parameters and eliminated catastrophic losses, you know that letting things play out is the best move.

3. Execution errors: Fat fingers kill profits. Messing up strikes, dates, quantities, or technical indicators. Heaven forbid you step away for lunch and miss something.

4. The ability to take advantage of the Law of Large Numbers by pumping out enough trades with positive Expected Value for things to swing in your favor. (I’ll discuss more in the next piece.)

Honestly, I had wanted to become a full-time trader years ago, but the technology just didn’t exist yet. Staring at a screen and mashing buttons all day is no way to live. And when you automate the boring stuff, not only does your trade entry become error-free, but it opens up your day to use your brain to find more winning strategies.

No kickbacks here: I used Trade Steward and it’s great. Trade Automation Toolbox is another highly regarded option. Neither existed 5 years ago so it’s exciting to see technology that makes full-time trading possible.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

Subscribe now

Drop any questions you have in the comments. And of course👇

Anyone who says “you can’t beat the market” clearly never heard of Return Stacking.

Wait, that’s you? Well, looks like I have my work cut out for me today…

Real talk: beating the market is actually very simple. Not easy, because there’s a lot to learn to put this together. But today is your primer.

👉Return Stacking means investing in two or more uncorrelated assets with the same money at the same time. Stack the returns on top of each other.

(My last blog was on correlation. Definitely check that out before you read further.)

The second thing you need (and please don’t be terrified) is leverage. Two things can be true at the same time.

Leverage can expose you to account-killing risk.

AND

Leverage can reduce risk while increasing your returns.

To get access to leverage, you need a margin account, which enables two things:

1. You can trade stocks with 2x your account value with margin interest. For example, if you have $100k in your account, you can buy $200k worth of stock. The first $100k with cash, the second $100k with margin interest.

But margin interest rates are absolutely predatory, so I never recommend this. But you still need a margin account because….

2. You can access leveraged instruments, meaning options and futures, which don’t use margin interest. And offer way more leverage still.

I won’t get into the specifics of options and futures in this piece, but 3 concepts are critical:

Notional Value: Total value of the assets you control.

Buying Power: What your broker requires as collateral to hold the position. (Different brokers have different requirements. In a Reg T margin account, stocks require 50%, futures require ~7%, short options require 15-20%, and credit spreads can be 1-2% depending on how a trade is structured.)

Cash: Actual dollars. Keep this above $0 and you never pay margin interest.

An example may clear this up. Let’s say you have $67,500 and you want to invest it all in the S&P 500.

Option 1: Buy 100 shares of SPY. It’s the most widely traded ETF tracking the S&P 500. Current price as of this writing: $675/share (thus why I picked that odd cash number.)

• Notional value: $67,500

• Buying power used: 50% ($33,750).

• Cash remaining: $0. (You can buy more, but you’re charged margin interest.)

• Average historical return: 10%

Option 2: Buy 2x /MES futures* contracts. They’re leveraged instruments that also track the S&P 500. Two contracts have approximately the same notional value as 100 shares of SPY, but only require ~7% buying power and cash.

• Notional value: ~$67,050

• Buying power used: ~7% ($4,800). 93% remaining.

• Cash remaining: $62,250

• Average historical return: 10%

The notional value and returns are approximately the same, but far less buying power and cash are tied up with the /MES futures.

So what could you do with that extra $62,250 in cash? Return Stack!

Let’s be conservative: buy treasury bills (using the SGOV ETF). They have a current return of ~4.2%, risk-free. (This is what I meant by reducing risk while increasing returns.)

Option 3: Buy 2x /MES + SGOV (60% notional for this example)

• Notional value: ~$67,500 in /MES + $40,800 SGOV

• Buying power used: 7% ($4,800) for /MES + 30% ($20,400) for SGOV = 37% total used. 63% remaining.

• Cash remaining: $21,900

• Average historical return of S&P + current 60% SGOV return: 12.5%

You just beat the market by 2.5% by Return Stacking. With cash & buying power to spare.

2.5% may not sound like much, but look at what it does to that $67,500 over 20 years:

• 10%: $454,106

• 12.5%: $711,794

Not the sexiest example, but you can Return Stack anything you want: gold, Bitcoin, or my favorite, 0DTE options.

Go down a few learning rabbit holes and generate life-changing returns, responsibly.

*Note: futures like /MES have different tax considerations (Section 1256). Research and consult your accountant before getting into that game.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

Subscribe now

Also, I do take requests. If anything I write is unclear or raises new questions, hit me up in comments or with a reply.

And if this is your first time here: subscribe! Also, forward this along to anyone you think might dig it.

If you want to learn what an undiversified trainwreck your 401(k) is, read further.

You may have heard this quote before:

“Diversification is the only free lunch.”

-Harry Markowitz, Nobel Prize-winning economist.

That’s the headline. The fine print (that everyone misses) is you need uncorrelated assets to get diversification. Otherwise, you’re fooling yourself.

Correlation is a measure of how one asset moves in relation to another. It ranges from -1 to +1.

0.0-0.3: Low correlation. Critical point: this doesn’t mean they move opposite, that would be negative correlation. It means their movements are random relative to each other.

0.3-0.7: Moderate correlation. Sometimes random, sometimes in-step.

0.7-0.99: High correlation. Almost the same damn thing.

1.0: Actually the same damn thing.

-0.1 to -1: Negative correlation, moving in opposite directions. (To the same incremental degrees as above)

Back to your 401(k). Our choices in those crappy things are limited. You probably have a large-cap, a mid-cap, a small-cap, and maybe a tech fund if you’re lucky. You divide up your cash between them and say, “Hey, I’m diversified.”

Problem is, you’re not. Not even a little.

Below is a correlation matrix. It shows the correlation between these types of funds. I picked common ETFs: SPY tracks the S&P 500 (large companies), MDY is mid-cap (medium-sized), IWM is small-cap, and QQQ is the Nasdaq tech-heavy index. I threw in my 0DTE options strategies for a comparison.

Notice anything? From 2022 to present, all the ETFs are highly correlated. They move the same way, almost all the time. No meaningful diversification.

And it gets worse! When the market goes crazy, like during the 2024 Carry Trade Fiasco, assets become more correlated, i.e., they all crash together.

What’s that you say? ETFs are boring? You trade tech stocks? Let’s see how that goes.

Similar issue. You’re still all-in on one concept.

Yes, I put my options strategies in here to peacock, but I’ll be fair: there are many ways to get true diversification. Just get out of the “all equities” mindset.

Look what happens when we add Gold, Bonds, and Bitcoin to the mix.

Way less correlation in normal times. And in times of stress, some things become more correlated (Bitcoin) while others (bonds) become inversely correlated.

And of course, you’ll notice my 0DTE options portfolio had the lowest levels of correlation in any scenario…

I’m not saying this to convince you to become an options nerd. But do yourself a favor: run a correlation analysis of whatever you invest in. (Download raw return data and ask any LLM to run one and/or ask it for the formulas to make one in Excel or Sheets.)

The last thing you want is to believe you’re diversified when you were really just invested in one damn thing all along. And when the market crashes, everything you have is in freefall together.

In my next piece, I’m going to build on this concept by demonstrating how ‘beating the market’ is actually quite easy with one of my favorite topics: Return Stacking.

Subscribe now

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

And of course, if this is your first time reading this newsletter, subscribe here:

When I tell people I quit my Chief Growth Officer job to trade 0DTE SPX options, they either:

1. Have no idea wtf I’m talking about and/or

2. Think I lost my mind to do some fringe degen YOLO meme stock crypto trading scam.

(That’s a lot of trading pejoratives into one sentence. Did I miss any?)

But nothing is further from the truth.

👉0DTE SPX trading is THE most mainstream form of trading you’ve never heard of.

Quick primer: SPX options are S&P 500 Index contracts traded on the CBOE (Chicago Board Options Exchange). It tracks the 500 largest U.S. companies. You never own actual stock. It settles to cash at expiration. 0DTE means zero days ‘til expiration (they expire the same day they’re traded).

I’ll put this in perspective with sports betting (which you know I hate):

SPX options trade $1.2 trillion in notional value. Every. Single. Day.

The entire sports betting industry? They did only $148 billion last year. For the whole year.

CBOE SPX volume > FanDuel + DraftKings + MGM + all casinos combined. In a few hours.

What about stocks? Daily options volume averages:

• Tesla: 1.0-2.5 million contracts per day

• Nvidia: 1.4-2.8 million contracts per day

• Apple: 0.9-1.7 million contracts per day

SPX? 3.1-4.1 million contracts per day.

But now you’re like “isn’t the 0DTE thing on the fringe?”

No. The exact opposite.

60% of all SPX options volume is 0DTE. Not year-long options. Not 30-day. Not weekly.

And of that, 50-60% of the volume is done by retail traders (people like you and me, not institutions).

As mainstream as it gets. But why is 0DTE so popular?

👉Buying Power Recycling

Putting on trades ties up some of your capital (known as buying power).

Let’s say a trade ties up $1000 in buying power. When you trade 30-day options, that $1000 is tied up for 30 days. Just one trade.

With 0DTE, you can use that $1000 today. And tomorrow. And the next day. And make 20x as many trades (all with positive expected value, if you’re smart).

With more trades, the law of large numbers kicks in. (Another topic for another day.)

👉Theta Decay is Steepest

Theta Decay is a core concept to trading options (and definitely a post by itself.)

When you’re an option seller, you’re rooting for them to lose their value (sell them at a high price and let them expire worthless).

But options don’t lose value linearly. They decay slowly at first, then accelerate.

About 50% of an option’s time value decays in the final week. A huge chunk on the last day. And a ton in the final hour.

Trading 0DTE gives you the most bang for your buck on a per-day basis.

👉Zero Gap Risk

Overnight news and weekend drama. None of it matters in 0DTE trading.

When the market closes, you have no positions on. You start fresh the next day. Off-hours crises have no effect on your portfolio.

There are more reasons still. I’ll talk about them soon. Stay tuned...

Subscribe here so you don’t have to scroll back to the top:

Subscribe now

Last thing before I get started: the stuff I talk about initially will be pretty foundational, then the discussions will get deeper into the weeds over time. But I don’t want to lose people.

If you have any questions, want more explanation, or even have a correction, hit me up with a reply. I’ll be happy to address it in a follow-up newsletter. I tend to keep these blogs short so the size doesn’t scare anyone away, but I also realize there’s more than can be said.

Onto the show…

What do crypto bros and Nobel Prize-winning PhDs have in common?

They’ve all blown up their trading accounts from sizing too damn big.

Last week, Bitcoin saw its largest liquidation in history. $19 billion wiped out in a day. One guy lost his entire $19 million account. Over 200 traders lost $1 million or more.

Mind you, Bitcoin dropped from $122k to $101k, a level not seen since…June.

Not exactly insane by Bitcoin standards. So how do you lose everything on a 17% drop?

Size. Too much leverage. I’ll define both:

Size = how big your position is. The total dollar value you’re controlling.

Leverage = controlling a bigger position than you have cash for. Borrowing or using margin to increase your size.

Now, crypto bros get dragged for their irrational exuberance. This would never happen to actual financial geniuses right?

Wrong. Google “Long-Term Capital Management.”

Tldr: In the mid-90s, a bigwig from Salomon Brothers started a fund with 2 Nobel Prize-winning economists doing bond arbitrage (don’t worry, you don’t need to know what that is).

They crushed it. Yearly returns of 21%, 43% and 41%. Then in 1998, they had $126 billion in assets with only $4.8 billion in actual capital.

Read that again. That’s 26:1 leverage. $26 in assets for every $1 they actually had.

Sh*t hit the fan. Russia defaulted on its debt (bad for bond arbitrage), and their capital dropped to $600M with the same assets.

200:1 leverage. The Fed bailed them out before they broke our financial system.

Speaking of nearly breaking the system: 2008. Lehman Brothers (30:1 leverage) and Bear Stearns (35:1 leverage). Big bets, crash, went bust.

If you’re really new to this: Melvin Capital’s short bets during the GameStop meme explosion. Technically speaking, that wasn’t leverage, but it was still massive size. (Short position losses can go to infinity; they don’t stop at 0. But that’s another topic for another day.)

Leverage for Dummies

It drives me crazy when people say ‘leverage kills.’ Leverage by itself is fine. It’s misunderstanding leverage, and taking on too much (often just more than you realize) that ruins people.

👉Key thing to remember: just because you have a margin account with access to leverage doesn’t mean you have to use it.

Short explanation:

Say you have $100 and buy 1 share of a $100 stock. No leverage.

If it drops 50%, you’re down 50%. You still have $50.

Now, let’s say you use that $100, this time with margin to buy 2 shares of that $100 stock. You now control $200 of stock with your $100.

The stock drops 50%. You just lost all of your money.

Cheat sheet:

• 4:1 leverage? A 25% drop wipes you out.

• 10:1 leverage? A 10% drop wipes you out.

• 26:1 leverage like LTCM? A 4% drop wipes you out.

Never forget:

👉Double-check your leverage. Adjust your size.

That is how you eliminate blow-up risk.

Missing that is what wipes out literally everybody, every time.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

Last chance to subscribe:

Subscribe now

If you know anyone who’d dig this, forward it along.

And now, onto our regularly scheduled blog newsletter thingee…

This isn’t a sob story. In 2012, my car got stolen. Don’t worry, it was a pile. They did me a favor.

At least I thought so at the time.

I’m a “drive it ‘til it’s dead” kind of guy. A used 1998 Grand Cherokee with electrical issues. Classic Chicago beater car. Takes you from point A to point B. That’s about it.

Why did someone steal it? For parts. So old, they quit making them.

When State Farm cut me a check for $6,000, I thought I hit the lottery. It wasn’t worth half that.

But let’s walk through the math:

2000-2012: I paid State Farm an average of ~$550 every six months for 12 years. Total: -$13,200

The claim in 2012. Total: +$6,000

2012-2025: I’ve paid an average of $425 every six months for 13 years with no claims. Total: -$11,050

👉In 2012, I was down -$7,200. Lifetime I’m down -$18,250.

And I thought they were doing me a favor…

That’s how the insurance works. And it’s exactly how Volatility Risk Premium (VRP) works.

How Insurance Companies Stay In Business

You buy insurance to hedge against a disaster. A car wreck, a house burning down, etc. Insurance companies pay out trillions every year. And still managed to make insane profits.

How? The premiums they charge are, on average, overstated compared to the actual risk.

That’s their edge. Insurance companies are experts in risk assessment above all else. They’re not gambling whether or not you’ll file a claim. They’ve already priced in that you will.

Options Work The Exact Same Way

In my last post, I talked about expected value (EV) and how you can structure options trades with positive EV.

The reason that’s possible? Volatility Risk Premium. Yes, premium, the same word insurance companies use for your recurring payments.

Long story short: buying a put option is like buying crash insurance. You pay a premium for it, and if the market tanks, you get a nice payout.

But, like insurance, VRP in options tends to be (but not always) overstated. Most of the time, it’s like paying for car insurance.

Who buys puts? Anyone worried about downside risk. Hedge funds, pension funds, endowments, everyday traders, etc.

Who sells puts? Also anyone. I do it, literally every trading day. Because sellers of premium usually (but not always) receive overstated premium prices. Just like insurance companies.

Does that mean it’s free money? Absolutely-f*cking-not. You can lose all your money selling too many options.

👉To make options trading work, you must become an expert in risk management, just like insurance companies.

Except I don’t worry about fires, car wrecks, or hurricanes.

I worry about Unit risk. Size risk. Directional risk. Gamma risk. Sequence risk. Correlation risk. Concentration risk. Liquidity risk. Tail risk. Execution risk. Counterparty risk. Margin risk.

I know that sounds like a lot. (There’s even more!)

But I’ll break down each one over the next few weeks, starting with Unit & Size risk (the primary account killers.)

If you’re new here, subscribe to keep getting these updates:

Subscribe now

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.

Not gonna lie, I wasn’t expecting 300+ people to sign up for this little newsletter on day 1. Especially since the overwhelming majority of sign-ups were from LinkedIn, where pretty much everyone knows (knew?) me as that-guy-who-rants-about-recruiting-and-sometimes-writes-scifi.

There’s no charge or upsell with this newsletter, so if you like what you read and know someone who may also, please forward this along so they can sign up here:

Zero Days To Give

Zero-day options trading. Zero margin calls.

By James Hornick

Whelp, here goes nothing…

I hate gambling. Ask any of my friends. If we’re on a trip and the group wants to hit the blackjack table, I bail. Every time.

Why? Because I’m not a f*cking moron.

I know, I know. Some people gamble for fun and I just gotta ruin everything…

My buzzkill nature about gambling came from my college internship, when I was a special events coordinator at a casino in Atlantic City. I helped set up tournaments that catered to the high roller customers (read: degenerate gamblers with high-paying day jobs to support their habit). They stopped getting invited when they lost all their money. It was gross.

Yet here I am. A full-time options trader.

Let me explain the difference. It starts with a concept called Expected Value.

Why Casinos Always Win (Math Alert!)

Expected value (EV) is a simple equation that tells you whether a bet is worth making.

EV = (Probability of Winning × Average Win) – (Probability of Losing × Average Loss)

Every casino game has negative EV. You WILL lose money over time. Full stop.

Blackjack: With perfect basic strategy (hitting, standing, and splitting according to optimal probabilities), the house edge is around 0.5%.

If you bet $100 on a hand:

EV = (0.4975 × $100) – (0.5025 × $100) = -$0.50

But most people don’t use perfect strategy, so their EV per hand is closer to:

EV = (0.49 × $100) – (0.51 × $100) = -$2.00

Roulette: There are 38 spots on an American wheel (1-36, 0, 00). Pays 35:1 if you win. The house edge is 5.26%.

If you bet $100 on a single number:

EV = (1/38 × $3,500) – (37/38 × $100)

EV simplified = $92.11 – $97.37 = -$5.26 per spin

👉The house has positive EV, you have negative EV. That’s how casinos stay in business.

With Options, You Can Be The Casino

I don’t know if I’m a professional trader as much as I’m a professional backtester.

Here are some stats from a 0DTE SPX trade that’s been a good moneymaker this year. Backtested from May 16, 2022 to October 8, 2025 with 262 occurrences:

• Win rate: 59.2%

• Average winner: $958

• Loss rate: 40.8%

• Average loser: -$667

Let’s throw that in our trusty EV formula, shall we?

Expected Value = (0.592 × $958) – (0.408 × $667) = $295 per trade

All of the trades that I (or any profitable trader) make have positive EV like this. Now, there are a million different ways where backtests can go wrong (you know, the whole “past performance is not indicative of future results” thing). I’ll write about that at some point in the future.

Tldr: this is an estimate of EV, not a guarantee. But it’s why I, a notorious gambling hater who assumes we’ve all done our homework, do not consider options trading “gambling.”

👉And it’s all possible because options trading is a two-sided marketplace.

In a casino, you’re the player. In the options world, you can choose to be the player or the house.

How do you recognize one side vs the other? You start by understanding where the true edge in options trading comes from.

Which I’ll get into in my next post. (We in the blogging game call that a hook. Kidding, I’m just done writing for the day.)

Spoiler: it’ll be a primer on Volatility Risk Premium.

Disclaimer: This content is for educational and entertainment purposes only. Nothing here should be construed as financial advice. I’m not a registered financial advisor, broker, or anything else that requires licensing. Trading options involves substantial risk and isn’t suitable for everyone. You can lose more than your initial investment. Do your own due diligence, understand the risks, and never trade with money you can’t afford to lose.