A Z-Score is a statistical measure.  A Z-Score tells how a single data point compares to normal data.  A Z-Score says not only whether a point was above or below average, but how unusual the measurement is.

The algorithm to compute a Z-Score is simple.  Start with a list of numbers representing common values for something.  Compute the mean of this list.  The mean is just a simple average.  Then compute the standard deviation of the list of numbers.  The standard deviation is the average distance between each number in the list, and the mean of the list.  Now take a new number that you want to compare to the list of numbers.  Subtract the mean of the list from that number, then divide the result by the standard deviation of the list.  The final result will be the Z-Score of the new number compared to the list of numbers.

The algorithm used to compute a Z-Score is almost identical to the algorithm used to compute volatility.  The difference is that a Z-Score only looks at the values of the normal data.  Volatility also looks at the order of the normal data.

Trade-Ideas uses Z-Scores and similar methods internally when evaluating market data.  It is not enough to know that a stock price is changing by a certain number of pennies or even a certain percent.  We always compare this to the normal fluctuations in the stock’s price, to see how unusual this even is.  If the Z-Score is too low, we do not report an alert; we consider this to be noise.  Z-Scores are often used in computing the stock specific filter values; larger Z-Scores lead to higher quality alerts.