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武汉大学金融科技研讨班(WHU Workshop on Financial Technology)

零、必读

  • 请新进的成员阅读公用文档。
  • 如果需要上传权限,请自行注册github账号,再邮件联系李斌添加。

一、课题组会

  • 研究生工作汇报,此部分内容请移步WeeklyMeeting私有库。

二、文献解读研讨班

  • 文献检索的方法
  • 请先阅读文献解读与PPT规范,在有更好的论文解读规范之前按照文献解读规范准备汇报。
  • 文献目录见组会安排文档。
  • 讲解结束后,请上传文献解读的pdf。pdf或文档按月存放,文件统一命名为: 日期姓名论文展示.pdf,比如:20200418_李玥阳_论文展示.pdf。
  • PPT版本可以上传至WeeklyMeeting。

三、研究方法学习班

研究方法学习班旨在帮助学生掌握编程和实证资产定价研究的基本方法,强调代码实现和数据证明,采用Python语言。

"Get your hands dirty." -- Idiom.

"Talk is cheap, show me the code." -- Linus Torvalds

"In God we trust, all others bring data." -- Edwards Deming

学习班包括三部分研究方法:

  1. 金融数据科学(Financial Data Science)

    本学习内容主要包括Python基础、基于Python的数据分析(含数据探索、数据整理、数据建模和数据交流等)、以及金融数据处理的内容。此部分内容的前身为机器学习(2017年开始),目前统称为金融数据科学,目录分为两部分。

  2. 机器学习(Machine Learning)

    运用Python实现机器学习的基本内容,由于经管类以应用为主,因此着重于机器学习基本概念的理解,以及相应的实现。

  3. 实证资产定价(Empirical Asset Pricing)

    用Python实现实证资产定价的基本方法,主要基于**市场的数据。内容以教材1的截面收益为主,同时也包括了时序收益和**市场的因子模型等。

  4. 机器学习与量化投资(Machine Learning and Algorithmic Trading)

    采用Python实现量化投资系统,并通过与机器学习的结合,实现量化投资系统的扩展。通过数个自洽的量化投资系统实现,鼓励学生在量化投资领域开拓进取。内容以教材为主。


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