How to Draw Trading Chart Channels by Hand
How to Interpret the Simple Moving Average on a Trading Chart
How to Create a Point-and-Figure Chart of Trading Prices

The Types of Trading Chart Moving Averages

You can adjust the moving average on a trading chart to make it track current prices more closely without sacrificing all the benefits of the averaging process. Moving averages come in a number of forms, each with its own benefits and drawbacks:

  • Weighted moving average (WMA): You can make the moving average more responsive to the latest prices by weighting the latest prices more heavily. You get the weighted moving average by multiplying each price in your series according to how fresh it is. In a 5-day moving average, for example, Day 5 (today) would be multiplied by 5, Day 4 by 4, Day 3 by 3, and so on. Remember to divide the total by the sum of the weights, not the sum of the days (5 + 4 + 3 + 2 + 1 = 15).

  • Exponential moving average (EMA): This moving average is hard to calculate, and fortunately, all the charting software packages can do it for you. The principle is to subtract today’s closing price from yesterday’s exponential moving average.

    Start with a simple moving average. Multiply the difference between today’s price and the moving average by a constant smoothing factor (the exponent). The factor is determined by the number of days you’re using in the moving average — say 10 days. The exponent is calculated by dividing 2 by 10, yielding 0.2 as the factor. If you’re using 20 days, you divide 2 by 20 and get 0.1 as the factor. Here’s why:

    • The factor minimizes the change between the existing moving average and the latest price, creating a smaller bridge than in a simple moving average, which has to bridge the entire distance between today’s price and yesterday’s. This factor gives the moving average a numerical value closer to the last price and thus makes it more representative of recent prices.

    • The fewer the number of days in the moving average, the bigger the factor. This principle closes the gap between the moving average and the latest price even more.

  • Adaptive moving average: Works like a long-term moving average in that it diminishes the effect of outliers, but without sacrificing sensitivity to trended prices. You always want a moving average to be as short as possible to identify the beginning of a trend quickly, but as long as necessary to avoid whipsaw losses.

  • Efficient prices follow a straight line. They receive an efficiency rating of 1. Prices that are inefficient resemble the meandering path of a drunken sailor. They get an efficiency rating of zero. Most prices are somewhere in between. The rating is then converted to a smoothing constant (which is confusing because in this application, it’s not constant, but changes depending on the numbers; constant is a term used by mathematicians for a term in a formula because it’s constantly there, whatever its numerical value). As the smoothing constant gets closer to 1, the moving average tracks the prices more closely. When the smoothing constant is zero, the moving average value doesn’t change and is carried over unchanged from yesterday — in other words, a spiky outlier is simply ignored.

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