Chicago, Illinois, United States
I'm currently a graduate student at the University of Chicago in the Financial Mathematics program with an expected graduation of Dec. 2025. Switching from Accounting, I have spent more than three years intensively studying, working and researching in Financial Engineering field. My academic background included strong training in mathematical modeling, statistical analysis, and programming, particularly in Python. To genuinely pursue what I am passionate about, I will continue to enhance my skills in C++, financial derivatives and machine learning. I look forward to rooting my career in Quantitative Finance, especially as a Quantitative Researcher or Quantitative Trader.
- Developed methods to evaluate the predictive power of cryptocurrency sentiment signals extracted from Reddit posts on their future returns, covering the top 50 coins representing 58% of message volume - Optimized sentiment signal using detailed user and message features, applying objective weighting methods such as Equal Weight, CRITIC, and EWM to enhance signal effectiveness - Conducted time series analysis, finding small coins are more impacted by sentiment but exhibit shorter-lived effects
- Explored pricing and risk hedging of option products using machine learning methods. - Built option volatility trading strategies; 5- and 20-days strategy return exceeded 90% at 0.03 slippage. - Visualized tracking error of products, generated VIX indices, back-tested strategies of research reports. - Collaborated in project team's Github repository, assisted in completion of daily reports.
- Developed trend trading strategies from scratch in Python, based on Bollinger Band and Turtle Trading. - Optimized band parameters and confirmed trends in opening position through oscillators, ATR, etc. - Analyzed distribution of each trade, and added Take Profit and Stop Loss points to closing positions. - Achieved expected strategy goals, including an average annualized return exceeding 10%, maximum drawdown limited to 15%, Sharpe ratio of around 1, a win rate between 40% and 50%, and an average of 6-10 trades per year.
- Analyzed financial statuses, market spaces and development potential via data from Bloomberg. - Produced a series of financial indicators in accordance with the requirements of the research report. - Wrote in-depth industry reports on IPO prospects of companies in high-tech electronics industry.
- Participated in the year-end audits of several state-owned enterprises and listed companies. - Prepared nearly 300 detailed account drafts based on A300, trial balance, journal entries. - Conducted audit testing on the final reports and continuously followed the revision process.