Traders know how hard it is to come up with systems that have a sustainable edge. Many people can look at a chart and determine trading rules that fit a particular chart perfectly. However, when they test their rules over a large period of market history, they learn that their system just doesn’t work.
Other traders will develop a system neglecting commissions and slippage. The trading system looks great, and the trader is very enthusiastic—until these real-life frictional costs are added in, and a winning system turns into a breakeven system or worse.
With a proper test and development method, it is common for traders to test dozens, hundreds or even thousands of trading ideas before they find one that works. A sound development process eliminates a lot of worthless trading systems. Yet, once a trading system passes initial development and testing, work is far from done. There are more steps necessary before committing real funds to a trading system. Following these “after development” rules can save a trader thousands of dollars, and eliminate a lot of heartache.
Monte Carlo simulation
One mistake traders make is to base their trading decisions on the shape of the historical backtest equity curve. Many will look at the equity curve and think “because the historical maximum drawdown is $X, I’ll probably never have a larger drawdown than that.” Or they will conclude “this system has made money every year, so it is safe to trade.”
Unfortunately, both of these assumptions are naïve. This is because the historical equity curve is just one of many possible paths the trading system could have taken over time. This is depicted in “Which way?” (below). Rearrange a few trades here and there, and the maximum drawdown could have been much worse, and profitable periods easily could have become losing periods. On the flip side, the performance could have been better than what it was, too.
Is there a way to analyze this phenomenon—to see the various different paths a trading system could have taken? Yes, there is, with a method called Monte Carlo analysis.
Monte Carlo analysis is based on the idea that trades in the past will occur in the future, just in some different, and unknown, order. By scrambling up the trades in a different order, different equity curves will be generated.
Although there are circumstances where Monte Carlo testing is not appropriate, for most trading systems, the analysis is valid and can provide insight into the trading system. You simply record each trade result and then pick trades out in various orders to create an equity curve. Computers obviously do this sampling process a lot faster, and create thousands of simulated equity curves.
When you analyze thousands of Monte Carlo simulations, you obtain statistics of what the biggest drawdown could be, what annual return is possible and your risk of ruin. If you have performance objectives for simulation results, you then can easily see if your system meets your goals. There is no way to obtain such information from just looking at the historical equity curve.