I'm a Technology Delivery Lead at DBS Bank with over 4 years of experience, currently driving data analytics, automation, and AI delivery in DBS Global Financial Markets while pursuing my Masters of Quantitative Finance at Singapore Management University (class of 2026).
Before finance, I was an aerospace engineer who fell in love with data science — I completed General Assembly's Data Science Immersive in 2021 and never looked back.
My mission is to combine two lifelong passions: investing and applied mathematics. When I'm not pricing derivatives or building trading strategies, you'll find me following global macro markets, playing rugby, or cheering for Onosato in sumo.
HFT strategy analysis with order book simulation, microstructure modeling, and backtesting framework implementation.
Swap curve bootstrapping, IRS pricing, forward swaps, no-arbitrage forward pricing with collateralised rates. Vasicek, Ho-Lee, Hull-White short rate models and convexity correction.
Binomial trees, Brownian motion simulation, exotic option pricing via Monte Carlo, Carr-Madan model-free pricing, SABR calibration, Black-Scholes dynamic hedging with tracking error analysis.
Portfolio optimization using FinRL and stable-baseline3 with PPO. Efficient frontier analysis and Monte Carlo simulations for allocation strategies.
TF-IDF, CountVectoriser, and RandomForest-based classification on Reddit posts. End-to-end NLP pipeline with sklearn.
Singapore HDB resale price analysis using RandomForest regression, K-clustering, and difference-in-difference modeling. Interactive Tableau dashboard.
Object-oriented options pricing framework in C++. Implements various payoff structures and pricing models with clean OOP architecture.
Daily CAPM+CRP, Total Return (div+buyback+growth), and FCFF DCF IRR estimates for Mag 7 and SPY. Interactive Plotly dashboard with spread analysis across all 3 methods.
Tracks daily sentiment from TraderChecklist views and CoT results, overlaid with SPY daily returns. Dark-themed Plotly dashboard with rolling averages and rangeslider zoom.
Client-side trading journal that tracks simple and hedged/options positions with automatic risk calculations. Stores daily snapshots in a GitHub repo. Features position sizing via risk-dollar budgeting, R/R ratios, and historical snapshot management with load/clone/import/export.
Client-side volatility dashboard that computes Historical Vol, GARCH/EWMA forecast, and Implied Vol from live option chains. All calculations run in-browser via Pyodide WASM — no server needed.
Daily expected return estimates for Mag 7 + SPY using three independent methods: CAPM+CRP (beta-predicted + country risk premium), Total Return (dividend yield + buyback yield + earnings growth), and FCFF DCF IRR (free cash flow to firm discounted cash flow internal rate of return). Updated daily via an automated pipeline.
Auto-updates daily. Covers AAPL, MSFT, GOOGL, AMZN, META, NVDA, TSLA, and SPY.
Live volatility analysis powered by Pyodide (Python WASM in the browser). Fetches real-time close prices and option chains from Yahoo Finance, then computes 30-day Historical Volatility, EWMA/GARCH forecast vol, and ATM Implied Volatility from the nearest expiry. All calculations run client-side — no backend required.
Enter any ticker to see Historical Vol, GARCH proxy, Implied Vol, and Expected One-Day Move.
Daily batch pipeline that scrapes FinTwit, Reddit, news RSS feeds, and market data to score global macro sentiment using VADER and FinBERT. Results are stored in SQLite and published as a live dashboard via GitHub Actions.
Trader Checklist Sentiment — Personal View + CoT Scores
Aggregates daily view scores from TraderChecklists and CoT results (Bullish=+1, Flat/Neutral=0, Bearish=-1). Overlayed with SPY daily returns for cross-reference. Updates on demand.
↗ Tracker Checklist Sentiment DashboardFinTwit/Reddit: Auto-updates daily via GitHub Actions. Tracker Checklist: On-demand via OpenClaw pipeline.
Daily trading journal for tracking position sizing, stop losses, and risk exposure. Supports simple positions (single stop — auto-computes position size and cost basis from risk-dollar budget) and hedged / options positions (two-leg stops with delta-adjusted cash exposure). All data syncs to your GitHub repo for persistence across devices — nothing stored server-side.
Credentials stored locally in your browser. Snapshots are saved to snapshots.json in the trading-journal repo.