If You Only Watch One Trading Strategy Video, Make It This

ELI5/TLDR

A trader named Steven Dux explains how he makes money by betting that hyped-up tiny stocks will fall. His whole edge is bookkeeping: for ten years he has logged every setup by hand and worked out, on average, how often each one happens, how often it works, and how far the stock drops. He shorts only when the float, market cap, volume, and price all line up, because each number tells him whether there are more trapped buyers wanting out than fresh buyers wanting in. Three named patterns — gap-up short, bounce short, first red day — are all the same bet dressed differently: when supply overwhelms demand, you ride the people stuck at the top down to the bottom.

The Full Story

The whole game is supply vs demand

Strip away the patterns and there is one idea underneath. A tiny stock that has rocketed up has a crowd of buyers sitting on it. Some are up and itchy; some are trapped and desperate to break even. The moment the stock weakens, those people sell. Their selling makes it weaker, which makes more of them sell. Dux opens a short into that cascade and rides their panic down.

“There’s more people selling apples than current day buying apples.”

That sentence is the entire method. Everything else — the float thresholds, the volume ratios, the dollar blocks — is just a way to measure whether the sellers outnumber the buyers before he commits.

Why the filters exist

He trades only small caps, and only ones that pass a gate: initial market cap between $1M and $100M, float between 1M and 50M shares, price above $3. Each filter has a reason, not a superstition.

Float is the number of shares actually available to trade. Low float plus high demand means violent moves up — which is exactly why he won’t short the average 25% drop on a 1–2M float name: it can spike another 10% first and shake him out. Below $3 the statistics fall apart. Three whole sectors get deleted on sight — biotech, energy, and Chinese stocks. He has the receipts:

“I have traded biotechs for many, many years… probably traded about three to 500 tickers. The net profit of biotechs probably maybe around 1 to 2 million on 500 tickers.”

Five hundred tickers for almost nothing. Chinese small caps are worse — thin volume, sudden halts, no exit. He has watched one go from 2 to 200 with shorts trapped inside. So he avoids the categories where the math has personally burned him.

Statistics as an emotional anaesthetic

The clever part isn’t the patterns, it’s the spreadsheet. Since 2015 he has logged each setup by hand: how often it happens per year, the win rate, the average fade. Gap-up short: ~50–70 times a year, ~75% win rate, fades ~26% from the high. Bounce short: ~30 times a year, 80–85% win. First red day: 5–10 times a year, win rate up to ~90%.

Multiply frequency × win rate × average reward and you get a number: roughly how much the year should produce if you just show up and execute. That number does something useful to the brain.

“Once you have simulated how much money you can make end of the year… it eliminates your emotion from being FOMO because you know, okay, well, I’ll just wait for the year.”

If you already know the year pays out across 50 trades, missing today’s trade costs you nothing. The fear of missing out evaporates because the future is, statistically, already accounted for.

The three patterns

Gap-up short. A small cap gaps up 100%+ overnight on a news pop. He waits — never shorts the open. The stock spikes, then consolidates for about an hour. Around 10–11am, when it shows the first crack on the breakdown, he sizes in full, stop-loss just above the consolidation high. Risk ~7%, reward ~26%, so roughly 1:3.5. The catch: if pre-market volume already exceeds 50M shares, it’s too crowded — algos are playing games and the short won’t work. He estimates the day’s volume as 5–10× the pre-market figure and checks that ~30% of it trades before 11am.

Bounce short. This one needs a backstory. Look at the one-year chart for a single huge volume candle — say 30M shares traded around $5 — after which the stock died and sat flat for two months. Everyone who bought that spike is trapped, down 70%. Now the stock gaps up toward $5 again. The trapped crowd’s first instinct is to dump and break even. He calls that buried supply the “dollar block” (shares stuck × price), and he wants it above $150M. Against it, today’s fresh buyers — scared off when the stock plummets 15–20% at the open — bring far less. When the trapped supply outweighs fresh demand 2:1 or more, he shorts heavily. His GME example ran 10:1 and crashed 50% on the open — $1.5M in fifteen minutes.

First red day. The hardest, most lucrative, and most dangerous. The setup: at least three consecutive green days, each on rising dollar-volume, no red or flat day breaking the streak (any interruption resets the count). Two-day versions exist but need a 1,000% range instead of 300%. The bet is that the parabola exhausts the moment retail money stops pouring in. His trick for timing the top is the “dollar block by market-cap bucket”: stocks with the same initial market cap tend to top out at roughly the same total dollars traded regardless of price. So he projects which day the stock will hit that exhaustion number, ignores it until then, shorts a quarter position when it does, then adds the rest the next morning when the volume dries up and the bounce never comes.

“Short selling has to be very precise or you’re taking a huge amount of risk and potential to lose more than 100%.”

That line matters. A short’s maximum gain is capped — a crash fades maybe 50% — but the loss is open-ended if the parabola keeps going. Shorting a stock that can run 2,000% means precision isn’t optional, it’s survival.

Size is its own enemy

The recurring confession: he became big enough to break his own patterns. Short more than ~30% of the available shares and you stop transferring retail’s money to your wallet — you’re just trading against yourself, pumping and dumping with no one on the other side. His rules cap him at ~10% of float and ~1% of volume. On Beyond Meat he could have pushed $12–13M but stopped at $7M, because covering a position that large moves the stock against him and costs an extra 5–6%.

“The bigger the size is, the more difficult the game becomes.”

Key Takeaways

Float is the lever. Low float + high demand = violent upside, so shorting “the average” gets you shaken out before the real drop. Section every setup by float band before deciding size.
The “dollar block” — trapped shares × their price — measures buried supply. Above ~$150M it’s a strong bounce-short candidate; the trapped crowd becomes your selling pressure.
Volume ratio = emotional intensity. Compare projected fresh demand against trapped supply. A 2:1 or 10:1 ratio isn’t just a number; it’s the size of the crowd about to panic-sell.
Pre-market volume × 5 to 10 ≈ full-day volume. If pre-market already exceeds 50M shares, the name is too crowded and the short loses its edge to algos.
Volume concentrates 9:30–11:30, with ~30% of the day’s total before 11am. Edge lives in the morning emotion; afternoons go quiet.
First red day requires an unbroken streak of 3+ consecutive green days on rising dollar-volume. One red or flat day resets the count entirely — which is why long multi-day runners keep going.
Same initial market cap → same dollar top. Stocks in the same market-cap bucket exhaust at roughly the same total dollars traded, letting him predict the topping day in advance.
Shorts have capped upside, uncapped downside. Max reward on a crash is ~50%; a parabola can run 2,000% against you. Precision is risk management, not perfectionism.
Never short more than ~30% of available shares. Past that you’re the only player and the pattern breaks. His cap: ~10% of float, ~1% of volume.
A strategy must rest on logic or psychology, then be tested by yourself before risking real money. If the “why” doesn’t make sense, it won’t hold.

Claude’s Take

The packaging is pure trading-guru theatre — “$27,000 to $50 million,” a $1.5M-in-15-minutes flex, and roughly five sponsor ad-reads (prop firms, journaling apps) stitched into a forty-minute clip. Set all of that on fire. What’s left underneath is unusually honest and specific for the genre.

The genuinely good idea is that he treats trading as actuarial work, not prophecy. He doesn’t claim to predict the next move; he claims to know the base rates of a narrow, well-defined event and sizes accordingly. The frequency × win-rate × reward math as an FOMO antidote is a real psychological insight — most retail blow-ups come from chasing, and “the year is already accounted for” genuinely defuses that. The supply/demand framing (trapped buyers as fuel, the dollar block, the volume ratio) is a coherent mental model, not vibes.

What to distrust: survivorship and selection. Hand-logged spreadsheets are not audited backtests, and “I win 75–90% of the time” is exactly what every blown-up trader believed right up until the trade that didn’t fade. He admits as much — biotechs netted almost nothing on 500 tickers, Chinese names trapped him 100%, losses pile up “on the way up” on first red day. The strategies are also brutally regime-dependent (you need a steady supply of hype-pumped US small caps) and capacity-constrained — his own size breaks his own patterns, which quietly tells you the edge is small and crowded. This is shorting tiny illiquid stocks: the polar opposite of anything resembling investing, and a near-perfect way to lose money for anyone without his decade of hand-tracked reps.

A 7. Below the engagement line because of the hype wrapper and the unverifiable claims, but the underlying framework — statistics as discipline, microstructure as the real signal — is more rigorous and more teachable than 95% of “trading strategy” content. Worth understanding as a model of how someone thinks; not worth imitating without their dataset.