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Yuyang-Yao75/README.md

Hi there 👋

I'm Yuyang Yao, a Master's student in Financial Mathematics @ Fudan University.
I’m passionate about Quantitative Finance, Artificial Intelligence, and Cross-disciplinary Research.

  • 🔭 I’m currently working on index futures arbitrage and option strategies
  • 🌱 I’m learning deep learning for time-series forecasting and exploring AI applications beyond finance
  • 👯 I’m open to collaboration on quant research and interdisciplinary AI projects
  • 💬 Ask me about quant research, financial AI, and trading strategies
  • 📫 How to reach me: GitHub | Email: yyyao75@163.com
  • 🎯 Research interests: cross-asset arbitrage, financial time series, AI4Finance, reinforcement learning, and the intersection of physics, AI, and markets
  • 🌍 Curiosity: I enjoy exploring how ideas from different fields connect and shape our understanding of the world

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  1. Attribution-Analysis-of-Options-Trading Attribution-Analysis-of-Options-Trading Public

    本项目提供一个基于希腊值的期权组合收益归因工具。脚本通过 iFinD 接口获取合约与行情数据，计算隐含波动率及 Delta/Gamma/Vega/Theta 等希腊值，并将当日盈亏分解到各因素。结果以 Excel 形式输出，便于历史回溯与组合表现研究。

    Python 2 1

  2. Scenario-Analysis-of-Options-Trading Scenario-Analysis-of-Options-Trading Public

    该项目用于对期权组合进行情景分析，评估在标的价格和波动率变化下的盈亏、保证金以及希腊值暴露情况。工具基于 iFinD 数据接口，结合 pandas、numpy、scipy 与 xlsxwriter 等库，实现自动化的风险测算与报表生成。

    Python 1 3

  3. Index-Component-Stock-Inclusion-Exclusion-Strategy Index-Component-Stock-Inclusion-Exclusion-Strategy Public

    本仓库围绕指数成分股的调入调出预测及调整策略回测展开，旨在帮助研究者或投资者理解和验证指数编制规则的量化实现以及成分股调整对策略表现的影响。

    Python 1

  4. Daily-Futures-Arbitrary-Strategy Daily-Futures-Arbitrary-Strategy Public

    本项目基于东证期货金工团队发布的《股指期货套利策略系列四：跨品种套利的基差、动量与季节性特征》研究报告进行策略复现与工程重构，旨在构建一个具备良好可读性、可扩展性和模块化设计的量化策略研究框架。

    Python 13 3

  5. Prompt-Enginneer-on-Recommend-System Prompt-Enginneer-on-Recommend-System Public

    传统的推荐系统依赖明确建模和大量特征工程，难以快速适配新的场景。本项目探索如何利用 大语言模型（LLM） 与提示词工程来完成电影重排任务，使其在更少人工特征的情况下仍能捕捉用户偏好。

    Python 1

  6. NLP-Project-on-Comment-Recognition NLP-Project-on-Comment-Recognition Public

    本项目针对 Kaggle IMDb Sentiment Analysis Challenge，预测电影评论的情感（正面 / 负面）。该项目作为 哥伦比亚大学 Patrick Houlihan 教授开设的《数据科学：机器学习与自然语言处理》课程的期末作业完成。项目最终获得 A+ 成绩，并获得国际顶尖创新人才培养项目的邀请。

    Python 3