Prop Firm Challenge: Unmasking Our EA's True Expected Value

A beginner-friendly summary of the verification: “Prop Firm Challenge: Unmasking Our EA’s True Expected Value”.

What’s the idea?

We’ve been looking into the Fintokei Quartz challenge, a popular prop firm offering where traders aim to prove their strategy on a simulated account to eventually manage real capital. The specific challenge we focused on was for a ¥1,000,000 account, with a ¥12,500 participation fee and a 1% risk set for each trade. Our goal was to calculate the “overall Expected Value” (EV) of taking on this challenge with a specific EA (Expert Advisor, or automated trading strategy). In simple terms, EV tells us, on average, how much profit or loss we expect to make each time we attempt the challenge, factoring in the costs and potential payouts.

How I tested it

To figure out the EV, we ran a Monte Carlo simulation. Think of Monte Carlo as rolling dice thousands of times to see all possible outcomes. We simulated countless challenge attempts, considering factors like the pass rate (which we estimated at 38%), the potential for withdrawals after successfully getting funded (around 9.3% of the time, based on previous data), and the expected lifespan of a funded account (estimated at 311 days before it might fail or get reset). Based on these simulations, our initial calculation for the overall EV was a positive ¥71,227. In other words, if you kept trying this challenge with our EA, you’d theoretically expect to earn ¥71,227 on average per attempt, after accounting for the participation fee and potential profits. Sounds great, right?

What happened?

Here’s where things get interesting, and a little bit sobering. We performed a “sensitivity analysis.” This is like testing how robust your house is by seeing what happens if you remove a key support beam. In our case, we wanted to see what would happen to the EV if our EA had absolutely “zero edge.” An “edge” in trading is your statistical advantage – like having a slightly better chance of winning than losing, or winning more when you win than you lose when you lose. If you have zero edge, it means your strategy is essentially a coin flip, with no inherent advantage. Shockingly, even when we simulated a scenario where our EA had no edge whatsoever, the EV still came out to a positive ¥45,753! This was a massive red flag. Why? Because prop firms are businesses, and like any business, they need to make money. They should have a “house advantage” – meaning that over the long run, the odds should slightly favor the firm, not the individual trader, especially one with zero edge. This result was definitive proof that our model was far too optimistic. So, what caused this overly rosy picture? The culprit was our simulation method. Our Monte Carlo simulation was based on daily returns. This meant it completely ignored what happens intraday – those quick, sharp dips and spikes that can trigger a loss limit. Prop firms often have strict daily drawdown limits (e.g., you can’t lose more than 5% in a single day) and overall maximum drawdown limits (e.g., you can’t lose more than 10% of your initial capital). By only looking at daily closes, our model was significantly underestimating the risk of hitting these critical intraday limits. In essence, our model assumed a smooth ride, when the reality of FX trading is often bumpy. It also assumed that after costs, we’d break even (or better), but in reality, with the prop firm’s structure, the odds are stacked against you if your edge isn’t truly robust.

What I learned

The biggest takeaway is this: we absolutely cannot trust that positive overall EV from our initial calculation. Given the identified flaws in our model and the fact that a zero-edge scenario still showed a profit, it’s highly probable that the actual expected value with our current, somewhat weak edge is negative. So, what now? We need to improve our approach:

1. Build a more realistic risk model: This means using higher-resolution data, like M1 (one-minute) charts, to accurately reflect intraday price movements and the true risk of hitting those daily and maximum drawdown limits. We need a model that doesn’t ignore the instant losses that can quickly end a challenge.
2. Find a truly robust edge: Our current EA’s edge isn’t strong enough. We need to go back to the drawing board and develop strategies with a genuinely significant and consistent statistical advantage. Until we make these improvements, our approach to prop firm challenges needs to change. We should view participating in them as taking a “small, calculated risk” where the maximum downside is limited to just the participation fee. It’s more like buying a lottery ticket than a guaranteed investment. This way, we’re not misled by overly optimistic calculations and can manage our expectations realistically!