Об этом курсе

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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.
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Прибл. 24 часа на выполнение
Английский
Субтитры: Английский

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Логотип Школа бизнеса EDHEC

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Программа курса: что вы изучите

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Неделя
1

Неделя 1

5 ч. на завершение

Analysing returns

5 ч. на завершение
14 видео ((всего 225 мин.)), 5 материалов для самостоятельного изучения, 1 тест
14 видео
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 Quiz
Неделя
2

Неделя 2

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 Quiz
Неделя
3

Неделя 3

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
Неделя
4

Неделя 4

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-Investing
Instruction prior to begin module 4 graded quizz2мин
To be continued (1)5мин
1 практическое упражнение
Module 4 Graded Quiz

Рецензии

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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....
Investment Management with Python and Machine Learning

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