- Publisher : O'Reilly Media; 1st edition (December 8, 2020)
- Language : English
- Paperback : 380 pages
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.