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    "# Factor-Based Regime Detection\n",
    "\n",
    "**Chapter 1 · §1.4 Market Regimes: Change Is the Constant**\n",
    "\n",
    "**Docker image**: `ml4t`\n",
    "\n",
    "## Purpose\n",
    "\n",
    "Demonstrates unsupervised learning for market regime detection using Gaussian Mixture Models (GMM)\n",
    "on factor returns from the AQR Century of Factor Premia dataset.\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "- Apply GMM clustering to factor return time series.\n",
    "- Evaluate cluster count with BIC, AIC, and silhouette scores.\n",
    "- Visualize regime timelines with historical event annotations.\n",
    "- Compare factor performance across regimes.\n",
    "\n",
    "## Book Reference\n",
    "\n",
    "Section 1.4 of Chapter 1, \"Market Regimes: Change Is the Constant\" — style-regime view that\n",
    "precedes the macro-regime view in `macro_regimes.py`.\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "- Familiarity with monthly return series and StandardScaler-style preprocessing.\n",
    "- Conceptual exposure to mixture models and information criteria.\n",
    "- AQR Century of Factor Premia parquet at `data/aqr_factors/` (download via\n",
    "  `uv run python data/factors/aqr_download.py` if missing).\n",
    "\n",
    "## Background\n",
    "\n",
    "Inspired by [Two Sigma's 2021 paper](https://www.twosigma.com/articles/a-machine-learning-approach-to-regime-modeling/)\n",
    "which uses GMM on an 18-factor \"Factor Lens\" to identify four market regimes. We work with\n",
    "AQR's longer history (1927+) over Value, Momentum, Carry, and Defensive across multiple asset\n",
    "classes. There is no objectively \"correct\" number of regimes; we sweep 2–6 clusters to show\n",
    "how granularity shapes the regime map.\n",
    "\n",
    "### Scope: descriptive, not predictive\n",
    "\n",
    "This notebook fits the GMM and the StandardScaler on the **entire factor-return history**\n",
    "and then assigns regime labels back over that same history. The result is an *ex-post*\n",
    "characterization of how the factor space partitions, useful for narrative and for\n",
    "visualizing where macro events fall within the partitioning. It is **not** a regime\n",
    "classifier that can be used for prediction: using these labels as features in a trading\n",
    "strategy would constitute look-ahead because the partition itself was estimated with\n",
    "future data. Lookahead-safe label construction (walk-forward fitting, point-in-time\n",
    "information sets, embargoed cross-validation) is introduced from Chapter 6 onward, and\n",
    "the case-study chapters (Ch16-20) demonstrate how predictive regime features are\n",
    "constructed and evaluated."
   ]
  },
  {
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     "exception": false,
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     "status": "completed"
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   },
   "source": [
    "## Imports"
   ]
  },
  {
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     "text": [
      ".venv/lib/python3.14/site-packages/kaleido/scopes/plotly.py:32: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.default_format is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.default_format instead.\n",
      "\n",
      "  self.default_format = \"png\"\n",
      ".venv/lib/python3.14/site-packages/kaleido/scopes/plotly.py:33: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.default_width is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.default_width instead.\n",
      "\n",
      "  self.default_width = 700\n",
      ".venv/lib/python3.14/site-packages/kaleido/scopes/plotly.py:34: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.default_height is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.default_height instead.\n",
      "\n",
      "  self.default_height = 500\n",
      ".venv/lib/python3.14/site-packages/kaleido/scopes/plotly.py:35: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.default_scale is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.default_scale instead.\n",
      "\n",
      "  self.default_scale = 1\n",
      ".venv/lib/python3.14/site-packages/plotly/io/_kaleido.py:133: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.mathjax is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.mathjax instead.\n",
      "\n",
      "  setattr(self._scope, name, value)\n",
      ".venv/lib/python3.14/site-packages/plotly/io/_kaleido.py:141: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.plotlyjs is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.plotlyjs instead.\n",
      "\n",
      "  scope.plotlyjs = os.path.join(package_dir, \"plotly.min.js\")\n",
      ".venv/lib/python3.14/site-packages/plotly/io/_kaleido.py:147: DeprecationWarning: \n",
      "Use of plotly.io.kaleido.scope.mathjax is deprecated and support will be removed after September 2025.\n",
      "Please use plotly.io.defaults.mathjax instead.\n",
      "\n",
      "  scope.mathjax = (\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Factor-Based Regime Detection — unsupervised regime detection using GMM on AQR factor returns.\"\"\"\n",
    "\n",
    "from __future__ import annotations\n",
    "\n",
    "import warnings\n",
    "from collections.abc import Iterable\n",
    "from dataclasses import dataclass\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import polars as pl\n",
    "from matplotlib.axes import Axes\n",
    "from matplotlib.colors import ListedColormap\n",
    "from ml4t.data.providers import AQRFactorProvider\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from utils.paths import get_output_dir\n",
    "from utils.reproducibility import set_global_seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "623a92ba",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
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   "outputs": [],
   "source": [
    "# Production defaults (Papermill overrides for testing)\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1fa9e3",
   "metadata": {
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     "exception": false,
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     "status": "completed"
    }
   },
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6d9e510",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "source": [
    "OUTPUT_DIR = get_output_dir(1, \"factor_regimes\")\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "set_global_seeds(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaee74c",
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     "status": "completed"
    }
   },
   "source": [
    "## Helper Functions\n",
    "\n",
    "A small container for GMM fit diagnostics, and a grid-search function\n",
    "that fits multiple cluster counts and reports BIC, AIC, and silhouette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33f5e26f",
   "metadata": {
    "execution": {
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   "source": [
    "@dataclass(frozen=True)\n",
    "class GmmFitResult:\n",
    "    \"\"\"Results from fitting a Gaussian Mixture Model.\"\"\"\n",
    "\n",
    "    model: GaussianMixture\n",
    "    labels: np.ndarray\n",
    "    probabilities: np.ndarray\n",
    "    bic: float\n",
    "    aic: float\n",
    "    silhouette: float"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040481db",
   "metadata": {
    "lines_to_next_cell": 2,
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     "exception": false,
     "start_time": "2026-06-14T19:26:19.415225+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Grid Search for Optimal Cluster Count\n",
    "Fit GMM across multiple cluster counts and evaluate with BIC, AIC, and silhouette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa2a737f",
   "metadata": {
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     "status": "completed"
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   "source": [
    "def fit_gmm_grid(\n",
    "    x: np.ndarray,\n",
    "    n_components_list: Iterable[int],\n",
    "    random_state: int = SEED,\n",
    ") -> dict[int, GmmFitResult]:\n",
    "    \"\"\"\n",
    "    Fit a grid of GaussianMixture models and report diagnostics.\n",
    "\n",
    "    BIC and AIC are more appropriate for mixture models than silhouette,\n",
    "    but silhouette can still be helpful as a coarse separation check.\n",
    "    \"\"\"\n",
    "    results: dict[int, GmmFitResult] = {}\n",
    "    for n in n_components_list:\n",
    "        model = GaussianMixture(\n",
    "            n_components=n,\n",
    "            covariance_type=\"full\",\n",
    "            random_state=random_state,\n",
    "            n_init=10,\n",
    "            reg_covar=1e-6,\n",
    "        )\n",
    "        model.fit(x)\n",
    "        labels = model.predict(x)\n",
    "        probs = model.predict_proba(x)\n",
    "        bic = float(model.bic(x))\n",
    "        aic = float(model.aic(x))\n",
    "        sil = float(silhouette_score(x, labels)) if n >= 2 else float(\"nan\")\n",
    "        results[n] = GmmFitResult(\n",
    "            model=model,\n",
    "            labels=labels,\n",
    "            probabilities=probs,\n",
    "            bic=bic,\n",
    "            aic=aic,\n",
    "            silhouette=sil,\n",
    "        )\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc1034a",
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     "exception": false,
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     "status": "completed"
    }
   },
   "source": [
    "## Load Factor Data"
   ]
  },
  {
   "cell_type": "code",
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   "id": "4832bc89",
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    "execution": {
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     "status": "completed"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m2026-06-14 15:26:19\u001b[0m [\u001b[32m\u001b[1mdebug    \u001b[0m] \u001b[1mRate limiter initialized      \u001b[0m \u001b[36mmax_calls\u001b[0m=\u001b[35m60\u001b[0m \u001b[36mperiod\u001b[0m=\u001b[35m60.0\u001b[0m \u001b[36mprovider\u001b[0m=\u001b[35maqr\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m2026-06-14 15:26:19\u001b[0m [\u001b[32m\u001b[1mdebug    \u001b[0m] \u001b[1mHTTP session initialized      \u001b[0m \u001b[36mmax_connections\u001b[0m=\u001b[35m10\u001b[0m \u001b[36mtimeout\u001b[0m=\u001b[35m30.0\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m2026-06-14 15:26:19\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mInitialized AQR factor provider\u001b[0m \u001b[36mdata_path\u001b[0m=\u001b[35mdata/factors/aqr\u001b[0m \u001b[36mname\u001b[0m=\u001b[35mAQRFactorProvider\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m2026-06-14 15:26:19\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mFetched AQR data              \u001b[0m \u001b[36mcolumns\u001b[0m=\u001b[35m44\u001b[0m \u001b[36mdataset\u001b[0m=\u001b[35mcentury_premia\u001b[0m \u001b[36mname\u001b[0m=\u001b[35mAQRFactorProvider\u001b[0m \u001b[36mregion\u001b[0m=\u001b[35mNone\u001b[0m \u001b[36mrows\u001b[0m=\u001b[35m1182\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m2026-06-14 15:26:19\u001b[0m [\u001b[32m\u001b[1mdebug    \u001b[0m] \u001b[1mHTTP session closed           \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AQR Century of Factor Premia: 1182 months, 44 factors\n",
      "Date range: 1926-07-01 00:00:00 to 2024-12-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "aqr_raw_pl = AQRFactorProvider().fetch(\"century_premia\")\n",
    "\n",
    "print(f\"AQR Century of Factor Premia: {aqr_raw_pl.height} months, {aqr_raw_pl.width - 1} factors\")\n",
    "date_range = aqr_raw_pl.select(\n",
    "    pl.col(\"timestamp\").min().alias(\"min\"), pl.col(\"timestamp\").max().alias(\"max\")\n",
    ")\n",
    "print(f\"Date range: {date_range['min'][0]} to {date_range['max'][0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee2c38d",
   "metadata": {
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     "duration": 0.002961,
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     "status": "completed"
    }
   },
   "source": [
    "## Understanding the Data\n",
    "\n",
    "The AQR \"Century of Factor Premia\" dataset contains monthly returns for various factor\n",
    "strategies across asset classes. Key columns we use:\n",
    "\n",
    "| Factor | Description |\n",
    "|--------|-------------|\n",
    "| **Equity indices Market** | Global developed equity market return (value-weighted) |\n",
    "| **All asset classes Value** | Cross-asset value factor (long cheap, short expensive) |\n",
    "| **All asset classes Momentum** | Cross-asset momentum (long top-quantile past returns, short bottom-quantile) |\n",
    "| **All asset classes Carry** | Cross-asset carry (long high yield, short low yield) |\n",
    "| **All asset classes Defensive** | Cross-asset low-risk anomaly |\n",
    "\n",
    "The \"Equity indices Market\" is what you'd earn investing in a global equity index -\n",
    "essentially the aggregate return from holding developed market stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5830ab00",
   "metadata": {
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     "exception": false,
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     "status": "completed"
    }
   },
   "source": [
    "## Select Factors"
   ]
  },
  {
   "cell_type": "code",
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   "id": "41f2da92",
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     "status": "completed"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 9 factors for regime detection:\n",
      "  All asset classes Value: 0.0% missing\n",
      "  All asset classes Momentum: 0.0% missing\n",
      "  All asset classes Carry: 0.0% missing\n",
      "  All asset classes Defensive: 0.0% missing\n",
      "  US Stock Selection Value: 0.0% missing\n",
      "  US Stock Selection Momentum: 0.5% missing\n",
      "  Equity indices Market: 0.0% missing\n",
      "  Fixed income Market: 0.0% missing\n",
      "  Commodities Market: 0.0% missing\n"
     ]
    }
   ],
   "source": [
    "factor_cols = [\n",
    "    \"All asset classes Value\",\n",
    "    \"All asset classes Momentum\",\n",
    "    \"All asset classes Carry\",\n",
    "    \"All asset classes Defensive\",\n",
    "    \"US Stock Selection Value\",\n",
    "    \"US Stock Selection Momentum\",\n",
    "    \"Equity indices Market\",\n",
    "    \"Fixed income Market\",\n",
    "    \"Commodities Market\",\n",
    "]\n",
    "\n",
    "available_cols = [c for c in factor_cols if c in aqr_raw_pl.columns]\n",
    "print(f\"Selected {len(available_cols)} factors for regime detection:\")\n",
    "for col in available_cols:\n",
    "    na_count = aqr_raw_pl.select(pl.col(col).is_null().sum()).item()\n",
    "    na_pct = na_count / aqr_raw_pl.height * 100\n",
    "    print(f\"  {col}: {na_pct:.1f}% missing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ab8310",
   "metadata": {
    "papermill": {
     "duration": 0.001819,
     "end_time": "2026-06-14T19:26:19.501553+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:19.499734+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Prepare Data for Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20175105",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:19.505558Z",
     "iopub.status.busy": "2026-06-14T19:26:19.505486Z",
     "iopub.status.idle": "2026-06-14T19:26:19.512837Z",
     "shell.execute_reply": "2026-06-14T19:26:19.512560Z"
    },
    "papermill": {
     "duration": 0.009812,
     "end_time": "2026-06-14T19:26:19.513157+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:19.503345+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factor data: 1176 months (1927 to 2024)\n"
     ]
    }
   ],
   "source": [
    "factors_pl = (\n",
    "    aqr_raw_pl.select([\"timestamp\"] + available_cols)\n",
    "    .sort(\"timestamp\")\n",
    "    .fill_null(strategy=\"forward\")\n",
    "    .drop_nulls()\n",
    ")\n",
    "\n",
    "factors_df = factors_pl.select(available_cols).to_pandas()\n",
    "factors_df.index = factors_pl[\"timestamp\"].to_pandas()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "factors_scaled = scaler.fit_transform(factors_df)\n",
    "\n",
    "print(\n",
    "    f\"Factor data: {len(factors_df)} months ({factors_df.index.min().year} to {factors_df.index.max().year})\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a7d6a9",
   "metadata": {
    "papermill": {
     "duration": 0.001806,
     "end_time": "2026-06-14T19:26:19.516888+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:19.515082+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Regime Detection with GMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "961554be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:19.521075Z",
     "iopub.status.busy": "2026-06-14T19:26:19.520999Z",
     "iopub.status.idle": "2026-06-14T19:26:21.445843Z",
     "shell.execute_reply": "2026-06-14T19:26:21.443826Z"
    },
    "papermill": {
     "duration": 1.927486,
     "end_time": "2026-06-14T19:26:21.446179+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:19.518693+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_660db\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_660db_level0_col0\" class=\"col_heading level0 col0\" >BIC</th>\n",
       "      <th id=\"T_660db_level0_col1\" class=\"col_heading level0 col1\" >AIC</th>\n",
       "      <th id=\"T_660db_level0_col2\" class=\"col_heading level0 col2\" >Silhouette (GMM)</th>\n",
       "      <th id=\"T_660db_level0_col3\" class=\"col_heading level0 col3\" >Silhouette (K-Means)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >n</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_660db_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "      <td id=\"T_660db_row0_col0\" class=\"data row0 col0\" >26169.8</td>\n",
       "      <td id=\"T_660db_row0_col1\" class=\"data row0 col1\" >25617.2</td>\n",
       "      <td id=\"T_660db_row0_col2\" class=\"data row0 col2\" >0.270</td>\n",
       "      <td id=\"T_660db_row0_col3\" class=\"data row0 col3\" >0.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_660db_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
       "      <td id=\"T_660db_row1_col0\" class=\"data row1 col0\" >26173.1</td>\n",
       "      <td id=\"T_660db_row1_col1\" class=\"data row1 col1\" >25341.7</td>\n",
       "      <td id=\"T_660db_row1_col2\" class=\"data row1 col2\" >0.115</td>\n",
       "      <td id=\"T_660db_row1_col3\" class=\"data row1 col3\" >0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_660db_level0_row2\" class=\"row_heading level0 row2\" >4</th>\n",
       "      <td id=\"T_660db_row2_col0\" class=\"data row2 col0\" >26258.9</td>\n",
       "      <td id=\"T_660db_row2_col1\" class=\"data row2 col1\" >25148.6</td>\n",
       "      <td id=\"T_660db_row2_col2\" class=\"data row2 col2\" >-0.024</td>\n",
       "      <td id=\"T_660db_row2_col3\" class=\"data row2 col3\" >0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_660db_level0_row3\" class=\"row_heading level0 row3\" >5</th>\n",
       "      <td id=\"T_660db_row3_col0\" class=\"data row3 col0\" >26387.6</td>\n",
       "      <td id=\"T_660db_row3_col1\" class=\"data row3 col1\" >24998.4</td>\n",
       "      <td id=\"T_660db_row3_col2\" class=\"data row3 col2\" >0.006</td>\n",
       "      <td id=\"T_660db_row3_col3\" class=\"data row3 col3\" >0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_660db_level0_row4\" class=\"row_heading level0 row4\" >6</th>\n",
       "      <td id=\"T_660db_row4_col0\" class=\"data row4 col0\" >26597.8</td>\n",
       "      <td id=\"T_660db_row4_col1\" class=\"data row4 col1\" >24929.8</td>\n",
       "      <td id=\"T_660db_row4_col2\" class=\"data row4 col2\" >0.003</td>\n",
       "      <td id=\"T_660db_row4_col3\" class=\"data row4 col3\" >0.077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7eb3c932dbe0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_regimes_list = [2, 3, 4, 5, 6]\n",
    "\n",
    "# Fit GMM grid\n",
    "gmm_grid = fit_gmm_grid(factors_scaled, n_regimes_list)\n",
    "\n",
    "# Also fit K-Means for comparison\n",
    "kmeans_results = {}\n",
    "for n in n_regimes_list:\n",
    "    kmeans = KMeans(n_clusters=n, random_state=SEED, n_init=10)\n",
    "    labels = kmeans.fit_predict(factors_scaled)\n",
    "    kmeans_results[n] = (kmeans, labels)\n",
    "\n",
    "# Report model selection criteria as a DataFrame (lower BIC/AIC is better; higher silhouette is better).\n",
    "selection_df = pd.DataFrame(\n",
    "    {\n",
    "        \"n\": n_regimes_list,\n",
    "        \"BIC\": [gmm_grid[n].bic for n in n_regimes_list],\n",
    "        \"AIC\": [gmm_grid[n].aic for n in n_regimes_list],\n",
    "        \"Silhouette (GMM)\": [gmm_grid[n].silhouette for n in n_regimes_list],\n",
    "        \"Silhouette (K-Means)\": [\n",
    "            silhouette_score(factors_scaled, kmeans_results[n][1]) for n in n_regimes_list\n",
    "        ],\n",
    "    }\n",
    ").set_index(\"n\")\n",
    "selection_df.style.format(\n",
    "    {\n",
    "        \"BIC\": \"{:.1f}\",\n",
    "        \"AIC\": \"{:.1f}\",\n",
    "        \"Silhouette (GMM)\": \"{:.3f}\",\n",
    "        \"Silhouette (K-Means)\": \"{:.3f}\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5529a616",
   "metadata": {
    "papermill": {
     "duration": 0.002853,
     "end_time": "2026-06-14T19:26:21.452160+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.449307+00:00",
     "status": "completed"
    }
   },
   "source": [
    "**Interpretation**: BIC is minimised at K=2 (26,170) and silhouette is maximised\n",
    "at K=2 (0.27); AIC keeps falling out to K=6 (24,930). BIC penalises model complexity\n",
    "more heavily than AIC, making it the standard choice when the goal is interpretability\n",
    "rather than maximum likelihood. We proceed with K=2 — a Risk-On / Risk-Off split that\n",
    "both BIC and silhouette select on this 1927-2024 panel.\n",
    "\n",
    "The silhouette scores turn negative for K≥4 (−0.02 at K=4, near-zero for K=5 and K=6),\n",
    "indicating that higher cluster counts produce overlapping, poorly separated regimes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdce1178",
   "metadata": {
    "papermill": {
     "duration": 0.002751,
     "end_time": "2026-06-14T19:26:21.457717+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.454966+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Model Selection Visualization\n",
    "\n",
    "The charts below show BIC, AIC, and silhouette scores for different cluster counts.\n",
    "Note the y-axis is scaled to highlight differences between models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a868709e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:21.464122Z",
     "iopub.status.busy": "2026-06-14T19:26:21.463849Z",
     "iopub.status.idle": "2026-06-14T19:26:21.466509Z",
     "shell.execute_reply": "2026-06-14T19:26:21.466022Z"
    },
    "papermill": {
     "duration": 0.00626,
     "end_time": "2026-06-14T19:26:21.466786+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.460526+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "metrics = {\n",
    "    \"BIC\": [gmm_grid[n].bic for n in n_regimes_list],\n",
    "    \"AIC\": [gmm_grid[n].aic for n in n_regimes_list],\n",
    "    \"Silhouette\": [gmm_grid[n].silhouette for n in n_regimes_list],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e297b56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:21.472914Z",
     "iopub.status.busy": "2026-06-14T19:26:21.472819Z",
     "iopub.status.idle": "2026-06-14T19:26:21.635684Z",
     "shell.execute_reply": "2026-06-14T19:26:21.635331Z"
    },
    "papermill": {
     "duration": 0.16651,
     "end_time": "2026-06-14T19:26:21.636108+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.469598+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "# BIC (lower is better) - zoomed y-axis\n",
    "ax = axes[0]\n",
    "bars = ax.bar(n_regimes_list, metrics[\"BIC\"], color=\"steelblue\", edgecolor=\"black\")\n",
    "best_idx = np.argmin(metrics[\"BIC\"])\n",
    "bars[best_idx].set_color(\"#D4A84B\")\n",
    "ax.set_xlabel(\"Number of Clusters\", fontsize=11)\n",
    "ax.set_ylabel(\"BIC (lower is better)\", fontsize=11)\n",
    "ax.set_title(\"Bayesian Information Criterion\", fontsize=12)\n",
    "ax.set_xticks(n_regimes_list)\n",
    "# Zoom y-axis to show differences more clearly\n",
    "bic_min, bic_max = min(metrics[\"BIC\"]), max(metrics[\"BIC\"])\n",
    "bic_range = bic_max - bic_min\n",
    "ax.set_ylim(bic_min - 0.1 * bic_range, bic_max + 0.1 * bic_range)\n",
    "\n",
    "# AIC (lower is better) - zoomed y-axis\n",
    "ax = axes[1]\n",
    "bars = ax.bar(n_regimes_list, metrics[\"AIC\"], color=\"steelblue\", edgecolor=\"black\")\n",
    "best_idx = np.argmin(metrics[\"AIC\"])\n",
    "bars[best_idx].set_color(\"#D4A84B\")\n",
    "ax.set_xlabel(\"Number of Clusters\", fontsize=11)\n",
    "ax.set_ylabel(\"AIC (lower is better)\", fontsize=11)\n",
    "ax.set_title(\"Akaike Information Criterion\", fontsize=12)\n",
    "ax.set_xticks(n_regimes_list)\n",
    "# Zoom y-axis to show differences more clearly\n",
    "aic_min, aic_max = min(metrics[\"AIC\"]), max(metrics[\"AIC\"])\n",
    "aic_range = aic_max - aic_min\n",
    "ax.set_ylim(aic_min - 0.1 * aic_range, aic_max + 0.1 * aic_range)\n",
    "\n",
    "# Silhouette (higher is better)\n",
    "ax = axes[2]\n",
    "bars = ax.bar(n_regimes_list, metrics[\"Silhouette\"], color=\"steelblue\", edgecolor=\"black\")\n",
    "best_idx = np.argmax(metrics[\"Silhouette\"])\n",
    "bars[best_idx].set_color(\"#D4A84B\")\n",
    "ax.set_xlabel(\"Number of Clusters\", fontsize=11)\n",
    "ax.set_ylabel(\"Silhouette (higher is better)\", fontsize=11)\n",
    "ax.set_title(\"Silhouette Score\", fontsize=12)\n",
    "ax.set_xticks(n_regimes_list)\n",
    "\n",
    "fig.suptitle(\n",
    "    \"GMM Model Selection: BIC and Silhouette Agree on K=2; AIC Prefers K=6\",\n",
    "    fontsize=14,\n",
    "    y=1.02,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd5a8e0",
   "metadata": {
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.002094,
     "end_time": "2026-06-14T19:26:21.640481+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.638387+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Regime Timeline Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1136340e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:21.645211Z",
     "iopub.status.busy": "2026-06-14T19:26:21.645119Z",
     "iopub.status.idle": "2026-06-14T19:26:21.648545Z",
     "shell.execute_reply": "2026-06-14T19:26:21.648253Z"
    },
    "papermill": {
     "duration": 0.006679,
     "end_time": "2026-06-14T19:26:21.649215+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.642536+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def plot_regime_timeline(\n",
    "    labels: np.ndarray,\n",
    "    dates: pd.Index,\n",
    "    n_regimes: int,\n",
    "    title: str,\n",
    "    ax: Axes | None = None,\n",
    ") -> Axes:\n",
    "    \"\"\"Plot regime assignments as a timeline heatmap (one row per regime).\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(16, 0.8 * n_regimes + 1))\n",
    "\n",
    "    years = [pd.Timestamp(ts).year for ts in dates]\n",
    "\n",
    "    # Create binary matrix: (n_regimes x n_months)\n",
    "    regime_matrix = np.zeros((n_regimes, len(labels)))\n",
    "    for i, label in enumerate(labels):\n",
    "        regime_matrix[label, i] = 1\n",
    "\n",
    "    # Plot each regime as a swim lane. origin='lower' so data row k anchors\n",
    "    # at y=k (matching the \"Regime k+1\" yticklabel below).\n",
    "    ax.imshow(\n",
    "        regime_matrix,\n",
    "        aspect=\"auto\",\n",
    "        cmap=\"Blues\",\n",
    "        vmin=0,\n",
    "        vmax=1,\n",
    "        extent=(0, len(labels), -0.5, n_regimes - 0.5),\n",
    "        interpolation=\"nearest\",\n",
    "        origin=\"lower\",\n",
    "    )\n",
    "\n",
    "    # Y-axis: regime labels\n",
    "    ax.set_yticks(range(n_regimes))\n",
    "    ax.set_yticklabels([f\"Regime {i + 1}\" for i in range(n_regimes)], fontsize=10)\n",
    "\n",
    "    # X-axis: decade markers\n",
    "    year_ticks = []\n",
    "    year_labels_list = []\n",
    "    for j, year in enumerate(years):\n",
    "        if j == 0 or years[j - 1] != year:\n",
    "            if year % 10 == 0:\n",
    "                year_ticks.append(j)\n",
    "                year_labels_list.append(str(year))\n",
    "\n",
    "    ax.set_xticks(year_ticks)\n",
    "    ax.set_xticklabels(year_labels_list, fontsize=9)\n",
    "    ax.set_xlabel(\"Year\", fontsize=11)\n",
    "    ax.set_title(title, fontsize=12)\n",
    "\n",
    "    # Add horizontal grid lines between regimes\n",
    "    for i in range(1, n_regimes):\n",
    "        ax.axhline(y=i - 0.5, color=\"white\", linewidth=2)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b91ad02d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:21.655567Z",
     "iopub.status.busy": "2026-06-14T19:26:21.655474Z",
     "iopub.status.idle": "2026-06-14T19:26:22.162688Z",
     "shell.execute_reply": "2026-06-14T19:26:22.162249Z"
    },
    "papermill": {
     "duration": 0.51108,
     "end_time": "2026-06-14T19:26:22.163125+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:21.652045+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x310 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x390 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x470 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x630 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create swim lane figures for each cluster count\n",
    "start_year = factors_df.index.min().year\n",
    "end_year = factors_df.index.max().year\n",
    "\n",
    "for n in n_regimes_list:\n",
    "    fig, ax = plt.subplots(figsize=(16, 0.8 * n + 1.5))\n",
    "    labels = gmm_grid[n].labels\n",
    "\n",
    "    plot_regime_timeline(\n",
    "        labels=labels,\n",
    "        dates=factors_df.index,\n",
    "        n_regimes=n,\n",
    "        title=f\"GMM Factor Regimes (n={n}) - {start_year} to {end_year}\",\n",
    "        ax=ax,\n",
    "    )\n",
    "\n",
    "    # Add metrics annotation\n",
    "    bic = gmm_grid[n].bic\n",
    "    aic = gmm_grid[n].aic\n",
    "    sil = gmm_grid[n].silhouette\n",
    "    ax.text(\n",
    "        0.02,\n",
    "        0.98,\n",
    "        f\"BIC: {bic:.0f}  |  AIC: {aic:.0f}  |  Silhouette: {sil:.3f}\",\n",
    "        transform=ax.transAxes,\n",
    "        fontsize=9,\n",
    "        verticalalignment=\"top\",\n",
    "        bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8),\n",
    "    )\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47691bc",
   "metadata": {
    "papermill": {
     "duration": 0.002708,
     "end_time": "2026-06-14T19:26:22.168729+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.166021+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Two-Regime View: Risk-On vs Risk-Off\n",
    "\n",
    "The two-regime split separates periods of broad factor outperformance from\n",
    "periods of broad underperformance. It is the model that BIC and silhouette\n",
    "select above; the analysis below characterises the two regimes empirically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c71ea098",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:22.175167Z",
     "iopub.status.busy": "2026-06-14T19:26:22.175016Z",
     "iopub.status.idle": "2026-06-14T19:26:22.178382Z",
     "shell.execute_reply": "2026-06-14T19:26:22.178100Z"
    },
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.007451,
     "end_time": "2026-06-14T19:26:22.178851+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.171400+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "# Historical events for annotation\n",
    "historical_events = {\n",
    "    1929: (\"Great Crash\", \"top\"),\n",
    "    1937: (\"Recession\", \"bottom\"),\n",
    "    1973: (\"Oil Crisis\", \"top\"),\n",
    "    1987: (\"Black Monday\", \"bottom\"),\n",
    "    2000: (\"Dot-com\", \"top\"),\n",
    "    2008: (\"GFC\", \"bottom\"),\n",
    "    2020: (\"COVID\", \"top\"),\n",
    "}\n",
    "\n",
    "# Use n=2 for cleaner, more stable regime identification\n",
    "n_regimes_book = 2\n",
    "labels_2 = gmm_grid[n_regimes_book].labels\n",
    "\n",
    "# Ex-post labeling for interpretability (NOT predictive)\n",
    "regime_equity_returns = factors_df[\"Equity indices Market\"].groupby(labels_2).mean()\n",
    "good_regime = regime_equity_returns.idxmax()\n",
    "bad_regime = 1 - good_regime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc858564",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.002633,
     "end_time": "2026-06-14T19:26:22.184291+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.181658+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Risk-On vs Risk-Off Regime Timeline\n",
    "Swim lane panel showing regime assignment alongside cumulative equity returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52af7ea4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:22.190357Z",
     "iopub.status.busy": "2026-06-14T19:26:22.190265Z",
     "iopub.status.idle": "2026-06-14T19:26:22.618575Z",
     "shell.execute_reply": "2026-06-14T19:26:22.618126Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.432075,
     "end_time": "2026-06-14T19:26:22.618964+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.186889+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_two_regime_view():\n",
    "    \"\"\"Create swim lane + cumulative return figure for the two-regime model.\"\"\"\n",
    "    # Create figure with swim lane panel + cumulative returns\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(14, 5.5), height_ratios=[1, 2], sharex=True)\n",
    "\n",
    "    # Panel 1: Regime swim lanes\n",
    "    ax1 = axes[0]\n",
    "\n",
    "    # Create binary matrix: row 0 = Risk-Off, row 1 = Risk-On\n",
    "    regime_matrix = np.zeros((2, len(labels_2)))\n",
    "    for i, label in enumerate(labels_2):\n",
    "        if label == good_regime:\n",
    "            regime_matrix[1, i] = 1  # Risk-On row\n",
    "        else:\n",
    "            regime_matrix[0, i] = 1  # Risk-Off row\n",
    "\n",
    "    # Use grayscale-friendly colors for swim lanes. origin='lower' so data\n",
    "    # row 0 (Risk-Off) anchors at the bottom of the axis and row 1 (Risk-On)\n",
    "    # at the top, matching the yticklabels below.\n",
    "    cmap_swim = ListedColormap([\"white\", \"#2d2d2d\"])\n",
    "    ax1.imshow(\n",
    "        regime_matrix,\n",
    "        aspect=\"auto\",\n",
    "        cmap=cmap_swim,\n",
    "        vmin=0,\n",
    "        vmax=1,\n",
    "        extent=[0, len(labels_2), -0.5, 1.5],\n",
    "        interpolation=\"nearest\",\n",
    "        origin=\"lower\",\n",
    "    )\n",
    "\n",
    "    # Y-axis: regime labels\n",
    "    ax1.set_yticks([0, 1])\n",
    "    ax1.set_yticklabels([\"Risk-Off\", \"Risk-On\"], fontsize=10)\n",
    "    ax1.axhline(y=0.5, color=\"gray\", linewidth=1.5)\n",
    "\n",
    "    # X-axis setup\n",
    "    years = [pd.Timestamp(ts).year for ts in factors_df.index]\n",
    "    decade_ticks = []\n",
    "    decade_labels = []\n",
    "    for j, year in enumerate(years):\n",
    "        if j == 0 or years[j - 1] != year:\n",
    "            if year % 10 == 0:\n",
    "                decade_ticks.append(j)\n",
    "                decade_labels.append(str(year))\n",
    "\n",
    "    # Add historical event markers (BLACK labels, not red)\n",
    "    for event_year, (event_label, pos) in historical_events.items():\n",
    "        try:\n",
    "            idx = next(j for j, y in enumerate(years) if y == event_year)\n",
    "            ax1.axvline(x=idx, color=\"black\", linestyle=\"-\", alpha=0.6, linewidth=1.5)\n",
    "            y_pos = 1.7 if pos == \"top\" else -0.7\n",
    "            va = \"bottom\" if pos == \"top\" else \"top\"\n",
    "            ax1.annotate(\n",
    "                event_label,\n",
    "                xy=(idx, y_pos),\n",
    "                xycoords=(\"data\", \"data\"),\n",
    "                ha=\"center\",\n",
    "                va=va,\n",
    "                fontsize=8,\n",
    "                color=\"black\",\n",
    "                fontweight=\"bold\",\n",
    "            )\n",
    "        except StopIteration:\n",
    "            pass\n",
    "\n",
    "    ax1.set_xlim(0, len(labels_2))\n",
    "    ax1.set_ylim(-0.5, 1.5)\n",
    "\n",
    "    # Panel 2: Cumulative equity returns colored by regime\n",
    "    ax2 = axes[1]\n",
    "    equity_returns = factors_df[\"Equity indices Market\"]\n",
    "    cum_returns = (1 + equity_returns).cumprod()\n",
    "\n",
    "    # Plot with regime coloring\n",
    "    for i in range(len(cum_returns) - 1):\n",
    "        color = \"#d0d0d0\" if labels_2[i] == good_regime else \"#2d2d2d\"\n",
    "        ax2.plot(\n",
    "            [i, i + 1], [cum_returns.iloc[i], cum_returns.iloc[i + 1]], color=color, linewidth=1.2\n",
    "        )\n",
    "\n",
    "    ax2.set_yscale(\"log\")\n",
    "    ax2.set_ylabel(\"Cumulative Return (log scale)\", fontsize=10)\n",
    "    ax2.set_xlabel(\"Year\", fontsize=10)\n",
    "    ax2.set_xticks(decade_ticks)\n",
    "    ax2.set_xticklabels(decade_labels, fontsize=9)\n",
    "    ax2.grid(True, alpha=0.3)\n",
    "\n",
    "    # Annotations\n",
    "    ax2.annotate(\n",
    "        f\"${cum_returns.iloc[-1]:.0f}\",\n",
    "        xy=(len(cum_returns) - 1, cum_returns.iloc[-1]),\n",
    "        xytext=(10, 0),\n",
    "        textcoords=\"offset points\",\n",
    "        fontsize=10,\n",
    "        fontweight=\"bold\",\n",
    "    )\n",
    "    ax2.annotate(\n",
    "        \"$1 invested in 1927\",\n",
    "        xy=(0, 1),\n",
    "        xytext=(10, 10),\n",
    "        textcoords=\"offset points\",\n",
    "        fontsize=9,\n",
    "        style=\"italic\",\n",
    "    )\n",
    "\n",
    "    fig.suptitle(\n",
    "        f\"Market Regimes: Risk-On vs Risk-Off ({start_year}-{end_year})\",\n",
    "        fontsize=12,\n",
    "        fontweight=\"bold\",\n",
    "        y=1.02,\n",
    "    )\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_two_regime_view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3550a5a9",
   "metadata": {
    "papermill": {
     "duration": 0.003091,
     "end_time": "2026-06-14T19:26:22.625304+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.622213+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Volatility by Regime\n",
    "\n",
    "A key question: does volatility differ meaningfully between regimes?\n",
    "If Risk-Off periods have higher volatility, this validates using regime\n",
    "detection for risk management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93f1e5b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:22.631851Z",
     "iopub.status.busy": "2026-06-14T19:26:22.631762Z",
     "iopub.status.idle": "2026-06-14T19:26:22.636133Z",
     "shell.execute_reply": "2026-06-14T19:26:22.635704Z"
    },
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.008281,
     "end_time": "2026-06-14T19:26:22.636545+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.628264+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "# Calculate rolling volatility (12-month window, annualized)\n",
    "equity_returns = factors_df[\"Equity indices Market\"]\n",
    "rolling_vol = equity_returns.rolling(12).std() * np.sqrt(12)\n",
    "\n",
    "# Create regime indicator series\n",
    "regime_indicator = pd.Series(labels_2, index=factors_df.index)\n",
    "\n",
    "# Calculate volatility statistics by regime\n",
    "vol_by_regime = []\n",
    "for regime, name in [(good_regime, \"Risk-On\"), (bad_regime, \"Risk-Off\")]:\n",
    "    mask = regime_indicator == regime\n",
    "    vol_in_regime = rolling_vol[mask].dropna()\n",
    "    vol_by_regime.append(\n",
    "        {\n",
    "            \"Regime\": name,\n",
    "            \"Mean Vol\": vol_in_regime.mean(),\n",
    "            \"Median Vol\": vol_in_regime.median(),\n",
    "            \"Max Vol\": vol_in_regime.max(),\n",
    "        }\n",
    "    )\n",
    "\n",
    "vol_df = pd.DataFrame(vol_by_regime).set_index(\"Regime\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5c97ca",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.002941,
     "end_time": "2026-06-14T19:26:22.642503+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.639562+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Volatility Regime Chart\n",
    "Regime bands with rolling volatility, showing how risk environments differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04db3558",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:22.648780Z",
     "iopub.status.busy": "2026-06-14T19:26:22.648715Z",
     "iopub.status.idle": "2026-06-14T19:26:22.972047Z",
     "shell.execute_reply": "2026-06-14T19:26:22.971789Z"
    },
    "papermill": {
     "duration": 0.327396,
     "end_time": "2026-06-14T19:26:22.972777+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.645381+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk-Off / Risk-On rolling-volatility ratio: 1.30x (30% higher volatility in Risk-Off).\n"
     ]
    },
    {
     "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>Mean (%)</th>\n",
       "      <th>Median (%)</th>\n",
       "      <th>Max (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Risk-On</th>\n",
       "      <td>9.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Off</th>\n",
       "      <td>12.7</td>\n",
       "      <td>10.9</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mean (%)  Median (%)  Max (%)\n",
       "Regime                                 \n",
       "Risk-On        9.8         8.7     29.7\n",
       "Risk-Off      12.7        10.9     31.8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_volatility_by_regime():\n",
    "    \"\"\"Plot regime bands and rolling volatility with comparison statistics.\"\"\"\n",
    "    # Create figure with regime bands panel + volatility\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(14, 5), height_ratios=[1, 3], sharex=True)\n",
    "\n",
    "    # Panel 1: Regime bands (same as the cumulative returns chart)\n",
    "    ax1 = axes[0]\n",
    "    regime_matrix = np.zeros((2, len(labels_2)))\n",
    "    for i, label in enumerate(labels_2):\n",
    "        if label == good_regime:\n",
    "            regime_matrix[1, i] = 1  # Risk-On row\n",
    "        else:\n",
    "            regime_matrix[0, i] = 1  # Risk-Off row\n",
    "\n",
    "    cmap_regime = ListedColormap([\"white\", \"#2d2d2d\"])\n",
    "    ax1.imshow(\n",
    "        regime_matrix,\n",
    "        aspect=\"auto\",\n",
    "        cmap=cmap_regime,\n",
    "        vmin=0,\n",
    "        vmax=1,\n",
    "        extent=[0, len(labels_2), -0.5, 1.5],\n",
    "        interpolation=\"nearest\",\n",
    "        origin=\"lower\",\n",
    "    )\n",
    "    ax1.set_yticks([0, 1])\n",
    "    ax1.set_yticklabels([\"Risk-Off\", \"Risk-On\"], fontsize=9)\n",
    "    ax1.axhline(y=0.5, color=\"gray\", linewidth=1)\n",
    "    ax1.set_title(\"Regime Classification\", fontsize=10)\n",
    "\n",
    "    # Panel 2: Volatility time series\n",
    "    ax2 = axes[1]\n",
    "\n",
    "    # Plot rolling volatility colored by regime\n",
    "    for i in range(len(rolling_vol) - 1):\n",
    "        if pd.notna(rolling_vol.iloc[i]):\n",
    "            color = \"#d0d0d0\" if labels_2[i] == good_regime else \"#2d2d2d\"\n",
    "            ax2.plot(\n",
    "                [i, i + 1],\n",
    "                [rolling_vol.iloc[i] * 100, rolling_vol.iloc[i + 1] * 100],\n",
    "                color=color,\n",
    "                linewidth=1.2,\n",
    "            )\n",
    "\n",
    "    # Add horizontal lines for regime means\n",
    "    ax2.axhline(\n",
    "        vol_df.loc[\"Risk-On\", \"Mean Vol\"] * 100,\n",
    "        color=\"#4a7c59\",\n",
    "        linestyle=\"--\",\n",
    "        linewidth=2,\n",
    "        label=f\"Risk-On avg: {vol_df.loc['Risk-On', 'Mean Vol'] * 100:.1f}%\",\n",
    "    )\n",
    "    ax2.axhline(\n",
    "        vol_df.loc[\"Risk-Off\", \"Mean Vol\"] * 100,\n",
    "        color=\"#8b4513\",\n",
    "        linestyle=\"--\",\n",
    "        linewidth=2,\n",
    "        label=f\"Risk-Off avg: {vol_df.loc['Risk-Off', 'Mean Vol'] * 100:.1f}%\",\n",
    "    )\n",
    "\n",
    "    # X-axis setup\n",
    "    years = [pd.Timestamp(ts).year for ts in factors_df.index]\n",
    "    decade_ticks = []\n",
    "    decade_labels = []\n",
    "    for j, year in enumerate(years):\n",
    "        if j == 0 or years[j - 1] != year:\n",
    "            if year % 10 == 0:\n",
    "                decade_ticks.append(j)\n",
    "                decade_labels.append(str(year))\n",
    "\n",
    "    ax2.set_xticks(decade_ticks)\n",
    "    ax2.set_xticklabels(decade_labels, fontsize=9)\n",
    "    ax2.set_xlabel(\"Year\", fontsize=10)\n",
    "    ax2.set_ylabel(\"Rolling 12-Month Volatility (%)\", fontsize=10)\n",
    "    ax2.legend(loc=\"upper right\", fontsize=10)\n",
    "    ax2.grid(True, alpha=0.3)\n",
    "\n",
    "    fig.suptitle(\n",
    "        f\"Equity Market Volatility by Regime ({start_year}-{end_year})\",\n",
    "        fontsize=12,\n",
    "        fontweight=\"bold\",\n",
    "        y=1.02,\n",
    "    )\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    vol_ratio = vol_df.loc[\"Risk-Off\", \"Mean Vol\"] / vol_df.loc[\"Risk-On\", \"Mean Vol\"]\n",
    "    print(\n",
    "        f\"Risk-Off / Risk-On rolling-volatility ratio: {vol_ratio:.2f}x \"\n",
    "        f\"({(vol_ratio - 1) * 100:.0f}% higher volatility in Risk-Off).\"\n",
    "    )\n",
    "    return (\n",
    "        (vol_df * 100)\n",
    "        .round(1)\n",
    "        .rename(columns={\"Mean Vol\": \"Mean (%)\", \"Median Vol\": \"Median (%)\", \"Max Vol\": \"Max (%)\"})\n",
    "    )\n",
    "\n",
    "\n",
    "vol_summary = plot_volatility_by_regime()\n",
    "vol_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1dea19c",
   "metadata": {
    "papermill": {
     "duration": 0.003522,
     "end_time": "2026-06-14T19:26:22.980370+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.976848+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Persist Figure 1.5 inputs\n",
    "\n",
    "The publication-quality version of Figure 1.5 is rendered by\n",
    "`book/01_process_is_edge/figures/scripts/generate_figure_1_5_factor_regimes_volatility.py`.\n",
    "That script reads the arrays persisted below so the book build does not\n",
    "re-fit the GMM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2669a64d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:22.987887Z",
     "iopub.status.busy": "2026-06-14T19:26:22.987789Z",
     "iopub.status.idle": "2026-06-14T19:26:22.990746Z",
     "shell.execute_reply": "2026-06-14T19:26:22.990408Z"
    },
    "papermill": {
     "duration": 0.007347,
     "end_time": "2026-06-14T19:26:22.991116+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.983769+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "ARTIFACT_DIR = OUTPUT_DIR / \"figure_1_5\"\n",
    "ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "np.savez(\n",
    "    ARTIFACT_DIR / \"inputs.npz\",\n",
    "    dates=factors_df.index.astype(\"datetime64[ns]\").astype(\"int64\"),\n",
    "    labels_2=np.asarray(labels_2, dtype=np.int64),\n",
    "    good_regime=np.int64(good_regime),\n",
    "    rolling_vol=rolling_vol.to_numpy(dtype=float),\n",
    "    risk_on_mean_vol=float(vol_df.loc[\"Risk-On\", \"Mean Vol\"]),\n",
    "    risk_off_mean_vol=float(vol_df.loc[\"Risk-Off\", \"Mean Vol\"]),\n",
    "    start_year=np.int64(start_year),\n",
    "    end_year=np.int64(end_year),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f499c5",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.003401,
     "end_time": "2026-06-14T19:26:22.998048+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:22.994647+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Factor Behavior by Regime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63bbbb24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-14T19:26:23.005424Z",
     "iopub.status.busy": "2026-06-14T19:26:23.005338Z",
     "iopub.status.idle": "2026-06-14T19:26:23.107607Z",
     "shell.execute_reply": "2026-06-14T19:26:23.107146Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.106452,
     "end_time": "2026-06-14T19:26:23.107892+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:23.001440+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Period: 1927-2024 (1176 months)\n",
      "Regime distribution: Risk-On 900 (76.5%), Risk-Off 276 (23.5%)\n"
     ]
    },
    {
     "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>Risk-Off (%)</th>\n",
       "      <th>Risk-On (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>5.3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Momentum</th>\n",
       "      <td>0.4</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity</th>\n",
       "      <td>2.2</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonds</th>\n",
       "      <td>4.2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carry</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Defensive</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Risk-Off (%)  Risk-On (%)\n",
       "Value               5.3          1.6\n",
       "Momentum            0.4          4.5\n",
       "Equity              2.2          9.5\n",
       "Bonds               4.2          0.8\n",
       "Carry              -0.6          3.5\n",
       "Defensive          -0.5          4.2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_factor_returns_by_regime():\n",
    "    \"\"\"Compare annualized factor returns between Risk-On and Risk-Off regimes.\"\"\"\n",
    "    # Calculate mean factor returns by regime\n",
    "    regime_labels_2 = pd.Series(labels_2, index=factors_df.index, name=\"regime\")\n",
    "    factor_by_regime = factors_df.copy()\n",
    "    factor_by_regime[\"regime\"] = regime_labels_2\n",
    "\n",
    "    # Mean returns by regime (annualized)\n",
    "    regime_means = factor_by_regime.groupby(\"regime\").mean() * 12\n",
    "    regime_means.index = [\"Risk-Off\" if i == bad_regime else \"Risk-On\" for i in regime_means.index]\n",
    "\n",
    "    # Rename columns for readability\n",
    "    col_renames = {\n",
    "        \"All asset classes Value\": \"Value\",\n",
    "        \"All asset classes Momentum\": \"Momentum\",\n",
    "        \"All asset classes Carry\": \"Carry\",\n",
    "        \"All asset classes Defensive\": \"Defensive\",\n",
    "        \"US Stock Selection Value\": \"US Value\",\n",
    "        \"US Stock Selection Momentum\": \"US Mom\",\n",
    "        \"Equity indices Market\": \"Equity\",\n",
    "        \"Fixed income Market\": \"Bonds\",\n",
    "        \"Commodities Market\": \"Cmdty\",\n",
    "    }\n",
    "    regime_means_display = regime_means.rename(columns=col_renames)\n",
    "\n",
    "    # Create bar chart comparing regimes\n",
    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "    x = np.arange(len(regime_means_display.columns))\n",
    "    width = 0.35\n",
    "\n",
    "    bars1 = ax.bar(\n",
    "        x - width / 2,\n",
    "        regime_means_display.loc[\"Risk-On\"].values * 100,\n",
    "        width,\n",
    "        label=\"Risk-On\",\n",
    "        color=\"#4a7c59\",\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.5,\n",
    "    )\n",
    "    bars2 = ax.bar(\n",
    "        x + width / 2,\n",
    "        regime_means_display.loc[\"Risk-Off\"].values * 100,\n",
    "        width,\n",
    "        label=\"Risk-Off\",\n",
    "        color=\"#8b4513\",\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.5,\n",
    "    )\n",
    "\n",
    "    # Add value labels\n",
    "    for bar, val in zip(bars1, regime_means_display.loc[\"Risk-On\"].values, strict=False):\n",
    "        height = bar.get_height()\n",
    "        ax.annotate(\n",
    "            f\"{val * 100:.1f}%\",\n",
    "            xy=(bar.get_x() + bar.get_width() / 2, height),\n",
    "            xytext=(0, 3 if height >= 0 else -10),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"center\",\n",
    "            va=\"bottom\" if height >= 0 else \"top\",\n",
    "            fontsize=8,\n",
    "            fontweight=\"bold\",\n",
    "        )\n",
    "\n",
    "    for bar, val in zip(bars2, regime_means_display.loc[\"Risk-Off\"].values, strict=False):\n",
    "        height = bar.get_height()\n",
    "        ax.annotate(\n",
    "            f\"{val * 100:.1f}%\",\n",
    "            xy=(bar.get_x() + bar.get_width() / 2, height),\n",
    "            xytext=(0, 3 if height >= 0 else -10),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"center\",\n",
    "            va=\"bottom\" if height >= 0 else \"top\",\n",
    "            fontsize=8,\n",
    "        )\n",
    "\n",
    "    ax.set_ylabel(\"Annualized Return (%)\", fontsize=11)\n",
    "    ax.set_xlabel(\"Factor\", fontsize=11)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(regime_means_display.columns, fontsize=10)\n",
    "    ax.axhline(y=0, color=\"black\", linestyle=\"-\", linewidth=0.5)\n",
    "    ax.legend(loc=\"upper right\", fontsize=10)\n",
    "    ax.grid(axis=\"y\", alpha=0.3)\n",
    "\n",
    "    n_on = (labels_2 == good_regime).sum()\n",
    "    n_off = (labels_2 == bad_regime).sum()\n",
    "    ax.set_title(\n",
    "        f\"Factor Returns by Market Regime\\nRisk-On: {n_on} months ({n_on / len(labels_2) * 100:.0f}%) | Risk-Off: {n_off} months ({n_off / len(labels_2) * 100:.0f}%)\",\n",
    "        fontsize=12,\n",
    "        fontweight=\"bold\",\n",
    "    )\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    # Display per-factor returns as a DataFrame for easy comparison.\n",
    "    period_label = (\n",
    "        f\"{factors_df.index.min().year}-{factors_df.index.max().year} ({len(factors_df)} months)\"\n",
    "    )\n",
    "    print(f\"Period: {period_label}\")\n",
    "    print(\n",
    "        f\"Regime distribution: Risk-On {n_on} ({n_on / len(labels_2) * 100:.1f}%), \"\n",
    "        f\"Risk-Off {n_off} ({n_off / len(labels_2) * 100:.1f}%)\"\n",
    "    )\n",
    "    key_factors = [\n",
    "        c\n",
    "        for c in [\"Value\", \"Momentum\", \"Equity\", \"Bonds\", \"Carry\", \"Defensive\"]\n",
    "        if c in regime_means_display.columns\n",
    "    ]\n",
    "    factor_summary = (regime_means_display[key_factors].T * 100).round(1)\n",
    "    factor_summary.columns = [f\"{c} (%)\" for c in factor_summary.columns]\n",
    "    return factor_summary\n",
    "\n",
    "\n",
    "factor_summary = plot_factor_returns_by_regime()\n",
    "factor_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5490b0c",
   "metadata": {
    "papermill": {
     "duration": 0.003793,
     "end_time": "2026-06-14T19:26:23.115766+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:23.111973+00:00",
     "status": "completed"
    }
   },
   "source": [
    "**Interpretation**: The regime split reveals distinct factor behavior:\n",
    "\n",
    "- **Value is countercyclical**: Returns *increase* during Risk-Off (+5.3% vs +1.6%),\n",
    "  consistent with the value premium's tendency to compensate for distress risk.\n",
    "- **Momentum is procyclical**: Much weaker during Risk-Off (+0.4% vs +4.5%),\n",
    "  reflecting momentum's vulnerability to sharp reversals.\n",
    "- **Carry and Defensive turn negative** in Risk-Off (−0.6% and −0.5%) — these\n",
    "  strategies that appear safe in calm markets lose money when conditions deteriorate.\n",
    "- **Bonds outperform in Risk-Off** (+4.2% vs +0.8%), consistent with the classic\n",
    "  flight-to-quality effect.\n",
    "\n",
    "For portfolio construction, this suggests Value and Bonds provide genuine\n",
    "diversification during stress, while Carry and Defensive do not."
   ]
  },
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   "source": [
    "## Regime Statistics\n",
    "\n",
    "Compare key metrics between Risk-On and Risk-Off periods."
   ]
  },
  {
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   "id": "b2ce203c",
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volatility ratio (Risk-Off / Risk-On): 2.23x\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_1f60c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1f60c_level0_col0\" class=\"col_heading level0 col0\" >Months</th>\n",
       "      <th id=\"T_1f60c_level0_col1\" class=\"col_heading level0 col1\" >% of Time</th>\n",
       "      <th id=\"T_1f60c_level0_col2\" class=\"col_heading level0 col2\" >Ann. Return</th>\n",
       "      <th id=\"T_1f60c_level0_col3\" class=\"col_heading level0 col3\" >Ann. Volatility</th>\n",
       "      <th id=\"T_1f60c_level0_col4\" class=\"col_heading level0 col4\" >Sharpe Ratio</th>\n",
       "      <th id=\"T_1f60c_level0_col5\" class=\"col_heading level0 col5\" >Max Drawdown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Regime</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1f60c_level0_row0\" class=\"row_heading level0 row0\" >Risk-On</th>\n",
       "      <td id=\"T_1f60c_row0_col0\" class=\"data row0 col0\" >900</td>\n",
       "      <td id=\"T_1f60c_row0_col1\" class=\"data row0 col1\" >76.5%</td>\n",
       "      <td id=\"T_1f60c_row0_col2\" class=\"data row0 col2\" >+9.5%</td>\n",
       "      <td id=\"T_1f60c_row0_col3\" class=\"data row0 col3\" >8.6%</td>\n",
       "      <td id=\"T_1f60c_row0_col4\" class=\"data row0 col4\" >1.11</td>\n",
       "      <td id=\"T_1f60c_row0_col5\" class=\"data row0 col5\" >-22.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f60c_level0_row1\" class=\"row_heading level0 row1\" >Risk-Off</th>\n",
       "      <td id=\"T_1f60c_row1_col0\" class=\"data row1 col0\" >276</td>\n",
       "      <td id=\"T_1f60c_row1_col1\" class=\"data row1 col1\" >23.5%</td>\n",
       "      <td id=\"T_1f60c_row1_col2\" class=\"data row1 col2\" >+2.2%</td>\n",
       "      <td id=\"T_1f60c_row1_col3\" class=\"data row1 col3\" >19.1%</td>\n",
       "      <td id=\"T_1f60c_row1_col4\" class=\"data row1 col4\" >0.12</td>\n",
       "      <td id=\"T_1f60c_row1_col5\" class=\"data row1 col5\" >-76.9%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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   "source": [
    "def compute_regime_statistics():\n",
    "    \"\"\"Calculate and display comprehensive regime statistics.\"\"\"\n",
    "    # Calculate comprehensive regime statistics\n",
    "    equity_returns = factors_df[\"Equity indices Market\"]\n",
    "    regime_series = pd.Series(labels_2, index=factors_df.index)\n",
    "\n",
    "    regime_stats_list = []\n",
    "    for regime, name in [(good_regime, \"Risk-On\"), (bad_regime, \"Risk-Off\")]:\n",
    "        mask = regime_series == regime\n",
    "        returns = equity_returns[mask]\n",
    "\n",
    "        # Basic counts\n",
    "        n_months = mask.sum()\n",
    "        pct_time = n_months / len(mask) * 100\n",
    "\n",
    "        # Return statistics (annualized)\n",
    "        mean_ret = returns.mean() * 12\n",
    "        vol = returns.std() * np.sqrt(12)\n",
    "        sharpe = mean_ret / vol if vol > 0 else 0\n",
    "\n",
    "        # Drawdown\n",
    "        cum_ret = (1 + returns).cumprod()\n",
    "        peak = cum_ret.cummax()\n",
    "        drawdown = (cum_ret - peak) / peak\n",
    "        max_dd = drawdown.min()\n",
    "\n",
    "        regime_stats_list.append(\n",
    "            {\n",
    "                \"Regime\": name,\n",
    "                \"Months\": n_months,\n",
    "                \"% of Time\": pct_time,\n",
    "                \"Ann. Return\": mean_ret,\n",
    "                \"Ann. Volatility\": vol,\n",
    "                \"Sharpe Ratio\": sharpe,\n",
    "                \"Max Drawdown\": max_dd,\n",
    "            }\n",
    "        )\n",
    "\n",
    "    regime_stats_df = pd.DataFrame(regime_stats_list).set_index(\"Regime\")\n",
    "\n",
    "    # Volatility ratio (printed; the table itself displays below).\n",
    "    vol_on = regime_stats_df.loc[\"Risk-On\", \"Ann. Volatility\"]\n",
    "    vol_off = regime_stats_df.loc[\"Risk-Off\", \"Ann. Volatility\"]\n",
    "    print(f\"Volatility ratio (Risk-Off / Risk-On): {vol_off / vol_on:.2f}x\")\n",
    "    return regime_stats_df.style.format(\n",
    "        {\n",
    "            \"% of Time\": \"{:.1f}%\",\n",
    "            \"Ann. Return\": \"{:+.1%}\",\n",
    "            \"Ann. Volatility\": \"{:.1%}\",\n",
    "            \"Sharpe Ratio\": \"{:.2f}\",\n",
    "            \"Max Drawdown\": \"{:.1%}\",\n",
    "        }\n",
    "    )\n",
    "\n",
    "\n",
    "compute_regime_statistics()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77297624",
   "metadata": {
    "papermill": {
     "duration": 0.0039,
     "end_time": "2026-06-14T19:26:23.148889+00:00",
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     "status": "completed"
    }
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   "source": [
    "**Interpretation**: The regime split is stark. Risk-On delivers a Sharpe of 1.11\n",
    "while Risk-Off drops to 0.12 — equity exposure during Risk-Off is essentially\n",
    "uncompensated risk. The −76.9% max drawdown in Risk-Off reflects the Great\n",
    "Depression and underscores that these are not mild corrections.\n",
    "\n",
    "The direct volatility ratio here (2.2x) is much larger than the rolling-window\n",
    "average reported earlier (1.3x). The rolling measure smooths over extreme months,\n",
    "while this calculation captures the full variance within each regime. The direct\n",
    "measure better reflects what a portfolio actually experiences during Risk-Off."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac76494",
   "metadata": {
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     "duration": 0.00365,
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     "status": "completed"
    }
   },
   "source": [
    "## Regime Duration Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e86a74cf",
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    "execution": {
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     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total regime transitions: 267 (avg 4.4 months between switches)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4073c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4073c_level0_col0\" class=\"col_heading level0 col0\" >Avg duration (months)</th>\n",
       "      <th id=\"T_4073c_level0_col1\" class=\"col_heading level0 col1\" >Max duration (months)</th>\n",
       "      <th id=\"T_4073c_level0_col2\" class=\"col_heading level0 col2\" >Episodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Regime</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4073c_level0_row0\" class=\"row_heading level0 row0\" >Risk-On</th>\n",
       "      <td id=\"T_4073c_row0_col0\" class=\"data row0 col0\" >6.7</td>\n",
       "      <td id=\"T_4073c_row0_col1\" class=\"data row0 col1\" >110</td>\n",
       "      <td id=\"T_4073c_row0_col2\" class=\"data row0 col2\" >134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4073c_level0_row1\" class=\"row_heading level0 row1\" >Risk-Off</th>\n",
       "      <td id=\"T_4073c_row1_col0\" class=\"data row1 col0\" >2.1</td>\n",
       "      <td id=\"T_4073c_row1_col1\" class=\"data row1 col1\" >14</td>\n",
       "      <td id=\"T_4073c_row1_col2\" class=\"data row1 col2\" >134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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     "metadata": {},
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   ],
   "source": [
    "# Calculate average duration\n",
    "durations = {}\n",
    "for regime, name in [(bad_regime, \"Risk-Off\"), (good_regime, \"Risk-On\")]:\n",
    "    regime_mask = labels_2 == regime\n",
    "    runs = []\n",
    "    current_run = 0\n",
    "    for val in regime_mask:\n",
    "        if val:\n",
    "            current_run += 1\n",
    "        elif current_run > 0:\n",
    "            runs.append(current_run)\n",
    "            current_run = 0\n",
    "    if current_run > 0:\n",
    "        runs.append(current_run)\n",
    "    durations[name] = (np.mean(runs), np.max(runs), len(runs))\n",
    "\n",
    "transitions = int(np.sum(np.diff(labels_2) != 0))\n",
    "\n",
    "duration_df = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"Regime\": name,\n",
    "            \"Avg duration (months)\": durations[name][0],\n",
    "            \"Max duration (months)\": durations[name][1],\n",
    "            \"Episodes\": durations[name][2],\n",
    "        }\n",
    "        for name in [\"Risk-On\", \"Risk-Off\"]\n",
    "    ]\n",
    ").set_index(\"Regime\")\n",
    "\n",
    "print(\n",
    "    f\"Total regime transitions: {transitions} \"\n",
    "    f\"(avg {len(labels_2) / transitions:.1f} months between switches)\"\n",
    ")\n",
    "duration_df.style.format({\"Avg duration (months)\": \"{:.1f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460f3685",
   "metadata": {
    "papermill": {
     "duration": 0.003783,
     "end_time": "2026-06-14T19:26:23.177175+00:00",
     "exception": false,
     "start_time": "2026-06-14T19:26:23.173392+00:00",
     "status": "completed"
    }
   },
   "source": [
    "**Interpretation**: With 267 transitions over 98 years, the model switches regime\n",
    "roughly every 4 months on average. Risk-Off episodes are short (avg ~2 months)\n",
    "— they capture acute stress periods rather than prolonged bear markets. This\n",
    "choppiness has practical implications: a strategy that reallocates based on these\n",
    "signals would trade frequently, incurring transaction costs that may erode the\n",
    "regime-timing benefit. Regime models are more useful for *understanding* market\n",
    "dynamics than for high-frequency tactical allocation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698d9e66",
   "metadata": {
    "papermill": {
     "duration": 0.003686,
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     "start_time": "2026-06-14T19:26:23.180821+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Key Takeaways\n",
    "\n",
    "- **BIC favors two regimes**: K=2 has the lowest BIC and highest silhouette score,\n",
    "  though AIC prefers more clusters. The two-state model offers the most interpretable\n",
    "  and stable regime split.\n",
    "- **Regimes capture distinct risk environments**: Risk-Off has 2.2x higher volatility,\n",
    "  a Sharpe of 0.12 (vs 1.11), and a max drawdown of −77%.\n",
    "- **Value is countercyclical**: +5.3% annualized during Risk-Off vs +1.6% during Risk-On.\n",
    "  Carry and Defensive, despite their names, turn negative in Risk-Off.\n",
    "- **Momentum is procyclical**: +4.5% during Risk-On vs +0.4% during Risk-Off — weakest\n",
    "  factor during market stress.\n",
    "- **Regime signals are noisy**: With transitions every ~4 months on average, this model\n",
    "  is better suited for understanding factor dynamics than for tactical allocation.\n",
    "\n",
    "**Next**: `macro_regimes.py` switches from style returns to macro indicators (UNRATE,\n",
    "DFF, T10Y2Y, CPIAUCSL) and validates the resulting clusters against S&P 500 volatility\n",
    "and drawdowns. See Chapter 1 §1.4 for the workflow context."
   ]
  }
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