{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "748b3a35",
   "metadata": {},
   "source": [
    "# Signal Quest Technical Masterclass\n",
    "\n",
    "<div style=\"padding:18px;border-left:8px solid #0E928C;background:#EFFAF8\">\n",
    "<b>Executable teaching artifact · research only</b><br>\n",
    "Every numerical result in this notebook is <b>Simulated</b> or <b>Illustrative</b>. Nothing is measured BTC or Polymarket performance, a live fill, financial advice, or permission to trade.\n",
    "</div>\n",
    "\n",
    "This notebook turns Chapters 4–13 into one deterministic evidence trace: metrics → temporal evaluation → a demanding tabular fallback → calibration → abstention → representation objectives → causal attention → cost-aware replay → fail-closed monitoring.\n",
    "\n",
    "**Epistemic vocabulary.** \\`Simulated\\` means generated by the seeded synthetic process below. \\`Illustrative\\` means formula-derived teaching arithmetic. \\`Implemented\\` means only that this notebook component executes; it does not establish market validity. \\`Design target\\` describes the absent research system.\n",
    "\n",
    "**Authority boundary.** The notebook has no market data, credentials, order API, live execution path, or autonomous code-execution authority."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d459f4c",
   "metadata": {},
   "source": [
    "## Learning contract\n",
    "\n",
    "By the end, you should be able to:\n",
    "\n",
    "1. reconstruct threshold metrics from a confusion matrix and distinguish ROC, precision–recall, and calibration;\n",
    "2. derive purge and embargo gaps from a temporal contract;\n",
    "3. explain why the scikit-learn gradient-boosting model here is a teaching fallback—not CatBoost and not a production candidate;\n",
    "4. calculate a causal attention mask and a masked-sequence objective;\n",
    "5. audit a replay that preserves fees, latency, partial fills, no fills, and no-trade decisions; and\n",
    "6. prove that a guardian fails closed and lacks order authority.\n",
    "\n",
    "The positive label is \\`Up = 1\\`. Threshold selection uses validation only. The test interval remains untouched until the complete forecast rule is frozen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13e48670",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:07.883861Z",
     "iopub.status.busy": "2026-08-06T13:42:07.883782Z",
     "iopub.status.idle": "2026-08-06T13:42:08.345573Z",
     "shell.execute_reply": "2026-08-06T13:42:08.345083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Implemented — portable runtime identity.** Python is checked by major/minor and every installed distribution must match the complete 64-package lock exactly. No interpreter path is retained."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>3.11.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>implementation</th>\n",
       "      <td>CPython</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>locked_packages_verified</th>\n",
       "      <td>64/64 exact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numpy</th>\n",
       "      <td>1.26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>3.0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matplotlib</th>\n",
       "      <td>3.11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed</th>\n",
       "      <td>8414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                value\n",
       "python                        3.11.15\n",
       "implementation                CPython\n",
       "locked_packages_verified  64/64 exact\n",
       "numpy                          1.26.4\n",
       "pandas                          3.0.5\n",
       "matplotlib                     3.11.1\n",
       "seed                             8414"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import contextlib\n",
    "import io\n",
    "import math\n",
    "import os\n",
    "import platform\n",
    "import re\n",
    "import warnings\n",
    "import tempfile\n",
    "from pathlib import Path\n",
    "from importlib.metadata import distributions\n",
    "from dataclasses import dataclass\n",
    "from enum import Enum\n",
    "\n",
    "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"signal-quest-mpl\"))\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.use(\"module://matplotlib_inline.backend_inline\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"FigureCanvasAgg is non-interactive\")\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "SEED = 8414\n",
    "rng = np.random.default_rng(SEED)\n",
    "np.set_printoptions(precision=4, suppress=True)\n",
    "pd.set_option(\"display.max_columns\", 30)\n",
    "pd.set_option(\"display.float_format\", lambda value: f\"{value:,.4f}\")\n",
    "\n",
    "COLORS = {\n",
    "    \"ink\": \"#17212B\", \"teal\": \"#0E928C\", \"coral\": \"#DF5D4D\",\n",
    "    \"gold\": \"#FFC857\", \"blue\": \"#415EB1\", \"paper\": \"#FFFFFF\",\n",
    "    \"mist\": \"#E9F5F4\", \"gray\": \"#7A8793\",\n",
    "}\n",
    "plt.rcParams.update({\n",
    "    \"figure.facecolor\": COLORS[\"paper\"], \"axes.facecolor\": COLORS[\"paper\"],\n",
    "    \"axes.edgecolor\": COLORS[\"ink\"], \"axes.labelcolor\": COLORS[\"ink\"],\n",
    "    \"text.color\": COLORS[\"ink\"], \"xtick.color\": COLORS[\"ink\"],\n",
    "    \"ytick.color\": COLORS[\"ink\"], \"font.size\": 10.5,\n",
    "    \"axes.titleweight\": \"bold\", \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False, \"figure.dpi\": 115,\n",
    "})\n",
    "\n",
    "EXPECTED_RUNTIME = {\"python_major_minor\": \"3.11\"}\n",
    "EXPECTED_LOCK = {'appnope': '0.1.4', 'asttokens': '3.0.2', 'attrs': '26.1.0', 'beautifulsoup4': '4.15.0', 'bleach': '6.4.0', 'comm': '0.2.3', 'contourpy': '1.3.3', 'cycler': '0.12.1', 'debugpy': '1.8.21', 'defusedxml': '0.7.1', 'executing': '2.2.1', 'fastjsonschema': '2.22.1', 'fonttools': '4.63.0', 'ipykernel': '7.2.0', 'ipython': '9.16.1', 'ipython-pygments-lexers': '1.1.1', 'jedi': '0.20.0', 'jinja2': '3.1.6', 'joblib': '1.5.3', 'jsonschema': '4.26.0', 'jsonschema-specifications': '2025.9.1', 'jupyter-client': '8.9.1', 'jupyter-core': '5.9.1', 'jupyterlab-pygments': '0.3.0', 'kiwisolver': '1.5.0', 'markupsafe': '3.0.3', 'matplotlib': '3.11.1', 'matplotlib-inline': '0.2.2', 'mistune': '3.3.4', 'narwhals': '2.24.0', 'nbclient': '0.11.0', 'nbconvert': '7.17.1', 'nbformat': '5.10.4', 'nest-asyncio': '1.6.0', 'numpy': '1.26.4', 'packaging': '26.3', 'pandas': '3.0.5', 'pandocfilters': '1.5.1', 'parso': '0.8.7', 'pexpect': '4.9.0', 'pillow': '12.3.0', 'platformdirs': '4.11.0', 'prompt-toolkit': '3.0.53', 'psutil': '7.2.2', 'ptyprocess': '0.7.0', 'pure-eval': '0.2.3', 'pygments': '2.20.0', 'pyparsing': '3.3.2', 'python-dateutil': '2.9.0.post0', 'pyzmq': '27.1.0', 'referencing': '0.37.0', 'rpds-py': '2026.6.3', 'scikit-learn': '1.9.0', 'scipy': '1.17.1', 'six': '1.17.0', 'soupsieve': '2.9.1', 'stack-data': '0.6.3', 'threadpoolctl': '3.6.0', 'tinycss2': '1.5.1', 'tornado': '6.5.7', 'traitlets': '5.16.1', 'typing-extensions': '4.16.0', 'wcwidth': '0.8.2', 'webencodings': '0.5.1'}\n",
    "\n",
    "def canonical_package_name(name: str) -> str:\n",
    "    return re.sub(r\"[-_.]+\", \"-\", name).lower()\n",
    "\n",
    "installed_lock = {\n",
    "    canonical_package_name(dist.metadata[\"Name\"]): dist.version\n",
    "    for dist in distributions()\n",
    "    if dist.metadata.get(\"Name\")\n",
    "}\n",
    "lock_missing = sorted(set(EXPECTED_LOCK) - set(installed_lock))\n",
    "lock_extra = sorted(set(installed_lock) - set(EXPECTED_LOCK))\n",
    "lock_mismatches = {\n",
    "    name: {\"expected\": version, \"observed\": installed_lock.get(name)}\n",
    "    for name, version in EXPECTED_LOCK.items()\n",
    "    if installed_lock.get(name) != version\n",
    "}\n",
    "runtime_matches = (\n",
    "    platform.python_version().startswith(EXPECTED_RUNTIME[\"python_major_minor\"] + \".\")\n",
    "    and not lock_missing\n",
    "    and not lock_extra\n",
    "    and not lock_mismatches\n",
    ")\n",
    "\n",
    "versions = pd.Series({\n",
    "    \"python\": platform.python_version(),\n",
    "    \"implementation\": platform.python_implementation(),\n",
    "    \"locked_packages_verified\": f\"{len(installed_lock)}/{len(EXPECTED_LOCK)} exact\",\n",
    "    \"numpy\": np.__version__,\n",
    "    \"pandas\": pd.__version__,\n",
    "    \"matplotlib\": matplotlib.__version__,\n",
    "    \"seed\": SEED,\n",
    "}, name=\"value\")\n",
    "display(Markdown(\"**Implemented — portable runtime identity.** Python is checked by major/minor and every installed distribution must match the complete 64-package lock exactly. No interpreter path is retained.\"))\n",
    "display(versions.to_frame())\n",
    "assert runtime_matches, {\n",
    "    \"python\": platform.python_version(),\n",
    "    \"expected_python\": EXPECTED_RUNTIME,\n",
    "    \"missing\": lock_missing,\n",
    "    \"extra\": lock_extra,\n",
    "    \"mismatches\": lock_mismatches,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d93446",
   "metadata": {},
   "source": [
    "## 1 · Build synthetic evidence with clocks\n",
    "\n",
    "**Simulated — not market data.** The generator below creates 960 ordered teaching rows. Its features resemble generic tabular signals only to make the evaluation pipeline concrete. The target comes from a declared latent Bernoulli process. It does not model a venue, order book, settlement source, or executable market.\n",
    "\n",
    "The feature lookback is 12 rows, label horizon is 5 rows, and illustrative availability allowance is 3 rows. The boundary gap is therefore conservatively set to 20 rows: \\`12 + 5 + 3\\`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d47b6747",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:08.347062Z",
     "iopub.status.busy": "2026-08-06T13:42:08.346963Z",
     "iopub.status.idle": "2026-08-06T13:42:08.356488Z",
     "shell.execute_reply": "2026-08-06T13:42:08.355988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Simulated — seeded synthetic generator.**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>event_time</th>\n",
       "      <th>receive_delay_ms</th>\n",
       "      <th>momentum</th>\n",
       "      <th>imbalance</th>\n",
       "      <th>spread</th>\n",
       "      <th>activity</th>\n",
       "      <th>time_wave</th>\n",
       "      <th>reference_probability</th>\n",
       "      <th>label_up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2026-01-01 00:00:00+00:00</td>\n",
       "      <td>35</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.5642</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>4.1862</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2026-01-01 00:05:00+00:00</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.0184</td>\n",
       "      <td>-0.7903</td>\n",
       "      <td>0.0627</td>\n",
       "      <td>4.4279</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.2693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2026-01-01 00:10:00+00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>-0.3108</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>0.0543</td>\n",
       "      <td>6.9573</td>\n",
       "      <td>0.0606</td>\n",
       "      <td>0.2736</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2026-01-01 00:15:00+00:00</td>\n",
       "      <td>86</td>\n",
       "      <td>-0.0320</td>\n",
       "      <td>0.7418</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>4.6290</td>\n",
       "      <td>0.0908</td>\n",
       "      <td>0.2796</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2026-01-01 00:20:00+00:00</td>\n",
       "      <td>103</td>\n",
       "      <td>-0.0143</td>\n",
       "      <td>-0.8465</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>5.1543</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2026-01-01 00:25:00+00:00</td>\n",
       "      <td>120</td>\n",
       "      <td>-0.0642</td>\n",
       "      <td>-0.8803</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>3.0065</td>\n",
       "      <td>0.1509</td>\n",
       "      <td>0.2675</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id                event_time  receive_delay_ms  momentum  imbalance  \\\n",
       "0       0 2026-01-01 00:00:00+00:00                35    0.0000    -0.5642   \n",
       "1       1 2026-01-01 00:05:00+00:00                52   -1.0184    -0.7903   \n",
       "2       2 2026-01-01 00:10:00+00:00                69   -0.3108     0.0621   \n",
       "3       3 2026-01-01 00:15:00+00:00                86   -0.0320     0.7418   \n",
       "4       4 2026-01-01 00:20:00+00:00               103   -0.0143    -0.8465   \n",
       "5       5 2026-01-01 00:25:00+00:00               120   -0.0642    -0.8803   \n",
       "\n",
       "   spread  activity  time_wave  reference_probability  label_up  \n",
       "0  0.0512    4.1862     0.0000                 0.2800         1  \n",
       "1  0.0627    4.4279     0.0303                 0.2693         0  \n",
       "2  0.0543    6.9573     0.0606                 0.2736         1  \n",
       "3  0.0467    4.6290     0.0908                 0.2796         1  \n",
       "4  0.0434    5.1543     0.1209                 0.2802         0  \n",
       "5  0.0577    3.0065     0.1509                 0.2675         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 960 | positive prevalence: 0.371 | seed: 8414\n"
     ]
    }
   ],
   "source": [
    "def sigmoid(x: np.ndarray | float) -> np.ndarray:\n",
    "    x_arr = np.asarray(x, dtype=float)\n",
    "    return 1.0 / (1.0 + np.exp(-np.clip(x_arr, -30, 30)))\n",
    "\n",
    "N = 960\n",
    "latent = np.zeros(N)\n",
    "innovations = rng.normal(0, 0.58, N)\n",
    "for t in range(1, N):\n",
    "    latent[t] = 0.88 * latent[t - 1] + innovations[t]\n",
    "\n",
    "momentum = pd.Series(latent).diff().rolling(4, min_periods=1).mean().fillna(0).to_numpy()\n",
    "imbalance = np.tanh(0.72 * latent + rng.normal(0, 0.50, N))\n",
    "spread = np.clip(0.045 + 0.018 * np.abs(latent) + rng.normal(0, 0.006, N), 0.008, None)\n",
    "activity = np.exp(np.clip(1.65 + 0.20 * latent + rng.normal(0, 0.22, N), 0.4, 3.0))\n",
    "time_wave = np.sin(np.arange(N) / 33.0)\n",
    "\n",
    "true_logit = -0.28 + 1.12 * momentum + 0.76 * imbalance - 3.2 * spread + 0.22 * time_wave\n",
    "true_probability = sigmoid(true_logit)\n",
    "y = rng.binomial(1, true_probability)\n",
    "reference_probability = np.clip(0.24 + 0.08 * sigmoid(0.55 * latent + 0.35 * time_wave), 0.05, 0.95)\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    \"row_id\": np.arange(N),\n",
    "    \"event_time\": pd.date_range(\"2026-01-01\", periods=N, freq=\"5min\", tz=\"UTC\"),\n",
    "    \"receive_delay_ms\": 35 + (np.arange(N) * 17) % 180,\n",
    "    \"momentum\": momentum,\n",
    "    \"imbalance\": imbalance,\n",
    "    \"spread\": spread,\n",
    "    \"activity\": activity,\n",
    "    \"time_wave\": time_wave,\n",
    "    \"reference_probability\": reference_probability,\n",
    "    \"label_up\": y,\n",
    "})\n",
    "FEATURES = [\"momentum\", \"imbalance\", \"spread\", \"activity\", \"time_wave\"]\n",
    "\n",
    "display(Markdown(\"**Simulated — seeded synthetic generator.**\"))\n",
    "display(data.head(6))\n",
    "print(f\"Rows: {len(data):,} | positive prevalence: {data.label_up.mean():.3f} | seed: {SEED}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fdc3550",
   "metadata": {
    "alt": "Two synthetic feature traces above a latent probability and sampled binary labels across ordered rows.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:08.357731Z",
     "iopub.status.busy": "2026-08-06T13:42:08.357647Z",
     "iopub.status.idle": "2026-08-06T13:42:08.494515Z",
     "shell.execute_reply": "2026-08-06T13:42:08.494111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1265x667 with 2 Axes>"
      ]
     },
     "metadata": {
      "alt": "Synthetic time series showing decision-time features and later binary labels."
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(11, 5.8), sharex=True, constrained_layout=True)\n",
    "axes[0].plot(data.row_id, data.imbalance, color=COLORS[\"teal\"], lw=1.4, label=\"synthetic imbalance\")\n",
    "axes[0].plot(data.row_id, data.momentum, color=COLORS[\"blue\"], lw=1.0, alpha=0.8, label=\"synthetic momentum\")\n",
    "axes[0].axhline(0, color=COLORS[\"ink\"], lw=0.8)\n",
    "axes[0].set_ylabel(\"feature value\")\n",
    "axes[0].legend(frameon=False, ncol=2, loc=\"upper right\")\n",
    "axes[1].plot(data.row_id, true_probability, color=COLORS[\"coral\"], lw=1.4, label=\"latent event probability\")\n",
    "axes[1].scatter(data.row_id[::8], data.label_up[::8], s=10, color=COLORS[\"ink\"], alpha=0.50, label=\"sampled label\")\n",
    "axes[1].set(xlabel=\"ordered synthetic decision row\", ylabel=\"probability / label\", ylim=(-0.05, 1.05))\n",
    "axes[1].legend(frameon=False, ncol=2, loc=\"upper right\")\n",
    "fig.suptitle(\"SIMULATED · Seeded teaching evidence, not market observations\", fontsize=14, fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f25b059",
   "metadata": {},
   "source": [
    "### Exercise 1 — contract defense\n",
    "\n",
    "Change the label horizon from 5 to 8 rows. Which split boundaries must move, and why would changing only the displayed gap fail to protect the experiment? Then identify one way \\`receive_delay_ms\\` could invalidate an event-time-only feature builder."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752f1be8",
   "metadata": {},
   "source": [
    "## 2 · Temporal split, purge, and embargo\n",
    "\n",
    "**Illustrative contract; implemented split.** Training ends before a 20-row purge. Validation follows. A separate 20-row embargo precedes the untouched test. These widths teach derivation from declared windows; they are not recommended durations for any real system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9a7857e",
   "metadata": {
    "alt": "Chronological train, purge, validation, embargo, and untouched-test blocks with a gap derived from lookback, horizon, and availability.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:08.496012Z",
     "iopub.status.busy": "2026-08-06T13:42:08.495932Z",
     "iopub.status.idle": "2026-08-06T13:42:08.569403Z",
     "shell.execute_reply": "2026-08-06T13:42:08.568985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1265x322 with 1 Axes>"
      ]
     },
     "metadata": {
      "alt": "Chronological train, purge, validation, embargo, and untouched-test partitions."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>component</th>\n",
       "      <th>rows</th>\n",
       "      <th>epistemic_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feature lookback</td>\n",
       "      <td>12</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>label horizon</td>\n",
       "      <td>5</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>availability allowance</td>\n",
       "      <td>3</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>derived gap</td>\n",
       "      <td>20</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                component  rows epistemic_status\n",
       "0        feature lookback    12     Illustrative\n",
       "1           label horizon     5     Illustrative\n",
       "2  availability allowance     3     Illustrative\n",
       "3             derived gap    20     Illustrative"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boundary</th>\n",
       "      <th>left_available_end</th>\n",
       "      <th>right_feature_start</th>\n",
       "      <th>disjoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train→validation</td>\n",
       "      <td>547</td>\n",
       "      <td>549</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation→test</td>\n",
       "      <td>727</td>\n",
       "      <td>729</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           boundary  left_available_end  right_feature_start  disjoint\n",
       "0  train→validation                 547                  549      True\n",
       "1   validation→test                 727                  729      True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LOOKBACK_ROWS = 12\n",
    "LABEL_HORIZON_ROWS = 5\n",
    "AVAILABILITY_ALLOWANCE_ROWS = 3\n",
    "GAP_ROWS = LOOKBACK_ROWS + LABEL_HORIZON_ROWS + AVAILABILITY_ALLOWANCE_ROWS\n",
    "\n",
    "split = {\n",
    "    \"train\": np.arange(0, 540),\n",
    "    \"purge\": np.arange(540, 560),\n",
    "    \"validation\": np.arange(560, 720),\n",
    "    \"embargo\": np.arange(720, 740),\n",
    "    \"test\": np.arange(740, 960),\n",
    "}\n",
    "assert GAP_ROWS == len(split[\"purge\"]) == len(split[\"embargo\"])\n",
    "assert max(split[\"train\"]) < min(split[\"purge\"]) < min(split[\"validation\"])\n",
    "assert max(split[\"validation\"]) < min(split[\"embargo\"]) < min(split[\"test\"])\n",
    "\n",
    "def support_interval(decision_row: int) -> dict[str, int]:\n",
    "    return {\n",
    "        \"feature_start\": decision_row - LOOKBACK_ROWS + 1,\n",
    "        \"feature_end\": decision_row,\n",
    "        \"label_start\": decision_row + 1,\n",
    "        \"label_end\": decision_row + LABEL_HORIZON_ROWS,\n",
    "        \"available_end\": decision_row + LABEL_HORIZON_ROWS + AVAILABILITY_ALLOWANCE_ROWS,\n",
    "    }\n",
    "\n",
    "boundary_support_audit = pd.DataFrame([\n",
    "    {\"boundary\": \"train→validation\",\n",
    "     \"left_available_end\": support_interval(int(max(split[\"train\"])))[\"available_end\"],\n",
    "     \"right_feature_start\": support_interval(int(min(split[\"validation\"])))[\"feature_start\"]},\n",
    "    {\"boundary\": \"validation→test\",\n",
    "     \"left_available_end\": support_interval(int(max(split[\"validation\"])))[\"available_end\"],\n",
    "     \"right_feature_start\": support_interval(int(min(split[\"test\"])))[\"feature_start\"]},\n",
    "])\n",
    "boundary_support_audit[\"disjoint\"] = (\n",
    "    boundary_support_audit[\"left_available_end\"] < boundary_support_audit[\"right_feature_start\"]\n",
    ")\n",
    "assert boundary_support_audit[\"disjoint\"].all(), boundary_support_audit.to_dict(\"records\")\n",
    "\n",
    "segments = [\n",
    "    (\"TRAIN\", 0, 540, COLORS[\"teal\"]),\n",
    "    (\"PURGE\", 540, 560, COLORS[\"coral\"]),\n",
    "    (\"VALIDATION\", 560, 720, COLORS[\"blue\"]),\n",
    "    (\"EMBARGO\", 720, 740, COLORS[\"gold\"]),\n",
    "    (\"UNTOUCHED TEST\", 740, 960, COLORS[\"ink\"]),\n",
    "]\n",
    "fig, ax = plt.subplots(figsize=(11, 2.8), constrained_layout=True)\n",
    "for label, start, end, color in segments:\n",
    "    ax.barh(0, end - start, left=start, height=0.48, color=color, edgecolor=\"white\")\n",
    "    ax.text((start + end) / 2, 0, f\"{label}\\n{end-start} rows\", ha=\"center\", va=\"center\",\n",
    "            color=\"white\" if label != \"EMBARGO\" else COLORS[\"ink\"], fontweight=\"bold\", fontsize=9)\n",
    "ax.annotate(f\"gap = lookback {LOOKBACK_ROWS} + horizon {LABEL_HORIZON_ROWS} + availability {AVAILABILITY_ALLOWANCE_ROWS}\",\n",
    "            xy=(550, 0.31), xytext=(480, 0.85), arrowprops={\"arrowstyle\": \"->\", \"color\": COLORS[\"coral\"]},\n",
    "            ha=\"center\", color=COLORS[\"coral\"], fontweight=\"bold\")\n",
    "ax.set(xlim=(0, N), ylim=(-0.55, 1.1), xlabel=\"ordered synthetic row\", yticks=[])\n",
    "ax.set_title(\"ILLUSTRATIVE CONTRACT · Chronology with purge and embargo\")\n",
    "plt.show()\n",
    "\n",
    "display(pd.DataFrame({\n",
    "    \"component\": [\"feature lookback\", \"label horizon\", \"availability allowance\", \"derived gap\"],\n",
    "    \"rows\": [LOOKBACK_ROWS, LABEL_HORIZON_ROWS, AVAILABILITY_ALLOWANCE_ROWS, GAP_ROWS],\n",
    "    \"epistemic_status\": [\"Illustrative\"] * 4,\n",
    "}))\n",
    "display(boundary_support_audit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8edb9f",
   "metadata": {},
   "source": [
    "### Exercise 2 — leakage audit\n",
    "\n",
    "A row at index 538 uses a 12-row lookback and its label resolves 5 rows later. Draw its full support. Explain why chronology without purging can still leak. Then propose a boundary assertion that operates on raw-event identifiers rather than row numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31856927",
   "metadata": {},
   "source": [
    "## 3 · Demanding tabular fallback\n",
    "\n",
    "**Design boundary.** CatBoost is absent and is not emulated. The intended teaching baseline is scikit-learn's \\`GradientBoostingClassifier\\`, clearly labeled a fallback challenger rather than CatBoost. A complex model earns attention only after it survives the same temporal split and calibration/replay contract.\n",
    "\n",
    "If the verified environment cannot import scikit-learn, the cell fails safely into a small NumPy boosted-stump teaching surrogate so later arithmetic remains inspectable. That branch is an environment limitation, not a substitute for the requested scikit-learn evidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e14ee296",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:08.570921Z",
     "iopub.status.busy": "2026-08-06T13:42:08.570826Z",
     "iopub.status.idle": "2026-08-06T13:42:09.431771Z",
     "shell.execute_reply": "2026-08-06T13:42:09.431275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>scikit-learn GradientBoostingClassifier fallba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>Implemented teaching fallback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatBoost imported</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scikit-learn available</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scikit-learn version</th>\n",
       "      <td>1.9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure detail</th>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fit rows</th>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>validation rows</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test rows</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    value\n",
       "model                   scikit-learn GradientBoostingClassifier fallba...\n",
       "status                                      Implemented teaching fallback\n",
       "CatBoost imported                                                   False\n",
       "scikit-learn available                                               True\n",
       "scikit-learn version                                                1.9.0\n",
       "failure detail                                                       none\n",
       "fit rows                                                              540\n",
       "validation rows                                                       160\n",
       "test rows                                                             220"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIMULATED teaching output. No market-performance claim.\n"
     ]
    }
   ],
   "source": [
    "SKLEARN_OK = False\n",
    "SKLEARN_ERROR = None\n",
    "_import_stderr = io.StringIO()\n",
    "with warnings.catch_warnings(), contextlib.redirect_stderr(_import_stderr):\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    try:\n",
    "        import sklearn\n",
    "        from sklearn.ensemble import GradientBoostingClassifier\n",
    "        SKLEARN_OK = True\n",
    "    except Exception as exc:\n",
    "        SKLEARN_ERROR = f\"{type(exc).__name__}: {exc}\"\n",
    "\n",
    "class NumpyBoostedStumps:\n",
    "    \"\"\"Small deterministic teaching surrogate; not sklearn and not CatBoost.\"\"\"\n",
    "    def __init__(self, n_estimators: int = 45, learning_rate: float = 0.08):\n",
    "        self.n_estimators = n_estimators\n",
    "        self.learning_rate = learning_rate\n",
    "        self.stumps: list[tuple[int, float, float, float]] = []\n",
    "\n",
    "    def fit(self, x: np.ndarray, target: np.ndarray) -> \"NumpyBoostedStumps\":\n",
    "        prevalence = np.clip(target.mean(), 1e-5, 1 - 1e-5)\n",
    "        self.base_logit = float(np.log(prevalence / (1 - prevalence)))\n",
    "        score = np.full(len(target), self.base_logit)\n",
    "        quantiles = np.linspace(0.15, 0.85, 8)\n",
    "        for _ in range(self.n_estimators):\n",
    "            residual = target - sigmoid(score)\n",
    "            best = None\n",
    "            for feature in range(x.shape[1]):\n",
    "                for threshold in np.unique(np.quantile(x[:, feature], quantiles)):\n",
    "                    left = x[:, feature] <= threshold\n",
    "                    if left.sum() < 8 or (~left).sum() < 8:\n",
    "                        continue\n",
    "                    left_value = float(residual[left].mean())\n",
    "                    right_value = float(residual[~left].mean())\n",
    "                    prediction = np.where(left, left_value, right_value)\n",
    "                    loss = float(np.mean((residual - prediction) ** 2))\n",
    "                    if best is None or loss < best[0]:\n",
    "                        best = (loss, feature, float(threshold), left_value, right_value)\n",
    "            _, feature, threshold, left_value, right_value = best\n",
    "            self.stumps.append((feature, threshold, left_value, right_value))\n",
    "            score += self.learning_rate * np.where(x[:, feature] <= threshold, left_value, right_value)\n",
    "        return self\n",
    "\n",
    "    def predict_proba(self, x: np.ndarray) -> np.ndarray:\n",
    "        score = np.full(len(x), self.base_logit)\n",
    "        for feature, threshold, left_value, right_value in self.stumps:\n",
    "            score += self.learning_rate * np.where(x[:, feature] <= threshold, left_value, right_value)\n",
    "        probability = sigmoid(score)\n",
    "        return np.column_stack([1 - probability, probability])\n",
    "\n",
    "x_train = data.loc[split[\"train\"], FEATURES].to_numpy()\n",
    "y_train = data.loc[split[\"train\"], \"label_up\"].to_numpy()\n",
    "x_val = data.loc[split[\"validation\"], FEATURES].to_numpy()\n",
    "y_val = data.loc[split[\"validation\"], \"label_up\"].to_numpy()\n",
    "x_test = data.loc[split[\"test\"], FEATURES].to_numpy()\n",
    "y_test = data.loc[split[\"test\"], \"label_up\"].to_numpy()\n",
    "\n",
    "if SKLEARN_OK:\n",
    "    model = GradientBoostingClassifier(\n",
    "        random_state=SEED, n_estimators=90, learning_rate=0.05,\n",
    "        max_depth=2, min_samples_leaf=18, subsample=0.85,\n",
    "    )\n",
    "    model.fit(x_train, y_train)\n",
    "    model_name = \"scikit-learn GradientBoostingClassifier fallback (not CatBoost)\"\n",
    "    model_status = \"Implemented teaching fallback\"\n",
    "else:\n",
    "    model = NumpyBoostedStumps().fit(x_train, y_train)\n",
    "    model_name = \"NumPy boosted-stump environment surrogate (not sklearn; not CatBoost)\"\n",
    "    model_status = \"LIMITATION — sklearn import failed closed\"\n",
    "\n",
    "p_val = model.predict_proba(x_val)[:, 1]\n",
    "p_test = model.predict_proba(x_test)[:, 1]\n",
    "model_record = pd.Series({\n",
    "    \"model\": model_name,\n",
    "    \"status\": model_status,\n",
    "    \"CatBoost imported\": False,\n",
    "    \"scikit-learn available\": SKLEARN_OK,\n",
    "    \"scikit-learn version\": sklearn.__version__ if SKLEARN_OK else \"unavailable\",\n",
    "    \"failure detail\": SKLEARN_ERROR or \"none\",\n",
    "    \"fit rows\": len(x_train),\n",
    "    \"validation rows\": len(x_val),\n",
    "    \"test rows\": len(x_test),\n",
    "})\n",
    "display(model_record.to_frame(\"value\"))\n",
    "print(\"SIMULATED teaching output. No market-performance claim.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61384676",
   "metadata": {},
   "source": [
    "## 4 · Confusion matrix and threshold metric table\n",
    "\n",
    "**Simulated held-out result.** The classification threshold is selected on validation by maximum F1, with deterministic tie-breaking toward the higher threshold. The frozen threshold is then applied once to the untouched synthetic test interval. Accuracy, precision, recall, specificity, F1, and MCC answer different questions; none certifies calibration or value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95bd54a3",
   "metadata": {
    "alt": "Two-by-two simulated untouched-test confusion matrix at the frozen validation-selected threshold.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:09.433607Z",
     "iopub.status.busy": "2026-08-06T13:42:09.433467Z",
     "iopub.status.idle": "2026-08-06T13:42:09.532188Z",
     "shell.execute_reply": "2026-08-06T13:42:09.531608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 621x529 with 2 Axes>"
      ]
     },
     "metadata": {
      "alt": "Confusion matrix with true-positive, false-positive, true-negative, and false-negative counts."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Simulated metric table — same matrix, different denominators.**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "      <th>question</th>\n",
       "      <th>epistemic_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>fraction of all hard decisions correct</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>precision</td>\n",
       "      <td>0.4648</td>\n",
       "      <td>reliability of positive decisions</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>recall</td>\n",
       "      <td>0.7416</td>\n",
       "      <td>share of positives detected</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>specificity</td>\n",
       "      <td>0.4198</td>\n",
       "      <td>share of negatives rejected</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f1</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>harmonic precision–recall summary</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mcc</td>\n",
       "      <td>0.1656</td>\n",
       "      <td>association using all four cells</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        metric  value                                question epistemic_status\n",
       "0     accuracy 0.5500  fraction of all hard decisions correct        Simulated\n",
       "1    precision 0.4648       reliability of positive decisions        Simulated\n",
       "2       recall 0.7416             share of positives detected        Simulated\n",
       "3  specificity 0.4198             share of negatives rejected        Simulated\n",
       "4           f1 0.5714       harmonic precision–recall summary        Simulated\n",
       "5          mcc 0.1656        association using all four cells        Simulated"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def confusion_counts(target: np.ndarray, probability: np.ndarray, threshold: float) -> dict[str, int]:\n",
    "    pred = probability >= threshold\n",
    "    return {\n",
    "        \"TN\": int(np.sum((target == 0) & (~pred))),\n",
    "        \"FP\": int(np.sum((target == 0) & pred)),\n",
    "        \"FN\": int(np.sum((target == 1) & (~pred))),\n",
    "        \"TP\": int(np.sum((target == 1) & pred)),\n",
    "    }\n",
    "\n",
    "def threshold_metrics(target: np.ndarray, probability: np.ndarray, threshold: float) -> dict[str, float]:\n",
    "    c = confusion_counts(target, probability, threshold)\n",
    "    tn, fp, fn, tp = c[\"TN\"], c[\"FP\"], c[\"FN\"], c[\"TP\"]\n",
    "    safe = lambda numerator, denominator: numerator / denominator if denominator else np.nan\n",
    "    precision = safe(tp, tp + fp)\n",
    "    recall = safe(tp, tp + fn)\n",
    "    specificity = safe(tn, tn + fp)\n",
    "    denominator = math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n",
    "    return {\n",
    "        \"threshold\": threshold,\n",
    "        \"accuracy\": safe(tp + tn, tp + tn + fp + fn),\n",
    "        \"precision\": precision,\n",
    "        \"recall\": recall,\n",
    "        \"specificity\": specificity,\n",
    "        \"f1\": safe(2 * precision * recall, precision + recall),\n",
    "        \"mcc\": safe(tp * tn - fp * fn, denominator),\n",
    "        **c,\n",
    "    }\n",
    "\n",
    "candidate_thresholds = np.linspace(0.05, 0.95, 181)\n",
    "val_sweep = pd.DataFrame([threshold_metrics(y_val, p_val, t) for t in candidate_thresholds])\n",
    "selected_threshold = float(val_sweep.sort_values([\"f1\", \"threshold\"], ascending=[False, False]).iloc[0].threshold)\n",
    "test_metric = threshold_metrics(y_test, p_test, selected_threshold)\n",
    "metric_table = pd.DataFrame({\n",
    "    \"metric\": [\"accuracy\", \"precision\", \"recall\", \"specificity\", \"f1\", \"mcc\"],\n",
    "    \"value\": [test_metric[k] for k in [\"accuracy\", \"precision\", \"recall\", \"specificity\", \"f1\", \"mcc\"]],\n",
    "    \"question\": [\n",
    "        \"fraction of all hard decisions correct\", \"reliability of positive decisions\",\n",
    "        \"share of positives detected\", \"share of negatives rejected\",\n",
    "        \"harmonic precision–recall summary\", \"association using all four cells\",\n",
    "    ],\n",
    "    \"epistemic_status\": \"Simulated\",\n",
    "})\n",
    "cm = np.array([[test_metric[\"TN\"], test_metric[\"FP\"]], [test_metric[\"FN\"], test_metric[\"TP\"]]])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5.4, 4.6), constrained_layout=True)\n",
    "image = ax.imshow(cm, cmap=\"GnBu\", vmin=0)\n",
    "for (row, col), value in np.ndenumerate(cm):\n",
    "    ax.text(col, row, str(value), ha=\"center\", va=\"center\", fontsize=20, fontweight=\"bold\",\n",
    "            color=\"white\" if value > cm.max() * 0.55 else COLORS[\"ink\"])\n",
    "ax.set(xticks=[0, 1], xticklabels=[\"Predicted Down\", \"Predicted Up\"],\n",
    "       yticks=[0, 1], yticklabels=[\"Actual Down\", \"Actual Up\"],\n",
    "       xlabel=f\"Frozen validation-selected threshold = {selected_threshold:.3f}\",\n",
    "       ylabel=\"Observed synthetic label\")\n",
    "ax.set_title(\"SIMULATED · Untouched-test confusion matrix\")\n",
    "fig.colorbar(image, ax=ax, shrink=0.74, label=\"count\")\n",
    "plt.show()\n",
    "\n",
    "display(Markdown(\"**Simulated metric table — same matrix, different denominators.**\"))\n",
    "display(metric_table)\n",
    "assert cm.sum() == len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f85e43",
   "metadata": {},
   "source": [
    "### Exercise 3 — denominators and policy\n",
    "\n",
    "Recompute precision and specificity directly from the displayed matrix. Then lower the threshold by 0.05 and predict which cells must weakly increase or decrease. Explain why a higher F1 would still not establish a calibrated probability or positive expected value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0ff5a4",
   "metadata": {},
   "source": [
    "## 5 · ROC and precision–recall threshold sweeps\n",
    "\n",
    "**Simulated ranking evidence.** ROC plots true-positive rate against false-positive rate. Precision–recall centers the positive class. The scalar reported here is stepwise average precision (AP), not an unnamed trapezoidal “PR-AUC.” Both plots show the frozen operating point; neither proves calibration or usefulness after costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60ced9d1",
   "metadata": {
    "alt": "Paired ROC and precision-recall threshold sweeps with a highlighted frozen operating point and explicit prevalence baseline.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:09.533865Z",
     "iopub.status.busy": "2026-08-06T13:42:09.533757Z",
     "iopub.status.idle": "2026-08-06T13:42:09.679227Z",
     "shell.execute_reply": "2026-08-06T13:42:09.678359Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1265x540.5 with 2 Axes>"
      ]
     },
     "metadata": {
      "alt": "ROC and precision-recall threshold sweeps for the same synthetic held-out scores."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "      <th>recall</th>\n",
       "      <th>fpr</th>\n",
       "      <th>precision</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0500</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.4045</td>\n",
       "      <td>0.5761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.9101</td>\n",
       "      <td>0.7939</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.5912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3500</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>0.4046</td>\n",
       "      <td>0.5093</td>\n",
       "      <td>0.5584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.3034</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.4030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.0674</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>0.5455</td>\n",
       "      <td>0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold  recall    fpr  precision     f1\n",
       "0     0.0500  1.0000 1.0000     0.4045 0.5761\n",
       "1     0.2000  0.9101 0.7939     0.4378 0.5912\n",
       "2     0.3500  0.6180 0.4046     0.5093 0.5584\n",
       "3     0.5000  0.3034 0.1374     0.6000 0.4030\n",
       "4     0.6500  0.0674 0.0382     0.5455 0.1200\n",
       "5     0.8000  0.0000 0.0076     0.0000    NaN\n",
       "6     0.9500  0.0000 0.0000        NaN    NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def roc_auc_rank(target: np.ndarray, probability: np.ndarray) -> float:\n",
    "    positives = probability[target == 1]\n",
    "    negatives = probability[target == 0]\n",
    "    comparisons = (positives[:, None] > negatives[None, :]).mean()\n",
    "    ties = (positives[:, None] == negatives[None, :]).mean()\n",
    "    return float(comparisons + 0.5 * ties)\n",
    "\n",
    "def average_precision_stepwise(target: np.ndarray, probability: np.ndarray) -> float:\n",
    "    order = np.argsort(-probability, kind=\"mergesort\")\n",
    "    sorted_target = target[order]\n",
    "    cumulative_tp = np.cumsum(sorted_target)\n",
    "    ranks = np.arange(1, len(target) + 1)\n",
    "    precision_at_rank = cumulative_tp / ranks\n",
    "    return float(precision_at_rank[sorted_target == 1].mean())\n",
    "\n",
    "test_sweep = pd.DataFrame([threshold_metrics(y_test, p_test, t) for t in candidate_thresholds])\n",
    "test_sweep[\"fpr\"] = 1 - test_sweep[\"specificity\"]\n",
    "roc_auc = roc_auc_rank(y_test, p_test)\n",
    "average_precision = average_precision_stepwise(y_test, p_test)\n",
    "positive_prevalence = float(y_test.mean())\n",
    "operating = test_sweep.iloc[(test_sweep.threshold - selected_threshold).abs().argmin()]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.7), constrained_layout=True)\n",
    "roc_view = test_sweep.sort_values([\"fpr\", \"recall\"])\n",
    "axes[0].plot(roc_view.fpr, roc_view.recall, color=COLORS[\"blue\"], lw=2.4)\n",
    "axes[0].plot([0, 1], [0, 1], \"--\", color=COLORS[\"gray\"], lw=1)\n",
    "axes[0].scatter([operating.fpr], [operating.recall], s=75, color=COLORS[\"coral\"], zorder=3, label=\"frozen threshold\")\n",
    "axes[0].set(xlabel=\"false-positive rate\", ylabel=\"true-positive rate\", xlim=(-0.02, 1.02), ylim=(-0.02, 1.02),\n",
    "            title=f\"ROC threshold sweep · rank AUC = {roc_auc:.3f}\")\n",
    "axes[0].legend(frameon=False)\n",
    "pr_view = test_sweep.sort_values(\"recall\")\n",
    "axes[1].plot(pr_view.recall, pr_view.precision, color=COLORS[\"teal\"], lw=2.4)\n",
    "axes[1].axhline(positive_prevalence, ls=\"--\", color=COLORS[\"gray\"], lw=1, label=f\"prevalence = {positive_prevalence:.3f}\")\n",
    "axes[1].scatter([operating.recall], [operating.precision], s=75, color=COLORS[\"coral\"], zorder=3, label=\"frozen threshold\")\n",
    "axes[1].set(xlabel=\"recall\", ylabel=\"precision\", xlim=(-0.02, 1.02), ylim=(-0.02, 1.02),\n",
    "            title=f\"Precision–recall sweep · stepwise AP = {average_precision:.3f}\")\n",
    "axes[1].legend(frameon=False)\n",
    "fig.suptitle(\"SIMULATED · Ranking views, not calibration or decision value\", fontsize=14, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "display(test_sweep.iloc[::30][[\"threshold\", \"recall\", \"fpr\", \"precision\", \"f1\"]].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29a7713",
   "metadata": {},
   "source": [
    "## 6 · Reliability, Brier score, and log loss\n",
    "\n",
    "**Simulated probability evidence.** Equal-width reliability bins compare mean prediction with observed frequency and expose sample counts. Brier score and log loss assess the probability forecast jointly; a lower Brier score alone must not be described as proof of better calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "caa2b201",
   "metadata": {
    "alt": "Reliability diagram with bin counts beside overlapping probability histograms for positive and negative synthetic labels.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:09.680784Z",
     "iopub.status.busy": "2026-08-06T13:42:09.680704Z",
     "iopub.status.idle": "2026-08-06T13:42:09.843481Z",
     "shell.execute_reply": "2026-08-06T13:42:09.842972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1265x529 with 2 Axes>"
      ]
     },
     "metadata": {
      "alt": "Reliability diagram and probability distributions used to compare calibration evidence."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin</th>\n",
       "      <th>count</th>\n",
       "      <th>mean_prediction</th>\n",
       "      <th>observed_frequency</th>\n",
       "      <th>calibration_gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 0.125]</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>-0.0230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0.125, 0.25]</td>\n",
       "      <td>45</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>0.3111</td>\n",
       "      <td>-0.1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0.25, 0.375]</td>\n",
       "      <td>74</td>\n",
       "      <td>0.3158</td>\n",
       "      <td>0.3378</td>\n",
       "      <td>-0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0.375, 0.5]</td>\n",
       "      <td>48</td>\n",
       "      <td>0.4292</td>\n",
       "      <td>0.4583</td>\n",
       "      <td>-0.0291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0.5, 0.625]</td>\n",
       "      <td>33</td>\n",
       "      <td>0.5571</td>\n",
       "      <td>0.6364</td>\n",
       "      <td>-0.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0.625, 0.75]</td>\n",
       "      <td>9</td>\n",
       "      <td>0.6946</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.1390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0.75, 0.875]</td>\n",
       "      <td>3</td>\n",
       "      <td>0.7822</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.4489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bin  count  mean_prediction  observed_frequency  \\\n",
       "0  (-0.001, 0.125]      8           0.1020              0.1250   \n",
       "1    (0.125, 0.25]     45           0.1855              0.3111   \n",
       "2    (0.25, 0.375]     74           0.3158              0.3378   \n",
       "3     (0.375, 0.5]     48           0.4292              0.4583   \n",
       "4     (0.5, 0.625]     33           0.5571              0.6364   \n",
       "5    (0.625, 0.75]      9           0.6946              0.5556   \n",
       "6    (0.75, 0.875]      3           0.7822              0.3333   \n",
       "\n",
       "   calibration_gap  \n",
       "0          -0.0230  \n",
       "1          -0.1256  \n",
       "2          -0.0221  \n",
       "3          -0.0291  \n",
       "4          -0.0792  \n",
       "5           0.1390  \n",
       "6           0.4489  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>value</th>\n",
       "      <th>interpretation</th>\n",
       "      <th>epistemic_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brier score</td>\n",
       "      <td>0.2319</td>\n",
       "      <td>mean squared probability error</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log loss</td>\n",
       "      <td>0.6585</td>\n",
       "      <td>proper score with sharp penalty for confident ...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         score  value                                     interpretation  \\\n",
       "0  Brier score 0.2319                     mean squared probability error   \n",
       "1     log loss 0.6585  proper score with sharp penalty for confident ...   \n",
       "\n",
       "  epistemic_status  \n",
       "0        Simulated  \n",
       "1        Simulated  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calibration_table(target: np.ndarray, probability: np.ndarray, bins: int = 8) -> pd.DataFrame:\n",
    "    frame = pd.DataFrame({\"target\": target, \"probability\": probability})\n",
    "    frame[\"bin\"] = pd.cut(frame.probability, bins=np.linspace(0, 1, bins + 1), include_lowest=True)\n",
    "    result = frame.groupby(\"bin\", observed=False).agg(\n",
    "        count=(\"target\", \"size\"), mean_prediction=(\"probability\", \"mean\"), observed_frequency=(\"target\", \"mean\")\n",
    "    ).reset_index()\n",
    "    result[\"calibration_gap\"] = result.mean_prediction - result.observed_frequency\n",
    "    return result[result[\"count\"] > 0].reset_index(drop=True)\n",
    "\n",
    "calibration = calibration_table(y_test, p_test)\n",
    "brier = float(np.mean((p_test - y_test) ** 2))\n",
    "clipped = np.clip(p_test, 1e-12, 1 - 1e-12)\n",
    "logloss = float(-np.mean(y_test * np.log(clipped) + (1 - y_test) * np.log(1 - clipped)))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.6), constrained_layout=True)\n",
    "axes[0].plot([0, 1], [0, 1], \"--\", color=COLORS[\"gray\"], label=\"perfect calibration\")\n",
    "axes[0].plot(calibration.mean_prediction, calibration.observed_frequency, \"o-\", color=COLORS[\"teal\"], lw=2, ms=7)\n",
    "for row in calibration.itertuples():\n",
    "    axes[0].annotate(f\"n={row.count}\", (row.mean_prediction, row.observed_frequency), xytext=(4, 5), textcoords=\"offset points\", fontsize=8)\n",
    "axes[0].set(xlabel=\"mean predicted probability\", ylabel=\"observed positive frequency\", xlim=(0, 1), ylim=(0, 1),\n",
    "            title=\"Reliability diagram\")\n",
    "axes[0].legend(frameon=False)\n",
    "axes[1].hist(p_test[y_test == 0], bins=14, alpha=0.72, color=COLORS[\"blue\"], label=\"Down labels\")\n",
    "axes[1].hist(p_test[y_test == 1], bins=14, alpha=0.72, color=COLORS[\"coral\"], label=\"Up labels\")\n",
    "axes[1].set(xlabel=\"predicted probability\", ylabel=\"count\", title=\"Forecast sharpness and overlap\")\n",
    "axes[1].legend(frameon=False)\n",
    "fig.suptitle(f\"SIMULATED · Brier = {brier:.4f} · log loss = {logloss:.4f}\", fontsize=14, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "display(calibration)\n",
    "display(pd.DataFrame({\n",
    "    \"score\": [\"Brier score\", \"log loss\"], \"value\": [brier, logloss],\n",
    "    \"interpretation\": [\"mean squared probability error\", \"proper score with sharp penalty for confident errors\"],\n",
    "    \"epistemic_status\": [\"Simulated\", \"Simulated\"],\n",
    "}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60f1e66",
   "metadata": {},
   "source": [
    "### Exercise 4 — ranking versus probability\n",
    "\n",
    "Apply any strictly increasing transformation to the test scores. Explain why rank AUC remains unchanged while Brier score, log loss, and the reliability curve may change. Which evidence would you require before using probability language on a selected high-score region?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445a1899",
   "metadata": {},
   "source": [
    "## 7 · Expected-value triggers and abstention\n",
    "\n",
    "**Illustrative formula, simulated inputs.** Under a teaching-only binary $1/$0 payoff, conservative per-unit edge is\n",
    "\n",
    "\\[\n",
    "e_c = p_{lower} - q_{exec} - c_{fees,slippage,latency}.\n",
    "\\]\n",
    "\n",
    "The deterministic policy acts only if data, artifact, calibration, and risk gates pass and \\`e_c ≥ margin\\`. Abstention is a policy output with a reason code—not a third settlement label and never permission for live execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "934b9ac2",
   "metadata": {
    "alt": "Conservative-edge distribution and reason-coded abstention, no-trade, and paper-intent counts.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:09.845098Z",
     "iopub.status.busy": "2026-08-06T13:42:09.844996Z",
     "iopub.status.idle": "2026-08-06T13:42:09.971265Z",
     "shell.execute_reply": "2026-08-06T13:42:09.970668Z"
    }
   },
   "outputs": [
    {
     "data": {
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iBnaPeF15bNn+XTPg+L1796VIwfwyc9IYtxYekYHjN9ZjeZO8KHfv+prf08AXBa6mzZpnJgdZtmCGqWQAAMJwjonCdcHnYcDgkabO0bd3zyD1FE91lMSJXzA3kK9Gsnrks6b1ji49PpEcb2aTdq2bh3g8pnzD/cZ7VL8ueRoXLl4yE5t9890wGfztF5IvTy7ZuWuP9Pr0CxN0Xv7jTHN94tg3uXJkD/57fC16HXd5cuWQOVPHmUlRps+ebxr1aPCvw1st5cvPPnSmu+vrK76+vh6v/aLrvgv9uSKJuYkUlrR2RuDvOdHZTPUC0bu07l0YAt0o+W/Zo4WWhG5W16HfjzNdRxvVr2OCCs/Kw4cPvS5vwL3A5Y0RfHkj5yS2IfL0GT5aHsOrmXmfdv3nzdNn+KTlthAjfD4fnZGvR+8vTEu//v0+k8gquO+pt8UtWqiA7P1ts5lBT7ssfPntYKndqKUsnjtNUiR/TSKbJ/8uBV/oU6fPmBmL9W5ugXx5nmkeAcAuYoj96oLP0pwFi2T85OlStlRx6di2VajqkdFthw4eMUYOHz0m65b9aAKfz+r8b0cPHj40Xe2/+LSXVK1U3izTnhoaDOz16ZeydMUaqV+3pjM9++6xrdt2SOuOXaRy+bLyQZdOJrC39dcd0rvvt3L12jUZPWyg275m33nH837y/N2MEYq0dkTg7znRcUmO793pVVodg8+1Bc3NQE2nlTYRVp7u4AVnwpQZ8t2wUVKjSkUZ2v9LeZb0DpC35XVtAaM/gjd8HpXNlc/NW49ef8n78kYWWqajx08EWa53eTToEdJn+LTrP2+aX20BpeP0uB7L6ubNW0/sUhEVOe7IaReHwK3VtLw6vklouro3b9vZNPdfMHNSqNZ9Xhx3y27pdzLQR/movEHvpnmidzp1/FJ9VK5Q1oxlUrZqXRk1fpJ8/fknEunK6+F3WMurLf08tW51+KzfAHk1aVL5qEeXZ5pPALCDx7+5j+p9gX9zlbfnmehCx5rt2fsLc0Nt0tjhQYJZWk85c/Z8kPXu3Llr6pJRsW4dVnozbtT4yWaMxKz/DZsU0vF408Ox6LwOi0b7Tr3yUhK5evWalCxWxG15qeKPnu/+829n4E+vYYOrO0XHffdpv/6S9JVXZNTQ/s7xrBvUrWVa733+9UBpXL+uGS9bhwrSnlNPvP6PZvvOEz2+HOeKwMMYBL7+Ck1aO2OMv+fE9SI3pIfr4PbZs2aWk6fOBIlEHz/xaHyTbFkye7V9bVLc56uBUq1yeRk3ctAzH0BfKx3eltfRJFxly5rZNCN3HZRYOZrgRsVB8bVMWrnSwf1dnTx9xlS4smXN9EzXf970mFUnAo3Bo82s9eHtMRtVOMp77L/vpIMGPnVSCG+PWR1vplGrt03wcOHsyZE2QOosr4cxlo6fOi1Zw/j56n7SINrxk5Fr0qHsWbOYv8c8dAPQ3yUdl/BJLl/5Vy5euiS5CpeRDDkLmUe9Jm3Ma4NGjDbPd/3x5zPKPQBELRkzpDMTY3k6x+h5Vl/LkD5dhOQtMtLJTt794GMpkDe3zJo81uNwGdmzZDbnIkfAxeFYFK5bh5UON6N1Zw3+Oc7J+tAhVlSuImWkZoPmbnWes2fPm+7BT3MdZhfZsz2qE8UKFFyO9d91pfb4cqbNmsVj3Sk6Hne6X44eO2GOp8DX4DlzZDN/tRWqg9YtHceY5+Mu+uy74GTP5vn6S7/fp86cdbseCU1aOyPwF8lpoE6j+9t37gpyotdZMLVJf0h00NCP+nxlZqT64fshboG2yKZapfKmW/SGTe6zZK1ev8n8UGrLoKimWqUK5u+a9Zvclq9et8k0Oa4WaAbj8F7/eatcsZzJ15oNgfO70fytXiVy5fdpVShb2lyIrA30+eiYbjpbnjfl1RN5o5btJX68uPLT7MmRcuIdh9IlipnfnsDl/X33HnMXWFsUh8WRY8dNS9EM6dJIZFIgX25J9mpSWRNoRnGtuB49fjLE8i6ZP10O/fGr6dbseMyZOt68phN96PP8eXM/0zIAQFShrV3KlCxuJrhynb1X/79x81YpX6bUE1tZR7fxwzt06SH58uSU2VPGmZvpnlStXME0IFi3cXOQurU2TKjyX5fN6MDRI2nfzsfnZH3UqVHFvP7bplWmx4XrdYnWTX7e+qvb+6xev9G0ytIeXdGJY2bo3wPdsNz5+x/O/etQtXJ5M6HkH3/+7ZZ2zbpN8mLixFLyv1aC0YE2iNFJd06cPB2kMc+x448CoSmSPx7Tu1rlCmYSo8CBU7320/dh6BgxM7/rdzDw9bF2n9YGM67189CktTULkZq/f4BVrlo9q1Tl2taJk6ethw8fWguXrLDeyJTbGjNhilvazVt/tdLnKGiNnTDVuWzx8lUmbau33zPvFdlp+eo0amUVKFnJOnDwsFm2Zv0mK222/NbXA4e6pf173wFT3m8HDff4XgcOHbGSp89hfTdslBXR2nTsYuUoWMravecv83zbjp1W5txFre4ff+6W7uy5C6ZMvXp/Eab1IwvNV8Zcha2t23aY53v+2mvlKlzGlMPV7Tt3THnbvfOBx/e5d++e+Qw7dOlhRWZ6bKbJms9atXaDOYYPHTlmFS5TxRzL+txV9vwlrP81e8v5/My581a+4uXN4+Sp01ZUoL89r2fMZc1fuMR68OCBder0Gats1brmtyrw70zx8jXMcodFS1dYA4Z+bx07ftIKCAiwfP38rF+2/2aVqVLXerNgKbM/Ipu5Py42x+GEqTOt+/fvWxcv/WPVbNDcKlS6inXr1m23tDXqN7PyFiv/xPfT74O+38gxE55xzgEg6tm7/6CVLnsB64OP+li3b98xD/1/ujcLOuuG0Z3WA3Uf6bko8HnIk4Yt2plzk9ad1aYtv5j6V59+A55DbiO/Lj17m/OyHmuu9JxfrV5Tq2jZatbho8dMnW7F6nVW6qx5rUHDR1vRjdb56jRubeo/f/z5t9k/O3f9Yeqw5av/z/Lz83em1fpdyYo1zfJTZ86adbU+lSJDTmvStNlWdDP6h8nmGPvsy/7W9Rs3zDWOXrvnLFTa1JV1fzn4+Nw018Jap7xw8ZLZzxOnzTLrL1i01IoudN/UatAi2Nf1O5gqSx5ryYrV5rt59PgJsy/1O6v7LKxp7SqG/hPRwUc8mc7Q9eW3g2Tl2g3mjqfO8qkz8rZr1SzInb9mb3WST3t1ky6d25tlpSrVMq1S9O5ojJhBB7Q8+tcOc7cvMtGuCF8NHCpLlq82Mxtpa5u3WjSR9zq1cxu3RCcEqFq3idkXX/Tu5VxepGxVs8+sh5bpMqytsRytHOdPnyAF8+d97mXScgwc8r0sWLTUjNmg4600b1xfenZ9x60F5plz56Vw6Spm8pWRg74J9fqRhXaJGDJyrMya95Ncv+EjiV9IZMax+KRXV3M330HvsmTMVVgqlittZnR1aPpWJ/nt993m/zpJjLb2dIwF2f/Lz6Rx/ToS2Zrwjx4/WabMnCOXr1w137c6NatKn4+7O8eVcEibLb+ZSW7lwtnmuc6M9v24SW7HqavRQweYO3+RzeTpc2TcpGmma75+NnpX/ItPPzTfV1f5ilcwYz3u2LTKPNfxXvS40GP5xKkz4ufnZ+5eli9TUrp36SypUgadPS8ymL9wiYwcM1FOnz0rsWLGMvnt1+fjIPmtUKO+nD13QY78tT3Y93L8drn+VgMA3FuR68RIf+3db57nzZ1T+n7SM0LqcJGR9hLYsm2HGSIjZqygHbi2rFnqdn66ffuOfP3dMFm0dKUZHzrpKy9Lq2aNpNu7HSLddUBEeL/XpzL/pyWmNWDglpNaj+3Xf7AsW7VW/Pz8JXmyV6Vdm+bSuX0be09aFwytx307eIQsXrbSjMGe+IUXpEbVitLn4x5BxkrTYYr6fv2d6TWh1wYp30gh73Zoa4696Ei/f2MnTjUt+bSHW7KkSU3LyG7vdgxSfz57/oL0/Wqgub5/8PCBpE2dWrq918FcT9lZ948/l8XLH10z+Pr6me9Y/PjxzPOund+Wru92cKbVMJZei0ycOssca5quZtVK0rd3ryDHohWKtHZF4C+K0R/N4AI92k9dT0h6Ee5IowEjDYAFJ7huAVGhvBps0R8Efd11gpAnlTlBgvhezeAVUWXSHyVHoCvwxBjerB8ZhZRfDf7Fih3LVF4dNGD78MHjcUJc6X551mNUPtPy3r1rjkFHVyUN5gceO8aVnpgic6U8pPLq91G5Bnxdv8P6iMyfZ2jLa47dhw89jrMU+LfL9bcaAOC5bqsXfhFdd4tstPvpg/sPgn1dh+QILigV1eqRz4OOy6yBmJCui9h3QfdbcNcr7Lsn0+PN2/pvaNLa5bvoSeBr/rB+N+9F099AAn8AAAAAAACADXH7DAAAAAAAALAhAn8AAAAAAACADRH4AwAAAAAAAGyIwB8AAAAAAABgQwT+AAAAAAAAABsi8AcAAAAAAADYEIE/AAAAAAAAwIYI/AEAAAAAAAA2ROAPgFfGTZwmPy1ezt4KR0O/Hyer1m6I0H06cepMmbNgUYTmAQAAwE6mzJgrM+YseGbvP3LMBFm6cs1zWw9A1EbgD4DT9+MmyaKlKz3ukZlzf5SVERykshsNum3a/Ivbsu+GjZI16zc9tzxo0G/pitXPbXsAAAB2t2DRUlm4dMUze/8pM+fK+k1bntt60emm/IrV6yI6G0C4I/AHwGnarHmyev1Gj3ukU/vW0qBuTfZWOOrepZNUq1LRbdm4SdPk562/sp8BAADg0fud35Za1So/t/Wi0035DT9vjehsAOEudvi/JQA7atGkQURnwXbat2kR0VkAAABAFPNWyybPdT0AURuBP9je/fv3ZdOWbXLg4GGJFTu2FMiXW4oVLuiW5ubNW6al26nTZyVRooRSvEghyZcnl1ua4aPHS4b06cxdMm0i/9fe/ZL0lVekTs2q8srLL7mlffDggWzc/IscPnpcLMuSbFkySfkyJSVWrFhu6e7du2fS7T942DzPmzunlCtdQmLEiOFxu3oH6s+/90n+vLnl8NFjkiB+fGnTIugJfNnKtXLg0GHp1e1diRkzphlj5PSZc+a1WLFimnyXKFZIcmTP5lzn64HDxMfnphw4dMT8X6VInswZnNIx/pK9mlTq/9fqb+zEqV5v39uyeuIof82qlUwXWF0/+WvJpGa1yvLyS0mCpH+afaqfUWB37tyVYaPGS+UKZaRwwfxur/0weYYkSfKiNK5fx7ms/+ARUqhAPrPN1es2mv35eorkUrdmNXnhhURBuhNkz5pZqlWuIHd9fWXoyHEm/7v3/OX8DDKkSyPNGtd/4j7y8/eXdRs2y5Fjx83nq9svUbRwkHRnzp2XVWs2mG0VLVwgyPfA1ekzZ2XV2o3i6+fnTKt3QRMlSiRNG9YL0/YBAADs5tz5i7JyzXq5feeOqSuWLBZ8HSgg4J6s2/izHDpyzNSRC+bLI6VKFPWYbtOWX+Tg4SMSJ04cKVIwvxTMn9dtrL506dJI7epV3K559Brl6LETEiNmTHkzW5YgdWBP66nrN3xMvfXsufOSOPELUqp4Ucn55uPrBMdwNHly5ZBK5cvI2g0/y74Dh+S1ZK+aa6EkL77o1b4KqVze5EXrnYOHj5HyZUuaazZXk6bPlgTx4rnVnb3Jt+ZL0/n6+pnrAkc9PHWqN6R188ZelQ2IzOjqC1s7evyElKlSV3p80lfOX7goPj4+5ke9c9cPnWl27vpDiparJqPGTxZ//wA5eOiI1GzQQt7v9ak8fPjQmW7StNnmZNrtwz6yZMVq8fPzN8GvctXqyZUr/zrT3bl7V6r/r5l8/PnXcuXfq3Lnzh0zPl7l2o3Maw7HT5ySslXrSe++38i169flxg0f6fbhZ9KgRTu3dLrddRs3S8f3e8qipSvk+o0bJjB06dJl6fPVALONwEHHz/r1NwEnR9AtUcKEkuTFxOahJ9nfdv0hVeo0kcEjxjjX09c0fZzYsZ1pNcgT3Bh/odm+t2X1xFH+Dl16yoo1680Jefyk6VKqUi0TzHX1tPvUE11v1PhJ8uff+4O8Nmv+T7Is0ADJehxt+WW7ya8eLxo4GzR8tFSs1cAEEYMb408rZbrP9W/cuHGcn0HChAmeuH/27j8oJcrXkG8HDZebt27L5StXpV3nbtKm4/smiOiwfNU6KVWxlixfvVZ8fX1l9PjJ5rvgiR7fpSrXNpVYTTt2wlST1tN4gN5uHwAAwG40kKR10sXLV4mfn5+p2301YKjHtHqNUbJSLfmy/xBzs/3qtWvSsWsvafZWZ3MN4rD/4CGT7pO+X8vFS5fl2rUb0q//EOnZ+4tgx+rTRgyVajWUvt98J9du3JBbt27J5OmzpUqdxiYgGNx6auu2HVK0bDWTdw2AaeBLr1s+6vNVkOFo9Ob6O90+enRz2NfP3EyvUKOB+Ny8GeK+8qZc3uTF39/f1M13/xG07j7vx8WyaNmqUOdbY6Na79b/xI0b11kPf8HlWgiI0izApgICAqyiZatZJSvVsq5eu+722t79B83fO3fvWjkLlbaq1m1i3fX1db6+ePkqK3n6HNYPk6c7l2m6bPmKW6vXbXQuu3DxkpU6a16rX/8hzmU/Llpm1j11+ozbNg8fPWb5+vmZ/9+7d88qUaGmVbpKHevWrdvONBcv/WNlyVPM+vyrgW7bzZq3mLV0xRrnshs+PtahI8fMdsZMmOK2nTXrN5nlrvn0ZNa8n6zXM+ayjh0/6VxWoGQlq0OXHh7Ta37bdu7mfO7t9kNTVk8c5V+2cq1zmb5Pqcq1zXs+fPgw3PapJ/9cvmLKM37S42PBQbfVvG1nt2VvZMptZS9Q0vrt991u+ypFhpzWqPGT3NJmz1/C6tX7C7dl6XMUtD7+/GvLG7fv3LHyFC1n1WrQwnlsqeMnTllps+W3ho8ab55f+feqlSFnIattp67WgwcPnOm+HTTcypy7qNWkdQfnsstX/jV5aP9ud+e+VYOGjw6S1tvtAwAA2I3WHbVupHXB+/fvO5cPHjHG1D3rNmntXKb1JK1nV6nT2Fx/OJw+e87UuwYM/d4819fyFS9vVazZwPLxuem2vb/3HXD+P2+x8laXnr2dz6fNmmfqmpcvX3FbZ9+Bg255C7yelkHrxJpXf/8At+sErf/Omvujc5nmU+uuW7ftcC47cfK0uZ4YMnLsE/eVN+XyNi+aTp+PHDMhyHYq1WpoNWjezm1ZaPKt6T74qM8TywJERbT4g21pF86Tp89Ijy6dgnTFdTQX37Bpq2mx9l7HtqbbqkOdGlUlW5bMMmveQrf1tMtmlYrl3J7nz5PbrbWY3oVSx0+edls3S6aMEj9ePPN/7Xp87MRJ+eDdDm7dP1Mkf810pdWZwLSLsMMrL78stao/HohXm6VnzZxRCuTLI7Pnu+dxzoKFpvl6hbKlgtxl07tnA4aMNM3X9Q6atmj8e98BCQtvtx/asnqiXYxrVqvkfK7v0+GtlnL4yDH548+/w2Wfrl63yewXx0MnOgmrN7NmcesWrPsqU8b0Hu9MPg1txXfpn8vSs+s7zmNLZUifVqpWKi/zFy79L91a09rw/XfedrbCVO91bCe+fr5B3vPuXV/p0qmdW9eQzu3beEzrzfYBAADsZuWaDXLz1i15v3N7t+F8Or/dRgICHrfgU9p19dz5C2Zit4QJHvfmSJMqpdSqVkXm/7TEPNchWS5c/McMl/OitkBzkStH9mDzotcfWs89ceqM23Id1ifwUEOutAXcDZ+b0vWdt02PEwcd1iVN6lQyK1A9P2OGdFKyeBHn8/Tp0kiO7FllVwh1XG/KFdq8hEZY8w3YBWP8wbY0KKRyBBqfwtXxkyfNXw3yBabj8q1Y4z6duwZvAnv11aSy78BB5/PaNaqaZvTN3upkxpMoUaywGZuiVPEiEjt2bGdTf7Vtx+9mjA89UTuCUjp237XrN0zT85eSPBrDLnOmDB7z37zR/6T7J31l1x9/mvExNIipzfc7tm3t3JbSrs4a+KpUvqxkyZThvy6lj17T7gBh5c32Q1tWTzJnDFp+DaapYydOmQDk0+5T7bLr2vWhcIF8YR7Tw9NxosHLS5evSHjS7tRKuy3/+tvvbmU+e/6CnDpz1jzXLtCeyqzjpryeIoXbshMnT3ksg459GTitt9sPaRxHAACAqOZx/epRndRBA3upUr3htsxRT/1566/mprVrnenkqdNmSCIdIkXrrCrw+HohqV+3lsyY+6PUbdLajFtdvEhBc/2hwS7Xm75By+D5WkjrblrX/n33HrflmTJ4vhb65/LlJ+bPm3KFNi+hEdZ8A3ZB4A+25Qg2PHwYfGuyGPIojSVB0zy0HgYJWMR3aRXooJMZ3L//wPlcW5utXjxXtmzbIVt/3SG//f6HGSNNAykLZ02WZMledZ7oNfCi4++5KluyuHnoWHwOOoGEJ3VqVpM+Xw80Y69p4E2De/fu3Zdmjf7nTPPL9p0ya95PMnRAP7fl2uLv+3GT5Gl4s/3QltUTj5/Pf5+r4yN62n3auH5dM8CwwxuvPwpyOQKY9x88Hh/FIfCYfQ7x4z9u/eYQK2YstzFWwsV/ZdayxIsb1+2lqhXLmYdyHMeeWla6jmMZ2rTebh8AAMBuAtdBXVmBrj8cacz4zS4t/pROOqEPb69fPNHeTRuW/2gCi1r31+sQredroG3BzEkeJ8Tzpt7n1bVQzJhy/8Hja6EwX5d5mZfHdfOg29ReK54mGglrvgG7IPAH23oze1bz96+9+5ytwwJztIDSGWAD3wnSO3PaPTcstEm9zqKlD7Vtx06p36ytuROnTfwdeSuUP6+Z0TWstBWWdkvWyRj69fnIBOCKFi5ouloGvntWoqj7rFd//Lk3yPvpHcGQut2GdvvhUdbDR44HWaazgamsmTOFy3Zcu/260oqSdmO97DKBi2MQ5YuX/jEtQ8NTaD4DR5n1jm6RQu4zDrvK8t/xr61g9S6www0fH9NVN7NL6z7Hd0JnpM7vMrO1ljdwWm+3DwAAYDeO+tWhI0fdZpe9ffuO6dabLFnSIHWmYoULSumSxby6fkmV8vVQ5UdvcLsGEXXikVZvv2e6EXds1yrEayEdwsg10KY9aMJ6LRSWcnmbF73Bn/iFF4LUzfWGvM4E7LpuaMUI5bUQEFUwxh9sS4Nu2lR86PfjTIDG1fadu8zfCmVLyxuvJ5dR4yaZk7TD3B8Xy9HjJ6Vl04ah3q4233ed5VeleuN1550lVb5MSXkzWxb5dvAI+ffqNbe0Ogvs5q2/er295o3rm7z3/fo7OXrshDRrVM/t9Yzp05m/v//xp3OZ7o/5Cx+NJeIq+WuvBpml92m3Hx5l1fFTtDWhw/UbPvLD5BlmTBDtTh1e2/FE7y4WLpjPjM3i99/4jWrY6PGS2GUswfCSPFkyrz+D2jWqSOpUKeXL/oNNYM7VrVu3TcBZ1ahaSV5MnNh8F1xbHQ4f/UOQMtSsVtmkHT5qvFtanT0tcFpvtw8AAGA31SpVMC3ttM6kM9A6jBw7QRImTBAkbYZ0aeWrgUPNjdfAAatffv3N/L9qxfKSLm1qGThslFt9UINRO3buDjYv2g326rXrnq8/YgV/yV+9ckVJmvQVGTHmB7nr+3gsZx22SLsft2wW+mshT7wpV2jyUrRwAVm7fpNbOl1PGyU8jeTJQn8tBEQFtPiDbWmru+kTR0m7zt2kbNV6JhCog8nu+Wuv5Mud09xxixcvrkweO0Le6tRVyteoL2VLFjPjsOlYb2+1bCqtmjUK9Xb17lOnrh9KhnRpJEP6dGawXb3jVjB/HmnTsokzbzMmjpZOXXtJsXLVpXzZkmYMuHPnL8re/QelVdOGUqZUca+2p+PbZc2SSWbO/dHc/dLAjSsd20OX9fi4r/y8ZZvEihVb/vjrb/nsww+kdYcubmkb1K0ln/T9xkx3r11dUyRPJu3btHiq7YdHWcuXLSWbf9luJpPQiUM2bv7FdDOdNuH7cN1OcHr36ib1m7eVanWbmBaN+w8ckiYN6kry5K9JeGtQr5YM/X6svN/rU3nt1VfNcdSscX2PaXVCmrlTx0uHLj2kaPnqpjvzyy8nkTNnz5vx93TyjhJFC5tWi2OGD5SO7/eUyrUbmYlHDh0+KpUqlHF2aXZwpNX3rFKnsRQqkM+My1K5QlmTNkaMmKHePgAAgN3o8D7jRgySdu98IJVqN5SihQrI0eMnpGSxIpIubRq3tHrNMWfqOOnQpaepp5YtVUKSJn3Z1Jm0hVvHtq1MnV3TzZo0Vtq+003KVK1r6lYvJEoou/b8JaVKFDMBL080MNa520em1ZzWHbXL65oNP5uxxps2fDwET2AaKJsydri07dxNKtZoYPKgdeeft26Tdzq8Za4NwoM35QpNXj7u0UXqNm4jVes2Ma0ttV6r46ynSuk+tmJY6uHfDhoh7/X4RFK89pqkTvVGmMf8BiKTGDq1b0RnAniWzJ2k33ebk2qC+PHMLLzZs2Xx2CLs9Jlz5g5dsSIFg3T9nTR9tpl5y9F83mHN+k2mBV2bFo+Cekrv+u3YuUuOnjgp8ePFlexZs7h1sXSlY+39tXe/GdBXZ6zS1mWuE10Et11XOpbHnj//lvTp0rrNfhs4jXZFeCnJi+a9dMy5KTPmmGBY7pxvOtPpDMV7/t4nvr5+8mrSV8xMWkoDe0lfedljN1pvtu9NWT3JVbiMlCtTUkYO+ka2btshBw8fldeSJZVKFcoGGcsvPPdpYNrNVQPC/gEBZqIW7W4we95PkjhxYrduwqPGT5a8uXNKyWLuAa/Fy1aKf8A9aVy/jnOZzrKcMUN6Z5dwB50oQ4OLfv4Bkjrl61K3VvUQj3GdYGXfgcPy4OEDSZcmtQlsB77rqS1RdSKOu35+ZjxDbTGp3bP1mNcu24ED2Ov/S1uscAEzK1yeouVMxWz8yMFh2j4AAIDdaE8TrV/duXPHjHmt9UDtqaJ17f/VqREkvda19aa0ji+XNnUqEzDUMapdaffWX82EdUclYcKEj260uwxdNGXGXNNl1rUu6+8fINt3/m4mvtNxBHXWWkfPmCetp+7cvWsaCJw9d0ESJ04kxYsUNjPfuho3cZpkz5o5yI30ZSvXyq1bt4K9UR2acnmbF9d6ra+/vwn+ad7m/bRE4sWN41Z3Dm2+dXz2vfsPiK+fv7yRIrnUr1szxHIBkR2BPwCRmmvgDxFbqc1dpKz07PqOGacSAAAAABD5McYfAMCNtqrUVrCud2f7Dx5hxoipW7MaewsAAAAAogjG+AMAuNGZ6HT8xwJ5c5txMbUb77HjJ2Vgvz5uMzYDAAAAACI3uvoCiNTCMh4fwqdrr86ypoNFv/LKS1KmZHEzsQoAAAAAIOog8AcAAAAAAADYEGP8AQAAAAAAADZE4A8AAAAAAACwIQJ/AAAAAAAAgA0R+AMAAAAAAABsiMAfAAAAAAAAYEME/gAAAAAAAAAbIvAHAAAAAAAA2BCBPwAAAAAAAMCGCPwBAAAAAAAANkTgDwAAAAAAABD7+T/0mnqm3CPGswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1265x517.5 with 2 Axes>"
      ]
     },
     "metadata": {
      "alt": "Expected-value threshold and abstention coverage under illustrative conservative costs."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>probability_lower</th>\n",
       "      <th>q_exec</th>\n",
       "      <th>total_cost_allowance</th>\n",
       "      <th>conservative_edge</th>\n",
       "      <th>decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>740</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>ABSTAIN_STALE_DATA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>741</td>\n",
       "      <td>0.5089</td>\n",
       "      <td>0.2900</td>\n",
       "      <td>0.0321</td>\n",
       "      <td>0.1868</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>742</td>\n",
       "      <td>0.6224</td>\n",
       "      <td>0.2842</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>0.3097</td>\n",
       "      <td>ABSTAIN_ARTIFACT_MISMATCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>743</td>\n",
       "      <td>0.2429</td>\n",
       "      <td>0.2736</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>-0.0594</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>744</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.0304</td>\n",
       "      <td>-0.2343</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>745</td>\n",
       "      <td>0.0733</td>\n",
       "      <td>0.2645</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>-0.2212</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>746</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>0.2698</td>\n",
       "      <td>0.0295</td>\n",
       "      <td>-0.1274</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>747</td>\n",
       "      <td>0.2281</td>\n",
       "      <td>0.2656</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>-0.0686</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>748</td>\n",
       "      <td>0.1745</td>\n",
       "      <td>0.2579</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>-0.1158</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>749</td>\n",
       "      <td>0.2858</td>\n",
       "      <td>0.2627</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>-0.0084</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>750</td>\n",
       "      <td>0.0617</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>-0.2284</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>751</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.2588</td>\n",
       "      <td>0.0319</td>\n",
       "      <td>-0.0681</td>\n",
       "      <td>NO_TRADE_INSUFFICIENT_EDGE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    row_id  probability_lower  q_exec  total_cost_allowance  \\\n",
       "0      740             0.3375  0.2840                0.0315   \n",
       "1      741             0.5089  0.2900                0.0321   \n",
       "2      742             0.6224  0.2842                0.0284   \n",
       "3      743             0.2429  0.2736                0.0287   \n",
       "4      744             0.0562  0.2600                0.0304   \n",
       "5      745             0.0733  0.2645                0.0301   \n",
       "6      746             0.1719  0.2698                0.0295   \n",
       "7      747             0.2281  0.2656                0.0310   \n",
       "8      748             0.1745  0.2579                0.0323   \n",
       "9      749             0.2858  0.2627                0.0315   \n",
       "10     750             0.0617  0.2576                0.0325   \n",
       "11     751             0.2226  0.2588                0.0319   \n",
       "\n",
       "    conservative_edge                    decision  \n",
       "0              0.0220          ABSTAIN_STALE_DATA  \n",
       "1              0.1868           TAKE_PAPER_INTENT  \n",
       "2              0.3097   ABSTAIN_ARTIFACT_MISMATCH  \n",
       "3             -0.0594  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "4             -0.2343  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "5             -0.2212  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "6             -0.1274  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "7             -0.0686  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "8             -0.1158  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "9             -0.0084  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "10            -0.2284  NO_TRADE_INSUFFICIENT_EDGE  \n",
       "11            -0.0681  NO_TRADE_INSUFFICIENT_EDGE  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coverage (paper intents / test opportunities): 0.436\n"
     ]
    }
   ],
   "source": [
    "policy = pd.DataFrame({\n",
    "    \"row_id\": split[\"test\"],\n",
    "    \"probability\": p_test,\n",
    "    \"probability_lower\": np.clip(p_test - 0.035, 0, 1),\n",
    "    \"q_exec\": data.loc[split[\"test\"], \"reference_probability\"].to_numpy(),\n",
    "    \"fee_allowance\": 0.010,\n",
    "    \"slippage_allowance\": 0.008 + 0.06 * data.loc[split[\"test\"], \"spread\"].to_numpy(),\n",
    "    \"latency_allowance\": 0.006 + data.loc[split[\"test\"], \"receive_delay_ms\"].to_numpy() / 50_000,\n",
    "    \"data_valid\": (split[\"test\"] % 37 != 0),\n",
    "    \"artifact_match\": (split[\"test\"] % 53 != 0),\n",
    "    \"calibration_valid\": True,\n",
    "    \"risk_ok\": (split[\"test\"] % 71 != 0),\n",
    "    \"label_up\": y_test,\n",
    "})\n",
    "policy[\"total_cost_allowance\"] = policy[[\"fee_allowance\", \"slippage_allowance\", \"latency_allowance\"]].sum(axis=1)\n",
    "policy[\"conservative_edge\"] = policy.probability_lower - policy.q_exec - policy.total_cost_allowance\n",
    "POLICY_MARGIN = 0.010\n",
    "\n",
    "def policy_reason(row: pd.Series) -> str:\n",
    "    if not row.data_valid:\n",
    "        return \"ABSTAIN_STALE_DATA\"\n",
    "    if not row.artifact_match:\n",
    "        return \"ABSTAIN_ARTIFACT_MISMATCH\"\n",
    "    if not row.calibration_valid:\n",
    "        return \"ABSTAIN_CALIBRATION_INVALID\"\n",
    "    if not row.risk_ok:\n",
    "        return \"ABSTAIN_RISK_LIMIT\"\n",
    "    if row.conservative_edge < POLICY_MARGIN:\n",
    "        return \"NO_TRADE_INSUFFICIENT_EDGE\"\n",
    "    return \"TAKE_PAPER_INTENT\"\n",
    "\n",
    "policy[\"decision\"] = policy.apply(policy_reason, axis=1)\n",
    "policy[\"eligible\"] = policy.decision.eq(\"TAKE_PAPER_INTENT\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.5), constrained_layout=True)\n",
    "axes[0].hist(policy.conservative_edge, bins=24, color=COLORS[\"teal\"], alpha=0.86)\n",
    "axes[0].axvspan(policy.conservative_edge.min(), POLICY_MARGIN, color=COLORS[\"coral\"], alpha=0.12, label=\"no-trade / abstain region\")\n",
    "axes[0].axvline(POLICY_MARGIN, color=COLORS[\"coral\"], lw=2, label=f\"margin = {POLICY_MARGIN:.3f}\")\n",
    "axes[0].set(xlabel=\"conservative per-unit edge\", ylabel=\"opportunities\", title=\"Expected-value trigger\")\n",
    "axes[0].legend(frameon=False)\n",
    "counts = policy.decision.value_counts().sort_values()\n",
    "axes[1].barh(counts.index.str.replace(\"_\", \" \"), counts.values, color=[COLORS[\"blue\"] if \"NO_TRADE\" in x else COLORS[\"coral\"] if \"ABSTAIN\" in x else COLORS[\"teal\"] for x in counts.index])\n",
    "axes[1].set(xlabel=\"decision count\", title=\"Reason-coded outcomes\")\n",
    "fig.suptitle(\"SIMULATED · A probability cannot override a failed deterministic gate\", fontsize=14, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "display(policy[[\"row_id\", \"probability_lower\", \"q_exec\", \"total_cost_allowance\", \"conservative_edge\", \"decision\"]].head(12))\n",
    "print(f\"Coverage (paper intents / test opportunities): {policy.eligible.mean():.3f}\")\n",
    "assert policy.decision.str.startswith((\"ABSTAIN\", \"NO_TRADE\", \"TAKE\")).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52418ba",
   "metadata": {},
   "source": [
    "### Exercise 5 — abstention is not free\n",
    "\n",
    "Increase the latency allowance and recompute coverage. Report conditional accuracy together with coverage; explain why conditional accuracy can rise merely because the policy acts less often. Add one unknown predicate and specify the only fail-closed interpretation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7bca3d",
   "metadata": {},
   "source": [
    "## 8 · Illustrative self-supervised masked-sequence objective\n",
    "\n",
    "**Illustrative — no encoder is trained.** A self-supervised objective derives a target from permitted inputs rather than the later settlement label. Here a causal prefix rule reconstructs selected masked values from the two most recent visible values. Low reconstruction loss would not establish directional skill, calibration, or replay value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d30e618",
   "metadata": {
    "alt": "Masked scalar sequence showing targets and causal-prefix reconstructions at four illustrative positions.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:09.972825Z",
     "iopub.status.busy": "2026-08-06T13:42:09.972702Z",
     "iopub.status.idle": "2026-08-06T13:42:10.083420Z",
     "shell.execute_reply": "2026-08-06T13:42:10.082916Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1242x460 with 1 Axes>"
      ]
     },
     "metadata": {
      "alt": "Masked-sequence self-supervised teaching objective over eligible historical positions."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>target</th>\n",
       "      <th>reconstruction</th>\n",
       "      <th>squared_error</th>\n",
       "      <th>epistemic_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1.1454</td>\n",
       "      <td>1.0117</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.5542</td>\n",
       "      <td>0.1978</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>-0.5089</td>\n",
       "      <td>-0.5590</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.9742</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>0.2849</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   position  target  reconstruction  squared_error epistemic_status\n",
       "0         5  1.1454          1.0117         0.0179     Illustrative\n",
       "1        10  0.1094          0.5542         0.1978     Illustrative\n",
       "2        15 -0.5089         -0.5590         0.0025     Illustrative\n",
       "3        20  0.9742          0.4404         0.2849     Illustrative"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_t = np.arange(24)\n",
    "sequence = np.sin(sequence_t / 3.0) + 0.03 * sequence_t\n",
    "masked_positions = np.array([5, 10, 15, 20])\n",
    "visible = sequence.copy()\n",
    "visible[masked_positions] = np.nan\n",
    "reconstruction = visible.copy()\n",
    "for position in masked_positions:\n",
    "    causal_history = reconstruction[:position][~np.isnan(reconstruction[:position])]\n",
    "    reconstruction[position] = causal_history[-2:].mean()\n",
    "masked_mse = float(np.mean((reconstruction[masked_positions] - sequence[masked_positions]) ** 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10.8, 4.0), constrained_layout=True)\n",
    "ax.plot(sequence_t, sequence, color=COLORS[\"blue\"], lw=2, label=\"permitted sequence target\")\n",
    "ax.scatter(masked_positions, sequence[masked_positions], s=90, facecolor=\"white\", edgecolor=COLORS[\"coral\"], lw=2.5, label=\"masked target\")\n",
    "ax.scatter(masked_positions, reconstruction[masked_positions], s=75, marker=\"x\", color=COLORS[\"teal\"], lw=2.5, label=\"causal-prefix reconstruction\")\n",
    "for position in masked_positions:\n",
    "    ax.axvline(position, color=COLORS[\"gray\"], lw=0.7, alpha=0.25)\n",
    "ax.set(xlabel=\"sequence position\", ylabel=\"illustrative scalar attribute\",\n",
    "       title=f\"ILLUSTRATIVE · Masked-sequence objective · masked MSE = {masked_mse:.4f}\")\n",
    "ax.legend(frameon=False, ncol=3)\n",
    "plt.show()\n",
    "\n",
    "display(pd.DataFrame({\n",
    "    \"position\": masked_positions,\n",
    "    \"target\": sequence[masked_positions],\n",
    "    \"reconstruction\": reconstruction[masked_positions],\n",
    "    \"squared_error\": (reconstruction[masked_positions] - sequence[masked_positions]) ** 2,\n",
    "    \"epistemic_status\": \"Illustrative\",\n",
    "}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec59e62f",
   "metadata": {},
   "source": [
    "## 9 · Causal attention mask and tensor calculation\n",
    "\n",
    "**Illustrative tensor arithmetic.** For one attention head, \\`Q=XW_Q\\`, \\`K=XW_K\\`, \\`V=XW_V\\`, logits are \\`QKᵀ/√d\\`, and an upper-triangular \\`−∞\\` mask removes future keys before row-wise softmax. A mask constrains only presented tokens; it cannot repair preprocessing leakage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "779a56a2",
   "metadata": {
    "alt": "Lower-triangular causal permission matrix beside the calculated attention-weight matrix.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:10.084750Z",
     "iopub.status.busy": "2026-08-06T13:42:10.084657Z",
     "iopub.status.idle": "2026-08-06T13:42:10.212179Z",
     "shell.execute_reply": "2026-08-06T13:42:10.211582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1207.5x483 with 3 Axes>"
      ]
     },
     "metadata": {
      "alt": "Causal attention permission mask and row-wise attention weights with future positions blocked."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Implemented toy invariant.** Changing only token 4 changed outputs at positions 1–3 by \\`0.00e+00\\`."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>output_dim_0</th>\n",
       "      <th>output_dim_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>query_position</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7449</td>\n",
       "      <td>0.3142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6659</td>\n",
       "      <td>0.5337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8124</td>\n",
       "      <td>0.4164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                output_dim_0  output_dim_1\n",
       "query_position                            \n",
       "0                     0.9000        0.1500\n",
       "1                     0.7449        0.3142\n",
       "2                     0.6659        0.5337\n",
       "3                     0.8124        0.4164"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.array([\n",
    "    [1.0, 0.0, 0.5],\n",
    "    [0.8, 0.4, 0.1],\n",
    "    [0.2, 1.0, 0.3],\n",
    "    [0.5, 0.3, 1.0],\n",
    "])\n",
    "W_Q = np.array([[0.7, -0.2], [0.1, 0.8], [0.5, 0.3]])\n",
    "W_K = np.array([[0.6, 0.1], [-0.3, 0.7], [0.4, 0.5]])\n",
    "W_V = np.array([[0.5, 0.2], [0.2, 0.9], [0.8, -0.1]])\n",
    "\n",
    "def causal_attention(tokens: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
    "    q, k, v = tokens @ W_Q, tokens @ W_K, tokens @ W_V\n",
    "    logits = q @ k.T / np.sqrt(q.shape[1])\n",
    "    permitted = np.tril(np.ones_like(logits, dtype=bool))\n",
    "    masked_logits = np.where(permitted, logits, -np.inf)\n",
    "    row_max = np.max(masked_logits, axis=1, keepdims=True)\n",
    "    exp_logits = np.exp(masked_logits - row_max)\n",
    "    weights = exp_logits / exp_logits.sum(axis=1, keepdims=True)\n",
    "    return weights, weights @ v, permitted\n",
    "\n",
    "attention_weights, attention_output, causal_mask = causal_attention(X)\n",
    "X_future_changed = X.copy()\n",
    "X_future_changed[3] = np.array([99.0, -50.0, 27.0])\n",
    "changed_weights, changed_output, _ = causal_attention(X_future_changed)\n",
    "future_perturbation_delta = float(np.max(np.abs(attention_output[:3] - changed_output[:3])))\n",
    "assert future_perturbation_delta < 1e-12\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10.5, 4.2), constrained_layout=True)\n",
    "axes[0].imshow(causal_mask, cmap=\"Greens\", vmin=0, vmax=1)\n",
    "axes[0].set(title=\"Permission mask\", xlabel=\"key position\", ylabel=\"query position\", xticks=range(4), yticks=range(4))\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        axes[0].text(j, i, \"allow\" if causal_mask[i, j] else \"block\", ha=\"center\", va=\"center\", fontsize=8,\n",
    "                     color=\"white\" if causal_mask[i, j] else COLORS[\"ink\"])\n",
    "heat = axes[1].imshow(attention_weights, cmap=\"YlGnBu\", vmin=0, vmax=1)\n",
    "axes[1].set(title=\"Row-wise causal attention weights\", xlabel=\"key position\", ylabel=\"query position\", xticks=range(4), yticks=range(4))\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        axes[1].text(j, i, f\"{attention_weights[i,j]:.2f}\", ha=\"center\", va=\"center\", fontsize=8)\n",
    "fig.colorbar(heat, ax=axes[1], shrink=0.78)\n",
    "fig.suptitle(\"ILLUSTRATIVE · Architectural masking ≠ causal-effect identification\", fontsize=13.5, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "display(Markdown(f\"**Implemented toy invariant.** Changing only token 4 changed outputs at positions 1–3 by \\`{future_perturbation_delta:.2e}\\`.\"))\n",
    "display(pd.DataFrame(attention_output, columns=[\"output_dim_0\", \"output_dim_1\"]).rename_axis(\"query_position\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c630c005",
   "metadata": {},
   "source": [
    "### Exercise 6 — test time itself\n",
    "\n",
    "Modify a future raw event before tokenization while keeping the eligible prefix fixed. Which components must rerun for an end-to-end perturbation test? Explain why testing only the triangular matrix cannot detect full-period normalization leakage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf180d8",
   "metadata": {},
   "source": [
    "## 10 · Cost-aware paper replay\n",
    "\n",
    "**Simulated — not a backtest or live fill claim.** This teaching replay consumes synthetic ask depth only after a declared latency haircut, applies a 1% illustrative fee, preserves partial and no fills, and records every no-trade decision. A binary contract pays one unit per filled unit only when the later synthetic label is positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d72b7c01",
   "metadata": {
    "alt": "Replay state counts for no-trade, no-fill, partial-fill, and full-fill beside cumulative simulated result under declared assumptions.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:10.213625Z",
     "iopub.status.busy": "2026-08-06T13:42:10.213534Z",
     "iopub.status.idle": "2026-08-06T13:42:10.322221Z",
     "shell.execute_reply": "2026-08-06T13:42:10.321693Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1265x529 with 2 Axes>"
      ]
     },
     "metadata": {
      "alt": "Event-driven paper-replay states and cumulative simulated result under costs and latency."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decision_id</th>\n",
       "      <th>policy_decision</th>\n",
       "      <th>execution_status</th>\n",
       "      <th>requested_units</th>\n",
       "      <th>filled_units</th>\n",
       "      <th>unfilled_units</th>\n",
       "      <th>gross_cost</th>\n",
       "      <th>fee</th>\n",
       "      <th>net_result</th>\n",
       "      <th>latency_ms</th>\n",
       "      <th>fill_detail</th>\n",
       "      <th>epistemic_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SQ-0741</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>NO_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>125</td>\n",
       "      <td>[]</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SQ-0755</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>PARTIAL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>55.0000</td>\n",
       "      <td>6.9917</td>\n",
       "      <td>0.0699</td>\n",
       "      <td>-7.0616</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.2688674275036334, 10.0), (0.28686742750363...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SQ-0756</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>FULL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>23.4508</td>\n",
       "      <td>0.2345</td>\n",
       "      <td>56.3147</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.2781354658390098, 30.0), (0.29613546583900...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SQ-0757</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>FULL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>23.3855</td>\n",
       "      <td>0.2339</td>\n",
       "      <td>-23.6193</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.2773181481673054, 30.0), (0.29531814816730...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SQ-0760</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>FULL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>23.4648</td>\n",
       "      <td>0.2346</td>\n",
       "      <td>-23.6995</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.2783105971962925, 30.0), (0.29631059719629...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SQ-0764</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>FULL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>23.3726</td>\n",
       "      <td>0.2337</td>\n",
       "      <td>-23.6064</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.2771578144620177, 30.0), (0.29515781446201...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SQ-0773</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>FULL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>24.3732</td>\n",
       "      <td>0.2437</td>\n",
       "      <td>55.3830</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.28966547684895655, 30.0), (0.3076654768489...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SQ-0774</td>\n",
       "      <td>TAKE_PAPER_INTENT</td>\n",
       "      <td>FULL_FILL</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>24.8426</td>\n",
       "      <td>0.2484</td>\n",
       "      <td>54.9090</td>\n",
       "      <td>125</td>\n",
       "      <td>[(0.2955323192124258, 30.0), (0.31353231921242...</td>\n",
       "      <td>Simulated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   decision_id    policy_decision execution_status  requested_units  \\\n",
       "1      SQ-0741  TAKE_PAPER_INTENT          NO_FILL          80.0000   \n",
       "15     SQ-0755  TAKE_PAPER_INTENT     PARTIAL_FILL          80.0000   \n",
       "16     SQ-0756  TAKE_PAPER_INTENT        FULL_FILL          80.0000   \n",
       "17     SQ-0757  TAKE_PAPER_INTENT        FULL_FILL          80.0000   \n",
       "20     SQ-0760  TAKE_PAPER_INTENT        FULL_FILL          80.0000   \n",
       "24     SQ-0764  TAKE_PAPER_INTENT        FULL_FILL          80.0000   \n",
       "33     SQ-0773  TAKE_PAPER_INTENT        FULL_FILL          80.0000   \n",
       "34     SQ-0774  TAKE_PAPER_INTENT        FULL_FILL          80.0000   \n",
       "\n",
       "    filled_units  unfilled_units  gross_cost    fee  net_result  latency_ms  \\\n",
       "1         0.0000         80.0000      0.0000 0.0000      0.0000         125   \n",
       "15       25.0000         55.0000      6.9917 0.0699     -7.0616         125   \n",
       "16       80.0000          0.0000     23.4508 0.2345     56.3147         125   \n",
       "17       80.0000          0.0000     23.3855 0.2339    -23.6193         125   \n",
       "20       80.0000          0.0000     23.4648 0.2346    -23.6995         125   \n",
       "24       80.0000          0.0000     23.3726 0.2337    -23.6064         125   \n",
       "33       80.0000          0.0000     24.3732 0.2437     55.3830         125   \n",
       "34       80.0000          0.0000     24.8426 0.2484     54.9090         125   \n",
       "\n",
       "                                          fill_detail epistemic_status  \n",
       "1                                                  []        Simulated  \n",
       "15  [(0.2688674275036334, 10.0), (0.28686742750363...        Simulated  \n",
       "16  [(0.2781354658390098, 30.0), (0.29613546583900...        Simulated  \n",
       "17  [(0.2773181481673054, 30.0), (0.29531814816730...        Simulated  \n",
       "20  [(0.2783105971962925, 30.0), (0.29631059719629...        Simulated  \n",
       "24  [(0.2771578144620177, 30.0), (0.29515781446201...        Simulated  \n",
       "33  [(0.28966547684895655, 30.0), (0.3076654768489...        Simulated  \n",
       "34  [(0.2955323192124258, 30.0), (0.31353231921242...        Simulated  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assumption</th>\n",
       "      <th>value</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>requested units</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fee rate</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>latency</td>\n",
       "      <td>125.0000</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>latency first-level depth haircut</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>Illustrative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          assumption    value        status\n",
       "0                    requested units  80.0000  Illustrative\n",
       "1                           fee rate   0.0100  Illustrative\n",
       "2                            latency 125.0000  Illustrative\n",
       "3  latency first-level depth haircut  10.0000  Illustrative"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@dataclass(frozen=True)\n",
    "class ReplayAssumptions:\n",
    "    requested_units: float = 80.0\n",
    "    fee_rate: float = 0.01\n",
    "    latency_depth_haircut: float = 10.0\n",
    "\n",
    "REPLAY = ReplayAssumptions()\n",
    "\n",
    "def consume_asks(levels: list[tuple[float, float]], requested: float) -> tuple[float, float, list[tuple[float, float]]]:\n",
    "    remaining = requested\n",
    "    gross = 0.0\n",
    "    fills: list[tuple[float, float]] = []\n",
    "    for price, available in levels:\n",
    "        quantity = min(max(available, 0.0), remaining)\n",
    "        if quantity > 0:\n",
    "            fills.append((price, quantity))\n",
    "            gross += price * quantity\n",
    "            remaining -= quantity\n",
    "        if remaining <= 1e-12:\n",
    "            break\n",
    "    return requested - remaining, gross, fills\n",
    "\n",
    "ledger_rows = []\n",
    "take_counter = 0\n",
    "for row in policy.itertuples(index=False):\n",
    "    if row.decision != \"TAKE_PAPER_INTENT\":\n",
    "        ledger_rows.append({\n",
    "            \"decision_id\": f\"SQ-{int(row.row_id):04d}\", \"policy_decision\": row.decision,\n",
    "            \"execution_status\": \"NO_TRADE\", \"requested_units\": 0.0, \"filled_units\": 0.0,\n",
    "            \"unfilled_units\": 0.0, \"gross_cost\": 0.0, \"fee\": 0.0, \"net_result\": 0.0,\n",
    "            \"latency_ms\": 125, \"fill_detail\": [], \"epistemic_status\": \"Simulated\",\n",
    "        })\n",
    "        continue\n",
    "    take_counter += 1\n",
    "    ask = float(row.q_exec + 0.008)\n",
    "    if take_counter == 1:\n",
    "        raw_depths = [0.0, 0.0, 0.0]\n",
    "    elif take_counter == 2:\n",
    "        raw_depths = [20.0, 15.0, 0.0]\n",
    "    else:\n",
    "        raw_depths = [40.0, 35.0, 30.0]\n",
    "    post_latency_depths = raw_depths.copy()\n",
    "    post_latency_depths[0] = max(0.0, post_latency_depths[0] - REPLAY.latency_depth_haircut)\n",
    "    levels = [(ask, post_latency_depths[0]), (ask + 0.018, post_latency_depths[1]), (ask + 0.038, post_latency_depths[2])]\n",
    "    filled, gross, fills = consume_asks(levels, REPLAY.requested_units)\n",
    "    fee = REPLAY.fee_rate * gross\n",
    "    unfilled = REPLAY.requested_units - filled\n",
    "    if filled == 0:\n",
    "        status = \"NO_FILL\"\n",
    "    elif unfilled > 1e-12:\n",
    "        status = \"PARTIAL_FILL\"\n",
    "    else:\n",
    "        status = \"FULL_FILL\"\n",
    "    settlement = float(row.label_up) * filled\n",
    "    ledger_rows.append({\n",
    "        \"decision_id\": f\"SQ-{int(row.row_id):04d}\", \"policy_decision\": row.decision,\n",
    "        \"execution_status\": status, \"requested_units\": REPLAY.requested_units,\n",
    "        \"filled_units\": filled, \"unfilled_units\": unfilled, \"gross_cost\": gross,\n",
    "        \"fee\": fee, \"net_result\": settlement - gross - fee, \"latency_ms\": 125,\n",
    "        \"fill_detail\": fills, \"epistemic_status\": \"Simulated\",\n",
    "    })\n",
    "\n",
    "replay_ledger = pd.DataFrame(ledger_rows)\n",
    "status_order = [\"NO_TRADE\", \"NO_FILL\", \"PARTIAL_FILL\", \"FULL_FILL\"]\n",
    "status_counts = replay_ledger.execution_status.value_counts().reindex(status_order, fill_value=0)\n",
    "assert (status_counts > 0).all(), status_counts.to_dict()\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.6), constrained_layout=True)\n",
    "axes[0].bar(status_counts.index.str.replace(\"_\", \" \"), status_counts.values,\n",
    "            color=[COLORS[\"gray\"], COLORS[\"coral\"], COLORS[\"gold\"], COLORS[\"teal\"]])\n",
    "axes[0].tick_params(axis=\"x\", rotation=20)\n",
    "axes[0].set(ylabel=\"decision records\", title=\"Replay outcome states\")\n",
    "axes[1].plot(replay_ledger.net_result.cumsum().to_numpy(), color=COLORS[\"blue\"], lw=2)\n",
    "axes[1].axhline(0, color=COLORS[\"ink\"], lw=0.8)\n",
    "axes[1].set(xlabel=\"ordered replay record\", ylabel=\"cumulative simulated units\", title=\"Conditional result under declared assumptions\")\n",
    "fig.suptitle(\"SIMULATED · Fees + latency + partial/no fills + no-trade retained\", fontsize=14, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "display(replay_ledger[replay_ledger.execution_status.ne(\"NO_TRADE\")].head(8))\n",
    "display(pd.DataFrame({\n",
    "    \"assumption\": [\"requested units\", \"fee rate\", \"latency\", \"latency first-level depth haircut\"],\n",
    "    \"value\": [REPLAY.requested_units, REPLAY.fee_rate, 125, REPLAY.latency_depth_haircut],\n",
    "    \"status\": [\"Illustrative\"] * 4,\n",
    "}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940ff2be",
   "metadata": {},
   "source": [
    "### Exercise 7 — replay skepticism\n",
    "\n",
    "Double the latency depth haircut and fee rate. Which records change from full to partial or no fill? Explain why a static midpoint table could not reproduce this transition. Identify one missing real-world execution variable that prevents this teaching replay from supporting a live-fill claim."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fcbe0b",
   "metadata": {},
   "source": [
    "## 11 · Fail-closed guardian and state machine\n",
    "\n",
    "**Implemented toy control; simulated health inputs.** The guardian may classify evidence, pause research output, quarantine a batch, record, and escalate. It has no credential, network, model-approval, policy-change, or order-control capability. Unknown evidence routes to escalation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96b5600c",
   "metadata": {
    "alt": "Fail-closed guardian state machine from read-only observation through predicate checks, containment, containment verification, audit record, and human escalation.",
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:10.323679Z",
     "iopub.status.busy": "2026-08-06T13:42:10.323595Z",
     "iopub.status.idle": "2026-08-06T13:42:10.433655Z",
     "shell.execute_reply": "2026-08-06T13:42:10.433227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1265x552 with 1 Axes>"
      ]
     },
     "metadata": {
      "alt": "Fail-closed guardian state machine from observation through containment verification, audit, and escalation."
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scenario</th>\n",
       "      <th>state</th>\n",
       "      <th>action</th>\n",
       "      <th>reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>healthy fixture</td>\n",
       "      <td>HEALTHY</td>\n",
       "      <td>record_audit_event</td>\n",
       "      <td>ALL_PREDICATES_VERIFIED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>stale source</td>\n",
       "      <td>PAUSED_RESEARCH</td>\n",
       "      <td>pause_research_report</td>\n",
       "      <td>FRESHNESS_OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>schema drift</td>\n",
       "      <td>QUARANTINED_BATCH</td>\n",
       "      <td>quarantine_batch</td>\n",
       "      <td>SCHEMA_CONTRACT_FAILED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unknown predicate</td>\n",
       "      <td>ESCALATED_HUMAN_REVIEW</td>\n",
       "      <td>request_human_review</td>\n",
       "      <td>INCOMPLETE_EVIDENCE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            scenario                   state                 action  \\\n",
       "0    healthy fixture                 HEALTHY     record_audit_event   \n",
       "1       stale source         PAUSED_RESEARCH  pause_research_report   \n",
       "2       schema drift       QUARANTINED_BATCH       quarantine_batch   \n",
       "3  unknown predicate  ESCALATED_HUMAN_REVIEW   request_human_review   \n",
       "\n",
       "                    reason  \n",
       "0  ALL_PREDICATES_VERIFIED  \n",
       "1             FRESHNESS_OK  \n",
       "2   SCHEMA_CONTRACT_FAILED  \n",
       "3      INCOMPLETE_EVIDENCE  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>state</th>\n",
       "      <th>reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>RECORDED_AUDIT</td>\n",
       "      <td>CONTAINMENT_VERIFIED_AND_RECORDED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unknown quarantine outcome</td>\n",
       "      <td>ESCALATED_HUMAN_REVIEW</td>\n",
       "      <td>CONTAINMENT_UNVERIFIED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         case                   state  \\\n",
       "0              verified pause          RECORDED_AUDIT   \n",
       "1  unknown quarantine outcome  ESCALATED_HUMAN_REVIEW   \n",
       "\n",
       "                              reason  \n",
       "0  CONTAINMENT_VERIFIED_AND_RECORDED  \n",
       "1             CONTAINMENT_UNVERIFIED  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scenario</th>\n",
       "      <th>sequence</th>\n",
       "      <th>state</th>\n",
       "      <th>audit_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>healthy path</td>\n",
       "      <td>0</td>\n",
       "      <td>OBSERVE</td>\n",
       "      <td>healthy path:0:OBSERVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>healthy path</td>\n",
       "      <td>1</td>\n",
       "      <td>VERIFY</td>\n",
       "      <td>healthy path:1:VERIFY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>healthy path</td>\n",
       "      <td>2</td>\n",
       "      <td>HEALTHY</td>\n",
       "      <td>healthy path:2:HEALTHY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>healthy path</td>\n",
       "      <td>3</td>\n",
       "      <td>RECORDED_AUDIT</td>\n",
       "      <td>healthy path:3:RECORDED_AUDIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>0</td>\n",
       "      <td>OBSERVE</td>\n",
       "      <td>verified pause:0:OBSERVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>1</td>\n",
       "      <td>VERIFY</td>\n",
       "      <td>verified pause:1:VERIFY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>2</td>\n",
       "      <td>PAUSED_RESEARCH</td>\n",
       "      <td>verified pause:2:PAUSED_RESEARCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>3</td>\n",
       "      <td>VERIFY_CONTAINMENT</td>\n",
       "      <td>verified pause:3:VERIFY_CONTAINMENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>4</td>\n",
       "      <td>RECORDED_AUDIT</td>\n",
       "      <td>verified pause:4:RECORDED_AUDIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>verified pause</td>\n",
       "      <td>5</td>\n",
       "      <td>ESCALATED_HUMAN_REVIEW</td>\n",
       "      <td>verified pause:5:ESCALATED_HUMAN_REVIEW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>failed verification</td>\n",
       "      <td>0</td>\n",
       "      <td>OBSERVE</td>\n",
       "      <td>failed verification:0:OBSERVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>failed verification</td>\n",
       "      <td>1</td>\n",
       "      <td>VERIFY</td>\n",
       "      <td>failed verification:1:VERIFY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>failed verification</td>\n",
       "      <td>2</td>\n",
       "      <td>PAUSED_RESEARCH</td>\n",
       "      <td>failed verification:2:PAUSED_RESEARCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>failed verification</td>\n",
       "      <td>3</td>\n",
       "      <td>VERIFY_CONTAINMENT</td>\n",
       "      <td>failed verification:3:VERIFY_CONTAINMENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>failed verification</td>\n",
       "      <td>4</td>\n",
       "      <td>ESCALATED_HUMAN_REVIEW</td>\n",
       "      <td>failed verification:4:ESCALATED_HUMAN_REVIEW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               scenario  sequence                   state  \\\n",
       "0          healthy path         0                 OBSERVE   \n",
       "1          healthy path         1                  VERIFY   \n",
       "2          healthy path         2                 HEALTHY   \n",
       "3          healthy path         3          RECORDED_AUDIT   \n",
       "4        verified pause         0                 OBSERVE   \n",
       "5        verified pause         1                  VERIFY   \n",
       "6        verified pause         2         PAUSED_RESEARCH   \n",
       "7        verified pause         3      VERIFY_CONTAINMENT   \n",
       "8        verified pause         4          RECORDED_AUDIT   \n",
       "9        verified pause         5  ESCALATED_HUMAN_REVIEW   \n",
       "10  failed verification         0                 OBSERVE   \n",
       "11  failed verification         1                  VERIFY   \n",
       "12  failed verification         2         PAUSED_RESEARCH   \n",
       "13  failed verification         3      VERIFY_CONTAINMENT   \n",
       "14  failed verification         4  ESCALATED_HUMAN_REVIEW   \n",
       "\n",
       "                                     audit_event  \n",
       "0                         healthy path:0:OBSERVE  \n",
       "1                          healthy path:1:VERIFY  \n",
       "2                         healthy path:2:HEALTHY  \n",
       "3                  healthy path:3:RECORDED_AUDIT  \n",
       "4                       verified pause:0:OBSERVE  \n",
       "5                        verified pause:1:VERIFY  \n",
       "6               verified pause:2:PAUSED_RESEARCH  \n",
       "7            verified pause:3:VERIFY_CONTAINMENT  \n",
       "8                verified pause:4:RECORDED_AUDIT  \n",
       "9        verified pause:5:ESCALATED_HUMAN_REVIEW  \n",
       "10                 failed verification:0:OBSERVE  \n",
       "11                  failed verification:1:VERIFY  \n",
       "12         failed verification:2:PAUSED_RESEARCH  \n",
       "13      failed verification:3:VERIFY_CONTAINMENT  \n",
       "14  failed verification:4:ESCALATED_HUMAN_REVIEW  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f156d\">\n",
       "  <caption>Guardian allowlist — order control is structurally absent</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f156d_level0_col0\" class=\"col_heading level0 col0\" >allowed_capability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f156d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f156d_row0_col0\" class=\"data row0 col0\" >pause_research_report</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f156d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f156d_row1_col0\" class=\"data row1 col0\" >quarantine_batch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f156d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f156d_row2_col0\" class=\"data row2 col0\" >read_health_metadata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f156d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f156d_row3_col0\" class=\"data row3 col0\" >record_audit_event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f156d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f156d_row4_col0\" class=\"data row4 col0\" >request_human_review</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13b6f2bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class GuardianState(str, Enum):\n",
    "    OBSERVE = \"OBSERVE\"\n",
    "    VERIFY = \"VERIFY\"\n",
    "    HEALTHY = \"HEALTHY\"\n",
    "    PAUSED = \"PAUSED_RESEARCH\"\n",
    "    QUARANTINED = \"QUARANTINED_BATCH\"\n",
    "    VERIFY_CONTAINMENT = \"VERIFY_CONTAINMENT\"\n",
    "    RECORDED = \"RECORDED_AUDIT\"\n",
    "    ESCALATED = \"ESCALATED_HUMAN_REVIEW\"\n",
    "\n",
    "ALLOWED_CAPABILITIES = {\n",
    "    \"read_health_metadata\", \"pause_research_report\", \"quarantine_batch\",\n",
    "    \"record_audit_event\", \"request_human_review\",\n",
    "}\n",
    "PROHIBITED_CAPABILITIES = {\n",
    "    \"place_order\", \"cancel_order\", \"resize_order\", \"approve_model\",\n",
    "    \"change_threshold\", \"modify_evidence\",\n",
    "}\n",
    "assert ALLOWED_CAPABILITIES.isdisjoint(PROHIBITED_CAPABILITIES)\n",
    "\n",
    "def guardian_check(*, freshness_ok: bool | None, schema_ok: bool | None,\n",
    "                   artifact_ok: bool | None, replay_ok: bool | None) -> dict[str, str]:\n",
    "    evidence = {\n",
    "        \"freshness_ok\": freshness_ok, \"schema_ok\": schema_ok,\n",
    "        \"artifact_ok\": artifact_ok, \"replay_ok\": replay_ok,\n",
    "    }\n",
    "    if any(value is None for value in evidence.values()):\n",
    "        return {\"state\": GuardianState.ESCALATED.value, \"action\": \"request_human_review\", \"reason\": \"INCOMPLETE_EVIDENCE\"}\n",
    "    if not schema_ok:\n",
    "        return {\"state\": GuardianState.QUARANTINED.value, \"action\": \"quarantine_batch\", \"reason\": \"SCHEMA_CONTRACT_FAILED\"}\n",
    "    if not freshness_ok or not artifact_ok or not replay_ok:\n",
    "        failed = \",\".join(key for key, value in evidence.items() if not value)\n",
    "        return {\"state\": GuardianState.PAUSED.value, \"action\": \"pause_research_report\", \"reason\": failed.upper()}\n",
    "    return {\"state\": GuardianState.HEALTHY.value, \"action\": \"record_audit_event\", \"reason\": \"ALL_PREDICATES_VERIFIED\"}\n",
    "\n",
    "scenarios = pd.DataFrame([\n",
    "    {\"scenario\": \"healthy fixture\", **guardian_check(freshness_ok=True, schema_ok=True, artifact_ok=True, replay_ok=True)},\n",
    "    {\"scenario\": \"stale source\", **guardian_check(freshness_ok=False, schema_ok=True, artifact_ok=True, replay_ok=True)},\n",
    "    {\"scenario\": \"schema drift\", **guardian_check(freshness_ok=True, schema_ok=False, artifact_ok=True, replay_ok=True)},\n",
    "    {\"scenario\": \"unknown predicate\", **guardian_check(freshness_ok=None, schema_ok=True, artifact_ok=True, replay_ok=True)},\n",
    "])\n",
    "assert scenarios.loc[scenarios.scenario.eq(\"unknown predicate\"), \"state\"].item() == GuardianState.ESCALATED.value\n",
    "assert not (PROHIBITED_CAPABILITIES & ALLOWED_CAPABILITIES)\n",
    "\n",
    "def verify_containment(action: str, postcondition_verified: bool | None) -> dict[str, str]:\n",
    "    if action not in {\"pause_research_report\", \"quarantine_batch\"}:\n",
    "        return {\"state\": GuardianState.ESCALATED.value, \"reason\": \"ACTION_NOT_ALLOWLISTED\"}\n",
    "    if postcondition_verified is not True:\n",
    "        return {\"state\": GuardianState.ESCALATED.value, \"reason\": \"CONTAINMENT_UNVERIFIED\"}\n",
    "    return {\"state\": GuardianState.RECORDED.value, \"reason\": \"CONTAINMENT_VERIFIED_AND_RECORDED\"}\n",
    "\n",
    "containment_checks = pd.DataFrame([\n",
    "    {\"case\": \"verified pause\", **verify_containment(\"pause_research_report\", True)},\n",
    "    {\"case\": \"unknown quarantine outcome\", **verify_containment(\"quarantine_batch\", None)},\n",
    "])\n",
    "assert containment_checks.loc[containment_checks.case.eq(\"unknown quarantine outcome\"), \"state\"].item() == GuardianState.ESCALATED.value\n",
    "\n",
    "ALLOWED_TRANSITIONS = {\n",
    "    (GuardianState.OBSERVE, GuardianState.VERIFY),\n",
    "    (GuardianState.VERIFY, GuardianState.HEALTHY),\n",
    "    (GuardianState.VERIFY, GuardianState.PAUSED),\n",
    "    (GuardianState.VERIFY, GuardianState.QUARANTINED),\n",
    "    (GuardianState.VERIFY, GuardianState.ESCALATED),\n",
    "    (GuardianState.HEALTHY, GuardianState.RECORDED),\n",
    "    (GuardianState.PAUSED, GuardianState.VERIFY_CONTAINMENT),\n",
    "    (GuardianState.QUARANTINED, GuardianState.VERIFY_CONTAINMENT),\n",
    "    (GuardianState.VERIFY_CONTAINMENT, GuardianState.RECORDED),\n",
    "    (GuardianState.VERIFY_CONTAINMENT, GuardianState.ESCALATED),\n",
    "    (GuardianState.RECORDED, GuardianState.ESCALATED),\n",
    "}\n",
    "\n",
    "def guardian_runbook(*, scenario: str, freshness_ok: bool | None, schema_ok: bool | None,\n",
    "                     artifact_ok: bool | None, replay_ok: bool | None,\n",
    "                     containment_verified: bool | None = True) -> list[dict[str, str]]:\n",
    "    decision = guardian_check(freshness_ok=freshness_ok, schema_ok=schema_ok,\n",
    "                              artifact_ok=artifact_ok, replay_ok=replay_ok)\n",
    "    states = [GuardianState.OBSERVE, GuardianState.VERIFY, GuardianState(decision[\"state\"])]\n",
    "    if states[-1] is GuardianState.HEALTHY:\n",
    "        states.append(GuardianState.RECORDED)\n",
    "    elif states[-1] in {GuardianState.PAUSED, GuardianState.QUARANTINED}:\n",
    "        states.append(GuardianState.VERIFY_CONTAINMENT)\n",
    "        verification = verify_containment(decision[\"action\"], containment_verified)\n",
    "        states.append(GuardianState(verification[\"state\"]))\n",
    "        if states[-1] is GuardianState.RECORDED:\n",
    "            states.append(GuardianState.ESCALATED)\n",
    "    ledger = []\n",
    "    for sequence, state in enumerate(states):\n",
    "        if sequence:\n",
    "            assert (states[sequence - 1], state) in ALLOWED_TRANSITIONS\n",
    "        ledger.append({\"scenario\": scenario, \"sequence\": sequence, \"state\": state.value,\n",
    "                       \"audit_event\": f\"{scenario}:{sequence}:{state.value}\"})\n",
    "    return ledger\n",
    "\n",
    "transition_ledger = pd.DataFrame(\n",
    "    guardian_runbook(scenario=\"healthy path\", freshness_ok=True, schema_ok=True,\n",
    "                     artifact_ok=True, replay_ok=True)\n",
    "    + guardian_runbook(scenario=\"verified pause\", freshness_ok=False, schema_ok=True,\n",
    "                       artifact_ok=True, replay_ok=True, containment_verified=True)\n",
    "    + guardian_runbook(scenario=\"failed verification\", freshness_ok=False, schema_ok=True,\n",
    "                       artifact_ok=True, replay_ok=True, containment_verified=False)\n",
    ")\n",
    "healthy_path = transition_ledger.loc[transition_ledger.scenario.eq(\"healthy path\"), \"state\"].tolist()\n",
    "verified_path = transition_ledger.loc[transition_ledger.scenario.eq(\"verified pause\"), \"state\"].tolist()\n",
    "failed_path = transition_ledger.loc[transition_ledger.scenario.eq(\"failed verification\"), \"state\"].tolist()\n",
    "assert healthy_path == [\"OBSERVE\", \"VERIFY\", \"HEALTHY\", \"RECORDED_AUDIT\"]\n",
    "assert verified_path == [\"OBSERVE\", \"VERIFY\", \"PAUSED_RESEARCH\", \"VERIFY_CONTAINMENT\",\n",
    "                         \"RECORDED_AUDIT\", \"ESCALATED_HUMAN_REVIEW\"]\n",
    "assert failed_path[-2:] == [\"VERIFY_CONTAINMENT\", \"ESCALATED_HUMAN_REVIEW\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 4.8), constrained_layout=True)\n",
    "ax.axis(\"off\")\n",
    "positions = {\n",
    "    \"OBSERVE\": (0.06, 0.55), \"VERIFY\": (0.23, 0.55), \"HEALTHY\": (0.43, 0.82),\n",
    "    \"PAUSED\": (0.43, 0.57), \"QUARANTINED\": (0.43, 0.30),\n",
    "    \"VERIFY_CONTAINMENT\": (0.65, 0.43), \"RECORDED\": (0.82, 0.66), \"ESCALATED\": (0.92, 0.32),\n",
    "}\n",
    "labels = {\n",
    "    \"OBSERVE\": \"OBSERVE\\nread-only\", \"VERIFY\": \"VERIFY\\npredicates\", \"HEALTHY\": \"RECORD\\nhealthy\",\n",
    "    \"PAUSED\": \"PAUSE\\nresearch\", \"QUARANTINED\": \"QUARANTINE\\nbatch\",\n",
    "    \"VERIFY_CONTAINMENT\": \"VERIFY\\ncontainment\", \"RECORDED\": \"RECORD\\naudit\",\n",
    "    \"ESCALATED\": \"ESCALATE\\nhuman review\",\n",
    "}\n",
    "node_colors = {\"OBSERVE\": COLORS[\"blue\"], \"VERIFY\": COLORS[\"gold\"], \"HEALTHY\": COLORS[\"teal\"],\n",
    "               \"PAUSED\": COLORS[\"coral\"], \"QUARANTINED\": COLORS[\"coral\"],\n",
    "               \"VERIFY_CONTAINMENT\": COLORS[\"gold\"], \"RECORDED\": COLORS[\"teal\"], \"ESCALATED\": COLORS[\"ink\"]}\n",
    "for key, (x, y_pos) in positions.items():\n",
    "    ax.text(x, y_pos, labels[key], ha=\"center\", va=\"center\", color=\"white\" if key not in {\"VERIFY\", \"VERIFY_CONTAINMENT\"} else COLORS[\"ink\"],\n",
    "            fontweight=\"bold\", bbox={\"boxstyle\": \"round,pad=0.8\", \"fc\": node_colors[key], \"ec\": COLORS[\"ink\"], \"lw\": 1.5})\n",
    "def arrow(a: str, b: str, label: str):\n",
    "    ax.annotate(\"\", xy=positions[b], xytext=positions[a], arrowprops={\"arrowstyle\": \"->\", \"lw\": 1.7, \"color\": COLORS[\"ink\"], \"shrinkA\": 52, \"shrinkB\": 52})\n",
    "    mid = ((positions[a][0] + positions[b][0]) / 2, (positions[a][1] + positions[b][1]) / 2)\n",
    "    ax.text(mid[0], mid[1] + 0.035, label, ha=\"center\", fontsize=8, color=COLORS[\"ink\"])\n",
    "arrow(\"OBSERVE\", \"VERIFY\", \"evidence\")\n",
    "arrow(\"VERIFY\", \"HEALTHY\", \"all pass\")\n",
    "arrow(\"VERIFY\", \"PAUSED\", \"stale / mismatch\")\n",
    "arrow(\"VERIFY\", \"QUARANTINED\", \"schema fail\")\n",
    "arrow(\"PAUSED\", \"VERIFY_CONTAINMENT\", \"contain\")\n",
    "arrow(\"QUARANTINED\", \"VERIFY_CONTAINMENT\", \"contain\")\n",
    "arrow(\"VERIFY_CONTAINMENT\", \"RECORDED\", \"verified\")\n",
    "arrow(\"VERIFY_CONTAINMENT\", \"ESCALATED\", \"unknown / failed\")\n",
    "arrow(\"RECORDED\", \"ESCALATED\", \"request review\")\n",
    "ax.set_title(\"DESIGN-TARGET BOUNDARY · Fail closed; no order authority\", fontsize=14, fontweight=\"bold\")\n",
    "plt.show()\n",
    "\n",
    "display(scenarios)\n",
    "display(containment_checks)\n",
    "display(transition_ledger)\n",
    "display(pd.DataFrame({\n",
    "    \"allowed_capability\": sorted(ALLOWED_CAPABILITIES),\n",
    "}).style.set_caption(\"Guardian allowlist — order control is structurally absent\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e41f06",
   "metadata": {},
   "source": [
    "## 12 · Optional local LLM draft path — disabled by default\n",
    "\n",
    "**Design target; no generated-code execution.** The next cell can request a *text draft* from a local Ollama endpoint using a Kimi model name only after a human deliberately changes the flag. The response remains an untrusted string for diff review. It is never passed to \\`exec\\`, \\`eval\\`, a shell, a file writer, or a tool. Network use is disabled during normal execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc72800b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:10.435090Z",
     "iopub.status.busy": "2026-08-06T13:42:10.434999Z",
     "iopub.status.idle": "2026-08-06T13:42:10.438148Z",
     "shell.execute_reply": "2026-08-06T13:42:10.437491Z"
    },
    "tags": [
     "llm-draft-disabled"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DISABLED · No Ollama/Kimi request made; no generated code obtained or executed.\n"
     ]
    }
   ],
   "source": [
    "ENABLE_LOCAL_OLLAMA_KIMI_DRAFT = False\n",
    "llm_draft_text = None\n",
    "\n",
    "if ENABLE_LOCAL_OLLAMA_KIMI_DRAFT:\n",
    "    import json\n",
    "    import urllib.request\n",
    "\n",
    "    request_body = json.dumps({\n",
    "        \"model\": \"kimi\",\n",
    "        \"prompt\": \"Draft one pure Python metric helper. Return text only; do not execute anything.\",\n",
    "        \"stream\": False,\n",
    "    }).encode(\"utf-8\")\n",
    "    request = urllib.request.Request(\n",
    "        \"http://127.0.0.1:11434/api/generate\", data=request_body,\n",
    "        headers={\"Content-Type\": \"application/json\"}, method=\"POST\",\n",
    "    )\n",
    "    with urllib.request.urlopen(request, timeout=10) as response:\n",
    "        llm_draft_text = json.loads(response.read().decode(\"utf-8\")).get(\"response\", \"\")\n",
    "    print(\"Draft captured as untrusted text for human review; execution remains prohibited.\")\n",
    "else:\n",
    "    print(\"DISABLED · No Ollama/Kimi request made; no generated code obtained or executed.\")\n",
    "\n",
    "assert llm_draft_text is None, \"Normal deterministic execution must keep the optional draft path disabled.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc59edbc",
   "metadata": {},
   "source": [
    "### Exercise 8 — authority audit\n",
    "\n",
    "Add a hypothetical guardian request to “repair the schema automatically.” Explain why this is remediation rather than containment. Identify the missing human approval, tests, rollback, and capability boundary. Then threat-model the disabled LLM cell: what changes would make it unsafe?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0017d0bc",
   "metadata": {},
   "source": [
    "## 13 · Executable evidence gate\n",
    "\n",
    "The final cell checks narrow invariants. Passing them means only that this notebook's deterministic teaching contracts executed in this environment. It does not promote any result to measured evidence or authorize production use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22a35749",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T13:42:10.439565Z",
     "iopub.status.busy": "2026-08-06T13:42:10.439476Z",
     "iopub.status.idle": "2026-08-06T13:42:10.447338Z",
     "shell.execute_reply": "2026-08-06T13:42:10.447000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>check</th>\n",
       "      <th>passed</th>\n",
       "      <th>evidence</th>\n",
       "      <th>epistemic_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>portable locked runtime</td>\n",
       "      <td>True</td>\n",
       "      <td>Python 3.11.15</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chronological non-overlap</td>\n",
       "      <td>True</td>\n",
       "      <td>ordered</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purge derived from contract</td>\n",
       "      <td>True</td>\n",
       "      <td>20 rows</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>embargo derived from contract</td>\n",
       "      <td>True</td>\n",
       "      <td>20 rows</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>raw support intervals disjoint</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'boundary': 'train→validation', 'left_availa...</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>confusion counts complete</td>\n",
       "      <td>True</td>\n",
       "      <td>220/220</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>probabilities finite</td>\n",
       "      <td>True</td>\n",
       "      <td>finite</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>causal future perturbation</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>replay preserves four states</td>\n",
       "      <td>True</td>\n",
       "      <td>NO_TRADE,NO_FILL,PARTIAL_FILL,FULL_FILL</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>guardian fails closed</td>\n",
       "      <td>True</td>\n",
       "      <td>escalated</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>guardian transition ledger complete</td>\n",
       "      <td>True</td>\n",
       "      <td>healthy, verified-containment, and failed-cont...</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>no order capability</td>\n",
       "      <td>True</td>\n",
       "      <td>structurally absent</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>optional LLM disabled</td>\n",
       "      <td>True</td>\n",
       "      <td>no request / no execution</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>scikit-learn GradientBoosting path</td>\n",
       "      <td>True</td>\n",
       "      <td>Implemented teaching fallback</td>\n",
       "      <td>Implemented notebook check</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  check  passed  \\\n",
       "0               portable locked runtime    True   \n",
       "1             chronological non-overlap    True   \n",
       "2           purge derived from contract    True   \n",
       "3         embargo derived from contract    True   \n",
       "4        raw support intervals disjoint    True   \n",
       "5             confusion counts complete    True   \n",
       "6                  probabilities finite    True   \n",
       "7            causal future perturbation    True   \n",
       "8          replay preserves four states    True   \n",
       "9                 guardian fails closed    True   \n",
       "10  guardian transition ledger complete    True   \n",
       "11                  no order capability    True   \n",
       "12                optional LLM disabled    True   \n",
       "13   scikit-learn GradientBoosting path    True   \n",
       "\n",
       "                                             evidence  \\\n",
       "0                                      Python 3.11.15   \n",
       "1                                             ordered   \n",
       "2                                             20 rows   \n",
       "3                                             20 rows   \n",
       "4   [{'boundary': 'train→validation', 'left_availa...   \n",
       "5                                             220/220   \n",
       "6                                              finite   \n",
       "7                                            0.00e+00   \n",
       "8             NO_TRADE,NO_FILL,PARTIAL_FILL,FULL_FILL   \n",
       "9                                           escalated   \n",
       "10  healthy, verified-containment, and failed-cont...   \n",
       "11                                structurally absent   \n",
       "12                          no request / no execution   \n",
       "13                      Implemented teaching fallback   \n",
       "\n",
       "              epistemic_status  \n",
       "0   Implemented notebook check  \n",
       "1   Implemented notebook check  \n",
       "2   Implemented notebook check  \n",
       "3   Implemented notebook check  \n",
       "4   Implemented notebook check  \n",
       "5   Implemented notebook check  \n",
       "6   Implemented notebook check  \n",
       "7   Implemented notebook check  \n",
       "8   Implemented notebook check  \n",
       "9   Implemented notebook check  \n",
       "10  Implemented notebook check  \n",
       "11  Implemented notebook check  \n",
       "12  Implemented notebook check  \n",
       "13  Implemented notebook check  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "### Notebook execution gate: **GO for synthetic teaching use**\n",
       "\n",
       "All requested executable paths ran. This remains research-only and non-Measured."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "audit = pd.DataFrame([\n",
    "    (\"portable locked runtime\", runtime_matches, f\"Python {platform.python_version()}\"),\n",
    "    (\"chronological non-overlap\", max(split[\"train\"]) < min(split[\"validation\"]) < min(split[\"test\"]), \"ordered\"),\n",
    "    (\"purge derived from contract\", len(split[\"purge\"]) == GAP_ROWS, f\"{GAP_ROWS} rows\"),\n",
    "    (\"embargo derived from contract\", len(split[\"embargo\"]) == GAP_ROWS, f\"{GAP_ROWS} rows\"),\n",
    "    (\"raw support intervals disjoint\", bool(boundary_support_audit[\"disjoint\"].all()),\n",
    "     boundary_support_audit.to_dict(\"records\")),\n",
    "    (\"confusion counts complete\", int(cm.sum()) == len(y_test), f\"{int(cm.sum())}/{len(y_test)}\"),\n",
    "    (\"probabilities finite\", bool(np.isfinite(p_test).all()), \"finite\"),\n",
    "    (\"causal future perturbation\", future_perturbation_delta < 1e-12, f\"{future_perturbation_delta:.2e}\"),\n",
    "    (\"replay preserves four states\", bool((status_counts > 0).all()), \",\".join(status_counts.index)),\n",
    "    (\"guardian fails closed\", scenarios.loc[scenarios.scenario.eq(\"unknown predicate\"), \"state\"].item() == GuardianState.ESCALATED.value, \"escalated\"),\n",
    "    (\"guardian transition ledger complete\", healthy_path == [\"OBSERVE\", \"VERIFY\", \"HEALTHY\", \"RECORDED_AUDIT\"] and verified_path[-3:] == [\"VERIFY_CONTAINMENT\", \"RECORDED_AUDIT\", \"ESCALATED_HUMAN_REVIEW\"] and failed_path[-1] == \"ESCALATED_HUMAN_REVIEW\", \"healthy, verified-containment, and failed-containment paths recorded\"),\n",
    "    (\"no order capability\", ALLOWED_CAPABILITIES.isdisjoint(PROHIBITED_CAPABILITIES), \"structurally absent\"),\n",
    "    (\"optional LLM disabled\", ENABLE_LOCAL_OLLAMA_KIMI_DRAFT is False and llm_draft_text is None, \"no request / no execution\"),\n",
    "    (\"scikit-learn GradientBoosting path\", SKLEARN_OK, model_status),\n",
    "], columns=[\"check\", \"passed\", \"evidence\"])\n",
    "audit[\"epistemic_status\"] = \"Implemented notebook check\"\n",
    "display(audit)\n",
    "critical_without_environment = audit[~audit[\"check\"].eq(\"scikit-learn GradientBoosting path\")]\n",
    "assert critical_without_environment.passed.all(), audit[~audit.passed].to_dict(\"records\")\n",
    "\n",
    "if SKLEARN_OK:\n",
    "    display(Markdown(\"### Notebook execution gate: **GO for synthetic teaching use**\\n\\nAll requested executable paths ran. This remains research-only and non-Measured.\"))\n",
    "else:\n",
    "    display(Markdown(\"### Notebook execution gate: **NO-GO for final delivery**\\n\\nThe notebook executed its fail-safe teaching surrogate, but the required scikit-learn GradientBoosting path could not run in the exact interpreter. Fix the environment incompatibility, then re-execute without changing notebook source.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3287f3d1",
   "metadata": {},
   "source": [
    "## Closing defense\n",
    "\n",
    "A threshold is not a probability. A probability is not expected value. Expected value is not a fill. A simulated fill is not live execution. A monitoring agent is not an RL policy, and a prompt is not a capability boundary.\n",
    "\n",
    "The defensible artifact is the trace: declared evidence → frozen procedure → probability assessment → deterministic policy → replay assumptions → reason-coded action or refusal → bounded guardian response.\n",
    "\n",
    "**Current claim boundary:** synthetic teaching evidence only. No verified historical corpus, CatBoost model, sequence encoder, TLOB/LiT model, live replay, measured latency, market performance, or trading authority exists in this notebook."
   ]
  }
 ],
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   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.15"
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  "signal_quest": {
   "catboost_present": false,
   "epistemic_status": "Synthetic teaching artifact",
   "live_execution_authority": false,
   "seed": 8414,
   "title": "Signal Quest Technical Masterclass"
  }
 },
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