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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

Logic relationships related to an array


An array could contain true and false as shown in the following lines of code. This data type is called Boolean.

>>>import numpy as np
>>>x=np.array([True,Talse,True,False],bool)
>>>a=any(x)  	# if one item is TRUE then return TRUE
>>>b=all(x)  	# if all are TRUE then return TRUE
>>>cashFlows=np.array([-100,50,40,30,100,-5])
>>>a=cashFlows>0  # [False,True,True,True,True,False]
>>>np.logical_and(cashFlows>0, cashFlows<60)
Array([False,True,True,False,False],dtype=bool)

The logical_and(), logical_or(), and logical_not() functions could be used to compare each data item included in an array as shown in the previous code example. In addition, we could save the index or subscripts of the logic comparison and call the array later as shown in the following lines of code:

>>>cashFlows=np.array([-100,50,40,30,100,-5])
>>>index=(cashFlows>0) # index is a Boolean variable...
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