Books

A short book outlining the key Python libraries used for backtesting of financial trading algos, along with fourteen implemented profitable strategies. Check this Jupyter Notebook for yourself. Read introduction of the book.

Primary Target audience:

• Anybody curious about financial/algorithmic trading who has not yet found the ideal environment for experimenting with market data and trading strategies.
• Professional asset managers and traders migrating to Python from any other programming language or any other Python backtesting framework.

Hands-on AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. Unlike other books, this one focuses on teaching intuition in designing complete trading strategies rather than explaining how to set up backtesting infrastructure. It utilizes QuantConnect and its market data from Algoseek and others.

The book explains more than twenty representative examples of AI in trading, written in Python, with performance tearsheets or research Jupyter notebooks covering Equity, Equity Options, Crypto, FX, and Index Option asset classes, with source code available in the book’s GitHub repository.

For further details, please visit the book’s website.