Chapter prerequisite graph

Solid arrows are required prerequisites; dotted arrows provide background. Absence of an edge does not imply absence of applications. The current graph has 45 nodes and 120 required edges, plus three background edges. All 118 historical required edges remain; 13→15 and 24→26 are added.

Diagram source

flowchart TD
  C1["Chapter 1 — Quantitative Finance and the Quant Industry"]
  C2["Chapter 2 — Python for Quantitative Research: Getting Returns and Time Indices Right"]
  C3["Chapter 3 — Financial Data Engineering"]
  C4["Chapter 4: Calculus, Rates of Change, Curvature and Local Decisions"]
  C5["Chapter 5: Linear Algebra, Portfolio Objects and Risk"]
  C6["Chapter 6 — Probability"]
  C7["Chapter 7 — Statistical Inference: uncertainty in research conclusions"]
  C8["Chapter 8 — Regression"]
  C9["Chapter 9 — Optimization"]
  C10["Chapter 10 — Financial Markets"]
  C11["Chapter 11 — Returns and Compounding"]
  C12["Chapter 12 — Financial Statements for Quantitative Research"]
  C13["Chapter 13 — Valuation for Quants"]
  C14["Chapter 14 — CAPM and Asset Pricing"]
  C15["Chapter 15 — Factor Models"]
  C16["Chapter 16 — Time Series I: Dependence and ARIMA"]
  C17["Chapter 17 — Time Series II: Unit Roots, Cointegration, and Prediction"]
  C18["Chapter 18 — Volatility Modeling and Forecasting"]
  C19["Chapter 19 — State-Space Models and the Kalman Filter"]
  C20["Chapter 20 — Modern Portfolio Theory"]
  C21["Chapter 21 — Practical Portfolio Construction"]
  C22["Chapter 22 — Quantitative Research Methodology"]
  C23["Chapter 23 — Cross-Sectional Research"]
  C24["Chapter 24 — Value Factors"]
  C25["Chapter 25 — Momentum"]
  C26["Chapter 26 — Quality"]
  C27["Chapter 27 — Carry, Defensive Investing, and Low Volatility"]
  C28["Chapter 28 — Multi-Factor Models: Preprocessing, Combination, and Constraints"]
  C29["Chapter 29 — Building a Backtesting Engine"]
  C30["Chapter 30 — Backtesting Biases"]
  C31["Chapter 31 — Financial Model Validation"]
  C32["Chapter 32 — Performance Statistics & Inference"]
  C33["Chapter 33 — Risk Management"]
  C34["Chapter 34 — Market Microstructure"]
  C35["Chapter 35 — Transaction Costs"]
  C36["Chapter 36 — Algorithmic Execution and Trade Scheduling"]
  C37["Chapter 37. A Foundational Stance on Machine Learning for Finance"]
  C38["Chapter 38: Financial ML Pipeline"]
  C39["Chapter 39 Regularized Linear Models"]
  C40["Chapter 40: Tree-Based Models"]
  C41["Chapter 41: Unsupervised Learning"]
  C42["Chapter 42: Deep Learning Foundations and Quantitative Applications"]
  C43["Chapter 43: Bayesian Inference, Causal Identification, and Alternative Text Data"]
  C44["Chapter 44 — Stochastic Calculus and Derivatives"]
  C45["Chapter 45 — Production Quant Pipelines"]
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  C11 -. background .-> C12
  C17 -. background .-> C18
  C33 -. background .-> C34

13→15 supplies valuation and claim interpretation; 24→26 supplies statement conventions and value/quality comparisons. 11→12 provides return context. 17→18 is background: Chapter 18 develops EWMA/ARCH/GARCH from given-mean innovations, squared increments and Chapter 16 dependence; it does not require unit roots, cointegration, VAR or EG. 33→34 adds risk context rather than prerequisites for quotes and books. The matching openings in Chapters 12, 15, 18, 26 and 34 in both languages were integrated and accepted in BIL23B. See the learning route for project prerequisites.