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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Toc

Table of Contents (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH Index

Tests of normality

The Shapiro-Wilk test is a normality test. The following Python program verifies whether IBM's returns are following a normal distribution. The last five-year daily data from Yahoo! Finance is used for the test. The null hypothesis is that IBM's daily returns are drawn from a normal distribution:

import numpy as np
from scipy import stats
from matplotlib.finance import quotes_historical_yahoo_ochl as getData 
#
ticker='IBM' 
begdate=(2009,1,1) 
enddate=(2013,12,31)
p =getData(ticker, begdate, enddate,asobject=True, adjusted=True)
ret = p.aclose[1:]/p.aclose[:-1]-1
#
print('ticker=',ticker,'W-test, and P-value') 
print(stats.shapiro(ret))
 ('ticker=', 'IBM', 'W-test, and P-value')
(0.9295020699501038, 7.266549629954468e-24)

The first value of the result is the test statistic, and the second one is its corresponding P-value. Since this P-value is so close to zero, we reject the null hypothesis. In other...

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