Moving Average MA

DEFINITION of moving average MA

Moving average or MA is a usual indicator in technical analysis. Oftenly using to examine price movements of assets while reducing the impact of random price spikes.


It gives help in analyzing previous movements of stocks and is only based on past prices. It can be of use in other predictive forms of analysis, but it is, first of all, a lagging indicator.

Calculating an MA requires some amount of data, depending on the length of the moving average.

A ten-day MA will require ten days of data, a one-year MA will require 365. 200 day is a very common way to use MA.


Traders are using moving average to determine levels of support and resistance.

We can recognize two main forms of moving average.

Simple moving average and exponential moving average. This is a straight calculation of average prices for a set number of time periods. The exponential moving average gives more importance to recent prices.
But also there is a weighted average.

An average that has multiplying factors to give different weights to data at different positions in the sample window.

A moving average is usually using with time series data to smooth out short-term fluctuations. And also, to highlight 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 accordingly.

For example, it is useful in the technical analysis of financial data, like stock prices, returns or trading volumes. It is also useful in economics to examine the gross domestic product, employment or other macroeconomic time series. 

Mathematically, a moving average is a type of convolution and so it can be viewed as an example of a low-pass filter used in signal processing.

If you want to use with non-time series data, a moving average filters higher frequency components without any specific connection to time. Although, typically some kind of order is implicit.

If you try to view this simplistically, it can be regarded as smoothing the data.