One of the most often used indicators utilized by professionals is the moving average.

Exponential moving average (EMA), the simple moving average (SMA), the weighted moving average (WMA) are the three forms of moving averages.

Normally, an average is calculated by averaging the most recent data. As a result, whenever you receive a new observation, you reject the previous one and recalculate the average. The “window” refers to the number of observations you maintain.

In terms of computing, this is problematic since it necessitates keeping a list of all the numbers in the window.

An exponential average is a weighted average of all the observations with an exponential series of weights. The last observation is assigned a significant amount of weight, say 90%. The observation before that receives 90% of the remaining 10% or 9 percent. Prior to that, there is 90% of the remaining 1% or 0.9 percent, and so on.

It’s easy to calculate: for each new observation, you just update your average to equal 10% of the old value + 90% of the new value.

By filtering away high-frequency noise from the signal, both types of averages end up “smoothing out” the data.

  • By filtering out the “noise” from random price movements, a moving average (MA) smooths out a market activity. Because it is dependent on historical prices, it is a trend-following indicator.

The average of a security over a specified number of time periods is known as SMA. More recent prices are given more weight by an EMA.

EMA and SMA

The main distinction between an EMA and an SMA is their sensitivity to changes in the data used to calculate them.

More specifically, the latest prices are given more weight by the EMA, the SMA assigns equal merit to all values. The two averages are similar in that they are both employed by technical traders to smooth out price volatility and are viewed in the same way. Because EMAs weigh current data more heavily than older data, they are more sensitive to recent price fluctuations than SMAs. This makes the findings of EMAs more timely, which is why many traders favor them.

EMA and Market

When used correctly, traders who utilize technical analysis find moving averages to be incredibly useful and informative. They are also aware that when these signals are misused or misread, they may cause disaster. By their very nature, all moving averages utilized in technical analysis are lagging indicators.

As a result, the conclusions gained from applying a moving average to a specific market chart should be to corroborate or signal the strength of a market move. Before a moving average signal that the trend has altered, the best moment to join the market has typically passed.

To some extent, an EMA helps to mitigate the detrimental effects of delays. The EMA computation “hugs” the price movement a little tighter and reacts faster since it gives greater weight to the most recent data. When an EMA is utilized to provide a trading entry signal, this is good.

In trending marketplaces, EMAs function best. The EMA indicator line will show an uptrend when the market is in a strong and persistent upswing, and vice versa when the market is in a downturn. A cautious trader will pay attention to the EMA line’s direction as well as the pace of change from one bar to the next.

Limitations

Many traders believe that the fresh data more closely reflects the current trend of the asset. Others, on the other hand, believe that emphasizing current dates causes a bias that leads to more false alarms.

Likewise, the EMA is entirely based on historical data. Many economists argue that markets are efficient, meaning that current market prices already represent all relevant data. If markets are efficient, historical data should teach us nothing about asset price trends in the future.