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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

Summary

In this chapter, we discussed the Black-Scholes-Merton option model in detail. In particular, we covered the payoff and profit/loss functions and their graphical representations of call and put options; various trading strategies and their visual presentations, such as covered call, straddle, butterfly, calendar spread, normal distribution, standard normal distribution, and cumulative normal distribution; delta, gamma and other Greeks; the put-call parity; European versus American options; and the binomial tree method to price options and hedging.

In the next chapter, Python Loops and Implied Volatility, first we will discuss several types of Python loops. Then, we will explain how to find the implied volatility for a call or put option. In addition, we will explain how to download real-world option data from several public available sources. Using that data, we will estimate implied volatility, volatility skewness, and their applications.

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