LinkedIn Profile My interest in quantitative finance began in a Foundations of Finance course, where mathematical models explained investor behavior and produced conclusions that matched financial intuition. It made me see markets not as random chaos, but as complex systems where disciplined research can uncover structure. To me, quantitative research is both a science and an art: rigorous in method, creative in discovery. I graduated from The Chinese University of Hong Kong, Shenzhen with a bachelor’s degree in Mathematics and Applied Mathematics, Financial Mathematics Stream. Through internships at quantitative funds, I explored crypto CTA strategies, factor research, and high-frequency signals across global markets. I built data pipelines, mined factors, designed trading signals, backtested strategies, and evaluated performance through Sharpe ratio, Calmar ratio, drawdown, transaction costs, and parameter stability. In one live portfolio, I contributed to a 5% monthly return with a Sharpe ratio of 2. These experiences taught me that quantitative strategies must evolve with changing markets. I believe research ideas gain value only when they are tested quickly, implemented carefully, and refined continuously. I am excited to connect with quantitative research teams and discuss how I can contribute to systematic, data-driven investment strategies.
