Research
Research interests and directions.
My research interests sit at the intersection of quantitative finance, data science, and machine learning.
Quantitative finance
- Systematic trading research and backtesting methodology
- Factor models, signal evaluation, and portfolio construction
- Volatility forecasting, risk measurement, and drawdown analysis
- Market data cleaning, feature engineering, and reproducible experiments
AI and data science
- Machine learning pipelines for structured and time-series data
- Experiment design, data preparation, and research reporting
- Data wrangling, visualization, and practical analytics workflows
Current direction
I am using this site to build a public archive of quantitative finance notes while developing stronger habits around transparent assumptions, reproducible code, and careful model evaluation.