I've been selling strangles on futures for 4 years (83% win rate, 130+ trades, 1.3 Profit Factor). Here's what I've learned about tail risk that changed how I size everything.

I want to share something that took me a while to figure out, and that I think a lot of premium sellers in this sub are probably not thinking about. This isn't a trade idea or a strategy pitch. It's more of a conceptual framework that changed how I approach position sizing and portfolio construction. **Background:** I sell 20-delta strangles on futures (currencies, grains, metals, energy, rates). 45 DTE, managed at 50% profit, 2x stop, 21 DTE time stop. Roughly following the tastytrade playbook but applied across uncorrelated futures instead of just equities. Over 130+ trades, the win rate has been 83.65%, average winner is 0.47x of risk, average loser is about 1x of risk, average hold 27 days. Profit factor around 1.3. Nothing spectacular per trade, but it compounds. I'm posting this because of something I noticed when I started really digging into the return distributions of the underlyings I trade, and I think it matters for anyone selling premium. **The thing most premium sellers get wrong (including me, for a long time):** We all know implied vol overstates realized vol. That's the variance risk premium. That's why selling premium works. No argument there. But here's what I wasn't thinking about carefully enough: WHY does implied vol overstate realized vol? The standard answer is "because hedgers overpay for insurance." True. But there's a deeper layer. [Leptokurtic Distribution, for reference.](https://preview.redd.it/px9hmdhergng1.png?width=400&format=png&auto=webp&s=72f44ae3a70a923f9faab24932600e41b8b6b895) Financial returns are leptokurtic. Fat tails, tall middles. This means two things are happening simultaneously: 1. Markets sit still more often than a normal distribution predicts (tall middle). This is why our win rate is 83% and not the 60-65% that raw deltas on the 20 delta strangles would suggest. The center of the distribution is "overpriced" relative to what actually happens. 2. Markets make extreme moves more often than a normal distribution predicts (fat tails). This is the risk we're getting paid to absorb, and it's MUCH bigger than most of us think or model. I started counting how many months various futures underlyings have made 3-sigma moves over the past 15 years and comparing that to what a normal distribution would predict. The results kind of blew my mind. Normal distribution says a 3-sigma monthly move should happen about 0.27% of the time. Over 180 months, you'd expect about 0.5 occurrences. What I actually found (so far, approximately): * Natural gas: 10 times (roughly 20x more frequent than normal predicts) * Crude oil: 7 times (\~14x) * Wheat: 7 times (\~14x) * Japanese yen: 6 times (\~12x) * British pound: 7 times (\~14x) * S&P 500: 5 times (\~10x) * Silver: 5 times (\~10x) These aren't outliers. This is just what the data looks like. Every single asset I checked had dramatically fatter tails than what a normal distribution would predict. At the 4-sigma level it's even more extreme (normal says basically zero should occur in 15 years; natural gas had 5). **Why this matters for sizing:** A lot of us (myself included, for a while) may use something loosely based on Kelly criterion (or partial Kelly) to size positions. The problem is that Kelly assumes you know the true distribution of outcomes. If you're feeding in your backtest win rate and average winner/loser, you're implicitly assuming the future distribution will look like the past sample. But if the true distribution is leptokurtic (it is), your backtest is almost certainly undersampling the tails. Your sample of 130 trades, or even 1000 trades, probably doesn't contain enough tail events to accurately represent their true frequency. This means Kelly-based sizing is almost always too aggressive. Not because Kelly is wrong mathematically, but because the inputs you're feeding it are wrong. The true loss distribution has fatter tails than your sample suggests, so the optimal bet size is smaller than Kelly tells you. I've moved to roughly half-Kelly on my strangles and I hold about 25% of the portfolio as a margin reserve specifically for vol spikes. After watching what happened to [OptionSellers.com](http://OptionSellers.com) and various accounts during Feb 2018 Volmageddon and March 2020, I think the margin reserve is possibly the single most important risk management tool for futures premium sellers and almost nobody talks about it (outside of tastytrade, sad to see Tom go...). **The second insight (this one is more speculative, but I think it's interesting):** If the tails are fatter than normal across all these markets, and if options pricing is based on models that assume thinner tails, then deep out-of-the-money options should be systematically underpriced. Not at-the-money options (those are efficiently priced by active hedging flow). The DEEP out-of-the-money ones. The 5-delta stuff that nobody looks at. And here's the kicker: the degree of underpricing varies enormously by asset class. SPX puts are actually expensive because every institution in the world is buying them for crash protection. But 5-delta wheat calls? 5-delta yen puts? The deep tails in these markets have almost no institutional buying pressure. The prices are set almost entirely by market makers using models that assume thinner tails than what actually occurs. I've started allocating a portion of my portfolio to buying cheap deep OTM options on the futures where the gap between actual tail frequency and model-implied tail frequency is widest. Not as a hedge for my strangles specifically (they're often on different underlyings). More as an independent trade that exploits the same distributional mispricing from the opposite side. It's a weird feeling to be selling premium on one set of underlyings while buying premium on another. But I think it's logically consistent: sell where the center of the distribution is overpriced (high IVR underlyings), buy where the tails are underpriced (whatever screens cheapest on a convexity-per-dollar basis). **I'm not saying any of this is proven.** The strangle side has 4 years of live data. The tail-buying side is newer and I'm still developing the framework. I could be wrong about the tail convexity piece. But the sizing insight (leptokurtosis means you should be more conservative than Kelly suggests) I'm pretty confident about. The data on tail frequency is just too consistent across too many markets to ignore. Curious what this sub thinks. Anyone else looking at this kind of cross-asset approach to premium selling? Or doing anything systematic with the deep OTM options?