A Moving Average is a lagging indicator (lagging price) which moves with each data point, taking a certain number of previous data points into consideration. A much used in the stock market is the 200 day moving average, which sums up the close of the last 200 trading days and divides that sum by 200, giving in effect an average close per day. As price moves each day, the oldest component of the average drops off and the newest component of the average is included. Thus the moving averages are moving with the price. As price goes up, the moving average goes up. As price goes down, the moving average goes down. If price goes sideways, the moving average goes sideways.

The 200 day daily close moving average is just an example. You can create a moving average from any sequential listing of numbers on any time frame or print - depending on the type of chart you are using. You could create a moving average of the daily highs and a separate moving average of the daily lows to create a trading channel. You could create a moving average of the final print on the minute charts of the emini. You could create a 2 day, 10 day, 100 day, or 1000 day moving average. You can create a moving average based on 15 minute data, 60 minutes data, weekly data, monthly data, yearly data - and everything in between and bigger and smaller.

Keep in mind that the most basic moving average is the simple moving average, which has been discussed above. There are actually several different types of moving averages which are each discussed in detail on their own page:

A simple moving average adds up the data pointed over a predefined number of data points in the past and simply divides by the count - giving every data point included in this set a equal weight in the outcome. You can check out our simple moving average page to get a better idea of how this value is calculated.

A weighted moving average uses a predefined number of data points in the calculation - as does the simple moving average. However, each data point, instead of having an equal weight in the final result, is multiplied by a different ration, and these multiplication results are added together to obtain the weighted moving average value. You can check out our weighted moving average page to get a better idea of how this value is calculated.

An exponential moving average, instead of using the data points for the calculations, uses today's data point and yesterday's exponential moving average calculation. All the previous data is contained and included within each exponential moving average calculation. However, similar to the weighted moving average above, more weight is given to the nearer data points. You can check out our exponential moving average page to get a better idea of how this value is calculated.