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Python for Finance Cookbook – Second Edition
Python for Finance Cookbook – Second Edition

Python for Finance Cookbook – Second Edition: Over 80 powerful recipes for effective financial data analysis , Second Edition

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Profile Icon Eryk Lewinson
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (36 Ratings)
Paperback Dec 2022 740 pages 2nd Edition
eBook
$39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Eryk Lewinson
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (36 Ratings)
Paperback Dec 2022 740 pages 2nd Edition
eBook
$39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Explore unique recipes for financial data processing and analysis with Python
  • Apply classical and machine learning approaches to financial time series analysis
  • Calculate various technical analysis indicators and backtest trading strategies

Description

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.

Who is this book for?

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.

What you will learn

  • Preprocess, analyze, and visualize financial data
  • Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models
  • Uncover advanced time series forecasting algorithms such as Meta's Prophet
  • Use Monte Carlo simulations for derivatives valuation and risk assessment
  • Explore volatility modeling using univariate and multivariate GARCH models
  • Investigate various approaches to asset allocation
  • Learn how to approach ML-projects using an example of default prediction
  • Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 30, 2022
Length: 740 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803243191
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Product Details

Publication date : Dec 30, 2022
Length: 740 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803243191
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(36 Ratings)
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4 star 13.9%
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Rubens C. Machado Jun 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Steven Fernandes May 22, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Financial Data Analysis with Python" is a must-read guide that efficiently uses Python libraries to process and analyze financial data. This new edition emphasizes exploratory data analysis, making data visualization more intuitive. Moreover, it introduces Streamlit, a tool for developing interactive web applications to display technical analysis results.The book is a trove of actionable instructions that boosts proficiency in financial data analysis for both personal and professional endeavors. It also wisely preempts potential issues, offering practical solutions. Briefly put, this book is a comprehensive and practical resource for anyone aiming to master Python in financial data analysis.
Amazon Verified review Amazon
Paul Gerber Feb 16, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is not only a comprehensive guide to finance but also a hands-on guide to coding in Python. The book uses various libraries such as pandas, numpy, matplotlib, and scikit-learn to demonstrate the application of Python in finance. There are other libraries I was not aware of such as yfinance and quantstats in Python.The author provides practical examples that show how to use Python to analyze financial data, from stock performance on the web, perform risk analysis, and build financial models. For example, the book covers topics such as calculating returns and volatility and visualizing financial data.Eryk uses very easy-to-understand language plus a great recipe process to help you learn and understand using Python in Finance. I especially love how he breaks each scenario into sections.How to do it...How it works...Then the bonus of wait, "There's more..."The code examples in the book are clear and concise, and the author provides detailed explanations of the concepts behind each example. This makes it easy for readers to understand the code and apply it to their own financial problems. The book provides a wealth of information and practical examples. It is a must-read for anyone looking to use Python in the field of finance and data science.
Amazon Verified review Amazon
Om S Mar 17, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The Python for Finance Cookbook is an excellent resource for financial analysts, data analysts and scientists, and Python developers who want to master financial data analysis using Python. The book provides a comprehensive overview of classical and modern approaches to financial data analysis and processing, including time series analysis, technical analysis, machine learning-based approaches, and deep learning.The book is well-structured, with clear explanations of the concepts, and includes practical examples and code snippets that enable readers to implement the techniques described in the book. The authors have also included tips on avoiding common mistakes and pitfalls, which is particularly helpful for those new to financial data analysis.One of the strengths of the book is its emphasis on exploratory data analysis, which enables readers to better understand financial data and draw meaningful conclusions from it. The use of Streamlit to create elegant, interactive web applications to present the results of technical analyses is also a useful feature of the book.Overall, the Python for Finance Cookbook is an excellent resource for anyone who wants to master financial data analysis using Python. The book is comprehensive, well-structured, and includes practical examples that enable readers to apply the techniques described in the book to real-world problems. I highly recommend this book to anyone who wants to become proficient in financial data analysis.
Amazon Verified review Amazon
Amazon Customer Jan 17, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A practical deep dive across many financial data topics with clear examples of application to real-world problems.As the author shows, financial data is quite accessible compared to many other domains which makes it a great playground to develop better methodological competence for both financially and non-financially focused readers.
Amazon Verified review Amazon
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