Python for Algorithmic Trading: From Idea to Cloud Deployment 160191

 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 efficient data analytics (with pandas, for example), to apply machine learning to stock market prediction (with scikit-learn, for example), or even to make use of Google’s deep learning technology with TensorFlow.

This is a book about Python for algorithmic trading, primarily in the context of alpha generating strategies (see Chapter 1). 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: 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).

Backtesting: 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.

Real-time data: 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.

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

Automation: 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.

 This book is for students, academics, and practitioners alike who want to apply Python in the fascinating field of algorithmic trading. The book assumes that the reader has, at least on a fundamental level, background knowledge in both Python programming and in financial trading. For reference and review, the Appendix A introduces important Python, NumPy, matplotlib, and pandas topics. The following are good references to get a sound understanding of the Python topics important for this book.