93 Out of 100 Traders Lose the Fee: Prop Challenge Math, Simulated

A trader running 32.4 million simulated trades found that 93 out of 100 attempts to pass an FTMO-style prop challenge fail once real trading costs are included — even with disciplined risk management.

The simulation, published on TradingView by user hectorldu under the title "Prop challenge with no edge: 93 of 100 lose the fee," tested a blunt question: how often can a prop-firm challenge be passed on risk management alone, with no genuine trading edge?

The setup copied FTMO challenge rules exactly: a profit target, a maximum daily loss and a maximum total loss, spread across two phases. The trader then simulated outcomes with random results — winning or losing each trade depended on nothing.

How the experiment worked

The design was systematic, not anecdotal:

• 324 different risk policies, varying position size per trade, stop placement, target and when to stop.
• 100,000 simulated paths per policy, meaning 32.4 million total trials.
• Random trade outcomes, isolating risk management from any predictive skill.

That scale matters. This isn't one trader's bad month; it's a statistical map of what happens when luck is the only input.

What the numbers show

Without costs, the ceiling sits near 33%. The best policy passes 33.5% of the time and gets paid 27.8%. Those are decent odds for a coin-flip strategy — but the picture collapses when friction enters.

With the real costs of trading CFDs in the challenge, measured at 0.13 R per trade, the best policy passes 19.1% of the time and gets paid just 6.7%. The author's summary is blunt: 93 out of 100 attempts lose the fee.

Costs, not risk tweaks, drive the gap. The simulation ran the same policies at three cost levels:

• At 0.05 R per trade: 24.7% pass, 14.0% get paid.
• At 0.13 R per trade: 19.1% pass, 6.7% get paid.
• At 0.20 R per trade: 15.6% pass, 5.4% get paid.

Halving costs roughly doubles the payout rate. No change to stop placement or position sizing comes close to that effect. Costs matter more than any risk tweak.

What does this mean for challenge takers?

The author frames the lesson in one line: "Risk management decides how you lose, not whether you win." It's the core finding of all 32.4 million paths. Good policy shapes the distribution of losses; it doesn't convert randomness into profit.

Passing without an edge is possible — roughly one attempt in five clears both phases even at full costs — but the author calls it "a lottery with the house on the other side." The prop firm collects fees from the 93 failures and pays out to the rare 6.7 who land the payout.

The practical test comes before payment, not after. The author's advice: "Before paying for a challenge, ask whether your method makes money after costs on data you did not use to design it." That's an out-of-sample check, the same discipline fund managers apply to backtests.

The author marks the work as educational content, not investment advice. Still, the arithmetic carries a commercial implication: with 324 policies tested and none beating a 19.1% pass rate after costs, the fee structure itself — not trader discipline — is the dominant variable in who keeps money at the end of an FTMO-style challenge.