I started my career in investment banking in 2005 in London at Deutsche Bank. I had the pleasure of working with some amazing people which sparked my interest in algorithmic and quantitative trading. After completing a masters in quantitative finance I started researching in the real world. I worked at Citi for a few years in the electronic trading group for the Euro government bond desk looking after parts of the electronic customer flow. This was during the 2008 GFC which was an interesting time to be involved in European gov bonds due to the volatility and break down in country correlations.

After deciding that I wanted to learn more about high-frequency trading I hit a roadblock. To be a good trader is a bit of a chicken and egg problem. Small firms are where the good people are and where you should go to learn but it is very hard to get hired if you are not an academic superstar or else someone that already a lot of experience. I spent the next eight years working for startups which were more willing to take people with less algo trading experience and learn bit by bit on much riskier ventures. At these firms, I managed to work with some talented people who taught me most of what I know about HFT system architectures, HPC and a lot about market-making strategies.

Most of what I have learned about trading however is from self-learning, reading books, papers and a huge amount of self-research. It has been a very long road and one that has been equal parts rewarding and frustrating. A lot of this work has been simply trying strategy ideas and researching forecasting techniques such as HMMs, MCMC, particle filters, FIR filters etc. Almost all of what I have looked at has been a dead end and arguably a waste of time but as time has progressed I have found a lot of equivalences between ideas that work.

My plan for the blog is to discuss research as I progress and to present it as a learning experience. Partly this is to not divulge too much proprietary information but also to highlight the reality that most research ends up in the bin. Research is exactly that, and anyone that has done a lot of it knows both the euphoria of finding a new idea and the sadness of giving up on something with significant time investment. I hope the ideas here can at least inspire people to continue their search for profitable ideas.