Skills Summary
Programming & Tools
- Python (Pandas, Polars, PyTorch, HuggingFace)
- C++, Linux, LeetCode (300+ problems solved)
- Machine Learning: LightGBM, XGBoost, Random Forest, CNN, LSTM
- Quantitative Tools: CVXPY, Barra, Factor Analysis
Domain Expertise
- Factor Mining & Evaluation Systems
- Multi-Factor Integrated Models
- Stock Market Prediction & Backtesting
- Large Language Models & Transformer Architecture
Education
Shanghai University of Finance and Economics
B.S. in Financial Statistics and Risk Management
First Prize in Chinese Chemistry Olympiad
People’s Scholarship
College Math Competition (2nd Prize)
Core Courses:
Probability & Statistics, Financial Mathematics, Machine Learning
Co-founder, SUFE Quantitative Association QITA
Peking University
Summer School Program
Course: Large Language Models - From Fundamentals to Frontiers
Studied Transformer architecture, classic LLMs, and HuggingFace/PyTorch frameworks. Conducted group presentations on cutting-edge research papers.
Internship Experience
Hainan
MY Capital (AUM: ¥30B)
Quantitative Researcher
Automated Factor Mining System
Developed high-performance factor evaluation framework with Polars. Implemented RL-based factor mining system using minute-level OHLC data, achieving out-of-sample IC > 0.09.
Daily Multi-Factor Integrated Model
Constructed multi-GPU parallel MoE/GRU architecture. Achieved top 3 groups with Sharpe > 2, 25% annualized return, and turnover < 6% in out-of-sample backtests.
Intraday K-Line Image Recognition
Converted 5-minute K-lines to grayscale images for CNN-based prediction. Achieved out-of-sample IC > 0.11 through 3-year rolling predictions.
Long Life Investment Co., Ltd. (AUM: ¥10B)
Quantitative Researcher
Cutting-edge DL Models Implementation
Reproduced and improved Attention-LSTM/MASTER models, increasing prediction IC to 0.12. Developed proprietary DL training framework.
Backtesting Performance
Achieved 19% annualized return, max drawdown < 6%, and Sharpe ratio > 2.5 under ¥10M+ capital with Barra neutralization.
Key Projects
Machine Learning Strategy Based on Multi-Frequency Factor Synthesis
- Constructed 100+ feature factors using 1-minute and daily market data
- Applied LightGBM for nonlinear factor integration, achieving overall IC 0.09 (daily avg IC 0.07)
- 40% annualized return with 15% max drawdown under ¥10M capital and 0.15% transaction cost
Machine Learning-Based Stock Limit-Up/Down Prediction
- Processed 330,000+ records of A-share data (fundamentals, limit events, Dragon & Tiger List)
- Designed 5-class prediction framework using XGBoost/CatBoost/Random Forest
- Achieved 75% accuracy for T+2 day 6-10% return category prediction