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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

Performance comparisons among stocks


In the following program, we compare the performance of several stocks in terms of their returns in 2013:

import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo
stocks = ('IBM', 'DELL', 'WMT', 'C', 'AAPL')
begdate=(2013,1,1)
enddate=(2013,11,30)
def ret_annual(ticker):
    x = quotes_historical_yahoo(ticker, begdate, enddate,asobject=True, adjusted=True)
    logret = log(x.aclose[1:]/x.aclose[:-1])
    return(exp(sum(logret))-1)
performance = []
for ticker in stocks:
    performance.append(ret_annual(ticker))
y_pos = np.arange(len(stocks))
plt.barh(y_pos, performance, left=0, alpha=0.3)
plt.yticks(y_pos, stocks)
plt.xlabel('Annual returns ')
plt.title('Performance comparisons (annual return)')
plt.show()

The related bar chart is shown in the following figure:

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