Andrew B. Collier / @datawookie

The Two-Fund Separation Theorem introduced by James Tobin, a Nobel Prize-winning economist, is a fundamental concept in investment theory. It addresses how investors can optimally allocate their assets. In an efficient market an optimal portfolio is a combination of a risk-free asset and a market portfolio.

Sprinkling status messages across you code using print() statements can be a good temporary fix for tracking down issues in your code.

But it can get messy and there’s no way for you to selectively disable them. Sure you can redirect output to a file or /dev/null but this is an all-or-nothing solution. How about disabling some messages and retaining others?

This is where the logging module comes into its own.

Setting the values of one or more parameters for a GARCH model or applying constraints to the range of permissible values can be useful.

The two quantities we have been modelling (the time-dependent average and standard deviation of the returns) represent respectively the (potential) risk and reward associated with an asset. The relationship between these two quantities is implicit in the GARCH model. However, sometimes the return depends directly on the risk. A variant of the GARCH model can take this explicit relationship into account.

Most often when you are creating a new Docker image it will be based on one of the standard Docker base images like ubuntu, alpine, python or nginx. But sometimes you might want to truly roll your own image. Starting with literally nothing. From scratch. Tabula rasa.

Is this a “good” model? How to validate a model and determine whether it’s a good representation of the training data and likely to produce robust and reliable predictions.

In general a parsimonious model is a good model. A model with too many parameters is likely to overfit the data. How do we determine when a model is “complex enough” but not “too complex”?

The models we have been looking at do not differentiate between positive and negative residuals: both errors are treated the same. However, this does not align with reality, where the volatility resulting from a large negative return is higher than that for the corresponding positive return.

In the previous post we assumed that returns had a normal distribution. This assumption implied that the distribution was symmetric and a positive return was as likely as the corresponding negative return. In reality this assumption is just not true and returns are asymmetrically distributed.

A GARCH (Generalised Autoregressive Conditional Heteroskedasticity) model is a statistical tool used to forecast volatility by analysing patterns in past price movements and volatility.

In the previous post we loaded stock data into R and then calculated return volatility, both for the entire time series and shorter intervals. We saw that volatility is not constant but can change appreciably with time. One way to get a clear view of changes in volatility is by calculating them using a moving or (“rolling”) window.

I’m going to be writing a series of posts which will look at some applications of R (and perhaps Python) to financial modelling. We’ll start here by pulling some stock data into R, calculating the daily returns and then looking at correlations and simple volatility estimates.

The current version of PyInstaller (6.5.0) doesn’t play nicely with the boto3 package. Here’s how to fix it.

I’ve hit my head against this issue from time to time, so it seems like I need to document the solution somewhere for each reference.

A collection of books read by my father, Frank Collier, for Tape Aids for the Blind. Dad was always an enthusiastic and patient reader. One of my earliest memories is of him reading to my sister and me in bed each morning. In retirement he devoted many hours to reading and editing books for Tape Aids for the Blind.

In the previous post Traefik was compared to NGINX. Now let’s take a look at a few simple Traefik setups. We’ll focus on specifying the host and port.

I’ve come across Traefik in a number of questions on Stack Overflow recently. I regularly use NGINX as a reverse proxy and sometimes find it to be a little obscure. Having an alternative would be helpful.

I have seen a few questions on Stack Overflow relating to building a simple standalone Next.js app in a Docker image. Here’s one way to do it.

There are many tools for generating CSS selectors and XPath expressions. However, short of using them in your code, how can you quick test them? In this post I’ll show how you can use your browser’s Developer Tools to establish that your CSS or XPath is doing what you intend.

The parse() function from the html-react-parser package converts HTML strings into React elements. It allows you to take HTML and render it as if it were JSX. This can be particularly useful when you’re working with content that comes as HTML from external sources (such as a CMS) and you want to include that content in your React components. It can also be used to filter and modify the React elements.