Like many of you, my journey in quantitative finance began back in 2008 when I was a young postgraduate student trying to figure out my next career move.
I began by self-studying as many quant finance texts as I could get my hands on, but soon noticed that the information I needed to know was pretty dense and quite difficult to understand without a lot of work. Further compounding the difficulty was the fact that there were no real courses and few certifications for the quant industry at this stage.
I really needed the key concepts organised and laid out in an easy to digest manner so that I wasn't wasting precious self-study time. Nothing of that sort really existed for quant job interview preparation on the Internet at the time, so I figured I'd do it myself.
I organised my notes, read all of the available mathematical finance and algorithmic trading textbooks, spoke to a bunch of people working in the industry and placed all of that information online so I could easily access my notes from anywhere to help me understand. And thus QuantStart.com was born.
In 2010 I was lucky enough to be employed as the lead quantitative developer for a London-based US-equity long-short startup quantitative hedge fund.
Over the next couple of years I gained a diverse set of skills across software development for high performance systems, statistical modelling for high frequency time series and startup formation of hedge fund structures.
The biggest lessons that I learnt during this period were less to do with sophisticated trading strategy development and more to do with institutional grade portfolio and risk management.
I now firmly believe, and teach accordingly, that the most important aspects for a prospective quant trader to learn are how to carry out sophisticated position sizing and risk management of their portfolios. I also strongly believe that even if you a retail trader, that you can learn a great deal from how quant hedge funds manage their risk.
In early 2012 I made the bold decision to concentrate on QuantStart full-time. I left the fund and worked tirelessly to post quant finance content across topics as diverse as university choice, time series analysis and deep learning.
One of the consistent themes running across all of the articles on QuantStart is the notion of continuous learning. Quant finance is a rapidly changing field. If you're not constantly learning, you will fall behind those that are.
My primary motivation for continuing to run QuantStart is to ensure that I keep learning about all of the new techniques in quant finance, which allows me to share them with the quant community so that we all grow together.
Coming from a scientific research background I still firmly believe in knowledge sharing, and am passionate about concepts such as free open-source software. While there is certainly a lot of proprietary knowledge in the world of quant finance, there is still plenty to share and we all become better by doing so.
I've gained a great deal of insight from the numerous conversations I've had with many of you, whether through email exchanges, catching up at quant conferences or through the post comments on the site. I hope you have learnt a similar amount!
The world is becoming more and more automated. Software is replacing many roles, including a lot of those in finance. Many aspects of finance are becoming systemised - particularly those related to trading of complex assets.
Now is the perfect time to train to become a quant - an individual who can model, design and code these automated risk and trading systems. Such a career provides a highly intellectually stimulating environment, surrounded by smart peers and excellent compensation compared to other industries.
Not only is quant finance great the entry level, where you're being paid very well to utilise your mathematics and programming skills, but it can lead to more exciting, and lucrative, roles in institutional risk management, quantitative development or, for the truly ambitious, a manager of your own quantitative investment fund.
The key skills of data science, mathematics, statistical machine learning and an ability to code algorithms are at the forefront of this development and are only going to become more important as time goes on.
It is my goal - and passion - to help you gain these skills via the resources on this site in order that you can achieve your dream career goals in the exciting, and ever-changing, world of quantitative finance.