开始日期: 2024-03-30
课时安排: 7周在线小组科研学习+5周不限时论文指导学习Prerequisites适合人群
适合年级 (Grade): 大学生及以上
适合专业 (Major): 金融工程、量化金融、金融数学、科技金融、投资学、统计和计算机相关专业，以及对金融工程、量化分析、AI、机器学习、量化投资和大数据金融工作感兴趣的同学;学生需要具备扎实的数学基础和编程基础
导师介绍
Miquel
纽约大学 New York University (NYU)教授
Miquel 导师兼任纽约大学Stern商学院教授，哥伦比亚大学教授和西班牙高等管理学院(ESADE)正教授。主要教授资产配置、金融大数据、金融科技和对冲基金课程。
Miquel 是一名在资产管理方面拥有20多年经验的金融市场从业者。他是人工智能金融研究所的创始人，全球人工智能(Global AI) 开发主管，《金融机器学习杂志》的联合编辑。他是金融数据专业研究所(FDPI)和CFA纽约量化投资集团的顾问委员会成员。他曾担任瑞银集团(UBS AG)执行董事，安道尔银行首席财务官(CFO)和首席信息官(CIO)，也是欧洲投资委员会成员。
他的研究领域包括资产配置、大数据算法交易和金融科技。学术合作包括2013年访问哥伦比亚大学金融与经济系，2010年访问弗里堡大学数学系，以及在印第安纳大学、西班牙高等管理学院、伦敦商学院等多个行业研讨会上发表演讲。
Miquel is an Adjunct Assistant Professor at NYU, an Adjunct Assistant Professor at Columbia University, and a full professor at ESADE School of Management in Spain. He teaches courses on asset allocation, financial big data, financial technology and hedge funds.
Miquel is a financial markets practitioner with more than 20 years of experience in asset management. He is the Founder of the Artificial Intelligence Finance Institute, Head of Development at Global AI and co-editor of the Journal of Machine Learning in Finance. He is a member of the Advisory boards of the Financial Data Professional Institute (FDPI) and CFA New York's Quantitative Investment Group. He has served as Executive Director of UBS AG, Chief Financial Officer (CFO) and Chief Information Officer (CIO) of Andorra Bank, and is a member of the European Investment Committee.
His research interests range from asset allocation, big data to algorithmic trading and Fintech. His academic collaborations include a visiting scholarship at Columbia University in 2013 in the Finance and Economics Department, at Fribourg University in 2010 in the Mathematics Department, and giving presentations at Indiana University, ESADE, London Business School and several industry seminars.
任职学校
纽约大学(New York University)简称“NYU”，毕业生综合就业能力排名世界第11位，极受雇主认可。被列为25所新常春藤名校之一。纽约大学在哲学、数学、会计与金融、法律、表演艺术、计算机科学等多个优势学科拥有世界顶尖的学术资源。斯特恩商学院 (Leonard N. Stern School of Business) 是蜚声世界的著名商学院，金融、商科等专业连续排名全美前三。
项目背景
在大数据和AI时代的金融行业，以量化交易、风险控制与管理、AI顾问为代表的智能金融创新方兴未艾，创新成果的获得离不开实用编程软件的开发。在许多机器学习算法编程语言中，Matlab、C++和Python是使用最广泛的。近年来，Python以其开源、易用、功能强大的特点，逐渐成为人工智能的金融工程定量分析中使用最频繁的定量分析软件。本课程的核心是如何使用Python完成金融大数据分析，还原金融行业真实的Python数据分析格式，帮助专业人士和学生完成从初学者到Python金融大数据分析专家的转变。
In the financial industry in the era of big data, financial innovations represented by quantitative trading, risk control and management, and robo-advisors are surging, and the gain of innovative results is inseparable from the development of practical programming software. Among many programming languages, Matlab, C++, and Python are the most widely used. In recent years, Python has gradually become the most frequently used quantitative analysis software in the quantitative analysis of financial engineering due to its open source, easy-to-use and powerful functions. The core of the program is how to use Python to complete financial big data analysis, restore the real Python data analysis format of the financial industry, and help professionals and students to complete their transformation from beginners to Python financial big data analysis experts.
项目介绍
本课程是一个特别的交叉学科课题，导师将金融数据分析、计算机编程(机器学习)与量化金融有机的结合起来，以目前华尔街对冲基金和量化金融公司的实战操练为蓝本将大学量化金融研究与实际金融交易市场有效结合。项目内容包括金融工程定价方法及其Python应用、马科维茨投资组合理论、资本资产定价模型、量化金融数据分析及其Python应用、金融大数据分析、利用机器学习、过滤和交易信号以及高频数据进行金融数据分析与研究，导师将结合数学和统计学分析金融量化模型，帮助学生掌握机器学习在量化金融的实践，在项目结束时提交项目报告，进行成果展示。
The program covers financial engineering pricing methods and their Python application, Markowitz portfolio theory, capital asset pricing model, quantitative financial data analysis and its Python application, financial big data, machine learning, filtering, and trading signals, and high-frequency data; combines mathematics and statistics to analyze financial quantitative models, and enables students to master the practice of machine learning in quantitative finance. Students will submit project reports at the end of the program, and present results.
项目大纲
Python金融数据分析：量化金融概论及其Python应用 Introduction and python basics. What is quantitative finance and why do we care about these tools? Why Python? When and how to use python to organize data?
数据处理：如何处理金融数据，什么是时间序列数据(比如股价、收益和收入数据)，如何获取和组织数据，如何处理数据。Working with data. Types of financial data and how to work with it. What is time series data? How to acquire and organize data? What to do with data once you have it?
数据可视化与商业智能工具Visualization and business intelligence tools
金融数据解读与呈现I Interpretation and presentation of financial data I
金融数据解读与呈现II Interpretation and presentation of financial data II
项目回顾与成果展示Program Review and Presentation
论文辅导 Project Deliverables Tutoring
项目收获
7周在线小组科研学习+5周不限时论文指导学习 共125课时
项目报告
优秀学员获主导师Reference Letter
EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表指导(可用于申请)
结业证书
成绩单
