Yves Hilpisch - Python for Algorithmic Trading: From Idea to Cloud Deployment

by Yves Hilpisch

Copyright 2021 Yves Hilpisch. All rights reserved.

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978-1-492-05335-4

[LSI]

Yuval Noah Harari

Finding the right algorithm to automatically and successfully trade in financial markets is the holy grail in finance. Not too long ago, algorithmic trading was only available and possible for institutional players with deep pockets and lots of assets under management. Recent developments in the areas of open source, open data, cloud compute, and cloud storage, as well as online trading platforms, have leveled the playing field for smaller institutions and individual traders, making it possible to get started in this fascinating discipline while equipped only with a typical notebook or desktop computer and a reliable internet connection.

Nowadays, Python and its ecosystem of powerful packages is the technology platform of choice for algorithmic trading. Among other things, Python allows you to do efficientdata analytics (with pandas, for example), to apply machine learning to stock market prediction (with scikit-learn, for example), or even to make use of Googles deep learning technology with TensorFlow.

This is a book about Python for algorithmic trading, primarily in the context of alpha generating strategies (see). Such a book at the intersection of two vast and exciting fields can hardly cover all topics of relevance. However, it can cover a range of important meta topics in depth.

These topics include:

Financial data is at the core of every algorithmic trading project. Python and packages like NumPy and pandas do a great job of handling and working with structured financial data of any kind (end-of-day, intraday, high frequency).

There should be no automated algorithmic trading without a rigorous testing of the trading strategy to be deployed. The book covers, among other things, trading strategies based on simple moving averages, momentum, mean-reversion, and machine/deep-learning based prediction.

Algorithmic trading requires dealing with real-time data, online algorithms based on it, and visualization in real time. The book provides an introduction to socket programming with ZeroMQ and streaming visualization.

No trading can take place without a trading platform. The book covers two popular electronic trading platforms: Oanda and FXCM.

The beauty, as well as some major challenges, in algorithmic trading results from the automation of the trading operation. The book shows how to deploy Python in the cloud and how to set up an environment appropriate for automated algorithmic trading.

The book offers a unique learning experience with the following features and benefits:

This is the only book covering such a breadth and depth with regard to relevant topics in Python for algorithmic trading (see the following).

The book is accompanied by a Git repository with all codes in a self-contained, executable form. The repository is available on the Quant Platform.

The coverage of two different online trading platforms puts the reader in the position to start both paper and live trading efficiently. To this end, the book equips the reader with relevant, practical, and valuable background knowledge.

Since the material and the code are self-contained and only rely on standard Python packages, the reader has full knowledge of and full control over what is going on, how to use the code examples, how to change them, and so on. There is no need to rely on third-party platforms, for instance, to do the backtesting or to connect to the trading platforms. With this book, the reader can do all this on their own at a convenient pace and has every single line of code to do so.

Although the reader should be able to follow along seamlessly, the author and The Python Quants are there to help. The reader can post questions and comments in the user forum on the Quant Platform at any time (accounts are free).

The Python Quants offer comprehensive online training programs that make use of the contents presented in the book and that add additional content, covering important topics such as financial data science, artificial intelligence in finance, Python for Excel and databases, and additional Python tools and skills.