Moving averages smooth price data, simplifying the up and downs of a market into a more understandable line that highlights the trend. However, the smoothing process introduces lag: The longer a moving average’s look-back period, the more the average trails behind changes in price direction. On the other hand, moving averages with short look-back periods respond more quickly to price changes but, because they reverse direction on minor price moves, they can lead to whipsaw losses. A moving average length that was appropriate last week might be inappropriate next week as market conditions change. One potential solution to this problem is to use a moving average that adjusts to market volatility by lengthening when the market is moving sideways and trading in a choppy fashion (making it less responsive) and shortening when the market is trending (making it more responsive).
In his book Smarter Trading (McGraw-Hill, 1995), Perry Kaufman detailed a method for calculating an adaptive moving average that fit this role. To see how it works, the following examples compare it to a simple moving average (SMA). First, two SMAs with different look-back periods will be compared to highlight the attributes of each. In this case, price crossing the moving average is not important; rather, it is the direction of the moving average that identifies the trend.
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