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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

Cumulative standard normal distribution


In Chapter 4, 13 Lines of Python to Price a Call Option, we used 13 lines of Python codes to price a call option since we have to write our own cumulative standard normal distribution. Fortunately, the cumulative standard normal distribution is included in the submodule of SciPy. The following example shows the value of the cumulative standard normal distribution at zero:

>>>from scipy.stats import norm
>>>norm.cdf(0)
0.5

Thus, we could simplify our call option model considerably using just five lines. The following code is a typical example of the benefits we can enjoy using various modules:

from scipy import log,exp,sqrt,stats
defbs_call(S,X,T,r,sigma):
    d1=(log(S/X)+(r+sigma*sigma/2.)*T)/(sigma*sqrt(T))
    d2 = d1-sigma*sqrt(T)
    return S*stats.norm.cdf(d1)-X*exp(-r*T)*stats.norm.cdf(d2)

Now, we could use the following function by inputting a set of values:

>>>price=bs_call(40,40,1,0.03,0.2)
>>>round(price...
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