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Portfolio Optimization Engines with AI: Black-Litterman, Hierarchical Risk Parity, neural allocators, entropy-based allocators - Couverture souple
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Synopsis
Reactive Publishing
Portfolio construction is no longer a static exercise. In an era of regime shifts, liquidity shocks, and nonlinear market behavior, traditional allocation models break down. The future belongs to adaptive engines, systems that learn, rebalance, and optimize dynamically.
Portfolio Optimization Engines with AI is a comprehensive guide to building next-generation allocation frameworks using machine learning, statistical modeling, and advanced optimization techniques. Designed for quants, systematic traders, and portfolio architects, this book shows you how to engineer intelligent allocation systems that outperform conventional methods.
Inside, you’ll learn how to:
Build AI-driven allocators using supervised, unsupervised, and reinforcement learning
Design risk models that capture volatility clusters, tail events, and correlation breakdowns
Implement classical, modern, and post-modern optimization frameworks:
Mean-variance
Black-Litterman
Hierarchical Risk Parity
Entropy-based allocators
Shrinkage and Bayesian models
Construct multi-asset portfolios built on equities, options, futures, and crypto
Build stress-testing engines for inflation shocks, volatility expansions, and liquidity crises
Evaluate durability using probabilistic scenario analysis and walk-forward testing
Deploy live, self-adjusting allocation engines with strict risk controls and override logic
Each chapter blends deep theory with executable models, real-world examples, and practical engineering guidance. The result is a definitive playbook for designing allocation systems that think, adapt, and evolve with the market.
If your goal is to build portfolios that are robust, intelligent, and structurally superior to traditional models, this book gives you the architecture to do it.
This is portfolio optimization for the AI era.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Portfolio Optimization Engines with AI: Black-Litterman, Hierarchical Risk Parity, neural allocators, entropy-based allocators (Algorithmic Alpha: Next-Gen Trading Systems for the Modern Market)
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Etat : New. Print on Demand. N° de réf. du vendeur I-9798274563710
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Portfolio Optimization Engines with AI
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Portfolio Optimization Engines with AI (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Reactive PublishingPortfolio construction is no longer a static exercise. In an era of regime shifts, liquidity shocks, and nonlinear market behavior, traditional allocation models break down. The future belongs to adaptive engines, systems that learn, rebalance, and optimize dynamically.Portfolio Optimization Engines with AI is a comprehensive guide to building next-generation allocation frameworks using machine learning, statistical modeling, and advanced optimization techniques. Designed for quants, systematic traders, and portfolio architects, this book shows you how to engineer intelligent allocation systems that outperform conventional methods.Inside, you'll learn how to: Build AI-driven allocators using supervised, unsupervised, and reinforcement learningDesign risk models that capture volatility clusters, tail events, and correlation breakdownsImplement classical, modern, and post-modern optimization frameworks: Mean-varianceBlack-LittermanHierarchical Risk ParityEntropy-based allocatorsShrinkage and Bayesian modelsConstruct multi-asset portfolios built on equities, options, futures, and cryptoBuild stress-testing engines for inflation shocks, volatility expansions, and liquidity crisesEvaluate durability using probabilistic scenario analysis and walk-forward testingDeploy live, self-adjusting allocation engines with strict risk controls and override logicEach chapter blends deep theory with executable models, real-world examples, and practical engineering guidance. The result is a definitive playbook for designing allocation systems that think, adapt, and evolve with the market.If your goal is to build portfolios that are robust, intelligent, and structurally superior to traditional models, this book gives you the architecture to do it.This is portfolio optimization for the AI era. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798274563710
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EUR 27,41
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Portfolio Optimization Engines with AI
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798274563710
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Portfolio Optimization Engines with AI (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Reactive PublishingPortfolio construction is no longer a static exercise. In an era of regime shifts, liquidity shocks, and nonlinear market behavior, traditional allocation models break down. The future belongs to adaptive engines, systems that learn, rebalance, and optimize dynamically.Portfolio Optimization Engines with AI is a comprehensive guide to building next-generation allocation frameworks using machine learning, statistical modeling, and advanced optimization techniques. Designed for quants, systematic traders, and portfolio architects, this book shows you how to engineer intelligent allocation systems that outperform conventional methods.Inside, you'll learn how to: Build AI-driven allocators using supervised, unsupervised, and reinforcement learningDesign risk models that capture volatility clusters, tail events, and correlation breakdownsImplement classical, modern, and post-modern optimization frameworks: Mean-varianceBlack-LittermanHierarchical Risk ParityEntropy-based allocatorsShrinkage and Bayesian modelsConstruct multi-asset portfolios built on equities, options, futures, and cryptoBuild stress-testing engines for inflation shocks, volatility expansions, and liquidity crisesEvaluate durability using probabilistic scenario analysis and walk-forward testingDeploy live, self-adjusting allocation engines with strict risk controls and override logicEach chapter blends deep theory with executable models, real-world examples, and practical engineering guidance. The result is a definitive playbook for designing allocation systems that think, adapt, and evolve with the market.If your goal is to build portfolios that are robust, intelligent, and structurally superior to traditional models, this book gives you the architecture to do it.This is portfolio optimization for the AI era. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798274563710
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Portfolio Optimization Engines with AI : Black-Litterman, Hierarchical Risk Parity, neural allocators, entropy-based allocators
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware - Reactive PublishingPortfolio construction is no longer a static exercise. In an era of regime shifts, liquidity shocks, and nonlinear market behavior, traditional allocation models break down. The future belongs to adaptive engines, systems that learn, rebalance, and optimize dynamically.Portfolio Optimization Engines with AI is a comprehensive guide to building next-generation allocation frameworks using machine learning, statistical modeling, and advanced optimization techniques. Designed for quants, systematic traders, and portfolio architects, this book shows you how to engineer intelligent allocation systems that outperform conventional methods.Inside, you'll learn how to: - Build AI-driven allocators using supervised, unsupervised, and reinforcement learning- Design risk models that capture volatility clusters, tail events, and correlation breakdowns- Implement classical, modern, and post-modern optimization frameworks: - Mean-variance- Black-Litterman- Hierarchical Risk Parity- Entropy-based allocators- Shrinkage and Bayesian models- Construct multi-asset portfolios built on equities, options, futures, and crypto- Build stress-testing engines for inflation shocks, volatility expansions, and liquidity crises- Evaluate durability using probabilistic scenario analysis and walk-forward testing- Deploy live, self-adjusting allocation engines with strict risk controls and override logicEach chapter blends deep theory with executable models, real-world examples, and practical engineering guidance. The result is a definitive playbook for designing allocation systems that think, adapt, and evolve with the market.If your goal is to build portfolios that are robust, intelligent, and structurally superior to traditional models, this book gives you the architecture to do it.This is portfolio optimization for the AI era. N° de réf. du vendeur 9798274563710
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EUR 31
Expédition à EUR 61,60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
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