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--- /dev/null
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@@ -0,0 +1,11 @@
+data/raw/*
+data/processed/*
+images/*
+models/*
+*.npy
+*.pkl
+*.pth
+.ipynb_checkpoints/
+__pycache__/
+.DS_Store
+.venv/
\ No newline at end of file
diff --git a/notebooks/01_data_overview_and_market_stats.ipynb b/notebooks/01_data_overview_and_market_stats.ipynb
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@@ -0,0 +1,878 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f7ef2de3",
+   "metadata": {},
+   "source": [
+    "# Data Overview and Market Statistics\n",
+    "\n",
+    "This notebook builds the foundational data object for the entire project: the **return matrix** $R \\in M_{T \\times N}(\\mathbb{R})$, where each row is a month and each column is a stock. Everything that follows in the sequals is essentially a linear algebra operation on this matrix or its covariance matrix $\\Sigma = \\frac{1}{T-1}X_c^TX_c$.\n",
+    "\n",
+    "The main goals of this notebook are as follows.\n",
+    "1. To assemble a clean survivorship-aware univerise of US large-cap equities (the columns of our return matrix $R$).\n",
+    "2. To build the return panel (fill in the matrix) from freely available price data.\n",
+    "3. To identify broad trends in cross-sectional dispesion, sector composition, and turnover. \n",
+    "4. To create a clean descriptive baseline fo the later factor and backtest notebooks. \n",
+    "\n",
+    "### Finance terms used in this notebook\n",
+    "\n",
+    "| Term | Meaning |\n",
+    "|------|---------|\n",
+    "| **Return** | Fractional price change: $r_{t} = p_t / p_{t-1} - 1$ |\n",
+    "| **Return panel** $\\mathbf{R}$ | The $T \\times N$ matrix of monthly returns — months (rows) × stocks (columns); the project's central object |\n",
+    "| **Cross-section** | A row of $\\mathbf{R}$ — all stocks at one date |\n",
+    "| **One stock's history** | A column of $\\mathbf{R}$ |\n",
+    "| **Universe** | The set of stocks (columns) we're allowed to hold — here the S&P 500 |\n",
+    "| **Sector** | A categorical partition of stocks (Technology, Financials, …); a 0,1 matrix $\\mathbf{D}$ |\n",
+    "| **Dispersion** | Cross-sectional standard deviation — how spread out returns are across a row |\n",
+    "| **Equal-weight index** | Mean of a row $\\bar{r}_t = \\tfrac{1}{N_t}\\mathbf{1}^\\top r_t$ (every stock at weight $w_i = 1/N_t$) |\n",
+    "| **Sharpe ratio** | Mean return / volatility — a signal-to-noise ratio (computed here for the equal-weight index) |\n",
+    "| **Survivorship bias** | Only stocks that *survived* until today appear; delisted names are missing, biasing returns upward |\n",
+    "\n",
+    "Throughout, I treat each stock-month as a separate observation unless explicitly noted otherwise. That matters because the universe changes over time — names enter and leave the index — so the matrix $\\mathbf{R}$ is *sparse* (has NaN entries) wherever a stock didn't trade yet or was delisted.\n",
+    "\n",
+    "## Outputs\n",
+    "\n",
+    "We produce a cleaned monthy return panel ($R$), a sector mapping table, and exploratory plots that motivate later notebooks. Topics include:\n",
+    "- single-factor diagnostics (vecotrs $f_t$ that predict rows of $R$),\n",
+    "- composite signal construction (linear combinations of orthogonalized factor vectors),\n",
+    "- backtesting (weight vectors $w$ and portfolio returns $w^Tr)$,\n",
+    "- risk decomposition (eigendecomposition of the covariance matrix $\\Sigma$). \n",
+    "\n",
+    "## Notebook Structure\n",
+    "1. [Setup and Imports](#setup-and-imports)\n",
+    "2. [Universe Construction](#universe-construction)\n",
+    "3. [Return Panel Assembly](#return-panel-assembly)\n",
+    "4. [Market Statistics](#market-statistics)\n",
+    "5. [Sector Composition](#sector-composition)\n",
+    "6. [Conclusion](#conclusion)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d02502aa",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Steup and Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eba6fb3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Setup and imports\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import yfinance as yf\n",
+    "import os\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "pd.set_option('display.max_rows', 200)\n",
+    "\n",
+    "palette = ['steelblue', 'coral', 'seagreen']\n",
+    "\n",
+    "os.makedirs('../data/raw', exist_ok=True)\n",
+    "os.makedirs('../data/processed', exist_ok=True)\n",
+    "os.makedirs('../images/01_market_stats', exist_ok=True)\n",
+    "\n",
+    "START_DATE = '2005-01-01'\n",
+    "END_DATE = '2025-12-31'\n",
+    "RANDOM_STATE = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46cd7224",
+   "metadata": {},
+   "source": [
+    "## Universe Construction\n",
+    "\n",
+    "We use the S&P 500 current constituents as a starting list and pull price history for each. This introduces **survivorship bias** (i.e., names that went bankrupt or were delisted between 2005 and today won't appear). In linear-algebraic terms, the columns of our return matrix $R$ are a non-random subset of all stocks that existed; the columns we don't see are exactly the ones that went to zero, which biases our return estimates upward. This can be properly handled with different data, but we work with this as is for the sake of simplicity. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f99f0d78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total constituents: 503\n",
+      "First 10: ['MMM', 'AOS', 'ABT', 'ABBV', 'ACN', 'ADBE', 'AMD', 'AES', 'AFL', 'A']\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>Symbol</th>\n",
+       "      <th>Security</th>\n",
+       "      <th>GICS Sector</th>\n",
+       "      <th>GICS Sub-Industry</th>\n",
+       "      <th>Headquarters Location</th>\n",
+       "      <th>Date added</th>\n",
+       "      <th>CIK</th>\n",
+       "      <th>Founded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MMM</td>\n",
+       "      <td>3M</td>\n",
+       "      <td>Industrials</td>\n",
+       "      <td>Industrial Conglomerates</td>\n",
+       "      <td>Saint Paul, Minnesota</td>\n",
+       "      <td>1957-03-04</td>\n",
+       "      <td>66740</td>\n",
+       "      <td>1902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AOS</td>\n",
+       "      <td>A. O. Smith</td>\n",
+       "      <td>Industrials</td>\n",
+       "      <td>Building Products</td>\n",
+       "      <td>Milwaukee, Wisconsin</td>\n",
+       "      <td>2017-07-26</td>\n",
+       "      <td>91142</td>\n",
+       "      <td>1916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ABT</td>\n",
+       "      <td>Abbott Laboratories</td>\n",
+       "      <td>Health Care</td>\n",
+       "      <td>Health Care Equipment</td>\n",
+       "      <td>North Chicago, Illinois</td>\n",
+       "      <td>1957-03-04</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>1888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ABBV</td>\n",
+       "      <td>AbbVie</td>\n",
+       "      <td>Health Care</td>\n",
+       "      <td>Biotechnology</td>\n",
+       "      <td>North Chicago, Illinois</td>\n",
+       "      <td>2012-12-31</td>\n",
+       "      <td>1551152</td>\n",
+       "      <td>2013 (1888)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ACN</td>\n",
+       "      <td>Accenture</td>\n",
+       "      <td>Information Technology</td>\n",
+       "      <td>IT Consulting &amp; Other Services</td>\n",
+       "      <td>Dublin, Ireland</td>\n",
+       "      <td>2011-07-06</td>\n",
+       "      <td>1467373</td>\n",
+       "      <td>1989</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Symbol             Security             GICS Sector  \\\n",
+       "0    MMM                   3M             Industrials   \n",
+       "1    AOS          A. O. Smith             Industrials   \n",
+       "2    ABT  Abbott Laboratories             Health Care   \n",
+       "3   ABBV               AbbVie             Health Care   \n",
+       "4    ACN            Accenture  Information Technology   \n",
+       "\n",
+       "                GICS Sub-Industry    Headquarters Location  Date added  \\\n",
+       "0        Industrial Conglomerates    Saint Paul, Minnesota  1957-03-04   \n",
+       "1               Building Products     Milwaukee, Wisconsin  2017-07-26   \n",
+       "2           Health Care Equipment  North Chicago, Illinois  1957-03-04   \n",
+       "3                   Biotechnology  North Chicago, Illinois  2012-12-31   \n",
+       "4  IT Consulting & Other Services          Dublin, Ireland  2011-07-06   \n",
+       "\n",
+       "       CIK      Founded  \n",
+       "0    66740         1902  \n",
+       "1    91142         1916  \n",
+       "2     1800         1888  \n",
+       "3  1551152  2013 (1888)  \n",
+       "4  1467373         1989  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Pull S&P 500 constituents from Wikipedia\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "\n",
+    "import requests\n",
+    "from io import StringIO\n",
+    "\n",
+    "url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n",
+    "\n",
+    "# Wikipedia requires a descriptive User-Agent per their API policy\n",
+    "headers = {\n",
+    "    'User-Agent': 'EquityFactorResearch/1.0 (quant research; contact@example.com)'\n",
+    "}\n",
+    "\n",
+    "resp = requests.get(url, headers=headers)\n",
+    "resp.raise_for_status()\n",
+    "\n",
+    "# match='Symbol' targets the constituents table directly\n",
+    "tables = pd.read_html(StringIO(resp.text), match='Symbol')\n",
+    "df_constituents = tables[0]\n",
+    "\n",
+    "df_constituents['Symbol'] = df_constituents['Symbol'].str.replace('.', '-', regex=False)\n",
+    "tickers = df_constituents['Symbol'].tolist()\n",
+    "\n",
+    "df_constituents.to_csv('../data/raw/constituents.csv')\n",
+    "\n",
+    "print(f\"Total constituents: {len(tickers)}\")\n",
+    "print(f\"First 10: {tickers[:10]}\")\n",
+    "df_constituents.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dee072b6",
+   "metadata": {},
+   "source": [
+    "A note on the CIK: CIK stands for **Central Index Key**. It's a unique identifier assigned by the U.S. Securities and Exchange Commission (SEC) to every entity (company, individual, or organization) that files disclosure documents with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "12116b17",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading cached prices from ../data/raw/prices_monthly.csv\n",
+      "\n",
+      "Price panel shape: (5282, 503)\n",
+      "Date range: 2005-01-03 00:00:00 to 2025-12-30 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Download price history via yfinance\n",
+    "==================================\n",
+    "\n",
+    "We use adjusted close to account for splits\n",
+    "and dividends, then resample to monthly returns.\n",
+    "\"\"\"\n",
+    "from tqdm import tqdm # progress bar\n",
+    "\n",
+    "raw_path = '../data/raw/prices_monthly.csv'\n",
+    "\n",
+    "if os.path.exists(raw_path):\n",
+    "    print(f\"Loading cached prices from {raw_path}\")\n",
+    "    df_prices = pd.read_csv(raw_path, index_col=0, parse_dates=True)\n",
+    "else:\n",
+    "    print(f\"Downloading price data for {len(tickers)} tickers ...\")\n",
+    "    df_raw = yf.download(\n",
+    "        tickers,\n",
+    "        start=START_DATE,\n",
+    "        end=END_DATE,\n",
+    "        progress=True,\n",
+    "        auto_adjust=True,\n",
+    "        threads=True\n",
+    "    )\n",
+    "\n",
+    "    # Use adjusted close\n",
+    "    df_prices = df_raw['Close']\n",
+    "    df_prices.to_csv(raw_path)\n",
+    "    print(f\"Saved to {raw_path}\")\n",
+    "\n",
+    "print(f\"\\nPrice panel shape: {df_prices.shape}\")\n",
+    "print(f\"Date range: {df_prices.index.min()} to {df_prices.index.max()}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c3466e",
+   "metadata": {},
+   "source": [
+    "## Return Panel Assembly\n",
+    "\n",
+    "We resample daily prices to month-end and compute simple monthly return \n",
+    "$$ r_{t,i} = p_{t,i}/p_{t-1,i} - 1. $$\n",
+    "Here $t$ is the time index, $i$ is the stock index, and $p_{t,i}$ is the closing date. The ration $p_{t,i}/p_{t-1,i}$ tells us what \\$1 invested ends up as, so we subtract 1 in order get the simple net return. \n",
+    "\n",
+    "The result is the return matrix $R \\in M_{T \\times N}(\\mathbb{R})$ with months as rows and stocks as columns. \n",
+    "\n",
+    "We are working with month-end for both simplicity, and the fact that a daily rebalance would contribute to large $\\ell^1$ distances between consecutive weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "249ffb9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Montly return panel shape: (251, 501)\n",
+      "Date range: 2005-02-28 00:00:00 to 2025-12-31 00:00:00\n",
+      "Unique tickers: 501\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>A</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>ABBV</th>\n",
+       "      <th>ABNB</th>\n",
+       "      <th>ABT</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</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>2005-02-28</th>\n",
+       "      <td>0.085481</td>\n",
+       "      <td>0.166710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.021546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-03-31</th>\n",
+       "      <td>-0.074999</td>\n",
+       "      <td>-0.071111</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-04-30</th>\n",
+       "      <td>-0.065315</td>\n",
+       "      <td>-0.134629</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.060572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-05-31</th>\n",
+       "      <td>0.157109</td>\n",
+       "      <td>0.102607</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.018714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-06-30</th>\n",
+       "      <td>-0.041233</td>\n",
+       "      <td>-0.074195</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.015961</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A      AAPL  ABBV  ABNB       ABT\n",
+       "Date                                                \n",
+       "2005-02-28  0.085481  0.166710   NaN   NaN  0.021546\n",
+       "2005-03-31 -0.074999 -0.071111   NaN   NaN  0.013699\n",
+       "2005-04-30 -0.065315 -0.134629   NaN   NaN  0.060572\n",
+       "2005-05-31  0.157109  0.102607   NaN   NaN -0.018714\n",
+       "2005-06-30 -0.041233 -0.074195   NaN   NaN  0.015961"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Build monthly return panel\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "# Resample to month-end and compute returns\n",
+    "df_monthly_prices = df_prices.resample('ME').last()\n",
+    "df_returns = df_monthly_prices.pct_change() # fractional change between current and prior\n",
+    "\n",
+    "# Drop the first row (NaN from pct_change)\n",
+    "df_returns = df_returns.iloc[1:]\n",
+    "\n",
+    "# Drop columns that are entirely NaN (delisted / no data)\n",
+    "df_returns = df_returns.dropna(axis=1, how='all')\n",
+    "\n",
+    "print(f\"Montly return panel shape: {df_returns.shape}\")\n",
+    "print(f\"Date range: {df_returns.index.min()} to {df_returns.index.max()}\")\n",
+    "print(f\"Unique tickers: {df_returns.shape[1]}\")\n",
+    "\n",
+    "df_returns.iloc[:5,:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9691c46b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved:\n",
+      "  - ../data/processed/returns_monthly.csv\n",
+      "  - ../data/processed/prices_monthly.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Save return panel\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "df_returns.to_csv('../data/processed/returns_monthly.csv')\n",
+    "df_monthly_prices.to_csv('../data/processed/prices_monthly.csv')\n",
+    "\n",
+    "print(\"Saved:\")\n",
+    "print(\"  - ../data/processed/returns_monthly.csv\")\n",
+    "print(\"  - ../data/processed/prices_monthly.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2260e830",
+   "metadata": {},
+   "source": [
+    "## Market Statistics\n",
+    "\n",
+    "Let's get a feel for the market itself.\n",
+    "- How many columns are available each month? We'll call this `breadth`, but this really counts how many of today's survivors had valid prices at month $t$. As we have ~501 stocks at 2025-12, this means that ~116 stocks from 2005 have been replaced with different ones. \n",
+    "- What does cross-sectional dispersion look like (the standard deviation across each row of $R$)?\n",
+    "$$ \\sigma_t^{XS} = \\sqrt{\\frac{1}{N_t - 1} \\sum_{i=1}^{N_t}(r_{t,i} - \\bar{r}_t)^2} $$\n",
+    "- How does the equal-weight market index behave? The equal-weight market index return at month $t$ is simply the mean of the row: $\\bar{r}_t = \\frac{1}{N_t} \\mathbf{1}^\\top r_t$, where $\\mathbf{1}$ is the all ones vector and $N_t$ is the number of stocks alive at date $t$. This is the return of a portfolio that holds every stock at equal weight $w_i = \\frac{1}{N_t}$. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1b1798b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average breadth: 455\n",
+      "Min breadth: 385\n",
+      "Max breadth: 501\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Universe breadth over time\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "# Count non-NaN returns each month\n",
+    "universe_breadth = df_returns.notna().sum(axis=1)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,5))\n",
+    "ax.plot(universe_breadth.index, universe_breadth.values, color='steelblue')\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Number of stocks with valid returns')\n",
+    "ax.set_title('Unverse Breadth Over Time')\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../images/01_market_stats/universe_breadth.png', dpi=150, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Average breadth: {universe_breadth.mean():.0f}\")\n",
+    "print(f\"Min breadth: {universe_breadth.min()}\")\n",
+    "print(f\"Max breadth: {universe_breadth.max()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0766c03b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average monthly CS dispersion: 0.0754\n",
+      "Annualized: 0.2610\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Cross-sectional return dispersion\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "# Cross-sectional standard deviation each month\n",
+    "cs_dispersion = df_returns.std(axis=1)\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(12,5))\n",
+    "ax.plot(cs_dispersion.index, cs_dispersion.rolling(12).mean(), color='coral', label='12m MA') # this smooths the last 12 months\n",
+    "ax.plot(cs_dispersion.index, cs_dispersion, color='steelblue', alpha=0.4, label='Monthly')\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Cross-sectional std dev')\n",
+    "ax.set_title('Cross-Sectional Return Dispersion')\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout\n",
+    "plt.savefig('../images/01_market_stats/cs_dispersion.png', dpi=150, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Average monthly CS dispersion: {cs_dispersion.mean():.4f}\")\n",
+    "print(f\"Annualized: {cs_dispersion. mean() * np.sqrt(12):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "aada924e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Annualized return: 0.1632\n",
+      "Annualized vol: 0.1659\n",
+      "Sharpe (rf=0): 0.984\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Equal-weight market index\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "ew_market = df_returns.mean(axis=1)\n",
+    "cum_ew = (1 + ew_market).cumprod()\n",
+    "\"\"\"\n",
+    "Mathematically, .cumprod() will do the following:\n",
+    "W_t = prod_{s=1}^t (1 + bar{r}_s) with W_0 = 1\n",
+    "\"\"\"\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,5))\n",
+    "ax.plot(cum_ew.index, cum_ew.values, color='seagreen')\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Cumulative return')\n",
+    "ax.set_title('Equal-Weight Universe Cumulative Return')\n",
+    "ax.set_yscale('log')\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../images/01_market_stats/ew_market_cumulative.png', dpi = 150, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "\n",
+    "ann_return = ew_market.mean() * 12\n",
+    "ann_vol = ew_market.std() * np.sqrt(12)\n",
+    "sharpe = ann_return / ann_vol\n",
+    "\n",
+    "print(f\"Annualized return: {ann_return:.4f}\")\n",
+    "print(f\"Annualized vol: {ann_vol:.4f}\")\n",
+    "print(f\"Sharpe (rf=0): {sharpe:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b53ad7a",
+   "metadata": {},
+   "source": [
+    "## Sector Composition\n",
+    "\n",
+    "Sectors are a categorial partition of the column index — each stock belongs to exactly one of the ~11 sectors (Technology, Financials, Healthcare, etc.). A binary encoding of sector membership gives a $\\{0,1\\}$-matrix $N \\times K$ matrix that will reappear in later notebooks. \n",
+    "\n",
+    "Sectors matter for two reasons. First, many factors (value, quality) have strong sector tilts that need to be projected out if we want a clean understanding of what is happening. Second, sector concentration in the unviverse drives how much idiosyncratic risk (stock-specific variance) a long-short portfolio will carry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f1ab950b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sector</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Industrials</th>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Financials</th>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Information Technology</th>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Health Care</th>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Consumer Discretionary</th>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Consumer Staples</th>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utilities</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Real Estate</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Materials</th>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Communication Services</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Energy</th>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        count\n",
+       "sector                       \n",
+       "Industrials                81\n",
+       "Financials                 76\n",
+       "Information Technology     74\n",
+       "Health Care                59\n",
+       "Consumer Discretionary     47\n",
+       "Consumer Staples           34\n",
+       "Utilities                  31\n",
+       "Real Estate                31\n",
+       "Materials                  26\n",
+       "Communication Services     23\n",
+       "Energy                     21"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "==================================\n",
+    "Sector mapping\n",
+    "==================================\n",
+    "\"\"\"\n",
+    "# Use GICS sector from the constituents table\n",
+    "df_sector = df_constituents[['Symbol', 'GICS Sector']].copy()\n",
+    "df_sector.columns = ['ticker', 'sector']\n",
+    "df_sector = df_sector.dropna(subset=['sector'])\n",
+    "\n",
+    "# Save for later\n",
+    "df_sector.to_csv('../data/processed/sector_mapping.csv', index=False)\n",
+    "\n",
+    "sector_counts = df_sector['sector'].value_counts()\n",
+    "\n",
+    "fig,ax = plt.subplots(figsize=(12,6))\n",
+    "ax.barh(sector_counts.index, sector_counts.values, color='steelblue')\n",
+    "ax.set_xlabel('Number of constituents')\n",
+    "ax.set_title('S&P 500 Sector Composition')\n",
+    "ax.invert_yaxis()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../images/01_market_stats/sector_composition.png', dpi=150, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "\n",
+    "display(sector_counts.to_frame('count'))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d80a6197",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "We have our return matrix $R$, which is a clean monthly panel over ~500 US large-cap stocks from 2005 to 2025, along with some valuable statistics and a sector mapping. In the next notebook, we take this matrix $R$ and ask the first factor question: which vectors $f_t \\in \\mathbb{R}^{N_t}$ have meaningful cross-sectional predictive power (i.e., point in roughly the same direction as the next month's return vector $r_{t+1}$), and how do they behave over time?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a9814a5
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+pandas
+numpy
+scipy
+scikit-learn
+matplotlib
+seaborn
+yfinance
+pandas-datareader
+joblib
+tqdm
+Pillow
+html5lib
+statsmodels
+requests
+torch
\ No newline at end of file