DataCamp course: Importing and managing financial data

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The team at DataCamp announced a new R/Finance course series in a recent email:

Subject: Data Mining Tutorial, R/Finance course series, and more!

R/Finance - A new course series in the works
We are working on a whole new course series on applied finance using R. This new series will cover topics such as time series (David S. Matteson), portfolio analysis (Kris Boudt), the xts and zoo packages (Jeffrey Ryan), and much more. Start our first course Intro to Credit Risk Modeling in R today.

I’m excited to announce that I’m working on a course for this new series! It will provide an introduction to importing and managing financial data.

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.

The tentative course outline is below. I’d really appreciate your feedback on what should be included in this introductory course! So let me know if I’ve omitted something, or if you think any of the topics are too advanced.

Introduction to importing and managing financial data

  1. Introduction and downloading data

  2. getSymbols design overview, Quandl

  3. Finding and downloading data from internet sources

  4. E.g. getSymbols.yahoo, getSymbols.FRED, Quandl

  5. Loading and transforming multiple instruments

  6. Checking for errors (i.e. summary stats, visualizing)

  7. Managing data from multiple sources

  8. Setting per-instrument sources and default arguments

  9. setSymbolLookup, saveSymbolLookup, loadSymbolLookup, setDefaults

  10. Handling instruments names that clash or are not valid R object names

  11. Aligning data with different periodicities

  12. Making irregular data regular

  13. Aggregating to lowest frequency

  14. Combining monthly with daily

  15. Combining daily with intraday

  16. Storing and updating data

  17. Creating an initial RData-backed storage

  18. Adjusting financial time-series

  19. Handling errors during update process

If you love using my open-source work (e.g. quantmod, TTR, xts, IBrokers, microbenchmark, blotter, quantstrat, etc.), you can give back by sponsoring me on GitHub. I truly appreciate anything you’re willing and able to give!