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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

Many useful applications


In this section, we discuss many issues, such as the 52-week high and low trading strategy, estimating the Roll (1984) spread, Amihud (2002) illiquidity measure, Pastor and Stambaugh (2003) liquidity measure, and CAPM, and running a Fama-French three-factor model, Fama-Macbeth regression, rolling beta, and VaR.

52-week high and low trading strategy

Some investors/researchers argue that we could adopt a 52-week high and low trading strategy by taking a long position if today's price is close to the minimum price achieved in the past 52 weeks and taking an opposite position if today's price is close to its 52-week high. The following Python program presents this 52-week's range and today's position:

from matplotlib.finance import quotes_historical_yahoo
from datetime import datetime
from dateutil.relativedelta import relativedelta
ticker='IBM'
enddate=datetime.now()
begdate=enddate-relativedelta(years=1)
p = quotes_historical_yahoo(ticker, begdate, enddate,asobject=True...
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