The results may surprise you. Moving averages smooth past price data so traders can more objectively see the recent trend. They filter out the noise which makes it much easier to see what direction a market is heading. The most common way to use moving averages is to look for moving average crossovers and this technique has been used by many successful trend followers. When a fast moving average such as a 5-day MA crosses over a slow moving average such as a day MA it signals a new uptrend is taking place and is a bullish signal for a trend follower, telling them to buy the market.
When the fast moving average crosses back under the slow moving average, it signals that the uptrend has come to an end and a new downtrend is in place. This is a bearish signal for a trend follower, telling them to close their long trade or go short the market. The biggest problem with moving averages like all technical indicators is that they are lagging indicators.
Since they make a calculation based on previous price data, they can only ever tell you what has happened in the past and not the future. For example, a 5-day moving average will be a lot more responsive to recent price moves than a day.
Thus, all moving averages are a trade-off between noise and lag. The simple moving average is fairly easy to calculate and so the indicator is carried by nearly all trading platforms. Nowadays, all you need to do is click a button and the moving average can be plotted onto your price chart. In the rest of this article, I shall go through nine different types of moving averages and then we shall put them to the test on historical stock market data to see which one is best. We have already seen how the simple moving average is calculated so the next most popular moving average is known as the exponential moving average EMA.
The exponential moving average works in the same way as the simple moving average but it gives greater weight to more recent price moves. More recent price data is weighted in an exponential fashion. It is therefore able to react faster to new trends but could therefore lead to more whipsaws. The EMA is also very popular and available on nearly all trading and technical analysis platforms. As the name suggests, the double exponential moving average DEMA is a faster version of the exponential moving average.
The indicator was first developed by Patrick Mulloy in a February article of Traders magazine. The most important thing to note is that this is a moving average that reacts quickly to new price moves. As such, it significantly reduces lag and reacts quickly to new price moves. This is another downside to using fast MAs.
The Wilders moving average was developed by J. Welles Wilder in his book: The indicator is calculated by altering the original exponential moving average formula. The weighted moving average WMA is designed to find trends faster but without whipsaws. This makes it faster than the typical EMA.
You can see how it works here. In essence, the linear regression line is projected forward indicating what would happen if the regression continued forward. The HMA is fairly complex to calculate so you can read more about the method here.
This is a moving average that is rarely found on popular trading platforms but is considered by some to be a very good indicator. The Guppy multiple moving average GMMA is different to the other MAs discussed here because it is a combination of several exponential moving averages at once.
Since it may interest readers, I will test the GMMA method as well but in a different way to the others. For the test, I will be using the following EMA parameters: It should be noted at this point that the tests are not designed to find the perfect settings but to get a rough idea as to which moving averages work best.
We will sell our position when the fast moving average crosses back under. The Wilders MA produced a compounded annualised return of 2. The worst performing average was in fact the Hull moving average. The worst performing moving average was tied between the Hull moving average and the least squares moving average.
Whenever it crosses back under, we will sell the stock and it will drop off the portfolio. The worst performer was the GMMA strategy. Firstly, longer term moving average crossovers work better than short-term crossovers. This is likely because they produce fewer whipsaws. Second, newer and more complex moving averages appear to be no better at finding trends than the more traditional moving averages.
There may be some truth to that. Indicators such as GMMA and least squares are not necessarily intended to be used in this manner. Personally, the conclusions confirm what I thought all along. Simple moving averages work just as well as complex ones at finding trends, and the trusted, exponential moving average is best.
All tests run using Amibroker using Norgate Premium Data. Then you'll love some of the free extras I've put together. Just enter your email address below to download and stay alerted to new content. You can unsubscribe at any time. I have to be an expert of them.
I am really flaggergasted with the amount of useless information you can find out on the web. Your blog belongs to the latter. Thanks for your writings! Thanks for your message Tom, moving averages are definitely a tool to get the hang of. They are flexible and can be used for many purposes, not just for identifying trends.
Hope to post more articles like this one soon. Similarly, using a too complex one which involves over curve-fitting is of little value for the future.
Simple works and you proven it again! Thanks, glad the article and the courses have been useful. I will definitely revisit moving averages and the Hull MA at some point. Thanks for posting these results JB. I would note that on his website he says this about the HMA and its use in crossover systems:.
The second half of the second paragraph should read:. Subscribe to the mailing list. Notify me of follow-up comments by email. Notify me of new posts by email. Guppy multiple moving average. Shows first 8 moving averages plotted together. Leave this field empty if you're human: TOM November 11, Your blog post s are really outstanding. JB Marwood November 17, 5: Anonymous November 17, Kayson Chan March 6, Thank you for your effort and i learnt alot taking your courses in udemy.
JB Marwood March 8, 3: Michael October 9, 5: I would note that on his website he says this about the HMA and its use in crossover systems: JB Marwood October 13, 1: Bob Malinowski January 25, 2: The second half of the second paragraph should read: JB Marwood January 25, 9: Hi Bob, nice spot.More...