Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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.

Arrow left icon
Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
Arrow right icon
View More author details
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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime