Extracting useful metrics from price info First, an admission: my stats knowledge is minimal - purely practical applications in a fairly narrow range. I'm mostly a mechanic with a good bit of experience building and wrenching on ML/DS systems in big data applications but little understanding of the underlying principles. I keep kicking myself about it, but have never found the time to go back to school and learn. :\
Next, the problem: I have some structured price data (date, time, open/high/low/close info on some stocks) that I want to do some analysis on - mainly, I want to get answers to questions like "what was the greatest amount of movement in stock X on Fridays?", "what was it during the last 3 hours, excluding the last 5 minutes of the day, on Wednesdays?", "what were the price movement extremes/mean/percentile rank at the selected time/day-of-week range?"
Now: I could write programs to do any of the above tasks - I'm anywhere from conversant to expert in a variety of programming languages. But I don't see how to get answers to this kind of questions in general, or how to look for any sort of existing framework for doing so. It just seems to me that the common principles of this kind of analysis, and the kind of framework that's needed for thinking about it is based on statistics - not the mechanics, but the approaches to take - which is exactly where the hole in my knowledge lies.
I suppose what I'm looking for is a clear statement of "yes, this can be done with an understanding of basic principles X, Y, and Z - here's where you can learn about it with a few hours/days of applying yourself" or "no, this isn't doable without a stack of degrees in quantum mechanics, French cooking, and xenobiology". If it is achievable with relatively short-term effort, both general ("here's how to approach this kind of problem") and specific ("here's a tool that will get you 99% of the way there") suggestions would be greatly appreciated.
 A: There are whole mathematical fields dedicated to that. Without any information on what price you want to look at, I assume you are talking about some sort of financial instruments.
One of those fields is technical data analysis, which look at patterns in price. There are some standards implementation, I am mainly thinking about quantmod, an R package. However you must know that extracting meaningfull signals from prices is rather hard, mostly because (1) future is difficult to predict from the past and (2) there is a whole industry specialized in detecting those signals and profit from them, which is intrinsically smoothing those signals and leave you with noise (see point 1). 
Another field is quantitative finance, which build models of the price and try to build on top of that with more rigorous assumptions. One of the main assumption is that there are no "free lunches", meaning that there is no reward without risk. The theory needed is rather difficult, but I am pretty sure that https://quant.stackexchange.com/ has a list of introductory materials. From a quantitative finance point of view, technical data analysis is seen as over-simplistic.
