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金融工程與智能金融專題: 基于機(jī)器學(xué)習(xí)的量化投資模型構(gòu)建與優(yōu)化策略研究【大學(xué)組】

閱讀 1013
2023-12-12

  開始日期: 2024-03-30

  課時(shí)安排: 7周在線小組科研學(xué)習(xí)+5周不限時(shí)論文指導(dǎo)學(xué)習(xí)Prerequisites適合人群

  適合年級(jí) (Grade): 大學(xué)生及以上

  適合專業(yè) (Major): 金融工程、量化金融、金融數(shù)學(xué)、科技金融、投資學(xué)、統(tǒng)計(jì)和計(jì)算機(jī)相關(guān)專業(yè),以及對(duì)金融工程、量化分析、AI、機(jī)器學(xué)習(xí)、量化投資和大數(shù)據(jù)金融工作感興趣的同學(xué);學(xué)生需要具備扎實(shí)的數(shù)學(xué)基礎(chǔ)和編程基礎(chǔ)

  導(dǎo)師介紹

  Miquel

  紐約大學(xué) New York University (NYU)教授

Miquel

  Miquel 導(dǎo)師兼任紐約大學(xué)Stern商學(xué)院教授,哥倫比亞大學(xué)教授和西班牙高等管理學(xué)院(ESADE)正教授。主要教授資產(chǎn)配置、金融大數(shù)據(jù)、金融科技和對(duì)沖基金課程。

  Miquel 是一名在資產(chǎn)管理方面擁有20多年經(jīng)驗(yàn)的金融市場(chǎng)從業(yè)者。他是人工智能金融研究所的創(chuàng)始人,全球人工智能(Global AI) 開發(fā)主管,《金融機(jī)器學(xué)習(xí)雜志》的聯(lián)合編輯。他是金融數(shù)據(jù)專業(yè)研究所(FDPI)和CFA紐約量化投資集團(tuán)的顧問委員會(huì)成員。他曾擔(dān)任瑞銀集團(tuán)(UBS AG)執(zhí)行董事,安道爾銀行首席財(cái)務(wù)官(CFO)和首席信息官(CIO),也是歐洲投資委員會(huì)成員。

  他的研究領(lǐng)域包括資產(chǎn)配置、大數(shù)據(jù)算法交易和金融科技。學(xué)術(shù)合作包括2013年訪問哥倫比亞大學(xué)金融與經(jīng)濟(jì)系,2010年訪問弗里堡大學(xué)數(shù)學(xué)系,以及在印第安納大學(xué)、西班牙高等管理學(xué)院、倫敦商學(xué)院等多個(gè)行業(yè)研討會(huì)上發(fā)表演講。

  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.

  任職學(xué)校

  紐約大學(xué)(New York University)簡(jiǎn)稱“NYU”,畢業(yè)生綜合就業(yè)能力排名世界第11位,極受雇主認(rèn)可。被列為25所新常春藤名校之一。紐約大學(xué)在哲學(xué)、數(shù)學(xué)、會(huì)計(jì)與金融、法律、表演藝術(shù)、計(jì)算機(jī)科學(xué)等多個(gè)優(yōu)勢(shì)學(xué)科擁有世界頂尖的學(xué)術(shù)資源。斯特恩商學(xué)院 (Leonard N. Stern School of Business) 是蜚聲世界的著名商學(xué)院,金融、商科等專業(yè)連續(xù)排名全美前三。

  項(xiàng)目背景

  在大數(shù)據(jù)和AI時(shí)代的金融行業(yè),以量化交易、風(fēng)險(xiǎn)控制與管理、AI顧問為代表的智能金融創(chuàng)新方興未艾,創(chuàng)新成果的獲得離不開實(shí)用編程軟件的開發(fā)。在許多機(jī)器學(xué)習(xí)算法編程語言中,Matlab、C++和Python是使用最廣泛的。近年來,Python以其開源、易用、功能強(qiáng)大的特點(diǎn),逐漸成為人工智能的金融工程定量分析中使用最頻繁的定量分析軟件。本課程的核心是如何使用Python完成金融大數(shù)據(jù)分析,還原金融行業(yè)真實(shí)的Python數(shù)據(jù)分析格式,幫助專業(yè)人士和學(xué)生完成從初學(xué)者到Python金融大數(shù)據(jù)分析專家的轉(zhuǎn)變。

  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.

  項(xiàng)目介紹

  本課程是一個(gè)特別的交叉學(xué)科課題,導(dǎo)師將金融數(shù)據(jù)分析、計(jì)算機(jī)編程(機(jī)器學(xué)習(xí))與量化金融有機(jī)的結(jié)合起來,以目前華爾街對(duì)沖基金和量化金融公司的實(shí)戰(zhàn)操練為藍(lán)本將大學(xué)量化金融研究與實(shí)際金融交易市場(chǎng)有效結(jié)合。項(xiàng)目?jī)?nèi)容包括金融工程定價(jià)方法及其Python應(yīng)用、馬科維茨投資組合理論、資本資產(chǎn)定價(jià)模型、量化金融數(shù)據(jù)分析及其Python應(yīng)用、金融大數(shù)據(jù)分析、利用機(jī)器學(xué)習(xí)、過濾和交易信號(hào)以及高頻數(shù)據(jù)進(jìn)行金融數(shù)據(jù)分析與研究,導(dǎo)師將結(jié)合數(shù)學(xué)和統(tǒng)計(jì)學(xué)分析金融量化模型,幫助學(xué)生掌握機(jī)器學(xué)習(xí)在量化金融的實(shí)踐,在項(xiàng)目結(jié)束時(shí)提交項(xiàng)目報(bào)告,進(jìn)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.

  項(xiàng)目大綱

  Python金融數(shù)據(jù)分析:量化金融概論及其Python應(yīng)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?

  數(shù)據(jù)處理:如何處理金融數(shù)據(jù),什么是時(shí)間序列數(shù)據(jù)(比如股價(jià)、收益和收入數(shù)據(jù)),如何獲取和組織數(shù)據(jù),如何處理數(shù)據(jù)。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?

  數(shù)據(jù)可視化與商業(yè)智能工具Visualization and business intelligence tools

  金融數(shù)據(jù)解讀與呈現(xiàn)I Interpretation and presentation of financial data I

  金融數(shù)據(jù)解讀與呈現(xiàn)II Interpretation and presentation of financial data II

  項(xiàng)目回顧與成果展示Program Review and Presentation

  論文輔導(dǎo) Project Deliverables Tutoring

  項(xiàng)目收獲

  7周在線小組科研學(xué)習(xí)+5周不限時(shí)論文指導(dǎo)學(xué)習(xí) 共125課時(shí)

  項(xiàng)目報(bào)告

  優(yōu)秀學(xué)員獲主導(dǎo)師Reference Letter

  EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級(jí)別索引國際會(huì)議全文投遞與發(fā)表指導(dǎo)(可用于申請(qǐng))

  結(jié)業(yè)證書

  成績(jī)單

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