Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

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.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Introduction to the pandas_reader module


Via this module, users can download various economics and financial via Yahoo! Finance, Google Finance, Federal Reserve Economics Data (FRED), and Fama-French factors.

Assume that the pandas_reader module is installed. For detail on how to install this module, see the How to install a Python module section. First, let's look at the simplest example, just two lines to get IBM's trading data; see the following:

import pandas_datareader.data as web
df=web.get_data_google("ibm")

We could use a dot head and dot tail to show part of the results; see the following code:

>>> df.head()
>>> 
                  Open        High         Low       Close   Volume  
Date                                                                  
2010-01-04  131.179993  132.970001  130.850006  132.449997  6155300   
2010-01-05  131.679993  131.850006  130.100006  130.850006  6841400   
2010-01-06  130.679993  131.490005  129.809998  130.000000  5605300   
2010...