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Python for Finance

You're reading from  Python for Finance

Product type Book
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Pages 408 pages
Edition 1st Edition
Languages
Author (1):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan

Table of Contents (20) Chapters

Python for Finance
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction and Installation of Python Using Python as an Ordinary Calculator Using Python as a Financial Calculator 13 Lines of Python to Price a Call Option Introduction to Modules Introduction to NumPy and SciPy Visual Finance via Matplotlib Statistical Analysis of Time Series The Black-Scholes-Merton Option Model Python Loops and Implied Volatility Monte Carlo Simulation and Options Volatility Measures and GARCH Index

The p4f module for options


In Chapter 3, Using Python as a Financial Calculator, we recommended the combining of many small Python programs as one program. In this chapter, we adopted the same strategy to combine all the programs in a big file p4f.py. For instance, the preceding Python program, that is, the bs_call() function is included. Such a collection of programs offers several benefits. First, when we use the bs_call() function, we don't have to type those five lines. To save space, we will only show a few functions included in p4f.py. For brevity, we will remove all the comments included for each function. Those comments are designed to help future users when issuing the help() function, such as help(bs_call()).

def bs_call(S,X,T,rf,sigma):
    from scipy import log,exp,sqrt,stats
    d1=(log(S/X)+(rf+sigma*sigma/2.)*T)/(sigma*sqrt(T))
    d2 = d1-sigma*sqrt(T)
    return S*stats.norm.cdf(d1)-X*exp(-rf*T)*stats.norm.cdf(d2)

The following program uses a binomial model to price a call...

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