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# Exponential Moving Average (EMA)

To measure an **exponential moving average**, unite a definite percentage of the actual value with an inverse percentage of the latter value of the **exponential moving average**.

For example:

If you have given 25% weight to the actual value, you should sum up 25% of the actual value to 75% of the previous moving average to get the actual moving average.

To define the corresponding weight which previous values should be given, use the period.

To determine the percentage, use the formula:

2/ (period+1)

For example:

A period of 7 will result in 25% (2/ (7+1)) of the actual value and you use 75% of the previous exponential moving average value).

Caution: All previous values (including values prior to the period) form an actual exponential moving average. The period is used as an approximate calculation of the time period for which values will stay essential in the estimation. At the start of a data series, the value is supposed to be zero so you may pay more attention to the values until the period is finished.

Moving Averages may turn out to be helpful for smoothing raw, noisy data, for example, daily prices. Price data can change very much from every day and still conceal if the price is growing or decreasing. You see even a more general picture of the basic trends can if you watch the moving price average.

Occasionally, moving averages are applied for defining the trend, but they can also be used to see whether data is opposing the trend. Entry and exit systems usually compare data to a moving average to determine if it is supporting a trend or starting a new one. That is why the exponential moving average is just one of the types of a moving average.

In an ordinary moving average, all price data has the same weight in the calculation of the average with the oldest eliminated value as each new value is added. In the exponential moving average equation, while the average is being measured, the most recent market action gets greater importance. Still the oldest pricing data in the exponential moving average is never eliminated.

A sell signal occurs if the short and intermediate term averages cross from the top to the bottom the longer-term average. On the contrary, a purchase signal happens if the short and intermediate term averages cross from bottom over the longer-term average. If you trade only 2 exponential moving averages in a crossover system, it is better to use longer-term averages.

It is rather important to know that a 5-day exponential moving average usually consists of over 5 days worth of data and can comprise data from all the life of a futures contract. So such moving averages can be more successfully searched by their actual "smoothing constants," as the number of days of data in the computation remains equal for the 5-day average as for the 10-day average. Exponential calculations are held at various moving average values depending on the point you start with.