What is a Moving Average and its Types?

A moving average is a statistical calculation that is used to analyze data points by creating a series of averages from different subsets of a full data set, where it the most commonly used with time series data, such as stock price. Let’s understand what is a moving average.

Types of Moving Averages

Here are the types:

Simple Moving Average

SMA or Simple Moving Average is a straightforward technical indicator that is obtained by summing the recent data points in a given set and dividing the total by the number of time periods. It can be a plot by calculating the average price of a stock over different time frames, so these are mainly formed based on the closing prices.

The formula for calculating SMA:

Simple Moving Average= (A1+ A2+ …………An) / n

Where,

A = it is the average in period n

n = the number of periods

Example of Simple Moving Average, where Ram, a stock trader, wants to calculate the SMA for the stock XYZ by looking at the closing price of the stock for the last five days.

The closing prices for the stock XYZ for the last 5 days are as follows:

Closing prices (XYZ)

INR 200

INR 550

INR 300

INR 800

INR 750

The SMA is then calculated as follows:

SMA= 200 + 550 + 300 + 800 + 750 / 5

SMA= INR 520

Weighted Moving Average

WMA or Weighted Moving Average counters the various drawbacks of SMA, so it puts more weight on the recent data instead of the past, so WMA follows the different price levels of the stock more strictly than SMA.

The formula for calculating WMA:

Weighted Moving Average: Price1 X n + (n-1) + ……Pricen / n X (n + 1) / 2

Exponential Moving Average

EMA or Exponential Moving Average, this involves complex calculation, as it is similar to WMA, EMA puts more weight on the latest prices of a financial instrument. So this happens due to a greater emphasis on recent prices.

The formula for calculating EMA:

EMAt = [ Vt (s / 1+d)] + EMAy X [ 1- (s/ 1+d)]

How a Moving Average Works?

So this works like:

It is calculated by taking the average of a fixed number of recent data points.
This process is applied for each new data point, resulting in a continuously updated average that reflects changes in the underlying data over time.
The new data becomes available, so the oldest data point is dropped, and the newest is added, creating a satisfying effect in the average.

Conclusion

In conclusion, this is more than just a line on a chart; it's a foundation tool for traders and analysts, so whenever you use a simple, weighted version, or exponential in this, which help you to visualise trends, time entries and exits, effectively, make informed trading decisions, time entirtes and exit effectively. We hope this blog has been helpful to you.

About the Author

Saniya

I am a writer, and this sentence speaks louder than anything, I love to play with words because I have a passion for writing easy and good-quality content that reflects simplicity. Readers like content that is straightforward with simple language. My priority has always been to deliver content that connects with the reader.

View All Articles by Saniya