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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

Activating our function using the import function

In the previous chapter, we learned that we could issue the import math command to upload the math module in order to use its included functions. Similarly, we can use the import function here. In other words, we have to upload or import it. Since we have the test01. py file saved under our default directory (Python33 in C:), we will use it, as shown in the following code:

>>>import test01
>>>dir()
['__builtins__', '__doc__', '__file__', '__loader__', '__name__', '__package__', 'test01']
>>>test01.pv_f(100,0.1,1)
90.9090909090909

Since test01 could be treated the same way as the math module discussed in Chapter 2, Using Python as an Ordinary Calculator, we have to use test01.pv() instead of pv_f(). See the following comparison. The ceil() function offers the smallest integer that is bigger than the input value:

>>>import math
>>...
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