Transcription
What if I told you this option strategy turned $10,000 into $1 million in just 1 year? Sounds insane, right? But I've personally traded this strategy for over 9 years, and in that time, I've made over $6 million across my portfolio with this strategy playing a major role in that success.
In this video, I'll break down the data, backtests, and research behind it, showing you exactly why it works. And if you stick around until the end, I'll even give you the actual code, calculator, and trade tracker so you can run the numbers yourself. I guarantee by the end of this video, you'll not only understand this strategy but you'll also have a powerful new tool to add to your trading arsenal.
Before we dive into the strategy itself, let's talk about something far more important: understanding Edge. Because without Edge, no strategy, no matter how well-structured, will ever be profitable in the long run. So, what exactly is Edge? In simple terms, it's a repeatable statistical advantage that gives you a positive expectancy over time. It's not about winning every trade; it's about having a systematic process that, when executed consistently, results in profitability over a large number of trades.
But here's where most traders go wrong: they confuse Edge with luck or random patterns in data. Just because something worked a few times in backtests does not mean it's an Edge. You need a valid, logical reason for why the Edge exists, otherwise, you're just data mining, and eventually, the market will correct itself, leaving you with nothing.
That brings up a critical question: If Edge is real, why hasn't it been arbitraged away by larger players? After all, institutions have billions of dollars and entire teams of quants searching for inefficiencies. So, why can I, as an individual trader, still monetize it? The answer lies in understanding where large players don't want to take risks. The best trade ideas come from areas where other market participants are offloading risk at any cost. They're not trying to optimize for maximum returns; they're simply looking for certainty. And when people are desperate for certainty, they overpay for protection, creating inefficiencies we can exploit.
But here's the key: every trade idea must be testable with data. If you can't quantify it, you're just gambling. Too many traders operate on gut feeling or intuition, but if you don't have a way to falsify bad ideas, then all you're doing is trading on hope. And hope is not a strategy.
Even when you find an Edge, you have to accept that Edges are noisy. They don't work every time, and results can vary in the short term. This is where most traders fail: they abandon a good system during a drawdown because they don't understand the natural variance of their strategy. And that's why research is so important. If you don't have the data to support your strategy, how will you know if you should keep trading through a drawdown or if your Edge just disappeared? Later in this video, I'll show you the data that answers this exact question.
So, what's the strategy? Selling earnings volatility. That's it. That's the strategy. It sounds simple, but there's a lot of nuance we will address with the data later in the video. It works because quarterly earnings events, which happen four times a year per stock, often account for the majority of the volatility a stock will experience in the entire year. By selling options ahead of earnings, we aim to profit from two key factors.
First is what is commonly referred to as implied volatility crush or IV crush, which is the rapid drop in implied volatility after earnings are announced. The second is stocks moving less than expected. Our hope is that options tend to overprice earnings moves, and when the stock doesn't move as much as anticipated, we profit from the discrepancy.
Now, why does this Edge exist? It exists because people hate uncertainty, especially big institutions and funds with tight risk controls. And uncertainty in a stock is directly reflected in implied volatility around earnings. Implied volatility spikes because traders, funds, and retail investors rush to buy protection. Many of these participants aren't concerned with whether implied volatility is over or underpriced; they just want to hedge their positions. We call them price-insensitive traders, and they create an opportunity for us.
Beyond hedgers, we also have speculators: traders looking to gamble on earnings moves. They often buy short-term upside call options. This increased demand pushes options prices and implied volatility even higher, further fueling our Edge. When you combine hedgers willing to overpay for protection, speculators driving up demand for options, and the general uncertainty leading to higher implied volatility, you get a market imbalance where options are systematically overpriced leading into earnings, and we can take the other side of that trade.
Now, obviously, this isn't just theory. I'm going to back this up with real data in the next section, but first, let's talk about the best ways to actually monetize this Edge using the correct structure. There are multiple ways to structure short volatility trades around earnings, but I focus on two: the short straddle and the long calendar spread. Let's break them down.
The short straddle is the most common short volatility strategy. It involves selling one call and one put of the same strike and same expiration, generally this will be the at-the-money strike. This means we profit when the stock moves less than the expected earnings move and when implied volatility collapses post-earnings. However, because we are selling short-term options, gamma risk is very high. Gamma measures how fast Delta changes, and near earnings with inflated implied volatility and short time to expiration, gamma is extremely sensitive. If the stock moves more than expected, our Vega gains from IV crush won't be enough to offset the loss from gamma, and we can take a big hit.
The long calendar spread involves selling a near-term (also known as front-month) option and buying a longer-term (also known as back-month) option at the same strike. It is usually executed with the shared strike being the at-the-money strike. It is a debit strategy because the option we are buying in the back will be more expensive in dollar terms than the one in the front. Typically, I use a 30-day expiration gap because it maximizes exposure to earnings volatility while keeping the structure stable.
Now, here's where people get confused. A calendar is technically long Vega, but this is misleading. The back-month option has more Vega than the front-month, making it seem like we want IV to rise. But in reality, what we want is IV to drop in the front-month more than it does in the back-month. Essentially, we want our Vega gains in the front contract to offset our Vega loss in the back. We are also short gamma because the option we sold in the front has a higher negative gamma than the positive gamma of the option we bought in the back. This means we want the stock to move as little as possible, making it so our gamma losses in the front are as small as possible since the back-month positive gamma will not be able to compensate. Lowering front-month implied volatility raises our profitability, but if back-month implied volatility also drops significantly, our structure collapses, limiting profits. Through testing, I found that a 30-day difference minimizes this issue.
So, which one is better? The straddle offers higher potential returns and lower commissions but also has higher tail risk, meaning when things go wrong, they go very wrong. The calendar offers better risk control and a smoother equity curve but generally has lower returns. That's why in the data section, I'm going to break down exactly which structure performs better over time based on thousands of historical earnings events.
Now, let's dive into the numbers. Everything I'm about to show you is backed by real historical market data, no cherry-picking, just raw numbers and analysis. We're working with a data set of 4,500 unique stocks spanning from 2007 to today, covering a total of 72,500 earnings events.
Now, within the data set, we track several key metrics: both predictor variables, which help us determine when to enter a trade, and target variables, which measure the success of each strategy. The key predictor variables are:
The term structure slopes. This measures the steepness of the implied volatility term structure, specifically it looks at the difference in implied volatilities between the front-month or near-term expirations and further out expirations of 45 days or higher. A steeper structure will show that near-term options expect large moves, but they expect volatility to fall back to normal levels over time.
There's also the term structure ratios. Similar to the term structure slope, but expressed as a ratio rather than a slope.
There's also a 30-day average volume, which is our liquidity measure. This will help us determine if liquidity plays a role in pricing of earnings events.
And finally, there is IV RV ratios, so comparing implied volatility to realized volatility over the period prior to earnings. This is used to see if there's any predictability coming from implied volatility being overpriced in the time before earnings.
Now, let's move on to our target variables, which measure how our strategy performed. We analyzed three primary return-based metrics. Note that all of these positions were open 15 minutes before the close of the trading period prior to the earnings announcement.
The first, we call straddle return jump. It is the return of the trade closed 15 minutes into the trading session after earnings.
We also have straddle return move, which is the return if we close the position with 15 minutes before market close on the day after earnings.
And the calendar return jump, which is the return of the calendar spread closed 15 minutes into the trading session after earnings.
All of these include realistic trading costs, including Interactive Brokers commissions and slippage estimates based on bid-ask spreads and volume. This ensures that everything analyzed reflects real-world trading conditions, not just theoretical models.
Let's look at the distribution of returns for the straddle jump strategy. When we plot the historical returns for the straddle jump, we immediately see something striking. Most returns cluster around small profits, but there's a long, dangerous left tail where rare but massive losses occur. In fact, 1% of the time, this strategy lost 130% or more, and 0.1% of the time, it lost over 41%. There's even one extreme outlier where the straddle lost over 9200% on a single trade. We can also notice the near 0% mean return, indicating that trading all events blindly will just break even over time. This is generally expected and shows that, on average, the market prices earnings events quite well.
Now, let's compare this to the calendar jump strategy. The calendar's distribution looks far more stable: lots of small, consistent winners and fewer extreme losses. The worst-case scenario for the calendar is losing the entire debit paid. We also noticed the near 0% mean return here, showing break-even if we just blindly traded all events.
Now, how did these strategies perform quarter by quarter? The straddle jump returns traded wildly around the zero line, showing no consistent long-term Edge when traded blindly. The calendar spread also showed random fluctuations around the zero line, showing that if trading all events, there is no real long-term Edge. This is why we have predictor variables to see if we can improve this.
Fact: One interesting observation is the move straddle held until near close the next day was a consistent loser, confirming the well-documented post-earnings announcement drift effect. Post-earnings announcement drift is a well-documented market phenomenon that results in prices reacting to earnings announcements slowly over time. This means that, for example, if a stock jumped 5% in the morning after earnings, it is more likely to continue to rise throughout the day. This means you could sell the position in the morning after the jump and take the opposite position or use shares to ride the predictable change. If you want a deep dive on post-earnings announcement drift and how to trade it, let me know in the comments. Because the jump shows more promise here, we will only consider that for the rest of the video. I just wanted to point out the post-earnings announcement drift effect that happens with earnings.
So, do any of our factors actually predict profitable trades? When we split the data into deciles (which is 10 equal groups based on each predictor variable), we found three factors that correlated strongly with success:
The term structure slope, specifically the nearest expiration to the 45-day expiration. The more negative the slope, the higher the returns for both the straddle and the calendar. Essentially, the steeper the implied volatility curve, the more overpriced short-term options tend to be. This shape of the slope is often referred to as backwardation. This means the near-term options are more overpriced in IV terms than later-term ones. The market expects volatility to drop back to normal levels after the event.
The 30-day average volume. Higher pre-earnings trading volume led to better returns for both the straddle and the calendar. This suggests that more volume, or more participants, will lead to a higher level of price insensitivity where demand is more likely to outpace supply.
And finally, the IV30/RV30 ratio. The higher this ratio, the better the expected return. This confirms that if implied volatility was overpriced in normal conditions, it was even more likely to be overpriced for earnings.
With all this analysis, we built a simple rule-based model that only trades when the term structure slope is sufficiently negative, the 30-day average volume is above a key threshold, and the IV30/RV30 is high enough to indicate implied vol overpricing. This filtered out 88% of events for the straddle and 90% of events for the calendar, leaving us with only the highest quality trades.
The final results of the model are that the straddle mean return became +9% (up from zero) but with a high variance (48% standard deviation). And the calendar mean return got up to 7.3% (up from zero) with a lower variance of a 28% standard deviation. The straddle had a max loss of -130%, where the calendar had a max loss of -105% (with the extra 5% being due to commissions). Both strategies were profitable, but the calendar was much more stable, making it easier to trade consistently.
So, after analyzing over 72,500 earnings events, we now have a data-backed trading strategy with clearly defined Edges. But this alone isn't enough. We still need to test how this strategy would perform in several scenarios, including what position size to use. In the next section, I'll walk through a Monte Carlo simulation to show the potential growth, drawdown, win rates, and more metrics you can expect when trading this strategy.
Now, let's dive into the results of both these models. First, we'll analyze the Kelly Criterion, which determines the optimal fraction of your portfolio to allocate to each trade. Theoretically, Kelly sizing maximizes long-term growth, but in practice, full Kelly is almost always too aggressive, leading to extreme volatility and an uncomfortably high risk of bankruptcy.
For the straddle, the suggested Kelly fraction is 6.5%, meaning if you had a $10,000 account, it recommends selling a straddle with a maximum of $650 in collected premium. For the calendar, the Kelly fraction is 60%, meaning the recommended position size would be a $6,000 debit. Right away, using the Kelly fraction as a proxy for the better strategy, this suggests that the calendar structure is likely superior. But here's where it gets interesting: for the calendar, 100% losses aren't rare. Even 60% is far too high. I'll show exactly why this is in a moment, but just looking at these numbers, the calendar appears to be the safer bet, as it recommends a much higher fractional allocation. However, before we can draw any conclusions, let's analyze this further.
Now, I don't just rely on backtests; I run Monte Carlo simulations to get a better picture of real-world variance. Why? Because backtests only show one historical path, while Monte Carlo runs thousands of different possible outcomes by randomly sampling from the data set. This gives us a far better idea of what could happen in the future and whether our strategy holds up across many different market conditions. These simulations are also a great way to validate whether our live trading results are within the expected range.
Now, let's break down the actual Monte Carlo results. Before we get started, though, I highly recommend you download the resources linked in the description. You'll find a PDF containing all the key data and figures I'll be discussing. That way, you can follow along and reference it later as you analyze the strategy for yourself.
For this study, I ran 10,000 simulated P&L paths over a span of 1,000 trading days (which is about 4 years) and another batch over 252 trading days (which is 1 year), all starting with a $10,000 portfolio. These simulations give us a much broader picture of potential outcomes than a single backtest ever could.
Let's start by looking at the full Kelly bet for the straddle strategy, which came out to 6.5% per trade. From the simulation, we see no bankruptcies and some paths even managed to hit a couple million dollars in returns over 4 years. Sounds great, right? Not so fast. When we examine the drawdown histogram, we can see a big problem: about 35% of all paths experience a max drawdown of over 45%, with some paths dropping as low as 80%. That means that at some point during these four years, more than a third of traders would have seen nearly half their capital wiped out or worse. And if we look at the longest drawdown duration, most of them lasted between 200 trading days, which means that if you hit a rough patch, you could be seeing in the red for months before seeing a recovery.
Now, ask yourself: Can you stomach seeing your account drop by 45% or more? Can you keep executing the strategy after losing 80% of your capital? Even though the average outcome after 4 years is around $2 million, some traders in this situation would still have less than $100,000 after the same period. This is why trading size is so important. The variance is real, and if you don't control risk, you won't survive long enough to see the gains.
Now, let's shift to the calendar strategy at full Kelly, which came out to a much higher 60% bet per trade. The results: 485 bankruptcies out of the 10,000 paths, that's about 5% of all accounts going to zero. Not only that, but when we check the drawdown histogram, we see an even more troubling trend: many of the drawdowns are in the 80 to 95% range. And the biggest warning sign: the distribution of drawdowns is skewed heavily towards 100%, meaning that at this bet size, you would eventually get wiped out if you traded long enough. This tells us one thing: betting at full Kelly has way too much variance. However, because of this aggressive sizing, this strategy was able to turn $10,000 into over $1 million in a few paths in under a year. That's how the crazy backtest results in the title happen. But here's the catch: this size will eventually lead to bankruptcy, period. I would never trade at this size and I strongly recommend against it. But if you're willing to risk everything for the shot at massive returns, that's your choice.
Let's use fractional Kelly going forward. Fractional Kelly bets let us reduce our variance while not reducing returns by as much. For instance, half or 50% Kelly reduces variance by 50% but only reduces returns by 25%. So, let's scale back to something more reasonable. Let's try a 30% Kelly bet for the straddle, this means dropping down to around 2% per trade. After 4 years, the mean portfolio size drops, but the max drawdown also drops significantly. The largest drawdown is now around 37%, and the average max drawdown is only 15%, which is much more manageable. The mean Sharpe ratio over the 4 years is around 3%, which is excellent.
For the calendar strategy, we take the 30% Kelly bet, which translates into around 18% per trade. The return graph looks really strong, but the drawdowns are still too high for my liking. The max is around 76%, with the mean around 40%, which is too high for me. So, I reduce it further to around a 10% Kelly, which is 6% per trade, and this finally brought the drawdowns in line with what I look for in a sustainable strategy. At this level, the Sharpe ratio was actually higher than the straddle, which is why this is the structure I personally prefer to trade between these two structures. Both are viable, but for me, the calendar strategy is a better balance of risk and reward. I've seen too many earnings events that move way more than expected, where a straddle would have resulted in a devastating loss. So, despite the straddle offering potentially higher returns, for my sanity and preservation of capital, I trade the calendar.
Let's now take a long-term view over 10 years, starting with $10,000, trading the calendar strategy at 10% Kelly. The mean ending portfolio value is around $6 million. This equates to a compound annual growth rate of around 90%. The max drawdown distribution sees a mean max drawdown of around 20%, a mean longest drawdown of around 6 months. The win rate is 66%, the expectancy per trade is $2.65, and the mean Sharpe ratio is 3.5. Overall, this strategy performs extremely well, but like I said earlier, position sizing is everything. No matter how strong an Edge is, if you size too aggressively, you will blow up. Risk management is the key to longevity, and preserving capital is always the number one priority.
In the files I shared in the description, I've shared a Python script that allows you to scan earnings events that fit the criteria we've discussed. You'll find a detailed document on how to set it up and run it at the link in the description. Here's how it works: you enter a ticker with an upcoming earnings event, and the program calculates all the key metrics from our model. It then provides a recommendation based on the data. If all three conditions are met, it flags the trade as "Recommended." If only two are met, but one of them is the term structure slope, it marks it as "Consider." If the term structure slope is not met, the trade is automatically labeled "Avoid." I personally only trade the recommended setups, but that choice is up to you.
Now, let's run the script and see what we get. Looking at Amazon, they have earnings tomorrow, and the model confirms that this is a "Green Recommended" setup. That means all conditions are met, and it qualifies as a high-probability trade. So, I executed the trade. I opened a February 7th - March 7th call calendar for a $3.33 debit. The following morning, about 15 minutes into the market open, Amazon had moved only 2.5%, which was well below the expected move priced into the options market. As a result, the position worked exactly as I wanted, and I closed for a nice profit of $9,300.
Now, if you look at the straddle trade, which I didn't take, you'll see it actually made even more while trading fewer contracts (20 contracts for the straddle versus 100 contracts for the calendar). This aligns with what we expect: the straddle has less commissions, and the straddle has higher potential returns when things go well. But when things go really bad, the calendar position takes a hit while the straddle can get absolutely crushed with massive losses. This is exactly why risk management is key, and why I personally prefer the calendar structure for this strategy. It offers a better balance of risk and reward.
If you want to run these calculations yourself, grab the Python script and setup guide in the description and test it on upcoming earnings events. If you made it to the end, please subscribe, like, and comment for any video you might want me to cover going forward. Thank you.