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Introduction to Portfolio Construction and Analysis with Python

Approx. 24 hours to complete

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Course Summary

Learn how to construct a portfolio using Python with this introductory course. Gain hands-on experience in constructing portfolios and learn how to navigate the financial markets.

Key Learning Points

Learn the basics of portfolio construction using Python
Gain hands-on experience with real-world data
Understand how to navigate the financial markets

Job Positions & Salaries of people who have taken this course might have

- USA: $62,000
- India: ₹4,50,000
- Spain: €40,000
- USA: $62,000
- India: ₹4,50,000
- Spain: €40,000
- USA: $106,000
- India: ₹20,00,000
- Spain: €70,000
- USA: $62,000
- India: ₹4,50,000
- Spain: €40,000
- USA: $106,000
- India: ₹20,00,000
- Spain: €70,000
- USA: $128,000
- India: ₹15,00,000
- Spain: €65,000

Learning Outcomes

Construct a portfolio using Python
Analyze financial data and make informed investment decisions
Understand the basics of navigating the financial markets

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming language
Familiarity with financial markets and investment strategies

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures

Similar Courses

Investment Management with Python and Machine Learning
Algorithmic Trading Strategies with Python

Related Education Paths

Notable People in This Field

Warren Buffett
Ray Dalio

Related Books

Description

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.

Knowledge

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

Outline

Analysing returns
Welcome video
Installing Anaconda
Fundamentals of Returns
Lab Session-Basics of returns
Measures of Risk and Reward
Lab Session-Risk Adjusted returns
Measuring Max Drawdown
Lab Session-Drawdown
Deviations from Normality
Lab Session-Building your own modules
Downside risk measures
Lab Session-Deviations from Normality
Estimating VaR
Lab Session-Semi Deviation, VAR and CVAR
Material at your disposal
Material for the Lab Sessions
Module 1- Key points
INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO
Before the Quiz
Module 1 Graded Quiz

An Introduction to Portfolio Optimization
The only free lunch in Finance
Lab Session-Efficient frontier-Part 1
Markowitz Optimization and the Efficient Frontier
Applying quadprog to draw the efficient Frontier
Lab Session-Asset Efficient Frontier-Part 2
Lab Session-Applying Quadprog to Draw the Efficient Frontier
Fund Separation Theorem and the Capital Market Line
Lab Session-Locating the Max Sharpe Ratio Portfolio
Lack of robustness of Markowitz analysis
Lab Session-Plotting EW and GMV on the Efficient Frontier
Module 2 - Key points
Module 2 Graded Quiz

Beyond Diversification
Limits of diversification
Lab session- Limits of Diversification-Part1
Lab session-Limits of diversification-Part 2
An introduction to CPPI - Part 1
An introduction to CPPI - Part 2
Lab session-CPPI and Drawdown Constraints-Part1
Lab session-CPPI and Drawdown Constraints-Part2
Simulating asset returns with random walks
Monte Carlo Simulation
Lab Session-Random Walks and Monte Carlo
Analyzing CPPI strategies
Lab Session-Installing IPYWIDGETS
Designing and calibrating CPPI strategies
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part1
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part2
Module 3 - Key points
ipywidgets installation - info
gbm function
Instruction prior to begin the module 3 graded quizz
Module 3 Graded Quiz

Introduction to Asset-Liability Management
From Asset Management to Asset-Liability Management
Lab Session-Present Values,liabilities and funding ratio
Liability hedging portfolios
Lab Session-CIR Model and cash vs ZC bonds
Liability-driven investing (LDI)
Lab Session-Liability driven investing
Choosing the policy portfolio
Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR
Beyond LDI
Lab Session-Naive risk budgeting between the PSP & GHP
Liability-friendly equity portfolios
Lab Session-Dynamic risk budgeting between PSP & LHP
Module 4 - Key points
Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?
Liability-Driven-Investing
Instruction prior to begin module 4 graded quiz
To be continued (1)
Module 4 Graded Quiz

Summary of User Reviews

Learn how to construct a portfolio using Python with this introductory course on Coursera. Users have praised the course for its practical approach to portfolio construction and its clear explanations. Overall, the course has received positive reviews from users.

Key Aspect Users Liked About This Course

Many users have praised the practical approach to portfolio construction that the course takes.

Pros from User Reviews

Clear explanations and step-by-step instructions.
Practical approach to portfolio construction.
Good for beginners who are new to Python and portfolio construction.
Course content is well-organized and easy to follow.
Instructors are knowledgeable and helpful.

Cons from User Reviews

Some users have found the course material to be too basic.
The course can be slow-paced at times.
Not enough emphasis on advanced portfolio construction techniques.
Some users have experienced technical issues with the platform.
Course videos can be lengthy and difficult to navigate.

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