A new version of quantmod is on CRAN! One really cool thing about this release is that almost all the changes are contributions from the community. Ethan Smith made more excellent contributions to getQuote() in this release. It no longer throws an error if one or more symbols are missing. And it handles multiple symbols in a semicolon-delimted string, just like getSymbols(). For example, you can get quotes for multiple symbols by calling getQuote("SPY;AAPL").
I pushed an updated microbenchmark to CRAN a couple weeks ago. There were two noteworthy changes, thanks to great contributions from @MichaelChirico and @harvey131. Michael fixed a bug in the check for whether the unit argument was a character string (#9, #10). The prior behavior was an uninformative error. Harvey added a feature to allow you to use a string for common checks: “equal”, “identical”, and “equivalent” (#16). So you don’t need to create a custom function to use all.
I just pushed a new release of quantmod to CRAN! I’m most excited about the update to getSymbols() so it doesn’t throw an error and stop processing if there’s a problem with one ticker symbol. Now getSymbols() will import all the data it can, and provide an informative error message for any ticker symbols it could not import. Another cool feature is that getQuote() can now import quotes from Tiingo. But don’t thank me; thank Ethan Smith for the feature request [#247] and pull request [#250].
xts version 0.11-2 was published to CRAN yesterday. xts provides data structure and functions to work with time-indexed data. This is a bug-fix release, with notable changes below: The xts method for shift.time() is now registered. Thanks to Philippe Verspeelt for the report and PR (#268, #273). An if-statement in the xts constructor will no longer try to use a logical vector with length > 1. Code like if (c(TRUE, TRUE)) will throw a warning in an upcoming R release, and this patch will prevent that warning.
xts version 0.11-1 was published to CRAN this morning. xts provides data structure and functions to work with time-indexed data. This release contains some awesome features that will transparently make your xts code even faster! There’s a new window.xts() method, thanks to Corwin Joy (#100, #240). Corwin also refactored and improved the performance of the binary search algorithm used to subset xts objects. Tom Andrews reported and fixed a few related regressions (#251, #263, #264).
This year marks the 10th anniversary of the R/Finance Conference! As in prior years, we expect more than 250 attendees from around the world. R users from industry, academia, and government will joining 50+ presenters covering all areas of finance with R. The conference will take place on June 1st and 2nd, at UIC in Chicago. You can find registration informationon the conference website, or you can go directly to the Cvent registration page.
First, the bad news: Google Finance no longer provides data for historical prices or financial statements, so we say goodbye to getSymbols.google() and getFinancials.google(). (#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.
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.
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.
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.