It has been pointed out that in the article Oversimplified Method for Finding Patterns in Stock Charts the actual method used was not described in detail. The process is pretty simple – you might even say oversimplified. It was just an error of omission, not an attempt to hide any proprietary methods. In fact, just as a gentle reminder – this blog is for the open discussion and exploration of advanced techniques for analyzing the stock market.

So, let’s review. I am not a chartist or a technical analyst, but am interested in understanding patterns that seem to have “legendary” status among those who dabble in these arts. One of my greatest concerns is that there is little work on validating chart patterns in an objective manner. So, for instance – how reliable, profitable and practical are these patterns?

Well, before we can approach those questions, we needed a way to detect patterns in a repeatable fashion. Pattern matching is a fascinating field, but we just needed a quit and dirty solution. Correlation was that simple solution. What was not described was how we are using correlation to find patterns in stock charts. We are going to use some data that never changes (a stock in the database that has stopped trading) and Excel to show this simple process.

AMNT stopped trading on 12/12/2005 and just so happens to have a really classic head and shoulders pattern in the last seven days of trading:

Using Excel, we can describe the process that is used to find the head and shoulders pattern (or any other pattern – given a template of the pattern). First, we need a template of the pattern we are looking for in the chart data. I explored the charting and technical analysis literature and “eye-balled” the patterns people were describing as head and shoulders. This is far from scientific, but a good start. What I came up with as a template for head and shoulders is:

Or, very simply using numbers from 0 to 100:

0
60
20
80
30
70
20

Seven data points gives us a template for what I think a head and shoulders pattern would look like. This template is certainly open to discussion, but this is more about the process than the correctness of the template. What is important is that we can use the above template to easily and quickly identify that general pattern. We have a template of 7 data point, so we need to look at 7 data points from the stock data we wish to search for patterns. We are using AMNT and the last 7 data points (looking at closing prices) are:

10.22
10.25
10.24
10.28
10.24
10.26
10.24

Now, open up Excel and paste the two columns of data into columns A and B of your worksheet. It doesn’t matter which column you put the data – template in A or template in B:

Now, using the Excel function CORREL we are going to find the correlation between the template and the stock data. Put the following formula into any cell that isn’t already being used:

=CORREL(A1:A7,B1:B7)

The result is a correlation of .940712, which is a pretty good match. You look at the pattern’s template and the actual stock data and see that it is a pretty good match. Pretty simple, huh?

For what we are doing, we have this process written in Java and every morning we search for patterns that have a correlation greater than .85 – why .85? No reason, just an arbitrary point. For those who wish to do this for themselves in other languages or technical analysis packages, you just need a correlation function and the ability to use that function to compare two sets of data. Once you have that setup, then you can play around with any number of templates describing different patterns and correlation cut points.