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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 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

The put-call ratio

The put-call ratio represents the perception of investors jointly towards the future. If there is no obvious trend, that is, we expect a normal future, then the put-call ratio should be close to one. On the other hand, if we expect a much brighter future, the ratio should be lower than one. The following code shows a ratio of this type over the years. First, we have to download the data from CBOE. Perform the following steps:

  1. Go to http://www.cboe.com/.
  2. Click on Quotes & Data on the menu bar.
  3. Click on CBOE Volume & Put/Call Ratios.
  4. Click on CBOE Total Exchange Volume and Put/Call Ratios (11-01-2006 to present) under Current.

Assume that the file named totalpc.csv is saved under C:\temp\. The code is given as follows:

import pandas as pd
from matplotlib.pyplot import *
data=pd.read_csv('c:/temp/totalpc.csv',skiprows=2,index_col=0,parse_dates=True)
data.columns=('Calls','Puts','Total','Ratio')
x=data.index
y=data.Ratio...
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