Farhad Chichgar — MSc Financial Technology & Data Science student at University of Bristol with a fascination for finding signal in noisy markets. My journey into quantitative finance started unconventionally through fantasy sports optimisation, where I discovered that the same mathematical rigour that generates alpha in markets can systematically beat millions of human decision makers. Since 2020, I've been actively investing and experimenting: starting in crypto during the DeFi summer, building positions across different chains, studying MEV bot strategies and learning how smart contract inefficiencies create arbitrage opportunities. This hands on experience with market microstructure spans Uniswap liquidity pools, high frequency DEX arbitrage, and the traditional finance challenges they mirror.
I'm drawn to problems at the intersection of statistical rigour and computational efficiency: how do you capture regime changes before they're obvious? Can microstructure patterns predict larger moves? Why do some factors decay while others persist? My risk modelling work at Solytics reinforced that robust models must survive contact with messy, real world data. Outside coursework, I'm working through Paleologo's Advanced Quantitative Methods for Trading and building ensemble models for the Hull Tactical competition. Seeking a graduate role where intellectual curiosity is valued and where I can contribute to research that makes it to production trading systems. Look below for a peak into my projects, research or any contact information.
> Developing ensemble models for S&P 500 return prediction for the Hull Tactical Kaggle competition.
> Working through Advanced Quantitative Methods for Trading by Giuseppe Paleologo.