What are MAs
The moving average (MA) is a popular technical indicator used to identify the beginning of a new
trend or the end or reversal of an existing trend. It helps track the progress of a trend by smoothing
out price data, making the underlying trend easier to see.
A moving average is calculated by averaging a set of data points over a specified period. For example,
a 10-day moving average of closing prices is calculated by summing the closing prices of the last 10
days and dividing by 10. As each new day is added, the average moves forward, hence the term
« moving average. »
There are three main types of moving averages:
- Simple Moving Average (SMA)
- Linearly Weighted Moving Average (WMA)
- Exponentially Smoothed Moving Average (EMA)
Moving averages are trend-following indicators, meaning they signal that a trend has started only after
it has already begun.
Strategies Using Moving Averages
- Using One Moving Average: When the market closes above the moving average, it signals a
bullish trend. Conversely, when it closes below the moving average, it indicates a bearish
trend. - Using Two Moving Averages: A bullish trend is signaled when a short-term moving average
crosses above a long-term moving average. A bearish trend is indicated when the short-term
average crosses below the long-term average.
Moving Average Overview
In statistics, a moving average, also known as a rolling average, rolling mean, or running average, is a
type of finite impulse response filter used to analyze data points by averaging different subsets of the
full data set. Given a series of numbers and a fixed subset size, the moving average is calculated by
averaging the initial subset, then shifting the subset forward and averaging again. This process is
repeated across the entire data series, producing a series of averages that form the moving average
line.
A moving average can apply unequal weights to data points within the subset to emphasize specific
values. It is commonly used with time series data to smooth short-term fluctuations and highlight
longer-term trends or cycles. The distinction between short-term and long-term depends on the
application, and the moving average parameters are set accordingly. Moving averages are widely used
in technical analysis of financial data, such as stock prices and trading volumes, as well as in economics
to examine metrics like GDP and employment.
Different types of moving averages include:
- Simple Moving Average (SMA)
- Cumulative Moving Average
- Weighted Moving Average (WMA)
- Exponential Moving Average (EMA)
Various types of moving averages are suitable for different trading styles and situations.