In the previous chapter, we've discovered, how to compute portfolios **with minimal risk** and **with the best ratio of expected return and corresponding risk**.

Now, you are going to **compute them on your own**.

First of all, in this chapter, you are going to compute **GMV** portfolio, which provides **the lowest risk**.

This course will help those who have knowledge of Python and Probability Theory and want to get some basic knowledge of Portfolio Management, including fundamental financial concepts like risk and return, basics of portfolio optimization and influence of factors on portfolio's behaviour.

To start, we'll explore some fundamental financial concepts that will be essential for the rest of the course. These include investing, diversification, portfolio, return and risk. We'll also learn how to use Python to work with them.

Now that we've covered the basics of portfolio analysis, it's time to explore methods for choosing optimal weights for our portfolio. Specifically, we'll discuss fundamental concepts in portfolio optimization, including Modern Portfolio Theory and its key component - the Efficient Frontier. Additionally, we'll learn how to balance high returns with low risk, and how to determine the weights for a portfolios that achieves the best possible return-risk trade-off or the lowest risk, all using Python.

Finally, after covering the fundamentals of portfolio analysis and optimization, we’re ready to dive into predicting our portfolio's behavior and understanding the impact of various factors on it. Practically, we'll explore the basics of Factor Analysis, including different types of factors and several models built around them.

Introduction to Portfolio Management with Python

Challenge: Computing GMV Portfolio

Solution

Challenge: Computing GMV Portfolio

Solution