Getting S&P 500 Stock Data from Quandl/Google with Python

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.

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Downloading S&P 500 Stock Data from Google/Quandl with R (Command Line Script)

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.

While most Americans have heard of the Dow Jones Industrial Average (DJIA), most people active in finance consider the S&P 500 stock index to be the better barometer of the overall American stock market. The 500 stocks included in the index are large-cap stocks seen as a leading indicator for the performance of stocks overall. Thus the S&P 500 and its component stocks are sometimes treated as “the market.”

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Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader

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.

Introduction

Having figured out how to perform walk-forward analysis in Python with backtrader, I want to have a look at evaluating a strategy’s performance. So far, I have cared about only one metric: the final value of the account at the end of a backtest relative. This should not be the only metric considered. Most people care not only about how much money was made but how much risk was taken on. People are risk-averse; one of finance’s leading principles is that higher risk should be compensated by higher returns. Thus many metrics exist that adjust returns for how much risk was taken on. Perhaps when optimizing only with respect to the final return of the strategy we end up choosing highly volatile strategies that lead to huge losses in out-of-sample data. Adjusting for risk may lead to better strategies being chosen.

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

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.

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On Programming Languages; Why My Dad Went From Programming to Driving a Bus

In Data Science from Scratch, a book introducing data science using Python, Joel Grus said the following about R (pg. 302):

Although you can totally get away with not learning R, a lot of data scientists and data science projects use it, so it’s worth getting familiar with it.

In part, this is so that you can understand people’s R-based blog posts and examples and code; in part, this is to help you better appreciate the (comparatively) clean elegance of Python; and in part, this is to help you be a more informed participant in the never-ending “R versus Python” flamewars.

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Where to Go from Here? Tips for Building Up R Experience

At the University of Utah, I teach the R lab that accompanies MATH 3070, “Applied Statistics I.”” None of my students are presumed to have any programming experience, and they never hesitate to remind me of that fact, especially when they are starting out. When I create assignments and pick problems, I often can write a one- or three-line solution in thirty seconds that students will sometimes spend four hours trying to solve. They then see my solution and slap their foreheads at its simplicity. I can be tricky with my solutions. For example, suppose you wish to find the sample proportion for a certain property. A common approach (or at least the one used in the textbook our course uses, Using R for Introductory Statistics by John Verzani) looks like this:

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