The Moving Average or PM is the most widely used indicator in technical analysis and with good reason, since it is one of the oldest technical indicators in existence.
A moving average is the average market price during a certain period of time.
A moving average shows the direction and duration of a trend. The purpose of a moving average is to show the trend in a smoothed.
Due to the fact that the moving average indicator is one of the most versatile and widely used in all indicators, is the basis for the design of most systems and strategies used today.
Here are some of the most common moving averages
The moving average is calculated with a predefined period of time.
The shorter the period, the greater the probability of a false signal.
The longer the period, the less sensitive the moving average. That is, there will be fewer but more accurate signals.
As its name implies, a moving average is an average of a changing body of data.
For example, a moving average of 50 cycles (50 MA where MA is Moving Average by its acronym in English), applied at the end, is formed by the sum of the closing prices of the last 50 periods, divided by 50 .
It is called moving average, since only the last period being evaluated, are being used to calculate the results. And so, the data being evaluated are moving forward with each day (or period) you go.
The analysis of moving averages may be made to any of the following types of price:
Opening price:
The period analysis is based on the opening price of each body.
Closing Price:
The period analysis is based on the closing price of each body.
Highest Price:
The period analysis is based on the highest price of each body.
Lowest Price:
The period analysis is based on the lowest price of each body.
Price:
The period analysis is based on the average price of each body.
Price = (High Price Low Price) / 2
Typical Price:
The typical price for the period is calculated as follows:
Typical Price = (Price High Price Low Price Close) / 3
Weighted Price:
The weighted price of the period is calculated as follows:
Weighted Price = (Price High Price Low Price Close Price Close) / 4
Types of Moving Averages
There are four common types of moving average
Single
Exponential
Smoothing (Smoothed)
Linearly Weighted (Linear Weighted)
Simple Moving Average (SMA):
The Simple Moving Average is undoubtedly the most widely used moving average today.
The Simple Moving Average is sometimes called an arithmetic moving average and is basically an average price over a period of time.
Is calculated by adding the closing prices of the two analyzed over a period of time and then divided into the same number of periods.
For example, the moving average of the last 10 days closing price, divided in 10.
Due to the fact that the simple moving average gives equal weight to each price period being evaluated, the longer the time period evaluated, the greater the smoothing of the latest data.
Here is an example of GBP / USD SMA 25
Exponential Moving Average (EMA):
The Exponential Moving Average indicator reacts faster to recent price changes than the simple moving average sum due to the fact that the closing prices of the current period the previous period, thus giving more weight to recent periods of price.
The period is used to determine the relative weight that should be allocated to prior periods.
The formula is used to determine the percentage.
Here is an example of GBP / USD 25 EMA
Smoothed Moving Average (SMMA):
Because the Smoothed Moving Average indicator, moving average smooths through the same allocation of weights to past prices that recent prices, it is advisable to use the SMMA with longer periods of time for best results
Here is the example of GBP / USD 25 but with SMMA:
(Notice how the curve is much smoother)
Linear Weighted Moving Average (LWMA)
A weighted moving average is calculated by multiplying each previous time period by a weight. The weight is based on the number of days moving average.
A Linear Weighted Moving Average gives more weight to more recent information than older data.
The fact is measured linearly means that the oldest data is given a value of 1, then the data that follows, a value of 2, then the data that follows a value of 3 and so on, until the last data receives a weight equal to the period.
So in a LWMA 25, the weight of the first day is 1, while the most recent day’s weight is 25. This gives 25 times more weight at today’s prices than to the past 25 days.
Here is the example of GBP / USD 25 but LWMA:
How to Operate Using Moving Averages:
The use of moving averages to Tranzas the Forex is a very, very broad, and here is the most common of which can benefit if applied and used properly.
Before you start is important to know that moving averages generally work best in trending markets than in markets without trend, as there may be much “noise” market, which means that false signals can be input and / or output due to the high and low price of a strong pair. However, moving averages indicate when a price is going to break, which leads to the first point:
Breaking of Moving Averages:
You must buy when prices move up and break above the moving average. Better yet if there is a complete candle above the moving average.
It must sell when prices move down and break below the moving average. Better yet if there is a complete candle below the moving average.
Crossing Moving Averages:
To Tranzas crossing moving averages, you must have at least two moving averages of different periods of time.
The moving average is less known as the fast moving average and the moving average is known as the most SLOW moving average.
In the following example, the moving average white 25 is FAST and SLOW moving average of 50 is yellow.
You must buy when the moving average of 25 FAST crosses above the moving average SLOW 50.
You should sell when the moving average of 25 FAST crosses below the moving average SLOW 50.
Tips to Operate moving averages
Remember to ALWAYS confirm your entry and exit points to other indicators when using any of the above strategies with moving averages. These other indicators may be, but not limited to: MACD, Momentum, RSI, Stochastics
