Yujia Huang
yujiahuang.research@gmail.com
About Me
I am an undergraduate student in SWUFE, enrolled in RIEM with a major in Finance (minor in Business Administration and the Honors Program in Mathematics). I have a solid background in quantitative strategies and blockchain technologies. I specialize in developing machine learning–based, high-frequency, and market-making strategies and have achieved over 300% returns through discretionary trading in cryptocurrency markets. I am interested in exploring emerging research directions at the intersection of finance, mathematics and computer science.
Experience
BigQuant
Jul 2025 – Sep 2025
Position: Quantitative Research Intern
- Developed 40+ daily-frequency stock selection strategies using machine learning models (LightGBM, XGBoost, CatBoost) with avg. Sharpe > 2.0; contributed to BigQuant SDK development and deployed live trading strategies integrating with QMT for automated execution
- Built high-frequency ETF timing and daily-frequency fund selection strategies covering index, sector, and Smart Beta
- Designed high-frequency futures strategies: cleaned, time-synchronized, and cross-validated 700K+ minute-level and tick data via DAI-SQL; replicated/developed 10+ strategies on Huatai Futures
- Developed daily-frequency deep learning cryptocurrency strategies using BigQuant visual modules and Binance data (DNN/CNN); summarized OKX order API for live deployment and iterative evaluation
China Blockchain Research Center
Jan 2024 – Apr 2024
Position: Research Intern
- Conducted in-depth research on modular Layer 2 solutions (e.g., Manta)
- Developed a six-part investment framework (Summary, Market, Project, Highlights, Valuation, Risks)
- Tracked L2 dominance (77.2%) and Celestia's 450% growth for valuation modeling
- Proposed strategic allocation, achieving simulated returns over 50%
- Monitored ZK ecosystem developments for entry/risk opportunities
WorldQuant Brain Consultant
Aug 2023 – Sep 2023
- Built a 50+ alpha-factor library through heterogeneous data and multi-factor modeling
- Designed position sizing models via nonlinear optimization
- Achieved Sharpe ratio of 1.9+
- Ranked top 3 globally in Alphathon Infinity Champions 002