Last week, we looked at simple data analysis techniques to test for persistence. But we only looked at a feature that is measured over a single day – the absolute range. Such a feature makes it easy to test persistence because you don’t have the problem of overlapping data. Each data point is entirely self-contained …
Towards the end of last year, we ran a couple of free Zoom webinars on: Here are the recordings: Basics of Edge Extraction Data analysis for Traders The colab research notebook for the second session can be found here. (To make sense of it you’ll want to watch the video.)
dplyr 1.1.0 was a significant release that makes several common data operations more syntactically intuitive. The most significant changes relate to joins and grouping/aggregating operations. In this post we’ll look at the changes to joins. First, install and load the latest version of dplyr: install.packages(“dplyr”) library(dplyr) A new approach to joins The best way to …
Holding data in a tidy format works wonders for one’s productivity. Here we will explore the tidyr package, which is all about creating tidy data. In particular, let’s develop an understanding of the tidyr::pivot_longer and tidyr::pivot_wider functions for switching between different formats of tidy data. In this video, you’ll learn: What tidy data looks like …
When data is too big to fit into memory, one approach is to break it into smaller pieces, operate on each piece, and then join the results back together. Here’s how to do that to calculate rolling mean pairwise correlations of a large stock universe. Background We’ve been using the problem of calculating mean rolling …
When you’re working with large universes of stock data you’ll come across a lot of challenges: Stocks pay dividends and other distributions that have to be accounted for. Stocks are subject to splits and other corporate actions which also have to be accounted for. New stocks are listed all the time – you won’t have …
Recently, we wrote about calculating mean rolling pairwise correlations between the constituent stocks of an ETF. The tidyverse tools dplyr and slider solve this somewhat painful data wrangling operation about as elegantly and intuitively as possible. Why did you want to do that? We’re building a statistical arbitrage strategy that relies on indexation-driven trading in …
Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr …
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 …
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 …
