An Introduction to Stock Market Data Analysis with Python (Part 2)

*This post is the second in a two-part series on stock data analysis using Python, based on a lecture I gave on the subject for MATH 3900 (Data Mining) at the University of Utah (read part 1 here). 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. This second post discusses topics including divising a moving average crossover strategy, backtesting, and benchmarking, along with practice problems for readers to ponder.

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.

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An Introduction to Stock Market Data Analysis with Python (Part 1)

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.

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Did Wages Detach from Productivity in 1973? An Investigation

This is the third and final blog post in my series on income inequality (read the other two here and here). This post discusses the detachment of compensation from productivity that occurred around 1973. I look at the data and use R for exploring this break, along with why it may have occurred. R code is with the analysis, in the spirit of reproducible research.

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Money is Power; The consequences of economic inequality

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.

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Playing the Game of Bank Bargains

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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