Quantitative Finance | Data Science | Machine Learning Enthusiast

I'm a quantitative researcher at heart – passionate about turning data into decisions. My background spans mathematics, statistics, and systematic trading, with hands‑on experience across Python, machine learning, and financial markets.

• 🔍 Currently: Risk Quant Intern at Singapore Exchange (SGX) – working on risk models, volatility modelling, and quantitative analytics.
• 💼 Previously: Data Engineering & Analytics Intern at Hong Kong ASTRI (3% acceptance rate), Research Project at Oxford Saïd Business School.
• 📈 Personal edge: 2+ years of systematic crypto trading (83% win rate, profit factor 6.9), built a hybrid forecasting model that cut prediction error by 50% (35 GitHub stars).
• 🎓 Education: MSc in Modelling & Simulation, NTU (4.57/5.00) | HBSc in Mathematics & Statistics, University of Toronto (with Distinction).

• Programming: Python (Pandas, NumPy, scikit‑learn, PyTorch), SQL, Git, Docker, UNIX/Linux, C++ (basic)
• Quantitative Methods: Stochastic Modelling, Time Series Analysis, Volatility Modelling, Optimisation, Statistical Inference
• Machine Learning: Predictive modelling, Feature engineering, Ensemble methods, Backtesting frameworks
• Languages: English (fluent), Mandarin (native)

• Systematic Signal Generation & Backtesting Framework – Hybrid SARIMAX‑SGD model for equity forecasting, reducing test MSE by 50%+ and training time by 29% (35 GitHub stars, technical Medium article with 1,200+ reads).

• Portfolio Optimisation & Algorithmic Trading Backtester – Built a multi‑asset backtesting framework under a 72‑hour hackathon deadline, processing 100K+ data points and reducing simulated drawdown risk by 20%.

• Kaggle Notebooks – Explore my public kernels on ML, time‑series forecasting, and quantitative competitions (including QRT Data Challenge).

Let’s connect and build data‑driven solutions together.