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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
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 (14) Chapters Close

Preface 1. Introduction and Installation of Python 2. Using Python as an Ordinary Calculator FREE CHAPTER 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

Summary

In this chapter, we introduced different types of loops. Then, we demonstrated how to estimate the implied volatility based on a European option (Black-Scholes-Merton option model) and on an American option. We discussed the for loop and the while loop, and their applications. For a given set of input values, such as current stock price, the exercise price, the time to maturity, the continuously compounded risk-free rate, and a call price (or put price), we showed how to estimate a stock's implied volatility. In terms of efficiency, we explained the binary search method and compared it with other approaches when estimating an implied volatility. In addition, we demonstrated how to download option data, such as put-call ratio, from Yahoo! Finance and the CBOE web page.

In the next chapter, we will focus on applications of Monte Carlo simulations on option pricing. Using random numbers drawn from a normal distribution, we could mimic the movements of a stock for a given set of...

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