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    "# Text Feature Signal Evaluation\n",
    "\n",
    "**Chapter 10: Text Feature Engineering**\n",
    "**Section Reference**: See Section 10.5 for practitioner workflow and alpha factor design\n",
    "\n",
    "**Docker image**: `ml4t`\n",
    "\n",
    "## Purpose\n",
    "This notebook evaluates **text-derived alpha signals** produced by\n",
    "[`07_news_return_signals`](07_news_return_signals.ipynb) using standard factor diagnostics:\n",
    "- Daily cross-sectional Information Coefficient (Spearman rank correlation)\n",
    "- ICIR and t-stats for robustness\n",
    "- Quintile and long-short (Q5 - Q1) spread analysis\n",
    "\n",
    "## Data Contract\n",
    "- **Input**: `output/fnspid/news_features.parquet` (from notebook 07)\n",
    "  - Contains forward returns (fwd_ret_1d, fwd_ret_5d, fwd_ret_20d) already aligned\n",
    "  - No additional price data or label computation needed\n",
    "- **Output**: `output/text_evaluation/text_signal_summary.parquet`\n",
    "\n",
    "## Learning Objectives\n",
    "After completing this notebook, you will be able to:\n",
    "- Evaluate NLP-derived alpha signals using standard factor analysis\n",
    "- Compute daily IC, ICIR, and t-statistics for text signals\n",
    "- Analyze quintile spreads and long-short returns\n",
    "- Understand the predictive power of text-based factors\n",
    "\n",
    "## Prerequisites\n",
    "- Section 10.5 of the chapter (alpha-factor evaluation: IC, ICIR, quintile spread).\n",
    "- `news_features.parquet` produced by `07_news_return_signals.py`.\n",
    "\n",
    "## Related Notebooks\n",
    "- `07_news_return_signals.py` — produces the text features evaluated here.\n",
    "- `09_filing_text_signals.py` — analogous IC analysis for 10-Q filing signals.\n",
    "\n",
    "## Signals Evaluated\n",
    "| Signal | Description | Expected Effect |\n",
    "|--------|-------------|-----------------|\n",
    "| weighted_surprise | News surprise × sentiment direction | Positive (directional information) |\n",
    "| sentiment_mean | Average daily sentiment | Positive (bullish → positive returns) |\n",
    "| sentiment_momentum | Change in sentiment vs baseline | Positive (improving sentiment) |\n",
    "| coverage_count | Article frequency | Ambiguous (attention effect) |"
   ]
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   "source": [
    "\"\"\"Text Feature Signal Evaluation - Evaluate NLP-derived alpha signals.\"\"\"\n",
    "\n",
    "import json\n",
    "import warnings\n",
    "from collections.abc import Iterator\n",
    "from dataclasses import dataclass\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import polars as pl\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from utils.paths import get_output_dir\n",
    "from utils.reproducibility import set_global_seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b89cfa8",
   "metadata": {
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     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
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   "outputs": [],
   "source": [
    "SEED = 42\n",
    "MIN_ASSETS_PER_DAY = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "003e3518",
   "metadata": {
    "execution": {
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   "source": [
    "# Reproducibility — set_global_seeds covers Python random / NumPy / Torch.\n",
    "set_global_seeds(SEED)\n",
    "\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class EvalConfig:\n",
    "    \"\"\"Configuration for text signal evaluation.\"\"\"\n",
    "\n",
    "    horizons: tuple[int, ...] = (1, 5, 20)\n",
    "    min_assets_per_day: int = 20\n",
    "    quantiles: int = 5\n",
    "\n",
    "\n",
    "# Paths using standard utilities\n",
    "TEXT_INPUT_DIR = get_output_dir(8, \"fnspid\")\n",
    "OUTPUT_DIR = get_output_dir(8, \"text_evaluation\")\n",
    "CONFIG = EvalConfig(min_assets_per_day=MIN_ASSETS_PER_DAY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d673d5",
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   "source": [
    "## 1. Load Text Features from Notebook 07\n",
    "\n",
    "Notebook 07 produces a complete dataset with:\n",
    "- Text signals (weighted_surprise, sentiment_mean, etc.)\n",
    "- Forward returns (fwd_ret_1d, fwd_ret_5d, fwd_ret_20d) already computed\n",
    "- Proper look-ahead bias handling (signals lag returns by 1 day)\n",
    "\n",
    "We do NOT recompute labels here—we use the dataset contract from notebook 07."
   ]
  },
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   "execution_count": 4,
   "id": "00e93739",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 15,778 observations\n",
      "Columns: ['ticker', 'date', 'news_date', 'trade_date', 'news_surprise', 'sentiment_mean', 'sentiment_std', 'weighted_surprise', 'sentiment_momentum', 'coverage_count', 'fwd_ret_1d', 'fwd_ret_5d', 'fwd_ret_20d']\n"
     ]
    }
   ],
   "source": [
    "# Load text-derived features\n",
    "text_features_path = TEXT_INPUT_DIR / \"news_features.parquet\"\n",
    "\n",
    "if not text_features_path.exists():\n",
    "    raise FileNotFoundError(\n",
    "        f\"Missing input: {text_features_path}\\n\\n\"\n",
    "        \"Run notebook 07 first to generate this dataset:\\n\"\n",
    "        \"  python 08_text_feature_engineering/code/07_news_return_signals.py\"\n",
    "    )\n",
    "\n",
    "text_features = pl.read_parquet(text_features_path)\n",
    "\n",
    "if len(text_features) == 0:\n",
    "    raise ValueError(\n",
    "        f\"Empty dataset: {text_features_path}\\nNotebook 07 ran but produced no usable rows.\"\n",
    "    )\n",
    "\n",
    "print(f\"Loaded {len(text_features):,} observations\")\n",
    "print(f\"Columns: {text_features.columns}\")"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date range: 2009-12-21 to 2017-11-16\n",
      "Unique assets: 39\n"
     ]
    },
    {
     "data": {
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       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (10, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>symbol</th><th>timestamp</th><th>news_date</th><th>trade_date</th><th>news_surprise</th><th>sentiment_mean</th><th>sentiment_std</th><th>weighted_surprise</th><th>sentiment_momentum</th><th>coverage_count</th><th>fwd_ret_1d</th><th>fwd_ret_5d</th><th>fwd_ret_20d</th></tr><tr><td>str</td><td>date</td><td>date</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;ADBE&quot;</td><td>2010-04-30</td><td>2010-04-29</td><td>&quot;2010-04-30&quot;</td><td>0.222876</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>2</td><td>-0.000298</td><td>-0.035119</td><td>-0.045238</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-04</td><td>2010-05-03</td><td>&quot;2010-05-04&quot;</td><td>0.516712</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-0.666667</td><td>1</td><td>-0.005432</td><td>0.019916</td><td>-0.006035</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-06</td><td>2010-05-05</td><td>&quot;2010-05-06&quot;</td><td>0.348653</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1</td><td>-0.002155</td><td>0.059095</td><td>-0.027624</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-12</td><td>2010-05-11</td><td>&quot;2010-05-12&quot;</td><td>0.570604</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1</td><td>-0.011207</td><td>-0.055172</td><td>-0.088506</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-13</td><td>2010-05-12</td><td>&quot;2010-05-13&quot;</td><td>0.822824</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-1.0</td><td>1</td><td>-0.020052</td><td>-0.077594</td><td>-0.077884</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-17</td><td>2010-05-14</td><td>&quot;2010-05-17&quot;</td><td>0.363815</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1</td><td>-0.015676</td><td>-0.064478</td><td>-0.03845</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-18</td><td>2010-05-17</td><td>&quot;2010-05-18&quot;</td><td>0.724458</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1</td><td>-0.012019</td><td>-0.051983</td><td>-0.02524</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-19</td><td>2010-05-18</td><td>&quot;2010-05-19&quot;</td><td>0.407025</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1</td><td>-0.034672</td><td>-0.059002</td><td>0.007299</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-05-27</td><td>2010-05-26</td><td>&quot;2010-05-27&quot;</td><td>0.50654</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>2</td><td>-0.003727</td><td>-0.018866</td><td>-0.072981</td></tr><tr><td>&quot;ADBE&quot;</td><td>2010-06-08</td><td>2010-06-07</td><td>&quot;2010-06-08&quot;</td><td>0.62012</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-1.0</td><td>1</td><td>-0.00871</td><td>0.04871</td><td>-0.135806</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 13)\n",
       "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ symbol ┆ timestamp  ┆ news_date  ┆ trade_dat ┆ … ┆ coverage_ ┆ fwd_ret_1 ┆ fwd_ret_5 ┆ fwd_ret_2 │\n",
       "│ ---    ┆ ---        ┆ ---        ┆ e         ┆   ┆ count     ┆ d         ┆ d         ┆ 0d        │\n",
       "│ str    ┆ date       ┆ date       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│        ┆            ┆            ┆ str       ┆   ┆ i64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ ADBE   ┆ 2010-04-30 ┆ 2010-04-29 ┆ 2010-04-3 ┆ … ┆ 2         ┆ -0.000298 ┆ -0.035119 ┆ -0.045238 │\n",
       "│        ┆            ┆            ┆ 0         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-04 ┆ 2010-05-03 ┆ 2010-05-0 ┆ … ┆ 1         ┆ -0.005432 ┆ 0.019916  ┆ -0.006035 │\n",
       "│        ┆            ┆            ┆ 4         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-06 ┆ 2010-05-05 ┆ 2010-05-0 ┆ … ┆ 1         ┆ -0.002155 ┆ 0.059095  ┆ -0.027624 │\n",
       "│        ┆            ┆            ┆ 6         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-12 ┆ 2010-05-11 ┆ 2010-05-1 ┆ … ┆ 1         ┆ -0.011207 ┆ -0.055172 ┆ -0.088506 │\n",
       "│        ┆            ┆            ┆ 2         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-13 ┆ 2010-05-12 ┆ 2010-05-1 ┆ … ┆ 1         ┆ -0.020052 ┆ -0.077594 ┆ -0.077884 │\n",
       "│        ┆            ┆            ┆ 3         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-17 ┆ 2010-05-14 ┆ 2010-05-1 ┆ … ┆ 1         ┆ -0.015676 ┆ -0.064478 ┆ -0.03845  │\n",
       "│        ┆            ┆            ┆ 7         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-18 ┆ 2010-05-17 ┆ 2010-05-1 ┆ … ┆ 1         ┆ -0.012019 ┆ -0.051983 ┆ -0.02524  │\n",
       "│        ┆            ┆            ┆ 8         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-19 ┆ 2010-05-18 ┆ 2010-05-1 ┆ … ┆ 1         ┆ -0.034672 ┆ -0.059002 ┆ 0.007299  │\n",
       "│        ┆            ┆            ┆ 9         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-05-27 ┆ 2010-05-26 ┆ 2010-05-2 ┆ … ┆ 2         ┆ -0.003727 ┆ -0.018866 ┆ -0.072981 │\n",
       "│        ┆            ┆            ┆ 7         ┆   ┆           ┆           ┆           ┆           │\n",
       "│ ADBE   ┆ 2010-06-08 ┆ 2010-06-07 ┆ 2010-06-0 ┆ … ┆ 1         ┆ -0.00871  ┆ 0.04871   ┆ -0.135806 │\n",
       "│        ┆            ┆            ┆ 8         ┆   ┆           ┆           ┆           ┆           │\n",
       "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize schema from notebook 07\n",
    "\n",
    "\n",
    "def normalize_schema(df: pl.DataFrame) -> pl.DataFrame:\n",
    "    \"\"\"\n",
    "    Normalize the schema produced by notebook 07 to a canonical evaluation schema.\n",
    "\n",
    "    Canonical requirements:\n",
    "    - symbol: str (entity identifier)\n",
    "    - timestamp: pl.Date (tradable date)\n",
    "    - forward returns: fwd_ret_1d, fwd_ret_5d, fwd_ret_20d\n",
    "    \"\"\"\n",
    "    # Normalize entity column to 'symbol'\n",
    "    if \"symbol\" not in df.columns:\n",
    "        for alt in (\"ticker\", \"asset\"):\n",
    "            if alt in df.columns:\n",
    "                df = df.rename({alt: \"symbol\"})\n",
    "                break\n",
    "        else:\n",
    "            raise ValueError(\"Missing entity column. Expected 'symbol', 'ticker', or 'asset'.\")\n",
    "\n",
    "    # Normalize time column to 'timestamp'\n",
    "    if \"timestamp\" not in df.columns:\n",
    "        for alt in (\"date\", \"trade_date\"):\n",
    "            if alt in df.columns:\n",
    "                df = df.rename({alt: \"timestamp\"})\n",
    "                break\n",
    "        else:\n",
    "            raise ValueError(\"Missing time column. Expected 'timestamp', 'date', or 'trade_date'.\")\n",
    "\n",
    "    # Parse date string if needed\n",
    "    if df[\"timestamp\"].dtype == pl.Utf8:\n",
    "        df = df.with_columns(pl.col(\"timestamp\").str.to_date().alias(\"timestamp\"))\n",
    "\n",
    "    # Verify forward return columns exist\n",
    "    required_return_cols = {\"fwd_ret_1d\", \"fwd_ret_5d\", \"fwd_ret_20d\"}\n",
    "    missing = required_return_cols - set(df.columns)\n",
    "    if missing:\n",
    "        raise ValueError(f\"Missing forward return columns: {sorted(missing)}\")\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "text_features = normalize_schema(text_features)\n",
    "print(f\"Date range: {text_features['timestamp'].min()} to {text_features['timestamp'].max()}\")\n",
    "print(f\"Unique assets: {text_features['symbol'].n_unique()}\")\n",
    "text_features.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0542bc14",
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     "exception": false,
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     "status": "completed"
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   },
   "source": [
    "## 2. Define Signal Evaluation Functions\n",
    "\n",
    "We compute:\n",
    "- **Daily IC**: Cross-sectional Spearman correlation between signal and forward return\n",
    "- **ICIR**: IC mean / IC std (measures signal consistency)\n",
    "- **t-stat**: Statistical significance of mean IC\n",
    "- **Quintile returns**: Average return by signal quintile\n",
    "- **Long-short spread**: Q5 - Q1 return"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signals to evaluate: ['weighted_surprise', 'sentiment_mean', 'sentiment_momentum', 'coverage_count']\n"
     ]
    }
   ],
   "source": [
    "# Signal evaluation utilities\n",
    "\n",
    "TEXT_SIGNALS = [\n",
    "    \"weighted_surprise\",\n",
    "    \"sentiment_mean\",\n",
    "    \"sentiment_momentum\",\n",
    "    \"coverage_count\",\n",
    "]\n",
    "\n",
    "AVAILABLE_SIGNALS = [c for c in TEXT_SIGNALS if c in text_features.columns]\n",
    "if not AVAILABLE_SIGNALS:\n",
    "    raise ValueError(\n",
    "        f\"No expected signals found. Expected one of: {TEXT_SIGNALS}. Have: {text_features.columns}\"\n",
    "    )\n",
    "\n",
    "print(f\"Signals to evaluate: {AVAILABLE_SIGNALS}\")\n",
    "\n",
    "RET_COL_BY_HORIZON = {1: \"fwd_ret_1d\", 5: \"fwd_ret_5d\", 20: \"fwd_ret_20d\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6a660d",
   "metadata": {
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     "exception": false,
     "start_time": "2026-06-13T02:50:58.312900+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Date Grouping Utility\n",
    "Helper to iterate over per-date cross-sections for IC computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1721cb6",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2026-06-13T02:50:58.315406+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def iter_date_groups(df: pl.DataFrame) -> Iterator[pl.DataFrame]:\n",
    "    \"\"\"Yield per-date groups for cross-sectional calculations.\"\"\"\n",
    "    for _, g in df.partition_by(\"timestamp\", as_dict=True).items():\n",
    "        yield g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e3eb96",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.001226,
     "end_time": "2026-06-13T02:50:58.321540+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.320314+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Daily Information Coefficient\n",
    "Compute daily cross-sectional Spearman IC for signal vs forward return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "934ea4b6",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2026-06-13T02:50:58.322800+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def daily_ic(\n",
    "    df: pl.DataFrame,\n",
    "    signal_col: str,\n",
    "    ret_col: str,\n",
    "    min_assets: int,\n",
    ") -> pl.DataFrame:\n",
    "    \"\"\"Compute daily cross-sectional Spearman IC for signal vs forward return.\"\"\"\n",
    "    records: list[dict[str, object]] = []\n",
    "    for g in iter_date_groups(df.select([\"timestamp\", signal_col, ret_col]).drop_nulls()):\n",
    "        if len(g) < min_assets:\n",
    "            continue\n",
    "        ic, _ = spearmanr(g[signal_col].to_numpy(), g[ret_col].to_numpy())\n",
    "        if np.isnan(ic):\n",
    "            continue\n",
    "        records.append({\"timestamp\": g[\"timestamp\"][0], \"ic\": float(ic)})\n",
    "    return pl.DataFrame(records).sort(\"timestamp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17decd2",
   "metadata": {
    "lines_to_next_cell": 2,
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     "duration": 0.001234,
     "end_time": "2026-06-13T02:50:58.329354+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.328120+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### IC Summary Statistics\n",
    "Compute mean, std, ICIR, and t-stat from a daily IC series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43e3eafe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T02:50:58.332249Z",
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    },
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     "duration": 0.004005,
     "end_time": "2026-06-13T02:50:58.334606+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.330601+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def summarize_ic(ic_df: pl.DataFrame) -> dict[str, float]:\n",
    "    \"\"\"Summarize IC series with mean, std, ICIR, and t-stat.\"\"\"\n",
    "    if len(ic_df) == 0:\n",
    "        return {\"ic_mean\": np.nan, \"ic_std\": np.nan, \"icir\": np.nan, \"t_stat\": np.nan, \"n\": 0}\n",
    "    values = ic_df[\"ic\"].to_numpy()\n",
    "    ic_mean = float(np.mean(values))\n",
    "    ic_std = float(np.std(values, ddof=1)) if len(values) > 1 else np.nan\n",
    "    icir = float(ic_mean / ic_std) if ic_std and ic_std > 0 else np.nan\n",
    "    t_stat = float(ic_mean / (ic_std / np.sqrt(len(values)))) if ic_std and ic_std > 0 else np.nan\n",
    "    return {\"ic_mean\": ic_mean, \"ic_std\": ic_std, \"icir\": icir, \"t_stat\": t_stat, \"n\": len(values)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebd03ab",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.001232,
     "end_time": "2026-06-13T02:50:58.337121+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.335889+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Quintile Long-Short Returns\n",
    "Compute daily long-short returns (top minus bottom quantile) for a signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3016d7d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T02:50:58.340057Z",
     "iopub.status.busy": "2026-06-13T02:50:58.339987Z",
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     "shell.execute_reply": "2026-06-13T02:50:58.342486Z"
    },
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     "duration": 0.004669,
     "end_time": "2026-06-13T02:50:58.343053+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.338384+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def quintile_long_short(\n",
    "    df: pl.DataFrame,\n",
    "    signal_col: str,\n",
    "    ret_col: str,\n",
    "    quantiles: int,\n",
    "    min_assets: int,\n",
    ") -> pl.DataFrame:\n",
    "    \"\"\"Compute daily long-short returns (top minus bottom quantile) for a signal.\"\"\"\n",
    "    d = df.select([\"timestamp\", \"symbol\", signal_col, ret_col]).drop_nulls()\n",
    "\n",
    "    # Rank within date, then bin into quantiles using rank percentiles.\n",
    "    d = d.with_columns(\n",
    "        (\n",
    "            (pl.col(signal_col).rank(method=\"average\").over(\"timestamp\") - 1)\n",
    "            / (pl.len().over(\"timestamp\") - 1).clip(lower_bound=1)\n",
    "        ).alias(\"rank_pct\")\n",
    "    )\n",
    "\n",
    "    # Quantile index 1..Q\n",
    "    d = d.with_columns(\n",
    "        ((pl.col(\"rank_pct\") * quantiles).floor().clip(0, quantiles - 1) + 1)\n",
    "        .cast(pl.Int64)\n",
    "        .alias(\"q\")\n",
    "    )\n",
    "\n",
    "    # Filter dates with insufficient cross-section\n",
    "    d = d.join(\n",
    "        d.group_by(\"timestamp\")\n",
    "        .agg(pl.len().alias(\"n\"))\n",
    "        .filter(pl.col(\"n\") >= min_assets)\n",
    "        .select([\"timestamp\"]),\n",
    "        on=\"timestamp\",\n",
    "        how=\"inner\",\n",
    "    )\n",
    "\n",
    "    qrets = d.group_by([\"timestamp\", \"q\"]).agg(pl.col(ret_col).mean().alias(\"q_ret\"))\n",
    "    wide = qrets.pivot(index=\"timestamp\", on=\"q\", values=\"q_ret\").sort(\"timestamp\")\n",
    "\n",
    "    top = str(quantiles)\n",
    "    bottom = \"1\"\n",
    "    if top not in wide.columns or bottom not in wide.columns:\n",
    "        return pl.DataFrame({\"timestamp\": [], \"ls_ret\": []})\n",
    "\n",
    "    return wide.with_columns((pl.col(top) - pl.col(bottom)).alias(\"ls_ret\")).select(\n",
    "        [\"timestamp\", \"ls_ret\"]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d87a2b5",
   "metadata": {
    "papermill": {
     "duration": 0.001249,
     "end_time": "2026-06-13T02:50:58.345602+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.344353+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 3. Evaluate Signals Against Forward Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e089098d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T02:50:58.348520Z",
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     "shell.execute_reply": "2026-06-13T02:51:00.963914Z"
    },
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     "duration": 2.617749,
     "end_time": "2026-06-13T02:51:00.964606+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:50:58.346857+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "TEXT SIGNAL EVALUATION SUMMARY\n",
      "================================================================================\n",
      "shape: (12, 7)\n",
      "┌────────────────────┬──────────────┬───────────┬──────────┬───────────┬───────────┬──────┐\n",
      "│ signal             ┆ horizon_days ┆ ic_mean   ┆ ic_std   ┆ icir      ┆ t_stat    ┆ n    │\n",
      "│ ---                ┆ ---          ┆ ---       ┆ ---      ┆ ---       ┆ ---       ┆ ---  │\n",
      "│ str                ┆ i64          ┆ f64       ┆ f64      ┆ f64       ┆ f64       ┆ i64  │\n",
      "╞════════════════════╪══════════════╪═══════════╪══════════╪═══════════╪═══════════╪══════╡\n",
      "│ weighted_surprise  ┆ 1            ┆ 0.001493  ┆ 0.344421 ┆ 0.004335  ┆ 0.153744  ┆ 1258 │\n",
      "│ sentiment_mean     ┆ 1            ┆ 0.001419  ┆ 0.350336 ┆ 0.00405   ┆ 0.143659  ┆ 1258 │\n",
      "│ sentiment_momentum ┆ 1            ┆ -0.00012  ┆ 0.330929 ┆ -0.000364 ┆ -0.012945 ┆ 1265 │\n",
      "│ coverage_count     ┆ 1            ┆ -0.001315 ┆ 0.347793 ┆ -0.003782 ┆ -0.133884 ┆ 1253 │\n",
      "│ sentiment_mean     ┆ 5            ┆ -0.000323 ┆ 0.347672 ┆ -0.000929 ┆ -0.032959 ┆ 1258 │\n",
      "│ sentiment_momentum ┆ 5            ┆ -0.001183 ┆ 0.348999 ┆ -0.003389 ┆ -0.120546 ┆ 1265 │\n",
      "│ weighted_surprise  ┆ 5            ┆ -0.001185 ┆ 0.343691 ┆ -0.003446 ┆ -0.12224  ┆ 1258 │\n",
      "│ coverage_count     ┆ 5            ┆ -0.002564 ┆ 0.349068 ┆ -0.007345 ┆ -0.25999  ┆ 1253 │\n",
      "│ sentiment_momentum ┆ 20           ┆ 0.011909  ┆ 0.331435 ┆ 0.035932  ┆ 1.277979  ┆ 1265 │\n",
      "│ sentiment_mean     ┆ 20           ┆ 0.007239  ┆ 0.33302  ┆ 0.021737  ┆ 0.770971  ┆ 1258 │\n",
      "│ weighted_surprise  ┆ 20           ┆ 0.004201  ┆ 0.329952 ┆ 0.012733  ┆ 0.451628  ┆ 1258 │\n",
      "│ coverage_count     ┆ 20           ┆ -0.003423 ┆ 0.342825 ┆ -0.009985 ┆ -0.353432 ┆ 1253 │\n",
      "└────────────────────┴──────────────┴───────────┴──────────┴───────────┴───────────┴──────┘\n"
     ]
    }
   ],
   "source": [
    "# Run evaluation for all signals and horizons\n",
    "summary_rows: list[dict[str, object]] = []\n",
    "daily_ic_rows: list[pl.DataFrame] = []\n",
    "daily_ls_rows: list[pl.DataFrame] = []\n",
    "\n",
    "for signal in AVAILABLE_SIGNALS:\n",
    "    for h in CONFIG.horizons:\n",
    "        ret_col = RET_COL_BY_HORIZON[h]\n",
    "        ic_df = daily_ic(text_features, signal, ret_col, CONFIG.min_assets_per_day)\n",
    "        ic_summary = summarize_ic(ic_df)\n",
    "        summary_rows.append(\n",
    "            {\n",
    "                \"signal\": signal,\n",
    "                \"horizon_days\": h,\n",
    "                **ic_summary,\n",
    "            }\n",
    "        )\n",
    "        daily_ic_rows.append(\n",
    "            ic_df.with_columns(pl.lit(signal).alias(\"signal\"), pl.lit(h).alias(\"horizon_days\"))\n",
    "        )\n",
    "\n",
    "        ls_df = quintile_long_short(\n",
    "            text_features, signal, ret_col, CONFIG.quantiles, CONFIG.min_assets_per_day\n",
    "        )\n",
    "        daily_ls_rows.append(\n",
    "            ls_df.with_columns(pl.lit(signal).alias(\"signal\"), pl.lit(h).alias(\"horizon_days\"))\n",
    "        )\n",
    "\n",
    "summary_df = pl.DataFrame(summary_rows).sort([\"horizon_days\", \"icir\"], descending=[False, True])\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"TEXT SIGNAL EVALUATION SUMMARY\")\n",
    "print(\"=\" * 80)\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db324c6",
   "metadata": {
    "papermill": {
     "duration": 0.0014,
     "end_time": "2026-06-13T02:51:00.967518+00:00",
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     "start_time": "2026-06-13T02:51:00.966118+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 4. Visualization: IC and Long-Short Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea95010d",
   "metadata": {
    "execution": {
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     "start_time": "2026-06-13T02:51:00.968838+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IC time series for the key signal (weighted_surprise, 1-day horizon)\n",
    "key_signal = \"weighted_surprise\"\n",
    "key_horizon = 1\n",
    "\n",
    "if key_signal in AVAILABLE_SIGNALS:\n",
    "    ic_df = daily_ic(\n",
    "        text_features, key_signal, RET_COL_BY_HORIZON[key_horizon], CONFIG.min_assets_per_day\n",
    "    )\n",
    "\n",
    "    if len(ic_df) > 10:\n",
    "        ic_vals = ic_df[\"ic\"].to_numpy()\n",
    "        ic_mean = np.mean(ic_vals)\n",
    "        ic_std = np.std(ic_vals, ddof=1)\n",
    "\n",
    "        fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "        # IC time series\n",
    "        axes[0].plot(range(len(ic_df)), ic_vals, alpha=0.7, linewidth=0.5)\n",
    "        axes[0].axhline(0, color=\"black\", linestyle=\"-\", linewidth=0.5)\n",
    "        axes[0].axhline(ic_mean, color=\"red\", linestyle=\"--\", label=f\"Mean IC: {ic_mean:.4f}\")\n",
    "        axes[0].fill_between(\n",
    "            range(len(ic_df)),\n",
    "            ic_mean - ic_std,\n",
    "            ic_mean + ic_std,\n",
    "            alpha=0.2,\n",
    "            color=\"red\",\n",
    "            label=\"±1 std\",\n",
    "        )\n",
    "        axes[0].set_xlabel(\"Trading Day\")\n",
    "        axes[0].set_ylabel(\"Information Coefficient\")\n",
    "        axes[0].set_title(f\"Daily Cross-Sectional IC ({key_signal}, {key_horizon}d)\")\n",
    "        axes[0].legend()\n",
    "\n",
    "        # IC histogram\n",
    "        axes[1].hist(ic_vals, bins=50, edgecolor=\"black\", alpha=0.7)\n",
    "        axes[1].axvline(0, color=\"black\", linestyle=\"-\", linewidth=0.5)\n",
    "        axes[1].axvline(ic_mean, color=\"red\", linestyle=\"--\", label=f\"Mean: {ic_mean:.4f}\")\n",
    "        axes[1].set_xlabel(\"Information Coefficient\")\n",
    "        axes[1].set_ylabel(\"Frequency\")\n",
    "        axes[1].set_title(\"IC Distribution\")\n",
    "        axes[1].legend()\n",
    "\n",
    "        plt.suptitle(f\"Text Signal IC Analysis: {key_signal}\")\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa7e0cca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T02:51:01.377676Z",
     "iopub.status.busy": "2026-06-13T02:51:01.377544Z",
     "iopub.status.idle": "2026-06-13T02:51:01.385324Z",
     "shell.execute_reply": "2026-06-13T02:51:01.384905Z"
    },
    "papermill": {
     "duration": 0.010397,
     "end_time": "2026-06-13T02:51:01.385594+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:51:01.375197+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quintile Returns (weighted_surprise, 1d forward return):\n",
      "shape: (5, 3)\n",
      "┌──────────┬──────────┬───────┐\n",
      "│ quintile ┆ mean_ret ┆ n_obs │\n",
      "│ ---      ┆ ---      ┆ ---   │\n",
      "│ str      ┆ f64      ┆ u32   │\n",
      "╞══════════╪══════════╪═══════╡\n",
      "│ Q1       ┆ 0.000995 ┆ 2574  │\n",
      "│ Q2       ┆ 0.000419 ┆ 3470  │\n",
      "│ Q3       ┆ 0.000602 ┆ 4700  │\n",
      "│ Q4       ┆ 0.0008   ┆ 1734  │\n",
      "│ Q5       ┆ 0.000216 ┆ 3300  │\n",
      "└──────────┴──────────┴───────┘\n"
     ]
    }
   ],
   "source": [
    "# Quintile returns for key signal\n",
    "if key_signal in AVAILABLE_SIGNALS:\n",
    "    # Compute quintile returns\n",
    "    d = text_features.select(\n",
    "        [\"timestamp\", \"symbol\", key_signal, RET_COL_BY_HORIZON[key_horizon]]\n",
    "    ).drop_nulls()\n",
    "\n",
    "    # Rank and assign quintiles\n",
    "    d = d.with_columns(\n",
    "        (\n",
    "            (pl.col(key_signal).rank(method=\"average\").over(\"timestamp\") - 1)\n",
    "            / (pl.len().over(\"timestamp\") - 1).clip(lower_bound=1)\n",
    "        ).alias(\"rank_pct\")\n",
    "    ).with_columns(\n",
    "        pl.when(pl.col(\"rank_pct\") < 0.2)\n",
    "        .then(pl.lit(\"Q1\"))\n",
    "        .when(pl.col(\"rank_pct\") < 0.4)\n",
    "        .then(pl.lit(\"Q2\"))\n",
    "        .when(pl.col(\"rank_pct\") < 0.6)\n",
    "        .then(pl.lit(\"Q3\"))\n",
    "        .when(pl.col(\"rank_pct\") < 0.8)\n",
    "        .then(pl.lit(\"Q4\"))\n",
    "        .otherwise(pl.lit(\"Q5\"))\n",
    "        .alias(\"quintile\")\n",
    "    )\n",
    "\n",
    "    # Average returns by quintile\n",
    "    quintile_returns = (\n",
    "        d.group_by(\"quintile\")\n",
    "        .agg(\n",
    "            [\n",
    "                pl.col(RET_COL_BY_HORIZON[key_horizon]).mean().alias(\"mean_ret\"),\n",
    "                pl.len().alias(\"n_obs\"),\n",
    "            ]\n",
    "        )\n",
    "        .sort(\"quintile\")\n",
    "    )\n",
    "\n",
    "    print(f\"\\nQuintile Returns ({key_signal}, {key_horizon}d forward return):\")\n",
    "    print(quintile_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f37ef5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T02:51:01.389688Z",
     "iopub.status.busy": "2026-06-13T02:51:01.389609Z",
     "iopub.status.idle": "2026-06-13T02:51:01.458789Z",
     "shell.execute_reply": "2026-06-13T02:51:01.458357Z"
    },
    "papermill": {
     "duration": 0.07172,
     "end_time": "2026-06-13T02:51:01.459183+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:51:01.387463+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot quintile returns\n",
    "if key_signal in AVAILABLE_SIGNALS:\n",
    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "    quintiles = quintile_returns[\"quintile\"].to_list()\n",
    "    returns_bps = [r * 10000 for r in quintile_returns[\"mean_ret\"].to_list()]\n",
    "\n",
    "    # Sequential blue gradient for ordinal quintile encoding (greyscale-safe)\n",
    "    quintile_colors = [\"#cfe2f3\", \"#9fc5e8\", \"#6fa8dc\", \"#3d85c6\", \"#0b5394\"]\n",
    "    bars = ax.bar(quintiles, returns_bps, color=quintile_colors)\n",
    "    ax.axhline(0, color=\"black\", linestyle=\"-\", linewidth=0.5)\n",
    "    ax.set_xlabel(f\"{key_signal} Quintile (Q1=Low, Q5=High)\")\n",
    "    ax.set_ylabel(f\"Average {key_horizon}-Day Forward Return (bps)\")\n",
    "    ax.set_title(f\"Quintile Returns: {key_signal}\")\n",
    "\n",
    "    # Add value labels\n",
    "    for bar, val in zip(bars, returns_bps, strict=False):\n",
    "        ax.annotate(\n",
    "            f\"{val:.1f}\",\n",
    "            xy=(bar.get_x() + bar.get_width() / 2, bar.get_height()),\n",
    "            xytext=(0, 3 if val >= 0 else -10),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"center\",\n",
    "            fontsize=10,\n",
    "        )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617aac52",
   "metadata": {
    "papermill": {
     "duration": 0.001749,
     "end_time": "2026-06-13T02:51:01.462907+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:51:01.461158+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 5. Save Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f4c556ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T02:51:01.467024Z",
     "iopub.status.busy": "2026-06-13T02:51:01.466905Z",
     "iopub.status.idle": "2026-06-13T02:51:01.474733Z",
     "shell.execute_reply": "2026-06-13T02:51:01.474351Z"
    },
    "papermill": {
     "duration": 0.010395,
     "end_time": "2026-06-13T02:51:01.475020+00:00",
     "exception": false,
     "start_time": "2026-06-13T02:51:01.464625+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved summary to: 08_financial_features/output/text_evaluation/text_signal_summary.parquet\n",
      "Saved daily IC to: 08_financial_features/output/text_evaluation/daily_ic.parquet\n",
      "Saved daily long-short to: 08_financial_features/output/text_evaluation/daily_long_short.parquet\n"
     ]
    }
   ],
   "source": [
    "# Create output directory\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Save summary\n",
    "summary_path = OUTPUT_DIR / \"text_signal_summary.parquet\"\n",
    "summary_df.write_parquet(summary_path)\n",
    "print(f\"Saved summary to: {summary_path}\")\n",
    "\n",
    "# Save daily IC series\n",
    "if daily_ic_rows:\n",
    "    daily_ic_df = pl.concat(daily_ic_rows, how=\"vertical_relaxed\")\n",
    "    daily_ic_path = OUTPUT_DIR / \"daily_ic.parquet\"\n",
    "    daily_ic_df.write_parquet(daily_ic_path)\n",
    "    print(f\"Saved daily IC to: {daily_ic_path}\")\n",
    "\n",
    "# Save daily long-short series\n",
    "if daily_ls_rows:\n",
    "    daily_ls_df = pl.concat(daily_ls_rows, how=\"vertical_relaxed\")\n",
    "    daily_ls_path = OUTPUT_DIR / \"daily_long_short.parquet\"\n",
    "    daily_ls_df.write_parquet(daily_ls_path)\n",
    "    print(f\"Saved daily long-short to: {daily_ls_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2ffc22",
   "metadata": {
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     "status": "completed"
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   "source": [
    "## Key Takeaways\n",
    "\n",
    "### Measured signal performance on this evaluation set\n",
    "1. All four text signals produce 1-day cross-sectional ICIRs within ±0.005\n",
    "   of zero (weighted_surprise 0.004, sentiment_mean 0.004,\n",
    "   sentiment_momentum −0.0005, coverage_count −0.004; n≈1,260 dates).\n",
    "   Per-signal t-stats are all in [−0.2, +0.2] at the 1-day horizon. The\n",
    "   construction methodology is demonstrated here; the magnitudes do not\n",
    "   support a ranking claim — none of the four signals is statistically\n",
    "   distinguishable from zero on this sample.\n",
    "2. The weighted_surprise quintile sort on 1-day forward returns is\n",
    "   non-monotonic and Q5 < Q1 (Q1 0.000998, Q2 0.000417, Q3 0.000604,\n",
    "   Q4 0.000786, Q5 0.000221). The predicted bullish quintile (Q5) earns\n",
    "   less than the predicted bearish quintile (Q1) — the bullish/bearish\n",
    "   framing in NB07 is **not** confirmed.\n",
    "3. ICIR magnitudes here are an order of magnitude or more below the\n",
    "   ICIR > 0.5 robustness threshold used in NB07's takeaway 4. The\n",
    "   notebook does not establish tradeability for any of the four signals\n",
    "   on the 2009-2017 FNSPID 50-ticker subset.\n",
    "\n",
    "### Evaluation Methodology\n",
    "- We use the exact dataset from notebook 07 (no label recomputation)\n",
    "- Forward returns are already aligned to tradable dates with proper lag\n",
    "- Daily cross-sectional IC avoids temporal autocorrelation issues\n",
    "\n",
    "### Limitations\n",
    "1. **Coverage bias**: Notebook 07 drops low-coverage tickers (see its takeaways)\n",
    "2. **Data sparsity**: News coverage is uneven across stocks and time\n",
    "3. **Decay**: Text signals may have shorter predictive horizons than momentum\n",
    "\n",
    "### Next Steps\n",
    "- Chapter 12: Train ML models on combined feature set (text + price features)\n",
    "- Chapter 17: Backtest text-enhanced strategies"
   ]
  },
  {
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   "execution_count": 16,
   "id": "5bbd04b5",
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     "status": "completed"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run metadata saved to: 08_financial_features/output/text_evaluation/run_metadata.json\n",
      "\n",
      "Text feature evaluation complete\n"
     ]
    }
   ],
   "source": [
    "# Save run metadata for reproducibility\n",
    "run_metadata = {\n",
    "    \"horizons\": list(CONFIG.horizons),\n",
    "    \"min_assets_per_day\": CONFIG.min_assets_per_day,\n",
    "    \"quantiles\": CONFIG.quantiles,\n",
    "    \"signals_evaluated\": AVAILABLE_SIGNALS,\n",
    "    \"n_observations\": len(text_features),\n",
    "    \"n_assets\": text_features[\"symbol\"].n_unique(),\n",
    "    \"date_range\": {\n",
    "        \"min\": str(text_features[\"timestamp\"].min()),\n",
    "        \"max\": str(text_features[\"timestamp\"].max()),\n",
    "    },\n",
    "    \"input_file\": str(text_features_path),\n",
    "}\n",
    "\n",
    "metadata_path = OUTPUT_DIR / \"run_metadata.json\"\n",
    "with open(metadata_path, \"w\") as f:\n",
    "    json.dump(run_metadata, f, indent=2)\n",
    "print(f\"Run metadata saved to: {metadata_path}\")\n",
    "\n",
    "print(\"\\nText feature evaluation complete\")"
   ]
  }
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