Book Image

Python for Finance

By : Yuxing Yan
Book Image

Python for Finance

By: Yuxing Yan

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
13
Index

The x.sum() dot function


After x is defined as a NumPy array, we could use x.function() to conduct related operations such as x.sum() as shown in the following lines of code:

>>>import numpy as np
>>>x=np.array([1,2,3])
>>>x.sum()
6
>>>np.sum(x)
6

If x is a NumPy array, we could have other functions with the same dot format as well: x.mean(), x.min(), x.max(), x.var(), x.argmin(), x.clip(), x.copy(), x.diagonal(), x.reshape(), x.tolist(), x.fill(), x.transpose(), x.flatten(), and x.argmax(). Those dot functions are useful because of the convenience they offer. The following commands show two such examples:

>>>cashFlows=np.array([-100,30,50,100,30,40])
>>>np.min(cashFlows)  
-100
>>>np.argmax(cashFlows)
0

The np.min() function shows the minimum value, while the np.argmin() function gives the location (that is, index) of the minimum value.