This is a lecture for MATH 4100/CS 5160: Introduction to Data Science, offered at the University of Utah, introducing time series data analysis applied to finance. This is also an update to my earlier blog posts on the same topic (this one combining them together). I strongly advise referring to this blog post instead of the previous ones (which I am not altering for the sake of preserving a record). The code should work as of July 7th, 2018. (And sorry for some of the formatting; WordPress.com’s free version doesn’t play nice with code or tables.)
This post is actually months late, but like with my last video course announcement, it’s better late than never. And besides, of my video courses, I had the most fun writing this one.
This news is a few weeks late, but better late than never!
DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not me. I, the author, neither take responsibility for the conduct of others nor offer any guarantees. None of this should be considered as financial advice; the content of this article is only for educational/entertainment purposes.
A few months ago I wrote a blog post about getting stock data from either Quandl or Google using R, and provided a command line R script to automate the task. In this post I repeat the task but with Python. If you’re interested in the motivation and logic of the procedure, I suggest reading the post on the R version. The Python version works similarly.
It’s been a long time since I’ve written a blog post. As I have written previously, I intentionally scaled back on my blogging. I didn’t want to scale back to the point that I have not written a post since July. But my life has been busy lately, a topic that may be the subject of a future post (for those who care about what goes on in my life). Continue reading