The perfect stop-loss does not exist. No matter what method you use to calculate your stops, they will never be perfect. In almost every case you will either set your stop too close and force an early exit, or you will set your stop too loose and give back too much of your profit. Setting stops is a no-win situation.
Despite the fact that you will never be able to completely optimize your stops, there is always room for improvement. Even a fractional improvement in the effectiveness of your stop-loss strategy could add up significantly over the course of a couple hundred trades. That is why many traders are constantly attempting to develop a better stop-loss strategy.
Michael Bryant from Adaptrade Software wrote a guest post for System Trader Success where he made his own attempt at creating a unique stop-loss strategy back in 2012. In his post, he explained the problems that traders encounter with the three most popular types of stops. Then, he attempted to create his own stop-loss strategy that would account for the volatility of the market being traded without having to be optimized for that market.
The Problem with Common Stops
The first common stop strategy that Michael discussed was using a fixed dollar amount for a stop. This is when a trader acknowledges that they are willing to lose a certain amount of money on a trade and sets a stop in a place that equates to that amount of loss. The problem with fixed stops is that they aren’t able to adjust for the volatility of a market. If your fixed stop is set inside a market’s normal daily trading range, it is almost certain to be triggered.
The next common stop strategy that Michael addresses is setting stops at key support and resistance levels. He explains that these levels are commonly associated with recent highs or lows. While this option can better account for volatility, these areas of support and resistance are known for pulling prices towards them. It is also likely that many other traders have stops set in these areas.
The third common stop strategy that Michael discusses is using a multiple of Average True Range (ATR) to calculate the stop location. He explains that this is a great way to account for a market’s volatility, but it also leaves us with another parameter for the system that will need to be optimized. This will make the system more complicated.
Michael’s Noise Tolerant Money Management Stop
The stop-loss strategy that Michael developed has two components:
It’s based on the idea that market movement consists of two components: trend and noise.
In order to calculate the noise in a given market, Michael’s first step is to draw a trendline from the earliest close to the most recent close in a given data set. Then, for each data point, he calculates the difference between the closing price and the trend line.
This gives him values that oscillate above and below a zero line that represents price relative to the current trend. The largest absolute value in this newly derived data set represents the greatest amount the price has strayed from the trend during that period. Michael uses this value to size his stop.
With all of the calculations coded into his strategy, there is no thinking or calculating to be done once the stop is set up. Michael points out that the only parameter that needs to be defined is the lookback period that is used to determine the data set. He suggests that because the goal is to properly size winning trades, the only good option for this value is to make it equal the length of an average winning trade according to backtesting.
In order to demonstrate this approach, Michael compares his stop strategy to a system that uses an optimized fixed stop. The results show that Michael’s stop improved the winning percentage a bit, but hurt the strategy in terms of total return and maximum drawdown. While his strategy doesn’t appear to be a significant improvement in this specific instance, it is still an interesting example of the development process.