Importing and Managing Financial Data in R
Learn how to access financial data from local files as well as from internet sources.
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Course Description
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.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Finance Fundamentals in R
Go To TrackQuantitative Analyst in R
Go To Track- 1
Introduction and Downloading Data
FreeA 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.
- 2
Extracting and Transforming Data
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.
- 3
Managing Data from Multiple Sources
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.
Setting default arguments for getSymbols()50 xpSet a default data source100 xpSet default arguments for a getSymbols source100 xpSetting per-instrument default arguments50 xpSet default data source for one symbol100 xpSave and load symbol lookup table100 xpHow *not* to specify the getSymbols() source50 xpHandling instrument symbols that clash or are not valid R names50 xpAccess the object using get() or backticks100 xpCreate valid name for one instrument100 xpCreate valid names for multiple instruments100 xp - 4
Aligning Data with Different Periodicities
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.
Making irregular data regular50 xpCreate a zero-width and regular xts object100 xpUse merge to make an irregular index regular100 xpAggregating to lower frequency50 xpAggregate daily data and merge with monthly data100 xpAlign series to first and last day of month100 xpAggregate to weekly, ending on Wednesdays100 xpAggregating and combining intraday data50 xpCombine data that have timezones100 xpMake irregular intra-day data regular100 xpFill missing values by trading day100 xpAggregate irregular intra-day data100 xp - 5
Importing Text Data, and Adjusting for Corporate Actions
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.
Importing text files50 xpImport well-formatted daily OHLC data100 xpImport text files in other formats100 xpHandle date and time in separate columns100 xpRead text file containing multiple instruments100 xpChecking for weirdness50 xpHandle missing values100 xpVisualize imported data100 xpCross reference sources100 xpAdjusting for corporate actions50 xpAdjust for stock splits and dividends100 xpDownload split and dividend data100 xpAdjust univariate data for splits and dividends100 xpWhen to adjust data50 xpCongratulations!50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Finance Fundamentals in R
Go To TrackQuantitative Analyst in R
Go To Trackcollaborators
prerequisites
Manipulating Time Series Data in R
Joshua UlrichQuantitative Analyst & member of R/Finance Conference committee
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, including quantmod. He is passionate about computational finance, algorithmic trading, risk management, and helping people solve problems.
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