Learning route and project milestones

Must Know is the research core; Should Know adds interpretation and implementation; Advanced needs stronger preparation; Optional is a route choice. Understand core definitions before selecting tools by the problem. Domain arrows are an overview, not the required-edge list; use the chapter DAG.

Chapters 1–3 establish the lifecycle, Python and time-data contracts. Preview return definitions in Chapter 11 and the market structure needed from Chapter 10.
Chapters 4–9 cover calculus, linear algebra, probability, inference, regression and optimization. Chapter 8 uses CAPM to practice regression; its equilibrium interpretation waits until Chapter 14.
Chapters 10–15 supply markets, statements, valuation and asset pricing. Complete Project 1 after Chapter 11 and Project 2 after Chapter 15.
Chapters 16–21 cover dependence, cointegration, volatility and portfolios. Chapter 19 can be revisited after basic portfolio theory. Complete Project 3 after Chapter 21.
Chapters 22–28 develop and combine alpha hypotheses. Small ledgers illustrate methods; assess strategy validity only after the backtesting, validation and cost material in Chapters 29–35.
Chapters 29–35 cover backtests, biases, validation, performance, risk and costs; add execution in Chapter 36, then Projects 4 and 5.
Chapters 37–40 form the financial-ML core and Project 6. Chapters 41–43 add tools chosen by the problem; Chapter 45 organizes research into an executable project.
Finish the Capstone. The derivatives route adds Chapter 44; stochastic calculus is not a prerequisite for starting equity-factor research.

Diagram source

flowchart TD
 P[Python 2] --> M[Calculus and algebra 4–5]
 M --> Q[Probability 6]
 Q --> S[Inference 7]
 S --> R[Regression 8]
 R --> O[Optimization 9]
 P --> D[Data and PIT 3]
 D --> F[Markets, returns, statements, valuation 10–13]
 F --> A[Asset pricing and factors 14–15]
 R --> T[Time series 16–19]
 A --> V[Portfolios 20–21]
 O --> V
 T --> V
 V --> H[Factor research 22–28]
 D --> B[Backtesting 29–32]
 H --> B
 B --> K[Risk 33]
 B --> E[Microstructure, costs, execution 34–36]
 K --> ML[Financial ML 37–43]
 E --> ML
 ML --> PROD[Engineering 45]
 PROD --> C[Capstone]
 Q --> STO[Derivatives 44: Advanced]
 M --> STO
 F --> STO
 STO -. optional derivatives route .-> C

Project	Prerequisite chapters	Completion criterion
Project 1: Financial data analysis	2, 3, 10, 11	Return, wealth, volatility and drawdown calculations checked against hand examples; historical data retain provenance.
Project 2: CAPM and Fama–French	8, 11, 14, 15	Check excess returns, units, HAC and exposure interpretation.
Project 3: Portfolio optimization	9, 18, 20, 21	Estimate inside training windows and compare rolling OOS baselines and costs.
Project 4: Multi-factor equity strategy	12, 23–26, 28–31, 35–36	Require PIT signals, historical universes, net returns and robustness; constructed sublabs do not close market evidence.
Project 5: Pairs trading	16–17, 29–31, 35–36	Select within training, freeze rules and reconcile self-financing OOS ledgers and costs.
Project 6: Machine-learning alpha	31, 35, 37–40, 43	Split by date, fit transforms inside training and compare baselines, net returns and stability.
Capstone	Methods from all six projects, 33, 45	18 report items and reproducible workflow; negative results can pass. Market execution has separate gates.

Early preparation: download plans/dictionaries for P1; French format parsing for P2; equal-weight baseline for P3; filing PIT dictionaries for P4; alignment for P5; label calendars for P6; hypothesis and data feasibility for the Capstone.

Derivatives route: 4, 6, 9, 10, 11→44→volatility and numerical pricing. Execution route: 10, 29, 33–36, 45→event data, matching and inventory risk. Both are advanced routes. Establish a reliable simple baseline before deciding whether deep learning, LLMs or state-space models are needed. Keep project historical acceptance, constructed sublabs and market evidence distinct; report insignificant alpha and failure after costs faithfully.