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Python for Finance
Python for Finance

Python for Finance: Apply powerful finance models and quantitative analysis with Python , Second Edition

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Python for Finance

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Key benefits

  • Understand the fundamentals of Python data structures and work with time-series data
  • Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib
  • A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance

Description

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.

Who is this book for?

This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data.

What you will learn

  • Become acquainted with Python in the first two chapters
  • Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models
  • Learn how to price a call, put, and several exotic options
  • Understand Monte Carlo simulation, how to write a Python program to
  • replicate the Black-Scholes-Merton options model, and how to price a few
  • exotic options
  • Understand the concept of volatility and how to test the hypothesis that
  • volatility changes over the years
  • Understand the ARCH and GARCH processes and how to write related
  • Python programs

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 30, 2017
Length: 586 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787125698
Category :
Languages :
Concepts :
Tools :

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Product Details

Publication date : Jun 30, 2017
Length: 586 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787125698
Category :
Languages :
Concepts :
Tools :

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Table of Contents

16 Chapters
1. Python Basics Chevron down icon Chevron up icon
2. Introduction to Python Modules Chevron down icon Chevron up icon
3. Time Value of Money Chevron down icon Chevron up icon
4. Sources of Data Chevron down icon Chevron up icon
5. Bond and Stock Valuation Chevron down icon Chevron up icon
6. Capital Asset Pricing Model Chevron down icon Chevron up icon
7. Multifactor Models and Performance Measures Chevron down icon Chevron up icon
8. Time-Series Analysis Chevron down icon Chevron up icon
9. Portfolio Theory Chevron down icon Chevron up icon
10. Options and Futures Chevron down icon Chevron up icon
11. Value at Risk Chevron down icon Chevron up icon
12. Monte Carlo Simulation Chevron down icon Chevron up icon
13. Credit Risk Analysis Chevron down icon Chevron up icon
14. Exotic Options Chevron down icon Chevron up icon
15. Volatility, Implied Volatility, ARCH, and GARCH 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 Half star icon Empty star icon 3.5
(33 Ratings)
5 star 39.4%
4 star 15.2%
3 star 15.2%
2 star 15.2%
1 star 15.2%
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Top Reviews

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N/A Feb 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent ! Can be a good textbook.
Feefo Verified review Feefo
Daniele Feb 12, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Amazing book with many interesting examples. Completely worth it
Amazon Verified review Amazon
Ana Isabel Bezerra Cavalcanti Oct 15, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Atendeu às minhas expectativas, em termos de pesquisas atuais e futuras.
Amazon Verified review Amazon
Jamie Jul 06, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I bought this eBook (with source code) from Packt. Great intro to Finance using Python. Chapter 10 and 12 are especially helpful as an addition to Hull's Options, Futures and Other Derivatives.
Amazon Verified review Amazon
Jaime May 21, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For the past 3 years I have been investing part of my time directing and managing SW financial implementations in parallel of providing consulting services on several industries leveraging my expertise on Data Advisory specifically data management, governance and analytics/big data.During this tenure I have implemented diverse strategies to support my teams having a quick and solid ramp up getting hands on using in several cases Python in an specific framework either for data management or solving complex financial challenges on tricky calculations.I have found this book a great overview and hands on guidance to who is in the Python learning path and has realized to focus its functionality and powerful use on problem solving in the Finance arena or Analytics for example.Taking in count Python is powerful, flexible, ad easy to learn, the author provides a good and strong basis to go from the basic use of Python calculating net present value to more complex financial problems like valuations, pricing options, Monte Carlo series usage and regressions. Even also the author shares guidance to interact with public data sources such as Yahoo! Finance or Google Finance.Last but not least the content of this book will provide guidance to create very useful graphs by using Matlotlib in a clear way.Enjoy your reading!Note: If you have no Finance background, I recommend reading this book getting access to electronic or printed dictionary or related finance terms reference.
Amazon Verified review Amazon
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