The ability to adjust moving averages according to their strategy’s needs is what determines successful traders from traders that lose money. This article will teach you how to adjust your moving averages and become one of the successful traders.

If you are trading a strategy based on moving averages, you know the difficulties of adjusting a moving average to track current prices. Choosing the right number of periods is a matter of constant trial and error, as too many periods can make your moving average too slow to react to price movements to generate valid signals, while too few periods make your moving average too quick to react to price outliers, which creates many false signals.

This article will help you adjust your moving average so it tracks current price movements as close as possible, but does not sacrifice the benefits of averaging.

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Most traders start by using a simple moving average (SMA) for their trading. While this is the most common moving average to use, there are other types of moving average that might create better results in your current market environment.

First of all, there is the weighted moving average (WMA). The WMA multiplies each period with a factor, using the largest factor for the last period and a continually declining factor for each preceding period. For a 5-period moving average, the first day from the end would by multiplied by 5, the second day by 4, and so on. This way of calculating a moving average places a bigger emphasis on recent price movements.

To emphasize recent price movements even more, you can use an exponential moving average (EMA). The EMA works similar to the WMA, but uses an exponential factor instead.

Adaptive moving averages have been developed as an attempt a have the moving average stay close to price, yet ignore single outliers. One of the most common adaptive moving averages is called Kaufman’s adaptive moving average (KAMA), after its inventor, Perry Kaufman.

To ignore single outliers, the KAMA uses an efficiency ratio that determines how straight prices move. If a period is perfectly in line with all its preceding periods, it receives an efficiency ratio of 1. If a period is completely out of line in relation to all the other periods, it receives an efficiency rating of 0. This efficiency rating is multiplied with the period’s price, thereby filtering out the influence of periods that stray from the smoothed price movement and emphasizing the influence of periods that are in line with the smoothed price movement. Other adaptive moving averages use similar concepts as the KAMA.

Traders have been debating about which moving average to use for decades. Every type of moving averages has its own strengths and weaknesses. While the weighted and the exponential moving averages are the most sensitive to price changes, they will generate the most false signals.

The simple moving average and especially the adaptive moving average are the best at creating a straight line with few false signals. On the other hand, the simple moving average takes the longest to react to changes in price direction.

The decision, which type of moving average to use, therefore depends largely on your strategy and your risk tolerance.

If you are experiencing difficulties with your current moving average, the important thing to understand is that endlessly modifying settings such as periods used will not necessarily help you get better results. Those things only make a difference in hindsight. Since you do not know the perfect number of periods to use for future movements in advance, the usefulness of these experiments is limited.

You can make a bigger impact by simple adjusting the type of moving average you use. When you are getting to many false signals, think about switching to an adaptive moving average such as the KAMA or a simple moving average. When you are getting your signals too late, think about using an exponential or weighted moving average.

These adjustments are simple, useful, and guaranteed to get generate the results you need, which is more than can be said for any experiment trying to find the perfect numbers for a moving average.

If you want to learn more about the use of moving averages in binary options, we recommend you to read the following: