What is the difference between simple moving average and exponential moving average?

Answer:

The only difference between the exponential moving average and simple moving average is the sensitivity each one shows to changes in the data used in the calculations.

More specifically, the exponential moving average (EMA) gives more weight to recent prices while simple moving average (SMA) assigns the same weight to all values. Two averages are similar because they are treated in the same manner, and both are often used by technical traders to smooth price fluctuations.

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SPF is the most common type of average used by technical analysts, and is calculated by dividing the sum of a set of prices for the total number of prices in the series. For example, the seven-period moving average can be calculated by summing the following seven prices and dividing the result by seven (also known as the arithmetic mean average).

Example
Given the following range of prices:
$10, $11, $12, $16, 17$, 19$, $20
The calculation of the AGR would look like this:
10$+11$+12$+16$+17$+$19+$20 = 105$
7-period SMA = $105/7 = 15

Since EMAS place more weight to recent data than on older data, they are more sensitive to recent price changes than the SMA are making the results of EMAS more timely and explains why the EMA is preferable to the average among many traders. As you can see from the chart below, traders with a short term may not be think about what kind of average is used, since the difference between the two averages usually some mere pennies. On the other hand, traders with a longer-term need to pay more attention to the average they use because the values can vary by a few dollars, which is enough for the price difference to ultimately prove the effect on profitability, especially when you are trading large number of stocks. (For associated reading, see: moving average strategies.)

As with all technical indicators there is no one type of average that a trader can use to guarantee success. (For more information, see the basics of moving averages.)

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