Wednesday, November 27, 2013
Friday, November 15, 2013
After seeing the announcement by @lauraegerdal of the SEC’s use of dygraphs to visualize market structure, I was inspired to experiment more with the great dygraphs + rCharts. I really wanted to see how responsive dygraphs would be with a fairly large dataset. Some Kenneth French US Industry data seemed just big enough to get a good feel for dygraph’s digestive abilities. See it in action here or click on the screenshot below.
Thursday, November 7, 2013
Wednesday, November 6, 2013
I wanted to analyze the EXIF information in a whole group of photos from a recent trip to Disney World. Of course I decided to use R and throw in some interactive charting with d3.js, rCharts, and and the new catcorrjs. Integrating the amazing and open-source Perl exiftool was a fun bonus. Click here or on the screenshot below for all the details.
On the trip I was baffled by some bad pictures. I realized the reason was ISO 6400, which was unintentionally set by the ring on our s100 when handing the camera to strangers for group shots.
Friday, October 25, 2013
Over the years I have really enjoyed this very thorough IESE Business School survey of market risk premium around the world.
Market Risk Premium and Risk Free Rate Used for 51 Countries in 2013
A Survey with 6,237 Answers
Fernandez, Pablo and Aguirreamalloa, Javier and Linares, Pablo
June 26, 2013
Available at SSRN: http://ssrn.com/abstract=91416
I thought a little d3/rCharts interactivity might really liven up the error bar plot. This is far from perfect, but I like the direction in which it is headed. Click here or on the screenshot below to see it live.
Thursday, October 24, 2013
Not finance, but I figured there might be some out there interested in the pictures from Flickr’s Explore. In addition to amazing photography, there is an abundance of information. In the short post below, I use R with rCharts, slidify, and Rflickr to take a look at the distribution of ISO speeds over the last 3 days' of pictures.
Wednesday, October 23, 2013
It has been a while since I discussed testing for overfitting in backtests. Since then, Marcos López de Prado and coauthors have done some very thoughtful work (see the bottom), and they even started a blog. Their newest paper builds on discoveries they made in their earlier work, and is an absolute must-read.
Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance (October 7, 2013)
Available at SSRN: http://ssrn.com/abstract=2308659
Translating scientific papers into code is not always easy, but I spent some time implementing some of the concepts in R, so that I can understand this more fully. Just as a word of encouragement to others out there, I am no math genius nor have any advanced math education, so please don’t be intimidated by formulas. Below you will see a slidify/rCharts discussion demonstrating these first steps. I plan to research this much more thoroughly. As always, I blog to interact, so please let me know what you are thinking.
Wednesday, October 9, 2013
Tuesday, October 8, 2013
This nice little tool Raw from DensityDesign transforms text from your clipboard to d3. For those yearning to access Raw from R, here is an easy way to do it.
Use this function read.excel from StatisticallySignificant’s post Copying Data from Excel to R and Back. Once you run the function, your data will be copied tab-delimited to the clipboard. Then simply paste the data into Raw and make some d3 charts.