Interview noteThis page restates a public interview in new words. Dollar results, win rates, and payouts are claims made by the speaker or the host. They are not this desk's record, and they are not a result to expect.
The firm spends this video on a free challenge simulator and on one worked example, not on a live trade. The host's claim is that the difference between people who pass and people who fail is often just doing this arithmetic before the first attempt, instead of learning it in their head over months. The numbers below are his example inputs and the simulation's outputs. He says none of it is an assurance of a pass.
What gets typed in
The first choice is calculation mode. Most people, he says, should use dollars, because that is how the tool is told whether drawdown and the gain goal are dollars or percentages. The core strategy in the example is what he calls strong but realistic: 2 to 1 reward against risk, and a 40 percent win rate. A viability meter sits on those inputs on purpose. He changes the win rate to 90 percent at the same 2 to 1 and the meter goes to zero, so a fantasy combination is visible before the simulation flatters it. Risk per trade in the example is a static $350.
The challenge rules he types, and he says they move with promotions, are a $100 challenge fee, a $140 activation fee, a $100,000 account, a $6,000 gain target, and a $6,000 max drawdown on an end-of-day trailing model. The funded side is the same 100K futures account, a 90/10 split as used at the firm at the time of the video, and two trades a day as the average, not a promise that every day will be two.
The consistency rule is a funded-account problem
He says the firm challenge accounts do not carry a consistency rule, so this block is for the funded account. At a 30 percent consistency rule, the tool shows the maximum gain allowed on one day and a simulated chance of violating the rule. That chance reads 0 percent for his inputs. He is careful about it. The zero applies only on paper, and only if the 2 to 1 and the 40 percent never change. Real trading has uncertainty. A zero is a simulated paper outcome, not a fact. A "great day" under those inputs, two trades, is $1,400 in theory, which sits $400 under the single-day maximum his risk implies. Drop the consistency rule to 10 percent and the same strategy shows a high simulated chance of a breach. The fixes the tool offers are the obvious ones: less risk per trade, fewer trades per day, or a larger payout target so one good day is not 30 percent or more of total gain. He sets the target payout at $6,000 to keep the example in one set of numbers.
Risk changes the pass rate and the bill
The first chart is simulated pass rate by risk per trade, from 0.1 percent of account size through 0.25, 0.33, and 0.5, up to 1.25 percent. At 0.1 percent the simulation prints a 100 percent pass rate. He says that is paper, that randomness exists, and that 0.1 percent can take forever to reach a payout. At 1.25 percent the pass rate falls to 62.5 percent. Higher risk reaches the target in fewer favorable outcomes and fails more often when they are not favorable. Trades to the first payout, on his strategy, are about 305 at 0.1 percent and about nine at 1.25 percent. A second view shows how many challenge attempts the simulation expects, the expected cost to get funded, the share of paths that fail five or more times, break-even return, days to funding, and the probability of a first payout. That last probability falls as risk rises.
The Monte Carlo he reads next is 5,000 simulations at the $350 risk and the 2 to 1. He states a 65.1 percent pass rate and a 34.9 percent fail rate, "two out of three," and in the same sentence the caption says a 30 percent win rate. The viability meter and the consistency example were built on 40 percent, so the 30 percent is inconsistent with the setup he described. The pass and fail shares he states for that run are 65.1 and 34.9. The same screen gives typical trades to pass, a worst-case attempt count, expected trades and expected cost to get funded (fee plus activation, times attempts), the percentage of simulations that failed more than five times, and the return on the 100K account required to break even against that cost.
A finer cut runs 5,000 simulations inside each risk profile, 50,000 in total, and reports passes, fails, and the median trades to pass. More risk, fewer trades to a theoretical pass, lower probability of passing. Expected cost at a 90 percent confidence interval rises with risk and the calendar shrinks. At 0.33 percent the simulated cost from passing through to funded was about $200, and it took much longer. At 1.25 percent it was about $600, often three attempts, at the 62.5 percent pass rate. A comparison table puts pass rate, typical trades, expected trades, expected days, cost, the 90 percent cost, and the five-plus-failure share side by side.
The host's instruction for using it is to read the how-to block first, because the outputs are easy to treat as facts. The tool is a cost planner and a compatibility check: where a stated win rate and payoff start to fall apart under a prop drawdown, and what the attempts are likely to cost before a single payout exists.