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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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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

Understanding the time value of money

In finance, we know that $100 received today is more valuable than $100 received one year later. If we use size to represent the difference, we could have the following Python program to represent the same concept:

from matplotlib.pyplot import *
fig1 = figure(facecolor='white')
ax1 = axes(frameon=False)
ax1.set_frame_on(False)
ax1.get_xaxis().tick_bottom() 
ax1.axes.get_yaxis().set_visible(False) 
x=range(0,11,2)
x1=range(len(x),0,-1)
y = [0]*len(x);
annotate("Today's value of $100 received today",xy=(0,0),xytext=(2,0.001),arrowprops=dict(facecolor='black',shrink=0.02))
annotate("Today's value of $100 received in 2 years",xy=(2,0.00005),xytext=(3.5,0.0008),arrowprops=dict(facecolor='black',shrink=0.02))
annotate("received in 6 years",xy=(4,0.00005),xytext=(5.3,0.0006),arrowprops=dict(facecolor='black',shrink=0.02))
annotate("received in 10 years",xy=(10,-0.00005...
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