This is the first post of a series on using TimeBase to stream real-time market data. TimeBase is a high performance event-based time series database and message broker. I used it on a proprietary trading desk that made markets in futures, and currently use it to build and test equity trading strategies. It was released as open-source in February 2021. Preface I am not affiliated with the company that created and maintains TimeBase (Deltix, now EPAM).
getSymbols() have been a core part of the R/Finance ecosystem for over 15 years. We want to change some things, but they would break existing code. We can make these changes in the new ‘rfimport’ package instead.
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.
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 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.
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).
A quantmod user asked an interesting question on StackOverflow: Looping viewFinancials from quantmod. Basically, they wanted to create a data.frame that contained financial statement data for several companies for several years. I answered their question, and thought others might find the function I wrote useful… hence, this post! I called the function stackFinancials() because it would use getFinancials() and viewFinancials() to pull financial statement data for multiple symbols, and stack them together in long form.