Reactive Publishing
Advanced Portfolio Construction with Python provides a practical, code-first guide to building sophisticated investment portfolios using three of the most powerful modern techniques: the Black-Litterman model, robust optimization, and Hierarchical Risk Parity (HRP).
Written for quantitative analysts, portfolio managers, and Python-savvy investors, this book bridges the gap between academic theory and real-world implementation. You will learn how to:
Each chapter includes clear explanations of the underlying mathematics followed by fully working Python examples and Jupyter-style workflows. The focus is on clarity, reproducibility, and practical application rather than abstract theory.
Whether you are looking to enhance your existing quantitative toolkit or move beyond classical portfolio optimization, this book delivers the technical depth and implementation details needed to build more resilient and sophisticated portfolios.
Ideal for:
Technical level: Intermediate to advanced. Readers should be comfortable with Python and basic linear algebra.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vendeur : California Books, Miami, FL, Etats-Unis
Etat : New. Print on Demand. N° de réf. du vendeur I-9798198673267
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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. N° de réf. du vendeur L0-9798198673267
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Vendeur : CitiRetail, Stevenage, Royaume-Uni
Paperback. Etat : new. Paperback. Reactive PublishingAdvanced Portfolio Construction with Python provides a practical, code-first guide to building sophisticated investment portfolios using three of the most powerful modern techniques: the Black-Litterman model, robust optimization, and Hierarchical Risk Parity (HRP).Written for quantitative analysts, portfolio managers, and Python-savvy investors, this book bridges the gap between academic theory and real-world implementation. You will learn how to: Apply the Black-Litterman model to combine investor views with market equilibriumImplement robust optimization methods that reduce sensitivity to estimation errorsConstruct diversified portfolios using Hierarchical Risk Parity, a powerful clustering-based approach that avoids many limitations of traditional mean-variance optimizationCode complete portfolio construction pipelines in Python using NumPy, pandas, SciPy, and scikit-learnEach chapter includes clear explanations of the underlying mathematics followed by fully working Python examples and Jupyter-style workflows. The focus is on clarity, reproducibility, and practical application rather than abstract theory.Whether you are looking to enhance your existing quantitative toolkit or move beyond classical portfolio optimization, this book delivers the technical depth and implementation details needed to build more resilient and sophisticated portfolios.Ideal for: Quantitative developers and financial engineersPortfolio managers seeking modern allocation techniquesAdvanced Python users working in finance and investmentTechnical level: Intermediate to advanced. Readers should be comfortable with Python and basic linear algebra. 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 9798198673267
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. Neuware. N° de réf. du vendeur 9798198673267
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