Behind The Scenes of a Profitable Short Selling Operation
“Gravity is the cleanest trade in the book.”
For most market participants, the playbook is simple: buy quality assets, hold on, and let compounding do its work. And honestly — it’s good advice. One glance at the S&P 500 over the last decade explains why it’s become the gold standard of investing.
Now, while this works for most investors, it doesn’t fit all.
There exists a class of participant who see the market as not just a way of capital to companies, but rather a mechanism that can be systematically leveraged for personal profit.
As quantitative traders, we are that class of participant.
To bring you up to speed, we still actively run our monthly-rebalanced long momentum strategy which has continued to perform exceedingly well:
A Junior Quant's Guide to Time-Series Momentum
"You’re not predicting the future. You’re betting yesterday keeps happening."
However, when it comes to the buy-side of markets, there’s really only so much that you can do.
Our long only momentum strategy has delivered strong results, but it remains tied to the broader market. If the bull market continues, it should keep outperforming — but in a market-wide drawdown, strategies like this inevitably take a hit.
And if a trading business can be dragged into months of losses just because the market slipped, that’s not a robust business. The core mandate of quantitative trading is simple: generate profits in all conditions: up or down.
To mitigate some of that one-way risk, we began gearing our operations to focus on short-selling:
At first, we messed around with purely quantitative strategies like chasing volatility and shorting small-cap stocks that became over-extended in pre-market trading.
Over time, we shifted toward deeper structural edges: how markets react to SEC filings and corporate actions, such as dilution events revealed in S-1 filings.
That pivot made the difference.
By anchoring our strategies to clear, rational mechanisms rather than noise, we found substantially more consistent success:
We last left off describing a rather trivial implementation of short selling a stock for 30 days after a given corporate event.
Since then, we’ve taken that seed idea and scaled it into something much bigger: a daily cash engine built on short selling.
In this piece, we’ll pull back the curtain on how we do it: synthetic index construction, complex order execution, and the mechanics of turning it into real profits.
So, without further ado, let’s get right into it.
It’s Like The S&P 500, But Reversed
You’ve probably heard the idea that small traders sometimes hold an edge over large institutions. On the surface, that sounds paradoxical, but sometimes, it’s true.
To see this, let’s examine our early approach of shorting a company for 30 days after they make a dilutive S-1 filing. When a company dilutes their own shares, it’s often seen as a desperate last-option move for raising cash, so most companies don’t do it (instead, good companies tend to buy back shares, reducing total float — the opposite of dilution).
These moves happen most often in nano-caps on the brink of delisting. A $40B hedge fund might see the same signal and know that the stock is headed lower, but they can’t size into a $50M market cap without distorting it or eating massive slippage; so they pass.
Smaller traders don’t have that constraint. You can press $100K into a setup like this; a rounding error to a megafund, but a high-expected-value trade for those willing to scoop up the scraps.
Of course, though, with every pro comes a con.
Shorting $5 nano-caps looks linear most of the time — until you meet the grim reaper: tail risk.
To see an example of this, let’s take a look at Forward Industries (NASDAQ: FORD):
In the market regime at time of writing (Q3 2025), it’s become a popular trend for stale, under-the-radar companies to announce that they’re running some new “crypto treasury” strategy, sending their shares higher on the news. A few days later, an SEC filing follows, revealing that the company is raising cash at those elevated levels. Not long after, the price drifts back down and the company is never heard from again.
The big picture is clear, but the risk remains that any one of these names could rally 10x on a gimmick theme before reality sets back in.
While these blowups are the exception rather than the rule, you can’t build a viable business if one day you wake up and lose 10x your capital for no good reason.
So, that sparked an idea:
What if instead of taking these single-stock wagers, we created an index of hundreds of these “toxic” assets to continuously short?
Idiosyncratic risk is unavoidable by nature, but it is largely hedge-able through sufficient diversification. So, what if we just created an inverse S&P 500 — instead of the 500 best companies, what about a universe of liquid, toxic companies only?
Such an index would virtually be a straight line down; but as a short seller, that’s the point. If we created such an index, we would aim to replicate it daily through short sale exposure by shorting the individual components.
Now, we already know that overnight borrow costs can be steep, so instead of just doing buy and hold, we opt for daily intraday exposure.
This has a few advantages:
No overnight borrow costs.
In the land of small-cap biotechs headed for failure, overnight borrow costs can exceed 200% on an annualized basis. While you’ll still have to incur per-share locate costs (e.g., $0.003/share), it will be dramatically cheaper than funding an overnight position.
No margin interest.
With most professional brokers, you can borrow up to 4x your starting capital under Reg-T margin. If positions are flattened before the close, there’s no interest expense.
Lower tail risk.
Idiosyncratic risk doesn’t vanish, but realized intraday volatility is generally dampened by higher liquidity and tighter spreads during regular hours; unlike pre/post-market, where thin books make stocks easier to whip around
The structure is simple: each day we borrow as much as we can, short the weakest companies, close positions by the bell, and reset to do it all again tomorrow.
Of course, in this business, the theory is the easy part. It’s execution that demands the real elbow grease.
Index Replication: Easier Than You Think
At first, the idea of buying or selling hundreds of securities at once seemed daunting. Would we have to eat the bid/ask spread on every name? How could we coordinate all the orders to go out at the same time?
Surprisingly, it’s much simpler than it sounds.
The key lies in one of the most underappreciated, but genius features of modern exchanges: opening and closing auctions.
These auctions are designed to match all eligible orders at a single, consolidated price — the official open or close of the trading day. Think of them like massive, well, auctions: every order submitted before the cutoff is considered, and the exchange determines a clearing price where the most volume can be matched.
If you’re running daily backtests with OHLC data, those “open” and “close” prices aren’t just the first or last trades of the day — they’re the actual prices set in these auctions (for daily OHLC data specifically).
To participate, you have to specify it explicitly with Market-On-Open (MOO) or Market-On-Close (MOC) orders.
Example: Say we want to short 200 names in our toxic basket at open. Instead of sending 200 separate aggressive market orders and getting picked off across 200 bid/ask spreads, we just submit 200 Market-On-Open (MOO) orders the night before.
When the opening auction fires at 9:30 AM, we’re filled at the official opening price, alongside the rest of the market — no fuss, no slippage.
This mechanism is what makes basket execution clean and scalable.
Once you're plugged into the auction logic, trading at the open or close becomes far more orderly and, more importantly, far closer to your backtest than you might expect.
The only real exception is in ultra-illiquid names where auction volume is thin (e.g., stocks with average daily notional volumes of just a few thousand dollars). Those are easily avoided with basic filters.
So, now that we have a way of getting in and out of the market with minimal slippage, the next step is generating the orders themselves.
That too is easier than you’d think.
Most professional trading platforms support basket order functionality: you just upload a pre-configured CSV of orders and fire it off with a single click.
We first generate the daily basket in Python by iterating through the names in our toxic index and writing the orders for submission:
Next, we upload the file to our trading platform:
Once it’s loaded, we do a quick sanity check to make sure everything looks right, then hit execute and let the auction handle the fills.
The only real bottleneck today is locates. Most names in the basket still require sourcing the cheapest borrow before we can short them. Thankfully, our broker provides an API for locate management, so this step should soon be automated.
And that’s really it.
In just three steps, we’ve built the ability to transact across virtually unlimited securities — simultaneously, cleanly, and at scale.
What’s In This Basket, Anyway?
Now, there are thousands of stocks and if you just do a naive “sort by worst performers”, you are likelier to find pure randomness than predictable, repeatable negative forward returns.
So, our basket is built only from segments where negative drift is expected for a specific reason:
Toxic structures (companies who skirted the standard IPO process)
Recent negative corporate actions
All dilution events in the last 30 days
All reverse splits in the last 30 days
This initial sweep will produce hundreds of tickers. From there, we narrow the list by adding features into a model to determine the best candidates to short each day:
Ticker
Important for categorical models like CatBoost to avoid recycling the same repeat losers.
Market cap classification
Ranked 0–5 by size.
Return metrics
Beta, momentum, price, t-n returns; all normalized (e.g., 1–5) instead of raw values.
This will return a smaller, but still sizable basket (e.g. 40 names) that we then short daily.
Because we can reasonably assume execution at the official auction prices, our backtests mainly modeled costs from commissions, exchange fees, and locates — all of which are relatively minimal.
By trading a broad basket of uncorrelated names, the portfolio’s realized volatility is structurally low by design. Each trade has an expected value of ~0.5% net of costs: not huge in isolation, but highly reliable in aggregate.
The flip side of that low volatility is that raw dollar PnL can feel modest unless size is scaled. Fortunately, most brokers offer 4x intraday leverage, which we treat not as a risk to suppress but as a strategic option.
Given the low correlation and muted downside risk across the basket, we’ve been steadily expanding the number of tickers per day to maximize the use of that “free” leverage window to boost dollar returns without meaningfully increasing exposure to blowups or tail events.
Final Thoughts
The strategy outlined here does the only job that actually matters: making money. A 0.5% edge won’t turn you into an overnight billionaire, but it will put you in the black at the end of the day and that’s what counts.
The old adage of “it takes money to make money” is unfortunately true, especially in this business. However, choosing to embrace this truth and prioritizing hedged, diversified wagers instead of chasing home runs is how you not only stay in the game, but actually walk away with something to show for it.
The deeper we’ve gone into short selling, the clearer it’s become: it’s far easier to predict what’s going to go down, than what’s going to go up.
We’re not saying go out there and root for American companies to fail, but we are saying that if one can spot a clearly poor investment, with a bit of elbow grease those poor investments can be turned into a profit.
Engineering Returns
One of the biggest lessons we’ve learned is the power of blending weaker strategies into a unified portfolio. On their own, each leg might look modest. But together, the Sharpe improves dramatically.
Take a basic example: shorting a single-stock YieldMax ETF like $CONY while going long the underlying stock, $COIN.
These covered call ETFs systematically lag in up markets due to capped upside, so the long-underlying / short-ETF trade tends to generate positive returns when markets rise and cushions losses when they fall. It works in principle, but performance can be choppy.
Now, scale that idea across the entire YieldMax suite: $TSLY/$TSLA, $NVDY/$NVDA, and so on. Still not perfect, but now you’ve diversified the idiosyncratic noise. Then, you can take it one step further: add in a structural broad market hedge like long $SPY vs. short IWM 0.00%↑ — a position that tends to pay off in market drawdowns, given the small-cap beta drag.
When you combine these strategies — each grounded in some repeatable, structural edge — you get a much smoother equity curve, where drawdowns in one leg are often buffered by gains in another. The key isn’t just diversification for its own sake, it’s intentional pairing, where each long/short is backed by a clear rationale.
These are just some of the levers we’ve been pulling to scale real-world execution without relying on crystal balls. We’ve still got more work to do; automating locates, further increasing our investable universe, and potentially using offshore brokers for higher leverage 👀, but this was just a way of offering an in-depth look at an area we’ve found some success in.
If it sparked an idea, challenged your thinking, or just showed you a new angle, then it did its job.
Good luck, and happy trading. 🫡🫡
Great article per usual