Friday, April 13, 2018

Goodbye Google, Hello Tiingo!

First, the bad news:

Google Finance no longer provides data for historical prices or financial statements, so we say goodbye to and (#221)  They are now defunct as of quantmod 0.4-13.

Now, the good news:

Thanks to Steve Bronder, getSymbols() can now import data from Tiingo! (#220)  This feature is part of quantmod 0.4-13, which is now on CRAN.  Windows and Mac binaries should be built in a day or two.

Tiingo is a web service that provides tools and data for financial analysis.  They provide daily price history for US stocks and ADRs, Chinese stocks, Mutual Funds, and ETFs.  There is up to 30+ years of history, including raw prices and split/dividend adjusted prices.

All this data is accessible for free, with reasonable symbol and bandwidth limits.  All you need to get started is a one-time registration for an API token.  You should see your API token just above the beginning of the metadata section, after logging in, of course.  Tiingo has a well-documented daily price data API that returns either JSON or CSV.

To get started, install the latest quantmod from CRAN.  Then you call:

  getSymbols("MSFT", src = "tiingo", api.key = "[your key]") 

Where you replace "[your key]" with the API key you receive after registration.  You can use setDefaults() to set your API key one time, and use it for all getSymbols.tiingo() calls.

  setDefaults("getSymbols.tiingo", api.key = "[your key]")

Other notable changes:
  • There is now a getQuote.alphavantage() that allows you to pull real-time quotes from Alpha Vantage.  Thanks to Ethan Smith! (#213, #223)
  • Speaking of Alpha Vantage, getSymbols.av() can now pull weekly and monthly adjusted prices. (#212)
  • The URL in getSymbols.oanda() and getFX() has been updated, so they work again. (#225)
  • no longer errors when a field has no data for all requested tickers. (#208)
  • saveChart() actually saves charts now (#154). Brilliant!

Monday, March 19, 2018

xts 0.10-2 on CRAN

This xts release contains mostly bugfixes, but there are a few noteworthy features. Some of these features were added in version 0.10-1, but I forgot to blog about it. Anyway, in no particular order:

  • endpoints() gained sub-second accuracy on Windows (#202)!
  • na.locf.xts() now honors 'x' and 'xout' arguments by dispatching to the next method (#215). Thanks to Morten Grum for the report.
  • na.locf.xts() and na.omit.xts() now support character xts objects. Thanks to Ken Williams and Samo Pahor for the reports (#42).

Many of the bug fixes were related to the new plot.xts() introduced in 0.10-0. And a handful of bugfixes were to make xts more consistent with zoo in some edge cases.

As always, I'm looking forward to your questions and feedback!  If you have a question, please ask on Stack Overflow and use the [r] and [xts] tags.  Or you can send an email to the R-SIG-Finance mailing list (you must subscribe to post).  Open an issue on GitHub if you find a bug or want to request a feature, but please read the contributing guide first!

Tuesday, January 9, 2018

R/Finance 2018: Call for Papers

R/Finance 2018: Applied Finance with R
June 1 and 2, 2018
University of Illinois at Chicago

Call For Papers

The tenth annual R/Finance conference for applied finance using R will be held June 1 and 2, 2018 in Chicago, IL, USA at the University of Illinois at Chicago. The conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing, market microstructure, and econometrics. All will be discussed within the context of using R as a primary tool for financial risk management, portfolio construction, and trading.

Over the past nine years, R/Finance has included attendees from around the world. It has featured presentations from prominent academics and practitioners, and we anticipate another exciting line-up for 2018.

We invite you to submit complete papers in pdf format for consideration. We will also consider one-page abstracts (in txt or pdf format) although more complete papers are preferred. We welcome submissions for both full talks and abbreviated lightning talks. Both academic and practitioner proposals related to R are encouraged.

All slides will be made publicly available at conference time. Presenters are strongly encouraged to provide working R code to accompany the slides. Data sets should also be made public for the purposes of reproducibility (though we realize this may be limited due to contracts with data vendors). Preference may be given to presenters who have released R packages.

Please submit proposals online at Submissions will be reviewed and accepted on a rolling basis with a final submission deadline of February 2, 2018. Submitters will be notified via email by March 2, 2018 of acceptance, presentation length, and financial assistance (if requested).

Financial assistance for travel and accommodation may be available to presenters. Requests for financial assistance do not affect acceptance decisions. Requests should be made at the time of submission. Requests made after submission are much less likely to be fulfilled. Assistance will be granted at the discretion of the conference committee.

Additional details will be announced via the conference website as they become available. Information on previous years' presenters and their presentations are also at the conference website. We will make a separate announcement when registration opens.

For the program committee:
Gib Bassett, Peter Carl, Dirk Eddelbuettel, Brian Peterson, 
Dale Rosenthal, Jeffrey Ryan, Joshua Ulrich

Friday, January 5, 2018

RQuantLib 0.4.4 for Windows

I'm pleased to announce that the RQuantLib Windows binaries are now up to 0.4.4!  The RQuantLib pre-built Windows binaries have been frozen on CRAN since 0.4.2, but now you can get version 0.4.4 binaries on Dirk's ghrr drat repo.

Installation is as simple as:

drat::addRepo("ghrr") # maybe use 'install.packages("drat")' first 
install.packages("RQuantLib", type="binary")

I will be able to create Windows binaries for future RQuantLib versions too, now that I have a Windows QuantLib build (version 1.11) to link against.

Dirk and I plan to talk with CRAN about getting the new binaries hosted there.  Regardless, they will always be available via the drat repo.

Friday, October 6, 2017

getSymbols and Alpha Vantage

Thanks to Paul Teetor, getSymbols() can now import data from Alpha Vantage!  This feature is part of the quantmod 0.4-11 release, and provides another another data source to avoid any Yahoo Finance API changes*.

Alpha Vantage is a free web service that provides real-time and historical equity data.  They provide daily, weekly, and monthly history for both domestic and international markets, with up to 20 years of history. Dividend and split adjusted close prices are available for daily data. They also provide near real-time price bars at a resolution of 1 minute or more, for up to 10 recent days.

All you need to get started is a one-time registration for an API key.  Alpha Vantage has clean, documented, public API that returns either JSON-encoded data or a CSV file.  The arguments to getSymbols.av() closely follow the native API, so be sure to use their documentation!

To get started, install the latest quantmod from CRAN.  Then you call:

  getSymbols("MSFT", src = "av", api.key = "[your key]") 

Where you replace "[your key"] with the API key you receive after registration.  You can use setDefaults() to set your API key one time, and use it for all getSymbols.av() calls.

  setDefaults("getSymbols.av", api.key = "[your key]")

*Speaking of API changes, this release also includes a fix for a Yahoo Finance change (#174).

Friday, July 7, 2017

xts 0.10-0 on CRAN!

A new, and long overdue, release of xts is now on CRAN!  The major change is the completely new plot.xts() written by Michael Weylandt and Ross Bennett, and which is based on Jeff Ryan's quantmod::chart_Series code.

Do note that the new plot.xts() includes breaking changes to the original (and rather limited) plot.xts().  However, we believe the new functionality more than compensates for the potential one-time inconvenience.  And I will no longer have to tell people that I use plot.zoo() on xts objects!

This release also includes more bug fixes than you can shake a stick at.  We squashed several bugs that could have crashed your R session.  We also fixed some (always pesky and tricky) timezone issues.  We've also done more sanity checking (e.g. for NA in the index), and provide more informative errors when things aren't right.  And last, but not least, unit tests are running again!

I'm sure you were hoping to see some examples of the new plot.xts() functionality.  Rather than clutter up this blog post with code, check out the basic examples, and the panel functionality examples that Ross Bennett created.

I'm looking forward to your questions and feedback!  If you have a question, please ask on Stack  Overflow and use the [r] and [xts] tags.  Or you can send an email to the R-SIG-Finance
mailing list (you must subscribe to post).  Open an issue on GitHub if you find a bug or want to request a feature, but please read the contributing guide first!

Wednesday, June 21, 2017

Importing and Managing Financial Data

I'm excited to announce my DataCamp course on importing and managing financial data in R! I'm also honored that it is included in DataCamp's Quantitative Analyst with R Career Track!

You can explore the first chapter for free, so be sure to check it out!

Course Description

Financial and economic time series 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 covers importing data from local files as well as from internet sources.

Course Outline

Chapter 1: Introduction and downloading data
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.

Chapter 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.

Chapter 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

Chapter 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.

Chapter 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.