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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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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 (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. 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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