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Python for Finance

Python for Finance

By : Yuxing Yan
3.9 (22)
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Python for Finance

Python for Finance

3.9 (22)
By: Yuxing Yan

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
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13
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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