11 Essential Moving Averages for Trading

Doji
May 24, 2024
7 min read

Updated: Oct 20, 2024

How to Use and Read Almost any Type of Moving Average

Moving averages are fundamental tools in technical analysis, primarily used to identify trends. More than smoothing out price data, moving averages can be used to improve many indicators, such as oscillators and volume. In this article, we will explore 11 different types of moving averages, their unique characteristics, and how to use them effectively in trading.

1. Simple Moving Average (SMA)

Description: Calculates the average of a security's price over a specified number of periods.
Usage: Commonly used to smooth out price data and identify trends. A 50-day SMA crossing above the 200-day SMA (a.k.a. Golden Cross) indicates a bullish trend.

Code (ProRealTime)

lookbackPeriod = 50

sumPrice = 0

for i = 0 to lookbackPeriod - 1 do

sumPrice = sumPrice + close[i]

SMA = sumPrice / lookbackPeriod

return SMA as "SMA"

Note: ProRealTime has this indicator as standard, called "Average".

2. Exponential Moving Average (EMA)

Description: Gives more weight to recent prices, making it more responsive to new information.
Usage: Effective for capturing short-term trends. The 20-day EMA is often used to identify short-term momentum.

Code (ProRealTime)

lookbackPeriod = 20

alpha = 2 / (lookbackPeriod + 1)

if barindex = lookbackPeriod then

sumPrice = 0

for i = 0 to lookbackPeriod - 1

sumPrice = sumPrice + close[i]

EMA = sumPrice / lookbackPeriod

elsif barindex > lookbackPeriod then

EMA = alpha * close + (1 - alpha) * EMA[1]

endif

return EMA as "EMA"

Note: ProRealTime has this indicator as standard, called "ExponentialAverage".

3. Weighted Moving Average (WMA)

Description: Assigns more weight to recent prices by multiplying each price by a weight.
Usage: Useful for identifying trends with reduced lag compared to SMA. It is often used in combination with other indicators.

Code (ProRealTime)

lookbackPeriod = 20

weights = 0

weightedSum = 0

for i = 0 to lookbackPeriod - 1 do

weight = lookbackPeriod - i

weights = weights + weight

weightedSum = weightedSum + close[i] * weight

WMA = weightedSum / weights

return WMA as "WMA"

Note: ProRealTime has this indicator as standard, called "WeightedAverage".

4. Wilder’s Moving Average

Description: A variation of the EMA that smooths the price data using a different formula, developed by J. Welles Wilder.
Usage: Commonly used in Wilder’s other indicators like the RSI to reduce noise and smooth out price data.

Code (ProRealTime)

lookbackPeriod = 14

WildMA = 0

if barindex = lookbackPeriod then

for i = 0 to lookbackPeriod - 1 do

WildMA = WildMA + close[i]

WildMA = WildMA / lookbackPeriod

elsif barindex > lookbackPeriod then

WildMA = (WildMA[1] * (lookbackPeriod - 1) + close) / lookbackPeriod

endif

return WildMA as "WilderMA"

Note: ProRealTime has this indicator as standard, called "WilderAverage".

5. Triangular Moving Average (TMA)

Description: A double-smoothed SMA, which applies the SMA twice to the price data.
Usage: Provides a very smooth line that is effective in identifying long-term trends but with more lag.

Code (ProRealTime)

lookbackPeriod = 20

sumPrice = 0

sumSMA = 0

for i = 0 to lookbackPeriod - 1 do

sumPrice = sumPrice + close[i]

SMA = sumPrice / lookbackPeriod

for i = 0 to lookbackPeriod - 1 do

sumSMA = sumSMA + SMA[i]

TMA = sumSMA / lookbackPeriod

return TMA as "TMA"

Note: ProRealTime has this indicator as standard, called "TriangularAverage".

6. Time Series Moving Average

Description: Uses regression analysis to fit a line through the price points and project it forward.
Usage: Suitable for forecasting and reducing lag.

Code (ProRealTime)

lookbackPeriod = 20

sumX = 0

sumY = 0

sumXY = 0

sumXX = 0

for i = 0 to lookbackPeriod - 1 do

sumX = sumX + i

sumY = sumY + close[i]

sumXY = sumXY + i * close[i]

sumXX = sumXX + i * i

slope = (lookbackPeriod * sumXY - sumX * sumY) / (lookbackPeriod * sumXX - sumX * sumX)

intercept = (sumY - slope * sumX) / lookbackPeriod

TSMA = slope * (lookbackPeriod - 1) + intercept

return TSMA as "TSMA"

Note: ProRealTime has this indicator as standard, called "TimeSeriesAverage".

7. Hull Moving Average (HMA)

Description: Designed to reduce lag and improve smoothness by using weighted moving averages.
Usage: Effective in fast-moving markets where quick response to price changes is crucial. It’s often used in short-term trading strategies.

Code (ProRealTime)

lookbackPeriod = 20

weightsHalf = 0

weightedSumHalf = 0

for i = 0 to lookbackPeriod / 2 - 1

weight = lookbackPeriod / 2 - i

weightsHalf = weightsHalf + weight

weightedSumHalf = weightedSumHalf + close[i] * weight

wmaHalf = weightedSumHalf / weightsHalf

weightsFull = 0

weightedSumFull = 0

for i = 0 to lookbackPeriod - 1

weight = lookbackPeriod - i

weightsFull = weightsFull + weight

weightedSumFull = weightedSumFull + close[i] * weight

wmaFull = weightedSumFull / weightsFull

weightsSqrt = 0

weightedSumSqrt = 0

for i = 0 to sqrt(lookbackPeriod) - 1

weight = sqrt(lookbackPeriod) - i

weightsSqrt = weightsSqrt + weight

weightedSumSqrt = weightedSumSqrt + (2 * wmaHalf - wmaFull)[i] * weight

HMA = weightedSumSqrt / weightsSqrt

return HMA as "HMA"

Note: ProRealTime has this indicator as standard, called "HullAverage".

8. Zero-Lag Moving Average

Description: Combines a simple moving average and a correction factor to eliminate lag.
Usage: Useful in fast markets where lag is a significant concern, providing timely signals without delay typical of SMAs or EMAs.

Code (ProRealTime)

lookbackPeriod = 20

alpha = 2 / (lookbackPeriod + 1)

if barindex < lookbackPeriod then

sumPrice = 0

for i = 0 to barindex do

sumPrice = sumPrice + close[i]

EMA = sumPrice / (barindex + 1)

else

EMA = alpha * close + (1 - alpha) * EMA[1]

endif

if barindex >= lookbackPeriod then

ZLMA = 2 * EMA - EMA[lookbackPeriod]

else

ZLMA = EMA

endif

return ZLMA as "Zero-Lag MA"

Note: ProRealTime has this indicator as standard, called "ZLEMA".

9. Double Exponential Moving Average (DEMA)

Description: Uses two EMAs to reduce lag and improve responsiveness.
Usage: Effective for short-term trend following, as it provides quicker signals compared to a single EMA.

Code (ProRealTime)

lookbackPeriod = 20

alpha = 2 / (lookbackPeriod + 1)

EMA1 = close[0]

for i = 1 to lookbackPeriod - 1

EMA1 = EMA1 + alpha * (close[i] - EMA1)

EMA2 = EMA1

for i = 1 to lookbackPeriod - 1

EMA2 = EMA2 + alpha * (EMA1[i] - EMA2)

DoEMA = 2 * EMA1 - EMA2

return DoEMA as "DEMA"

Note: ProRealTime has this indicator as standard, called "DEMA".

10. Triple Exponential Moving Average (TEMA)

Description: Uses three EMAs to further reduce lag.
Usage: Ideal for capturing short-term trends with minimal lag, making it useful in high-frequency trading.

Code (ProRealTime)

lookbackPeriod = 20

alpha = 2 / (lookbackPeriod + 1)

EMA1 = close[0]

for i = 1 to lookbackPeriod - 1

EMA1 = EMA1 + alpha * (close[i] - EMA1)

EMA2 = EMA1

for i = 1 to lookbackPeriod - 1

EMA2 = EMA2 + alpha * (EMA1[i] - EMA2)

EMA3 = EMA2

for i = 1 to lookbackPeriod - 1

EMA3 = EMA3 + alpha * (EMA2[i] - EMA3)

TrEMA = 3 * EMA1 - 3 * EMA2 + EMA3

return TrEMA as "TEMA"

Note: ProRealTime has this indicator as standard, called "TEMA".

11. Adaptive Moving Average (AMA)

Description: Adjusts the smoothing constant based on the volatility and price trend.
Usage: Provides a balance between smoothness and responsiveness, suitable for dynamic trading environments.

Code (ProRealTime)

lookbackPeriod = 20

fastest = 2 / (2 + 1)

slowest = 2 / (30 + 1)

if barindex < lookbackPeriod then

AMA = close

else

volSum = 0

for i = 0 to lookbackPeriod - 1 do

volSum = volSum + abs(close[i] - close[i + 1])

efficiencyRatio = abs(close - close[lookbackPeriod]) / volSum

sc = efficiencyRatio * (fastest - slowest) + slowest

smoothingConstant = sc * sc

AMA = AMA[1] + smoothingConstant * (close - AMA[1])

endif

return AMA as "AMA"

Note: ProRealTime has this indicator as standard, called "AdaptiveAverage".

Comparison of Different Types of Moving Averages

It may be difficult to understand the difference just by reading, so let's put these moving averages on a chart and have a look. All of the below have a 20 candle lookback period.

As you can see, the difference between 20-period averages can be huge, depending on how you calculate. But they're all good to keep in your trading toolbox for different purposes. Try them out and discover which ones can improve your strategies.

FAQ

Q: Which moving average is best for day trading?

A: For day trading, the Exponential Moving Average (EMA) is often the go-to. It's super responsive to recent price changes, helping you catch those short-term moves. The shorter the period, like a 9 or 12 EMA, the better it is for quick trades.

Q: Which moving average is best for swing trading?

A: Swing traders usually prefer the Simple Moving Average (SMA). It smooths out the price data nicely and helps you focus on the overall trend without getting distracted by the noise. A 50 or 200-period SMA can be great for identifying those medium-term trends.

Q: Which moving average is best for scalping?

A: Scalping needs something fast and responsive, so the Hull Moving Average (HMA) is a great choice. It's designed to reduce lag and give you smoother signals. You’ll find it helpful for those ultra-short-term trades.

Q: Which moving average is best for generating signals?

A: The Triple Exponential Moving Average (TEMA) is awesome for generating signals. It combines multiple EMAs to give you faster and more accurate signals, which is perfect for making timely entry and exit decisions.

Q: Which moving average is best for defining trends?

A: The Time Series Moving Average is excellent for defining trends. It uses linear regression to create a best-fit line for your data, helping you clearly see the direction of the trend. Perfect for getting a good sense of where the market is headed.

Q: Which moving averages are best to combine?

A: Combining moving averages can give you a better picture. A popular combo is the 50-period SMA with the 200-period SMA for longer-term trends, and the 9-period EMA with the 21-period EMA for shorter-term signals. This way, you get the best of both worlds – smooth trends and responsive signals.