Publications by Pete
Digging into the VIX
I wanted to revisit using some sort of volatility filter for systematic trading. In particular, if we are trading SPX, can we somehow use the VIX to produce better risk adjust returns? This is not about trading volatility, but more about using additional “out of band” data in our systems.I thought I would take a look at the VIX, what is it, a...
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What can we use the VIX for?
In part 1, we took a look at VIX and the relationship it had between historical volatility and realized volatility.Continuing on, I thought I would take a look at next day returns and the VIX. There is a relationship between SPX and VIX in that when SPX drops, VIX typically rises. This leads the question: does a high VIX serve as a useful indicat...
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A volatility filter using historical vol
We have been looking at a way to improve risk adjusted returns by using a volatility filter. Although we could use VIX or equivalent, it turns out that historical volatility will work just as well, if not a little better.You can see part 1 here Digging into the VIX, and part 2 here What can we use VIX for?Although the mean return of how we slic...
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Type conversion and you (or and R)
Types and type conversion can be a tricky and intricate topic, and sometimes can lead to some real head-scratcher issues in R. Hence a somewhat confusing title.This is for people still relatively new to R, and I will skip some gory details. Actually I will skip most of them, the canonical source for type and conversion information is ...
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Building models over rolling time periods
Often I have some idea for a trading system that is of the form “does some particular aspect of the last n periods of data have any predictive use for subsequent periods.”I generally like to work with nice units of time, such as 4 weeks or 6 months, rather than 30 or 126 days. It probably doesn’t make a meaningful difference in most cases, ...
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The case for data snooping
When we are backtesting automated trading systems, accidental data snooping or look forward errors are an easy mistake to make. The nature of the error in this context is making our predictions using the data we are trying to predict. Typically, it comes from a mistake with our calculations of time offsets somewhere.However, it can be a useful to...
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Another Rcpp/RcppArmadillo success story
I finally had an excuse to try out RcppArmadillo and was amazed at just how well it worked out.The exact details are a bit beyond a blog post, but basically I had a matrix of roughly 80,000×2 double valued samples (call it A) and needed to find where in another, much smaller matrix (call it B) each of the samples was closest too.Th...
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Book Review: Applied Predictive Modeling by Max Kuhn and Kjell Johnson
This is a gem of a book.From the introduction:We intend this work to be a practitioner’s guide to the predictive modeling process and a place where one can come to learn about the approach and to gain intuition about the many commonly used and modern, powerful models.…it was our goal to be as hands-on as possible, enabling the rea...
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Just for fun: attractors in R
I have a borderline unhealthy obsession with attractors. I thought I got it out of my system, but here we are. For whatever reason, I felt like making some in R.You can find the R code here. It uses the attractor function to define density in a matrix, which is how often a given point gets hit. Then we plot the log scaled version of ...
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RcppArmadillo cheatsheet
I have been using RcppArmadillo more and more frequently, so thought I would make a cheatsheet/cookbook type reference that translates common R operations into equivalent arma code.I have put them up on a github wiki page here.The functions are all pretty basic and not particularly robust. In particular they do not do any bounds or s...
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