Lévy Processes in Algorithmic Trading With Python Advanced Stochastic Models for High-Frequency Trading and Risk Management

Reactive Publishing

In modern financial markets, traditional models like Black-Scholes fail to capture the complexity of asset price movements, especially during periods of volatility and extreme events. Lévy processes offer a powerful alternative by extending Brownian motion to account for jump dynamics, heavy-tailed distributions, and market microstructure effects-making them essential for algorithmic traders, quants, and risk analysts.

This book provides a practical, code-driven approach to implementing Lévy processes in Python for high-frequency trading (HFT), quantitative strategies, and risk modeling. Readers will learn how to simulate, calibrate, and apply advanced stochastic models such as Variance Gamma, Normal Inverse Gaussian, and Jump-Diffusion to real-world financial data.

Introduction to Lévy Processes - Understanding how they extend Brownian motion for financial modeling
Simulating Lévy Processes in Python - Monte Carlo methods, Variance Gamma, and Jump-Diffusion models
High-Frequency Trading Applications - Using Lévy-driven models for price prediction and strategy development
Risk Management and Tail Events - Modeling extreme market movements and improving portfolio resilience
Parameter Estimation & Calibration - Implementing Maximum Likelihood Estimation (MLE) and Machine Learning techniques
Advanced Python Implementations - Full code examples using NumPy, SciPy, pandas, and JAX for speed optimization

Designed for quantitative traders, financial engineers, and algorithmic strategists, this book combines rigorous theory with hands-on Python code to give you a competitive edge in modern financial markets. Whether you are a quant developer, hedge fund researcher, or a data scientist, this book will elevate your understanding of financial modeling and trading strategy design.