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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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Python for Finance Cookbook – Second Edition

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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 : 9781803238838
Category :
Languages :

What do you get with eBook?

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Product Details

Publication date : Dec 30, 2022
Length: 740 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803238838
Category :
Languages :

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Table of Contents

17 Chapters
Acquiring Financial Data Chevron down icon Chevron up icon
Data Preprocessing Chevron down icon Chevron up icon
Visualizing Financial Time Series Chevron down icon Chevron up icon
Exploring Financial Time Series Data Chevron down icon Chevron up icon
Technical Analysis and Building Interactive Dashboards Chevron down icon Chevron up icon
Time Series Analysis and Forecasting Chevron down icon Chevron up icon
Machine Learning-Based Approaches to Time Series Forecasting Chevron down icon Chevron up icon
Multi-Factor Models Chevron down icon Chevron up icon
Modeling Volatility with GARCH Class Models Chevron down icon Chevron up icon
Monte Carlo Simulations in Finance Chevron down icon Chevron up icon
Asset Allocation Chevron down icon Chevron up icon
Backtesting Trading Strategies Chevron down icon Chevron up icon
Applied Machine Learning: Identifying Credit Default Chevron down icon Chevron up icon
Advanced Concepts for Machine Learning Projects Chevron down icon Chevron up icon
Deep Learning in Finance Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(37 Ratings)
5 star 86.5%
4 star 13.5%
3 star 0%
2 star 0%
1 star 0%
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bin Sep 11, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is very comprehensive, with useful knowledge points. It is really a highly recommended book!
Subscriber review Packt
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
David Zhang May 01, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Die 2. Version ist nochmal viel kompakter als die erste von vor 3 Jahren. Grundlegende als auch tiefgreifende Prozesse der Statistik und Programmierung werden gut erklärt dargestellt. Die fast 800 Seiten des Buches decken theoretisch mehr als nur einen ganzen Semester ab.
Amazon Verified review Amazon
Ram Seshadri Feb 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was recently given the Python for Finance cookbook to review by Packt based on my experience with Finance and ML. I have to say that this is one hell of a book!! It is one of the most comprehensive and sweeping write-ups of Python in Finance I have read. Just for starters: it’s 720 pages long.Second, it has over 15 chapters covering everything from downloading and processing Time series data to EDA to modeling and finally explaining and evaluating results.The book provides over 80 recipes for everything from ARIMA to Garch to ML to Monte Carlo. The subjects range from derivatives evaluation to asset management and Bitcoin forecasting.The book has tons and tons of code. Every page is filled with step by step instructions with code and charts and graphs. I can go and on. If there is only one book that you plan to buy for learning to apply Python to financial problems, this is probably the book to buy. Highly recommended!
Amazon Verified review Amazon
Asha Jan 19, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Having just started as a Junior Data Scientist this book was really helpful for time series analysis and forecasting. It's not for beginners you need to have some basic understanding of Python and data analysis to get the most out of this book. I don't work in the Finance industry but it was nice to learn more about financial data.
Amazon Verified review Amazon
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