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Mastering Python for Finance

You're reading from   Mastering Python for Finance Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784394516
Length 340 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Python for Financial Applications FREE CHAPTER 2. The Importance of Linearity in Finance 3. Nonlinearity in Finance 4. Numerical Procedures 5. Interest Rates and Derivatives 6. Interactive Financial Analytics with Python and VSTOXX 7. Big Data with Python 8. Algorithmic Trading 9. Backtesting 10. Excel with Python Index

Merging the data


Since the earliest dates in the text files are 31.12.1986 and 04.01.1999 for the STOXX Europe 600 and VSTOXX data file respectively, we will require both the datasets to begin from a common date at 04.01.1999. We will also use values from the SX5E and V2TX columns to retrieve our EURO STOXX 50 Index and VSTOXX historical data values. The following Python code extracts these values into a new Pandas DataFrame object:

import datetime as dt

cutoff_date = dt.datetime(1999, 1, 4)
data = pd.DataFrame(
{'EUROSTOXX' :stoxxeu600['SX5E'][stoxxeu600.index >= cutoff_date],
 'VSTOXX':vstoxx['V2TX'][vstoxx.index >= cutoff_date]})

Now, let's take a look at our DataFrame information:

>>> print data.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4072 entries, 1999-01-04 00:00:00 to 2014-11-18 00:00:00
Data columns (total 2 columns):
EUROSTOXX    4071 non-null float64
VSTOXX       4046 non-null float64
dtypes: float64(2)

Also, let's take a look at the top...

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