About Me
I am a freshman at Penn State University double majoring in Computer Science and Statistics.
I am interested in the combination of Software Engineering and Statistics/Data Science in Financial Markets. My goal is to build robust, event-driven trading systems and analyze market microstructure using quantitative approaches.
Currently, I am researching meta filtration and machine learning optimization in trading models and building my own automated trading system in Python.
Featured Research
Project Kintoun: NQ Futures Strategy
Status: Completed (Dec 2025)
An event-driven algorithmic trading strategy designed for the Nasdaq-100 E-Mini Futures (NQ). This project explores how Market Microstructure (Break & Retest) combined with Volume and Regression Analysis can filter out false breakouts in variable volatility environments.
- Technique: Event-driven simulation (partially vectorized).
- Data Handling: Timezone-aligned, degradation-filtered 1-minute OHLCV data.
- Key Metric: significantly improved expectancy (Profit Factor) and risk-adjusted return (Sharpe) compared to naive ORB strategies.
View the full Analysis & Backtest ➞
Technical Stack
| Domain | Skills |
|---|---|
| Languages | Python, C++, Java, R |
| Libraries | Pandas, NumPy, Scikit-Learn, MatPlotLib |
| Quant | Time-Series Data Analysis, Backtesting, Risk Management |
| Tools | Git, VS Code, Jupyter, Linux/Ubuntu, Quarto |