The concept of moving average in statistics is used to analyze the time series data. Moving averages simply measure the average price or exchange rate of a currency pair over a specific time frame. For example, if we take the closing prices of the last 10 days, add them together and divide the result by 10, we have created a 10-day simple moving average(SMA).

    "A moving average is an average of a security's price over a specific time period"

 In finance it is most often applied to stock prices, returns or trading volumes and used to smooth out short-term fluctuations, thus highlighting longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set.

The moving average is one of the most versatile and widely used of all technical indicators and one of the oldest technical indicator. The moving average is calculated with a certain predefined period. The shorter the period is, the higher the probability of false signals is. The longer the period is, the weaker the sensibility of the moving average is.


The most commonly used time frames for moving averages are 10, 20, 50, and 200 periods on a daily chart. As always, the longer the time frame, the more reliable the study. However shorter term moving averages will react more quickly to the market's movements and will provide earlier trading signals.


  "The Simple Moving Average (SMA) indicator is calculated by summing the closing prices of the currency for a period of time and then dividing this total by the number of time periods"

"An exponential moving average (EMA) is calculated by combining a certain percentage of the current value with an inverse percentage of the previous value of the exponential moving average"

"The Double Exponential Moving Average (DEMA) is a combination of a single exponential moving average and a double exponential moving average. The advantage is that gives a reduced amount of lag time than either of the two separate moving averages alone"


  • A moving average of equal length period will completely eliminate the periodic fluctuation
  • A moving average of equal length will be linear if the series changes on the average by constant per time unit and its fluctuation are periodic.
  • Even when the data show periodic fluctuation, a moving average of unequal length, no matter how small the difference is between the duration of periodicity of original series, and the length of the moving average, the moving average cannot completely remove the periodic variations in the original series. The averaging process then only tends to smooth out somewhat the short-run highs and lows

                    Thus, we can say that the moving average may constitute a satisfactory trend for a series that is basically linear and that is regular in duration and amplitude. However a method of moving average is very useful technique in analyzing a time series data. First of all, in all problems in which the trend of the time series is clearly not clear and in which we are concerned only with the general movement of the time series, whether it is a trend or a cycle or both, it is customary to study the smoothing behavior of the series by the use of moving average. Secondly, the characteristic of a moving average is the basis of the seasonal analysis.




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