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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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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 (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH Index

Extracting output data

In this section, we'll be discussing different ways to extract our output data to different file formats.

Outputting data to text files

The following code will download the daily price data for Microsoft and save it to a text file:

import pandas_datareader.data as getData
import re
ticker='msft'
f=open("c:/temp/msft.txt","w")
p = getData.DataReader(ticker, "google")
f.write(str(p))
f.close()

The first several saved observations are shown in the following screenshot:

Outputting data to text files

Saving our data to a .csv file

The following program first retrieves IBM price data, and then saves it as a .csv file under c:/temp:

from matplotlib.finance import quotes_historical_yahoo_ochl as getData
import csv
f=open("c:/temp/c.csv","w")

ticker='c'
begdate=(2016,1,1)
enddate=(2017,1,9)
p = getData(ticker, begdate, enddate,asobject=True,adjusted=True)

writer = csv.writer(f)
writer.writerows(p)
f.close()

In the preceding code, we rename...

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