diff --git a/project-templates/python/alternative-data-universe-quivergovernmentcontractuniverse/research.ipynb b/project-templates/python/alternative-data-universe-quivergovernmentcontractuniverse/research.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a1b2c3d4",
+   "metadata": {},
+   "source": [
+    "![QuantConnect Logo](https://cdn.quantconnect.com/web/i/icon.png)\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5f6a7b8",
+   "metadata": {},
+   "source": [
+    "## Quiver Government Contracts Research\n",
+    "\n",
+    "This notebook studies whether government contract dollar volume helps explain future returns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c9d0e1f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qb = QuantBook()\n",
+    "# Daily bars will have an end_time that matches the following midnight.\n",
+    "qb.settings.daily_precise_end_time = False\n",
+    "# Anchor the research clock to the start of 2026.\n",
+    "qb.set_start_date(2026, 1, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b4c5d6",
+   "metadata": {},
+   "source": [
+    "### Build a Government Contract Universe\n",
+    "\n",
+    "Select assets with frequent, sizable US government contracts, then inspect the returned universe history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e7f8a9b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (580, 4)\n",
+      "Columns: ['agency', 'amount', 'description', 'value']\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></th>\n",
+       "      <th>agency</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>description</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">2025-10-04</th>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>General Services Administration</td>\n",
+       "      <td>138.66</td>\n",
+       "      <td>78C10M0</td>\n",
+       "      <td>138.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>General Services Administration</td>\n",
+       "      <td>346.65</td>\n",
+       "      <td>78C10M0</td>\n",
+       "      <td>346.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>General Services Administration</td>\n",
+       "      <td>452.85</td>\n",
+       "      <td>CARTRIDGE; TONER I.A.W. HEWLETT PACKARD P/N: C...</td>\n",
+       "      <td>452.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>General Services Administration</td>\n",
+       "      <td>905.70</td>\n",
+       "      <td>CARTRIDGE; TONER I.A.W. HEWLETT PACKARD P/N: C...</td>\n",
+       "      <td>905.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>General Services Administration</td>\n",
+       "      <td>374.20</td>\n",
+       "      <td>CARTRIDGE; TONER I.A.W. HEWLETT PACKARD P/N: C...</td>\n",
+       "      <td>374.20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                       agency  amount  \\\n",
+       "time       symbol                                                       \n",
+       "2025-10-04 DNOW VR22RBI5MBFP  General Services Administration  138.66   \n",
+       "           DNOW VR22RBI5MBFP  General Services Administration  346.65   \n",
+       "           DNOW VR22RBI5MBFP  General Services Administration  452.85   \n",
+       "           DNOW VR22RBI5MBFP  General Services Administration  905.70   \n",
+       "           DNOW VR22RBI5MBFP  General Services Administration  374.20   \n",
+       "\n",
+       "                                                                    description  \\\n",
+       "time       symbol                                                                 \n",
+       "2025-10-04 DNOW VR22RBI5MBFP                                            78C10M0   \n",
+       "           DNOW VR22RBI5MBFP                                            78C10M0   \n",
+       "           DNOW VR22RBI5MBFP  CARTRIDGE; TONER I.A.W. HEWLETT PACKARD P/N: C...   \n",
+       "           DNOW VR22RBI5MBFP  CARTRIDGE; TONER I.A.W. HEWLETT PACKARD P/N: C...   \n",
+       "           DNOW VR22RBI5MBFP  CARTRIDGE; TONER I.A.W. HEWLETT PACKARD P/N: C...   \n",
+       "\n",
+       "                               value  \n",
+       "time       symbol                     \n",
+       "2025-10-04 DNOW VR22RBI5MBFP  138.66  \n",
+       "           DNOW VR22RBI5MBFP  346.65  \n",
+       "           DNOW VR22RBI5MBFP  452.85  \n",
+       "           DNOW VR22RBI5MBFP  905.70  \n",
+       "           DNOW VR22RBI5MBFP  374.20  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def select_assets(data: List[QuiverGovernmentContractUniverse]) -> List[Symbol]:\n",
+    "    # Group by ticker and keep names with 3+ contracts totalling over $50K.\n",
+    "    contracts_by_symbol: dict[Symbol, list[QuiverGovernmentContractUniverse]] = {}\n",
+    "    for d in data:\n",
+    "        contracts_by_symbol.setdefault(d.symbol, []).append(d)\n",
+    "    return [s for s, ds in contracts_by_symbol.items()\n",
+    "            if len(ds) >= 3 and sum(x.amount or 0 for x in ds) > 50_000]\n",
+    "\n",
+    "# Add the Quiver Government Contract universe.\n",
+    "universe = qb.add_universe(QuiverGovernmentContractUniverse, select_assets)\n",
+    "# Request universe history of the last 90 days.\n",
+    "universe_history = qb.universe_history(universe, qb.time - timedelta(90), qb.time - timedelta(1), flatten=True).rename_axis(['time', 'symbol']).drop(columns=['time'])\n",
+    "# Print the returned shape and columns.\n",
+    "print(f\"Shape: {universe_history.shape}\")\n",
+    "print(f\"Columns: {list(universe_history.columns)}\")\n",
+    "universe_history.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1d2e3f4",
+   "metadata": {},
+   "source": [
+    "### Universe Diagnostics\n",
+    "\n",
+    "Inspects the raw contract amount distribution and visualizes how the daily universe size evolves chronologically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a5b6c7d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Universe days: 19\n",
+      "Mean basket size per day: 2.6\n",
+      "\n",
+      "count        580.000\n",
+      "mean       20138.930\n",
+      "std       105049.143\n",
+      "min            0.000\n",
+      "25%          259.200\n",
+      "50%          618.250\n",
+      "75%         1741.500\n",
+      "max      1776510.800\n",
+      "Name: amount, dtype: object\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count selected assets by day.\n",
+    "universe_size = universe_history.reset_index().groupby(['time', 'symbol']).size().groupby('time').size()\n",
+    "print(f\"Universe days: {len(universe_size)}\")\n",
+    "# Store the selected symbol list.\n",
+    "unique_assets = list(universe_history.index.levels[1].unique())\n",
+    "print(f\"Mean basket size per day: {universe_size.mean():.1f}\")\n",
+    "print('')\n",
+    "print(universe_history.amount.describe().map('{:0.3f}'.format))\n",
+    "universe_size.plot(title='Daily Universe Size', ylabel='Universe Size');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9f0a1b2",
+   "metadata": {},
+   "source": [
+    "### Daily Universe Prices\n",
+    "\n",
+    "Fetch daily price history for every symbol that appears in the universe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c3d4e5f6",
+   "metadata": {},
+   "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></th>\n",
+       "      <th>close</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>open</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>symbol</th>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">DNOW VR22RBI5MBFP</th>\n",
+       "      <th>2025-10-04</th>\n",
+       "      <td>15.770</td>\n",
+       "      <td>15.8300</td>\n",
+       "      <td>15.330</td>\n",
+       "      <td>15.39</td>\n",
+       "      <td>1016905.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-10-07</th>\n",
+       "      <td>15.910</td>\n",
+       "      <td>16.2600</td>\n",
+       "      <td>15.822</td>\n",
+       "      <td>15.86</td>\n",
+       "      <td>1095631.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-10-08</th>\n",
+       "      <td>15.510</td>\n",
+       "      <td>15.9200</td>\n",
+       "      <td>15.285</td>\n",
+       "      <td>15.81</td>\n",
+       "      <td>1093552.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-10-09</th>\n",
+       "      <td>15.660</td>\n",
+       "      <td>15.7999</td>\n",
+       "      <td>15.560</td>\n",
+       "      <td>15.65</td>\n",
+       "      <td>778108.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-10-10</th>\n",
+       "      <td>14.690</td>\n",
+       "      <td>15.6800</td>\n",
+       "      <td>14.690</td>\n",
+       "      <td>15.56</td>\n",
+       "      <td>952082.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">MDLN YYDBHHPS742T</th>\n",
+       "      <th>2025-12-25</th>\n",
+       "      <td>44.280</td>\n",
+       "      <td>45.0100</td>\n",
+       "      <td>43.550</td>\n",
+       "      <td>43.95</td>\n",
+       "      <td>4363660.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-12-27</th>\n",
+       "      <td>44.085</td>\n",
+       "      <td>45.4500</td>\n",
+       "      <td>43.610</td>\n",
+       "      <td>44.50</td>\n",
+       "      <td>8118252.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-12-30</th>\n",
+       "      <td>42.090</td>\n",
+       "      <td>43.8000</td>\n",
+       "      <td>41.735</td>\n",
+       "      <td>43.57</td>\n",
+       "      <td>5517778.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-12-31</th>\n",
+       "      <td>41.820</td>\n",
+       "      <td>43.2500</td>\n",
+       "      <td>41.620</td>\n",
+       "      <td>42.05</td>\n",
+       "      <td>4102917.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026-01-01</th>\n",
+       "      <td>42.000</td>\n",
+       "      <td>43.0700</td>\n",
+       "      <td>41.210</td>\n",
+       "      <td>41.94</td>\n",
+       "      <td>5587994.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>816 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               close     high     low   open     volume\n",
+       "symbol            time                                                 \n",
+       "DNOW VR22RBI5MBFP 2025-10-04  15.770  15.8300  15.330  15.39  1016905.0\n",
+       "                  2025-10-07  15.910  16.2600  15.822  15.86  1095631.0\n",
+       "                  2025-10-08  15.510  15.9200  15.285  15.81  1093552.0\n",
+       "                  2025-10-09  15.660  15.7999  15.560  15.65   778108.0\n",
+       "                  2025-10-10  14.690  15.6800  14.690  15.56   952082.0\n",
+       "...                              ...      ...     ...    ...        ...\n",
+       "MDLN YYDBHHPS742T 2025-12-25  44.280  45.0100  43.550  43.95  4363660.0\n",
+       "                  2025-12-27  44.085  45.4500  43.610  44.50  8118252.0\n",
+       "                  2025-12-30  42.090  43.8000  41.735  43.57  5517778.0\n",
+       "                  2025-12-31  41.820  43.2500  41.620  42.05  4102917.0\n",
+       "                  2026-01-01  42.000  43.0700  41.210  41.94  5587994.0\n",
+       "\n",
+       "[816 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Extract unique assets\n",
+    "symbols = list(universe_history.index.get_level_values(1).unique())\n",
+    "# Fetch daily historical price metrics using the earliest timestamp available in the index.\n",
+    "history = qb.history(symbols, universe_history.index[0][0] - timedelta(1), qb.time, Resolution.DAILY)\n",
+    "history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7b8c9d0",
+   "metadata": {},
+   "source": [
+    "### Align Contract Volume And Returns\n",
+    "\n",
+    "Build a joined table of contract dollar volume and future returns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3d56c7cd",
+   "metadata": {},
+   "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></th>\n",
+       "      <th>dollarvolume</th>\n",
+       "      <th>futurereturn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2025-10-04</th>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>222382.01</td>\n",
+       "      <td>0.027290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-10-08</th>\n",
+       "      <th>SYK R735QTJ8XC9X</th>\n",
+       "      <td>450063.66</td>\n",
+       "      <td>0.024027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-10-10</th>\n",
+       "      <th>DNOW VR22RBI5MBFP</th>\n",
+       "      <td>294421.70</td>\n",
+       "      <td>-0.087404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-11-15</th>\n",
+       "      <th>SENEA R735QTJ8XC9X</th>\n",
+       "      <td>798649.20</td>\n",
+       "      <td>0.031759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025-11-18</th>\n",
+       "      <th>SENEA R735QTJ8XC9X</th>\n",
+       "      <td>798649.20</td>\n",
+       "      <td>0.026921</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               dollarvolume  futurereturn\n",
+       "time       symbol                                        \n",
+       "2025-10-04 DNOW VR22RBI5MBFP      222382.01      0.027290\n",
+       "2025-10-08 SYK R735QTJ8XC9X       450063.66      0.024027\n",
+       "2025-10-10 DNOW VR22RBI5MBFP      294421.70     -0.087404\n",
+       "2025-11-15 SENEA R735QTJ8XC9X     798649.20      0.031759\n",
+       "2025-11-18 SENEA R735QTJ8XC9X     798649.20      0.026921"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = (\n",
+    "    universe_history.reset_index().groupby(['time', 'symbol']).agg(dollarvolume=('amount', 'sum'))\n",
+    "    .join(history.open.unstack('symbol').sort_index().pct_change(2, fill_method=None).shift(-2).stack().rename('futurereturn'), how='inner')\n",
+    ")\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5d6e7f8",
+   "metadata": {},
+   "source": [
+    "### Analyze Relationships Between Factor and Future Returns\n",
+    "\n",
+    "Drop the 1% outliers on each side of the factor and fit a line of best fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "21099a93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Factor: dollarvolume\n",
+      "Observations: 46\n",
+      "Mean future return: 0.84%\n",
+      "Alpha: 1.09%\n",
+      "Beta: -0.00%\n",
+      "R-squared: 1.94%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "factor = 'dollarvolume'\n",
+    "# Assign the factor values.\n",
+    "x = dataset[factor]\n",
+    "y = dataset['futurereturn']\n",
+    "# Drop the 1% outliers on each side of the factor.\n",
+    "lower, upper = x.quantile([0.01, 0.99])\n",
+    "mask = x.between(lower, upper)\n",
+    "x, y = x[mask], y[mask]\n",
+    "# Fit a simple linear model.\n",
+    "slope, intercept = np.polyfit(x, y, 1)\n",
+    "r_squared = x.corr(y) ** 2\n",
+    "# Print the linear model statistics.\n",
+    "print(f\"Factor: {factor}\")\n",
+    "print(f\"Observations: {len(x)}\")\n",
+    "print(f\"Mean future return: {y.mean():.2%}\")\n",
+    "print(f\"Alpha: {intercept:.2%}\")\n",
+    "print(f\"Beta: {slope:.2%}\")\n",
+    "print(f\"R-squared: {r_squared:.2%}\")\n",
+    "# Plot the factor values against future returns.\n",
+    "plt.scatter(x, y, alpha=0.6)\n",
+    "plt.plot(x.sort_values(), intercept + slope * x.sort_values(), color='tab:red', label='Linear fit')\n",
+    "plt.axhline(0, color='black', linewidth=1, alpha=0.4)\n",
+    "plt.title(f'{factor} vs Future Return')\n",
+    "plt.xlabel(factor)\n",
+    "plt.ylabel('Future Return')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ac413aa",
+   "metadata": {},
+   "source": [
+    "Create a box plot of dollar-volume quantile buckets compared to future returns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c7d8e9f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Factor: dollarvolume\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_efca2\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_efca2_level0_col0\" class=\"col_heading level0 col0\" >bucket</th>\n",
+       "      <th id=\"T_efca2_level0_col1\" class=\"col_heading level0 col1\" >mean_factor</th>\n",
+       "      <th id=\"T_efca2_level0_col2\" class=\"col_heading level0 col2\" >min_future_return</th>\n",
+       "      <th id=\"T_efca2_level0_col3\" class=\"col_heading level0 col3\" >max_future_return</th>\n",
+       "      <th id=\"T_efca2_level0_col4\" class=\"col_heading level0 col4\" >mean_future_return</th>\n",
+       "      <th id=\"T_efca2_level0_col5\" class=\"col_heading level0 col5\" >std_future_return</th>\n",
+       "      <th id=\"T_efca2_level0_col6\" class=\"col_heading level0 col6\" >observations</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_efca2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_efca2_row0_col0\" class=\"data row0 col0\" >(48.149, 25000.0]</td>\n",
+       "      <td id=\"T_efca2_row0_col1\" class=\"data row0 col1\" >11731.199</td>\n",
+       "      <td id=\"T_efca2_row0_col2\" class=\"data row0 col2\" >-2.68%</td>\n",
+       "      <td id=\"T_efca2_row0_col3\" class=\"data row0 col3\" >9.74%</td>\n",
+       "      <td id=\"T_efca2_row0_col4\" class=\"data row0 col4\" >2.10%</td>\n",
+       "      <td id=\"T_efca2_row0_col5\" class=\"data row0 col5\" >3.31%</td>\n",
+       "      <td id=\"T_efca2_row0_col6\" class=\"data row0 col6\" >11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_efca2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_efca2_row1_col0\" class=\"data row1 col0\" >(25000.0, 44524.0]</td>\n",
+       "      <td id=\"T_efca2_row1_col1\" class=\"data row1 col1\" >39489.625</td>\n",
+       "      <td id=\"T_efca2_row1_col2\" class=\"data row1 col2\" >-0.11%</td>\n",
+       "      <td id=\"T_efca2_row1_col3\" class=\"data row1 col3\" >4.80%</td>\n",
+       "      <td id=\"T_efca2_row1_col4\" class=\"data row1 col4\" >1.53%</td>\n",
+       "      <td id=\"T_efca2_row1_col5\" class=\"data row1 col5\" >1.61%</td>\n",
+       "      <td id=\"T_efca2_row1_col6\" class=\"data row1 col6\" >8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_efca2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_efca2_row2_col0\" class=\"data row2 col0\" >(44524.0, 76500.0]</td>\n",
+       "      <td id=\"T_efca2_row2_col1\" class=\"data row2 col1\" >53859.619</td>\n",
+       "      <td id=\"T_efca2_row2_col2\" class=\"data row2 col2\" >-3.06%</td>\n",
+       "      <td id=\"T_efca2_row2_col3\" class=\"data row2 col3\" >4.51%</td>\n",
+       "      <td id=\"T_efca2_row2_col4\" class=\"data row2 col4\" >0.62%</td>\n",
+       "      <td id=\"T_efca2_row2_col5\" class=\"data row2 col5\" >2.53%</td>\n",
+       "      <td id=\"T_efca2_row2_col6\" class=\"data row2 col6\" >9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_efca2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_efca2_row3_col0\" class=\"data row3 col0\" >(76500.0, 222382.01]</td>\n",
+       "      <td id=\"T_efca2_row3_col1\" class=\"data row3 col1\" >154137.782</td>\n",
+       "      <td id=\"T_efca2_row3_col2\" class=\"data row3 col2\" >-3.74%</td>\n",
+       "      <td id=\"T_efca2_row3_col3\" class=\"data row3 col3\" >7.10%</td>\n",
+       "      <td id=\"T_efca2_row3_col4\" class=\"data row3 col4\" >0.50%</td>\n",
+       "      <td id=\"T_efca2_row3_col5\" class=\"data row3 col5\" >3.30%</td>\n",
+       "      <td id=\"T_efca2_row3_col6\" class=\"data row3 col6\" >9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_efca2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_efca2_row4_col0\" class=\"data row4 col0\" >(222382.01, 1581867.01]</td>\n",
+       "      <td id=\"T_efca2_row4_col1\" class=\"data row4 col1\" >599279.908</td>\n",
+       "      <td id=\"T_efca2_row4_col2\" class=\"data row4 col2\" >-8.74%</td>\n",
+       "      <td id=\"T_efca2_row4_col3\" class=\"data row4 col3\" >3.18%</td>\n",
+       "      <td id=\"T_efca2_row4_col4\" class=\"data row4 col4\" >-0.74%</td>\n",
+       "      <td id=\"T_efca2_row4_col5\" class=\"data row4 col5\" >3.72%</td>\n",
+       "      <td id=\"T_efca2_row4_col6\" class=\"data row4 col6\" >9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7cb1003120d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Split factor values into quantile buckets.\n",
+    "buckets = pd.qcut(x, q=5, duplicates='drop')\n",
+    "# Summarize each bucket with distribution statistics.\n",
+    "summary = dataset.assign(bucket=buckets).groupby('bucket', observed=True).agg(\n",
+    "    mean_factor=(factor, 'mean'),\n",
+    "    min_future_return=('futurereturn', 'min'),\n",
+    "    max_future_return=('futurereturn', 'max'),\n",
+    "    mean_future_return=('futurereturn', 'mean'),\n",
+    "    std_future_return=('futurereturn', 'std'),\n",
+    "    observations=('futurereturn', 'size')\n",
+    ").reset_index()\n",
+    "summary['bucket'] = summary['bucket'].astype(str)\n",
+    "# Display the bucket summary.\n",
+    "print(f\"Factor: {factor}\")\n",
+    "display(summary.style.format({\n",
+    "    'mean_factor': '{:.3f}',\n",
+    "    'min_future_return': '{:.2%}',\n",
+    "    'max_future_return': '{:.2%}',\n",
+    "    'mean_future_return': '{:.2%}',\n",
+    "    'std_future_return': '{:.2%}'\n",
+    "}))\n",
+    "# Plot the return distribution for each bucket.\n",
+    "groups = [y[buckets == b].values for b in buckets.cat.categories]\n",
+    "plt.boxplot(groups, labels=[str(b) for b in buckets.cat.categories])\n",
+    "plt.axhline(0, color='black', linewidth=1, alpha=0.4)\n",
+    "plt.title(f'Future Return by {factor} Bucket')\n",
+    "plt.xlabel(f'{factor} Bucket')\n",
+    "plt.ylabel('Future Return')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Foundation-Py-Default",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
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+ },
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+ "nbformat_minor": 5
+}