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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

Getting data from Quandl

Quandl is a provider of alternative data products for investment professionals, and offers an easy way to download data, also via a Python library.

A good starting place for financial data would be the WIKI Prices database, which contains stock prices, dividends, and splits for 3,000 US publicly traded companies. The drawback of this database is that as of April, 2018, it is no longer supported (meaning there is no recent data). However, for purposes of getting historical data or learning how to access the databases, it is more than enough.

We use the same example that we used in the previous recipe – we download Apple's stock prices for the years 2000-2010.

Getting ready

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