You're reading from Machine Learning for Algorithmic Trading - Second Edition

Product typeBook

Published inJul 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781839217715

Edition2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1)

Stefan Jansen

Common alpha factors implemented in TA-Lib

The TA-Lib library is widely used to perform technical analysis of financial market data by trading software developers. It includes over 150 popular indicators from multiple categories that range from overlap studies, including moving averages and Bollinger Bands, to statistic functions such as linear regression. The following table summarizes the main categories:

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Machine Learning for Algorithmic Trading - Second Edition

Published in: Jul 2020Publisher: PacktISBN-13: 9781839217715

Author (1)

Stefan Jansen

Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems. Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank. He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.
Read more about Stefan Jansen

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Function Group	# Indicators
Overlap Studies	17
Momentum Indicators	30
Volume Indicators	3
Volatility Indicators	3

You're reading from Machine Learning for Algorithmic Trading - Second Edition

Common alpha factors implemented in TA-Lib

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Author (1)

Hands-On Financial Trading with Python

Learn Algorithmic Trading

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