In the realm of technical analysis, averages play a crucial role in smoothing out price data and identifying trends. Moving averages, in particular, are widely employed by traders and analysts to gain insights into market trends, filter out noise, and make informed decisions. This article delves into the concept of averages, introduces moving averages, and explores different types, such as simple or weighted.
What is an Average?
An average is a statistical measure that represents a central or typical value within a set of data. It is calculated by summing up all values in a dataset and dividing the sum by the total number of values. Averages provide a way to analyze data and discern patterns by smoothing out fluctuations and highlighting trends.
Introduction to Moving Averages:
Moving averages take the concept of averages a step further by incorporating a time dimension. Rather than considering the entire dataset, moving averages focus on a specific window or period of time. This moving window ‘slides’ through the data, recalculating the average at each step. The markets often move through trends. With the shift to a moving average from a simple average, the indicator gains the ability to follow trends, adapting to price and trend changes.
Moving averages are used in many different forms in technical analysis, such as identifying trends, reducing noise, or serving as supports and resistances during those trends. Because they are calculated directly from the price and have the ability to adapt to changes, moving averages may be the most popular indicator among traders.
There are different versions that calculate moving averages. Here are some of the most commonly used ones:
- Simple Moving Average (SMA): The moving version of the standard average, calculated by the sum of closing prices over ‘n’ periods divided by ‘n’. With every closing candle, the oldest one is excluded from the calculation.
- 2- Exponential Moving Average (EMA): The exponential moving average is calculated using the simple moving average but assigns more weight to recent price data instead of using the same weight for the entire series. EMA is more useful for sharp trend changes and is able to follow prices more rapidly.
- Triangular Moving Average (TMA): The triangular moving average gives more weight to the center of the series, making it generally smoother than the SMA.
- Weighted Moving Average (WMA): The weighted moving average assigns different weights to various data points across the series, typically giving more weight to recent data to capture price changes more quickly.
- Smoothed Moving Average (SMMA): Smooths the moving average to be less affected by sharp volatility. It is useful for following trends when prices are whipsawing during those trends, reducing noise. SMMA can be used as longer-term supports or resistances.
- Variable Moving Average (VMA): The variable moving average automatically adjusts the smoothing factor depending on volatility. Due to its higher adaptability, it is often useful for tracking shorter trends.
(Different Moving Averages with Period of 100)
