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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

Normal distribution, standard normal distribution, and cumulative standard normal distribution

In finance, normal distribution plays a central role. This is especially true for option theory. The major reason is that it is commonly assumed that the stock prices follow a log normal distribution while the stock returns follow a normal distribution. The density of a normal distribution is defined as follows:

Normal distribution, standard normal distribution, and cumulative standard normal distribution

Here, μ is the mean and σ is the standard deviation.

By setting μ as 0 and σ as 1, the preceding general normal distribution density function collapses to the following standard normal distribution:

Normal distribution, standard normal distribution, and cumulative standard normal distribution

The following code generates a graph for the standard normal distribution. The SciPy's stats.norm.pdf() function is used for the standard normal distribution. The default setting is with a zero mean and unit standard deviation, that is, the standard normal density function:

>>>from scipy import exp,sqrt,stats
>>>stats.norm.pdf(0)
0.3989422804014327...
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