Here’s a round-up of our new articles this week. They cover options trading, digital signal processing, data munging and Kris’s luxurious
Recently, we wrote about calculating mean rolling pairwise correlations between the constituent stocks of an ETF. The tidyverse tools dplyr
Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools
In this post, we look at tools and functions from the field of digital signal processing. Can these tools be
How might we calculate rolling correlations between constituents of an ETF, given a dataframe of prices? For problems like this,
Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL;
In this post, we’re going to show how a quant trader can manipulate stock price data using the dplyr R
Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick
In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on
If you want to make money trading, you’re going to need a way to identify when an asset is likely
There are 2 good reasons to buy put options: because you think they are cheap because you want downside protection.
We’ve been working on visualisation tools to make option pricing models intuitive to the non-mathematician. Fundamental to such an exercise