It does. Thanks. I played around with it and yes while raw data can be exported to excel, it still requires a lot of manipulation. Example: If I want values for the 'Time' that High/Low was made, I have to first output all the data from the High/Low study and then do all sorts of sorting to get the time during which High/Low was made. Not terribly time intensive though. Would be nice to know how to get it in one step.
Yeah R is pretty good for statistical work. If you're going in that direction checkout Roger Peng's class in coursera. It's part of the data analisys specialization n u can take it for free.
Use the INDEX MATCH formula to get the time/date that the H/L were made. You can do it in Sierra Charts, but if you are pulling the data from one sheet to another, be aware of the problem with comparing serial date-time values between sheets - you get incorrect values returned (see link below). A work around is to put the INDEX MATCH in the same sheet as the data and pull it over with a simple cell reference. http://www.sierrachart.com/index.php?l=doc/doc_WorksheetFunctions.html
Yes nice to get validation for it. I had browsed coursera earlier and saved the following for possible followup https://www.coursera.org/course/datascitoolbox Not sure if I need to go all in on data analysis . Although the 'Data Smart' book was a great eyeopener to what is possible.
I will look at it JT and be sure to ask you any followup questions. Amazing how despite having used excel for years, I wasn't even aware of VLookup, Pivots, Index, Match. Market making me catch up now.
I got started into computer science from courseras data science courses (they had different names back in 2012 but were basically the same idea) There's a course from GATech called computational investments that leans a lot more towards analyzing financial data on python.
A note on Quandl, which is referenced above: I wasted some time running continuous futures contracts through Hoadley Tools, not realizing the data sets were full of holes. Now, I zoom the graphs within Quandl before downloading and can find the holes and error spikes and make a decision as to whether the data can be repaired quickly or whether it's garbage. Unfortunately, a lot of the futures data is garbage. I like Quandl, but they're still new and apparently they don't curate the thousands and thousands of data sets they have available. I hope they improve in the future, but in the meantime, check their data visually or with some simple stats before you use it, and if you need a very clean price series, you're probably going to have to buy it.