Personal website for Dean Markwick. If you like stats, sports and rambling, you've come to the right place. All rights reserved.
Factor models attempt to explain asset returns. You can approach this in two ways: define the factors you think are relevant, or use statistical learning to build the relevant factors from the data. My previous post took the first approach. This post will use principal component analysis (PCA) to let the data tell us which factors are most relevant in an FX factor model.
I’ve been tinkering around with the free tier on Cloudflare and have managed to churn out a couple of side projects. Of course, I had a little help with various AI systems, but it was assisted rather than vibe-coded.
I’ve been reading The Elements of Quantitative Investing to branch out from my usual high-frequency finance to something slower or mid-frequency. Factor models are a big part of this quant topic, and I’m trying to get a deeper understanding by following the book and applying the process to FX data.
My day job is in quant trading, but there’s another fascinating world: quantitative investing. While I focus on latencies and execution, quant investors are busy building the most efficient portfolios and ensuring they extract pure alpha. Not one to stay in my lane, I’m using this blog post as an opportunity to dive into the world of quant investing and level up my knowledge.
It’s been an interesting start to the Premier League. All of the promoted teams (Sunderland, Leeds and Burnley) are outside the relegation zone, with Wolves and West Ham struggling at the bottom. So I want to look back at the other seasons and work out the average number of points throughout the season that characterises relegation teams, and how many points do you need to avoid relegation?
I’m fortunate enough to be participating in a lecture series at work that covers deep learning and its applications in finance. This will be a series of posts documenting what I learn and implementing the ‘homework’ (I’m 32, how am I still getting homework?) using Julia and Flux.
Cyclical embedding (or encoding) is a basic transformation for numerical variables that follow a cycle. Let’s explore how they work.
A big part of market microstructure is price impact and understanding how you move the market every time you trade. In the simplest sense, every trade upends the supply and demand of an asset even for a tiny amount of time. The market responds to this change, then responds to the response, then responds to that response, etc. You get the idea. It’s a cascading effect of interactions between all…
A new paper hit my feed Choosing trading strategies in electronic execution using importance sampling. I’ve only encountered sampling as part of a statistical computing course as part of my PhD, and I had never strayed away from Monte Carlo sampling, but this practical example provided an intuitive understanding of its importance and utility.
People are never short of a trade idea. There is a whole industry of researchers, salespeople and amateurs coming up with trading ideas and making big calls on what stock will go up, what country will cut interest rates and what the price of gold will do next. Alpha capture is about systematically assessing ideas and working out who has alpha and generates profitable ideas and who is just making…