Monte Carlo Simulation: Stress-Testing Your Own Results

[Fairman]

Monte Carlo Simulation: Stress-Testing Your Own Results

Your trade history is one sequence out of many that could have occurred. Monte Carlo simulation explores the others, and shows how bad things might get.

The method is simple. Take your list of past trade results in R. Randomly reorder them, or randomly draw them with replacement, to build many new sequences of the same length. For each sequence, compute the equity curve, maximum drawdown, and ending value. Repeat thousands of times.

The output is a distribution. You might find that your actual maximum drawdown was 8 percent, but in 5 percent of simulated sequences it exceeded 18 percent. That tells you the drawdown you experienced was on the lucky side, and that you should be prepared for worse.

It also shows the probability of ruin under your sizing, and the range of outcomes over the next hundred trades, which is wider than most people expect.

Spreadsheets can perform simple simulations with random functions, and free tools exist for the purpose. Programmers can do it in a few lines of code.

Remember the limits. The simulation assumes future trades resemble the past ones and that outcomes are independent. It cannot capture regime changes or behavior changes.

Still, it improves risk planning, by supplying realistic worst-case figures for sizing decisions.

Practical step: if you can, run a simple simulation of your trade list, and find the 95th percentile of maximum drawdown.

[Fairman]

Even a basic spreadsheet version is useful. Shuffle the R column a few hundred times with a random sort and record the worst drawdown each time. It takes maybe twenty minutes to set up. In Google Sheets, for example, you can put RAND() next to each result, sort by it, then use a running sum and track the lowest point below the previous peak. Copy that a few hundred times or use a small script. What most people find is that their real drawdown was somewhere in the middle of the range, and the bad end of the range is a lot worse. Better to see that in a spreadsheet than in the account.

[Fairman]

The 95th percentile drawdown is a good number to size from. If you can't stomach that one, the risk per trade is too high, even if your actual history looked fine. Say the simulation shows a 95th percentile drawdown of 15R. At 1 percent risk that's about a 14 percent drop in the account, allowing for compounding. At 2 percent it's closer to 26 percent. Ask yourself honestly which of those you could sit through without changing the strategy or quitting. For most people it's the smaller one. Picking risk from the bad case rather than the average case is one of the simplest ways to stay in the game.