Andrew B. Collier / @datawookie

Manfred Schroeder touches on the topic of percolation a number of times in his encyclopaedic book on fractals (Schroeder, M. (1991) Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. Percolation has numerous practical applications, the most interesting of which (from my perspective) is the flow of hot water through ground coffee!

I recently enjoyed reading O’Hara, R. B., & Kotze, D. J. (2010). Do not log-transform count data. Methods in Ecology and Evolution, 1(2), 118–122. doi:10.1111/j.2041-210X.2010.00021.x.

I need to mount a directory from my laptop on my desktop machine using sshfs. At first I was not making the mount terribly regularly, so I did it manually each time that I needed it. However, the frequency increased over time and I was eventually mounting it every day (or multiple times during the course of a day!). This was a perfect opportunity to employ some automation.

Some years ago I allowed myself to accept a challenge to read the Top 100 Novels of All Time (complete list here). This list was put together by Richard Lacayo and Lev Grossman at Time Magazine.

To start with I could tick off a number of books that I had already read. That left me with around 75 books outstanding. I knuckled down. The Lord of the Rings had been on my reading list for a number of years, so this was my first project. A little unfair for this trilogy to count as just one book… but I consumed it with gusto! One down. Other books followed. They were also great reads. And then I hit a couple of books that were just, well, to put it plainly, heavy going. I am sure that they were great books and my lack of enjoyment was entirely a reflection on me and not the quality of the books. No doubt I learned a lot from reading them. But it was hard work! At this stage it occurred to me that the book list was constructed from a rather specific perspective of what constituted a great book. A perspective which is quite different from my own. I had to admit defeat: my literary tastes will have to mature a bit before I attack this list again!

The logs generated by expert advisors and indicators when running live on MetaTrader are displayed in the Experts tab at the bottom of the terminal window. Sometimes it is more convenient to analyse these logs offline (especially since the order of the records in the terminal runs in a rather counter-intuitive bottom-to-top order!). However, because writing to the log files is buffered, there can be a delay before what you see in the terminal is actually written to disk.

In my previous post I used the tm package to do some simple text mining on the Complete Works of William Shakespeare. Today I am taking some of those results and using them to generate word clusters.

Time zones on MetaTrader can be slightly confusing. Two specific time zones are important:

• the time zone of the broker’s server and
• your local time zone.

And these need not be the same.

I am starting a new project that will require some serious text mining. In the interests of bringing myself up to speed on the {tm} package, I thought I would apply it to the Complete Works of William Shakespeare and just see what falls out.

The first order of business was getting my hands on all that text. Fortunately it is available from a number of sources. I chose to use Project Gutenberg.

A client came to me with some conformance data. She was having a hard time making sense of it in a spreadsheet. I had a look at a couple of ways of presenting it that would bring out the important points.

The Data

The data came as a spreadsheet with multiple sheets. Each of the sheets had a slightly different format, so the easiest thing to do was to save each one as a CSV file and then import them individually into R.

Writing code from scratch to do parallel computations can be rather tricky. However, the packages providing parallel facilities in R make it remarkably easy. One such package is foreach. I am going to document my trail of discovery with foreach, which began some time ago, but has really come into fruition over the last few weeks.

First we need a reproducible example. Preferably something which is numerically intensive.

max.eig <- function(N, sigma) {
  d <- matrix(rnorm(N**2, sd = sigma), nrow = N)
  #
  E <- eigen(d)$values
  #
  abs(E)[[1]]
}

This function generates a square matrix of uniformly distributed random numbers, finds the corresponding (complex) eigenvalues and then selects the eigenvalue with the largest modulus. The dimensions of the matrix and the standard deviation of the random numbers are given as input parameters.

Maximum-Likelihood Estimation (MLE) is a statistical technique for estimating model parameters. It basically sets out to answer the question: what model parameters are most likely to characterise a given set of data? First you need to select a model for the data. And the model must have one or more (unknown) parameters. As the name implies, MLE proceeds to maximise a likelihood function, which in turn maximises the agreement between the model and the data.

Following up on my previous post as a result of an excellent suggestion from Andrej Spiess. The data are indeed very heteroscedastic! Andrej suggested that an alternative way to attack this problem would be to use weighted correlation with weights being the inverse of the measurement variance.

A week or so ago a colleague of mine asked if I knew how to calculate correlations for data with uncertainties. Now, if we are going to be honest, then all data should have some level of experimental or measurement error. However, I suspect that in the majority of cases these uncertainties are ignored when considering correlations. To what degree are uncertainties important? A moment’s thought would suggest that if the uncertainties are large enough then they should have a rather significant effect on correlation, or more properly, the uncertainty measure associated with the correlation. What is the best (or at least correct) way to proceed? Somewhat surprisingly a quick Google search did not turn up anything too helpful.

Debugging an indicator or expert advisor (EA) can be a tricky business. Especially when you are doing the debugging remotely. I write my MQL code to include copious amounts of debugging information to log files. The contents of these log files can be used to diagnose any problems. This articles tells you where you can find those files.

Continuing on my quest to document the Comrades Marathon results, today I have put together a chart showing the winners of both the men and ladies races since 1980. Click on the image below to see a larger version.

Following up on my previous posts regarding the results of the Comrades Marathon, I was planning on putting together a set of models which would predict likelihood to finish and probable finishing time. Along the way I got distracted by something else that is just as interesting and which produces results which readily yield to qualitative interpretation: Conditional Inference Trees as implemented in the R package party.

Just to recall what the data look like:

I am busy with a project where I need to calibrate the Heston Model to some Asian options data. The model has been implemented as a function which executes a Monte Carlo (MC) simulation. As a result, the objective function is rather noisy. A number of algorithms exist for this sort of problem, and here I simply give a brief overview of some of them.

When you receive the code for an expert advisor or indicator which we have developed for you, it will come in a package consisting of include files (with a .mqh extension) and source code files (with a .mq4 extension). What do you do with them?

Comrades Marathon runners are awarded a permanent green race number once they have completed 10 journeys between Durban and Pietermaritzburg. For many runners, once they have completed the race a few times, achieving a green number becomes a possibility. And once the idea takes hold, it can become something of a compulsion. I can testify to this: I am thoroughly compelled! For runners with this goal in mind, every finish is one step closer to a green number. They are slowly chipping away, year after year and the idea of bailing is anathema. However, once the green number is in the bag, does the imperative to complete the race fade?

Following up on a suggestion from my previous post, here are the statistics for medal count versus age. Every point on the plot is the number (see colour legend on right) of athletes who have achieved a given number of medals by a particular age.