Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition

by Eryk Lewinson

Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problems Purchase of the print or Kindle book includes a free eBook in the PDF format Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions. You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses. Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them. This book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems. Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.

Genres: Finance, Programming

740 pages, Paperback

Published December 30, 2022

Reviews

[Josua Naiborhu]

A concise and good read for going through every concept alongside hands-on instances on each chapter especially for the part of utilizing time series use cases in finances on how to take advantage of cross validation on time series problem namely expanding window validation and sliding window validation.

[Charles]

would have been great if the code actually worked

[Direnç]

It could have been a great book — or maybe it was. However, I got sick of the errors in the code examples. This made the book effectively useless.

[Jake Losh]

This was a very good book. Lots of very hands on and applied examples and a fantastic overview of the key concepts. You're not going to be an expert in time series modeling or ML after reading this, but you'll at least have some idea of what the current, standard toolsets are and how they can apply to solving common problems in finance and economics.