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Python for Finance Cookbook

You're reading from   Python for Finance Cookbook Over 50 recipes for applying modern Python libraries to financial data analysis

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789618518
Length 432 pages
Edition 1st Edition
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (12) Chapters Close

Preface 1. Financial Data and Preprocessing 2. Technical Analysis in Python FREE CHAPTER 3. Time Series Modeling 4. Multi-Factor Models 5. Modeling Volatility with GARCH Class Models 6. Monte Carlo Simulations in Finance 7. Asset Allocation in Python 8. Identifying Credit Default with Machine Learning 9. Advanced Machine Learning Models in Finance 10. Deep Learning in Finance 11. Other Books You May Enjoy

Pricing American options using Quantlib

In the previous recipe, we showed how to manually code the Longstaff-Schwartz algorithm. However, we can also use already existing frameworks for valuation of derivatives. One of the most popular ones is QuantLib. It is an open source C++ library that provides tools for the valuation of financial instruments. By using Simplified Wrapper and Interface Generator (SWIG), it is possible to use QuantLib from Python (and some other programming languages, such as R or Julia). In this recipe, we show how to price the same American put option that we priced in the Pricing American options with Least squares Monte Carlo recipe, but the library itself has many more interesting features to explore.

How to do it...

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