{"product_id":"advanced-portfolio-construction-with-python-black-litterman-robust-optimization-and-hierarchical-risk-parity-9798198673267","title":"Advanced Portfolio Construction with Python: Black-Litterman, Robust Optimization, and Hierarchical Risk Parity","description":"\u003cp\u003e • Author(s): Alice Schwartz | Vincent Bisette\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Investments \u0026amp; Securities - Portfolio Management\u003c\/p\u003e\u003cp\u003e\u003cb\u003eReactive Publishing\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAdvanced Portfolio Construction with Python\u003c\/b\u003e 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).\u003c\/p\u003e\u003cp\u003eWritten 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eApply the Black-Litterman model to combine investor views with market equilibrium\u003c\/li\u003e\n\u003cli\u003eImplement robust optimization methods that reduce sensitivity to estimation errors\u003c\/li\u003e\n\u003cli\u003eConstruct diversified portfolios using Hierarchical Risk Parity, a powerful clustering-based approach that avoids many limitations of traditional mean-variance optimization\u003c\/li\u003e\n\u003cli\u003eCode complete portfolio construction pipelines in Python using NumPy, pandas, SciPy, and scikit-learn\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eEach 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.\u003c\/p\u003e\u003cp\u003eWhether 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eIdeal for: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eQuantitative developers and financial engineers\u003c\/li\u003e\n\u003cli\u003ePortfolio managers seeking modern allocation techniques\u003c\/li\u003e\n\u003cli\u003eAdvanced Python users working in finance and investment\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eTechnical level: Intermediate to advanced. Readers should be comfortable with Python and basic linear algebra.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47890304237719,"sku":"9798198673267","price":3518.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798198673267.webp?v=1781179464","url":"https:\/\/atlanticbooks.com\/products\/advanced-portfolio-construction-with-python-black-litterman-robust-optimization-and-hierarchical-risk-parity-9798198673267","provider":"Atlantic Books","version":"1.0","type":"link"}