I am a quant researcher focused on execution at both the high (parent) and low (order routing) level. I also dabble in market-making and alpha signals when there is a spare 5 minutes at work.

Previously, I was a quant at BestX and spent the day researching financial markets with a focus on transaction cost analysis (TCA). Hopefully I shared some interesting insights about the world of FX, fixed income and equity trading and built some cool product features.

I have a PhD in Statistical Science from University College London (UCL) and my thesis was Bayesian Nonparametric Hawkes Processes with Applications. I also have an MRes in Computer Science and an MPhys in Theoretical Physics from the University of Manchester.

I run a dashboard for crypto liquidity and pre-trade analytics: https://cryptoliquiditymetrics.com/

I’m an open source contributor with multiple projects ongoing: dirichletprocess, HawkesProcesses.jl, AlphaVantage.jl, CoinbsePro.jl, AlpacaMarkets.jl.

I’ve written guest posts for QuestDB:

Want me to write for you or have something else interesting? Email me at dean[dot]markwick[at]talk21[dot]com

Talks

Articles

Some are behind a paywall.

Outside the Day Job

I also have a keen interest in sports modelling and how statistics can be used in professional sports and gambling, I enjoy a good film, watching a boxset too quickly and a bit of a foodie. Here I am enjoying some delicious Korean BBQ.

Delicious BBQ

My blog is also aggregated on https://www.r-bloggers.com/ and https://www.juliabloggers.com/.

Recent Post:

    A Statistical FX Factor Model

    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.