Quantitative Research
derivatives pricing, portfolio optimization, risk analysis, alpha research, and machine learning applications in quantitative finance.
Featured Projects
| Project | Description | Links |
|---|---|---|
| Systematic Alpha Research | Strategy engineering pipeline featuring Walk-Forward Analysis, Global Parameter Optimization, and Regime Stress Testing for robust signal generation. | Docs · Github |
| Option Pricing & Risk | Black-Scholes, binomial trees, and Monte Carlo simulations for pricing vanilla and exotic derivatives. Includes advanced Greeks analysis and implied volatility surface construction. | Docs · Github |
| Portfolio Optimization | Modern Portfolio Theory (MPT), Hierarchical Risk Parity (HRP), and Black-Litterman models implemented for quantitative asset allocation and risk budgeting. | Docs · Github |
| Equity Market Crisis Prediction | Machine Learning models designed to predict equity market crashes using macro-economic and technical feature sets. | Docs · Github |
| Regime Shift Detection | Identifying structural breaks in market behavior. | Docs · Github |
Research Areas
Systematic Strategy Design
Robust signal engineering using Python. Focuses on Walk-Forward Validation to eliminate overfitting, utilizing strategies like Adaptive Moving Averages (AMA) and Volatility Breakouts (MABW).
Derivatives & Risk Analytics
Stochastic calculus implementations for pricing options, computing real-time Greeks, and modeling Value-at-Risk (VaR/CVaR) via historical and Monte Carlo methods.
Market Regime Analysis
Time series forecasting and regime shift detection using Hidden Markov Models (HMM) and Supervised Learning to adapt strategies to changing market volatility.
Tech Stack
| Category | Tools |
|---|---|
| Languages | Python, SQL, R, C++ |
| ML/DL | PyTorch, TensorFlow, scikit-learn, XGBoost, Optuna |
| Quant | QuantLib, VectorBT, Backtrader, Zipline |
| Data | Pandas, NumPy, Polars, Dask |
| Viz | Plotly, Matplotlib, Seaborn |
Contact
- GitHub: github.com/xxxxyyyy80008