I am a Finance undergraduate at East China University of Science and Technology, with a minor in Computer Science and Technology. I am interested in programming, financial data analysis, and building tools that make research workflows more useful and efficient.
My path from English to Finance led me to study mathematics, statistics, econometrics, and computing. I enjoy turning a financial question or a repetitive task into something I can investigate with data or implement in software.
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I developed a bilingual financial-report research workbench with AI-assisted coding tools, including Codex and Cursor. It connects official CNINFO/HKEX filing discovery, PDF evidence selection, operating summaries, seven-section research memos, independent risk review, and exports.
The project grew out of my exposure to investment research and my interest in turning repetitive financial-analysis tasks into practical software. Its engineering features include explicit evidence-gap states, accounting-scope checks, targeted repair, resumable background jobs, and protected figures and references in English exports.
The public v8.1 snapshot includes source code, historical examples, architecture notes and a regression suite. The local release check passed 296 tests plus 8 subtests; this does not establish audited financial accuracy. Development notes explain the AI-assisted approach and current limitations.
Japan Travel Ledger is a local-first Android expense tracker for travel in Japan. It supports expense entry, manual categories, spending summaries, JPY-to-CNY conversion, and CSV export.
Programming Coursework Portfolio brings my coursework into one collection, organized by course and individual assignment:
- Financial Numerical Analysis: 14 assignment folders covering Python, numerical methods and financial applications.
- Financial Engineering: 4 assignments on futures hedging, option sensitivity and implied volatility.
- Big Data and Financial Computing: 8 assignments, a course paper and supplementary practice on asset pricing, portfolios, volatility and risk.
Browse the collection for assignment topics, Python implementations and selected Matplotlib figures, with English and Chinese documentation.
- Python: pandas, NumPy, statsmodels, and Matplotlib for financial-data cleaning, regression analysis, numerical methods, and visualization.
- Financial analysis: coursework and empirical projects involving CAPM, Fama–French models, Fama–MacBeth regressions, and a random forest model applied to Chinese AI-related stocks.
- Stata: OLS, hypothesis testing, interaction models, robust standard errors, panel-data models, and time-series robustness checks.
- SPSS: multivariate statistical methods, including clustering, principal component analysis, and factor analysis.
- AI product internship: acceptance testing of an enterprise AI-agent platform, issue reporting, and collaboration with product and engineering colleagues.
- Investment research internship: financial-statement review, industry-data collection, and contributions to industry research reports.
I want to deepen my understanding of software development, applied machine learning, and the evaluation of AI tools in financial workflows. I am particularly interested in how these methods can support investment research and better-informed decisions.