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The professional pathway I chose to walk <br>could perfectly be described by what I like to do. |
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These are the topics where I have been building my professional profile, mainly focused on quantitative finance, portfolio management, and risk management as well as quant development. Usually as a transversal topic on all those topics there is machine learning algorithms such as supervised/unsupervised learning, deep learning and reinforcement learning. Comming soon natural language procesing (NLP) as well.
The good investment decision process/result is the main interest of my professional path so far. On this regard there are plenty of topics I like to research including economic behaviour/performance, macroeconomic analysis, asset allocation, portfolio performance, P&L analysis, trading cost analysis (TCA), P&L attribution (PLA), among others.
Because you cannot deal with uncertainty without consider the relevance of good risk management, there is other field that I like. This field results important in the way it is a mechanism of control of losses, in some sense it may help you to keep under control the emotions of quantitative portfolio management and algo/quant trading. Main interest on market, conterparty, and liquidity risk.
I have been a lot intrigated about taking investment decisions under uncertainty, and especially how to deal with it having quantitative tools. I do have experience working with algorithmic trading strategies through broker's APIs using programming laguages as python, C++, C# and Java. Along with it the passion of machine learning applied on algo/quant-trading.
In the current world of finance and capital markets there is no chance to compete without having deep knowledge of how to build tools that support your decision taking process, and that's why a good complement of quantitative development is relevant. My experience has been framed on programming languages such as python, C++, Java and recently Ocaml, Julia and Rust. Some other tools which support this taste is AWS with services like S3, EC2, lambda, athena and Sagemaker.
The complement of capital market's performance itself is the economic analysis. Almost every movement in capital markets is stemmed from feelings produced from economic facts reading for each agent composing the markets.Because of that it is also interesting to assess the economic facts not only the current ones but also the previous ones in order to identify patters if it applies.
If you want to know all my public work till now, I do invite you to visit my github personal repository, where you can see a couple of scripts in some programming languages like python (including jupyter notebooks), javascript and C++. I should warn to you some scripts are not finished yet, but I am putting all my effort to finish it soon.