Moving Average

Moving Average (MA) serves as a fundamental tool in technical analysis. Let’s delve into its fundamentals and methods of construction.

Moving Average

What is Moving Average?

Moving Average (MA), also known as a sliding average, is derived from a series of mean values within various subsets of a complete dataset. In trading, MA is applied on price charts to facilitate technical analysis, with the data typically representing prices over a defined period. Let’s illustrate with an example of Moving Average below:

Moving Average

Essentially, each price point (equating to one day) reflects the mean value of a subset of prices from the 30 closest preceding candles, typically the closing price. Connecting these mean values yields a Moving Average line. For a more detailed understanding, let’s proceed to explore the construction of Moving Averages.

Types of Moving Averages

1. Simple Moving Average (SMA)

The most basic form of Moving Average, SMA, is calculated by averaging a subset of values near it. SMA is associated with a parameter indicating the number of elements in the subset used for computation. For instance, SMA 5 entails calculating the average price of the five closest candles preceding the one being evaluated. Similarly, SMA 20, SMA 50, SMA 100, and so forth are utilized.

The formula for constructing SMA involves computing the simple average:

Moving Average

– SMA(n): SMA value at the candle being evaluated
– n: the number of elements in the subset used to construct SMA
– A1 → An: Price of the n closest candles to the point being analyzed (inclusive of the current candle and the preceding ones).

Let’s proceed with a concrete example:

| Session | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Price | 10 | 15 | 23 | 17 | 29 | 35 | 44 |
| SMA 3 | | | 16.0| 18.3| 23.0| 27.0| 36.0|
| SMA 5 | | | | | 18.8| 23.8| 29.6|

For session 3, the SMA 3 value is computed by averaging the prices of the three preceding sessions closest to it: (10 + 15 + 23) / 3 = 16. Likewise, the SMA 3 for session 4 is determined by averaging the prices of sessions 2, 3, 4. This process can be applied to calculate SMA values at various sessions. Connecting these calculated SMA 3 values will form an SMA 3 line.

2. Exponential Moving Average (EMA)

EMA differs from SMA in that it assigns higher weights to data of prices of candles closer to the point being analyzed. Specifically, the formula for calculating EMA is as follows:

Moving Average

– EMAi(n): EMA(n) at the point being calculated
– EMAi-1(n): EMA(n) at the previous point
– Pi: Price at the point being evaluated

The formula indicates that the weight of the current price Pi is greater than that of previous price levels. W is also known as the smoothing factor.

3. Other Types of Moving Averages

Additional types of Moving Averages include Linear Weighted Moving Average (WMA), Double Exponential Moving Average (DEMA), Triple Exponential Moving Average (TEMA), Quadruple Exponential Moving Average (QEMA), among others.

Moving Average

Characteristics of Moving Averages

Moving Averages tend to appear smoother than prices, making them useful for predicting price trends, support, or resistance levels. The longer the MA, the gentler its slope or the smoother it appears. As shown below, the MA 50 line is less steep than the MA 20 line, and the MA 20 line adheres closer to the price than the MA 50 line.

Moving Average


Given that Moving Averages are based on average price values, they can partially filter out price noise. However, they are classified as lagging indicators since they are constructed based on past price data.

EMA lines are devised to reduce the lag of SMA lines by assigning greater weight to the current price level compared to past price levels. Consequently, many traders prefer EMA over SMA. Nevertheless, due to lower lag, EMA lines often generate more noisy signals than SMA lines. The selection of which MA line to apply depends on individual trading preferences.

Moving Average

Moving Averages are considered among the simplest technical indicators, and they serve as the foundation for constructing various other technical indicators such as MACD, Bollinger Bands, Keltner Channels, etc.