Moving averages are one of the technical indicators that investors and traders employ to determine the direction of the trend.
It adds all the data points in any financial security and then divides the sum by the number of data points during the time frame for reaching an average.
It is known in the context of the “moving” average as it is continuously recalculated and is based on the most recent prices. learn all about moving average trading from top experts
The use of the moving average is by analysts to identify the strength and weakness of an asset by analyzing the movements of the asset’s price.
This indicator shows the prior price change of security, which traders utilize to determine the likely direction of the asset’s price.
The indicator can be described as lagging since it follows the price movement of any other asset in providing a signal or indicating the direction of price movement that any company has.
Table Of Contents
- Simple Moving Average:
- Exponential Moving Average (EMA):
- Weighted Moving Average(WMA):
- Double Exponential Moving Average (DEMA):
- The Triple Exponential Moving Average (TEMA):
- Linear Regression (or) Least Square Moving Averages:
The bottom line:
Below are 6 types of Moving Averages used by traders to trade in the market for stocks:
1. Simple Moving Average:
The SMA is the most simple moving average, which is calculated by adding the latest data points and then multiplying the total by the number of time spans.
This SMA indicator is utilized by traders to create signals that indicate when it is time to exit or enter the market.
An SMA is an indicator that is lagging since it is calculated based on historical price data of an upcoming time frame that can be calculated for various kinds of prices like high open, low, and close.
Investors use this indicator to make buy or sell and trade signals for securities. It it also assists in identifying areas of resistance and support.
A stock trader would like to determine the simple moving average of a particular stock by simply taking the closing price for the past five days.
The closing prices of the last five days were as follows: Rs.23, Rs.23.40, Rs.23.20, Rs.24, and Rs.25.50. The SMA is calculated according to:
SMA = (Rs.23 + Rs.23.40 + Rs.23.20 + Rs.24 + Rs.25.50) / 5
SMA = Rs.23.82
The SMA of the most recent 9 months from Nifty 50 is depicted as a line on the chart below:
2. Exponential Moving Average (EMA):
EMA is the second type of moving average which gives extra weight to price data and allows it to be more responsive to the most recent information points.
EMA has a greater sensitivity to price changes compared to the SMA because it assigns an equal amount of weight to every price change within the specified time.
Three steps are in formulating EMA:
In the beginning, we must find the moving average that is simple for a certain time.
Then , we have to calculate the multiplier to be used for the weighting of the moving average that is exponential.
The final step is the formula for the calculation of the current EMA through the time beginning with the first EMA to the latest time frame with the use of the multiplier, price, and the prior date’s EMA value. Formula:
Current EMA = [Closing Price – EMA (Previous Time Period)] x Multiplier + EMA (Previous Time Period)
The EMA of the most recent 9 intervals in Nifty 50 is shown in a straight line on the price charts below:
3. Weighted Moving Average(WMA):
WMA is yet another kind of moving average, which traders employ to generate trading direction and make the buy or sell decision.
It assigns more weightage to the latest data points and less weightage to older data points.
The calculation is done by multiplying every number of data points by a weighting.
The weighted moving average is utilized by traders to generate signals for trading. If, for instance, the prices are higher than the weighted average moving average, this indicates that there is in an upward trend.
However, if prices are lower than the weighted movement, it is a sign that the trend is in the direction of down.
The WMA of the most recent 9 periods from Nifty 50 is depicted as a line in the price charts below:
4. Double Exponential Moving Average (DEMA):
DEMA is an upgraded version of EMA since it gives greater weightage to current data points.
It also reduces the amount of lag it is also more responsive, which aids traders on short-term trading in spotting the reversals of trends rapidly.
Let’s take a look at the Nifty 50 price for the last 9 days:
The blue line is an uncomplicated moving average line. The purple line represents the exponential move average (EMA) and the yellow line indicates the of DEMA line.
Based on the chart above We can conclude that DEMA is close to the prices and also has the lowest deviation.
As the DEMA line is a mirror of the prices of stocks closest and is, therefore, the most sensitive to the volatility of the stock. Variability changes are excellent indicators for a trend reversal and, consequently, the stock market trades.
Watch our Webinar on the Magic of Moving Averages
5. The Triple Exponential Moving Average (TEMA):
TEMA reduces the lag time of EMAs and allows them to be more responsive to prices.
Following when the Double Exponential Moving Average (DEMA) was created around 1994. Patrick Mulloy created the Triple Exponential Moving Average (TEMA).
Like DEMA The TEMA reduces the time delay between the different EMA.
The main difference the two formulas DEMA the formula for DEMA and TEMA is that the formula for TEMA employs the Triple-smoothed EMA along with the double-smoothed and single-smoothed EMAs that are used in the formula used for DEMA.
Below, TEMA is shown as the yellow line. DEMA is represented by the color purple
So, this indicator made by combining these three EMAs creates an average that is much closer to price bars than DEMA.
6. Linear Regression (or) Least Square Moving Averages:
A least-square moving mean (LSMA) computes the regression line with the least squares of the previous time frames which result in forwarding projections for the current time frame.
The indicator assists in determining what might occur in the event that the regression line is extended.
The indicator is built on the sum of the least-squares method to find straight lines that is the most appropriate for data of the specific time period.
You can also view the following video in order to grasp the meaning behind this indicator.