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How might we calculate rolling correlations between constituents of an ETF, given a dataframe of prices? For problems like this, the tidyverse really shines. There are a number of ways to solve this problem … read on for our solution, and let us know if you'd approach it differently! First, we load some packages and some data that we extracted...

Modern data science is fundamentally multi-lingual. At a minimum, most data scientists are comfortable working in R, Python and SQL; many add Java and/or Scala to their toolkit, and it's not uncommon to also know one's way around JavaScript. Personally, I prefer to use R for data analysis. But, until recently, I'd tend to reach for Python for anything more...

In this post, we're going to show how a quant trader can manipulate stock price data using the dplyr R package. Getting set up and loading data Load the dplyr package via the tidyverse package. if (!require('tidyverse')) install.packages('tidyverse') library(tidyverse) First, load some price data. energystockprices.RDS contains a data frame of daily price observations for 3 energy stocks. prices <- readRDS('energystockprices.RDS')...

Every aspiring millionaire who comes to the markets armed with some programming ability has implemented a systematic Get Rich Quick (GRQ) trading strategy. Of course, they don't work. Deep down even the greenest of newbies knows this. Yet, still, we are compelled to give them a try, just once, just for fun (or so we tell ourselves). In this series,...

In this post, we are going to construct snapshots of historic S&P 500 index constituents, from freely available data on the internet. Why? Well, one of the biggest challenges in looking for opportunities amongst a broad universe of stocks is choosing what stock "universe" to look at. One approach to dealing with this is to pick the stocks that are...

If you want to make money trading, you're going to need a way to identify when an asset is likely to be cheap and when it is likely to be expensive. You want to be a net buyer of the cheap stuff and a net seller of the expensive stuff. Thanks, Capitain Obvious. You're welcome. How does this relate to...

There are 2 good reasons to buy put options: because you think they are cheap because you want downside protection. In the latter case, you are looking to use the skewed payoff profile of the put option to protect a portfolio against large downside moves without capping your upside too much. The first requires a pricing model. Or, at the...

We've been working on visualisation tools to make option pricing models intuitive to the non-mathematician. Fundamental to such an exercise is a way to model the random nature of asset price processes. The Geometric Brownian Motion (GBM) model is a ubiquitous way to do this. We can represent the price of an asset at time [latex] t [/latex] as the state...