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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 Yahoo Finance

One of the most popular sources of free financial data is Yahoo Finance. It contains not only historical and current stock prices in different frequencies (daily, weekly, monthly), but also calculated metrics, such as the beta (a measure of the volatility of an individual asset in comparison to the volatility of the entire market) and many more. In this recipe, we focus on retrieving historical stock prices.

For a long period of time, the go-to tool for downloading data from Yahoo Finance was the pandas-datareader library. The goal of the library was to extract data from a variety of sources and store it in the form of a pandas DataFrame. However, after some changes to the Yahoo Finance API, this functionality was deprecated. It is still good to be familiar with this library, as it facilitates downloading data from sources such as FRED (Federal...

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