Different Types Of Moving Average.

Now we are going to discuss different types of moving average(MA):

Simple Moving Average (SMA)

Simple, in other words, arithmetical moving average is calculated by summing up the prices of instrument closure over a certain number of single periods (for instance, 12 hours). This value is then divided by the number of such periods.

The volatility of the forex market is much more smoothed at the long periods of time due to the equal weight given for the daily price by SMA. Only the long-term trends bay me seen out of the long-term averages as far as any insignificant fluctuations get smoothed. For finding put short-term trends the short-term averages are taken, however they still give the long term expense.

The prices are mostly located close to the moving average but still aside from it. The moving average changes following the trend changes giving the additional data of the trend strength taking the slope steepness as its basis.

Exponential Moving Average (EMA):

Exponentially smoothed moving average is calculated by adding the moving average of a certain share of the current closing price to the previous value. With exponentially smoothed moving averages, the latest prices are of more value. P-percent exponential moving average will look like:

As moving averages are sometimes applied for the trend defining, they can also be used to see whether data is opposing the trend. Entry and exit systems usually compare data to a moving average to determine if it is supporting a trend or starting a new one. That's why the exponential moving average is just one of the types of a moving average.

In an ordinary moving average, all price data has the same weight in the calculation of the average with the oldest eliminated value as each new value is added. And in the exponential moving average equation as the average is being measured the most recent market action gets greater importance. Still the oldest pricing data in the exponential moving average is never eliminated.

A sell signal occurs if the short and intermediate term averages cross from the top to the bottom the longer term average. On the contrary, a purchase signal happens if the short and intermediate term averages cross from bottom over the longer term average. If you trade only 2 exponential moving averages in a crossover system it's better to use longer term averages.

It's rather important to know that a 5-day exponential moving average usually consists of over 5 days worth of data and can comprise data from all the life of a futures contract. So such moving averages can be more successfully searched by their actual "smoothing constants," as the number of days of data in the computation remains equal for the 5-day average as for the 10-day average. Exponential calculations are held at various moving average values depending on the point you start with.

Weighted moving average:

A weighted average is any average that has multiplying factors to give different weights to different data points. Mathematically, the moving average is the convolution of the data points with a moving average function; in technical analysis, a weighted moving average (WMA) has the specific meaning of weights which decrease arithmetically. In an n-day WMA the latest day has weight n, the second latest n − 1, etc, down to zero.
By looking at the moving average of the price, a more general picture of the basic trends can be seen MA are useful for smoothing raw, noisy data, such as daily prices. Price data can change greatly every day without demonstrating if the price is increasing or decreasing.

Moving averages can be used to see trends, that's why they can also be used to predict if data is bucking the trend. A weighted moving average is measured by multiplying each of the previous day's data by a weight. The weight in its turn is based on the number of days in the moving average. In this example, the first day's weight is 1.0 while the value on the most recent day is 5.0. This gives 5 times more weight to today's price than the price 5 days before.

Soothed Moving Average:

A Smoothed Moving Average is sort of a cross between a Simple Moving Average and an Exponential Moving Average, only with a longer period applied. The Smoothed Moving Average gives the recent prices an equal weighting to the historic ones. The calculation does not refer to a fixed period, but rather takes all available data series into account. This is achieved by subtracting yesterday’s Smoothed Moving Average from today’s price. Adding this result to yesterday’s Smoothed Moving Average, results in today’s Moving Average.

In a Simple Moving Average, the price data have an equal weight in the computation of the average. Also, in a Simple Moving Average, the oldest price data are removed from the Moving Average as a new price is added to the computation. The Smoothed Moving Average uses a longer period to determine the average, assigning a weight to the price data as the average is calculated. Thus, the oldest price data points in the Smoothed Moving Average are never removed, but they have only a minimal impact on the Moving Average, which is similar to how an Exponential Moving Average places more weight on the more recent data.