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.
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.
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.
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.
A new release of quantmod is now on CRAN! The only change was to address changes to Yahoo! Finance and their effects on getSymbols.yahoo(). GitHub issue #157 contains some details about the fix implementation. Unfortunately, the URL wasn’t the only thing that changed. The actual data available for download changed as well. The most noticeable difference is that the adjusted close column is no longer dividend-adjusted (i.e. it’s only split-adjusted). Also, only the close price is unadjusted; the open, high, and low are split-adjusted.
I assume that you’re reading this because you are one of many people who were affected by the changes to Yahoo Finance data in May (2017). Not only did the URL change, but the actual data changed as well! The most noticeable difference is that the adjusted close column is now only split-adjusted, whereas it used to be split- and dividend-adjusted. Another oddity is that only the close prices is unadjusted (strangely, the open, high, and low are split-adjusted).