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.
Этот курс входит в специализацию ''Специализация Investment Management with Python and Machine Learning'
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Специализация Investment Management with Python and Machine LearningШкола бизнеса EDHECОб этом курсе
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 1 из 4 в программе
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Прибл. 24 часа на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
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
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 1 из 4 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Прибл. 24 часа на выполнение
Английский
Субтитры: Английский
от партнера
Школа бизнеса EDHEC
Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .
Программа курса: что вы изучите
5 ч. на завершение
Analysing returns
5 ч. на завершение
14 видео ((всего 225 мин.)), 5 материалов для самостоятельного изучения, 1 тест
14 видео
Welcome video3мин
Installing Anaconda3мин
Fundamentals of Returns10мин
Lab Session-Basics of returns29мин
Measures of Risk and Reward9мин
Lab Session-Risk Adjusted returns28мин
Measuring Max Drawdown10мин
Lab Session-Drawdown30мин
Deviations from Normality9мин
Lab Session-Building your own modules12мин
Downside risk measures8мин
Lab Session-Deviations from Normality30мин
Estimating VaR10мин
Lab Session-Semi Deviation, VAR and CVAR27мин
5 материалов для самостоятельного изучения
Material at your disposal5мин
Material for the Lab Sessions10мин
Module 1- Key points2мин
INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO10мин
Before the Quiz10мин
1 практическое упражнение
Module 1 Graded Quiz1ч
4 ч. на завершение
An Introduction to Portfolio Optimization
4 ч. на завершение
10 видео ((всего 172 мин.)), 1 материал для самостоятельного изучения, 1 тест
10 видео
Lab Session-Efficient frontier-Part 123мин
Markowitz Optimization and the Efficient Frontier9мин
Applying quadprog to draw the efficient Frontier11мин
Lab Session-Asset Efficient Frontier-Part 220мин
Lab Session-Applying Quadprog to Draw the Efficient Frontier38мин
Fund Separation Theorem and the Capital Market Line7мин
Lab Session-Locating the Max Sharpe Ratio Portfolio25мин
Lack of robustness of Markowitz analysis5мин
Lab Session-Plotting EW and GMV on the Efficient Frontier20мин
1 материал для самостоятельного изучения
Module 2 - Key points2мин
1 практическое упражнение
Module 2 Graded Quiz1ч
5 ч. на завершение
Beyond Diversification
5 ч. на завершение
15 видео ((всего 236 мин.)), 4 материалов для самостоятельного изучения, 1 тест
15 видео
Lab session- Limits of Diversification-Part119мин
Lab session-Limits of diversification-Part 222мин
An introduction to CPPI - Part 17мин
An introduction to CPPI - Part 210мин
Lab session-CPPI and Drawdown Constraints-Part129мин
Lab session-CPPI and Drawdown Constraints-Part228мин
Simulating asset returns with random walks10мин
Monte Carlo Simulation6мин
Lab Session-Random Walks and Monte Carlo22мин
Analyzing CPPI strategies11мин
Lab Session-Installing IPYWIDGETS5мин
Designing and calibrating CPPI strategies12мин
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part119мин
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part221мин
4 материала для самостоятельного изучения
Module 3 - Key points2мин
ipywidgets installation - info5мин
gbm function10мин
Instruction prior to begin the module 3 graded quizz10мин
1 практическое упражнение
Module 3 Graded Quiz45мин
9 ч. на завершение
Introduction to Asset-Liability Management
9 ч. на завершение
12 видео ((всего 327 мин.)), 5 материалов для самостоятельного изучения, 1 тест
12 видео
Lab Session-Present Values,liabilities and funding ratio22мин
Liability hedging portfolios12мин
Lab Session-CIR Model and cash vs ZC bonds1ч 8мин
Liability-driven investing (LDI)10мин
Lab Session-Liability driven investing51мин
Choosing the policy portfolio14мин
Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR33мин
Beyond LDI11мин
Lab Session-Naive risk budgeting between the PSP & GHP44мин
Liability-friendly equity portfolios10мин
Lab Session-Dynamic risk budgeting between PSP & LHP40мин
5 материалов для самостоятельного изучения
Module 4 - Key points2мин
Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?1ч 30мин
Liability-Driven-Investing1ч
Instruction prior to begin module 4 graded quizz2мин
To be continued (1)5мин
1 практическое упражнение
Module 4 Graded Quiz1ч
Рецензии
Лучшие отзывы о курсе INTRODUCTION TO PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON
от партнера DH26 мая 2020 г.
Enjoyable course. One has to be conversant with basic Phyton to follow this course. What I learnt the most is the ability to use Phyton coding to demonstrate the concept of portfolio investment.
от партнера SK8 дек. 2019 г.
The course is great, I enjoyed very detailed Python labs, they taught me a lot. But almost no support from the staff in the forums. If you don't understand something, you are on your own.
от партнера TT26 дек. 2019 г.
Very useful course that covered some simple investment strategies and taught you to implement it using python. Recommended to those who want to enter the quantitative investment world.
от партнера XX7 мар. 2020 г.
One of the most hardcore, challenging but knowledge-harvesting courses that I ever had. Highly recommend, and you will be better off if you type in all codes VJ did simultaneously.
Специализация Investment Management with Python and Machine Learning: общие сведения
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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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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