Python has been hot for years and it’s a good tool for financial analysis. This book covers the topic of finance using Python.

The book is divided into 15 chapters. In chapters 1 and 2, it covers basic Python (Python 3 specifically) like setting up the development environment for loops and modules, including NumPy, SciPy, and Matplotlib. In chapter 3, it introduces basic concepts and formulas associated with finance. Chapter 4 shows how to get data from web sources like Yahoo or Google. Chapter 5 is about interest rates. Chapter 6 presents how to run a linear regression. Chapter 7 focuses on the capital asset pricing model (CAPM), extending from chapter 6. Chapter 8 discusses time-series analysis, and chapter 9 discusses mean and risk estimation of the portfolio. Options and futures are discussed in chapter 10, and chapter 11 focuses on estimating the value at risk (VaR). In chapter 12, the author presents Monte Carlo simulation. Chapter 13 works with credit risk analysis. Chapter 14 presents different option models compared to chapter 9, while chapter 15 is on volatility.

The contents in the book are presented clearly. I’m a computer science person. Reading through this book, I have two feelings: (1) The first two chapters covering Python are not deep enough for a Python beginner. Readers unfamiliar or beginning with Python will need other books or online tutorials; and (2) I think I need a finance book to get a better understand of the formulas and models.

Personally, I think the coverage in this book is extensive, but the source code needs some work since it is not updated. Readers can find the source code online. However, some of it uses application programming interfaces (APIs) that are discontinued, for example, quotes_historical_yahoo_ochl(). At times, readers will have to search online if the source code doesn’t work well.

Overall, I think this is a helpful book for readers who have some Python programming experience and an understanding of finance concepts.

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