Using Monte Carlo Tests to Set Risk for Profit Targets
Summary
This article develops a way to estimate per-trade risk for a target balance and trading horizon. It first uses a fixed-fraction compounding model to relate win rate, reward-to-risk ratio, number of trades, and target growth. It then describes Monte Carlo simulations that vary risk and reward-to-risk assumptions, measuring target attainment, final balances, drawdowns, losing streaks, and trades required. The examples compare systems with different win rates and report combinations that pass a stated threshold of more than 50% successful runs.
The results illustrate the trade-off: higher risk can increase the chance or speed of reaching a target while producing deeper drawdowns, and a stronger reward-to-risk ratio can allow lower risk in some scenarios. The article presents these outputs as planning inputs rather than guarantees. Its conclusions depend on assumed win rates, reward-to-risk ratios, trade counts, and simulation design; actual performance can differ, and the reproduced text has gaps in formulas and some repeated or inconsistent scenario descriptions. Traders should treat the figures as model-specific rather than universal risk recommendations.
Key ideas
- Fixed-percentage risk on current equity compounds differently from risking a fixed amount based on initial capital.
- The proposed simulations vary win rate, reward-to-risk ratio, risk per trade, and trade count to estimate target attainment.
- A success-rate threshold makes the selection of minimum risk more reproducible than visual inspection alone.
- Higher risk can improve target attainment or reduce the required trades while sharply increasing drawdown.
- Monte Carlo outputs depend on the assumed trade statistics and do not guarantee future results.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.