Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

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 maximum price achieved in the past 52 weeks and taking an opposite position if today's price is close to its 52-week low. Let's randomly choose a day of 12/31/2016. The following Python program presents this 52-week's range and today's position:

import numpy as np
from datetime import datetime 
from dateutil.relativedelta import relativedelta 
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
#
ticker='IBM' 
enddate=datetime(2016,12,31)
#
begdate=enddate-relativedelta(years=1) 
p =getData(ticker, begdate, enddate,asobject=True, adjusted=True) 
x=p[-1] 
y=np.array(p.tolist())[:,-1] 
high=max(y) 
low=min(y) 
print(" Today, Price High Low, % from low ") 
print(x[0], x[-1], high, low, round((x[-1]-low)/(high-low)*100,2))

The corresponding output is shown as follows:

According to the 52-week...