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Each previous EMA value accounts for a small portion of the current value. The formula for an EMA incorporates the previous period's EMA value, which in turn incorporates the value for the EMA value before that, and so on. After the first calculation, the normal EMA formula is used. The exponential moving average in the spreadsheet starts with the SMA value (22.22) for its first EMA value. The SMA calculation is straightforward and requires little explanation: the 10-day SMA simply moves as new prices become available and old prices drop off. The Weighting Multiplierīelow is a spreadsheet example of a 10-day simple moving average and a 10-day exponential moving average for Intel. Third, calculate the exponential moving average for each day between the initial EMA value and today, using the price, the multiplier, and the previous period's EMA value. Second, calculate the weighting multiplier. An exponential moving average (EMA) has to start somewhere, so a simple moving average is used as the previous period's EMA in the first calculation. First, calculate the simple moving average for the initial EMA value. There are three steps to calculating an exponential moving average (EMA). You need far more than 10 days of data to calculate a reasonably accurate 10-day EMA. EMAs differ from simple moving averages in that a given day's EMA calculation depends on the EMA calculations for all the days prior to that day. The weighting applied to the most recent price depends on the number of periods in the moving average. Prices the prior four days were lower and this causes the moving average to lag.Įxponential moving averages (EMAs) reduce the lag by applying more weight to recent prices. For example, the moving average for day one equals 13 and the last price is 15. Also, notice that each moving average value is just below the last price. Notice that the moving average also rises from 13 to 15 over a three-day calculation period. In the example above, prices gradually increase from 11 to 17 over a total of seven days. The third day of the moving average continues by dropping the first data point (12) and adding the new data point (17). The second day of the moving average drops the first data point (11) and adds the new data point (16). The first day of the moving average simply covers the last five days. The example below shows a 5-day moving average evolving over three days.ĭaily Closing Prices: 11,12,13,14,15,16,17įirst day of 5-day SMA: (11 + 12 + 13 + 14 + 15) / 5 = 13 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.