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Python for Finance

You're reading from  Python for Finance

Product type Book
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Pages 408 pages
Edition 1st Edition
Languages
Author (1):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan

Table of Contents (20) Chapters

Python for Finance
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction and Installation of Python Using Python as an Ordinary Calculator Using Python as a Financial Calculator 13 Lines of Python to Price a Call Option Introduction to Modules Introduction to NumPy and SciPy Visual Finance via Matplotlib Statistical Analysis of Time Series The Black-Scholes-Merton Option Model Python Loops and Implied Volatility Monte Carlo Simulation and Options Volatility Measures and GARCH Index

Estimation of IRR via a for loop


In the first two chapters, we learned that we could apply the Internal Rate of Return (IRR) rule to evaluate our project with a set of forecasted current and future cash flows. Based on a for loop, we could calculate the IRR of our project. The two related functions, NPV() and IRR_f(), are shown as follows:

def npv_f(rate, cashflows):
    total = 0.0
    for i, cashflow in enumerate(cashflows):
        total += cashflow / (1 + rate)**i
    return total

Here, the key is finding out what kinds of values the intermediate variables i and cashflow would take. From the previous section, we know that i will take values from 0 to the number of cash flows and that cashflow would take all values from the variable called cashflows. The total+=x statement is equivalent to total=total+x. One issue is that if we enter -1 as our rate, the function would not work. We could add an if command to prevent this from happening (refer to the succeeding solution for the IRR() function...

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