Diving into the data
You can continue to add additional layers of complexity until you simply turn the computer loose on data. This is referred to by many as data mining, or looking at all combinations of variables in the system. Opponents of this approach associate it with curve-fitting, and argue that if you measure enough noise, you are likely to find some aberrant behavior. There is some validity to this argument, and a reason you should look for confirmation from two or more indicators.
I refer to my data mining model as PTA8. Each day, the model looks at seven different tape measures over 21 different time frames and quantifies the market's performance in similar past instances. It looks for days where you get extremely rare performance over 30 similar data observations. Three-standard deviation results are rare, but consider the data from May 18 (see "Breadth analysis," below).
ADTn is the percent of advance in n days. Looking at the first row, the ADT1 is equal to 84.27 and the 100-day exponential moving average is 1.16. XMA100 is a 100-day exponentially smoothed price trend indicator normalized to -3 to +3 standard deviations. The value for the XMA100 indicates it is at +1.16 standard deviations. The model looks for ADT1s between 82.57 and 86.39 with an XMA100 between 0.75 and 1.56. The average three-, six- and 12-month forward performance follows and is normal. The same approach is taken for two to 21 days in the subsequent rows.