Introduction to Portfolio Risk Management in Python Course

Name: Introduction to Portfolio Risk Management in Python
Rating: 4.853968253968254 (315 reviews)

Introduction to Portfolio Risk Management in Python

IntermediateSkill Level

4.8+

Updated 04/2026

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

Course Description

This course will teach you how to evaluate basic portfolio risk and returns like a quantitative analyst on Wall Street. This is the most critical step towards being able to fully automate your portfolio construction and management processes. Discover what factors are driving your portfolio returns, construct market-cap weighted equity portfolios, and learn how to forecast and hedge market risk via scenario generation.

Prerequisites

Introduction to Financial Concepts in Python Manipulating Time Series Data in Python

Univariate Investment Risk and Returns

Learn about the fundamentals of investment risk and financial return distributions.

Financial returns

50 XP

Financial timeseries data

100 XP

Calculating financial returns

100 XP

Return distributions

100 XP

Mean, variance, and normal distribution

50 XP

First moment: Mu

100 XP

Second moment: Variance

100 XP

Annualizing variance

100 XP

Skewness and kurtosis

50 XP

Third moment: Skewness

100 XP

Fourth moment: Kurtosis

100 XP

Statistical tests for normality

100 XP

Start Chapter

Portfolio Investing

Level up your understanding of investing by constructing portfolios of assets to enhance your risk-adjusted returns.

Portfolio composition and backtesting

50 XP

Calculating portfolio returns

100 XP

Equal weighted portfolios

100 XP

Market-cap weighted portfolios

100 XP

Correlation and co-variance

50 XP

The correlation matrix

100 XP

The co-variance matrix

100 XP

Portfolio standard deviation

100 XP

Markowitz portfolios

50 XP

The efficient frontier

50 XP

Sharpe ratios

100 XP

The MSR portfolio

100 XP

The GMV portfolio

100 XP

Start Chapter

Factor Investing

Learn about the main factors that influence the returns of your portfolios and how to quantify your portfolio's exposure to these factors.

The Capital Asset Pricing model

50 XP

Excess returns

100 XP

Calculating beta using co-variance

100 XP

Calculating beta with CAPM

100 XP

Adjusted R-squared

50 XP

Alpha and multi-factor models

50 XP

The Fama French 3-factor model

100 XP

p-values and coefficients

100 XP

Economic intuition in factor modeling

50 XP

The efficient market and alpha

100 XP

Expanding the 3-factor model

50 XP

The 5-factor model

100 XP

Alpha vs R-squared

50 XP

Start Chapter

Value at Risk

In this chapter, you will learn two different methods to estimate the probability of sustaining losses and the expected values of those losses for a given asset or portfolio of assets.

Estimating tail risk

50 XP

Historical drawdown

100 XP

Historical value at risk

100 XP

Historical expected shortfall

100 XP

VaR extensions

50 XP

Changing VaR and CVaR quantiles

100 XP

Parametric VaR

100 XP

Scaling risk estimates

100 XP

Random walks

50 XP

A random walk simulation

100 XP

Monte Carlo simulations

100 XP

Monte Carlo VaR

100 XP

Understanding risk

50 XP

Start Chapter

Introduction to Portfolio Risk Management in Python

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Don’t just take our word for it

*4.8

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Antoine

9 hours ago

Anh

10 hours ago

The phrase "CAPM model" is not accurate, as M in CAPM stands for "model", so it's usually referred to as the CAPM. The writing on the slides about Cvar is not accurate, though the explanation is correct. 2.5% is not exceeding, but an average expected loss once VaR is exceeded. The concept of running max in drawdown could be explained better. Drawdown formula could be linked to the more common (Peak - Trough)/Peak formula. Sorted function seems to be phasing out for Pandas DataFrame, so .sort_values could be used.

Fabian

4 days ago

Thuy Ly

5 days ago

6 days ago

Juan Fernando

last week

Antoine

Fabian

Thuy Ly

FAQs

Is this course suitable for beginners?

Yes, this course is suitable for beginners. It covers the fundamentals of investment risk and returns, portfolio investing, factor investing and Value at Risk, along with interactive coding challenges.

Will I receive a certificate at the end of the course?

Yes, upon completion of this course, you will receive a Certificate of Completion from DataCamp.

Who will benefit from this course?

This course is perfect for professionals in the finance and investment industry, such as financial analysts, portfolio managers, and other positions where understanding and managing financial risk is important.

What techniques will I learn in this course?

During this course you will learn various techniques related to portfolio risk management, such as constructing market-cap weighted equity portfolios, estimating probability of sustaining losses and expected value of losses for a given asset, and forecasting and hedging market risk via scenario generation.

What is Value at Risk?

Value at Risk (VaR) is a statistical measure used to estimate the probability of a portfolio suffering losses over a certain period of time. VaR measure is often used to assess the amount of risk in a portfolio and it is typically expressed in terms of a percentage of the portfolio value.

Introduction to Portfolio Risk Management in Python

Training a Team?

Course Description

Prerequisites

Univariate Investment Risk and Returns

Portfolio Investing

Factor Investing

Value at Risk

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Is this course suitable for beginners?

Will I receive a certificate at the end of the course?

Who will benefit from this course?

What techniques will I learn in this course?

What is Value at Risk?

Join over 19 million learners and start Introduction to Portfolio Risk Management in Python today!

Grow your data skills with DataCamp for Mobile

Course Description

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Will I receive a certificate at the end of the course?

Who will benefit from this course?

What techniques will I learn in this course?

What is Value at Risk?

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