The Mathematical Trading Trick A 23-Year-Old Exploited For +969.8% In 12 Months (Full System Reveal)

ELI5 / TLDR

A 23-year-old named Martin Luke won a US trading championship with a 969.8% return in a year, while losing on roughly four out of every five trades. The whole thing rests on one boring idea: if your losses are tiny and your occasional wins are large, you make money even when you’re usually wrong. He achieved this by entering trades right next to a price floor, so his stop-loss (the automatic “get me out” exit) could sit very close, which let him buy a lot more shares for the same dollar of risk. One big winner could erase thirty-odd small losers and still leave him ahead.

The Full Story

Being wrong most of the time, profitably

The headline numbers are deliberately shocking: 209 winners, 731 losers, a 22% win rate. Most people would read that as a slow-motion account blow-up. The video’s argument is that the win rate is almost irrelevant on its own. What matters is the average size of a win versus the average size of a loss.

The tool here is expected value — the average outcome per trade if you repeated it forever. You weight each possible result by how often it happens. Martin’s math, as presented:

Expected value equals win rate times average win minus loss rate times average loss. That’s 0.22 × 15% minus 0.78 × 2.5%, which gives us 3.3% minus 1.95% equals positive 1.35% per trade.

So each trade is worth about +1.35% on average. Do that across ~940 trades and it compounds hard. The provocative comparison: a trader with a 60% win rate but symmetric 1:1 payoffs has less than half the expected value per trade. Being right more often is not the same as making more money.

Why the stop-loss size is the whole game

The clever part is a piece of arithmetic Martin lifted into a strategy. He measures every trade in R multiples — how many times your risk you stand to make. The formula is simply trade return divided by stop width.

R multiple equals trade return divided by stop width. This is a simple division, but because the stop width is in the denominator, as it approaches zero, the R multiple approaches infinity.

Concretely: a stock you enter at $100 aiming for $125 (a 25% gain). With a 3% stop you risk 3 to make 25, an 8.3R trade. Halve the stop to 1.5% and the same trade becomes a 16.7R trade. Same stock, same entry, same target — only the distance to your exit changed. Below roughly a 2% stop, the payoff stops rising gently and starts shooting up. This is why he obsesses over getting his stop to 1.5% or tighter.

The tight stop is secretly a position-size lever

Here’s the part that does the real work, and it’s easy to miss. If you decide in advance to risk a fixed dollar amount per trade — say 0.5% of the account — then a tighter stop lets you buy more shares, because your loss-if-wrong stays the same.

The worked example: a $50,000 account risking $250 per trade. With a 3% stop you can afford 333 shares (a $8,300 position). Tighten the stop to 1.5% and you can afford 667 shares (a $16,700 position) — double the shares for the identical $250 of risk. If the stock then runs, you make double the profit. So the tight stop compounds the edge twice: once through the R multiple, once through the bigger position.

The tight stop didn’t just improve a ratio on paper. It put him in twice the shares.

The practical caveat the video glosses over: a 1.5% stop only “works” if your entry is genuinely right next to support. Otherwise you just get stopped out by ordinary noise. Which is why the entry method matters more than anything.

Finding the right stocks

Martin runs three scanners. A pre-market gap scanner for news-driven movers, a “potent” scanner for yesterday’s strongest names, and a weekend “leader” scanner for the biggest 30-day movers. The signal he’s really hunting is a theme — when several stocks from the same industry light up together (his example: a cluster of metals or quantum-computing names), he reads it as a sector heating up.

He then sorts candidates by how their moving averages are stacked. (A moving average is just the average price over the last N days, redrawn each day — a smoothed trend line. “EMA” weights recent days more heavily.) When the 9-day sits above the 21-day sits above the 50-day, that’s the strongest uptrend — his buy list. The reverse stack is weak. He only trades the fastest names in the hottest sectors, stocks that routinely move 5%+ in a day.

The setups: entering near the floor, not at the breakout

The key shift from 2024 to 2025 was abandoning breakout entries (buying as a stock pushes through a ceiling) in favour of pullback entries (buying as it dips back to a rising support line and bounces). The breakout buyer enters high with his stop far below; the pullback buyer enters low with his stop just beneath the support he’s leaning on.

The Nvidia example makes the contrast stark: the pullback trader risked 1.6% to capture 27% (a 17:1 trade); the breakout trader, forced to put his stop all the way down at the base low, risked 21% to capture 16% — less than 1:1. Same stock, same move, wildly different math, purely because of where and when they entered.

His second setup is the anchored VWAP break-and-retest. VWAP is the average price since a chosen date, weighted by how much volume traded — a read on who’s been winning, buyers or sellers, since that anchor. He anchors it to a capitulation low (the moment panic selling exhausts itself) and waits for price to come back and bounce off it.

The highest-conviction version is what he calls a multiple-edge entry: several independent support levels — the anchored VWAP, a moving average, a round number like $100 — all converging in one tight zone.

If a trader buys at the anchored VWAP and another buys at the 9 EMA and another buys at the previous highs, then there would be the highest probability point that the stock will find support.

Different traders watching different tools, all stepping in at the same place, concentrate buying pressure exactly where he wants it.

One full trade, start to finish

The IonQ example (a quantum-computing stock, September 2025) walks the whole method: the weekly chart shows a tight “coil” (volatility shrinking while the trend holds), the daily confirms a base under a repeatedly-rejected $43 ceiling, and the 5-minute chart is where he actually pulls the trigger — entering at $44.20 with a stop at $43.40, under 2% risk. On a $150,000 account risking 0.5% ($750), that 80-cent stop let him buy 937 shares, a $41,000 position — about 28% of the portfolio, all from risking half a percent.

The stock ran to $73. That single trade made ~$27,000 from $750 of risk — a 36R trade. As the video puts it: he could lose 36 trades in a row and one winner brings him back to break-even. And because the whole quantum sector moved together, he held three or four such trades at once.

The honest part: December

After eleven months of this, December 2025 handed him a 26% drawdown — almost entirely self-inflicted.

December, this is overtrading… I keep jumping between longs and shorts but they don’t have much follow-through. And then I just got slapped in both ways.

The market turned choppy and directionless. The same tight-stop system that prints money in a trending market becomes a liability in a range: every small stop-out is survivable, but 70–80 trades a month at a 22% win rate means death by a thousand cuts. His own fix is blunt — trade less, and stop trading when the environment turns hostile. The edge lives in the entries; when the entries lose their predictive power, the machinery just grinds the account down.

Key Takeaways

Expected value per trade = (win rate × average win) − (loss rate × average loss). A 22% win rate with large wins can beat a 60% win rate with symmetric payoffs.
R multiple = trade return ÷ stop width. Because stop width is the denominator, shrinking it makes the payoff rise hyperbolically — the effect accelerates below about a 2% stop.
A tighter stop is also a position-sizing multiplier: at a fixed dollar risk, halving the stop width roughly doubles the share count, doubling profit on winners.
Halving a stop from 3% to 1.5% turned an 8.3R trade into a 16.7R trade on the identical entry and target.
Pullback entries (buy the bounce off rising support) give a tighter stop and lower cost basis than breakout entries (buy through the ceiling), because the entry sits right next to the floor.
“Multiple-edge entry”: when an anchored VWAP, a moving average, and a round number all converge in one zone, independent buyers cluster there, raising the odds of a bounce.
Anchored VWAP = volume-weighted average price since a chosen anchor date (often a capitulation low); it indicates whether buyers or sellers have controlled price since that point.
EMA stacking (9 above 21 above 50) signals the strongest uptrend; the inverse stack flags the weakest names.
Multi-timeframe confirmation: the daily/weekly chart says where to buy, the 1- or 5-minute chart says when — this is how he achieves sub-2% stops.
Entries are mechanical and exact; exits are discretionary (partial sells into strength, final exit on first daily close below the 9 EMA). Tight entries protect capital; flexible exits maximize winners.
The same tight-stop system that thrives in trends produces large drawdowns in choppy markets — small losses compound across dozens of trades. The defence is to trade less when conditions deteriorate.
He started with $1,300, survived a 50% drawdown, and traded for 2.5 years before even returning to break-even.

Claude’s Take

The title is engineered for clicks (“mathematical trading trick,” “exploited”) and the framing oversells a fairly old idea: asymmetry. Traders have known for decades that you can be wrong most of the time and still win if losses are small and wins large — this is the entire “cut losers, ride winners” tradition, and Ed Thorp (name-dropped at the end) built a career on edge-plus-sizing. The “trick” is real but it is not a secret.

What the video gets genuinely right, and explains more clearly than most, is the second-order effect of the tight stop — that it doubles as a position-size lever. That mechanical link between stop width and share count is the actual insight, and it’s underappreciated. The R-multiple arithmetic is correct.

Where it earns scepticism: the whole edifice depends on entries that are right often enough at a tight stop. The video asserts this edge exists (pullbacks to converging support) but offers cherry-picked Nvidia and IonQ charts during a roaring 2025 momentum tape, including the quantum-computing mania. A 22% win rate with a 1.5% stop is brutally unforgiving — you need the wins to be both large and frequent enough, and in a chop you get neither. To the video’s real credit, it does not hide this: the December drawdown section is honest and is the most useful part for a sceptic, because it shows the system’s failure mode rather than burying it. A 969.8% year is also, by definition, a single sample from a competition designed to reward extreme risk-taking — survivorship and luck are doing more work than the narration admits.

Net: a clearly-taught lesson on expected value, R multiples, and stop-as-position-sizer, wrapped in influencer packaging and one extraordinary (and probably not repeatable) result. Worth it for the position-sizing mechanics; discount the implied promise that you too can compound 1.35% a trade across 940 trades. Score 6 — solid pedagogy on the math, real transparency about the downside, but the survivorship framing and clickbait pull it back.