Joshua Ulrich is a Quantitative Analyst & Programmer for an algorithmic market maker, a member of the R/Finance Conference organizing committee, and founder of the Saint Louis R User Group. He is the creator of TTR, and co-author of several other packages for R and finance. He is passionate about computational finance, algorithmic trading, risk management, and helping people solve problems.
If you've ever done anything with financial or economic time series, you know the data come in various shapes, sizes, and periodicities. Getting the data into R can be stressful and time-consuming, especially when you need to merge data from several different sources into one data set. This course will cover importing data from local files as well as from internet sources.
A wealth of financial and economic data are available online. Learn how getSymbols() and Quandl() make it easy to access data from a variety of sources.
You've learned how to import data from online sources, now it's time to see how to extract columns from the imported data. After you've learned how to extract columns from a single object, you will explore how to import, transform, and extract data from multiple instruments.
Learn how to simplify and streamline your workflow by taking advantage of the ability to customize default arguments to `getSymbols()`. You will see how to customize defaults by data source, and then how to customize defaults by symbol. You will also learn how to handle problematic instrument symbols.
You've learned how to import, extract, and transform data from multiple data sources. You often have to manipulate data from different sources in order to combine them into a single data set. First, you will learn how to convert sparse, irregular data into a regular series. Then you will review how to aggregate dense data to a lower frequency. Finally, you will learn how to handle issues with intra-day data.
You've learned the core workflow of importing and manipulating financial data. Now you will see how to import data from text files of various formats. Then you will learn how to check data for weirdness and handle missing values. Finally, you will learn how to adjust stock prices for splits and dividends.