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Book synopsis

Reactive Publishing

Path signatures and rough path theory represent some of the most powerful mathematical tools available for analyzing complex, irregular time series data in quantitative trading.

This practical guide introduces Python developers and quantitative analysts to the core concepts of path signatures and rough path theory, with a strong focus on real-world implementation. You will learn how to apply geometric feature engineering and signature-based models to extract meaningful information from high-frequency and noisy financial data.

What You Will Learn:

• The mathematical foundations of path signatures and rough path theory
• How to compute and use signature features in Python for time-series analysis
• Techniques for geometric feature engineering on sequential data
• Implementation of signature kernels and signature-based machine learning models
• Practical approaches for regime detection and modeling market dynamics
• Code examples using modern Python libraries for quantitative finance

Written for practitioners with intermediate Python and basic machine learning knowledge, this book bridges advanced mathematical theory with clear, reproducible code. Each chapter includes hands-on examples that demonstrate how these methods can be integrated into existing trading and research workflows.

Whether you are exploring new feature engineering approaches or seeking more robust ways to model non-linear and path-dependent market behavior, this book provides the technical foundation and practical guidance needed to work effectively with signature methods in quantitative trading.

Note: This book focuses on educational techniques and does not provide trading advice or performance guarantees.

Book details

• Full title: Path Signatures and Rough Path Theory for Quantitative Trading in Python
• Subtitle: A Practical Guide to Geometric Feature Engineering and Signature Models
• Author: James Preston, Alice Schwartz
• Language: English
• Binding: Paperback
• Number of pages: 488
• EAN: 9798199799140
• ID: 52770891
• Publisher: Independently published
• Weight: 585 g
• Dimensions: 229 × 152 × 31 mm
• Date of publishing: 03. June 2026

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