Old data is dropped as new data becomes available, causing the average to move along the time scale. As its name implies, a moving average is an average that moves. Most moving averages are based on closing prices for example, a 5-day simple moving average is the five-day sum of closing prices divided by five. Try experimenting with both types of moving averages, different timeframes, and different securities to find the best fit.Ī simple moving average is formed by computing the average price of a security over a specific number of periods. Your moving average preferences will depend on your objectives, analytical style, and time horizon. Keep the lag factor in mind when choosing the right moving average for your chart. The 50-day SMA fits somewhere between the 10- and 100-day moving averages when it comes to the lag factor. Even with the January-February decline, the 100-day SMA held the course and did not turn down. The chart above shows the SPDR S&P 500 ETF (SPY) with a 10-day EMA closely following prices and a 100-day SMA grinding higher. a 10-day moving average.Ĭlick here for a live version of the chart. It takes a larger and longer price movement for a 100-day moving average to change course vs. Longer-term moving averages are like ocean tankers-lethargic and slow to change. In contrast, a 100-day moving average contains lots of past data that slows it down.

Short-term moving averages are like speedboats-nimble and quick to change.

In addition, the type of moving average affects the lag: EMAs with the more recent data weighted more heavily will lag less than an SMA, which gives equal weight to data further in the past.Ī 10-day moving average will hug prices quite closely and turn shortly after prices turn. The longer the moving average, the more the lag. Because moving averages are based on past data, they tend to lag behind price data.

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