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Mastering Python for Finance. - Second Edition

You're reading from  Mastering Python for Finance. - Second Edition

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789346466
Pages 426 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
James Ma Weiming James Ma Weiming
Profile icon James Ma Weiming
LinkedIn
James Ma Weiming is a software engineer based in Singapore. His studies and research are focused on financial technology, machine learning, data sciences, and computational finance. James started his career in financial services working with treasury fixed income and foreign exchange products, and fund distribution. His interests in derivatives led him to Chicago, where he worked with veteran traders of the Chicago Board of Trade to devise high-frequency, low-latency strategies to game the market. He holds an MS degree in finance from Illinois Tech's Stuart School of Business in the United States and a bachelor's degree in computer engineering from Nanyang Technological University.
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Table of Contents (16) Chapters

Preface 1. Section 1: Getting Started with Python
2. Overview of Financial Analysis with Python 3. Section 2: Financial Concepts
4. The Importance of Linearity in Finance 5. Nonlinearity in Finance 6. Numerical Methods for Pricing Options 7. Modeling Interest Rates and Derivatives 8. Statistical Analysis of Time Series Data 9. Section 3: A Hands-On Approach
10. Interactive Financial Analytics with the VIX 11. Building an Algorithmic Trading Platform 12. Implementing a Backtesting System 13. Machine Learning for Finance 14. Deep Learning for Finance 15. Other Books You May Enjoy

The Dow Jones industrial average and its 30 components

The Dow Jones Industrial Average (DJIA) is a stock market index that comprises the 30 largest US companies. Commonly known as the Dow, it is owned by S&P Dow Jones Indices LLC and computed on a price-weighted basis (see https://us.spindices.com/index-family/us-equity/dow-jones-averages for more information on the Dow).

This section involves downloading the datasets of Dow and its components into pandas DataFrame objects for use in later sections of this chapter.

Downloading Dow component datasets from Quandl

The following code retrieves the Dow component datasets from Quandl. The data provider that we will be using is WIKI Prices, a community formed by members...

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Authors (1)

Profile icon James Ma Weiming
LinkedIn
James Ma Weiming is a software engineer based in Singapore. His studies and research are focused on financial technology, machine learning, data sciences, and computational finance. James started his career in financial services working with treasury fixed income and foreign exchange products, and fund distribution. His interests in derivatives led him to Chicago, where he worked with veteran traders of the Chicago Board of Trade to devise high-frequency, low-latency strategies to game the market. He holds an MS degree in finance from Illinois Tech's Stuart School of Business in the United States and a bachelor's degree in computer engineering from Nanyang Technological University.
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See other products by James Ma Weiming

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