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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.

Product type Paperback

Published in Apr 2014

Publisher

ISBN-13 9781783284375

Length 408 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Financial Technology

Author (1):

Yuxing Yan

View More author details

Table of Contents (14) Chapters

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

Launching Python from Anaconda

For the Window version, we locate the executable python.exe file at c:\anaconda and then click on it. The following Python window will appear:

To test whether we have correctly installed NumPy and SciPY, we need to type the following two commands to import them. If there is no error, it means that we have installed them correctly.

>>>import numpy as np
>>>import scipy as sp

In the last several chapters, we know that we could use from numpy import * instead of import numpy as np to make all functions included in the NumPy module into our namespace. However, most developers prefer to use import numpy as np. From now on, we will follow this tradition. The second reason is that using sp.pv() instead of pv() makes it clearer that the pv() function is from a module called sp. To generate a Python icon on the desktop, we generate a shortcut first, and then move it from c:\anaconda to our desktop.