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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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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

Installation of NumPy and SciPy

In the previous chapter, we discussed the module dependency and how it might be difficult to install a new module because it depends on many other modules. Fortunately, several super packages, such as Anaconda and Enthought Canoy, could be used to install several (or many) modules simultaneously. In this book, Anaconda is used, since it contains both NumPy and SciPy. To install it, we have to perform the following two steps:

  1. Go to http://continuum.io/downloads.
  2. According to your machine, choose the appropriate package, such as Windows 32-bit / 280M / md5: 91a6398f63a8cc6fa3db3a1e9195b3bf.

After installation, we will see the anaconda folder in C: for the Windows system. The Python version accompanying Anaconda is 2.7. In addition to NumPy and SciPy, Anaconda contains the other three modules we plan to discuss in this book. For a complete list of modules, about 124, included in Anaconda, you can check these out at http://docs.continuum.io/anaconda/pkgs.html.

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