Moving Average (MA) is a price based, lagging (or reactive) indicator that displays the average price of a security over a set period of time. A Moving Average is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance. Essentially, Moving Averages smooth out the “noise” when trying to interpret charts. Noise is made up of fluctuations of both price and volume. Because a Moving Average is a lagging indicator and reacts to events that have already happened, it is not used as a predictive indicator but rather an interpretive one, used for confirmations and analysis. In fact, Moving Averages form the basis of several other well-known technical analysis tools such as Bollinger Bands and the MACD. There are a few different types of Moving Averages which all take the same basic premise and add a variation. Most notable are the Simple Moving Average (SMA), the Exponential Moving Average (EMA) and the Weighted Moving Average (WMA).
Simple Moving Average (SMA)
Simple Moving Average (SMA) Moving averages visualize the average price of a financial instrument over a specified period of time. However, there are a few different types of moving averages. They typically differ in the way that different data points are weighted or given significance. A Simple Moving Average (SMA) is an unweighted moving average. This means that each period in the data set has equal importance and is weighted equally. As each period ends, the oldest data point is dropped and the newest one is added to the beginning. Please note that of all the moving averages the SMA lags price the most.
Exponential Moving Average (EMA)
An Exponential Moving Average (EMA) is very similar to (and is a type of) a weighted moving average. The major difference with the EMA is that old data points never leave the average. To clarify, old data points retain a multiplier (albeit declining to almost nothing) even if they are outside of the selected data series length.
Weighted Moving Average (WMA)
A Weighted Moving Average (WMA) is similar to the simple moving average (SMA), except the WMA adds significance to more recent data points. Each point within the period is assigned a multiplier (largest multiplier for the newest data point and then descends in order) which changes the weight or significance of that particular data point. Then, just like the SMA, once a new data point is added to the beginning, the oldest data point is thrown out.
Smoothed Moving Average (SMMA)
The Smoothed Moving Average (SMMA) is similar to the Simple Moving Average (SMA), in that it aims to reduce noise rather than reduce lag. The indicator takes all prices into account and uses a long lookback period. Old prices are never removed from the calculation, but they have only a minimal impact on the Moving Average due to a low assigned weight. By reducing the noise it removes fluctuations and plots the prevailing trend. The SMMA can be used to confirm trends and define areas of support and resistance. It is often used in combination with other signals and analysis techniques.
Volume Weighted Moving Average (VWMA)
Volume Weighted Moving Average (VWMA) The Volume-weighted Moving Average (VWMA) emphasizes volume by weighing prices based on the amount of trading activity in a given period of time. Users can set the length, the source and an offset. Prices with heavy trading activity get more weight than prices with light trading activity. In periods of low market volume, the SMA and the VWMA are close in value. The indicator can be used to identify and trade trends. Price crossing it could point to a directional change. The VWMA is often used in combination with other signals and analysis techniques.
Double Exponential Moving Average (DEMA)
Double Exponential Moving Average (DEMA) The Double Exponential Moving Average (DEMA) was developed by Patrick Mulloy for the purpose of reducing lag and increasing responsiveness. This fast-acting moving average allows traders to spot trend reversals quickly, resulting in better entries into newly formed trends. The indicator is obviously based on the Exponential Moving Average (EMA) but it follows the price more closely. Its calculation and usage somewhat resemble the Hull Moving Average (HMA). It helps traders spot the prevailing trend and is often used in combination with other signals and analysis techniques.
Fractal Adaptive Moving Average (FRAMA)
Fractal Adaptive Moving Average (FRAMA) The Fractal Adaptive Moving Average (FRAMA) is an intelligent, adaptive moving average that was developed by John Ehlers. It takes the importance of price changes into account and follows price closely with significant moves while remaining flat if price ranges. The FRAMA takes advantage of the fact that markets are fractal and dynamically adjusts the lookback period based on this fractal geometry. The actual calculation is very elaborate and complicated. The FRAMA is often used in combination with other signals and analysis techniques.