This blog is a follow up to a blog explaining how to pull Intrinio financial data into R and R-Studio. In that blog I showed the basics of how to get the data flowing. In this blog I take it one step further and provide custom functions that will allow you to pull historical data into R very efficiently. I plan to build quant models, predicting historical prices based on historical metrics for a stock, and use a subset of the historical data to back test my models. This blog explains how to get the data for such an analysis.
Update 05/22/16- Check out this blog as well showing how to create a for loop in R to get multiple pages of data via API. This example shows the best way (known to the author) to parse JSON from an API in R.
Update 11/30/2017- Feel free to skip ahead to this recently released package that does the hard work for you.
Data analysts everywhere know that most of their time is spent gathering, cleaning, and formatting data. The actual analysis and interpreting the results is fun, fast, and easy once the data is structured how you need it. This blog shows how easy Intrinio makes it to complete the nasty part of analysis by using the Intrinio API to pull financial data into R.
Intrinio provides many data feeds via API and if you learn to use the API in conjunction with R, you can spend a lot more of your time running the analysis and analyzing the results and a lot less time on data entry.
If you don't use R this might not be the blog for you, but if you do, this blog will show you in step by step fashion how to save yourself a lot of money, and make yourself a lot of time, by using the Intrinio API in the R terminal or RStudio.
Feel free to skip ahead to this recently released package that does the hard work for you.