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Reactive Publishing

Advanced Options Engineering with Python: Quantitative Strategies for Dynamic Hedging, Volatility Trading, and Risk Control by Hayden Van Der Post delivers the definitive guide to mastering the next generation of derivatives modeling and risk architecture.

This book bridges the worlds of quantitative finance and real-world trading execution, showing you how to build, test, and deploy advanced Python-based frameworks for managing volatility, hedging dynamically, and engineering payoff structures that adapt to market behavior.

Inside, you'll learn how to:

• Design algorithmic hedging systems that respond to changing volatility regimes

• Implement delta, gamma, and vega neutral strategies in live trading environments

• Apply quantitative models for volatility surface calibration and term structure analysis

• Engineer Python-based risk controls for portfolio stability and tail protection

• Build end-to-end volatility arbitrage and dispersion trading frameworks

• Backtest strategies with robust statistical rigor and real market data

Written for professional traders, quantitative analysts, and risk engineers, this book blends mathematical depth with practical code and trading intuition. Each chapter reveals how to combine financial theory with Python automation to build adaptable, data-driven trading systems designed for today's fast-moving derivatives markets.

Whether you manage complex portfolios or trade volatility as a standalone asset, Advanced Options Engineering with Python is your blueprint for mastering the edge where quantitative precision meets trading execution.

Parametre knihy

• Celý názov: Advanced Options Engineering with Python
• Podnázov: Quantitative Strategies for Dynamic Hedging, Volatility Trading, and Risk Control
• Autor: Hayden Van Der Post, Alice Schwartz
• Jazyk: Angličtina
• Väzba: Brožovaná
• Počet strán: 572
• EAN: 9798268757583
• ID: 50561607
• Nakladateľ: Independently published
• Hmotnosť: 757 g
• Rozmery: 229 × 152 × 29 mm
• Dátum vydania: 07. October 2025

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