Shoutout for Atlassian

In the last two days I was able to download and install a great stack of software from Atlassian, which would theoretically allow a small team of 5-10 people to organize software development on enterprise-grade tools:

  • JIRA for issue tracking and planning, with support for Agile project management
  • STASH for hosting and integrating your git repositories
  • Crucible + Fish Eye for Code review
  • Bamboo for Continuous Integration
  • Confluence for Documentation

As a novice to server administration I was able to install the software, set the packages up for integrated work and import my projects from github – including issues, tags and so on, in about 10 working hours. Using the software is rather easy, maybe with the exception of the CI-server, which probable is more a sign of my lack of knowledge in this area, and a lack of templates in the R domain. And the price is a snap: for each package there is a starter licence setting you back 10$, covering 5-10 seats, which Atlassian will donate. So, if you are thinking of starting a startup, you could probably do worse than locking yourself into the Atlassian stack.

SAS on the road

I like some of the ideas what SAS might need that truck for:

PS: Clustering whiskies

I played some more with the app from yesterday, and deployed it with a more usefull user interface and some new functionality on By the way, thanks to the guys at for hosting the app on their servers.


Clustering whiskies by taste

Lately I was wondering how to integrate my wordpress blog with a shiny app. This post is an example for the collaboration between these platforms following the advice from this thread in the shiny google group. Looking for a good example for an app, I stumbled upon this article from Luba Gloukhov on the Revolution blog. I shamelessly copied most of the code from Luba. Obviously the layout of this blog is not ideal for the layout of the app, but I will work this out on another day.

You can find the code for the app on github. I also made a standalone-app with some more functions on

Payday lending – how low can they go?

A post from TMM referenced the British payday lender wonga. As this is a way of credit which is either uncommon or illegal in Germany, a word about their business model: They give out very small credits (upper limit 400 pound) for a short time, up to 30 days. For this they will charge 5.5 pounds in fees and roughly 1% interest per day, based on a yearly rate of 365% .

Nice business, if you can get it. What peeked my interest was the question: How much losses can they take on these loans and still make a decent return on investment? After fiddling around with the numbers I pulled from thin air, the result is: They can troll the bottom of the sea, and still make a living. Here is my reasoning:

I simulate a credit portfolio of 100,000 credits within a business year of 365 days, ignoring weekends and holidays. Loan size and duration are uniformly distributed within the limits set by wonga. I assign a probability of default of between 80% and 100%. I will define the time reference, in which this probability applies, in the next step. So this value can be interpreted as “the creditor will go bust within the next (x) days with (y) likelihood”. I further assume an equity of 1 mil pounds to start the business. This ensures that the lender himself does not get in the red.

numYears =1
numCost = 100000*numYears
numDays = 365*numYears
equity = 1e6

credits = data.frame(id = 1:numCost,
                     loan = round(runif(numCost, min=1, max =400),0),
                     duration = as.integer(runif(numCost, min=1, max = 30)),
                     loanday = as.integer(runif(numCost,min=1,max=numDays)),
                     pd = runif(numCost, min = 0.8, max = 1))

credits$repayday = credits$loanday + credits$duration

I calculate the value of the outstanding amount on the repayment day as the size of the loan + 1% interest per day of duration. I assume that the fixed amount of 5.50 pounds coveres the fixed costs of the credit. To calculate the actual repayment made to the lender I make a random draw to simulate the possibility of default, where the base probability of default is wheighted with the duration of the credit in reference the time reference chosen for the probability of default. Usually this is one year, but I will vary it in the analysis.

In the next step I calculate the cashflow and the balance sheet during the business year, and calculate the return on investment as the value of the balance sheet at the end of the year, divided by the equity.

calcRoi = function(credits, defaultDenum=365)
  credits$repayment = (credits$loan* (1+credits$duration/100))* 
    rbinom(numCost, 1, (1-credits$pd*credits$duration/defaultDenum)) 

  cashflow = data.frame(day = 1:numDays, outflow = 0, inflow = 0, balance = 0)
  cashflow$balance[1]= equity
  for(day in 1:numDays)
    cashflow$outflow[day] = sum(credits$loan[which(credits$loanday == day)])
    cashflow$inflow[day] = sum(credits$repayment[which(credits$repayday == day)])
  (ROI = cashflow$balance[nrow(cashflow)]/equity)

I now calculate the ROI for different time references in the range from 1 month to 1 year and plot the result
plot of chunk unnamed-chunk-3
The red line is the break-even and the blue line marks 10% ROI.

As you can see, the payday lender will make a decent ROI of 10% even with an average probability of default of 90% within the next 3-4 months, even if he writes off the defaulted loans completely. The crucial part of the business thus will be walking the fine line of selecting creditors who are nearly busted, so they are desperate enough to apply to this loan, but not yet busted, so that the lender will lose on a too large part of the credit portfolio.