Here's a useful trick that you might not have seen before. Suppose we have some data with includes rows with values missing, then we can use the below formula to apply linear interpolate to fill in the missing datapoints, without having to laboriously type in the interpolation formula long hand (which I used to do all the time)
There's some interesting literature from the world of forecasting and natural sciences on the best way to aggregate predictions from multiple models/sources.
For a well-written, moderately technical introduction, see the following by Jaime Sevilla:
Jaime’s article suggests a geometric mean of odds as the preferred method of aggregating predictions. I would argue however that when it comes to actuarial pricing, I'm more of a fan of the arithmetic mean, I'll explain why below.
I wrote a quick Python script to download the latest odds from PredictIt, and then output to an Excel file. I've pasted it below as an extract from a Jupyter notebook:
PredictIt is an online prediction website, mainly focused on Political events:
I think it's great that PredictIt allow access like this, before I realised the API exists I was using Selenium to scrape the info through Chrome, which was much slower to run, and also occasionally buggy.
I work as an actuary and underwriter at a global reinsurer in London.