Python for Algorithmic Trading
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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).
