Investment Risk and Return Analysis In Python

This book is designed for finance students and investment professionals who want a practical understanding of how to evaluate investment risks and returns using Python.

Starting from the foundational concepts of risk and return, the book walks you through step-by-step calculations using real stock data. You will learn how to measure different types of returns, understand the statistical properties of return distributions, and apply formal normality tests — all using Python’s scientific computing ecosystem.

What You Will Learn

• Calculate simple (discrete) and logarithmic returns from stock price data
• Visualise the distribution of returns using histograms
• Understand and compute the four moments of a distribution: mean, variance, skewness, and kurtosis
• Test whether financial returns follow a normal distribution using the Shapiro-Wilk test
• Apply these techniques to real stock data using pandas, numpy, scipy, matplotlib, and seaborn

Prerequisites

A basic familiarity with Python and introductory statistics is helpful but not required. Every concept is introduced from first principles.