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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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Toc

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

Converting the interest rates


Assume that bank A offers 5 percent compounding monthly, while bank B offers 5.1 percent compounding quarterly. Which bank should we borrow from in order to enjoy a lower interest rate? These examples are associated with conversion between different interest rates. First, let's look at the following formula used to estimate effective annual rate (EAR) for a given Annual Percentage Rate (APR).

Here, m is the compounding frequency within one year. For example, if the annual rate is 5 percent compounding semiannually, its equivalent effective annual rate will be 5.0625 percent. From the two banks' offers, we would choose the offer of bank A since the cost of borrowing (effective annual rate) is cheaper, as shown in the following code:

>>>(1+0.05/2)**2-1
0.05062499999999992
>>>(1+0.051/4)**4-1
0.051983692114066615

For a mortgage estimate, if the annual rate is 5 percent, compounding monthly, the effective monthly rate will be 0.41667 (0.05/12)....

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