Weighted Moving Average: What It Means in Technical Trading

Written by Lawrence PinesUpdated Cited by Forbes, The Guardian, Stanford University +48+ more

The Weighted Moving Average emphasizes recent price action, helping commodity traders spot trend changes and smoother entry or exit signals faster than a simple average.

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What Is the Weighted Moving Average?

The weighted moving average (WMA) places more importance on recent price moves. Therefore, the WMA reacts more quickly to price changes than the regular simple moving average.

How to Calculate WMA

Here is a basic example (three-period) of how the weighted moving average is calculated:

  • Prices for the past three days have been $5, $4, and $8.
  • Since there are three periods, the most recent day ($8) gets a weight of 3, the second most recent day ($4) receives a weight of 2, and the first day ($5) receives a weight of just 1.
  • The calculation is as follows: [(3 x $8) + (2 x $4) + (1 x $5)] / 6 = $6.17

Now compare the weighted moving average value of 6.17 to the simple moving average calculation of 5.67. Note how the large price increase that occurred on the most recent day was better reflected in the weighted moving average calculation.

WMA Chart Example

This chart of Wal-Mart stock illustrates the visual difference between a 10-day weighted moving average and a 10-day simple moving average.

weighted moving average reacts faster to price changes than the simple moving average
Please note, this is an example – not a recommendation.

Potential buy and sell signals for the weighted moving average indicator are discussed in-depth on the simple moving average indicator page.

Where to Trade Commodities Using Technical Analysis

Further Reading on Trend Indicators

These trend tools complement Weighted Moving Average: Andrew’s Pitchfork, Gann Fans, and Triangular Moving Average.

FAQ

What Does Weighted Moving Average Forecast?

The weighted moving average (WMA) measures market momentum by assigning more weight to recent data than to past data.

This is done by giving each data point a different weighting factor based on its recency. The resultant moving average follows prices more closely than a simple moving average for the same period.

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