"Quantitative Portfolio Management: Data, Models, and Implementation"
"Quantitative Portfolio Management: Data, Models, and Implementation" bridges the critical gap between theoretical alpha research and live trading operations. This book is written for quantitative portfolio managers, researchers, and developers who require an end-to-end, implementation-focused guide to building systematic investment systems. Beyond mere theory, it addresses the messy reality of financial data and the engineering rigor required to transition a strategy from a research environment to a production-grade capital allocation engine.
Readers will traverse the full lifecycle of a systematic strategy, starting with the architecture of a robust research data stack that prevents lookahead bias and ensures data integrity. You will learn to construct stable factor risk models, implement cost-aware portfolio optimization, and design event-driven backtests that accurately simulate market microstructure and liquidity. The text provides a deep dive into the mathematical and operational mechanics necessary to minimize implementation shortfall and manage model risk effectively.
Designed as a self-contained professional reference, this text emphasizes the "how" of execution alongside the "why" of mathematics. By integrating advanced financial theory with practical data engineering and operational governance, it serves as the definitive blueprint for modern quantitative asset management. Whether you are scaling a single strategy or managing a multi-asset platform, this book provides the tools to quantify u
