Product guide

Python for Finance Cookbook, 2nd Edition

Edition: Python for Finance Cookbook is a recipe-based guide to applying Python libraries and quantitative methods to financial data. Its topics range from data preparation and visualization to forecasting, portfolio allocation, volatility models, backtesting, and machine learning. The listing says readers need working knowledge of Python, particularly pandas and NumPy, and familiarity with financial concepts.

At a glance

Covers financial data preparation, exploratory analysis, visualization, and interactive dashboards with Streamlit.
Includes classical finance topics such as CAPM, factor models, GARCH volatility modeling, Monte Carlo valuation, and asset allocation.
Addresses statistical and machine-learning time-series forecasting, including ARIMA, Prophet, and deep-learning methods.
Includes technical indicators, strategy backtesting, and a machine-learning example focused on credit default prediction.
The listing states that print or Kindle purchase includes a free PDF eBook.

Price

$37.98Save 24%

Reference price: ~~$49.99~~

Price checked: Nov 14, 2026

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Pros and cons

Pros

Broad coverage connects data handling and visualization with quantitative finance and machine-learning applications.
Recipe-based topics include practical workflows such as forecasting, backtesting, and interactive dashboards.
Customer feedback describes the explanations as detailed and the content as useful for study and work.

Cons

The book is not positioned for Python beginners. The listing says working knowledge of Python, especially pandas and NumPy, is necessary.
The intended reader should already be familiar with financial concepts, which may make the material less suitable as a first finance text.

Technical specifications

9 specifications

Book details
Publisher	Packt Publishing
Publication date	December 30, 2022
Edition	2nd ed.
Language	English
Print length	740 pages
ISBN-10	1803243198
ISBN-13	978-1803243191

Dimensions and installation
Weight listed	2.75 pounds
Dimensions	7.5 x 1.67 x 9.25 inches

Who is it for?

A good fit for

Financial analysts and data analysts or scientists who already know Python and want finance-focused examples.
Python developers with familiarity with financial concepts who want recipes covering time series, econometrics, machine learning, or trading strategy analysis.
Readers seeking a substantial reference across several financial data workflows rather than an introduction to Python.

Less suited to

Readers looking for a beginner-level introduction to Python or its core data libraries.
Readers without familiarity with financial concepts who need foundational finance instruction.

Customer reviews on Amazon

4.2 / 5 · 81 ratings

5 stars58%58%
4 stars27%27%
3 stars5%5%
2 stars1%1%
1 star9%9%

Customer feedback highlights informative, detailed explanations and content considered useful for both study and work, with some customers calling it a reference worth keeping. The aggregate ratings are mostly in the upper star categories, though a smaller one-star share shows that experiences are not uniformly positive. This feedback may help readers gauge the book's perceived usefulness, but does not establish how well its advanced Python and finance material will fit an individual reader's background.

Positive themes

Customers describe the content as informative and useful for study and work.
Feedback praises detailed explanations and considers the book a helpful reference.

Frequently asked questions

What experience does the listing recommend?

It says working knowledge of Python is necessary, particularly pandas and NumPy, and describes the intended audience as familiar with financial concepts.

What topics does the book cover?

Listed subjects include financial data preparation and visualization, time-series forecasting, CAPM and factor models, GARCH, Monte Carlo simulations, asset allocation, backtesting, and machine learning.

Does the listing mention a digital copy?

It says purchasing the print or Kindle book includes a free eBook in PDF format.

Is this an introductory Python book?

No. The listing expects working knowledge of Python and its data libraries, so readers new to Python may need a separate foundation first.