Most Popular Moving Averages Explained - Inveslo
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23 August @ 04:11

Most Popular Moving Averages Explained

The moving average (MA) is a tool for smoothing out price data by continually updating its average price. The average is calculated over a specified time period, such as 10 days, 20 minutes, or 30 weeks. Using a moving average in your trading has advantages, as well as options when it comes to the type of moving average you should use. Moreover, both long-term investors and short-term traders can benefit from moving average strategies.

Why Use a Moving Average

Moving averages help reduce the amount of noise on a price chart. If you want to get a basic idea of how the price is moving, you can look at the direction of the moving average. Generally, if the angle is upward, the prices are moving up (or have been recent); if the angle is downward, the prices are moving down overall; if it is moving sideways, the prices are probably in a range.

Additionally, moving averages can be used as support or resistance. During an uptrend, a 50-day, 100-day, or 200-day moving average can act as a support level for the price. See the figure below as an example of what this means. This occurs because the average serves as support (floor), so prices bounce up off it. Moving averages may act as resistance in a downtrend; like ceilings, they may cause prices to drop again after hitting them.

Moving Averages and Their Types

Different methods are available for calculating a moving average. Here we look at the two most popular moving averages:

  • Simple Moving Average (SMA)
  • Exponential Moving Average (EMA)

The ten-day simple moving average (SMA) takes the ten most recent daily closing prices and divides them by five to create a new average each day. The singular flowing line is formed by connecting the averages one after another.

The exponential moving average (EMA) is another popular type of moving average. Compared to the previous calculation, it is more complex because it gives more weight to the last few prices. When plotting a 50-day SMA and a 50-day EMA on the same chart, you will find that the EMA responds to price changes faster than the SMA, mainly because recent price data has an additional weighting, which means that it reacts more quickly to price change.

Moving averages are calculated automatically by charting software and trading platforms, so no manual calculations are necessary.

There is no better type of MA than another. Sometimes, an EMA works best in a stock market or financial market, while other times, an SMA may be more effective. A moving average's effectiveness will also depend on the time frame it is set at (regardless of its type).

Suggested read: Key Principles Of Technical Analysis

The Moving Average Length

The most common moving average lengths are 10, 20, 50, 100, and 200. A trader can apply these lengths to any chart timeframe (one minute, daily, weekly, etc.). Timeframe and length of a moving average are both essential factors in its effectiveness.

When a MA is based on a short time span, it will react to price changes much quicker than a MA based on a more extended look-back period. The 20-day moving average is seen to track the price more closely in the figure below than the 100-day moving average.

Chart, histogram

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Putting It All Together

Creating a smooth line can simplify price data with a moving average. As a result, it is easier to see the trend. Simple moving averages do not react as quickly to price changes as exponential moving averages. This can be good in some cases, but it could also lead to false signals in others. Moving averages with a shorter look-back period (20 days, for example) will also respond to price changes more quickly than moving averages with a longer look-back period (200 days, for example).

Crossovers between moving averages are popular forex trading strategies for entering and exiting a trade. It is also possible to identify areas of potential support or resistance using MAs. Although moving averages may appear predictive, they are always based on historical data and show the average price over a period of time.