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Lévy Processes in Algorithmic Trading with Python: Advanced Stochastic Models for High-Frequency Trading and Risk Management
Reactive PublishingIn 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.
Key Topics 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.
Get your copy today and master the power of Lévy processes in algorithmic trading!
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.
Key Topics 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.
Get your copy today and master the power of Lévy processes in algorithmic trading!
417 pages, Kindle Edition
Published March 11, 2025
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