How far does combining strategies push the prop pass rate?
Adding more risk to this strategy actually improves the pass rate for prop firm challenges, but only up to a certain point.
How Monte Carlo works (simulated example): replay thousands of possible account fates and judge the whole range of luck.
Adding more risk to this strategy actually improves the pass rate for prop firm challenges, but only up to a certain point. When running a Monte Carlo simulation (a method of testing the robustness of a strategy by resampling historical daily returns) over 750 days with a 1% risk per component, the results are promising. The first step of the challenge shows a 77% pass rate, with an overall pass rate of 64%. Because this strategy maintains a low drawdown, or the peak-to-trough decline in account equity, there is room to increase position sizing. However, pushing the risk beyond 1.5% causes the failure rate due to maximum loss limits to climb too quickly. To see how this holds up under pressure, I ran an M1 intraday verification for the 2022 to 2025 period using a 1.5% risk setting.
| Metric | Result |
|---|---|
| Performance | +35.8% |
| Profit Factor | 1.52 |
| M1 Worst Single-Day Loss | 2.67% |
| Daily Disqualification Count | 0 |
| Hidden Disqualifications | None |
| This result is a clear pass. Unlike other strategies I have tested, such as the Gold Donchian breakout, this approach uses a diversification of edges that prevents the portfolio from sinking all at once during the day. In other words, it avoids the common trap of giving back accumulated profits before the market closes, which is a critical trait for prop firm success. | |
| My current conclusion is that a combination of uncorrelated, weak edges is the most realistic path toward consistent withdrawals. This setup offers low drawdowns, high stability, and a reliable pass rate of around 64%. | |
| Before I commit to live trading, there are two final hurdles to clear. First, I need to perform a rigorous out-of-sample (OOS) test. This means selecting my strategy components based on data from 2016 to 2021 and then verifying the combination performance on the 2022 to 2024 period. This helps eliminate selection bias, where a strategy looks great only because it was perfectly fitted to past data. Second, I need a concrete plan for tail risks like the 2020 COVID market crash. This will likely involve keeping the risk per component between 0.7% and 1%, which keeps the total drawdown around 10% even during extreme market events. |