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{ "item_title" : "Lévy Processes in Algorithmic Trading with Python", "item_author" : [" Reactive Publishing", "Alice Schwartz", "Hayden Van Der Post "], "item_description" : "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. L vy 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 L vy 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 Covered: Introduction to L vy Processes - Understanding how they extend Brownian motion for financial modelingSimulating L vy Processes in Python - Monte Carlo methods, Variance Gamma, and Jump-Diffusion modelsHigh-Frequency Trading Applications - Using L vy-driven models for price prediction and strategy developmentRisk Management and Tail Events - Modeling extreme market movements and improving portfolio resilienceParameter Estimation & Calibration - Implementing Maximum Likelihood Estimation (MLE) and Machine Learning techniquesAdvanced Python Implementations - Full code examples using NumPy, SciPy, pandas, and JAX for speed optimizationDesigned 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 L vy processes in algorithmic trading ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/831/384/9798313844978_b.jpg", "price_data" : { "retail_price" : "29.99", "online_price" : "29.99", "our_price" : "29.99", "club_price" : "29.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Lévy Processes in Algorithmic Trading with Python|Reactive Publishing

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

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Overview

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. L vy 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 L vy 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 Covered:

Introduction to L vy Processes - Understanding how they extend Brownian motion for financial modeling
Simulating L vy Processes in Python - Monte Carlo methods, Variance Gamma, and Jump-Diffusion models
High-Frequency Trading Applications - Using L vy-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 L vy processes in algorithmic trading

This item is Non-Returnable

Details

  • ISBN-13: 9798313844978
  • ISBN-10: 9798313844978
  • Publisher: Independently Published
  • Publish Date: March 2025
  • Dimensions: 9 x 6 x 0.81 inches
  • Shipping Weight: 1.16 pounds
  • Page Count: 394

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