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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
Languages
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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 2. Using Python as an Ordinary Calculator FREE CHAPTER 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

Indentation is critical in Python


Indentation plays a vital role in Python. Let's look at an R program. Anything between a pair of curly braces belongs to the same logic block. If we have multiple lines, the indentation is not important for R programs, as shown in the following code:

pv_f<-function(fv,r,n) {   # this is an R program
    pv<-fv*(1+r)^(-n)
pv
}

To achieve the same result in Python, we use indentations instead. This means that all the lines with the same indentation belong to the same scope, as shown in the following code:

def pv_f(fv,r,n):
    pv=fv/(1+r)**n
    return pv

The following are the ways to input values:

  • In the preceding example, pv_f(100,0.1,1), we input three values, 100, 0.1, and 1. There is no ambiguity that 100 is the future value, 0.1 is the discount rate, and 1 is the number of periods since the input variables are arranged this way. This is the first way to input values into a function.

  • The second way to input values is based on key words. The advantage...

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