"Building systematic investment strategies through mathematics, statistics, and machine learning."
My work focuses on developing statistically robust and reproducible quantitative models for financial markets. Research interests include stochastic processes, derivatives pricing, time series modelling, optimization, and systematic trading. My repositories document end-to-end quantitative research—from mathematical modelling and data engineering to backtesting and performance evaluation—with an emphasis on reproducibility and rigorous analysis.
| Category | Skills |
|---|---|
| Programming Languages | Python, SQL, C++, Java |
| Quantitative Methods | Statistical Modelling, Volatility Forecasting, Hypothesis Testing, Derivative Pricing, Time Series Analysis, Backtesting, Monte Carlo Simulation |
| Mathematics | Probability Theory, Statistics, Linear Algebra, Calculus, Stochastic Processes, Optimization |
| Libraries | NumPy, pandas, SciPy, scikit-learn, statsmodels, Matplotlib, arch |
- Statistical Arbitrage
- Volatility Modelling
- Portfolio Optimization
- Yield Curve Modelling
- Market Microstructure
- Reinforcement Learning for Trading
Idea Generation
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Data Collection
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Feature Engineering
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Statistical Testing
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Model Development
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Backtesting
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Risk Analysis
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Execution
"Markets reward discipline, not prediction."
