Direct Answer
A Weighted Moving Average (WMA) is a moving average that assigns linearly increasing weight to more recent prices instead of weighting every period equally like a Simple Moving Average (SMA). For an n-period WMA, the most recent price gets weight n, the one before it n-1, and so on down to a weight of 1 for the oldest price in the window.
Key Takeaways
- WMA gives the most recent price in the lookback window weight n, the price before it weight n-1, and so on down to weight 1 for the oldest price, unlike an SMA, where every price counts equally.
- For an n-period WMA, the sum of the weights is n(n+1)/2, and dividing the sum of weighted prices by that number pulls the result closer to the latest price than a simple average of the same window.
What Is a Weighted Moving Average?
A Weighted Moving Average (WMA) is a moving average that assigns linearly increasing weight to more recent prices instead of weighting every period equally like a Simple Moving Average (SMA). For an n-period WMA, the most recent price gets weight n, the one before it n-1, and so on down to a weight of 1 for the oldest price in the window. Because the weighting steps down in a straight line rather than all at once, WMA sits between SMA (equal weighting) and EMA (exponential weighting) in how strongly it favors the latest data.
The Formula
WMA = (sum of Pricei × Weighti) ÷ (sum of Weights)
For an n-period window, the most recent price is multiplied by n, the price before it by n-1, continuing down to the oldest price in the window multiplied by 1. The sum of the weights for an n-period WMA is:
Sum of Weights = n(n+1) ÷ 2
WMA builds on the same idea as the simple and exponential moving averages: all three are ways of averaging a price window, but they differ in how much weight each individual price inside that window is given.
Worked Example
Take a 3-period WMA with prices of $10, $12, $14 (oldest to newest).
Weights: the oldest price ($10) gets weight 1, the middle price ($12) gets weight 2, and the newest price ($14) gets weight 3. Sum of weights = 1 + 2 + 3 = 6.
WMA = (10 × 1 + 12 × 2 + 14 × 3) ÷ 6 = (10 + 24 + 42) ÷ 6 = 76 ÷ 6 = 12.67
Compare that to a simple average of the same three prices, (10 + 12 + 14) ÷ 3 = 12.00. The WMA of 12.67 is pulled closer to the most recent price ($14) than the simple average, because the most recent price carries three times the weight of the oldest one instead of an equal share.
How WMA Is Commonly Used
Trend direction
As with any moving average, price trading above a rising WMA is commonly read as an uptrend, and price trading below a falling WMA as a downtrend. Because WMA weights recent prices more heavily than an SMA of the same length, it can shift direction a bit sooner after a price move begins.
Crossovers
Traders sometimes plot a shorter-period WMA against a longer-period WMA (or against an SMA/EMA of a different length) and treat a cross of the shorter line above the longer one as bullish, and below as bearish, the same crossover logic used with other moving-average pairs, carried over to WMA's specific weighting.
Support and resistance
A rising or falling WMA line is sometimes watched as dynamic support or resistance, where price is expected to react around the line rather than pass through it cleanly, a tendency, not a guarantee.
Common Period Lengths
WMA has one primary parameter, the number of periods (n) in the lookback window. There's no single correct setting; the period is commonly chosen to match the timeframe being traded, and any specific value should be verified against your own platform and backtested before relying on it.
| Period length | Responsiveness | Common use |
|---|---|---|
| 10 | Faster, more sensitive to recent moves | Shorter-term/swing trading |
| 20 | Balanced | General-purpose trend following |
| 50 | Slower, smoother | Longer-term trend context |
Why WMA Produces False Signals
- Choppy, non-trending markets, price can cross a WMA line repeatedly in a sideways market with no sustained follow-through, generating whipsaw signals.
- Still a lagging calculation, even though WMA reacts faster than an SMA of the same length, it's still built entirely from past prices, so a WMA-based signal confirms a move that has already started rather than predicting one.
- Sensitivity to a single large price swing, because the most recent price carries the heaviest weight, one unusually large move can pull the WMA sharply even if it doesn't represent a genuine change in trend.
Common Mistakes
- Assuming WMA and EMA behave identically, both weight recent prices more, but WMA's weighting is linear and cuts off at the edge of the window, while EMA's weighting decays exponentially and technically never reaches zero. The two won't always agree.
- Treating every WMA crossover as a standalone trade trigger, most traders pair a WMA signal with broader trend, volume, or price-structure context rather than acting on the crossover alone.
- Using a period length copied from another asset or timeframe without testing it, the period that works for a given market and timeframe isn't automatically appropriate elsewhere.
Limitations
WMA is a lagging indicator: every input is a past closing price, so it confirms a shift in trend after it begins rather than forecasting it. It also inherits the general limitation of any single moving average, it describes what price has done, not what it will do next, and is commonly used alongside other tools rather than in isolation.
A Linear Weighting and Where It Sits Between the Alternatives
A weighted moving average assigns linearly declining weights to older prices, which places it between a simple average, where every period counts equally, and an exponential one, where the decay is geometric and older data never fully drops out. Knowing where it sits is most of what is needed to choose it.
The practical difference is in how the average behaves when an old value leaves the window. A simple average jumps when a large historical value drops out; a weighted one has already reduced that value's influence, so the transition is smoother. That is a real if modest advantage on data containing occasional extreme periods.
The mistake is expecting the weighting choice to matter more than the period. The lookback length dominates the behaviour of any moving average, and the difference between weighting schemes at the same period is small compared with the difference between two periods under the same scheme.
Every version also summarises past prices and lags by construction. Adjusting the weights redistributes that lag across the window rather than removing it, and no scheme produces an average that reflects the current price.
WMA FAQs
What is a Weighted Moving Average (WMA)?
A Weighted Moving Average is a moving average that assigns linearly increasing weight to more recent prices instead of weighting every period equally like a Simple Moving Average. In an n-period WMA, the most recent price carries weight n, the price before it n-1, and so on down to a weight of 1 for the oldest price in the window.
How is WMA calculated?
WMA equals the sum of each price multiplied by its weight, divided by the sum of the weights. For an n-period WMA, the sum of the weights is n(n+1)/2. The most recent price gets weight n and the oldest price in the window gets weight 1, with every price in between stepping down by 1.
What's the difference between WMA and EMA?
Both weight recent prices more heavily than older ones, but they use different weighting schemes. WMA applies weights that decrease in a straight line, one step at a time, and then drop off completely outside the lookback window. EMA applies weights that decay exponentially and, mathematically, never fully reach zero, so a small influence from prices before the window persists. In practice this typically makes EMA react at least as fast as WMA for the same period length, though the exact responsiveness depends on the smoothing constant chosen.
What's the difference between WMA and SMA?
A Simple Moving Average gives every price in the lookback window equal weight, so an old price at the start of the window counts exactly as much as yesterday's close. WMA instead scales each price's weight linearly by how recent it is, which pulls the resulting average closer to the latest prices than an SMA of the same length would sit.
Is WMA a leading or lagging indicator?
Lagging. WMA is calculated entirely from past closing prices, so like any moving average it confirms a change in trend after it has already begun rather than predicting it in advance.
What period length is commonly used for WMA?
There's no single universal setting, traders commonly cite periods such as 10, 20, or 50, chosen to match the timeframe and holding period they're trading, and the exact value should be verified against your own charting platform and backtested rather than assumed.
How does the linear weighting scheme differ from exponential weighting?
Linear weighting assigns weights that decrease in equal steps and drops observations entirely once they leave the window. Exponential weighting decays weights geometrically and never fully discards older data. The practical consequence is that a linear average changes abruptly when a significant observation exits the window, while an exponential one fades its influence gradually.
Does heavier weighting on recent prices reduce lag without cost?
It reduces lag and increases sensitivity to short-term movement, which produces more direction changes in choppy conditions. Lag and noise sit at opposite ends of the same trade-off, and no weighting scheme escapes it. Choosing a weighting is choosing where on that spectrum to sit rather than finding a superior alternative.
Where is this weighting scheme used as a building block rather than on its own?
Several composite indicators construct their output from multiple weighted averages of different lengths, combining them to reduce lag further than a single average allows. In those constructions the weighted average is an intermediate step rather than the displayed line. Understanding its behaviour therefore helps in interpreting indicators that never mention it in their name.