I’m a big fan of Ernie Chan’s quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them. Ernie explicitly states that …
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 …
Here’s a round-up of our new articles this week. They cover crash protection, sloppy, noisy regressions, and data-munging skills. Finding Options for Effective Crash Protection Large capital losses can be devastating to your trading account. A couple of weeks ago, we explained how you can use SPY put options to protect your portfolio against severe market …
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 …
One way we can quantify a stock’s movement relative to the market index is by calculating its “beta” to the market. To calculate the beta of MSFT to SPY (for example) we: calculate daily MSFT returns and daily SPY returns align the returns with one another regress MSFT returns against SPY returns. This shows the …
Here’s a round-up of our new articles this week. They cover options trading, digital signal processing, data munging and Kris’s luxurious moustache… Trading Insanity! Every new trader tries out a few insane trading ideas! In a new series on the blog, Kris explores three insane trading strategies that tempted him back when he didn’t know any …
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 …
In this post, we look at tools and functions from the field of digital signal processing. Can these tools be useful to us as quantitative traders? What’s a Digital Signal? A digital signal is a representation of physical phenomena created by sampling that phenomena at discrete time intervals. If you think about the way we …
