A Quant's Guide to Cross-Section Maxxing [Code Included]
Rank stuff, long the top, short the bottom, go home.
Quantitative trading is about many things; buying low and selling high, chasing momentum, capturing yield, arbitraging price imbalances — the whole lot.
But time and time again, it comes back to one idea:
The cross-section, the cross-section, the cross-section.
Now, if you don’t spend the majority of your life in code terminals and academic papers, the word cross-sectional might not mean much.
In practice, it’s just shorthand for “in relation to everything else.”
Looking at something on its own rarely tells the full story, but a broader context does (or at least helps).
For instance, if you’re looking at a 6’0 basketball player, you may deem him to be rather tall; however, if that same player is standing in a group where everyone else is at least 6’8, he suddenly doesn’t look as tall.
So instead of asking, “how much is X?”, the better question becomes, “how much is X relative to its peer group?”
That shift, from absolute to relative, is where many of the most persistent edges in markets begin.
To see just how powerful this concept is, let’s pay a visit to our old friend:
The Options Market
Despite the innumerable complexities of the options market, at the end of the day, it still largely boils down to buying low and selling high.
But how do you know what’s “low” and what’s “high”?
In options, that estimate is typically based around implied volatility:
Now, implied volatility is a rather tricky thing:
On one hand, it clusters, mean reverts, and often signals stress in the underlying.
On the other hand, it’s a model-derived construct — a number inferred from prices, but not a tangible asset you can hold.
To see what we’re getting at, let’s have a look at some data.
What Goes Up, Must Come Down — Sometimes
To begin, let’s have a look at the price of the S&P 500 with an overlay indicating regimes of high implied volatility:
As demonstrated, while it may not be a crystal ball, it is indeed useful at isolating periods where future realized vol is higher than usual.
But more importantly, every high vol regime ends.
When investors get worried and start paying a premium for options, implied vol spikes, but it never usually stays high.
To see another example of this, let’s look at the constant-maturity 10d implied vol for a random stock, say CELH:
The same dynamic appears, where periods of extreme relative implied volatility are typically followed by subsequent mean reversion.
We can take this a step further and zoom out to look at the behavior across a broad basket of liquid options:
At this point, you may say to yourself:
If implied vol is so predictive (simply just short it when it gets high, buy when it’s low), why can’t you just print money?
Well, this is where the theoretical nature of implied vol starts to kick in.
In the above examples, we used a constant 10-day implied volatility series — similar in spirit to how the VIX9D tracks a constant 9-day maturity.
Sticking to a constant maturity is analytically clean, but actually trading it is a bit messier:
To see this, let’s pitch a sample trade scenario:
You’ve run your model and are fairly certain that 10 days from now, the implied vol for the 10 day option maturity will be 20 points lower (e.g., IV 100% → 80%). How do you generate a profit?
From here, there are a few schools of thought:
Roll the 10-day tenor continuously.
Each day, pull the option series closest to expiring in 10 days and short at-the-money straddles.
This keeps you exposed to the exact maturity your model is based on, but it introduces path dependency. If the stock moves 5% on day one, you must close and reopen at a new strike and are thus constantly reshaping the position.
Short the nearest 10-day strip and hold to expiration.
Instead of rolling daily, enter once and hold until expiration.
This has a cleaner execution, but is now exposed to realized volatility risk. Even if implied volatility decreases as predicted, a large underlying move can overwhelm the trade if at expiration, one leg of the straddle is deep in-the-money.
Roll daily and delta-hedge continuously.
In theory, this isolates pure volatility exposure.
In practice, it requires constant trading, capital, precise automation, and optimized transaction costs. It’s elegant academically, but operationally heavy.
So, while you might be able to run a cross-sectional rank of vol to isolate stocks likeliest to make a larger move than normal, it’s a bit more difficult to fully capitalize on it with the same options that are already pricing in the move.
Now, while this highlights the inherent difficulty of options, fortunately, not everything is as hard.
To see that, let’s pay another visit to a dear friend:
The Stock Market
The power of the cross-section shines much more cleanly back in delta-one land, sometimes, almost too cleanly.
To demonstrate this, we’ll pull from a real-world strategy we’ve been running for quite some time:
Cross-Sectional Mean Reversion
Now, when it comes to stock prices, the adage of “what goes up, must come down” has so many anti-cases that it’s unreliable on its own.
However, not all stocks are the same.
What if there existed a class of stocks that structurally trended downwards for bone-deep economic reasons?
If we were able to isolate those, we could sell the highest tranche when it goes up, then buy it back lower after some time.
Thankfully, such a class exists — but not without some quirks.
To begin, let’s pull the full stock universe on a given date t:
Now, 5,000 names is quite a big breadth. To make things easier for ourself, it will help to first classify these stocks by size — we’ll go with a simple market cap classification to start:
Market Cap Tier Mapping:
5 → Mega Cap (≥ $200B)
4 → Large Cap ($10B – $200B)
3 → Mid Cap ($2B – $10B)
2 → Small Cap ($250M – $2B)
1 → Micro Cap ($50M – $250M)
0 → Nano Cap (< $50M)That absolute bottom tranche, the 0s, is where our bread and butter lies.
As we’ve addressed before, these nanocap names, while capacity-constrained, represent the worst of the worst and almost uniformly head to bankruptcy in a series of dilutions, shady financings, and de-listings — a target rich environment for a short seller.
Once we have our target universe at that point-in-time, we can then move onto structuring this into a trade strategy.
Note: Accurate point-in-time market cap data is extremely important for running this. Nano-caps today may not have been nano caps yesterday, and in these names especially, the shares outstanding values can change by the month. We used Alphanume’s historical market cap endpoint to ensure we avoid look-ahead bias.
Now, we don’t want to risk getting into an overfitted quantitative framework, so for simplicity, we’ll define our strategy as follows:
For all nanocap stocks on day t, we calculate the distance of the stock price from a fixed-period moving average (e.g., 10 day TWAP)
We use the prior day’s market cap as the current day’s wouldn’t be known until the end of the day.
We then rank all stocks in the grouping from highest percentile to lowest, based on that distance
If SMX is 40% above its fixed period average price, it will rank higher than IBIO, which may be 8% above its average.
We short an equal-weighted basket of the 10 most-extended names and hold until the next rebalancing cycle.
Repeat.
Extremely simple, but let’s see what the data says:
From exclusively shorting nano-cap stocks in this manner, each basket posted an average negative return of -9%, with a 76% success rate and a Sharpe ratio exceeding 2.
Now, before you see this as a money printer, there is a rather large caveat that makes real-world implementation more challenging: capacity and borrow constraints.
To understand this, let’s take a look at the most recent basket of outputs:
Because these names are less liquid than average, when available shares are in short supply, the borrow costs to short sell can sometimes get astronomical:
Gross Basket PnL (without borrow cost): +15.3%
Net Basket PnL (with borrow cost): +12.6%
So, although borrow costs add some friction, it doesn’t always totally override the available profits from the strategy.
Additionally, because these stocks are already so small, you may be able to find share locates for a notional size of few thousand bucks each, but scaling up beyond that gets tricky.
We’ll include the code for this below so that you can replicate the full strategy historically and in real-time.
At this point, you can hopefully see the key value that cross-sectional universe selection provided in getting to this point.
If we were to run this exact same strategy but just on a random grouping of all stocks, we would certainly be run over by momentum continuation in large caps, passive buying from index funds, or the million other factors that send good stocks upwards.
Now, to drive the point home one final time, let’s look at another asset class where the cross-section expresses itself even more cleanly:
The Crypto Market
To begin, there’s no need to overcomplicate things: we want to simply replicate the same approach as with our equities strategy, but applied to a 24/7 market.
We’ll start with a baseline working assumption:
In crypto, outside of the majors (BTC, ETH, SOL), most tokens lack durable cash flows, governance rights, or economic outputs that justify persistent long-term appreciation. That doesn’t mean they can’t rally, but overall, we expect some structural decay.
With that in mind, we can adopt another short-only bias.
To implement this, we’ll have to constrain our universe to tokens with perpetual futures contracts. Perpetuals allow both long and short exposure without fixed expiration, making them a suitable instrument for our approach.
Once we have a defined universe of tradeable contracts, we apply the same cross-sectional framework used in equities.
For each token, calculate the distance from a fixed-period average (10-day VWAP, as data is continuous and makes the calculation more stable)
We rank the universe from highest to lowest based on that distance, then isolate the top decile
Next, we short an equal-weighted basket of the names in the top decile and hold until the next rebalancing date.
Repeat.
Let’s see how a simple strategy as such performed:
Looking at the data, exclusively shorting a universe of perpetual futures in this manner produced an average basket return of -2%, with a 64% success rate. As with equities, the edge emerges not from predicting direction outright, but from ranking relative extension within a structurally biased universe.
Now, as always, the difficult part of this doesn’t come from the strategy, but interestingly enough, from regulatory constraints.
As of this writing, if you’re a U.S. citizen, it is extremely difficult to get access to the exchanges that have the most lucrative short opportunities.
In our example, we used the MEXC exchange as it typically ranks highest in terms of available universe size:
Typically, access to these exchanges can be found with a bit of leg-work (dedicated IP from a “friendly” country), but perhaps we were sloppy once, which resulted in this:
As of this writing, funds remain locked pending KYC review. Given opaque compliance structures and unclear data handling standards, we elected not to pursue further escalation.
When paired with the limited capacity of these markets (some futures have a max size of $200), we frankly decided to move on.
However, if you’re in a crypto-friendly country and have access to established but expansive exchanges like Binance, it won’t hurt to give this a deeper look.
We’ll include the code for this below so that you can replicate the full strategy historically and in real-time.
Final Thoughts
In asset class after asset class, we have seen the power of the cross-section.
Not just looking at one stock or option has done on its own, but rather what one has done relative to all others.
Like with most businesses, coming up with a viable strategy or idea isn’t the hardest part; it’s the execution. Whether that’s managing interest financing costs of borrowed shares or finding accessible crypto execution venues; much of the edge comes from the operational side of the trade.
For each strategy discussed here, we are publishing the exact scripts used in our research so you can verify every calculation and observe how the framework behaves on a real-time, forward-looking basis.
If this piece sparked a new idea, challenged an old assumption, or gave you something concrete to test, then it did its job. Quantitative trading remains one of the most intellectually demanding and structurally rewarding fields in finance, and we are glad you took the time to explore it with us.
Code + Strategy Lab
In the past, when we shared code, we simply published the files and left the rest to you: database setup, API wiring, data normalization, and all the surrounding infrastructure required to make it run. It worked, but it wasn’t scalable.
So we structured it properly.
The Alphanume Strategy Lab is a public GitHub repository containing production-oriented quantitative research built directly on Alphanume’s market data APIs. Each strategy is implemented as a reproducible workflow; from point-in-time universe construction to signal generation and forward evaluation.
This is not a SaaS dashboard or a black-box signal feed. It is a transparent reference implementation of how systematic research is actually conducted using structured data.
If you want to replicate the strategies discussed here, build new ones on top of production-grade datasets, or see exactly how we integrate Alphanume endpoints into live research pipelines, this is where you start.
And if there is a dataset you believe should exist, reach out. Alphanume was built because we needed better data. There is a strong chance the next dataset will be built for the same reason.
Alphanume Strategy Lab (GitHub)
Production-grade quantitative research. Fully reproducible. Built on structured market data.
Good luck, and trade well.