Blog Posts by Andrew B. Collier / @datawookie

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.

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 (looking at attrition rates and medal allocations), 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!

It is a bit of a mission to get the complete data set for this year’s Comrades Marathon. The full results are easily accessible, but come as an HTML file. Embedded in this file are links to the splits for individual athletes. With a bit of scripting wizardry it is also possible to download the HTML files for each of the individual athletes. Parsing all of these yields the complete result set, which is the starting point for this analysis.

A couple of years ago I implemented an automated trading algorithm for a strategy called the “Cable Morning Trade”. The basis of the strategy is the range of GBPUSD during the interval 05:00 to 09:00 London time. Two buy stop orders are placed 5 points above the highest high for this period; two sell stop orders are placed 5 points below the lowest low. All orders have a protective stop at 40 points. When either the buy or sell orders are filled, the other orders are cancelled. Of the filled orders, one exits at a profit equal to the stop loss, while the other is left to run until the close of the London session.

A balanced experimental design is one in which the distribution of the covariates is the same in both the control and treatment groups. However, although often achievable in an experiment, for observational data this ideal is seldom achieved.

A package was recently released to generate plots in the style of xkcd using R. Being a big fan of the cartoon, I could not resist trying it out. I set out to produce something like one of Hans Rosling’s bubble plots.

I’ve just finished coding a swing alert indicator for a client. The rules are rather straightforward and it all depends on two simple moving averages (by default with periods of 25 and 5).

I am going to be using the party package for one of my projects, so I spent some time today familiarising myself with it. The details of the package are described in Hothorn, T., Hornik, K., & Zeileis, A. (1999). “party: A Laboratory for Recursive Partytioning” which is available from CRAN.

In the previous installment we generated a few plots using numerical data straight out of the National Health and Nutrition Examination Survey. This time we are going to incorporate some of the categorical variables into the plots. Although going from raw numerical data to categorical data bins (like we did for age and BMI) does give you less precision, it can make drawing conclusions from plots a lot easier.

We will start off with a simple plot of two numerical variables: age against BMI.

In the previous installment we generated some simple descriptive statistics for the National Health and Nutrition Examination Survey data. Now we are going to move on to an area in which R really excels: making plots and visualisations.