These past few weeks I’ve been writing about a new package I created, MCHT. Those blog posts were basically tutorials demonstrating how to use the package. (Read the first in the series here.) I’m done for now explaining the technical details of the package. Now I’m going to use the package for purpose I initially had: exploring the distribution of time separating U.S. economic recessions.
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.)
I recently saw a Reddit thread in r/PoliticalDiscussion asking the question “If the economy is still booming 2020, how should the Democratic address this?” This gets to an issue that’s been on my mind since at least 2016, maybe even 2014: when will the current period of economic growth end?
Around September of 2016 I wrote two articles on using Python for accessing, visualizing, and evaluating trading strategies (see part 1 and part 2). These have been my most popular posts, up until I published my article on learning programming languages (featuring my dad’s story as a programmer), and has been translated into both Russian (which used to be on backtest.ru at a link that now appears to no longer work) and Chinese (here and here). R has excellent packages for analyzing stock data, so I feel there should be a “translation” of the post for using R for stock data analysis.
There was once a time where only the most extreme leftists would accuse Hillary Clinton of not being a true progressive, prior to, say, 2008. Even after 2008, Hillary Clinton was seen as perhaps being a more moderate Democrat, but still, ultimately, a progressive. Republicans certainly would call Clinton a leftist and still continue to believe so.
THIS POST IS OUT OF DATE: AN UPDATE OF THIS POST’S INFORMATION IS AT THIS LINK HERE! (Also I bet that WordPress.com just garbled the code in this post.)
I’m keeping this post up for the sake of preserving a record.
This post is the first in a two-part series on stock data analysis using Python, based on a lecture I gave on the subject for MATH 3900 (Data Science) at the University of Utah. In these posts, I will discuss basics such as obtaining the data from Yahoo! Finance using pandas, visualizing stock data, moving averages, developing a moving-average crossover strategy, backtesting, and benchmarking. The final post will include practice problems. This first post discusses topics up to introducing moving averages.
NOTE: The information in this post is of a general nature containing information and opinions from the author’s perspective. None of the content of this post should be considered financial advice. Furthermore, any code written here is provided without any form of guarantee. Individuals who choose to use it do so at their own risk.
This is my second post in a series of blog posts about income inequality. This post (again, an essay written for a thesis that never materialized) discusses why income inequality matters, from both a political and economic perspective. You can read the first post in the series here.
I am beginning a new series on economics focusing on income and wealth inequality, which may last for a couple weeks. This first post (originally written in 2014 as preliminary work for a thesis research project that ended up simply not happening) does not deal directly with income inequality; instead, I discuss the political economy of banking, reviewing a book and a journal article on the topic. Next week’s post will make clear the relationship of the content in this post with income inequality.
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