CS & Economics @ UIUC | GPA: 4.0/4.0 | CFA Level II Candidate
I build systematic tools for quantitative investing — risk analytics, signal generation, portfolio visualization, and ML-driven alpha research.
Core Engine
- clawdfolio — Production AI portfolio monitor. Multi-broker sync (Longport, Moomoo), risk metrics (VaR, Sharpe, Beta, Max Drawdown), options strategy playbook, 20+ automated finance workflows.
Visualization
- investment-dashboard — React/TypeScript portfolio dashboard with real-time P&L tracking, holdings analysis, and risk radar. Frontend layer for clawdfolio.
- QQQ-200D-Deviation-Dashboard — Market timing tool monitoring 200-day MA deviation with historical percentile ranking.
ML Research
- crypto-return-prediction — 24-hour crypto return prediction for 355 assets. LightGBM ensemble, time-series CV, momentum/volatility feature engineering. HKU Web3 Quant Competition.
- ESG-Driven-Stock-Value-Prediction — Stock value prediction using ESG factors. Random Forest with walk-forward backtesting.
- Risk & Portfolio — VaR, CVaR, Sharpe, Beta, Max Drawdown, HHI concentration
- Options — Covered call & cash-secured put lifecycle, Greeks-based guardrails
- Signal Generation — RSI, SMA/EMA, Bollinger Bands, 200-DMA deviation
- ML for Finance — LightGBM, Random Forest, time-series CV, ESG alternative data
- Stack — Python, TypeScript, React, pandas, scikit-learn, GitHub Actions

