Python for Finance, 3rd Edition

Part IV. Python for Asset Management

Asset management provides a natural setting for end-to-end portfolio workflows: from investment objectives and data structures to portfolio construction, signals, and reporting. This part treats the Efficient Market Hypothesis (EMH) as the working baseline for liquid markets and shows how Python supports systematic, benchmark-aware strategies built on that assumption.

This part develops an asset management toolkit on top of the financial data science stack:

• Chapter 17 defines the asset management problem space, including asset classes, mandates, benchmarks, constraints, and the data structures that describe universes and holdings.

• Chapter 18 turns expected returns, risk estimates, and constraints into portfolios via optimization and risk analysis, emphasizing implementable solutions.

• Chapter 19 connects research signals and forecasts to rankings, weights, and trades, including turnover, transaction costs, and capacity considerations.

• Chapter 20 focuses on the operational layer: reporting, performance attribution, risk dashboards, and the systems that keep portfolios observable and auditable.

• Chapter 21 packages these ideas into a small, self-contained asset management library in Python that works off the example dataset and provides reusable building blocks for research, portfolio construction, and reporting.

Throughout this part, EMH acts as the background assumption for passive and benchmark-aware design, while leaving room for ...