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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Walk-Forward Analysis Demonstration with 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.

Finally I can apply a walk-forward analysis!

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Getting Started with backtrader

A few weeks ago, I ranted about the R backtesting package quantstrat and its related packages. Specifically, I disliked that I would not be able to do a particular type of walk-forward analysis with quantstrat, or at least was not able to figure out how to do so. In general, I disliked how usable quantstrat seemed to be. The package’s interface seems flexible in some areas, inflexible in others, due to a strange architecture that I eventually was not willing to put up with anymore.

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Order Type and Parameter Optimization in quantstrat

DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not the author. The author takes no responsibility for the conduct of others nor offers any guarantees.

Introduction

You may have noticed I’ve been writing a lot about quantstrat, an R package for developing and backtesting trading strategies. The package strikes me as being so flexible, there’s still more to write about. So far I’ve introduced the package here and here, then recently discussed the important of accounting for transaction costs (and how to do so).

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Transaction Costs are Not an Afterthought; Transaction Costs in quantstrat

DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not the author. The author takes no responsibility for the conduct of others nor offers any guarantees.

Introduction: Efficient Market Hypothesis

Burton Malkiel, in the finance classic A Random Walk Down Wall Street, made the accessible, popular case for the efficient market hypothesis (EMH). One can sum up the EMH as, “the price is always right.” No trader can know more about the market; the market price for an asset, such as a stock, is always correct. This means that trading, which relies on forecasting the future movements of prices, is as profitable as forecasting whether a coin will land heads-up; in short, traders are wasting their time. The best one can do is buy a large portfolio of assets representing the composition of the market and earn the market return rate (about 8.5% a year). Don’t try to pick winners and losers; just pick a low-expense, “dumb” fund, and you’ll do better than any highly-paid mutual fund manager (who isn’t smart enough to be profitable).

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

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.

This post is the second in a two-part series on stock data analysis using R, based on a lecture I gave on the subject for MATH 3900 (Data Science) at the University of Utah. (You can read the first post here.) In these posts, I 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 post discusses moving average crossover strategies,backtesting, and benchmarking.

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 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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