Blog Posts by Andrew B. Collier / @datawookie

Mounting a sshfs volume via the crontab

Linux

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.

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Top 250 Movies at IMDb

R web scraping
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. So 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. Read More →

Flushing Live MetaTrader Logs to Disk

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.

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MetaTrader Time Zones

Time zones on MetaTrader can be slightly confusing. There are two important time zones:

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

And these need not be the same.

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Text Mining the Complete Works of William Shakespeare

R
I am starting a new project that will require some serious text mining. So, 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. Read More →

Presenting Conformance Statistics

R
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. Read More →

The Wonders of {foreach}

R {foreach}
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. Read More →

Fitting a Model by Maximum Likelihood

R

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.

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Correlations with Uncertainty: Bootstrap Solution

R
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. Read More →

Finding Your MetaTrader Log Files

Debugging an indicator or expert advisor (EA) can be a tricky business. Especially when you are doing the debugging remotely. So 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. Testing Logs When you are running an EA under the strategy tester, the log files are written to the tester\logs directory (see the red rectangle in the directory tree above). Read More →

A Chart of Recent Comrades Marathon Winners

R running
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. The analysis started off with the same data set that I was working with before, from which I extracted only the records for the winners. winners = subset(results, gender.position == 1, select = c(year, name, gender, race. Read More →

Comrades Marathon Inference Trees

R running
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: Read More →

Optimising a Noisy Objective Function

R

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. There are a number of algorithms for dealing with this sort of problem, and here I simply give a brief overview of some of them.

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Compiling Indicators and Expert Advisors

MetaTrader

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). So, what do you do with them?

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Are Green Number Runners More Likely to Bail?

R running
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. Read More →

The Green Number Effect

R running
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. There is clear evidence of a Green Number Effect: many people hang on for ten medals and then pack it in. There is also weaker evidence of a Double Green Number Effect. Read More →

Age Distribution of Comrades Marathon Athletes

R running

I can clearly remember watching the end of the 1989 Comrades Marathon on television and seeing Wally Hayward coming in just before the final gun, completing the epic race at the age of 80! I was in awe.

Since I have been delving into the Comrades Marathon data, this got me thinking about the typical age distribution of athletes taking part. The plot below indicates the ages of athletes who finished the race, going all the way back to 1984. You can clearly spot the two years when Wally Hayward ran (1988 and 1989). My data indicates that he was only 79 on the day of the 1989 Comrades Marathon, but I am not going to quibble over a year and I am more than happy to accept that he was 80!

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