Moving averages clean the price data to create a trend following indicator. They don't predict price direction, but instead define the current path with a lag. Moving averages lag because they're predicated on past prices. Not surprisingly lag, moving averages help smooth price actions and filter the noise. They also form the inspiration for many other technical indicators and overlays, such as Bollinger Bands, MACD and the McClellan Oscillator . The two most well-known types of moving averages will be the Simple Moving Average (SMA) and the Exponential Moving Average (EMA). These moving averages can be utilized to identify the path of the pattern or define potential support and resistance levels.

Simple Moving Average Calculation

A simple moving average is formed by processing the average price of a protection over a particular number of periods. Many moving averages derive from closing prices. A 5-day simple moving average is the five day time sum of closing prices divided by five. As its name implies, a moving average can be an average that techniques. Old data is usually dropped as fresh data comes available. This causes the common to move along enough time scale. Below is an exemplory case of a 5-time moving average evolving over three times.

Daily Closing Prices: 111,112,113,114,115,116,117

First day of 5-day SMA: (111 + 112 + 113 + 114 + 115) / 5 = 113

Second day of 5-day SMA: (112 + 113 + 114 + 115 + 116) / 5 = 114

Third day of 5-day SMA: (113 + 114 + 115 + 116 + 117) / 5 = 115

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The first day of the moving average simply covers the last five days. The second day of the moving average drops the first data point (111) and adds the new data point (116). The third day of the moving average continues by dropping the first data point (112) and adding the new data point (117). In the example above, prices gradually increase from 111 to 117 over a total of seven days. Notice that the moving average also rises from 113 to 115 over a three day calculation period. Also notice that each moving average value is just below the last price. For example, the moving average for day one equals 113 and the last price is 115. Prices the prior four days were lower and this causes the moving average to lag.

Exponential Moving Average Calculation

Exponential moving averages decrease the lag by applying more excess weight to recent prices. The weighting applied to the most recent price depends upon the number of periods in the moving average. There are three guidelines to calculating an exponential moving average. First, calculate the easy moving typical. An exponential moving average (EMA) has to begin somewhere so a simple moving average is utilized as the prior period's EMA in the 1st calculation. Second, calculate the weighting multiplier. Third, calculate the exponential moving average. The formula below is usually for a 10-day EMA.

SMA: 10 period sum / 10

Multiplier: (2 / (Time periods + 1) ) = (2 / (10 + 1) ) = 0.1818 (18.18%)

EMA: {Close - EMA(previous day)} x multiplier + EMA(previous day).

Download Excel File For Calculate Moving Average

A 10-period exponential moving average applies an 18.18% weighting to the most recent price. A 10-period EMA can also be called an 18.18% EMA. A 20-period EMA applies a 9.52% weighing to the most recent price (2/(20+1) = .0952). Notice that the weighting for the shorter time period is more than the weighting for the longer time period. In fact, the weighting drops by half every time the moving average period doubles.

If you want to us a specific percentage for an EMA, you can use this formula to convert it to time periods and then enter that value as the EMA's parameter:

Time Period = (2 / Percentage) - 1

3% Example:  Time Period = (2 / 0.03) - 1 = 65.67 time periods

It is a great indicator of trend direction. Nonetheless it does give fake signals in sideways styles. There is lots of trading stratergy available with moving averages like ema crossover trading strategy, sma crossover trading strategy, supertrend with ema crossover strategy etc.