{
  "chapter": "black-litterman.md",
  "source_provenance": "portfolio-optimization-provenance.json",
  "data_status": "Hypothetical one-year simple returns; no empirical market observations",
  "visuals": {
    "design_system": "QuantCorner / QuantSeras; preserve the current light book layout, purple and teal accents, readable Thai typography, accessible dark theme and offline operation.",
    "route": "no-image-generator",
    "image_generator_used": false,
    "generator": "scripts/make_portfolio_optimization_figures.py generates 13 SVGs; scripts/render_optimization_roadmap.py renders the editable Excalidraw roadmap.",
    "svg_files": [
      "assets/images/optimization-black-litterman-roadmap.svg",
      "assets/images/optimization-black-litterman-beliefs.svg",
      "assets/images/optimization-black-litterman-weights.svg"
    ],
    "editable_diagram": {
      "rendered_file": "assets/images/optimization-black-litterman-roadmap.svg",
      "source_file": "assets/diagrams/optimization-black-litterman-roadmap.excalidraw",
      "spec": {
        "layout": "Two input branches: market to prior, prior and views to posterior, posterior to allocation",
        "stages": [
          "market weights and covariance",
          "reverse optimization prior",
          "P-Q-Omega views",
          "posterior expected excess returns",
          "portfolio allocation"
        ],
        "requirements": [
          "Each arrow names the object passed to the next stage.",
          "Market inputs, investor views and allocation outputs remain visually distinct.",
          "The SVG and Excalidraw source express the same five stages.",
          "Text remains legible at 320 CSS pixels and in both site themes.",
          "No PDF artwork or source portrait is embedded."
        ]
      }
    },
    "labels": "SVGs include explicit titles, descriptions, axes, units and source-aligned numerical values. Chapter alt text and figure captions explain the takeaway without relying on color alone.",
    "revision": "2026-09-18: three static illustrations and the interactive prior/posterior chart rebuilt for the standalone lesson."
  },
  "notebook": "notebooks/black-litterman.ipynb",
  "notes": [
    "The prior is reverse-optimized from market weights, not copied from the expected returns used in the previous chapter.",
    "Return covariance Sigma is held fixed for allocation; posterior uncertainty of the mean is a distinct object."
  ],
  "learning_revision": {
    "editorial": "New concrete introduction; fixed market lambda distinguished from investor lambda.",
    "controls": [
      "independently enabled views",
      "two Q values",
      "two positive diagonal Omega multipliers",
      "investor risk aversion",
      "no-view and negative-view presets"
    ],
    "return_axis": "adaptive to negative and positive values",
    "weights": "prior and posterior including residual risk-free allocation",
    "portraits": [
      {
        "path": "assets/images/fischer-black.jpg",
        "source": "https://commons.wikimedia.org/wiki/File:Fischer_Black.JPG",
        "rights": "Public-domain dedication by Dalmatine; original retained; photograph date unknown",
        "sha256": "e06d7fd8a49854f624c803b34c688c35db49d1da34d6a67545941f659fe66d7d"
      }
    ],
    "litterman_portrait": "Linked interview only; no open reuse license established for Jake Armour photograph.",
    "date": "2026-09-18"
  },
  "part_iii_revision": {
    "date": "2026-09-19",
    "title": "Black–Litterman Portfolio",
    "source_file": "JA252.2 Notes1 (1).pdf",
    "sha256": "eebd6f73231ea0c249afb91aaf551dd45d9e54f3a2898fac88d5e8bf66106c20",
    "pages": [
      101,
      131
    ],
    "coverage": {
      "101-104": "motivation, two derivations, roadmap",
      "105-107": "Bayes and terminology; biographical digression omitted",
      "108-118": "choice of prior, reverse optimization, lambda and tau",
      "119-123": "views, dimensions, independence and four Omega methods",
      "124-126": "posterior mean and covariance",
      "127-131": "allocation and four risk-aversion examples"
    },
    "corrections": [
      "Likelihood is p(Q|mu), not likelihood times prior",
      "GMV weights do not require means, but expected GMV return does",
      "Prior is informative, conditional on equilibrium assumptions",
      "Tau=1/120 is a teaching assumption; monthly count alone does not justify annual mean covariance",
      "Recomputed all values with lambda_market exactly 2.24 rather than mixing rounded inputs",
      "Lambda=1 is not universally exact Kelly or an investor-wide risk threshold"
    ],
    "private_source_published": false
  }
}