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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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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 (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures 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:

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

The results are shown as follows:

Tests of normality

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 words, we conclude that IBM's daily returns do not follow a normal distribution...

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