Advanced Portfolio Construction with Python: Black-Litterman, Robust Optimization, and Hierarchical Risk Parity

Name: Advanced Portfolio Construction with Python: Black-Litterman, Robust Optimization, and Hierarchical Risk Parity
Brand: Independently Published
SKU: 9798198673267
Price: 3518.0 INR
Availability: InStock

by Alice Schwartz , Vincent Bisette

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ISBN13: 9798198673267
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: May 26, 2026
Pages: 432
Original Price: GBP 31.11
Language: English
Edition: N/A
Item Weight: 518 grams
BISAC Subject(s): Investments & Securities / Portfolio Management

About The Book

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:

Apply the Black-Litterman model to combine investor views with market equilibrium
Implement robust optimization methods that reduce sensitivity to estimation errors
Construct diversified portfolios using Hierarchical Risk Parity, a powerful clustering-based approach that avoids many limitations of traditional mean-variance optimization
Code complete portfolio construction pipelines in Python using NumPy, pandas, SciPy, and scikit-learn

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:

Quantitative developers and financial engineers
Portfolio managers seeking modern allocation techniques
Advanced Python users working in finance and investment

Technical level: Intermediate to advanced. Readers should be comfortable with Python and basic linear algebra.

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