Neuware - Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and indivisual traders using online platforms. The tool of choice for many traders today is Python and its ecosystems of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy and sell-side institutions make heavy use of Python. Ny exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading; Learn how to retrieve financial data from public and proprietary data sources; Explore vectorization for financial analytics with NumPy and pandas; Master vectorized backtesting of different algorithmic trading strategies; Generate market predictions by using machine learning and deep learning; Tackle real-time processing of streaming data with socket programming tools; Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms.

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Detalles bibliogr�ficos

T�tulo: Python for Algorithmic Trading : From Idea to Cloud Deployment
Editor: O'reilly Media Jan 2021
Fecha de publicaci�n: 2021
Idioma: Ingl�s
ISBN 10: 149205335X
ISBN 13: 9781492053354
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading.

You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field.

Set up a proper Python environment for algorithmic trading
Learn how to retrieve financial data from public and proprietary data sources
Explore vectorization for financial analytics with NumPy and pandas
Master vectorized backtesting of different algorithmic trading strategies
Generate market predictions by using machine learning and deep learning
Tackle real-time processing of streaming data with socket programming tools
Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

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Dr. Yves J. Hilpisch is founder and managing partner of The Python Quants (http://tpq.io), a group that focuses on the use of open source technologies for financial data science, algorithmic trading and computational finance. He is the author of the books Python for Finance (O'Reilly, 2014), Derivatives Analytics with Python (Wiley, 2015) and Listed Volatility and Variance Derivatives (Wiley, 2017). Yves lectures on computational finance at the CQF Program (http://cqf.com), on data science at htw saar University of Applied Sciences (http://htwsaar.de), and is the director for the online training program leading to the first Python for Finance University Certificate (awarded by htw saar).

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