We tested the strategies of the traders who get paid millions. What we found: their edge is mostly how they run accounts.
Why I built the Prop Scale Planner
I watched the interviews. These traders show huge prop payouts, and the channels verify them. So I took every payout as real.
I tested their strategies. I coded each trader's own rules from their own words and tested them on 4 years of NQ, judging each year on its own. Most rules had a low win rate. Only 4 of 48 made money year after year.
So I asked a better question: how do they get paid so much? I read 171 transcripts to find out. Their real strategy is not only the trading. It is the business side: many accounts, small strict losses, big risk only to pass, payouts as soon as the rules allow, and smart spending on evaluations.
Then I built the planner. The Prop Scale Planner does that business math for you, with your own money and your firm's rules. And it adds the part that makes it stronger: a strategy with a tested edge.
Leaderboard: every trader, claim vs their own rules
One row per strategy, all 6 channels, all judged the same way. Click a column title to sort by it. Each row is the trader's own rule (headline version), coded from their episode / page, on NQ 1-minute data Dec 2022 – Nov 2026 (2026 = MNQ). All trades are pooled, and each year is judged on its own (the few Dec 2022 days count with 2023). WR = win rate · avg RR = average result in R after costs · PF = profit factor · t = t-score on all trades · Random = the same trades at a random time · Years green = years with 5+ trades that made money. WORKS = 30+ trades, t 2+ and every year green · GOOD = 3 of 4 years green, avg RR above 0, and the Max fixed 1:2 test green too (a real but smaller edge) · MAYBE = t 1+, avg RR above 0 and at most one red year · NO = the rest · too few = under 30 trades or under 3 years (ES and gold: only 2026 data). Click a Chart Fanatics row for every combo. Combo counts (WORKS / MAYBE) use the strict rule only. TopstepTV (John Hoagland) and John Grady are left out: they are not typical prop-firm traders.
Trader
Their claim
Their rule (headline)
Trades
WR
avg RR
PF
t
Random
Years green
2023
2024
2025
2026
Max 1:2
Combos judged
WORKS
MAYBE
Random WORKS
Verdict
How they turn trading into big payouts
Their payouts are real and verified by the channels. Here is how they get them, in their own words from the episodes, plus two simulations of the account rules.
Verified payouts
Chart Fanatics features traders with verified results, and we take every payout shown as real money. A payout total is the sum from many accounts, after all the evaluations it took to get them. That is the key: the payout comes from the whole account business, not from one trade setup alone.
Many accounts. Apex allows 20, all copied with one click. Kane says he bought about 500 accounts; JJ Simon about 2,200 evaluations.
Tiny, strict losses. Okala always uses a 10 pt stop. Kane moves to break-even fast. Fabio has a daily max loss.
Bigger size when it works. Okala: "if you're hot, step on the gas." Kane rides news days (an $8k day on a 25k account). Fabio adds 3–4 positions on trend days.
Things the video does not show. Kane: "the discretion is my edge." Fabio reads order-book (MBO) data and says his model is not a standalone strategy. Candle data cannot test either.
The surprise: the prop deal itself pays
1,000 make-believe traders with no edge: 1 random trade a day (1:2, avg RR −0.05 after costs), copied onto 20 Apex-like accounts for 2 years ($25 eval + $79 activation, $2,000 payout cap, 6 payouts per account, rebuy when blown).
Median payouts: $96,000. Luckiest 1%: $392,000.
80% ended up ahead after paying for evaluations.
Why: a blown account costs about $104, but a funded one can pay up to $12,000. Small cost, big possible payout. A big part of the edge is in the account rules, not only the chart.
Discipline is the real line
Same simulation, 20 accounts, 1 trade a day, sloppier trading.
avg RR per trade
Median net (20 accounts)
Traders ahead
−0.05 (strict losses)
+$65,000
80%
−0.15
−$2,310
48%
−0.30 (typical losing trader)
−$29,200
10%
That fits the industry numbers: only 4–7% of prop buyers ever get paid. The guests are the ones who keep every loss small and run many accounts.
Big months come from size and copies
The host says Kane took $130k to $1.4M in about a month. Months like that come from bigger size on strong days, copied onto many funded accounts, with very strict loss control. Size is the lever; strict losses make it safe.
What you can copy
Their risk control and the many-accounts approach. Most of their entries did not beat random entries when tested on their own, so pair the method with a tested edge. Adding size into winners (Fabio and Okala both do it) is not tested yet.
Limits of this model
The prop model is simpler than the real rules. Real firms add consistency rules and payout reviews, so real numbers would be lower — and firms like Apex keep tightening caps for this reason.
Re-checked from the full transcripts: Kane, Okala, Fabio
Max supplied the full episode transcripts. Each model was re-coded from the trader's own words (quotes and timestamps below), then re-tested with every entry × stop × target × exit, a random twin for every trade, and Max's fixed 50 pt 1:2 stop. All trades Dec 2022 – Nov 2026 pooled and each year judged alone (Kane with ES: 2026 only, so too few years). WORKS = t 2+ and every year green. GOOD = 3 of 4 years green, avg RR above 0, and that trader's Max 1:2 test green.
How they really run their prop accounts
Their own words, from the transcripts. The money comes from the account method, and it shows up on the channels that feature payout stars.
Channel
Transcripts
Who is on it
Prop accounts (their words)
Money machine
Guest by guest
Guest
Claim
Many accounts
Spread the trades
Evaluation
Once funded
Burned accounts
Spend → payouts
Data notes
Their famous statistics, checked
Claim
Who
Claimed
Measured here
So what
What makes a successful prop trader: the proof
The account machine: how the method turns small edges into big payouts
A trader copies one rule onto many prop accounts, rebuys every account that blows up, and posts the total of all payouts. The simulation below does exactly that with each trader's real backtest trades, and with random trades (no edge at all).
Accounts copied
Eval price
Evals bought
$ spent on evals
Payouts
$ paid out
Best month
Net after evals
RANDOM trades: $ paid out
best month
net
Assumed generic 50k account: pass at +$3,000 before a $2,500 trailing drawdown, $1,000 daily loss stop; funded payouts of everything above a $2,600 buffer every 8 trading days (min $500, 100% of the first $25k then 90%); a blown account is replaced at once. All accounts take the same trades (a trade copier), started 5 trading days apart. Risk per trade = the trader's own size. Generic rows include no activation fee. Apex-like rows: $25 evaluation + $79 activation per funded account, payouts capped at $2,000 each and at most 6 payouts per account before it must be replaced. No consistency rule is modelled, so real payouts would be a bit lower.
Each trader
Insights
Method, data and limits
Research only. Not financial advice. Built from public strategy pages and episode transcripts; payouts are as shown and verified on each channel. Trader or channel? Contact us to correct anything. Data: NQ 1m Dec 2022–Nov 2025, MNQ / MES / MGC 2026.