本課程是 Investment Management with Python and Machine Learning 專項課程專項課程的一部分

Vijay Vaidyanathan, PhD 另外 +1 位授課教師

提供方

Investment Management with Python and Machine Learning 專項課程北方高等商学院

課程信息

127,913 次近期查看

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. 
This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction. 

As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods.

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第 1 門課程（共 4 門）

Investment Management with Python and Machine Learning 專項課程

可靈活調整截止日期

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完成時間大約為24 小時

英語（English）

字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）

您將學到的內容有

Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques

Write custom Python code to estimate risk and return parameters

Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios

Build custom utilities in Python to test and compare portfolio strategies

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 1 門課程（共 4 門）

Investment Management with Python and Machine Learning 專項課程

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為24 小時

英語（English）

講師

授課教師評分

4.89/5 （240 個評分）

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.

CEO

32,152 個學生

2 門課程

Lionel Martellini, PhD

EDHEC-Risk Institute, Director

Finance

34,309 個學生

3 門課程

提供方

北方高等商学院

Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .

Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform. These various components make EDHEC a centre of knowledge, experience and diversity, geared to preparing new generations of managers to excel in a world subject to transformational change. EDHEC in figures: 8,600 students in academic education, 19 degree programmes ranging from bachelor to PhD level, 184 professors and researchers, 11 specialist research centres.

教學大綱 - 您將從這門課程中學到什麼

內容評分94%（2,653 個評分）

週

週 1

完成時間為 6 小時

Analysing returns

完成時間為 6 小時

14 個視頻（總計 225 分鐘）, 5 個閱讀材料, 1 個測驗

14 個視頻

Welcome video3分鐘

Installing Anaconda3分鐘

Fundamentals of Returns10分鐘

Lab Session-Basics of returns29分鐘

Measures of Risk and Reward9分鐘

Lab Session-Risk Adjusted returns28分鐘

Measuring Max Drawdown10分鐘

Lab Session-Drawdown30分鐘

Deviations from Normality9分鐘

Lab Session-Building your own modules12分鐘

Downside risk measures8分鐘

Lab Session-Deviations from Normality30分鐘

Estimating VaR10分鐘

Lab Session-Semi Deviation, VAR and CVAR27分鐘

5 個閱讀材料

Material at your disposal5分鐘

Material for the Lab Sessions10分鐘

Module 1- Key points2分鐘

INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO10分鐘

Before the Quiz10分鐘

1 個練習

Module 1 Graded Quiz1小時

週

週 2

完成時間為 4 小時

An Introduction to Portfolio Optimization

完成時間為 4 小時

10 個視頻（總計 172 分鐘）, 1 個閱讀材料, 1 個測驗

10 個視頻

The only free lunch in Finance11分鐘

Lab Session-Efficient frontier-Part 123分鐘

Markowitz Optimization and the Efficient Frontier9分鐘

Applying quadprog to draw the efficient Frontier11分鐘

Lab Session-Asset Efficient Frontier-Part 220分鐘

Lab Session-Applying Quadprog to Draw the Efficient Frontier38分鐘

Fund Separation Theorem and the Capital Market Line7分鐘

Lab Session-Locating the Max Sharpe Ratio Portfolio25分鐘

Lack of robustness of Markowitz analysis5分鐘

Lab Session-Plotting EW and GMV on the Efficient Frontier20分鐘

1 個閱讀材料

Module 2 - Key points2分鐘

1 個練習

Module 2 Graded Quiz1小時

週

週 3

完成時間為 5 小時

Beyond Diversification

完成時間為 5 小時

15 個視頻（總計 236 分鐘）, 4 個閱讀材料, 1 個測驗

15 個視頻

Limits of diversification8分鐘

Lab session- Limits of Diversification-Part119分鐘

Lab session-Limits of diversification-Part 222分鐘

An introduction to CPPI - Part 17分鐘

An introduction to CPPI - Part 210分鐘

Lab session-CPPI and Drawdown Constraints-Part129分鐘

Lab session-CPPI and Drawdown Constraints-Part228分鐘

Simulating asset returns with random walks10分鐘

Monte Carlo Simulation6分鐘

Lab Session-Random Walks and Monte Carlo22分鐘

Analyzing CPPI strategies11分鐘

Lab Session-Installing IPYWIDGETS5分鐘

Designing and calibrating CPPI strategies12分鐘

Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part119分鐘

Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part221分鐘

4 個閱讀材料

Module 3 - Key points2分鐘

ipywidgets installation - info5分鐘

gbm function10分鐘

Instruction prior to begin the module 3 graded quizz10分鐘

1 個練習

Module 3 Graded Quiz45分鐘

週

週 4

完成時間為 9 小時

Introduction to Asset-Liability Management

完成時間為 9 小時

12 個視頻（總計 327 分鐘）, 5 個閱讀材料, 1 個測驗

12 個視頻

From Asset Management to Asset-Liability Management7分鐘

Lab Session-Present Values,liabilities and funding ratio22分鐘

Liability hedging portfolios12分鐘

Lab Session-CIR Model and cash vs ZC bonds1 小時 8 分

Liability-driven investing (LDI)10分鐘

Lab Session-Liability driven investing51分鐘

Choosing the policy portfolio14分鐘

Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR33分鐘

Beyond LDI11分鐘

Lab Session-Naive risk budgeting between the PSP & GHP44分鐘

Liability-friendly equity portfolios10分鐘

Lab Session-Dynamic risk budgeting between PSP & LHP40分鐘

5 個閱讀材料

Module 4 - Key points2分鐘

Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?1 小時 30 分

Liability-Driven-Investing1小時

Instruction prior to begin module 4 graded quizz2分鐘

To be continued (1)5分鐘

1 個練習

Module 4 Graded Quiz1小時

關於 Investment Management with Python and Machine Learning 專項課程

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.

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Introduction to Portfolio Construction and Analysis with Python