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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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Daniele Feb 12, 2016

Amazing book with many interesting examples. Completely worth it

Amazon Verified review

Ana Isabel Bezerra Cavalcanti Oct 15, 2020

Atendeu às minhas expectativas, em termos de pesquisas atuais e futuras.

Jamie Jul 06, 2018

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.

Jaime May 21, 2014

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.

bruce Todd puls Jun 17, 2016

I love this book, well written.

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

What do you get with eBook?