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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

Continuously compounded interest rate


In the previous section, our compounding frequency could be annual (m=1), semiannual (2), quarterly (4), monthly (12), or daily (365). If the compounding frequency increases further and further, such as by the hour, minute, and second, the limit is called continuously compounded. The following is the conversion formula:

Here, Rc is the continuously compounded rate, ln() is a natural log function, APR is the annual percentage rate, and m is the compounding frequency per year. For the natural log function, refer to the following code:

>>>import math
>>>math.e
2.718281828459045
>>>math.log(math.e)
1.0

For example, if a given APR of 5 percent is compounded semiannually, its corresponding continuously compounded rate will be 4.9385225 percent, as shown in the following code:

>>>import math
>>>2*math.log(1+0.05/2)
0.04938522518074283

In the next chapter, for a call option, the risk-free rate used is continuously...

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