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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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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

Generating our own module p4f

We could combine many small Python progams as one program, such as p4f.py. For instance, the preceding Python program called bs_call() function is included. Such a collection of programs offers several benefits. First, when we use the bs_call() function, we don't have to type those five lines. To save space, we only show a few functions included in p4f.py. For brevity, we remove all comments included for each function. Those comments are designed to help future users when issuing the help() function, such as help(bs_call()):

def bs_call(S,X,T,rf,sigma):
    from scipy import log,exp,sqrt,stats
    d1=(log(S/X)+(rf+sigma*sigma/2.)*T)/(sigma*sqrt(T))
    d2 = d1-sigma*sqrt(T)
    return S*stats.norm.cdf(d1)-X*exp(-rf*T)*stats.norm.cdf(d2)

def binomial_grid(n):
    import networkx as nx 
    import matplotlib.pyplot as plt 
    G=nx.Graph() 
    for i in range(0,n+1):     
        for j in range(1,i+2):         
            if i<n:             
      ...
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