# How to Trade Options Like a Quant (Even If You’re Not One)

https://www.youtube.com/watch?v=MAQBCz9ChkY

[00:00] Most traders don't last, but I've been a consistently profitable full-time trader for nearly a decade.
[00:06] This isn't theory.
[00:06] This is the quantbased approach I use every single day.
[00:10] In this video, I'll show you exactly how I research signals, build and back test models, structure high probability trades, and manage risk so you can start trading like a pro.
[00:18] At its core, trading is about finding things that are either underpriced or overpriced.
[00:23] It's fundamentally simple, no different than any other business.
[00:25] The goal is to buy low and sell high.
[00:27] Imagine seeing a product at Target for $5, knowing it sells on eBay for 25.
[00:31] You buy it at Target and flip it on eBay for a profit.
[00:35] Trading operates in the same way, just at a much larger scale and involving assets that are significantly more complex to value accurately.
[00:40] To approach trading quantitatively, we need a scientific framework, one that removes emotion and adds discipline.
[00:46] Here's the general structure I follow.
[00:48] First, we have to propose a hypothesis.
[00:50] This is your trade idea, your rationale for why a certain asset might be mispriced.
[00:54] For instance, maybe you believe Apple's options are underpriced because the market is underestimating upcoming volatility.
[00:59] That gives you a reason to
[01:01] Consider buying options.
[01:03] Or perhaps you think Tesla stock is overvalued, so you consider a short position.
[01:07] The point is, great traders are always looking for tradable and testable ideas.
[01:10] Over time, your ability to generate good hypotheses improves.
[01:14] It's a skill that develops with experience and curiosity.
[01:16] Once you have your hypothesis, the next step is to look for falsifying evidence.
[01:20] This comes from the scientific principle of falsifiability popularized by Carl Pauper.
[01:27] In science and in trading, we don't try to prove ideas true.
[01:29] We try to disprove them.
[01:31] Confirmatory evidence is never conclusive.
[01:33] There's always a chance that you just haven't looked hard enough or encountered the exception yet.
[01:37] But if you find solid evidence that contradicts your hypothesis, that's definitive.
[01:40] It means your hypothesis is wrong and must be revised or discarded.
[01:45] In trading terms, this means rigorously testing your thesis.
[01:47] If it holds up, great.
[01:49] Put on the trade.
[01:52] If not, move on.
[01:54] The goal is to survive long enough to make another trade with better odds.
[01:56] Assuming your hypothesis survives falsification, the next step is choosing the right trade structure.
[01:59] You want a
[02:01] Structure that captures the specific risk you're targeting at the lowest possible cost.
[02:04] For example, if you believe a stock will go up with no opinion on volatility or timing, then just buy the stock.
[02:10] Keep it simple.
[02:11] There's no reason to complicate it.
[02:13] But if you think volatility is overpriced and have no directional view, selling a straddle might be the better play because it gives you the maximum exposure to Vega and theta at the money.
[02:21] Position sizing is just as critical.
[02:23] Generally, your size should reflect your perceived edge.
[02:27] If your hypothesis has a strong edge, you can size up.
[02:29] One popular method is fractional Kelly sizing.
[02:33] Estimate your edge and the trade's variance.
[02:35] Calculate your Kelly fraction, then size down from there to stay conservative and account for any estimation errors.
[02:40] Trade management follows the same principle of falsification.
[02:43] If at any point new information disproves your initial hypothesis, exit the trade regardless of profit or loss.
[02:50] Whether you've hit your price target or your thesis is no longer valid, the decision to exit should not depend on the P&L.
[02:56] In fact, in a perfect world, the P&L would have no influence on your exit decision.
[03:00] Personally, I use what I call a day zero mentality.
[03:02] Every point during the trade, I ask, if I were putting on this trade today with the current information, would I still do it?
[03:10] If the answer is no, or if my thesis has been falsified, I exit.
[03:12] This could mean taking a win.
[03:14] It could also mean cutting a loser.
[03:16] Either way, acting on falsification rather than hope is what separates good traders from emotional ones.
[03:22] Now, all of this sounds great in theory, but you might be wondering, how do I come up with a solid hypothesis, and how do I actually test it?
[03:29] That's exactly where Option Quants comes in.
[03:31] It's a platform I help build to publish and share the actual tools I use every day, the same ones I've developed for the past 10 years to trade profitably.
[03:38] This isn't a signal service or some generic scanner.
[03:40] It's a real analysis platform built for institutional level trade research.
[03:43] In the next part of this video, I'll walk you through exactly how I use it from the lens of an options and volatility trader and show you how to turn raw ideas into structured, testable strategies.
[03:54] Trading is a business just like any other.
[03:56] And just like any business, if we want to stay in the game, we have to make more than we lose.
[03:59] That's the bottom line.
[04:01] Success in trading requires structure and
[04:02] No legitimate business would risk everything on a single product launch or a high-stakes client.
[04:07] The same applies to trading.
[04:09] We cannot let a single trade jeopardize our entire operation.
[04:13] Longevity in this game comes from consistency, risk management, and the ability to weather the inevitable bad days.
[04:19] Preserving capital is paramount.
[04:21] Capital is the lifeblood of our business.
[04:23] The more we have, the more opportunities we can seize.
[04:25] And with time, compounding does the heavy lifting, but large drawdowns, they're the enemy of compounding.
[04:30] They set us back and compromise everything we're trying to build.
[04:34] So, while we can't let one trade take us out, that doesn't mean avoiding risk altogether.
[04:38] Growth comes from taking calculated risks with a positive expected value.
[04:41] Just like any other smart business invests in new products, markets, or talent.
[04:45] The key is understanding what risks you're intentionally taking and being paid to take and managing or hedging the rest.
[04:50] If you're trading volatility, for example, and you don't have a directional view, then your focus is on Vega exposure.
[04:56] You'll want to hedge out delta risk as much as possible.
[04:58] Now, let's talk about the difference between risk premiums and inefficiencies.
[05:01] Risk premia are predictable, repeatable, and
[05:03] Systematic mispricings in financial markets.
[05:05] They represent the compensation you receive for taking on certain types of risk that others want to avoid.
[05:11] One classic example is the equity risk premium.
[05:14] If you invest in the stock market, you're already participating in it.
[05:17] The extra return you earn over risk-free assets like US Treasury bonds is compensation for uncertainty of equities.
[05:24] Stocks can crash, businesses can fail, and in exchange for taking on that risk, investors expect a higher return.
[05:30] But the focus of this video is a lesser known yet incredibly powerful premium, the variance risk premium or VRP.
[05:36] VRP is built on the observation that implied volatility, what the market expects, is typically higher than realized volatility, what actually happens.
[05:44] This consistent overestimation creates a structural edge in selling options.
[05:49] Essentially, you're being paid more for the risk you're taking than what that risk actually ends up costing you.
[05:54] Think of it like this.
[05:56] Just as equities tend to outperform bonds over time due to the risk premium, selling volatility can outperform equities because of the added layer of convex risk.
[06:03] The fact that option sellers collect small gains most of the time but
[06:05] Face the risk of rare large losses.
[06:07] There are other risk premium worth noting as well.
[06:10] For example, the earnings event premium which I back tested in a previous video.
[06:14] The idea is simple.
[06:15] Implied moves heading into earnings are often overstated.
[06:17] This is for a variety of reasons and if you're curious on why, make sure you go watch that video after you're done this one.
[06:21] On the dashboard here, you'll see that cumulative straddle returns across many tickers are consistently negative.
[06:27] That means if you had sold those straddles, you would have profited over time.
[06:30] And the beauty is you can skip the ones that don't show a clear edge, and consistent overpricing.
[06:35] On the other hand, inefficiencies are something different.
[06:37] They're usually short-lived or one-off mispricings.
[06:40] These arise from temporary issues like low liquidity, sudden imbalances in supply and demand, market panics, execution delays, overreactions among many other reasons.
[06:49] Sometimes structural events like index rebalancing or force selling creates these fleeting opportunities as well.
[06:54] Inefficiencies tend to be more profitable, but they're also harder to find and harder to consistently exploit.
[06:59] So, what's the takeaway?
[07:01] We want to build our trading business on risk premia, the repeatable, consistent sources of edge.
[07:03] That's our
[07:05] foundation.
[07:07] But at the same time, we should stay alert and be ready to capitalize on inefficiencies when they appear.
[07:12] As mentioned previously, the volatility risk premium, VRP for short, refers to the consistent tendency for implied volatility to overstate future realized volatility.
[07:21] But in trading circles, when people talk about the VRP, they're usually referring specifically to equity indices like the S&P 500.
[07:27] And that distinction matters.
[07:28] A common mistake among options traders is to apply VRP logic indiscriminately to individual stocks.
[07:33] Take Apple for example.
[07:35] Over the past 2 years, implied volatility on Apple has underestimated realized volatility.
[07:39] This is clearly shown in both the VRP chart and the straddle back test.
[07:43] A long straddle on Apple, betting that volatility will exceed expectations, would have been profitable over time, while a short straddle would have lost money.
[07:49] That's the opposite of what you'd expect if you assume that the VRP applied equally across all assets.
[07:55] That said, VRP isn't exclusive to indices.
[07:56] You can also find it in other areas of the market, most reliably in ETFs, which tend to carry a lot less idiosyncratic risk than individual stocks.
[08:04] A simple systematic way to monetize VRP is by selling
[08:06] Straddles or buying butterflies on spy.
[08:08] These strategies allow traders to capture the gap between implied and realized volatility.
[08:13] For those who aren't full-time traders, a well-designed VRP strategy can offer a relatively hands-off way to improve long-term portfolio returns.
[08:20] When we evaluate these strategies over time by return on margin, we can see that the results are consistently positive.
[08:25] But as with any short volatility strategy, there's a trade-off.
[08:29] Frequent small wins punctuated by rare but sometimes sharp losses often during periods of heightened market stress like the 2008 crash or the covid pandemic.
[08:37] This risk profile — small steady gains and occasional large losses — is the hallmark of volatility selling.
[08:43] For example, running a short straddle with a 10% margin allocation slightly outperforms traditional buy and hold benchmark.
[08:49] Increasing that to 15% boosts returns significantly but also increases maximum drawdowns up to around 35%.
[08:55] Interestingly, the butterfly strategy outperforms the straddle in most respects.
[09:01] It shows a higher total return and a smoother equity curve with milder drawdowns, making it a more attractive option for long-term implementation.
[09:06] If you're looking for a clean, scalable way to generate returns, sticking to a consistent VRP strategy, especially using SPY, can be highly effective, but we can do a lot better by adding some intelligence to our approach.
[09:18] First, we define a universe of liquid ETFs.
[09:20] So, we apply the filter to only have ETFs and require an average 20-day option volume of at least 2500 contracts.
[09:27] When we analyze the return distributions across this ETF universe, we find they resemble what we observed with SPY alone.
[09:32] Consistent mean returns around 2% for 30-day positions.
[09:36] So far, so good.
[09:36] But with better filtering, we can push those returns higher.
[09:39] This is where signal research comes in.
[09:41] If you want to follow along with these examples or explore and test some of these signals yourself, head over to Option Quants.
[09:46] The link's right down in the description.
[09:47] And while you're down there, don't forget to like the video if you're finding it valuable and subscribe for more deep dive content just like this.
[09:53] One of the most intuitive signals is the ratio of implied to realized volatility, specifically the 30-day implied volatility divided by the past 30-day realized volatility.
[10:02] This ratio is a rough proxy for the VRP itself.
[10:04] And the data backs it up.
[10:06] There's a strong positive relationship between higher
[10:07] IVRV ratios and future returns.
[10:10] The regression analysis shows that for every one unit increase in IVRV, the expected straddle return rises by about 2.36% and butterfly returns rise by nearly 4%.
[10:19] That said, financial data is rarely clean.
[10:21] As you can see here, the raw scatter plots are noisy and without the regression overlay, the signal would be very easy to miss.
[10:28] When working with financial data, the signal to noise ratio is extremely low and you need to keep your eyes open for any relationship to improve predictability.
[10:36] In the next step where we're going to build a statistical model, we want our inputs to be more normally distributed.
[10:41] The IVRV ratio, as it turns out, has a heavy right tail resembling a lognormal distribution.
[10:47] By applying a log transformation, we improve its distribution and boost the model's explanatory power.
[10:50] The result, a higher R squar and a more effective predictor.
[10:54] Next up, let's look at the one-year implied volatility percentile.
[10:58] This is one of my favorites to show people, and it often trips a lot of people up because the results are counterintuitive.
[11:03] Contrary to what you might hear on platforms like Tasty Trade, the data shows that selling volatility when implied volatility is
[11:08] Low, not high, actually performs better.
[11:10] The regression shows a clear negative relationship with high R squared.
[11:14] When we break the data into deciles, we see that the worst performance and the only consistent negative returns occur when selling in the top two deciles when IV is at its highest.
[11:25] In these cases, long Vall trades tend to work better.
[11:27] This flips conventional wisdom on its head where we're told to always sell high implied volatility.
[11:32] Then let's look at the flat forward volatility ratio.
[11:34] An underutilized but incredibly powerful signal.
[11:37] It differs from traditional forward volatility by solving for a common implied volatility level that balances both maturities.
[11:43] When we take the ratio of flat forward to standard forward volatility, we get a signal with surprisingly strong predictive power, an R squared around 1%.
[11:51] That might sound small, but in financial data terms, it's quite impressive and even stronger than IVRV.
[11:57] These signals, especially when combined, form a much more robust way to trade the VRP.
[12:01] Blindly selling volatility might work okay, but applying a data-driven filter based on IVRV, implied percentile, and flat forward ratio gives us a major edge.
[12:10] We've identified our best performing signals, we're ready to build a predictive model.
[12:14] We'll set up a regression using these features as inputs.
[12:17] Before going too far, it's important to highlight a few key principles when building any model.
[12:20] Earlier in our research, we defined a train test split.
[12:23] This means we're training and optimizing our model on one part of the data while holding back a separate portion that remains completely unseen.
[12:31] The test set, which is the data we held back, is our best approximation of how the model will perform on future data.
[12:36] So, it's crucial that we don't touch it during optimization.
[12:38] If we peek at the test results and then make changes based on that feedback, we're essentially training on the test set, defeating the entire purpose of having the test set in the first place.
[12:47] And without a test set, it's impossible to have true out of sample evaluation and understand how the model might perform going forward.
[12:54] This is a common trap in finance and machine learning and one of the fastest ways to build a model that looks great on paper but fails in the real world.
[13:00] We'll start with a simple linear regression model.
[13:01] This type of model generates a formula that predicts expected return based on our input signals.
[13:07] Initially, we'll use it to identify any trades where the model predicts a return greater than 0%.
[13:11] Expected, since the mean return of our data is around 2%, this gives us way too many trades.
[13:16] To make it more manageable and realistic, we'll increase the entry threshold to only include trades where the predicted return is above 5%.
[13:22] This is still too many trades for me.
[13:23] Bumping it up to 8% brings us down to around 2 to 3,000 trades total.
[13:28] This works out to around a few hundred trades per year, which is much more practical.
[13:31] This filtered model shows promising results.
[13:33] We get a higher mean return, lower variance, and a significantly higher Kelly fraction, which is a good measure of capital efficiency.
[13:39] Now that we're done optimizing, we can finally look at the test set.
[13:42] The model continues to perform well.
[13:44] Higher return, lower variance, and again, stronger Kelly fraction.
[13:47] While the variance reduction is a nice bonus, it's really the mean return we care about most here.
[13:51] And in a lot of cases, you'll find the variance actually goes up when reducing the number of trades you're taking.
[13:56] Overall, this test set is a strong sign that the model is generalizing well.
[13:59] As an alternative, we also test a logistic regression model.
[14:03] Unlike linear regression, this model doesn't predict a continuous return.
[14:07] It outputs a binary outcome, yes or no.
[14:09] For example, we might ask whether the trade is likely to
[14:11] Generate a return above a certain threshold, such as 0%.
[14:15] Initially, the model predicts yes for nearly every trade.
[14:18] So, we increase the minimum return threshold to 10% and tighten the probability cutoff from the default of 50 to a more selective 55%.
[14:25] This means we'll only take trades where the model is at least 55% confident that the return will exceed 10%.
[14:31] This version of the model looks even more promising than the linear regression.
[14:35] More trades, higher average return, and lower variance.
[14:38] But when we check the performance on the test set, we notice it doesn't generalize quite well.
[14:42] In contrast, the linear model holds up more consistently out of sample.
[14:45] So we'll stick with that for back testing.
[14:47] Running the back test, we see that the model significantly outperforms both buy and hold and the basic VRP strategies we tested earlier, especially in the test period, which has been marked by strong bullish trends.
[14:58] This is typically when short volatility trades struggle.
[14:59] So the model's ability to maintain strong performance here is especially encouraging.
[15:04] With a bit of extra research and signal filtering, we've managed to build a strategy that meaningfully enhances our ability to collect the VRP.
[15:11] From here, we can save the model and use it to generate entry.
[15:13] Signals going forward.
[15:14] The beauty of this process is that the possibilities are wide open.
[15:18] There's an entire world of market behavior waiting to be uncovered through signal research.
[15:21] I spend hours digging into this kind of work and consistently uncover new interesting patterns, and I encourage you to do the same.
[15:28] With the right tools and curiosity, you'll be surprised about what you can find.
[15:32] Building a model and systematically collecting the volatility risk premium is a solid foundation for any options trader.
[15:38] But as full-time traders, our objective is to go beyond systematic strategies.
[15:42] Our real edge lies in actively identifying inefficiencies, short-lived, often hidden opportunities to exploit mispricings and volatility.
[15:49] To do this, we return to the framework laid out in part one.
[15:52] A structured scientific approach rooted in hypothesis generation, falsification, and careful trade construction.
[15:57] Before we can identify inefficiencies, we need a firm grasp on how volatility is priced.
[16:01] There are two primary methods for this.
[16:03] Absolute valuation and relative valuation.
[16:07] Absolute valuation looks solely at the asset in question.
[16:09] A great example here is QE, a China focused ETF.
[16:12] According to our expensive volatility
[16:13] Screener, QEB stands out with significantly elevated implied volatility compared to its realized volatility.
[16:20] Historical back tests and VRP charts reinforce this, showing that barring one major move, selling volatility on Qu has been consistently profitable.
[16:28] The volatility cone confirms that current implied volatility is at the high end of its historical range.
[16:31] Specifically, 30-day realized volatility is hovering around 52% while 30-day implied is closer to 68%.
[16:39] This mispricing is also visible in the term structure where the slope is unusually steep.
[16:44] The 30 to 60-day forward factor is approximately 80%, indicating that the market is anticipating a sharp drop in volatility following the upcoming 30-day expiration.
[16:52] This brings us to a key question.
[16:54] Is such a steep drop in implied volatility justified?
[16:57] Looking at the data, there's no clear reason to expect such a rapid volatility decline.
[17:00] Given QuB's history of persistent IV overpricing, the current spread between IV and RV and the shape of the term structure, it appears QuB's implied volatility is mispriced on an absolute basis.
[17:12] Importantly, there's nothing in the available data that contradicts this.
[17:14] Conclusion.
[17:16] We then shift to relative valuation, which compares QuB's volatility to other assets.
[17:20] The first point of comparison is with the broad market.
[17:21] So, we'll compare it to SPY.
[17:23] QB's implied volatility is above the 75th percentile of its historical IV to SPY ratio.
[17:29] While realized volatility is also elevated, it's still within the 75th percentile, indicating that the market is likely overestimating future volatility expectations for QB relative to SPY.
[17:39] A more revealing comparison is with CQS, another China focused ETF.
[17:42] QB's implied volatility is not only significantly higher than CQ's, but it also lies well outside the normal range.
[17:48] In contrast, QB's realized volatility is within expected bounce.
[17:53] This suggests that implied volatility is expensive relative to a closely comparable asset.
[17:58] From a macro perspective, one could argue that elevated IV is driven by ongoing trade tensions and tariff uncertainty, which naturally raises concerns around Chinese related ETFs.
[18:07] And while that narrative makes sense, it's important to recognize that macro events like these often lead to inefficiencies.
[18:11] Traders tend to overestimate the probability or
[18:15] Magnitude of adverse outcomes, which inflates options premiums as sellers demand higher compensation for perceived risk.
[18:23] This imbalance between supply and demand, where more participants are looking to hedge than to sell volatility, can create attractive opportunities.
[18:30] In fact, these are exactly the kinds of setups I look for all the time.
[18:33] When historical patterns consistently show overpricing, and the data doesn't support the elevated fear, it's often a sign that the market has mispriced risk, and that's where our edge lives.
[18:42] Given this analysis, I've opened a short straddle position on QuB for the May 16th expiration, which is 34 days out.
[18:49] I'll monitor the trade closely and using my day zero mentality, my plan is to exit if realized volatility starts climbing up towards implied or if implied volatility compresses back towards the 50% range.
[18:58] So, I didn't originally plan on having this part in the video because I honestly thought I'd be holding this position a lot longer, but it turns out this web position worked out very well.
[19:07] So, I opened it last Thursday and Friday, the 10th and 11th of April, and today is the 14th of April and V dropped from where we opened it around 68% all the way down to my.
[19:15] Price target of around 50%.
[19:18] So, I decided to close and take the nice gain.
[19:20] Not too bad for holding a position over the weekend.
[19:23] I want to stress that while this one worked out about as good as it gets, not all trades work out this way.
[19:27] Sometimes I'm wrong and I lose money.
[19:29] Also, a lot of the times you win, you actually end up holding to expiration and having to delta hedge to realize that difference between implied and realized.
[19:37] We've covered how to approach trading like a business, how to systematically harvest the volatility risk premium, and how to go a level deeper, identifying and exploiting inefficiencies through disciplined data-driven analysis.
[19:48] Whether you're running a model-based strategy or digging into absolute and relative valuations like we did with QB, the goal is the same.
[19:53] To find edge, manage risk, and execute with precision.
[19:57] If this breakdown gave you new insights or even helped you think about the markets differently, I'd love to hear about it.
[20:02] Drop a comment, ask a question, or let me know what you want to see next.
[20:05] And if you haven't already, check out Option Quants.
[20:07] It's the platform I help build to make this kind of analysis faster, easier, and more actionable.
[20:12] Thanks for watching.
[20:15] Stay sharp, stay curious, and I'll see you in the next one.