Simple moving average (SMA):

If EUR/USD had closed in each of the last 10 days at price of 1.10, the current value of the SMA would be 1.10. However, if 5 days closed at 1.10 and another 5 days at 1.20, the average would be 1.15. With each successive day, a new number would be added and the oldest one dropped, changing the 10-day average. Hence the naming “moving”. “Simple” in turn comes as a result of the equal weights of all observations. To get a simple moving average we just sum all the prices and divide by the number of observations (P1+P2+P3…P10) / 10. In TA, closing price data is most commonly used.

Chart 1: Simple Moving Average (21-period SMA). Chart: MetaTrader4

Chart 1: Simple Moving Average (21-period SMA). Chart: MetaTrader4

Trading platforms can automatically perform these arithmetic operations and plot the moving average line on the chart. The above example is a 21-period SMA. Each observation reports the weekly closing price.

When the price is below the average, bears are considered to control the market. Conversely, quotes above the moving average suggest a bull market.

Chart 1 clearly shows the strengths and weaknesses of this indicator. It can detect the start of a trend relatively early and probably more important – to smooth out price fluctuations during the trend. For this reason, moving averages are a good trend-following indicator. However, when prices move sideways, moving averages can generate a number of false buy and sell signals (second part of the chart).

Averages are most often used in combination of two or more. For example, if the object of analysis is a daily chart, one can look for logic in the following settings: 21-period (about a month of trading), 50-day average (more than 2 months of trading) and 200-day average (nearly a year of trading history). Thus, at a glance, short-term, medium-term and long-term trends are identified.

For intraday traders on a 1-hour chart, a 24-period average might be interesting. It will effectively cover the last twenty-four hours, and thus all trading sessions around the world.

Chart 2: SMA 21, 50, 200-period (red, white, blue)

Chart 2: SMA 21, 50, 200-period (red, white, blue). Chart: MetaTrader4

Chart 2 shows a combination of 21, 50 and 200-day SMA. The intersection of the long average (200 period) by the short ones (21 and 50 period) is considered key. This is a point at which a new trend may be emerging.

The strong EURUSD decline has offered a number of opportunities for sellers. Such a placement of the averages has confirmatory significance. The faster ones (21 and 50 SMA) are closer to prices and the fan of averages” has dissolved. The 200-day average is expectedly lagging – the old data is weighing on it. First indications of an end to the decline come with the upside break of the 50 SMA from the 21 SMA. It is likely that at this point, some market participants will begin to exit their short positions.

The presence of many false signals during the sideways movement is noteworthy. If a position is opened on each break of the 200-day average from the fast ones, such price consolidation period could lead to a series of losses. Of course, trend-following indicators doesn’t work well when trend doesn’t exist!

However, the effect of combining more averages is also visible. The slow one reflects the main trend and plays the role of last resistance or support. The fast ones cover shorter periods and react quickly to price changes. 

Simple moving averages are subject to criticism as they do not adequately represent the current market reality. How relevant would be the closing price two hundred days ago to the events of yesterday and today? Exponential moving averages seek to answer this question.

Exponential moving averages (EMA):

The exponential moving average gives more weight to the latter data. This gives traders confidence that the current market factors are reflected appropriately. Setting parameters are a matter of individual preference. If the objective is to make the average sensitive to the last trading period, then a weight of 90% can be assigned to the last price. This will allocate the remaining weight of 10% to the rest of the observations, increasingly reducing the importance of data for older periods.

Chart 3: SMA vs EMA (21 Weekly SMA (red) and EMA (blue))

Chart 3: SMA vs EMA (21-Weekly SMA (red) and 21-Weekly EMA (blue))

Obviously, exponential averages have pros and cons. Compared to SMA, exponential averages react faster to market changes and move closer to recent prices. This can be clearly seen in the chart above, which alternates tops and bottoms. The sensitivity of exponential averages makes them suitable for traders trying to catch the trend in its very early stage (or exit a position early). At the same time, their quick reaction is what generates false signals.

Chart 4: Multiple EMA (21, 50, 200-day periods). Chart: MetaTrader4

Chart 4: Multiple EMAs – 21, 50, 200-period (red, white, blue). Chart: MetaTrader4

In Chart 4, the EMAs are positioned slightly closer to the prices compared to the SMAs from Chart 2. In this case, there were not as many false signals during the consolidation, since the 200-day EMA was only broken by the fast ones once or twice at the end of that period.

In conclusion:

Simple moving averages and exponential moving averages are highly exploited indicators by technical analysts. They give the best results during a trend, as they manage to smooth out the small corrective movements.

When quotes are below moving averages, sellers open their positions with more confidence. When prices are above the moving averages, it’s buyers’ time. Moving average settings are a matter of individual preference. Usually more than one moving average is used, which is intended to capture price action from different periods.

Averages have another application. They form support and resistance levels. For longer-term traders, the 200-day average is indicative of the underlying trend. When prices break it, the market situation reverses.

Simple averages have been criticized for giving equal importance to all observations. Exponentials solve this problem by giving more weight to the most recent observations.

Besides simple and exponential, there are other variations of averages in technical analysis.

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