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

Amazon.com Price:  $34.97 (as of 01/03/2019 04:19 PST- Details)

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Description

Learn and implement more than a few Quantitative Finance concepts the usage of the popular Python libraries

About This Book

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

Who This Book Is For

This book assumes that the readers have some basic knowledge related to Python. Alternatively, 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 couple of 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

In Detail

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 more than a few finance subjects. In other words, the first edition focuses more on Python, even as the second edition is really 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 more than a few 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 be able to 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.

Style and approach

This book takes a step by step approach in explaining the libraries and modules in Python, and how they may be able to be used to implement more than a few aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.

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