Monte Carlo Analysis of Position Sizing and Losing Streaks
Summary
The article uses Monte Carlo simulations to examine how per-trade risk affects growth, drawdowns, and the ability to withstand losing streaks. It compares synthetic systems with different win rates and reward-to-risk ratios, all presented as having positive expectancy, and contrasts fixed risk with risk based on the changing account balance. The core lesson is that position size determines how severely a sequence of losses affects capital, while win rate alone does not describe a system’s edge.
The reported simulations include 100 runs of 500 trades for estimates of losing-streak ranges, with illustrative systems ranging from a 30% to an 83% win rate. The discussion argues that the familiar 1%–2% guideline is a starting point rather than a universal rule, and that higher risk may be considered only after a system is tested. These results are examples from simulated systems, not proof of future performance; the conclusions depend on the assumed win rates, reward-to-risk ratios, and simulation setup. The article also notes that aggressive sizing can create substantial drawdowns even for high win-rate systems.
Key ideas
- Positive expectancy does not prevent long sequences of consecutive losses.
- Monte Carlo runs can illustrate how win rate relates to plausible losing-streak lengths.
- Drawdown severity depends on both losing streak length and the fraction risked per trade.
- Risk based on current balance generally compounds outcomes differently from fixed monetary risk.
- Simulation results depend on assumed system characteristics and do not guarantee live outcomes.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.