I’m drawn to markets because they reward clear thinking and tight execution. I’m building toward quantitative research and trading roles where I can turn data into signals, wire those signals into code, and be accountable to results. I began on the research side at UBS, where I wrote modular Python backtests for technical-indicator strategies. I focused on cleaner entry/exit logic, position sizing, and validation to reduce overfitting and improve drawdown control. Earlier finance and audit work sharpened my instincts for data integrity and edge cases. In competition settings, I’ve built a delta-neutral volatility portfolio and an ETF arbitrage bot for RITC x CMU 2025, operating within strict position, net/gross, and execution limits. That experience reinforced my comfort with fast feedback loops, inventory risk, and slippage diagnostics. I work mainly in Python and R, with additional experience in C++ and MATLAB. I care about reproducible research, out-of-sample testing, and clean engineering. Long term, I want to design signals across horizons and help run systematic portfolios that are explainable, robust, and tied to PnL.
