Mutual Information in Trading Systems: Measuring Nonlinear Dependencies in Financial Markets with Python

By: Preston, James

Reactive Publishing

Financial markets are driven by complex relationships that traditional correlation measures often fail to capture. Many trading strategies rely on linear assumptions, yet real market dynamics frequently involve nonlinear dependencies that remain hidden when using standard statistical tools.

Mutual Information in Trading Systems introduces a rigorous framework for identifying and analyzing nonlinear relationships in financial data using information theory. The book explains how mutual information can reveal structural dependencies between variables such as price movements, volatility regimes, and macro signals that conventional methods overlook.

Through practical Python implementations, readers learn how to estimate mutual information, apply it to market datasets, and incorporate information-theoretic metrics into systematic trading research. The techniques presented are useful for analyzing feature relationships, improving signal selection, and understanding the informational structure of financial time series.

Topics covered include:

Foundations of information theory and entropy

Mutual information estimation techniques

Nonlinear dependency analysis in market data

Feature selection for trading models

Python implementations for quantitative research

Applications in systematic trading and financial data analysis

Designed for quantitative researchers, data scientists, and traders working with Python, this book provides a structured introduction to applying information-theoretic methods within modern trading systems.

• Format: Paperback
• Language: English
• ISBN: B0GRNF2722
• ISBN13: 9798251073997
• Release Date: March 2026
• Publisher: Independently Published
• Length: 432 Pages
• Weight: 1.27 lbs.
• Dimensions: 0.9" x 6.0" x 9.0"