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December 23rd, 2011, 07:31 AM  #1 
Newbie Joined: Dec 2011 Posts: 1 Thanks: 0  Overlaying multiple graphs onto the same graph
Hey, I'm currently writing a piece of software that does all the backend mathematics in determining whether a stock should be purchased or not. I have had no trouble writing the EMA, SMA, Bollinger band, Keltner Channels or ADL calculations up to this point. The problem I'm having now is writing some functions that look for divergences in different data sets. Here is a great example and please let me know if my thinking is correct that what I "want" to do is wrong, and what I should be doing: The ADL (AccumulationDistribution line) is calculated and has values like the following for each time interval (represented as t): t0: 4774 t1: 4855 t2: 12019 http://stockcharts.com/school/data/medi ... msheet.png And the prices for those same times could be something like: t0: 52.13 t1: 50.02 t2: 50.05 With a picture of the chart (prices) with the ADL (pink) on separate graphs: http://stockcharts.com/school/data/medi ... cidisc.png NOW THE PROBLEM: Calculating divergences between the price and the ADL will be skewed because the change in price versus the change in ADL via slope will never be really that steep, so I'm not sure what to do at this point. I saw this graph, which seems to plot the ADL with the stock price: http://stockcharts.com/school/data/medi ... xaffrm.png but I have NO idea how to do that, or what it is even called. Is it normalization? Overlaying graphs? What's the backend math on normalizing these values to put them on the same graph, thereby allowing a CORRECT divergence test to take place???? Is my thinking even correct? Can I instead do a percentage change in price, versus a percentage change in ADL and use the percents as a normalization technique?? Google has failed me in trying to solve this problem. Here's the webpage that I got all of those pictures from if you want to peruse that too: http://stockcharts.com/school/doku.php? ... on_distrib Thanks in advance and I hope it wasn't too rambly. 

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