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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

Credit rating

Nowadays, there are three major credit ratings agents in the USA: Moody's, Standard, and Poor's and Fitch. Their websites are http://www.moodys.com/, http://www.standardandpoors.com/en_US/web/guest/home, and https://www.fitchratings.com/site/home. Although their ratings have different notations (letters), it is easy to translate one letter rating from a rating agency to another one. Based on the following link at http://www.quadcapital.com/Rating%20Agency%20Credit%20Ratings.pdf, a dataset called creditRatigs3.pkl is generated, which can be downloaded at the author's website, http://canisius.edu/~yany/python/creditRatings3.pkl. Assume that it is located under C:/temp/.

The following codes show its contents:

import pandas as pd
x=pd.read_pickle("c:/temp/creditRatings3.pkl")
print(x)
       Moody's S&P Fitch  NAIC  InvestmentGrade
0      Aaa   AAA   AAA     1                1
1      Aa1   AA+   AA+     1                1
2      Aa2    AA    AA...
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