Volatility trading systems python par preston james (6 résultats)

Taschenbuch. Etat : Neu. Neuware - Reactive PublishingThe volatility surface is broken. Rough volatility explains why.This book delivers the most accessible, practical, and comprehensive guide to rough volatility, fractional Brownian motion (fBM), and Hurst-driven modeling ever written for quantitative traders and financial engineers.If you have struggled to understand why standard stochastic volatility models fail, or why modern markets behave with 'memory' and microstructure-induced roughness, this book gives you the complete framework, intuition, and Python workflows to build the next generation of volatility models and trading systems.What This Book Teaches You- The core intuition behind rough volatilityWhy volatility is not smooth, why it cannot be modeled with classical Brownian motion, and how fractional processes capture long-memory behavior in real markets.- Fractional Brownian motion (fBM) from zero to masteryStep-by-step construction, parameterization, simulation, and calibration. Learn Hurst exponents the right way - intuitively first, then rigorously.- Full rough volatility model implementations in PythonIncluding: - The Rough Bergomi Model- Fractional Stochastic Volatility (fSV)- Multi-factor fractional models- Hybrid neural/fractional architecturesAll presented with clean, reusable code templates.- How rough volatility transforms tradingUse fractional features to detect volatility clustering, regime shifts, option mispricings, and structural breaks that traditional models miss.- Complete volatility trading systemsConcrete, plug-and-play strategies built from fractional features: - Hurst-driven volatility filters- fBM momentum/anti-momentum signals- Rough volatility mean-reversion engines- Volatility-of-volatility predictorsIncludes full Python implementations.Why Rough Volatility MattersClassical models assume volatility is smooth, Markovian, and memoryless. Modern markets are the opposite. They are rough, self-similar, and exhibit long-range dependence - and this book shows how to model that structure directly.Rough volatility is not an academic curiosity. It is the new foundation of volatility modeling at banks, hedge funds, and derivatives desks worldwide.Who This Book Is ForQuant traders, systematic volatility researchers, derivatives professionals, financial engineers, and advanced Python quants who want to push beyond Black-Scholes-era assumptions and into the future of stochastic modeling.If you want to understand how volatility really behaves, and build trading systems that exploit it, this is the most practical, complete, and modern guide available.…

Taschenbuch. Etat : Neu. Neuware - Reactive PublishingVolatility is no longer a side-note in quant trading. It is the engine. Volatility Trading Systems with Python takes you inside the architecture of real, deployable vol-driven strategies, from building arbitrage-free surfaces to constructing gamma-scalping engines, dynamic hedging models, and automated risk responses. This is a hands-on, Python-first guide designed for traders, quants, and system designers who want to master the mechanics of volatility as a tradeable asset.Inside, you'll build full pipelines: implied vol surface construction, intraday vol metrics, vega-neutral and gamma-positive systems, automated hedge adjusters, microstructure-aware risk triggers, and real-market backtests. Every chapter focuses on production-ready frameworks that translate directly into deployable edge, giving you a complete view of volatility as both a model and a profit-center.If you trade options, run quant strategies, or design automated systems, this book becomes a cornerstone - the bridge between volatility theory and live execution.…

Etat : New. Print on Demand.

Etat : New. Print on Demand.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.