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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

Exercises

1. Write a Python program to price a call option.

2. Explain the empty shell method that is used while writing a complex Python program.

3. Explain the logic behind the so-called comment-all-out method when writing a complex Python program.

4. Explain the usage of a return value when we debug a program.

5. When we write the CND, we could define a1, a2, a3, a4, and a5 separately. What are the differences between the following two approaches?

Current approach:

(a1,a2,a3,a4,a5)=(0.31938153,-0.356563782,1.781477937,-1.821255978,1.330274429)

An alternative approach:

a1=0.31938153
a2=-0.356563782
a3=1.781477937
a4=-1.821255978
a5=1.330274429

6. What are the definitions of effective annual rate, effect semi-annual rate, and risk-free rate for the call option model? Assuming that the current annual risk-free rate is 5 percent, compounded semi-annually, which value should we use as our input value for the Black-Scholes call option model?

7. What is the call premium when the stock is traded at...

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