Time Series Calendar Heat Maps Using R

[This article was first published on Intelligent Trading, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I came across an interesting blog that showcased Charting time series as calendar heat maps in R . It is based upon a great algorithm created by Paul Bleicher,CMO of Humedica. I’ll let you link to the other blog to see more details on the background and original source code.

I made a very small modification to allow %daily changes, rather than price values.
stock.dailychange<-100*(diff(stock.data$Adj.Close,lag=1)/y[1:length(stock.data$Adj.Close)-1]) calendarHeat(stock.data$Date[1:length(stock.data$Date)-1], stock.dailychange, varname="SPY daily % changes(CL-CL)")

Fig 1. Calendar Heat Map for SPY time series 2005-Present

What's interesting is you can see how unusual events tend to Cluster (heteroscedasticity) , and a preponderance of low change days (as would be expected in close to Gaussian distributions). Using the regions of clustering might help warn of impeding catastrophic regimes (as seen in late 08), similar to using VIX as a proxy. In addition, the 10,000 foot bird's eye view, might allow you to spot pockets of order for further evaluation.

To leave a comment for the author, please follow the link and comment on their blog: Intelligent Trading.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)