stefan-jansen--machine-learning-for-trading/08_financial_features/05_feature_selection.ipynb

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    "# Feature Selection and Deduplication\n",
    "\n",
    "**Chapter 8: Feature Engineering**\n",
    "**Section Reference**: 8.6 — Combining Features and Controlling Search\n",
    "\n",
    "**Docker image**: `ml4t`\n",
    "\n",
    "## Purpose\n",
    "\n",
    "A feature engineering pipeline produces many candidates — different lookbacks,\n",
    "transforms, and interaction variants. This notebook demonstrates how to reduce\n",
    "that set to a focused, production-ready collection using systematic selection\n",
    "and deduplication.\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "1. Compute cross-sectional IC and rank features by predictive power\n",
    "2. Apply correlation filtering to remove redundant features\n",
    "3. Cluster near-duplicate features and select representatives\n",
    "4. Use Benjamini–Hochberg FDR to control false discovery across multiple tests\n",
    "5. Assess feature stability via bootstrap IC\n",
    "6. Compare IC-based and ML-based (LightGBM) importance rankings\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "- Run [`03_financial_features`](../case_studies/etfs/03_financial_features.ipynb)\n",
    "  to produce `financial.parquet`\n",
    "- Requires `ml4t-diagnostic` and `ml4t-engineer` libraries\n",
    "\n",
    "## References\n",
    "\n",
    "- Harvey, Liu, and Zhu (2016) — Multiple testing in factor research\n",
    "- Meinshausen and Bühlmann (2010) — Stability selection\n",
    "\n",
    "**Output**: Selected feature list for downstream Chapter 9 use"
   ]
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   "source": [
    "## Setup"
   ]
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    "\"\"\"Feature Selection and Deduplication — reduce feature candidates to a focused production-ready set.\"\"\"\n",
    "\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import polars as pl\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from ml4t.diagnostic.metrics import pooled_ic\n",
    "from scipy.cluster.hierarchy import fcluster, leaves_list, linkage\n",
    "from scipy.spatial.distance import squareform\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from data import load_etfs\n",
    "from utils.paths import get_case_study_dir, get_output_dir\n",
    "from utils.reproducibility import set_global_seeds\n",
    "from utils.style import COLORS"
   ]
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     "parameters"
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   "source": [
    "START_DATE = \"2006-01-01\"\n",
    "N_BOOTSTRAP = 50\n",
    "MAX_SYMBOLS = 0\n",
    "SEED = 42"
   ]
  },
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   "source": [
    "set_global_seeds(SEED)"
   ]
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   "source": [
    "## 1. Load Features from ETF Case Study\n",
    "\n",
    "The ETF case study produced features in `case_studies/etfs/features/`."
   ]
  },
  {
   "cell_type": "code",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: (396186, 59)\n",
      "Labels: (468562, 3)\n"
     ]
    }
   ],
   "source": [
    "CASE_DIR = get_case_study_dir(\"etfs\")\n",
    "FEATURES_PATH = CASE_DIR / \"features\" / \"financial.parquet\"\n",
    "\n",
    "if not FEATURES_PATH.exists():\n",
    "    raise FileNotFoundError(\n",
    "        f\"Features file not found at {FEATURES_PATH}. \"\n",
    "        \"Please run case_studies/etfs/03_financial_features.py first.\"\n",
    "    )\n",
    "\n",
    "features_df = pl.read_parquet(FEATURES_PATH)\n",
    "prices_df = load_etfs()\n",
    "\n",
    "# Apply date filter\n",
    "features_df = features_df.filter(pl.col(\"timestamp\") >= pl.lit(START_DATE).str.to_date())\n",
    "prices_df = prices_df.filter(pl.col(\"timestamp\") >= pl.lit(START_DATE).str.to_date())\n",
    "\n",
    "if MAX_SYMBOLS > 0:\n",
    "    top_symbols = (\n",
    "        features_df.group_by(\"symbol\")\n",
    "        .len()\n",
    "        .sort(\"len\", descending=True)\n",
    "        .head(MAX_SYMBOLS)[\"symbol\"]\n",
    "    )\n",
    "    features_df = features_df.filter(pl.col(\"symbol\").is_in(top_symbols))\n",
    "    prices_df = prices_df.filter(pl.col(\"symbol\").is_in(top_symbols))\n",
    "\n",
    "# Compute forward returns on-demand\n",
    "labels_df = (\n",
    "    prices_df.sort([\"symbol\", \"timestamp\"])\n",
    "    .with_columns(\n",
    "        (pl.col(\"close\").shift(-21).over(\"symbol\") / pl.col(\"close\") - 1).alias(\"fwd_return_1m\")\n",
    "    )\n",
    "    .select([\"timestamp\", \"symbol\", \"fwd_return_1m\"])\n",
    "    .drop_nulls()\n",
    ")\n",
    "\n",
    "print(f\"Features: {features_df.shape}\")\n",
    "print(f\"Labels: {labels_df.shape}\")"
   ]
  },
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     "name": "stdout",
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     "text": [
      "Available features: 57\n",
      "   1. ret_5d\n",
      "   2. ret_10d\n",
      "   3. ret_21d\n",
      "   4. ret_42d\n",
      "   5. ret_63d\n",
      "   6. ret_126d\n",
      "   7. ret_189d\n",
      "   8. ret_252d\n",
      "   9. skip_recent_12_1\n",
      "  10. skip_recent_6_1\n",
      "  11. vol_21d\n",
      "  12. vol_63d\n",
      "  13. vol_126d\n",
      "  14. vol_252d\n",
      "  15. sharpe_5d\n",
      "  16. sharpe_10d\n",
      "  17. sharpe_21d\n",
      "  18. sharpe_42d\n",
      "  19. sharpe_63d\n",
      "  20. sharpe_126d\n",
      "  21. sharpe_189d\n",
      "  22. sharpe_252d\n",
      "  23. mom_accel_short\n",
      "  24. mom_accel_medium\n",
      "  25. mom_accel_long\n",
      "  26. vol_ratio_short\n",
      "  27. vol_ratio_medium\n",
      "  28. rsi_7\n",
      "  29. rsi_14\n",
      "  30. macd_line\n",
      "  31. adx_14\n",
      "  32. cci_14\n",
      "  33. cci_21\n",
      "  34. stoch_k\n",
      "  35. aroon_diff\n",
      "  36. sma_ratio_50\n",
      "  37. sma_ratio_200\n",
      "  38. ema_ratio_26\n",
      "  39. bb_pctb_20\n",
      "  40. natr_14\n",
      "  41. chop_14\n",
      "  42. hurst_100\n",
      "  43. max_dd_63d\n",
      "  44. max_dd_126d\n",
      "  45. vol_ratio_21d\n",
      "  46. vol_ratio_63d\n",
      "  47. obv_zscore_63d\n",
      "  48. pct_positive_63d\n",
      "  49. dist_52w_high\n",
      "  50. dist_52w_low\n",
      "  51. corr_spy_tlt_63d\n",
      "  52. ret_126d_rank\n",
      "  53. sharpe_126d_rank\n",
      "  54. vol_63d_rank\n",
      "  55. regime\n",
      "  56. yield_curve_slope\n",
      "  57. yield_curve_zscore\n"
     ]
    }
   ],
   "source": [
    "all_feature_cols = [c for c in features_df.columns if c not in [\"timestamp\", \"symbol\"]]\n",
    "print(f\"Available features: {len(all_feature_cols)}\")\n",
    "for i, col in enumerate(all_feature_cols, 1):\n",
    "    print(f\"  {i:2d}. {col}\")"
   ]
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   "source": [
    "## 2. Compute Information Coefficient (IC)\n",
    "\n",
    "IC measures the Spearman rank correlation between features and forward returns.\n",
    "We compute IC **cross-sectionally** (per date, then average). Pooled IC\n",
    "conflates time-series drift with cross-sectional predictive power."
   ]
  },
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     "text": [
      "Analysis dataset: (394233, 60)\n"
     ]
    }
   ],
   "source": [
    "# Merge features with forward returns\n",
    "analysis = features_df.join(\n",
    "    labels_df.select([\"timestamp\", \"symbol\", \"fwd_return_1m\"]),\n",
    "    on=[\"timestamp\", \"symbol\"],\n",
    "    how=\"inner\",\n",
    ").drop_nulls(subset=[\"fwd_return_1m\"])\n",
    "\n",
    "print(f\"Analysis dataset: {analysis.shape}\")"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature IC Rankings (top 15) — Newey-West with 12 lags:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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: (15, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>feature</th><th>ic</th><th>t_stat_iid</th><th>t_stat_NW</th><th>n_obs</th><th>ic_abs</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;ret_252d&quot;</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4759</td><td>NaN</td></tr><tr><td>&quot;skip_recent_12_1&quot;</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4759</td><td>NaN</td></tr><tr><td>&quot;vol_252d&quot;</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4759</td><td>NaN</td></tr><tr><td>&quot;sharpe_252d&quot;</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4759</td><td>NaN</td></tr><tr><td>&quot;mom_accel_long&quot;</td><td>NaN</td><td>NaN</td><td>NaN</td><td>4759</td><td>NaN</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;natr_14&quot;</td><td>0.05722</td><td>10.388454</td><td>10.353586</td><td>4759</td><td>0.05722</td></tr><tr><td>&quot;vol_126d&quot;</td><td>0.056434</td><td>10.029753</td><td>9.94651</td><td>4759</td><td>0.056434</td></tr><tr><td>&quot;vol_63d&quot;</td><td>0.05091</td><td>9.110108</td><td>9.051793</td><td>4759</td><td>0.05091</td></tr><tr><td>&quot;vol_63d_rank&quot;</td><td>0.05091</td><td>9.110108</td><td>9.051793</td><td>4759</td><td>0.05091</td></tr><tr><td>&quot;vol_21d&quot;</td><td>0.050691</td><td>9.232101</td><td>9.179108</td><td>4759</td><td>0.050691</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (15, 6)\n",
       "┌──────────────────┬──────────┬────────────┬───────────┬───────┬──────────┐\n",
       "│ feature          ┆ ic       ┆ t_stat_iid ┆ t_stat_NW ┆ n_obs ┆ ic_abs   │\n",
       "│ ---              ┆ ---      ┆ ---        ┆ ---       ┆ ---   ┆ ---      │\n",
       "│ str              ┆ f64      ┆ f64        ┆ f64       ┆ i64   ┆ f64      │\n",
       "╞══════════════════╪══════════╪════════════╪═══════════╪═══════╪══════════╡\n",
       "│ ret_252d         ┆ NaN      ┆ NaN        ┆ NaN       ┆ 4759  ┆ NaN      │\n",
       "│ skip_recent_12_1 ┆ NaN      ┆ NaN        ┆ NaN       ┆ 4759  ┆ NaN      │\n",
       "│ vol_252d         ┆ NaN      ┆ NaN        ┆ NaN       ┆ 4759  ┆ NaN      │\n",
       "│ sharpe_252d      ┆ NaN      ┆ NaN        ┆ NaN       ┆ 4759  ┆ NaN      │\n",
       "│ mom_accel_long   ┆ NaN      ┆ NaN        ┆ NaN       ┆ 4759  ┆ NaN      │\n",
       "│ …                ┆ …        ┆ …          ┆ …         ┆ …     ┆ …        │\n",
       "│ natr_14          ┆ 0.05722  ┆ 10.388454  ┆ 10.353586 ┆ 4759  ┆ 0.05722  │\n",
       "│ vol_126d         ┆ 0.056434 ┆ 10.029753  ┆ 9.94651   ┆ 4759  ┆ 0.056434 │\n",
       "│ vol_63d          ┆ 0.05091  ┆ 9.110108   ┆ 9.051793  ┆ 4759  ┆ 0.05091  │\n",
       "│ vol_63d_rank     ┆ 0.05091  ┆ 9.110108   ┆ 9.051793  ┆ 4759  ┆ 0.05091  │\n",
       "│ vol_21d          ┆ 0.050691 ┆ 9.232101   ┆ 9.179108  ┆ 4759  ┆ 0.050691 │\n",
       "└──────────────────┴──────────┴────────────┴───────────┴───────┴──────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute cross-sectional IC per date\n",
    "ic_by_date = analysis.group_by(\"timestamp\").agg(\n",
    "    [pl.corr(col, \"fwd_return_1m\", method=\"spearman\").alias(col) for col in all_feature_cols]\n",
    ")\n",
    "\n",
    "# Summary statistics. The daily IC series is serially correlated (overlapping\n",
    "# information sets, slow-moving common factors). We report both the i.i.d.\n",
    "# t-stat and a Newey-West HAC t-stat from regressing the IC time series on a\n",
    "# constant. HAC is the headline used for the BH-FDR step in §5.\n",
    "NW_MAXLAGS = 12\n",
    "ic_results = {}\n",
    "for col in all_feature_cols:\n",
    "    daily_ics = ic_by_date[col].drop_nulls().to_numpy()\n",
    "    if len(daily_ics) < 10:\n",
    "        ic_results[col] = {\"ic\": np.nan, \"n\": len(daily_ics)}\n",
    "        continue\n",
    "\n",
    "    mean_ic = np.mean(daily_ics)\n",
    "    std_ic = np.std(daily_ics, ddof=1)\n",
    "    # Yield NaN on both branches when std_ic == 0 so the iid and HAC outputs\n",
    "    # are internally consistent for degenerate series.\n",
    "    if std_ic > 0:\n",
    "        t_stat_iid = mean_ic / (std_ic / np.sqrt(len(daily_ics)))\n",
    "        nw = sm.OLS(daily_ics, np.ones(len(daily_ics))).fit(\n",
    "            cov_type=\"HAC\", cov_kwds={\"maxlags\": NW_MAXLAGS}\n",
    "        )\n",
    "        t_stat_nw = float(nw.tvalues[0])\n",
    "    else:\n",
    "        t_stat_iid = np.nan\n",
    "        t_stat_nw = np.nan\n",
    "    ic_results[col] = {\n",
    "        \"ic\": mean_ic,\n",
    "        \"ic_std\": std_ic,\n",
    "        \"t_stat_iid\": t_stat_iid,\n",
    "        \"t_stat_NW\": t_stat_nw,\n",
    "        \"n\": len(daily_ics),\n",
    "    }\n",
    "\n",
    "ic_df = (\n",
    "    pl.DataFrame(\n",
    "        [\n",
    "            {\n",
    "                \"feature\": k,\n",
    "                \"ic\": v[\"ic\"],\n",
    "                \"t_stat_iid\": v.get(\"t_stat_iid\"),\n",
    "                \"t_stat_NW\": v.get(\"t_stat_NW\"),\n",
    "                \"n_obs\": v[\"n\"],\n",
    "            }\n",
    "            for k, v in ic_results.items()\n",
    "        ]\n",
    "    )\n",
    "    .with_columns(pl.col(\"ic\").abs().alias(\"ic_abs\"))\n",
    "    .sort(\"ic_abs\", descending=True)\n",
    ")\n",
    "\n",
    "print(f\"\\nFeature IC Rankings (top 15) — Newey-West with {NW_MAXLAGS} lags:\")\n",
    "ic_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb34c0f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.151498Z",
     "iopub.status.busy": "2026-06-13T03:31:48.151428Z",
     "iopub.status.idle": "2026-06-13T03:31:48.347166Z",
     "shell.execute_reply": "2026-06-13T03:31:48.346507Z"
    },
    "papermill": {
     "duration": 0.198359,
     "end_time": "2026-06-13T03:31:48.347619+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.149260+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IC bar chart\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ic_pd = ic_df.to_pandas().sort_values(\"ic_abs\", ascending=True)\n",
    "colors = [COLORS[\"positive\"] if ic > 0 else COLORS[\"negative\"] for ic in ic_pd[\"ic\"]]\n",
    "ax.barh(ic_pd[\"feature\"], ic_pd[\"ic\"], color=colors)\n",
    "ax.axvline(0, color=\"black\", linewidth=0.5)\n",
    "ax.axvline(0.02, color=\"orange\", linestyle=\"--\", alpha=0.7, label=\"IC threshold (0.02)\")\n",
    "ax.axvline(-0.02, color=\"orange\", linestyle=\"--\", alpha=0.7)\n",
    "ax.set_xlabel(\"Information Coefficient (Spearman)\")\n",
    "ax.set_title(\"Feature IC Ranking\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4181b05e",
   "metadata": {
    "papermill": {
     "duration": 0.001915,
     "end_time": "2026-06-13T03:31:48.351607+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.349692+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 3. Correlation Filtering\n",
    "\n",
    "Highly correlated features provide overlapping information. We compute\n",
    "correlation on the full panel (all dates × symbols), then remove features\n",
    "with |r| > 0.9 — keeping the one with higher IC in each redundant pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24153c2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.356338Z",
     "iopub.status.busy": "2026-06-13T03:31:48.356159Z",
     "iopub.status.idle": "2026-06-13T03:31:48.509839Z",
     "shell.execute_reply": "2026-06-13T03:31:48.509387Z"
    },
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.15681,
     "end_time": "2026-06-13T03:31:48.510272+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.353462+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation matrix: 57 × 57 features\n"
     ]
    }
   ],
   "source": [
    "feature_matrix = features_df.select(all_feature_cols).drop_nulls()\n",
    "corr_np = feature_matrix.corr().to_numpy()\n",
    "\n",
    "print(f\"Correlation matrix: {corr_np.shape[0]} × {corr_np.shape[1]} features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff5bfbf",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.002696,
     "end_time": "2026-06-13T03:31:48.515898+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.513202+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### Remove Redundant Features\n",
    "Greedily drop the weaker member of each highly correlated pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08d9830c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.521924Z",
     "iopub.status.busy": "2026-06-13T03:31:48.521829Z",
     "iopub.status.idle": "2026-06-13T03:31:48.524636Z",
     "shell.execute_reply": "2026-06-13T03:31:48.524273Z"
    },
    "papermill": {
     "duration": 0.006324,
     "end_time": "2026-06-13T03:31:48.524886+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.518562+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def filter_correlated_features(\n",
    "    corr_matrix: np.ndarray,\n",
    "    feature_names: list[str],\n",
    "    ic_scores: dict[str, float] | None = None,\n",
    "    threshold: float = 0.9,\n",
    ") -> tuple[list[str], list[str]]:\n",
    "    \"\"\"Remove highly correlated features, keeping the one with higher IC.\"\"\"\n",
    "    removed = set()\n",
    "    n = len(feature_names)\n",
    "\n",
    "    for i in range(n):\n",
    "        if feature_names[i] in removed:\n",
    "            continue\n",
    "        for j in range(i + 1, n):\n",
    "            if feature_names[j] in removed:\n",
    "                continue\n",
    "            if abs(corr_matrix[i, j]) > threshold:\n",
    "                if ic_scores:\n",
    "                    ic_i = abs(ic_scores.get(feature_names[i], 0))\n",
    "                    ic_j = abs(ic_scores.get(feature_names[j], 0))\n",
    "                    to_remove = feature_names[j] if ic_i >= ic_j else feature_names[i]\n",
    "                else:\n",
    "                    to_remove = feature_names[j]\n",
    "                removed.add(to_remove)\n",
    "\n",
    "    kept = [f for f in feature_names if f not in removed]\n",
    "    return kept, list(removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5a73fe5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.529799Z",
     "iopub.status.busy": "2026-06-13T03:31:48.529697Z",
     "iopub.status.idle": "2026-06-13T03:31:48.532266Z",
     "shell.execute_reply": "2026-06-13T03:31:48.531928Z"
    },
    "papermill": {
     "duration": 0.00544,
     "end_time": "2026-06-13T03:31:48.532496+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.527056+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Filtering (threshold=0.9):\n",
      "  Before: 57 features\n",
      "  After:  43 features\n",
      "  Removed: ['rsi_14', 'cci_14', 'vol_ratio_21d', 'sma_ratio_50', 'vol_126d', 'stoch_k', 'vol_63d', 'rsi_7', 'cci_21', 'ret_252d', 'sharpe_189d', 'ret_126d', 'max_dd_126d', 'vol_21d']\n"
     ]
    }
   ],
   "source": [
    "ic_scores = {row[\"feature\"]: row[\"ic\"] for row in ic_df.to_dicts()}\n",
    "\n",
    "kept_after_corr, removed_by_corr = filter_correlated_features(\n",
    "    corr_matrix=corr_np,\n",
    "    feature_names=all_feature_cols,\n",
    "    ic_scores=ic_scores,\n",
    "    threshold=0.9,\n",
    ")\n",
    "\n",
    "print(\"Correlation Filtering (threshold=0.9):\")\n",
    "print(f\"  Before: {len(all_feature_cols)} features\")\n",
    "print(f\"  After:  {len(kept_after_corr)} features\")\n",
    "print(f\"  Removed: {removed_by_corr}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7c0ad4",
   "metadata": {
    "papermill": {
     "duration": 0.001863,
     "end_time": "2026-06-13T03:31:48.536303+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.534440+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 4. Clustering and Deduplication\n",
    "\n",
    "Even after removing pairs above 0.9, many features remain near-duplicates.\n",
    "Hierarchical clustering groups similar features so we can pick one\n",
    "representative per cluster — preserving diversity across families while\n",
    "removing redundancy within them.\n",
    "\n",
    "**Linkage choice**: We use **average linkage** (not Ward) because Ward\n",
    "assumes Euclidean distance. Correlation-based distances don't satisfy\n",
    "this assumption; average and complete linkage work with any distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "405b0ddd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.540500Z",
     "iopub.status.busy": "2026-06-13T03:31:48.540427Z",
     "iopub.status.idle": "2026-06-13T03:31:48.543279Z",
     "shell.execute_reply": "2026-06-13T03:31:48.542832Z"
    },
    "papermill": {
     "duration": 0.005409,
     "end_time": "2026-06-13T03:31:48.543551+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.538142+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "# Build correlation matrix for surviving features\n",
    "surv_idx = [all_feature_cols.index(f) for f in kept_after_corr]\n",
    "surv_corr = corr_np[np.ix_(surv_idx, surv_idx)]\n",
    "\n",
    "# Distance = 1 - |ρ| (NaN correlations treated as uncorrelated → distance 1.0)\n",
    "dist_matrix = 1 - np.abs(np.nan_to_num(surv_corr, nan=0.0))\n",
    "np.fill_diagonal(dist_matrix, 0)\n",
    "dist_matrix = (dist_matrix + dist_matrix.T) / 2\n",
    "dist_matrix = np.clip(dist_matrix, 0, 2)\n",
    "\n",
    "dist_condensed = squareform(dist_matrix, checks=False)\n",
    "link = linkage(dist_condensed, method=\"average\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69015817",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.547728Z",
     "iopub.status.busy": "2026-06-13T03:31:48.547658Z",
     "iopub.status.idle": "2026-06-13T03:31:48.870314Z",
     "shell.execute_reply": "2026-06-13T03:31:48.869873Z"
    },
    "papermill": {
     "duration": 0.325691,
     "end_time": "2026-06-13T03:31:48.871123+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.545432+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clustered heatmap\n",
    "leaves = leaves_list(link)\n",
    "reordered_names = [kept_after_corr[i] for i in leaves]\n",
    "reordered_corr = surv_corr[np.ix_(leaves, leaves)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 12))\n",
    "n_feats = len(reordered_names)\n",
    "sns.heatmap(\n",
    "    reordered_corr,\n",
    "    annot=(n_feats <= 25),\n",
    "    fmt=\".2f\",\n",
    "    annot_kws={\"size\": 6},\n",
    "    cmap=\"RdBu_r\",\n",
    "    center=0,\n",
    "    vmin=-1,\n",
    "    vmax=1,\n",
    "    ax=ax,\n",
    "    xticklabels=reordered_names,\n",
    "    yticklabels=reordered_names,\n",
    "    cbar_kws={\"label\": \"Correlation\"},\n",
    ")\n",
    "ax.set_title(\"Feature Correlation (Clustered, Average Linkage)\")\n",
    "ax.tick_params(axis=\"both\", labelsize=8)\n",
    "plt.setp(ax.get_xticklabels(), rotation=60, ha=\"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c4c78c",
   "metadata": {
    "papermill": {
     "duration": 0.00239,
     "end_time": "2026-06-13T03:31:48.876365+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.873975+00:00",
     "status": "completed"
    }
   },
   "source": [
    "The block structure reveals which features are essentially measuring the\n",
    "same thing. Within each block, correlations are high (>0.7), confirming\n",
    "that one representative per cluster is sufficient. Between blocks,\n",
    "correlations are low — genuine diversification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35983144",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.881665Z",
     "iopub.status.busy": "2026-06-13T03:31:48.881573Z",
     "iopub.status.idle": "2026-06-13T03:31:48.885132Z",
     "shell.execute_reply": "2026-06-13T03:31:48.884672Z"
    },
    "papermill": {
     "duration": 0.006749,
     "end_time": "2026-06-13T03:31:48.885388+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.878639+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Factor Clusters (5 groups) ===\n",
      "\n",
      "Cluster 1:\n",
      "      ret_5d: IC = -0.0101\n",
      "      ret_10d: IC = -0.0055\n",
      "      ret_21d: IC = -0.0025\n",
      "      ret_42d: IC = 0.0047\n",
      "      ret_63d: IC = 0.0201\n",
      "      ret_189d: IC = 0.0411\n",
      "      skip_recent_12_1: IC = nan\n",
      "      skip_recent_6_1: IC = 0.0241\n",
      "      vol_252d: IC = nan\n",
      "      sharpe_21d: IC = -0.0083\n",
      "      sharpe_42d: IC = -0.0024\n",
      "      sharpe_63d: IC = 0.0076\n",
      "      sharpe_126d: IC = 0.0026\n",
      "      sharpe_252d: IC = nan\n",
      "      mom_accel_short: IC = -0.0261\n",
      "      mom_accel_medium: IC = -0.0200\n",
      "      mom_accel_long: IC = nan\n",
      "      vol_ratio_short: IC = 0.0067\n",
      "      vol_ratio_medium: IC = -0.0159\n",
      "      macd_line: IC = 0.0038\n",
      "      adx_14: IC = -0.0068\n",
      "      aroon_diff: IC = -0.0093\n",
      "      sma_ratio_200: IC = 0.0335\n",
      "      ema_ratio_26: IC = 0.0005\n",
      "      bb_pctb_20: IC = -0.0147\n",
      "      natr_14: IC = 0.0572\n",
      "      chop_14: IC = 0.0000\n",
      "      max_dd_63d: IC = -0.0203\n",
      "      vol_ratio_63d: IC = 0.0105\n",
      "      obv_zscore_63d: IC = -0.0145\n",
      "      pct_positive_63d: IC = 0.0294\n",
      "      dist_52w_high: IC = -0.0061\n",
      "    → dist_52w_low: IC = 0.0764\n",
      "      ret_126d_rank: IC = 0.0234\n",
      "      sharpe_126d_rank: IC = 0.0026\n",
      "      vol_63d_rank: IC = 0.0509\n",
      "Cluster 2:\n",
      "    → corr_spy_tlt_63d: IC = nan\n",
      "      regime: IC = nan\n",
      "      yield_curve_slope: IC = nan\n",
      "      yield_curve_zscore: IC = nan\n",
      "Cluster 3:\n",
      "    → hurst_100: IC = 0.0054\n",
      "Cluster 4:\n",
      "    → sharpe_5d: IC = -0.0111\n",
      "Cluster 5:\n",
      "    → sharpe_10d: IC = -0.0098\n",
      "\n",
      "Representatives: ['dist_52w_low', 'corr_spy_tlt_63d', 'hurst_100', 'sharpe_5d', 'sharpe_10d']\n"
     ]
    }
   ],
   "source": [
    "# Assign clusters and select representatives by highest |IC|\n",
    "N_CLUSTERS = 5\n",
    "clusters = fcluster(link, N_CLUSTERS, criterion=\"maxclust\")\n",
    "\n",
    "print(f\"\\n=== Factor Clusters ({N_CLUSTERS} groups) ===\\n\")\n",
    "representatives = []\n",
    "\n",
    "for c in range(1, N_CLUSTERS + 1):\n",
    "    cluster_factors = [kept_after_corr[i] for i, clust in enumerate(clusters) if clust == c]\n",
    "    best = max(cluster_factors, key=lambda f: abs(ic_scores.get(f, 0)))\n",
    "    representatives.append(best)\n",
    "\n",
    "    print(f\"Cluster {c}:\")\n",
    "    for f in cluster_factors:\n",
    "        marker = \"  →\" if f == best else \"   \"\n",
    "        print(f\"  {marker} {f}: IC = {ic_scores.get(f, 0):.4f}\")\n",
    "\n",
    "print(f\"\\nRepresentatives: {representatives}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9333366c",
   "metadata": {
    "papermill": {
     "duration": 0.002346,
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     "exception": false,
     "start_time": "2026-06-13T03:31:48.887898+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 5. Multiple Testing Correction (BH-FDR)\n",
    "\n",
    "With many features tested, some appear significant by chance.\n",
    "Benjamini–Hochberg FDR controls the expected false discovery rate.\n",
    "\n",
    "**Inference**: the p-values fed into BH-FDR come from the **Newey-West HAC**\n",
    "t-statistic on each feature's daily IC series (matching the table above and\n",
    "the headline measure in `06_robustness_sensitivity.py`). The i.i.d. t-stat\n",
    "would overstate significance because daily ICs share slow-moving common\n",
    "factors and overlapping information sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "96df45e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.895411Z",
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     "shell.execute_reply": "2026-06-13T03:31:48.914716Z"
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     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features tested:                 48\n",
      "Significant at p<0.05 (raw):     31\n",
      "Significant after BH-FDR:        29\n",
      "False discoveries prevented:     2\n",
      "\n",
      "Features surviving FDR correction:\n",
      "  - ret_63d\n",
      "  - ret_126d\n",
      "  - ret_189d\n",
      "  - skip_recent_6_1\n",
      "  - vol_21d\n",
      "  - vol_63d\n",
      "  - vol_126d\n",
      "  - sharpe_5d\n",
      "  - sharpe_10d\n",
      "  - sharpe_189d\n",
      "  - mom_accel_short\n",
      "  - mom_accel_medium\n",
      "  - vol_ratio_medium\n",
      "  - rsi_7\n",
      "  - cci_14\n",
      "  - cci_21\n",
      "  - aroon_diff\n",
      "  - sma_ratio_200\n",
      "  - bb_pctb_20\n",
      "  - natr_14\n",
      "  - max_dd_63d\n",
      "  - max_dd_126d\n",
      "  - vol_ratio_21d\n",
      "  - vol_ratio_63d\n",
      "  - obv_zscore_63d\n",
      "  - pct_positive_63d\n",
      "  - dist_52w_low\n",
      "  - ret_126d_rank\n",
      "  - vol_63d_rank\n"
     ]
    }
   ],
   "source": [
    "from ml4t.diagnostic.evaluation.stats import benjamini_hochberg_fdr\n",
    "\n",
    "ic_pvalues = []\n",
    "ic_feature_names = []\n",
    "for col in all_feature_cols:\n",
    "    daily_ics = ic_by_date[col].drop_nulls().to_numpy()\n",
    "    # Exclude degenerate series (constant or non-finite) so they do not\n",
    "    # contribute NaN p-values, which would still inflate BH's denominator and\n",
    "    # tighten the per-rank threshold for every valid feature. Mirror the same\n",
    "    # std_ic > 0 guard the IC-ranking loop above uses.\n",
    "    if len(daily_ics) < 20 or np.std(daily_ics, ddof=1) == 0 or not np.isfinite(daily_ics).all():\n",
    "        continue\n",
    "    nw = sm.OLS(daily_ics, np.ones(len(daily_ics))).fit(\n",
    "        cov_type=\"HAC\", cov_kwds={\"maxlags\": NW_MAXLAGS}\n",
    "    )\n",
    "    p_val = float(nw.pvalues[0])\n",
    "    if not np.isfinite(p_val):\n",
    "        continue\n",
    "    ic_pvalues.append(p_val)\n",
    "    ic_feature_names.append(col)\n",
    "\n",
    "if ic_pvalues:\n",
    "    bh_result = benjamini_hochberg_fdr(ic_pvalues, alpha=0.05, return_details=True)\n",
    "\n",
    "    n_significant_raw = sum(p < 0.05 for p in ic_pvalues)\n",
    "    n_significant_fdr = sum(bh_result[\"rejected\"])\n",
    "\n",
    "    print(f\"Features tested:                 {len(ic_pvalues)}\")\n",
    "    print(f\"Significant at p<0.05 (raw):     {n_significant_raw}\")\n",
    "    print(f\"Significant after BH-FDR:        {n_significant_fdr}\")\n",
    "    print(f\"False discoveries prevented:     {n_significant_raw - n_significant_fdr}\")\n",
    "\n",
    "    survivors = [ic_feature_names[i] for i, r in enumerate(bh_result[\"rejected\"]) if r]\n",
    "    if survivors:\n",
    "        print(\"\\nFeatures surviving FDR correction:\")\n",
    "        for f in survivors:\n",
    "            print(f\"  - {f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e474420e",
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     "duration": 0.002395,
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     "exception": false,
     "start_time": "2026-06-13T03:31:48.917836+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 6. Selection Pipeline\n",
    "\n",
    "Applying the filters in sequence: correlation filtering → IC filtering →\n",
    "top-K selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a07abb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:31:48.925841Z",
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    },
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     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IC Filtering (|IC| >= 0.01):\n",
      "  Before: 43 features\n",
      "  After:  18 features\n"
     ]
    }
   ],
   "source": [
    "# IC filtering\n",
    "IC_THRESHOLD = 0.01\n",
    "kept_after_ic = [f for f in kept_after_corr if abs(ic_scores.get(f, 0)) >= IC_THRESHOLD]\n",
    "\n",
    "print(f\"IC Filtering (|IC| >= {IC_THRESHOLD}):\")\n",
    "print(f\"  Before: {len(kept_after_corr)} features\")\n",
    "print(f\"  After:  {len(kept_after_ic)} features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0ecc865",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-06-13T03:31:48.937028Z"
    },
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     "duration": 0.006171,
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     "exception": false,
     "start_time": "2026-06-13T03:31:48.931475+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top-10 Selected Features:\n",
      "   1. dist_52w_low (IC=0.0764)\n",
      "   2. natr_14 (IC=0.0572)\n",
      "   3. vol_63d_rank (IC=0.0509)\n",
      "   4. ret_189d (IC=0.0411)\n",
      "   5. sma_ratio_200 (IC=0.0335)\n",
      "   6. pct_positive_63d (IC=0.0294)\n",
      "   7. mom_accel_short (IC=-0.0261)\n",
      "   8. skip_recent_6_1 (IC=0.0241)\n",
      "   9. ret_126d_rank (IC=0.0234)\n",
      "  10. max_dd_63d (IC=-0.0203)\n"
     ]
    }
   ],
   "source": [
    "# Top-K selection\n",
    "TOP_K = 10\n",
    "ic_ranked = sorted(kept_after_ic, key=lambda f: abs(ic_scores.get(f, 0)), reverse=True)\n",
    "final_features = ic_ranked[:TOP_K]\n",
    "\n",
    "print(f\"\\nTop-{TOP_K} Selected Features:\")\n",
    "for i, f in enumerate(final_features, 1):\n",
    "    print(f\"  {i:2d}. {f} (IC={ic_scores[f]:.4f})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311a13e7",
   "metadata": {
    "lines_to_next_cell": 2,
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     "end_time": "2026-06-13T03:31:48.942852+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:31:48.940484+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 7. Stability Selection via Bootstrap IC\n",
    "\n",
    "Stability selection tests whether features remain important across bootstrap\n",
    "samples. Features that rank highly in >80% of samples are considered stable.\n",
    "\n",
    "> **Caveat**: The bootstrap below samples individual rows (date × symbol),\n",
    "> pooling across dates. A more rigorous approach bootstraps by *date*\n",
    "> (block bootstrap), preserving cross-sectional structure. The pooled\n",
    "> version here is a quick filter; production systems should use\n",
    "> time-aware resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f62717f",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-06-13T03:31:48.951352Z"
    },
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     "start_time": "2026-06-13T03:31:48.945245+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def bootstrap_ic(\n",
    "    df: pl.DataFrame,\n",
    "    feature_cols: list[str],\n",
    "    return_col: str = \"fwd_return_1m\",\n",
    "    n_bootstrap: int = 50,\n",
    "    sample_frac: float = 0.8,\n",
    ") -> pl.DataFrame:\n",
    "    \"\"\"Compute IC across bootstrap samples to assess stability.\n",
    "\n",
    "    Uses the global numpy seed set in the preamble via ``set_global_seeds(SEED)``.\n",
    "    \"\"\"\n",
    "    n_samples = len(df)\n",
    "    sample_size = int(n_samples * sample_frac)\n",
    "\n",
    "    results = {f: [] for f in feature_cols}\n",
    "\n",
    "    for _ in range(n_bootstrap):\n",
    "        indices = np.random.choice(n_samples, size=sample_size, replace=True)\n",
    "        sample = df[indices.tolist()]\n",
    "        y = sample[return_col].to_numpy()\n",
    "\n",
    "        for col in feature_cols:\n",
    "            x = sample[col].to_numpy()\n",
    "            mask = np.isfinite(x) & np.isfinite(y)\n",
    "            if mask.sum() < 30:\n",
    "                results[col].append(np.nan)\n",
    "                continue\n",
    "            ic = pooled_ic(x[mask], y[mask])\n",
    "            results[col].append(ic)\n",
    "\n",
    "    stability_data = []\n",
    "    for col in feature_cols:\n",
    "        ics = np.array(results[col])\n",
    "        valid = ics[~np.isnan(ics)]\n",
    "        if len(valid) == 0:\n",
    "            continue\n",
    "        stability_data.append(\n",
    "            {\n",
    "                \"feature\": col,\n",
    "                \"ic_mean\": np.mean(valid),\n",
    "                \"ic_std\": np.std(valid),\n",
    "                \"ic_ir\": np.mean(valid) / (np.std(valid) + 1e-8),\n",
    "                \"positive_pct\": np.mean(valid > 0) * 100,\n",
    "            }\n",
    "        )\n",
    "\n",
    "    if not stability_data:\n",
    "        return pl.DataFrame(\n",
    "            {\"feature\": [], \"ic_mean\": [], \"ic_std\": [], \"ic_ir\": [], \"positive_pct\": []}\n",
    "        )\n",
    "    return pl.DataFrame(stability_data).sort(\"ic_ir\", descending=True)"
   ]
  },
  {
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   "execution_count": 19,
   "id": "40947c2f",
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     "shell.execute_reply": "2026-06-13T03:32:26.932430Z"
    },
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     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability Selection (50 bootstrap samples):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>feature</th><th>ic_mean</th><th>ic_std</th><th>ic_ir</th><th>positive_pct</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;natr_14&quot;</td><td>0.112569</td><td>0.001795</td><td>62.698844</td><td>100.0</td></tr><tr><td>&quot;dist_52w_low&quot;</td><td>0.074668</td><td>0.001652</td><td>45.188178</td><td>100.0</td></tr><tr><td>&quot;vol_63d_rank&quot;</td><td>0.047153</td><td>0.001877</td><td>25.117785</td><td>100.0</td></tr><tr><td>&quot;ret_126d_rank&quot;</td><td>0.016202</td><td>0.001979</td><td>8.185987</td><td>100.0</td></tr><tr><td>&quot;skip_recent_6_1&quot;</td><td>0.013648</td><td>0.001797</td><td>7.593609</td><td>100.0</td></tr><tr><td>&quot;ret_189d&quot;</td><td>0.000568</td><td>0.001692</td><td>0.335922</td><td>62.0</td></tr><tr><td>&quot;pct_positive_63d&quot;</td><td>-0.001916</td><td>0.00171</td><td>-1.120629</td><td>14.0</td></tr><tr><td>&quot;sma_ratio_200&quot;</td><td>-0.003097</td><td>0.001846</td><td>-1.677291</td><td>8.0</td></tr><tr><td>&quot;mom_accel_short&quot;</td><td>-0.013379</td><td>0.001936</td><td>-6.909346</td><td>0.0</td></tr><tr><td>&quot;max_dd_63d&quot;</td><td>-0.072808</td><td>0.002083</td><td>-34.958715</td><td>0.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 5)\n",
       "┌──────────────────┬───────────┬──────────┬────────────┬──────────────┐\n",
       "│ feature          ┆ ic_mean   ┆ ic_std   ┆ ic_ir      ┆ positive_pct │\n",
       "│ ---              ┆ ---       ┆ ---      ┆ ---        ┆ ---          │\n",
       "│ str              ┆ f64       ┆ f64      ┆ f64        ┆ f64          │\n",
       "╞══════════════════╪═══════════╪══════════╪════════════╪══════════════╡\n",
       "│ natr_14          ┆ 0.112569  ┆ 0.001795 ┆ 62.698844  ┆ 100.0        │\n",
       "│ dist_52w_low     ┆ 0.074668  ┆ 0.001652 ┆ 45.188178  ┆ 100.0        │\n",
       "│ vol_63d_rank     ┆ 0.047153  ┆ 0.001877 ┆ 25.117785  ┆ 100.0        │\n",
       "│ ret_126d_rank    ┆ 0.016202  ┆ 0.001979 ┆ 8.185987   ┆ 100.0        │\n",
       "│ skip_recent_6_1  ┆ 0.013648  ┆ 0.001797 ┆ 7.593609   ┆ 100.0        │\n",
       "│ ret_189d         ┆ 0.000568  ┆ 0.001692 ┆ 0.335922   ┆ 62.0         │\n",
       "│ pct_positive_63d ┆ -0.001916 ┆ 0.00171  ┆ -1.120629  ┆ 14.0         │\n",
       "│ sma_ratio_200    ┆ -0.003097 ┆ 0.001846 ┆ -1.677291  ┆ 8.0          │\n",
       "│ mom_accel_short  ┆ -0.013379 ┆ 0.001936 ┆ -6.909346  ┆ 0.0          │\n",
       "│ max_dd_63d       ┆ -0.072808 ┆ 0.002083 ┆ -34.958715 ┆ 0.0          │\n",
       "└──────────────────┴───────────┴──────────┴────────────┴──────────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stability = bootstrap_ic(df=analysis, feature_cols=final_features, n_bootstrap=N_BOOTSTRAP)\n",
    "print(f\"Stability Selection ({N_BOOTSTRAP} bootstrap samples):\")\n",
    "stability"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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9+PM6bdm+76Z2jo4OGjtplk6duaADh47r05kL1bxx3XTj3g8ePqHqVSrdNIwBAAAAAHfSs0S92tU0ecwQzft2uRb9+IvM9vYqVaKoOrR+TJJUoVwpvTrwGX3z4y+a+fWPerhKJb30XHeNmfSl7TOqPVRRndo118jx0xQVfc32OKxXBjyjD6fM1ktDxsrbq7DeeKm3Tpw6f9eaOrRpKicnJ327dLWmffW9nJ2cVK5MCXXvlPEwW6yIr+ZPG6f536/UF3O+U3hEpAp5uOuB8mX01g1j8/+tbq1qWrNhm778eokSEhPl41VYDeo+nG48+gt9uurjz+epW98hSkpO1o5136hvjyd1+Uqo3hg+UU5OjurYtqkaP1pLsbHx6T6/9eMNlZiYrH6vvi87Ozs99WQrPdm2WYa/1418vT3Vv3dnzZy3WB9OmaM2jzfUyKHXH8dWwM1VjzWsoz93H1DjR2vd9bOeaP2YnntlpK7FxqV75NjZC5fUvsdgSZKzk5OKFfXVsFeeU9sWjSRdH9IwcfQb+mT6Qr00dFy6R7ClafJobb3xYm99t2yNPv1ykYr6+2j4kAGqWb2yrc0rA3rqi1nfasWvm+XjXVg/zp2s6JgYjZk0SxGRUfIo6K7HGtRW/z7XJzO83T53K43q19LTnVtryvQFSk626NFHHtZzPZ/U3G9+SteueFE/Pdagtt4cOVnRMdfUoG4NDR38XLo2GzbvVP/ene+6PQEAAID8yJSUlHjvM4QBedzgt8erbKnienNQnwy1f2/cF6pUvrSefbpDNldmPGnPSV84c/xt2+zYc1BfzP5Wi76cILP93XuEAAAAAPkN3d2BW4iOidXm7Xv019/H1OWJxzO83iv9e8j1Pz7jPj+JT0jUiCEDCOgAAADAbdDdHbiFZ18erpiYWA16/mmVKlE0w+sV8fdRt44ts7Gy+1uzRo/kdgkAAACAodHdHQAAAAAAg6C7OwAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCH9LqxWqywWi6xWhu4DAAAAALIXIf0uUlJS1KhdX6WkpOR2KQAAAACAPI6QDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEJ6HhIXH5/bJQAAAAAAMsGc2wXgv5k2e6FmzF5ke52cbFFsXJziExJltVplMpnk4uwkN1dXOTj8/9c7aEBvDR7QJzdKBgAAAABkECH9PhMTE6vLQSG3XW61WhUXn6C4+ISb1gMAAAAAGBsh/T7j7u6mov6+Sk62KDQ84q7tfbw85eBglru7Ww5UBwAAAADIDMak32cGD+ijo3s3qHWLJhlq36blYzq6dwNd3QEAAADgPkBIv0/9tHJthtotW/FrNlcCAAAAAMgqhPT7UFx8vGLjMjaTe2xcvOL/NT4dAAAAAGBMhPT7kKuLi9xcXTLU1s3VRS4uztlcEQAAAAAgKxDS71OdO7TOULsuHdtkcyUAAAAAgKxCSL/PTJu9UJVrt9DaDVsy1P7X9ZtVuXYLTZu9MJsrAwAAAABkFo9gu8/c7Tnp/5b2mDaekw4AAAAAxkdIv8+kPSc9TXKyRbFxcYpPSJTVapXJZJKLs5PcXF3l4GBOtx4AAAAAwNhMSUmJ1twuwsgsFosateurravny2w29jWN+PgEJokDAAAAgPsYY9LzEAI6AAAAANzfCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABiEObcLuFFAYJDGTJqlqOgYFXBz0YghA1W2dPF0ba4EhWrslFk6efqCivr7aOHM8emWr1y7WYsWr5LValWt6pU17JW+MpvNd10GAAAAAEBuM9Sd9I8/n6cn2zbVj/Mmq9dTT2jclFk3tXF1ddHAZ7vpg3cG3bTsclCI5ixYqi+njNSSr6coIjJKP6/ZdNdlAAAAAAAYgWFCekRklI6dOqtWzRtIkpo2rKPg0AgFBAala+dRsICqV6kkF2enmz7j96271bBeTXl5FpLJZFKnds21YfOOuy4DAAAAAMAIDNPXOyQ0Qt6ehWS2t5ckmUwm+fl4KTg0XCWK+WfoM4JDwuXv5217XcTPR8Eh4Xdd9m9LV27QslUbJElWq/Wevg8AAAAAAP+VYUK6kXTt0EJdO7SQJFksFjVq1zd3CwIAAAAA5AuG6e7u6+OpsIhIWVJSJF2/gx0cGi4/H68Mf4afr5eCgsNsr68Eh8rP1+uuywAAAAAAMALDhHTPQh6qVL601m3cLknatG2PfL09M9zVXZKaNnxE23buV3hEpKxWq5av3qjHm9S76zIAAAAAAIzAlJSUaJhB1xcCLmvclNmKir4mN1cXDR8yQOXLlND4T+eoUb2aalS/lhISEvVUv6FKTrboWmycChcqqNbNG2rQ890lSSvWbNKiH1dJkmpUe1Bvv/qc7TFrd1p2O2nd3beuns/j2gAAAAAA2cpQId2ICOkAAAAAgJximO7uAAAAAADkd4R0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcyIS4+PrdLAAAAAJCHENKB/+jw0ZN6ZehoFatYT0Ur1FOxivX0ytDROnz0ZG6XBgAAAOA+Z0pKSrTmdhFpAgKDNGbSLEVFx6iAm4tGDBmosqWL39Ru5drNWrR4laxWq2pVr6xhr/SV2WzWL+u26Mef19nahYRF6OGqD+ijUa/rSlCouj73psqVLmFbPn7kaype1O+ONVksFjVq11dbV8+X2WzOui+L+8a02Qs1Y/YiSVJ8fIKuRkXftm1hj4JycXGWJA0a0FuDB/TJkRoBAAAA5A2GSp0ffz5PT7ZtqnYtG+v3rbs1bsoszZs6Nl2by0EhmrNgqeZPHyfPwh56a/Qn+nnNJnXt0ELtWzVR+1ZNbG2fGfCOWjV91Pba1cVFC2eOz7Hvg7whJiZWl4NCMtT2alS0LcTHxMRmZ1kAAAAA8iDDdHePiIzSsVNn1ap5A0lS04Z1FBwaoYDAoHTtft+6Ww3r1ZSXZyGZTCZ1atdcGzbvuOnzjhw/rauR0WpUv2aO1I+8y93dTUX9feX6zx3yu3F1cVZRf1+5u7tlc2UAAAAA8hrD3EkPCY2Qt2chme3tJUkmk0l+Pl4KDg1XiWL+tnbBIeHy9/O2vS7i56PgkPCbPm/V2i1q3bxBui7q8QmJev6VkUpJTVXj+rXVt0dH2dsb5joFDGrwgD4aPKCPilWsl6H2JpNJR/duyOaqAAAAAORFhgnpWSk+IUEbtuzQV599YHvPy7OQVn73hTwLeSgq+ppGjp+m75etUa+n2t+0/tKVG7Rs1fWQZbUaZsg+clFcfLxi4zI2k3tsXLzi4xNsY9MBAAAAIKMME9J9fTwVFhEpS0qKzPb2slqtCg4Nl5+PV7p2fr5eCrz8//HBV4JD5eebvs3vf+xW2VLFVaZUMdt7jo4O8nT0kCR5FCyg9q0aa/2mHbcM6V07tFDXDi0k/X/iOORvri4ucnN1yVBQd3N1IaADAAAAuCeG6evtWchDlcqX1rqN2yVJm7btka+3Z7qu7pLUtOEj2rZzv8IjImW1WrV89UY93iR9N+RVazfriRsmkJOuj3m3WCySpKSkZG3evlcVy5XKxm+EvKZzh9YZatelY5tsrgQAAABAXmWYO+mS9Parz2vclNla8MNKubm6aPiQAZKk8Z/OUaN6NdWofi0VK+Kr/r27aOCbYyRJNao9qE7tmtk+40LAZZ06e1HN/xXc/z58UnMWLpOdnZ1SUlJU6+HK6tujY859Ody30h7BlpxsyVD7X9dvVuVN23gEGwAAAID/zFDPSTcinpOOCVNm6uNPv/zP6739xot6d8hL2VARAAAAgLyK1AncRdoj2NIkJ1sUGxen+IREWa1WmUwmuTg7yc3VVQ4O5nTrAQAAAMB/wZ30u+BOOu6EWdwBAAAAZCXDTBwH3I8I6AAAAACyEiEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhzbhdwo4DAII2ZNEtR0TEq4OaiEUMGqmzp4je1W7l2sxYtXiWr1apa1Str2Ct9ZTabtf/gUb0xYpJKFS9iazv7s9FydnK843oAAAAAABiBoe6kf/z5PD3Ztql+nDdZvZ56QuOmzLqpzeWgEM1ZsFRfThmpJV9PUURklH5es8m2vFTxIlo4c7ztf2kB/W7rAQAAAACQ2wwT0iMio3Ts1Fm1at5AktS0YR0Fh0YoIDAoXbvft+5Ww3o15eVZSCaTSZ3aNdeGzTvu+vn3uh4AAAAAADnFMH29Q0Ij5O1ZSGZ7e0mSyWSSn4+XgkPDVaKYv61dcEi4/P28ba+L+PkoOCTc9jrwSoiefXm47O3s1K5lY3V5okWG1rvR0pUbtGzVBkmS1WrNui8JIEvExcfL1cUlt8sAAAAAspxhQnpWqFS+tFZ8+4UKuLkqJDRcb46cLI+C7nq8Sb3/9DldO7RQ1w7Xw73FYlGjdn2zoVoAdzJt9kLNmL3I9jo52aLYuDjFJyTKarXKZDLJxdlJbq6ucnD4/6Fs0IDeGjygT26UDAAAAGSaYUK6r4+nwiIiZUlJkdneXlarVcGh4fLz8UrXzs/XS4GXQ2yvrwSHys/3ehs3N9cbPs9LLR6rr4OHT+jxJvXuuB4A44mJidXloJDbLrdarYqLT1BcfMJN6wEAAAD3K8OMSfcs5KFK5Utr3cbtkqRN2/bI19szXVd3SWra8BFt27lf4RGRslqtWr56o+1OeVj4VaWmpkqSYuPitX3XX6pYvvRd1wNgPO7ubirq7ysfL88Mtffx8lRRf1+5u7tlc2UAAABA9jElJSUaZtD1hYDLGjdltqKir8nN1UXDhwxQ+TIlNP7TOWpUr6Ya1a8lSVqxZpMW/bhKklSj2oN6+9XnZDabtWTFei3/ZaPs7e2VkpKiZo0fUb9enWUyme643p2kdXffuno+j2sDcsErQ0dr0Q/L79quT4/O+mLS+zlQEQAAAJB9DBXSjYiQDuSuYhXrKTYu/q7t3FxdFHhyZw5UBAAAAGQfw3R3B4B/i4uPz1BAl64PcYn/1/h0AAAA4H5DSAdgWK4uLnJzzdij1txcXeTi4pzNFQEAAADZi5AOwNA6d2idoXZdOrbJ5koAAACA7EdIB2BoA5/vme456Lfi4GDWwOd75lBFAAAAQPYhpAMwpGmzF6py7RZ6qs/LKuDqese2BVxd1a33IFWu3ULTZi/MoQoBAACArMd05QAMKSYmVpeDQjLU9mpUtK5GRdvWAwAAAO5XhHQAhuTu7qai/r63XGa1WmUymW67HgAAAHC/4jnpd8Fz0gEAAAAAOYUx6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMwvzvNzr3eUMm091XXLbg0+yoBwAAAACAfOumkP76i71s/z53MVArf92kTu2ay9/XW0EhYfp5ze96ovVjOVkjAAAAAAD5wk0hvfGjtWz/nv/9z/r0w7dUsngR23uN6tfUBxNn6tmnO+RMhQAAAAAA5BN3HJN+8VKQ/H29073n7+eti5eCsrUoAAAAAADyozuG9CoPltfEqV8rKjpGkhQZFaMp0xfooQfK5UhxAAAAAADkJzd1d7/Re2++oPcnTFfb7oPk6OCopOQkVatcUaPfHpRT9QEAAAAAkG/cMaT7entq5pSRCgkNV2h4pHy8CsnXxyunagMAAAAAIF+5Y3f3ISMnSZJ8fbz00APlbAF92PtTsr8yAAAAAADymTuG9AOHT9zy/YOHT2ZLMQAAAAAA5Ge37O7+48/rJEkWS4rt32kCrwTLy9Mj+ysDAAAAACCfuWVI37J9ryQpxZJi+7ckmexM8izkoRFDBuZMdQAAAAAA5CO3DOnTJw2XJH0x+zu9OqBnjhYEAAAAAEB+dccx6TcG9AsBl7V5+x4Fh4Rne1EAAAAAAORHt7yT/sWsb1WhXCm1ebyhJGn9pj81ZtIsFXBzUUJikiaOflOP1KySo4UCAAAAAJDX3fJO+h879ql6lYq2119+vUQv9+uutUu+1Jsv9da8b37KsQIBAAAAAMgvbhnSI6NiVNTfV5IUEBiksIir6tSuuSSpbYtGOh9wOecqBAAAAAAgn7hlSHd2dlRsXLwk6fCx0ypXuoScnZ1syy2WlJypDgAAAACAfOSWIb1W9Yf0+axvdPDISS1ZsU4N69W0LTt3IVA+3oVzrEAAAAAAAPKLW4b0Vwf01JWgML05fKI8CrqrR5c2tmXrNv3JpHEAAAAAAGQDU1JSojW3izAyi8WiRu36auvq+TKbbzkZPgAAAAAAWeKOz0kHAAAAAAA5h5AOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAyFNKHvT8l3eshIydlSzEAAAAAAORnGQrpBw+fTPf6wOET2VIMAAAAAAD5maEe/B0QGKQxk2YpKjpGBdxcNGLIQJUtXfymdivXbtaixatktVpVq3plDXulr8xms/YeOKIZcxcrPiFBJpn0aN2HNej57rKzs9OVoFB1fe5NlStdwvY540e+puJF/XLyKwIAAAAAcFu3Del/HTouSbJarUpJTdGBQ8dllVSj6gPZVszHn8/Tk22bql3Lxvp9626NmzJL86aOTdfmclCI5ixYqvnTx8mzsIfeGv2Jfl6zSV07tJB7ATeNfW+wihXxVWJSkl595yP9+ts2tWvZWJLk6uKihTPHZ1v9AAAAAABkxm27u3+1cJm+WrhMcxf9pKSkZM3559+SJGvWFxIRGaVjp86qVfMGkqSmDesoODRCAYFB6dr9vnW3GtarKS/PQjKZTOrUrrk2bN4hSapUvrSKFfGVJDk5OqpC2VK6Ehya9cUCAAAAAJANbnsnffqk4bZ/t+wyIN1rmbK+kJDQCHl7FpLZ3v76jzCZ5OfjpeDQcJUo5m9rFxwSLn8/b9vrIn4+Cg4Jv+nzwiMitWnbbk0eM8T2XnxCop5/ZaRSUlPVuH5t9e3RUfb2N1+nWLpyg5at2iDpek8CAAAAAAByQgbHpGdDKs9GsbFxGvb+FPXq1k4PViwrSfLyLKSV330hz0Ieioq+ppHjp+n7ZWvU66n2N63ftUMLde3QQpJksVjUqF3fnCwfAAAAAJBPZfA56dY7vswKvj6eCouIlCUl5fqPsFoVHBouPx+vdO38fL0UFBxme30lOFR+vv9vExsXr9eHT1Kj+rXUo0tb2/uOjg7yLOQhSfIoWEDtWzVmlnoAAAAAgKFkKKQvmjkh3evvZn+c5YV4FvJQpfKltW7jdknSpm175Ovtma6ruyQ1bfiItu3cr/CISFmtVi1fvVGPN6knSYqLT9AbwyeqXu1qeq7nk+nWi4iMksVikSQlJSVr8/a9qliuVJZ/DwAAAAAA7lWGurvfeKf6Vq+zytuvPq9xU2ZrwQ8r5ebqouFDBkiSxn86R43q1VSj+rVUrIiv+vfuooFvjpEk1aj2oDq1ayZJ+nH5Oh09cVYJCYnasn2PJKlZo7rq27Oj/j58UnMWLpOdnZ1SUlJU6+HK6tujY7Z8DwAAAAAA7oUpKSmRmdHuIG1M+tbV82U2G+qx8gAAAACAPCaDY9IBAAAAAEB2I6QDAAAAAGAQhHQAAAAAAAziliH99627NfrjGbdc4YOJM7X5n0nZAAAAAABA1rllSF+2coOebNvslis82baZlvy8PluLAgAAAAAgP7plSL9w6bKqV6l0yxWqVq6g8wGXs7UoAAAAAADyo1uG9Pj4RCUmJd9yhaRkixISErO1KAAAAAAA8qNbhvRSJYpo/8Gjt1xh/8GjKlWiSLYWBQAAAABAfnTLkN65/eOaNPVr/X3kZLr3Dx09pcnTFqjLEy1ypDgAgDHExcfndgkAAAD5gvlWb7Zv1UQXL13RoGEfyserkLy9PBUWHqGwiEj16NxG7Vo2zuk6AQDZbNrshZoxe5HtdXKyRbFxcYpPSJTVapXJZJKLs5PcXF3l4PD/Px+DBvTW4AF9cqNkAACAPOeWIV2SBvV7Wh3bNtPev47oalS0Cnm4q06NKipWxDcn6wMA5JCYmFhdDgq57XKr1aq4+ATFxSfctB4AAACyxm1DuiQVK+JLKAeAfMLd3U1F/X2VnGxRaHjEXdv7eHnKwcEsd3e3HKgOAAAgfzAlJSVa//3m57O+ueuKrw3slS0FGY3FYlGjdn21dfV8mc13vKYBAHnCK0NHa9EPy+/ark+Pzvpi0vs5UBEAAED+ccuJ42JiYu/6PwBA3vTTyrUZardsxa/ZXAkAAED+c8tbwyOGDszpOgAABhAXH6/YuIzN5B4bF6/4+AS5uDhnc1X5U1x8vFxdXHK7DAAAkMPovw0AsHF1cZGbq0uGgrqbqwsBPZOYUR8AAPwbIR0AkE7nDq0zNCa9S8c2OVBN3saM+gAA4N9uOSYdAJD/TJu9UJVrt9DaDVsy1P7X9ZtVuXYLTZu9MJsry7vSZtT38fLMUHsfL08V9fdlRn0AAPIwQjoAQNL/7+pm5PFrkhQaHqHLQSHc1c2EwQP66OjeDWrdokmG2rdp+ZiO7t1AV3cAAPKwDHV3j46JVVx8+vGJ/r7e2VIQACB3pN3VTZPR8dHc1c28/zKjPo+9AwAgb7tjSN//9zGNmzxbwaFhslolk0m2/9/+66I7rQoAuM8MHtDntndomcU9+zCjPgAAuNEdQ/rEL75Wh9aPqV2rxnJxdsqpmgAABkMozD7MqA8AAG50xzHpoeERerZHB/l4FVYBN9d0/wMAAFmjc4fWGWrHjPoAAOR9dwzpDR55WAcPn8ipWgAAyFeYUR8AAPzbHbu7u7q4aNj7n6h+nery8vRIt+y1gb2ytTAAAPK6uz0n/d/SZt5nRn0AAPKuO4Z0i8WiJo/WksQJAQAAWY0Z9QEAwL+ZkpISrbldhJFZLBY1atdXW1fPl9mcoSfWAQCQacziDgBA/pTh1BkbF3/9+Wv/cGPyOAAAsg0BHQCA/OmOIf1KcJg++uwrHTxyUsnJyemW8Zx0AAAAAACy1h1nd588bb7cC7hp9qfvy8XZWfOnjVPDejX19qvP51R9AAAAAADkG3cM6YePndZ7b76giuVKyWQyqUK5Unrvjf76/qdfc6o+AAAAAADyjTuGdHs7Ozn+M5usm6uLoqKvyc3NVSGhETlSHAAAAAAA+ckdx6RXKFdSew8cVb3a1VS9SiVN+OwrOTs5qnTJYjlVHwAAAAAA+cYd76S/98YLKlWiqCTpjZd6y8O9gCyWFI0cOjBHigMAAAAAID+54510P18v278Lebjr3Tf6Z3tBAAAAAADkV3d9Tvov67Zo3e9/6mpUtL75coL2/31MEVej9HiTejlRHwAAAAAA+cYdu7vP/26Ffli+Vi0eq6fgkHBJkrdnIX23dHWOFAcAAAAAQH5yx5C+cu1mfTJ2qDq0aSqT6fp7xYv66dLlkJyoDQAAAACAfOWOIT0hIVFenoX/eXU9pVssKXJ0vGsveQAAAAAA8B/dMaQ/9GA5/fTLb+ne+2XdFlWrXDFbiwIAAAAAID+64y3x11/srVfenqDV6/9QfEKCXnj9A12NjNIXH72TU/UBAAAAAJBv3DGkFyviq+/mfKw/dx/QlaBQ+fl4qUG9h+Xi7JxT9QEAAAAAkG/cdXC5s5OjmjV6JCdqAQAAAAAgX7tlSP981jd3XfG1gb2yvBgAAIDcEhcfL1cXl9wuAwCQz90ypC9evk6lSxRV5QfKSVZrTtcEAACQLabNXqgZsxfZXicnWxQbF6f4hERZrVaZTCa5ODvJzdVVDg7/P00aNKC3Bg/okxslAwDymVuG9EH9umvN+q06duKs2rVsrNaPN5BnIY+crg0AACBLxcTE6nJQyG2XW61WxcUnKC4+4ab1AADICbcM6b26tVevbu115PgZ/bJui54Z8I6qPlhB7Vs1UYO6NWRvf8cntwEAABiSu7ubivr7KjnZotDwiLu29/HylIODWe7ubjlQHQAAkikpKfGu/dkTk5L09bc/65sff9FnE95W7YcfypZiAgKDNGbSLEVFx6iAm4tGDBmosqWL39Ru5drNWrR4laxWq2pVr6xhr/SV2WzO1LLbsVgsatSur7aunn/XtgAA4P7wytDRWvTD8ru269Ojs76Y9H4OVAQAwHV3vCUeGxevn9f8rsFvjde637erz9MdVKl86Wwr5uPP5+nJtk3147zJ6vXUExo3ZdZNbS4HhWjOgqX6cspILfl6iiIio/Tzmk2ZWgYAAPKXn1auzVC7ZSt+zeZKAABI75Yhfe+BI/pg4kx1efYNHTh0XC/06aqfFn6mAc92lXuB7OnuFREZpWOnzqpV8waSpKYN6yg4NEIBgUHp2v2+dbca1qspL89CMplM6tSuuTZs3pGpZQAAIP+Ii49XbFx8htrGxsUr/l/j05G14uIz9rsAgPzilv23X33nI5UqUUTdOraSewFXnb8YqPMXA9O1eerJVllaSEhohLw9C8lsby9JMplM8vPxUnBouEoU87e1Cw4Jl7+ft+11ET8fBYeEZ2oZAADIP1xdXOTm6pKhoO7m6iIXF+ccqCpvY1Z9AMi4W4b0h6tWkkkm7T949JYrmUymLA/pRrJ05QYtW7VB0vVZXiWpa9enZDLlZlUAACCrFHQ1KzYuY+06deqc/QXlcafPnte5MxckSXYOLjK7esp0w4lV2qz6sXHxssRFKDX5+gWUWdPDtfHXn3Oj5DwtJSWViaABA1m+/Kd0r28Z0mdMGpEjxdzI18dTYRGRsqSkyGxvL6vVquDQcPn5eKVr5+frpcDL/390ypXgUPn5emVq2b917dBCXTu0kPT/ieOWLv2RieMAALjPpd3RtVgdMtTeYnXQiYAY7uhmUtp2v9us+iaTSQ5uXrZZ9Qey3e8ZvReA+5dhLqF5FvJQpfKltW7jdknSpm175Ovtma6ruyQ1bfiItu3cr/CISFmtVi1fvVGPN6mXqWUAACB/SHtOekYevyZJoeERuhwUwnPSM2nwgD46uneDWrdokqH2bVo+pqN7NxAWMyFtX0/7X2h4hOLiE2y9RNN6L6Tt42n/Y18Hcp+hbg2//erzGjdlthb8sFJuri4aPmSAJGn8p3PUqF5NNapfS8WK+Kp/7y4a+OYYSVKNag+qU7tmknTPywAAQP6Q9pz0NBm9u8hz0rPGf5lVn0ffZU7avn633gtp0novsK8DuS9Dz0nPz3hOOgAA+UN8fAKTxGWjuPh4Fa2Q8V6MV07t4veRBV4ZOlqLflh+13Z9enTmwghgEIbp7g4AAJCbCITZK21W/YxgVv2s8196LwAwBkI6AAAAckTnDq0z1K5LxzbZXEn+EBcfn6FHDUpSbFy84uMTsrkiABlBSAcAAEC2mjZ7oSrXbqG1G7ZkqP2v6zercu0WmjZ7YTZXlrfRewG4PxHSAQAAkK2YVT/30HsBuP8wExoAAACyFbPq556Bz/fUD8tWKTnZcts2Dg5mDXy+Zw5WBeBOmN39LpjdHQAAIPswq372mDZ7oWbMXiTp+ja+GhV927aFPQrafgeDBvTm+fRALiN1AgAAINcQ0LNH2hCDjLgaFW0L8QwxAHIfIR0AAADIY/49xOBGaUMMbrcegNxFd/e7oLs7AAAAACCnMLs7AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgzLldAAAAAADc76bNXqgZsxfdclmq1So7k+mWywYN6K3BA/pkZ2m4zxDSAQAAACCTYmJidTko5J7WA25ESAcAAACATHJ3d1NRf19JUnx8gq5GRd+2bWGPgnJxcbatB9zIlJSUaM3tIozMYrGoUbu+2rp6vsxmrmkAAAAAuL3DR0+qabseSk623LaNg4NZm9f8oIcerJCDleF+wcRxAAAAAJBFZs377o4BXZKSky2aNe+7HKoI9xtCOgAAAABkkZ9Wrs1Qu2Urfs3mSnC/IqQDAAAAQBaIi49XbFx8htrGxsUrPj4hmyvC/YiQDgAAAABZwNXFRW6uLhlq6+bqYps8DrgRIR0AAAAAskjnDq0z1K5LxzbZXAnuV4R0AAAAAMikabMXqnLtFlq7YUuG2v+6frMq126habMXZnNluN/wTDEAAAAAyKSYmFhdDgrJcPvQ8AjbesCNCOkAAAAAkEnu7m4q6u9re52cbFFsXJziExJltVplMpnk4uwkN1dXOTiY060H3MiUlJRoze0ijMxisahRu77aunq+zGauaQAAAAD4b+LjE5gkDhnGmHQAAAAAyEYEdPwXhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQhHQAAAAAAAyCkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYhDm3C5Ck1NRUfTpzkXbsOShJ6t6ptbp1bHnLtgGBQRozaZaiomNUwM1FI4YMVNnSxZWYlKRR46fr3MVAOTk6qnChghr2Sl+VKOYvSRo0bJyCgsNVwM1FktSmRSP16NwmZ74gAAAAAAAZYIiQvnbjdp27GKjFcyfrWmyc+r48XLWqV1bZ0sVvavvx5/P0ZNumateysX7fulvjpszSvKljJUkd2zZV/TrVZTKZtGTFek347CvNmDTCtu5rLz6jJo/WzrHvBQAAAADAf2GI7u4b/9ipjm2ayt7eTh4FC6h5k3rasHnHTe0iIqN07NRZtWreQJLUtGEdBYdGKCAwSE6Ojnr0kYdlMpkkSVUeLK8rwWE5+j0AAAAAAMgMQ4T0oJBw+ft6214X8fNWUEj4Te1CQiPk7VlIZnt7SZLJZJKfj5eCQ29u++PP69S4fs10782Yu1jPDHxHIz6cqsArIVn8LQAAAAAAyJwc6e7+wuujFRAYdMtlC6Z/mOU/b/73K3TpcrCmfvSu7b33h70kP18vWa1WLV25QUNHTdb3cybecv2lKzdo2aoNkiSr1Zrl9QEAAAAAcCs5EtLnfDb6jsv9fb0UFBKmqpUrSJKuBIfJ39frpna+Pp4Ki4iUJSVFZnt7Wa1WBYeGy8/n/22/XbJaW7bv1RcfvSNnZyfb+37/fJ7JZFK3ji01bc73ioqOkUdB95t+TtcOLdS1QwtJksViUaN2ff/rVwYAAAAA4D8zRHf3Zo3qasWvm5SSkqqo6GvauGWnmjepd1M7z0IeqlS+tNZt3C5J2rRtj3y9PW0zuH+/bI02bN6hzye8I/cCbrb1LCkpirgaZXu9aetueRYueMuADgAAAABAbjElJSXmen/ulJRUfTJzoXbuOWi70929U2tJ0tYd+7R1536998YLkqQLAZc1bspsRUVfk5uri4YPGaDyZUooJDRcHXu9pmJFfOXq4ixJcnBw0NwvPlB8QoIGDf1QScnJsjPZycOjgF4b8IwqlCt119rS7qRvXT1fZrMhJsMHAAAAAORRhgjpRkZIBwAAAADkFEN0dwcAAAAAAIR0AAAAAAAMg5AOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgzDndgGSlJqaqk9nLtKOPQclSd07tVa3ji1v2TYgMEhjJs1SVHSMCri5aMSQgSpburgkqVOf1+XoYJaTo6MkqU/3Dnr8sXp3XQ8AAAAAACMwREhfu3G7zl0M1OK5k3UtNk59Xx6uWtUr3zJEf/z5PD3ZtqnatWys37fu1rgpszRv6ljb8rHvvaKK5Ur95/UAAAAAAMhthujuvvGPnerYpqns7e3kUbCAmjeppw2bd9zULiIySsdOnVWr5g0kSU0b1lFwaIQCAoPu+Pn3uh4AAAAAADnJEHfSg0LC5e/rbXtdxM9bh4+dualdSGiEvD0LyWxvL0kymUzy8/FScGi4ShTzlySNmfSlrFarKlcqp0HPd1fhQgUztN6Nlq7coGWrNkiSrFZrln9fAAAAAABuJUdC+guvj77tXesF0z/Msp8zc/II+ft6y2KxaNb8pRo7eZY+GTfsP39O1w4t1LVDC0mSxWJRo3Z9s6xGAAAAAABuJ0dC+pzPRt9xub+vl4JCwlS1cgVJ0pXgMPn7et3UztfHU2ERkbKkpMhsby+r1arg0HD5+Xj98znX78abzWZ179RK3fsNy9B6AAAAAAAYgSHGpDdrVFcrft2klJRURUVf08YtO9W8Sb2b2nkW8lCl8qW1buN2SdKmbXvk6+2pEsX8FZ+QoJhrsba2GzbvsE0gd6f1AAAAAAAwClNSUmKuD7pOSUnVJzMXaueegzKZTOrWsaW6d2otSdq6Y5+27tyv9954QZJ0IeCyxk2Zrajoa3JzddHwIQNUvkwJBV4J0btjP1dqaqqsVquK+fvqjZd6q4i/zx3Xu5u07u5bV8+X2WyIIfwAAAAAgDzKECHdyAjpAAAAAICcYoju7gAAAAAAgJAOAAAAAIBhENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAADynLj4+Nwu4Z6Yc7sAAAAAAADuxbTZCzVj9iLb6+Rki2Lj4hSfkCir1SqTySQXZye5ubrKweH/8XfQgN4aPKBPbpR8V4R0AAAAAMB9KSYmVpeDQm673Gq1Ki4+QXHxCTetZ1SEdAAAAADAfcnd3U1F/X2VnGxRaHjEXdv7eHnKwcEsd3e3HKju3jAmHQAAAABwXxo8oI+O7t2g1i2aZKh9m5aP6ejeDYbt6i4R0gEAAAAA97mfVq7NULtlK37N5koyj5AOAAAAALhvxcXHKzYuYzO5x8bFK/5f49ONhpAOAAAAALhvubq4yM3VJUNt3Vxd5OLinM0VZY4hJo5LTU3VpzMXaceeg5Kk7p1aq1vHlrdsGxAYpDGTZikqOkYF3Fw0YshAlS1dXFHRMXrl7Qm2dgmJSbp8JUSrF8+QR8ECGjRsnIKCw1XA7fovr02LRurRuU32fzkAAAAAQLbq3KG1Fv2w/K7tunQ0fgY0REhfu3G7zl0M1OK5k3UtNk59Xx6uWtUrq2zp4je1/fjzeXqybVO1a9lYv2/drXFTZmne1LHyKOiuhTPH29p9u2S1/jp0XB4FC9jee+3FZ9Tk0do58p0AAAAAADlj4PM99cOyVUpOtty2jYODWQOf75mDVd0bQ3R33/jHTnVs01T29nbyKFhAzZvU04bNO25qFxEZpWOnzqpV8waSpKYN6yg4NEIBgUE3tV21boueaJ2xGf4AAAAAAPefabMXqnLtFnqqz8sq4Op6x7YFXF3VrfcgVa7dQtNmL8yhCv87Q9xJDwoJl7+vt+11ET9vHT525qZ2IaER8vYsJLO9vSTJZDLJz8dLwaHhKlHM39bu7yMnFXMtVg3q1ki3/oy5izV7wVKVKVlMLz3fXcWK+N6ynqUrN2jZqg2SJKvVmunvBwAAAADIejExsbocFJKhtlejonU1Ktq2nlHlSEh/4fXRt7zbLUkLpn+Y5T9v1botavN4Q1uYl6T3h70kP18vWa1WLV25QUNHTdb3cybecv2uHVqoa4cWkiSLxaJG7fpmeY0AAAAAgMxxd3dTUf9b33y1Wq0ymUy3Xc+ociSkz/ls9B2X+/t6KSgkTFUrV5AkXQkOk7+v103tfH08FRYRKUtKisz29rJarQoODZefz//bxsUn6Pc/dmnuF2PSrev3z+eZTCZ169hS0+Z8r6joGHkUdM/ktwMAAAAA5IbBA/po8IA+uV1GljLEmPRmjepqxa+blJKSqqjoa9q4ZaeaN6l3UzvPQh6qVL601m3cLknatG2PfL0903V1/23LTpUvW1KlSxa1vWdJSVHE1Sjb601bd8uzcEECOgAAAADAUAwxJr1184Y6evKsnnp+iEwmk57u3Ebly5SQJG3dsU9bd+7Xe2+8IEl6+9XnNW7KbC34YaXcXF00fMiAdJ+1au0WdWzzWLr3kpOTNWTkZCUlJ8vOZCcPjwKaOPrNHPluAAAAAABklCkpKZGZ0e4gbUz61tXzZTYb4poGAAAAACCPMkR3dwAAAAAAQEgHAAAAAMAwCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADMKc2wUYndVqlSRZLJZcrgQAAAAAkBfZ29vLZDJJIqTfVUpKiiSpacf+uVwJAAAAACAv2rp6vszm6/HclJSUaM3legwtNTVVSUlJ6a5sGFWvF9/VN19OyO0y8hW2ee5gu+c8tnnOY5vnPLZ57mC75zy2ec5jm+eO+2m7cyf9P7Czs5Ozs3Nul5EhJpPJdvUFOYNtnjvY7jmPbZ7z2OY5j22eO9juOY9tnvPY5rnjft3uTBwHAAAAAIBBENLzkC5PtMjtEvIdtnnuYLvnPLZ5zmOb5zy2ee5gu+c8tnnOY5vnjvt1uzMmHQAAAAAAg+BOOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAO47qampuV1CtiCk3ycsKSmSpJSUvLkjAgCQncLCr+rg4RO5XUa+ExEZpf0Hj+Z2GQDymIuXruj02Yuys7PLk0GdkH4fuHjpil54bbTOXwyUvb0dQT2HBQQGKTwiMrfLyBfSDrInz1zQleCwXK4m/7Barz+JMyExKZcryR8sFktul5DvJCdbtHbjdi1cvIrAmIOSky1atvI3rVy7Wbv3HcrtcvKFq5HRuhIUqqjoGNt7acd4ZI/EpCQlJl3/+3k5KESxcfG5XFH+sH33AfUZNFwnz1zIk0GdkH4f2L7rL504fV5vjpis02cvEtRziCUlRQkJiZo8bb7WbNia2+XkaVHRMYqMipGdnZ1iY+M0bvIsxccn5HZZ+YbJZNJfh47r2yWrlZKSygldNjpzPkDDx03V+YuXc7uUfMXBwawWj9VX5Upl9fOa33Xk+OncLilfcHAwq0uHx+XlWUhbduyjJ0M2O30uQENGTta4KbM1ZfoCzV6wVNL1YzzH9exhtVq1edteLV2xQb9t2am3R3+m2Ni43C4rT7NarbJarerRuY16P9VeLw0ZqzPnA/JcUCek3weqVq6oQc9319OdW+vVdz9SRGSU7O351WWXtD9kiYlJcnZ20ovPPaVf1m/R7v2Hc7myvCkuPkFzv1mu+d+v0NXIaLm5ucrZ2UneXoUlpR9rxElG1gkKCdM3S36xvf7r72Mq7OHOsSUbBYWEadT46Tpy4rQ+nblQAYFBuV1SvpB2DLl0OVjnLgTq2Mlz3FHPAWnbPSAwWBcCrmjbjv1avHwtf0uzSWxcvD7+fK6e6dZO40e+pnYtm2jPX0f04SdzJF0P6sh6JpNJTR6tpaUr1+vjz+fpxee6ydfHK7fLyvNMJpN27DkoBwezvL0KacDrH+jEqXN5KqhzNnYfKODmoj/3HNATrZuoyxMt1GvAu2rz1Es6eeaCbaw6so7JZNLxU+fU4ZlXtHbjNrm6OKvP0x108PAJRcfE5nZ5eY6ri7MeeqCc4uMT9OPP63Q5KEQVy5VWQXc3SZKdnZ2tGzYnGVkn8HKIfln3h+Z+85MkKTTsquwI6Nkq8HKI2rVsrF++ny6PggU04bOv0gX1tBMLLkZlLTs7Ox04dFyTps1Xn6c76JUXeqp0iaL6ec3vOnyMO+rZxc7OTn8dOq4Jn36lF597Sh+8M0iehQvp9627dPDIydwuL89JSEiU1WpVzWoPyqNgAT1Ss4pGDRuo8xcDNf2rH3K7vDwp7VgdGBSiVs0ayMersPb/fUxR0ddsPV45T896aefp4z+Zozo1q+qTcW+pZ9d2GvDGGJ0+l3fuqHNGZlBp/3GnpqaqWBE/ubu5ycXZWV2eeFx29ialpKTI27OQzPb2uVxp3mSS5FnIQ6fPBWjVui3atHWPLgeFKjQ8IrdLy1PSDqKtmjVQnZpVFH41UtO/+kG/rN+ijz6bq1ETpmnS1K81/pPZioiMyuVq85bqVSrqzUF9tHPv31qyYr1KlSiqQh7uCrwSojPnL+nkmQs6fS4gt8vMU2o9XFmtmj0qSRrz7mB5exbShM++snV9Dwm7fnzhYlTWO3rirGpUfUAVy5VS40drqUXTRxUSGqGvFi3TvgPcUc8uFwIuq9bDlVW+TAk9XPUBdevYQidOndeC71do596/c7u8PMXLs5BKlSiqJSvWKz4hQSaTSSWK+Wtw/x4KuBzEPC/ZIG2o2Htjv9ATrR/TnM9Ha++BI5o1f4ns7e108MhJ/frbNoJ6Ngi8EqI6Nauo+kMVVcTPW/16ddIjtaqoz0vv6dQ/Y9Tvd/f/N8hjIqOuT/SRNu7czs5ODg5mFS/mp1/WbdHLb32onl3aqXun1npm4Du6FhvHXZcsdP7iZR05fkaVKpRRy6aP6vzFy+reqbUczGat+327PpwyxzYWBpljtVplZ2enmGvXeyc83qSeGtWvJTs7O1V5sLwqVSitFk0fVfPGddW5/ePyLOSRyxXnDWn7rtlsVs1qD6hfr85a9/t2fTH7W/348zpNm/O9PvtykabO/o5xddnAy7OQ7Xcw5t3B8ipcSFPnfKs1G7aqz0vv6fzFQI4vmRQUEqZNW3fr6IkzCgu/Kun6do+5FqvomFhZrVaVL1NCDz1YXl6ehWy9dpA5ly4H6+c1v2v9pj9tE2fZ2dkpKDjU1qZUiaJqWK+mnJ2d5O9Ll+DMirgapXMXAhX6z37evHFdXQ4K0cYtu5SQkChJqlCulK5di1NcPJOZZbXzFwP1w7Jf9fqLvVSsiK/cXF00ecxQHT52SqMmTNN7Yz9XYY+C3FDLpIDAIM395id98+MvOn7qnCSpiJ+3jp08q30HjtoCeYO6NVSremWFX80bN3XMuV0A/u/s+Ut6Y/hE1az+oN5/6yXZ29spKSlZjo4Osre31/S5P6hvj47q3qm1pOuhpoCbay5XnTekpqYqJSVVS1asU8y1WBUu5KGXnu+uz7/8RpevhOiDdwZpzW9bVapEUe5yZZG08USLflylcqVLqFH9WmpUr6ZMMmnbrv0KCg5Tk0dry7Mw4TyrWK1WmUwmnTh9XvZ2dnJzdVG92tVkMpm06MdVKluquN4c1EeSFBV9TR4FC+RyxXmTyWRSSkqq7O3tNPa9wXrh9dGaOPVrjX77JZUuWSy3y7uvnb94WW9/8IlqVHtQ5y8Eyt/PW+1aNla92tX048/rtHj5Wj1Ss4rs7e3119/HNOyV51ShXKncLvu+d/7iZQ3/8As1rl9Ly1ZtUEBgsPr16qSWj9XX4p/W6v2PZuiZbu0UGRmt3fsPaXD/HuzrmXT67EWNmzJbbq4uKuheQMWK+mpw/x4KDg3X3gNHFH41Sl2eeFxh4ZG6GhUtkzh3ySqpqalKTU3Vn7sP6uTZC9q2c7/q16kuSfLxKqwvPnpXBw4fV8+u7fRAhTK2v7347y4EXNYHE79U7RoPKTg0XMdOntWYdwerTKlieqxBHf3621YFBoWobKni+umXjRr77ssqVaJontjmpqSkRC7ZG0BU9DW9MfxjVSpfRtEx1+Ti4qwRQwbYlkdERuno8bNqWK+GpOsHiLzQlSO3pf1HHBsbJ7d/LngEXgnRzHmLdTkoRMWL+qmgu7uGDn7W1jYv/IdvBEeOn9bkaQvUs2tb7T94TMkWi+rVqqbHH6unzdv3aPO2PXqu55MqVaJobpeap+zed0jjPpmtByuUVVJysnp0bqNHalXV7n2H9MnMRWraqI4GPtvNFiKRvc5fvKw3R0zSGy/1UqP6tWx30TnG/HexcfEaPu4LtXisvtq1bKxjJ89q3OTZcnJy0GsDe8nX21PT5/4gS0qKQkIj9FzPjmpUv1Zul33fCw4J19BRU9StYwt1aNNUW3fs08EjJ/V4k3p6oEIZJSYladT46XJwMOtyUIiee6aTGtWrmdtl39fiExI0ZORkdW7/uB5rUFtHjp/RV4t+kpenh0a/PUibt+/Rlu17dfLMBZlMJvXt0VGPN6mX22Xf99LO/9L+PiYnW/TL+i3a+9cRPVKzqjq2bZrbJeYpV4LD9Pp7H+vZpzuobYtG2rHnoDb+sUvdOrRQwYLuKujupnUbt+uX9X/Is7CH2rZopGaNHsntsrMMId0g4hMStGf/EVWtXEEhoeGatWCpPAsV1IihA29qy8lz1kg72O7c+7cWLV4le3t7+XgX1ntvvCB7ezstW7VB23cd0M69f2v5ws/k6+PJiXMWOX0uQEtXrFflSmXVoU1TRUVf089rftely8Gq/XBltWrWQFcjo1W4UMHcLjVPOXHqnL5dukZ9uj+hggULaPO2Pdq0dbeee6aTHqlZRTv2HJSbq4uqPVQxt0u9r8XFJ8jVxTlDbXfsOShHBwfVergyAT2TUlJSNW7KbHXt8LgeeqC8JOnTmYuUkpKquPh4jRr2omKuxcrR0UHRMbHy8SrMRdcssP/gUcXExqnJo7WVkpKqngPeUhE/H0XHxKpS+dJ6pls7FfHzkSXFoti4eHkW8mC7Z1JU9DUNHTVZw98coNIlr981DA2/qjGTvlS50iX0xku9JV3vJuzk5Chfb0+2eSalbb+9B45o7cbtci/gqvJlSqpdy8ZasmK9jp44q8qVyqpbx5a5XWqeERkVo/1/H7MF7/6vvS8Hs1nlypTUxj926qNRr6t6lUpKTEpSSkqqXF2c89R+TtIzCBdnZ9WrXU2FCxVUpQplNPDZroqIjNbYybMkXT/Q7j1wRJII6FkgJSXVNuHHZ19+ox5d2ur1F3vp4qUremv0FElSlyda6L03+uuHrybKz9crz/xHbwSHjp7UsVNnte/gMYWFX5VHwQLq8sTj8vX21M69fys8IpKAnsXCwq9qwQ8rdSU4TOXLlpSvt6eaNXpETRs9ohlzf9DOvX+rfp3qBPRMOnchUK++M0HnLgRmaLKg+nWqq9bDlW2vOc7cO0uKRXHx8fr1t+36c/cBLVmxXmfPX1KzRnV05PgZnT4XIPcCbnJydJTPP494ZHvfu7Q5dGpWr6wmj9aWJG3atlvNG9fTZ+Pf1oSRr+lKcKj++HOf7O3t5OToaJtbhO2eOR4FC+ihB8pr1brNioq+JpPJJF9vTw3u30NXgkJ18dIVSVKJYv7y9faUxDbPLJPJpF37DmnS1K9VuVI5eXkW0nfL1mj+dyvUrWNLlStdXIeOnlJwSHhul3rfCwu/qoDAIBXycLcF9O27/lK1hypp5pSRGjr4WT3ZtpnW/f6nUlNT5eToaLswnpf2c9KegTg6Otj+Xb5sKb3Qp4sSEhL10tBxevXdj5h4IgukTa5ib28nq9WqgEtX9GTbZmpYr4bKli6uOZ+Ntk1+I0neXoVVsngRSTwWKTPStl3aUws6tWuunl3aKtli0ZY/9yriapQKuLnq6c6t1a9XZ3l5FsrFavMmBwcHNahXQ3Hx8Vq0eJWk6/v3Yw3rqFXzBsxvkQUCAoM0YvxUtWrWQGVKFbvpmP3vY0haiE97ykFeOrnIKZcuB2vBDyu1duM2me3NGvLys4qMitbvf+zSgUPH9e4b/VSzemVVebC8Ev95lCMy7/zFQL04ZKwOHDoui8Vie79uraoa8GxXSZKfr5fq16muVOv9/ygkI4i5FqugkDDbI0nr16muq5HR+m3LDtvjYSuVL62ExCTbhKzIOhaLRVt37NfAvk+pc/vm6tWtvUYNe1Hbdu1XQGCQOrRpqhef6yY/JkTMlLPnL2n4h1N1+uzFdI9Ra1C3hl4d0NP22rOwhwq4ueTpob9MHGdQ9vZ2erBiWdWqXlnTvvpeo98epIerPpDbZd3XUlNTNW7yLCVbLJoxaYRMJpMio2O09rft6tDmMdtVuGaN6qqg+80TZnECfW/Suh7t2HNQh46ekr29nZ7u1FqtmjVQcrJFu/cfUlJSslo2fVRenoXkXoCZlrNC2na/EhwmVxdnubo4q+3jjWS2N2vjlp36dslqPdOtnXy9PdX1iRZycODPQWYdOHxCzRvXVbeOLWVJSdHmrXvk4uIkj4LuqvJg+XTHkJSUVJnt7RUdE6vxn87R+2+9KBfnjHWRx3UXAi7rvbFfqE7NKvp59UbFJySqU7vmGjlsoJwcHRWfkCAXZ2ft//uYjhw/o55d2+Z2yXnClaBQvTv2cz31ZKubzkvcC7jZjj2Hjp7SijWbNHTws7lUad5x7kKgPvp8rkySfH081b1TG9WtVVUBgUE6cvyMwiOi1L5VYyUlWRQWflVOjo65XXKeYzablZqaqt37D6lx/Zoym80qVaKovDwLKT4hUSWK+fOkiEwKDgnX6I9nqHvn1mp6h7HlR0+c0cpfN+uVG0J7XpR3Lz/c56xWq4JDwjV1zvcaNexFNX60FndyM8nOzk6jhr2olJRUDR11vUt7r27t9UDFMpoyfYFCw6/q6Ikz+mPHPnnS1TrLmEwmbd/1l6bN+V61azykpSs3aNyUOTp99qLat2qiGtUe1MEjJ5WUbLn7hyFD0k6St+7cr1ETpuvjL+bpy/k/6vzFy2rV7FG1aFpfO/f+rYWLV0oSAT2LxMXF6+KlK0pOtmjoyMn6Y8c+LVv1m75bulpb/txra5c2r0jMtViNHD9VXTu0IKD/R7GxcZrw6Vfq2rGFXn+xlwb27abwiEgdP3VOkZHXu2E7OTrq4OETGv3xDL3c/2mVK10il6vOG84HXFbdWtXUuX1zWVJStHTlBi1fvdG2j5tMJm3etkcTPv1KL/d/WjWrV77LJ+JOLl66ovc/mq6uHVpoytihsre316q1myVJXTu0UKN6NXUtNk5DR07RBxNn6vlenVS+bMncLToPSOvpFHMt1tZbpGG9GrIkW7Rp2x5JUmhYhEJCIxiGmkWCQsJUsXxptWvRWElJyZr//QrNnLdYqzf8IUlKTErSrn2HNGrCdL34XDc9UrNKLlecvZg4Lhf8l0kNzl0IVJlSxZhQKAtFREbprfc/kWchD0384E2dv3hZ875drvMBl+Xs5KjeT7Vnxt8sFBQSpjGTvtTbrz6vK8FhmrNwmTwKFlByskWvDXxG5cuWVEhYhG3cHLLG9l1/ae43yzV57BAt/GGVduw5oKqVK6pnl7YqXbKo1m/6U6VKFNWDFcvmdql5xuFjp7Xi1016oEJpBYeEa1C/pxUSFqHvl62RR0F39e3RMV1Af2/sF+rXqxO9pO5BYlKSpkxfoHYtm6hSuVJ6/pVR8vP1Umj4VVV5sLzatWisqpUr6Mjx00pNtapq5Qp5akKh3LRt51/6YfmvGj/iNb3/0XT5ensqKTlZFkuKqj1UUd06ttSyVRtUsngR1amRt0+is5vFYtE3P66Wi4uT7fG7aXfVPxk3TG6uLra2QSFhcnRwkGdhJubLjIirUbK3t5dHwQLaunO/5i76SZXKl1bF8qXU5YkW+nbJau09cEQJCYm6Fhuvfr076bEGdXK77Dxhx56DWrpyg6aMHar3P5ohF2cnlSlVTAt+WKk+3Tvo6c6ttf/vY5KkmtUezOVqsx8hPZf8vnW3zl24pH69Ot9y+b8PsMzonrUiIqM0dOQU+fl4acKo1yRJIaHhcnJykkfBAvyByyInTp9XbFy8fLwK69o/d77mfD5almSLOjzzqpo2rKM3Xupte/wdMidtv01KStbSlRv0cNVKCguP1MLFK9W7+xP6dslqFfYoqL49OxLOs0F8QoJGfzxTly4Hq2HdGnrxuadkMpm0ZMV6XQi4oiEv97n+yMe4eL3zwWd6vlcn1SCg/2dWq1UpKSl6/6MZSkpOVnBohCqWLakRQwfqQsBlffn1EtWs/qC6dWzJsTwbBAQGafaCpapepZKCQ8L1cv+nlZCYpKUr1isoJJzu7Vns1JkLKuheQH6+XrJYLLp0OVjvjv1csz55XwXd3RQdE0s36yySkJikmfN+0NXIaHVo01Tf/PiLWjdvoGRLijZu2am6taupR+c2ioq+pgsBl1XIw10lixfhOJNFkpMtemnoWPl4eap0yaIa2LebJGnXvkOaNX+JZk4ZIUcHh3yzrUl9ueDs+UtasmK9at2hC1hq6vVrJwmJSYqKvkZAz2KehTw0eewQhV+N1OC3xkuSfH285FHw+lj0/HIAyA5pvT5OnDqnjz+fK19vT5Uo5q/gkHDVq11NTo6OOh9wWY0fra1O7R8noGehtKEF879foY5tHpO/r7d+XLFOI4cOVJNHa6tCuVJycXFKN0klskZqaqpcnJ01auhAlSxWRBcvBWnVui3ate+Qlq5cryYNatmOK+cuBGpw/6cJ6P9RQkKipOv7udls1shhL+rNl/qoyaO11KxxXUlSqRJF9UDFMgoNi8jNUvO0EsX85ePtqR9++lWh4RGKjY2Ts5OjihXxVVR0jBISEtNN+ITMqVCulG0yMrPZrEIeBVXAzVUF3d108MhJzZj3gyIio3K5yrzB2clRDR6pIXf3Apr//Qo93qSeWjdvqKYN66h7p9bas/+wZs1fIo+CBVTtoYq2iYU5Z8y81NRUOTiYNbBvN0VERunAoeO2ZXZ2Jnl55r8nQ5D8ckhacDl/8bK+WnS9u29aF8d//zG7sTvkqPHTFB1zLcfrzSvuNI7fs5CHPhr1umLj4nXs5NkcrCpvM5lM2nfgqL5ZslrP9XxSxYv6SbrePXXz9r36atEyDR83Ve1bNtZDD5TL5WrzlkuXg7VkxXo91rCO3Nxc5ezspIIF3HTyzAUdPHxC5y5cUvdOrRmbmw3s7OyUkpIqNzdXjRg6QDWqPaCAS0FavX6LXh3wjOrUqGI7HlV5sLwqVSiTyxXfX86ev6TRE2dq87Y9tu3o7OSoIv4+sqSk6Pc/dikuPkH7/z6mtRu36ZFaVSXlrxO6nJB2vvLqgJ5q3riuwiOitGTlBq3ZsFUz5i1W2xaN5OzslKdnXM5tjo4OKlbEV7v2HdLkqfPVoG4N26PtcO/S9u1HalVV62YN5OjgoJ9+2aio6Gsq4Oaq6lUqqVO7Zjpy/IwCAoNyudq8J+2YUeXB8urVrZ1iYuP0wcQv9duWnfp81rfq0KZpvpsQke7u2Sw+IUGODo6yt7fT+YuX5ebmovnfr9CJU+fUt8eTalivhqT/d1O9MaC/O/ZzPf9Mp3wx7iK7REbFqJCH+x3bJCdb5OBgprtSFvpl3RaN//QrvfhcN/Xp3sH2/qp1W5Twzyyo9WpXy8UK857T5wI0cvxUVXmwgoa/+YIkKTYuXnO/Wa7QsAgdPn5aQwY9azvmIHukpqamCyhx8QlydXHm+JIJoeFX9crbE2Q226t6lUqqV6uqGtaradue12Lj1PflESpdspiCQsL00nNPqUFd9vPscuPwu9+37lZoWIQCAoPVqH5N1f3n4giyR2pqqkLDrqrLs2/I389bQwf3Vb3a1Ti+ZFLa9tvz12EFhYTriVZNdPDISa36dZPs7e01qF93eRR0V1x8ghITk1SYyYWzldVqVXTMNX27dI0KurupXOkSql+ner7bzwnp2WzjH7u0edsetWrWQJ/MWKivvhgtezs7zf1muZKSkvVYwzq2sJK288Vci9WID6fquZ5PMqFQJgSHhGvm14v17NMdVaZUsVu2+fdY//x2AMhOP6/5XZ/OXKSPRr2u+nWq37ScbZ313v9oho6fOquP339TpUsWlXQ9wFgsKYq5FqsSxfzZ7pmQmJSkiKvRKuLnrfMXA2VJSVX5MrfulfDv7cx2v3d/HzmpgMAgNaxXQwt+WKXo6Bg1aVBbDerWsF0QSUhMUlRUjFJTU1XE3yeXK877/n0xypKSIrO9Pft5DoiOidWQkZPUr1dnLnZnoe27/tKXXy/RoH7dbecsR46f0c9rfldiYpKGvNxHHgXvfNMHdxcUEqaLAVf0cNUHbjv07lbHkfx4bKE/UjZr3riuTp65oFETpum9N/vLs5CHbZZfJydHrd24TX/uPiBJtgmf3hg+Uc/26EhAvwc3dm+3s7eT2WzWleBQSdcD+Y3SAnp0TKxmzP0hXx4AstOTbZvptYG9NHbyLG3dse+m5WzrrJO2b3/wziBVLFdak6fPt3XHK+DmqkIe7ipRzF8S2/1eWa1WHT56Wr9t3qG53yzX6I9nytnp9l3v0uYVsVgsSkpKZrtnQrWHKqp+neryKOiu5595UgULumvL9r3645/jSkhYhGS1ys/Xi4CeQ24M6FarVWZ7e0kcX7LS7YbrFXR305h3B9vuoDP+P/POnr+kGXMXa/zIV1W/TnUdOX5GixavUqUKpdWxTVPZ29srNDwyt8vME7bu2K8pMxZq38GjSkxKumWbtOPIjft22t/U/ISQno2SkpIlSQ3q1lDxYv6aNX+pbYf0LOyhXk+1l3sBN/n6eNnWcXR00IfDX6WL+z0ymUzae+CI1m7cJjdXFz3WoI6mTF+g8IhI2dvb2f7o3TisYPi4L1SvdjVOLv6jtGeI3knn9s31XM8nNfrjmboaGc3JRDaxt7ezBfWx7w2WV+FCGv3xTMbNZSGTyaTKD5TVX4eO67ulq9Wy6aO2+RZuN69IdEysJk2dr2txcblRcp7iWfj6mNsCbq7q26Oj3N3d9NffxzR7wVL1e2XU9aCOLJH2d/KvQ8f125adWrtx223bpqSk2m4wREUzf869StvmcfEJio29fry41TlJamqqrFarivh5KzX1+rZn/H/mOTs7qmL50tq4ZZdmzFuspSs36Oc1v+uDj2eqyoPl9cZLvW/bawoZk7aPd+vYUk0a1NZ3S9doz/4jN91AS5OScr23TtqEoflxAu38941zQNqOeOMEKwtnfChJevHNsZKu//HbvusvvfJCz5v+w0+bxRP/Tdr2nv/9Cn0yY6Heev8TlS5ZVA3r1dC8b5fb7mb9e9x/v16dVPMOM+3jZiFhEZo6+7sMhcBuHVvqu9kfq3ChgpxMZFJkVIzOnr90y2U3BvUP3hkkPx9PJp3MQlarVS7Ozqpfp7rq1q6mkLAI7dhzUFarVXZ2drYT69TUG56DPu5ztWr2KJM6ZaHU1FQVdHfTawN76UpQmJasWK83B/WxzbKMzDOZTPpz9wFNnjZfskpjJs3SkhXrb7qze+Pf0vc/mqHExMTcKfg+l9aL78/dB/TW6E/02nsfa9LUr3U5KOQWba//fq7Fxmnx8rW2AIPMMZvNKlemuPYdPKpqlStq+Jv9NfGDN+XgYFZysoVH3GWhPX8dVkREpOLiEzThs6+0Y89BWSyWdG1uPLa88s5HOn8xMJeqzV2MSc9iNx5sl63aIG/PwipVoqh6dm0rSer/2vuyWqWo6BgN6ve0mjV6JJcrzjvCwq/K26uwzp6/pN+37pKTo6P2HTwqj4LuOnT0lCaMfNU2o/K12Di9M+Yz9e/VmWEF9yDmWqzeev8TlS9bUk892crWlfpWbhz3z5CCe5eQmKTZ85coITFJnds3V/myJW/Z7t/zLCBz0vbZK0GhcnZ2UuFCBRWfkKDpXy1WcnKy2rVsLHt7O23b9Zee7tRGHgUL/NNDZ6qef4Z5RbLLlaBQPf/qKL37en81frQWx5YsFBwSrvc/mq4x776sM+cvac7CpZo4+k15exW2tUkbg562r/ft0YGL3fcgbb/969BxTZ46X28M6q3ChQpq2pzv5e/rrbdefc62X98YXIaOmqKX+z2tag9VzOVvkHdYrValpKbKbG+vA4eO65OZi9S/V2c1frRWbpeWZ5w8c0FDR03W2PdeUfWHKmr+9yu0ccsuvfhcN9WtXU1me/ubbqTl5/N0zuSymMlk0o49BzVn4TI9+3RHSdKsBUv05dc/SpK++vwD9e7eXh+Nel3NGj1yx0eEIeOCQ8I1euJMzZq/RF6eHgoNu6rKlcrpg3cGqU6Nh5SUnKSk5P9fqfvjz316oXeXfPsffmZYLBa5F3DTwL7ddPDwCX359Y8KCgm7Zdu0g+212Djt+eswJ9GZ4OzkqOZN6sne3k5rf9+ui5eu3LKdvb2d7ao0wwsyz2QyaeuOfRo6aopeGjpO879focTEZL3Qp4scHR20ZMV6vTX6U1V9sII8ChZQQkKihoycrOcI6NkqLCJSE0e/aQvoyDomO5PKly2lg0dOat63y/XBOy/L26uwVv66SXsPHJGkdAH9+WeeJKD/RyGh4ZL+36X98pUQtW7eQLUffkjlSpfQB++8rF37DmnV2s2S0v8tfXfs53rpuacI6P/Rv+/W/pvJZJKsVtt5zQu9O3N8yaTAKyE6dPSU7XViYpIeqVFF1f/Zd/v26KhK5Uvpg4lfave+Q7KkpBDQb0BIz2Jh4Vf1629bNebdlxUVc03nAy7rg7cH6ec1v2vOwmWSpMca1LHdBSO03JuAwCBN++p7zZq/RHv+Oiw/Xy8N7t9DO/Yc1A/L18rV1VkTp36tkNAItW/VREvnf6qqlSvYDrZtWzRS9SqVcvlb3J/MZrO2/LlXCxevUrNGdXX42Gl9+fWPN3XNu/GkYtj7n8jR4dazeOLu0rqxp6amKiQ0Qut+/1NLVqzX6XMBt2xrNpsVHROr8Z9+pbj4hJwuN0+5eOmKFi9fpw9HvKIhL/fRiVPntXTl9a6/L/fvoX69OuvzCe/o0UceltVqVXBouN57o79q5OMTi3uRkJikiMgoSdKFgMsKvHJzV98bVa1cQVUrV7C95m9p5p0+e1EnTp2TZ2EPnTl3UdO/+kEfjnhVJYr56/Cx0/rhp7W2CeISk5Jsd9Dz80n0vZo4dX6643f41Uit2/Sn7XVBdzd1aP2YXF1cJMn2t/SN4RO5wXAPIq5Gadj7n+j02Yt3bGc2m1WsiK+GDxmgRvXpoZNZ23bu1+C3xuvgkZOSrs+7teXPfdq9/7CtTZMGtVWudHF5FvaQ2d5eiUlJem/sF/k+oEuE9Czn7VVYL/TpKklatHiVRr89SI81rKMHKpTRDz/9qoDAIK7KZdL5i4EaNWG6zPb2MplMmvDpXB07eVYPVCijaRPfU0H3AnJwcFBAYJDW/LZVyckWOf3zmAcOtpmTmpqqqOhrmvftcj3dqbX69uyo77+aqNCwq5o6+3tbUL9xXO47Yz7TwGe7clEkE+zt7bTnr8P66PO5erZHB/Xr1Ulx8Qn69bet6e6o39hNbMSHU9W6eQO5ujjnYuX3t5NnLmjqnO9Uopi/Spcspjo1qqjbky119vwlfbdsjULDIlS6ZFHbvCImk0mlShRV6ZK3fuQjbm//waNasWaTFi9fq+EfTtWdDtVpk1ampKTKYrFwXM8iCxev1I8r1stsb6/2rZuo8gPl9OXXP2r1+j/00edzNajf07aT5piYWL3z+vPcQb9HE0e/ISdHBw17f4okqU/3DvLz8dTboz/V1cho7f/7mNZt2i5vr0K2df46dFyD+/fgb+k98CzsIVcXZ02ZvuC287qk8fIslO5pKJyz/3dp26x7p9bq3rm1xk76Un8dOq5K5UtrwLNdNXnafK1Ys0lb/tyrr79boVcG9NQD/wxHdXJ01Ltv9Mv3AV0ipGda2o4YFBJm+w+/RDF/XY2MVnKyRUX8vHX2/CX5+Xhp4czxKlHMnxOKTIiIjNKYSbPUvVMrvfjcU3qu55OqXqWSgkOudx0r4Oaqpzq2Ut8eHdXrqfaqV7u6HBzMbPNMSEhItE2KZWdnp5TUFLk6O6vcP8HE1cVZr73YS9t27dd3S39VfEKC7OzorpRZ/54Q6K+/j6tRvZqqXKmcnmzbTG0eb6gduw/q2yWrderMBUn6/4RlY7/Q8888qdoPP5QbpecZfj6ecnBw0OlzATp+6pxSU1NVs9qD6tS+uc5duPOJHv6b+nWqa8eeg5q9cKme6thSRf19Jd38GKqUlOtjRqNjYvXxF3MVF8/EWffq39v2pee66+KlIJ04fV5NGz6iXt3aydXFRVHR1/TqgGfUsF4N2zreXoVtvyNkTGJSkq3LdXBohPx9vXXo6CkNH/eFJOm9N16QJSVFwz/8QjPn/aiX+/VI97ezwSMPE9DvQVpPtA9HvKpiRXw14bOvbhvU055WEHMtVktWrJfEzZ17kbbNdu07pJSUFLm6umjYqCn669BxdevYUv16ddaGLTu0/vc/9WyPDqpcqZyk//+uOLZcx8RxWeCPP/dp6pzv5OjooBLF/PXh8FclSQPf/EDS9RmZB/fvocca1pHE5FmZkZCYpFVrN6vt4w3l5uYqSfr483lyc3PR4P49brkO2/venb94WROnzpOLs5PMZrM+fv8NSdI7Yz5TcrJFU8YOlSQdOX5aS1f+ps7tm6tq5QpKTU3VxKlfq1WzBnT7vQfnLgRq0rSv9e7r/VXEz1tms1kLflipK8Gheue1fkpNvf5oknFTZishIVEv9OmiUiWKKi4+QW+N/kT9enVmu9+DtGPF2fOXlJqaKi/PQnJzddG4KbPk4uyszk88rorlSslkMikq+po8ChbI7ZLzhLTt/s2SX7TvwFEV9fdR+1ZNVKFsSZnNZiUkJMrZ2cm236ddiOrbo6NqPcyd3P8qbdI3STp15oJKligiJ0dHpaamatLU+apUobSebNssl6vMWywpKdr71xGdu3BJxYr4acmK9ZoydqhSU1PVc8A7erBiGX044vq5Y2RUjEwmkzwKFrBdFOEc5t6kHVti4+Ll5np96MCkafN1/OQ5DX/zBZUtXdzW9t8Tlj3fk7kWMuPkmQt6c8REjXvvFVUoV0pLVqzXd0tX6+PRb6pG1QeUnGyRnZ2d7fHI7OM3I6RnUuCVEC36cZXat2yiiuVK6cUhY+Xv66Ux776sxKRk7TtwVL4+nnqgQhl2wkyIir6mlNQUeRbykMVikdlstp2wzft2uaxWqV+vTjp09JTs7Ex66IHyuV3yfe9CwGV9MPFLPdm2qeo/8rBeGjJWk8cMUemSxRRxNUqTps7XxUtX1LZFIy1dtUEjhwxQzeqVbft5QmKSnJ0cc/tr3HcuXrqi98Z9oQ6tH9NTT7ayvX/uQqAGDRunZ7q2U8tmjyo0LEIz5i7Wy/2ftl2FPnj4hFxdnFWhXKncKv++t3Xnfn397c+qWL6ULly8rJf791CJYv6aMn2+JJOe6dpWlTieZ4kbL4o4OJglXe+JNmrCdDk6mNW355MKC7+qg0dOqusTj8vNzZWZ8zMpOCRc3y1brcH9eyrZYtFHn32lk2cuqvdT7VXtoYqKjonV+x9N1/SJw3kcbBayWq06e+GSxn/ylYJCwvT+Wy/pkZpVJF3vNdX7pfdUrIivPhv/di5XmnekHV927DmojX/sUiEPd1WtXEFNHq2t8Z/OUcClIA0Z3Ffly5RIdwGQHoBZY/f+w1r/+3aNGDrQ9rt4/6MZ2vLnXn0+/m16hWQA3d0zITIqRt2fHyoHs1lVHiwvR0cHzfpklELDI/X2B5/KyclRjR+tZRtnwQndvbk+Bn2aIq5GS7o+sYckpf5zhdne3l4+3oV18dIVjZsyW9ExsblWa14RGxevGfMWq1P75urQpqk83AvIyclRa37bpi9mfavU1FRNGPWaWjdvIDc3F737en/bFee0/ZyAfm927z+slk3r66knW8mSkqItf+7Vrn2H5Fm4oGZ9Mkq/b92lz2Yu0vhPvtLTXdrYArokVa9SiYCeCUeOn9aixav02fi3VbpEMaWkpqqIn7c8ChbQ0MHPKTnZIvt/7kByPM88k8mkrTv3a+zkWfpu6RqNnTxLB4+c1LBX+iouIVGz5i/R8A+nqlzpEnJzc1V8QoLe/uBT9e3ZkRPoe+Tn66V2LZsoLPyqomOuacy7g9Wza1tdCLisQUPH6e8jJ+Ts7KR9B4/mdql5islkkq+3p5ycHFSmZDHt2X/YNqmns7OTFn05QZcuB+v4qXO5XGneYTKZtGvfIc38+kd169hS+w8e04ZNOyRdH1rg7+ejD6fMVlw8Q/SyQyEPd+3c97d27v3b9vfy0Uce1oMVyyqFJ89kCHfSM+nP3Qc04sOpWvTlBBUrcn0MRVJSsga88YHeeb2fLaDj3lwIuKxxU2arXcvGt+1+t2TFev2yfotMMumFPl3UoG6NHK4ybzp/MVClSxZTamqqRk2YLqvVqj7dn9CsBUtlMklTxg7L7RLzpPnfr1BwSLiGDu6rISMnqYCbq6JjYuXi4qQXeneRv5+3LJYUxcXHq6i/L3d0s9DWHfsUF58os9le3y5ZrXHDB6uov6927zukh6s+IDt7O1s3YWTe4WOn9fmsbzVl7FCt3bhNv23ZqQkjX5OXZyElJCbp7PkAOTo62ibmO3HqnBwcHNJ1UUXG/Ps48c6Yz3Q1MloTRr4mz8IekqS/j5zUuk1/asfug3rn9X62O724d2nbPTnZIgcHs+ITEnQh4Iq+W7pGHgULaMjLz+rM+QCFhEaofp3quV1unpGamvpPT5G56vVUe0VGxmj63B80YeRr8vP1sg1XOn0uwHZ8Wbh4papWrshQsSyQtt9//9Ov2rX3bz3WsI5KFPXXtK++14ihA1SudAnOXTKAkJ4Ftu/6Sx9OmaOvvhidbrIbdr7MiYiM0rODRqjLE4+rb4+OslqtWvPbVpl0faxWWhif+81yzf3mJ02b+J5qVnswl6u+v52/eFlbd+xT7+5P2N6LuRarvX8dUdNGj0iSYmPj9Oq7H2vCqNfk6+2ZW6XmWfsPHtWGzTtVrkwJhYRFaNDz3RV4JUTfLVujYv6+6tm1bW6XmGekHadDwiLk6+2pjX/s0jc//iI3VxeNGDpA/r7e2r3/sGbM/UFj3xtsm/EX9+7Gv40HD59QUEiY7O3t9d3SNf+/KLL/sMqXLSHPQh65XG3ekJCYpJ17DuqxhnV06OgpHT1xRt07tdaw96coJSVVb7/6vK1re2JSkpKTLSrg5sp5TCalbb9d+w5py/a9Kl2yqKpWrqAHK5bVvgNH9dMvvyk6JlYRkVF6bcAzeqRW1dwuOc9IG1/+6cxFcnJy1JFjpzXsledUumRRbd62R8dPndMLfbrK3p4OxdkpNPyq9h88ph9/Xic/H0+1eKy+7VwSd8femQUa1K2hEUMHqEf/t23PduUPW+Z5FvLQAxVK69SZCwoIDNKQkZO1c8/fOnzstD6Y+KVWrdsiSWr7eEN9Nv5t1az2II/KyISz5y9p9MczbN1507gXcEt3UD159qIcHcw89zyLhIZf1bGTZ23Pby1buoSuBIdq1brNcnW+/vi0YkV8VcTXW2ERkblYad5y43jFz7/8RiGh4Wpcv5ZcXJzk5OSoiKtR2rpjn6bO/k79encmoGeRtG0+a8ESXboSrO+WrdHPq3/XhJGv2QL69LnfKzY2PrdLzTPsTCZdDLyiHi+8pYlffK2H/xkLOumDITKZTPpkxkJdCQ6TdP3xRwX+mZSV85jMMZlM+nP3AU2b873q16mmVWu3aO43y/XHn/tU6+HKeun57nq07sN6/cVeBPQskHb+d/DISW3auluS5OLspG9+/EXvvN5PpUsW1YlT5/TVNz+pauUK6QI6546Zd6tt6ONVWK2aPaqZk0fog3deVtNGj7Ct/wNCehZ59JGHNW7EK7aQjsxJewzDpA+GyGq16pmB76h82ZIa+95gvfXqcxrc/2kdOHRcqampKuLvQ7e8TIpPSNCMeYv11JOt1LNrW1mtVkVFX1NysiVdu78OHdcXs77VM93aq5CHey5Vm3ecv3hZbw6fpKUrN6jPoOFatW6LCnm46703X1Bhj4I6e+GS1m/6U/v/PqZV6zarwSMP53bJeYbJZNLeA0c0bc73eurJVvL18ZKDg1mfjHtLri7O+uGntVr3+58a1K+7GtWryYlFFrl0OVi//rZNLR97VO1aNJa7m5scHR0UFnH1+pNSZn+n/r27cFEkC4SEhmvl2s1ydLw+DvpCwBU5OJhV6YZheFPGDlVcfIImfPqVEpOScrHavCckLEJzFi7T2PcGy9nZSdL1C64/r/ldW3fsU/GifurRuY3q1KjC8SULpF0AHP/JbHkWLihJevG5p/R4k7oaOmqyPpg4U+M/+0oD+3ZTg7o10m1zLkjdu4DAIMUnJNxxGzo6OtgmB2Vfzzi6u2cDuohljbTuSpK08tdNavN4I9t/5CvXbtbRE2f09qvPs62zgCUlRSM/nKZXB/SUr4+XRnw4VanWVEVHX1PXji3VsF4NXQi4ovc/mq6X+/WwPS+XbX/vLgeFaNioT9TrqfZq83hD/bZlp6bO/k5zPh8tX29PRVyN0poNWxUaHqGQ0Ktq17KxGtZjvoWsNHnaApUrU1yd2jVXSkqqrNZU28SUkhQXnyDX/7V332FRHl0Dh38sHaQIggIi2DU2LCGKhdhrjCWW2I0t1lgSY42KJdEUu0nsmtgSe4u9xIJKVBBFUUAEQRSRDruw7H5/8O6++KZ8iayukHNflxcCu3B4GGbnzDNzxtrKiBEWLymp6XTsPYoOrZsxfeIwIL+Gy7yvV6NQmJCXp6FD66Y0erOO9C+FpNVqCbp+CydHe9xKl8LW1obg0DucOneF+w/imP3pSJydHElOSaOkoz3hEdFUreRt7LCLjRu37lLS0R4Lc3OSU9NYuHQ9q76aTkZmNkPGfUa92tUZNaS3bBkzoEcJiUyfv4xxw/viU6vac0cNXrl2ExtrK6ysLKlUXvZDG4JWqyU+IZFFy9YTMHU0DvZ/fONGN5ZX5eRgaSEFhf8JuZP+EsgfvmGYmir0d9Q7t2+uT9Bv3o5g575jNG/iK9faALRaLepcNRYW5sTEJfDzvmO4u7kwb9pY6vu8wZ5DJ8nMzKZKRS++nDNJEnQDuR56h9bNG9G+VRO0Wi1+vj54uLtiaWGOVqvFqaQDfXt0ZMLIAUyfNEx/3YXhqHJyUKvzAFDn5R/teC0kjF8vXgXkhAJDc3Sw46uAjzlx5hKxcQlA/h2WOVNGMfvTUUyfOExfPEv6l8IxMTGhXp3quJUuxYwFK1j34258alVjYO/OlHZ1ZsaCFZwLvMqYyQuIjomXBN0AdP1zWHgki5ZvQKlUUdrVmceJSXiXc8fayor09EyqVSrPoPe7SIJuYAqFgjKupfCpVY28PA2a/4wfY+MSqF2jCjWrV9IXiZP+5cXp2rmJiQlupUuRl6fh0eOn+vG67q3u/7qz50dMCOBRQqJRYi6qJEkXr7WCe4Y0Gg03bt3ls89XMmJQD96SPVwGYWJigpWVJTWqVWTKnCWER0Tzfrf2mJubMbR/d0ww4cHDRwCUdS+tf44onLbN/Xi78ZtA/vW0sbYiNzeP9IwsTExMeJqUjConFwBbG2v948SL0Q0snjx9pl/W+1b9WqxYu42o6IdYWlgQfu8+S777Ub+VQ6GQl0hDa/RmHeZNH8OICQHEJ+RvD9No8n83ljIpYhC6tp6enomtrQ3tWjYhKvohG7ftw9nJkbHD+lClYjk2bd/PqCG98C7nbuSIiwcTExOCQ+9w9NRF+vd8h8oVvdBqtfl301PSWLB4DVPnLqVrp5ZyzQ1A185j4xJ48vQZFhbmhN6O4ODRs5iaKrCwMOfWnUhWrttOamq6kaMtPkxMTAi5Gc7Bo2e5cCUYe/sSKJUq/Xhd91adl6dP0GfMX85HH/bDrYyLMUMvcsz+/4cI8XrInyV1Zu60MdSoVlHu5hqI7jr27NKWjMws1v6wmxbNfGnqXJKI+7E8S06lpIO9scMsFpRKlX5vopmZmX6gptVqUalySE1Lx8bGiuuhd1i+egsLZn5EGddS0s4LSdfGz1+6ztZdhyjn4UauWs2k0QMZ9P67jJv6OfXr1CA6No5hA7pTu0YVY4dcrBUstrp1zUL98aXSzguvYEHEnfuPM31i/iocMzNTjp68wOYd+xnQqzMTRg4gNS0dB3s7eS01oJBbd9lz6CQ2NlZoNBoUCgVv1a+FSpVDYlIyrZo1lCJxBqIrzLd6006mjh9C1crlmTR6ACvW5E+8lvN0Y9f+Ewwb0F1/eoEovGylkuCb4YTcDKe0izNnzgdxLSSMpo3qk52txNOjDAPffxcrS4v8s+cDljK0v5w9/yJkT7owqqxsJcGhd/Dz9eHSbzdIepZCxzbN/vTxBQcTBfesi3/mfwdlBd//YccBTp27Qq03KhMceoeh/bvTzK++sUItNqKiH7J6807atWiMf+MGmJiY/O73MOuLVfj5+vDzvqMM6NVZrrsBXQsJY8Xa7Xwxazybtu0jOiaeL2aNx66ELRFRMZiZ5e9d9C7nIUnLK3Lu0jUsLSyk8KeBBQaFsGrdDj4eO4g6BSaczl78jd0HTlCzemWGDeiuTyKFYW3deZj9R87w6UcfUKdGld9dY+lfDCPk1l2+XL6BT8YMos5/TiwACA27x97Dp3B1caJuzWr41q8l17yQ/ur6LVu9lbT0DLq/04rgm+H41KxK9SoVUKpymDF/OX17dJSz51+QJOnCqHJz1UyYsQilUoVSlfO7zrYgXVKela1Eq9XqlwCLv0+pytHvs/2rRP3+gzjMzExRqXKoVKGcvMAVUmJSMmM//RwzM1Pq1KxKw/q1aNKw3u8S9b7Dp5CWkcm0CUOleJaB7dhzBC/P/JULqzftZP70sbiVcSEsPJIK3p6y/9yIpJ0bjlKpYkrAErp0bEHThvU5dvoiew+fonqVCoz/sB8nzl6irHtpqhWo8C4Mo+Ckx7of93D6/BXGDe8rk1AvybHTF3mS+Ix+PTuhysnB3MxMJp1eAl3/fO3GbcIjonFzLcUbVSvg6pK/OmHrzsMkp6Qxemjv556Xla3kaVIy5cq6GSPsYkFaszAajUaDubkZH/TpQlT0Qxwd7PQJesHCE7r3TU0VZGRm8eHEADnq7gU8S05lzeadXPrtBoA+QdQp+H55Lw88PcpQqUI5o8Ra3DxKSKR/z06sXDQNSwsLzl74jfOXrqHRaPTXPS9Pg4ebK5PHDZbiWQZ05dpNTp+7gkar5ed9x/jx54METB2NWxkXAoNC2LRtP9nZSmOH+a8m7dxwrKwseaNqRXbsPsLk2V8TG5fA+93bc+VqKLFxCbTybygJ+kuiUCjQaPLHLkP6daXJW3X5asVGUtMyjBxZ8ZT4NJlfTp4nN1eNpYUFCoWCkFt32bHnCEqVHCdoKLptBd+s2kxubi5bdh7mu40/64t/enqU5mH8Y7KVSv3YXaPRYGNtJQl6IUmSLoxCq9WiUCh4/CSJko4OLPtiav7xGfOWAfmFJxKTkoHnq0NODVjKpNEDqVLRy5jhF0m5uWqeJqVw4fJ1rgaHAX+cqMPvJ0lkEF04tWtUodGbdXCwt+ODvl2wt7fj7IXf+DUwv4r402cpmJoq+Pyz8XImtwEULCi0/sfdVKnkTd1a1Qi5GU6zRvUp5eTIzdsRfLt+B53aNqOko9RcEEVTwbYeFf2Q5JQ02rdqQvd3WjNycC+GD3yPqhW9sbAw1x9HJV6egon6h4N78vXcj3GwL2HkqIo+XTtPS8/UJ+A9u7Slorcni5Zv4NHjp1wLCeOLJWspV9ZNVkYZUFh4JN9t+InF8ydT3qssWdnZKBQK1m/Zw6OERDw93OjXsyPWVlb6LaiyosEwZLm7eOV0S2cuXL7OyrXbWTh7Ap4eZcjMzKL/yOnUrF6RTm38+XLFRhbPn0xZ99KkZ2QyZc4Shg3oLsUnXoDumj9KSGTTjv2YYEIr/4bU93njuc/D85MioWH38PP1MWLkxVNaeiYbtu5Bo9Fga2PDgSNnWL5wmlT8NaDroXf4asVGOrVpxvvdOwBw+twVNu84gLOTI0qlil7d2uknRWQiShRVgUEhLFu9hfLlPLh1J5Kxw/rQ1K8elhYW/BZ8iyXf/ciwAd3x92tg7FCLPF1f8f/1GbpVUtKvFJ7uWl+8Eszm7fvx8nTH1saacSP6Ehkdy4Yte3mcmISlhQW9u7WnScO6xg65WElNyyDu0WOys1UsX7OVhbMmcCPsLku//5E6NaoyZ8oozMykDvnLIEm6MIorV0NZunoLn4wZhE+taiSnpFHS0R6lKocpcxajUCjo3P5t3m78Jjk5ucz7ejXdOrWUBP0F/O+g4mlSMuu37EWj1dDav9FzibpGo9VvK5gwfRGjh/SWa25gBfctTp71DddD7zBtwlCaN/U1cmTFS3pGJn2GfYqrizPrls3Rf/zJ02dYW1miVOXg4lxSEnRRpD2IjWdqwFImjxuMT61qHDx6lj2HTjFm2PtUqejFpu37qVOjCo3fqittvZB01+9qcBihYfco616a2jWr6M87/6PJ7ozMLCKiYuR1tJAuXw3l2/U7mDphKMdOX+Tnfcdo5d+Qzz75EIDklDQszM2wtbWRdl5IuuunyslBq9HqT6RZv2UPanUewwe+x/XQOxw5cZ7e3dpT3svDyBEXX5Kki1ciPuEJublqvDzdycvTsHLddmpWr8SbdWtw4uwldu4/TkVvT0YO7omrizMZmZk42Nvpn/80KZlSziWN+BMUTbrONjj0DoFBITg62FG7RhUqeJdlxZptAPj7NXiu+ml6RiZT5y5lWP/uf1rETxTeo4REPhj3GVPHD6WZX30ZWBiQOi8PM1PT/ER9+BTerFtDP5iTitaiONGdfx4wdbS+bX+7fgdRDx7y5ZxJZGZlY2tjLf2LgQQGhbB89Vb69uzIqnU7eLvJm3Ro1YQa1SoBv5/s/mjqQj4a0VeOdSyEtPRMVqzdSp/uHXkY/5hN2/cxdlgfZi5YQZ2aVQmYOtrYIRY75wKvsuvACUrY2tC+VRMav1WXg0fPsu+X0zSoW4Oz539j3Ii+NGxQ29ihFmsyUhEvnVqt5vyl62RkZqPKycHUVIGHmwuzvljJ9HnLUavzGDe8LwqFCTFxCZiaKvQJum5vtCToL0Z3Xu7nS9ZibWVFdEw8K9Zu51LQDUZ+0AulKodT56+QmpaOiYkJGZlZfPLZ1wzt100S9Jfs6bMUFs2eqE/QheGYmZqizsvDroQtW77/In+1wn/qXUiCLoqy/+0rrKwsCLp+k6OnLurb9hvVKlLapRSA/hQUSdALLzomnu82/sTC2RNwLeWEg10JniYls+fQKW7fjQLyfz+67WJTApYwbngfSdALyd7OlhEDe2Bjbcm2XYcJmDqG2jWqUK9Oda7fuM3dyAfGDrFYiY6JZ+uuX2jbojG13qjM7IXfEhgUQruWjWnfsgmPEhIZO7yPJOivgGwiEC9Valo6E6YvYtzwvpR1L83QcbOZNnEo3d9pjbenB+5urriVLsWjhESioh9SwtbmuefLOegvpuBypf2/nGHSqIH41q9FekYml6+GcuTkBRr5+vBB364oVSoc7O3QaDTsOnCCUUN6y6DiBShVOWRlZ+Pk6MCD2HjMzMzwcHP908fXeqPyc+/LINqwdIm6vZ0tG1fM4/2hk7kX+YDKUnRSFFG6fv3GrbvcjXyAaykn6tWpzrQJQ1mxdhsP4x9T3suDjVv3MmJQD2OHW+w42JdgzqejSU5JY8XabWxcOY+I+zGMmbwAuxI2uJdxxcG+BBmZWUye9Q0jBvWQyW4DcXZyJDomjqfPkrG3syU6Jh5VTi6rF8/CrYyLscMr8nR9y7OUVGLjE2jfqgntWzUBwMbGmtkLv2XGx8Pp9k4runZq+bfqMojCk+Xu4qVb9+Mejp2+wKLZE/lp71Fu343i04+GULWSNwCnzl1h7eZdjPygJ00b1TdusMXI1eAwHB3t2Hf4NG5lXOjVpS0KhYLYuAQWLl3PzI9HUNrV+bnnKJUq/f4j8c9cvBJMeEQ0NtZWHDh6lkWzJ+Be5o+TdN1y7Lw8DVqtRoquGMCfDRh011r3VoiiSNe+r1wNZd43q3m78ZvcuHWX2jWq4O/XABOFCdt2HcbVxZnGvj74+frIILqQdNcvNS3juQrtuw+e5FFCIqOH9ib83n3Wb9nLsIHvUam8J3l5GtZv2YNv/VrUkclug5v1xSoiomLIVav5cHBPWkgtF4P59eJVlq3egqODHRqNlvXLA/Sf233wJEu//5Hdmxbj6GAvN9BeEUnSxUujGxQ/S05l6tylPE1KZtGciRw5cYGg6zeZNnEYFbw8OHY6kJKO9vqzoYVhrFq3neCb4dSrXZ1spYp2LRtTvUoFYuMSCPjye+ZMGfmnSaT457RaLSMmBhAZHctHw/vSuX1z/ccLDpR1BYV0++zGDO2DvZ2tscIusgoWQrSzs0WtzvvTvbdqtVomQkSRFx4RzbHTF2n8Vl3q1a7OvcgHHDl1AStLS4YN6P7cRJQk6IYRGBTCt+t3UKNaJcp5uvF+t/YcOv4rR05coOYblThzPohJowfSwKeG/jm6OgDC8NRqNbfv3sfGxoqK3p7Szg3kYfxjtvx8iJb+b+FRxpVRk+dTp0YVZn86Sv+YJ4lJuLo4/8VXEYYmoxbx0piZmnLmfBAbt+2jx7tt+PXiVT6a+gVLFkzBRGHCzAUrCJgyig6tmwIyqDC0Tm39ycpW8Wbdmhw5dYE1m3dhYWHOw/jHDBvQXRJ0A9K13WZ+9bG1sSY8Iprbd6OoXKEcZmZm+hUKGs1/j7ebPm8Zg95/VxL0F2RiYsL5S9fZuG0v9nYlcHK0p2unVtSoVvF3VZbNzMxIS8/k533HGNKvq5EjF+Kf0Wq1qNV5fLFkHQlPnlK3VnXy8jRUruhFUnIqK9Zuo1fXds/1JfJaWngPYuPZsHUvA3p1JjU9naDrt8jLy6Nfj06oVLkkPHnKmKHv6xN0Xb8jCfo/90fjvz/6mJmZ2XNbxaSdF15Kajq9h3xC986t9W1544q5DBk3i0/nLGbhrAkA+gRdxuqvjqxXEC9Nbq6aY6cvMuqDXnRs04yFsyfQtWNLJs34krYt/GjYoDZKVY7+8fJHX3ixcQn6AjZl3UuTkprGxaBgpk8cxpB+3ejQqikzPx6Bv18DKVZmALpreP9BHLFxCfj7NWDx/MmkZ2Sxa/9xEp4kERx6hx17j5KZmYVCoUvQlzOkX1f98Xfi79Nd8/CIaNZv2c2Ekf3p3bUdnmXdWLluGw9i4//wGKTp85ZRu0blv/rSQryWTExMMDc346uASXiVdeP85WukpWcAUMrJETtbGzQajZGjLB50/cujhERyc9V0bN2MVm83pPXbfrRr0ZjQW/fYuHUf3Tq1ZNQHvfRH24GMYV6ULukLC4/kWkgYsXEJAPp9zwWp8/Ly36rVMoYxEEcHOxbNmcjBo2d5GP8YAAd7O9YunUPYnUjuRj547lpLO391ZLm7eGlycnIZPXk+ndr4826H5mi1WrKVKgaNnoGpqYL1ywOwtrIydpjFhlKp4tsNP/Hrxd/o3L45zZv4UsLWmkXLNjB8UA8qlfc0dojF0rlL11j/4x6qVS5PZHQso4e+TwUvDz5fsg5ThYJrN24zdfxQmjSsS7ZSyaSZXzG0f3fq1a5u7NCLlMzMLBSmCqytrIi4H8sPOw5ga2PN5HGDgfyleCvWbqNZowa0ervhc8ewTZu7jCH9uspZxaLI0k04JSYl8/FnX2FuZkYDnxr8FnyLvu91pLnszTWYwKAQ5n75PR7upXmc+JTvv/4MtzIuZGZmERh0g19Onmf8h/3w9Chj7FCLjYtXglny3Y84OdpTpnQpGjaoTbuW+YXLdEcLFtwqNnPBcmZNHolTSQcjR158XLh8nflfr2Htstn6lZayVcy4JEkXL9WRk+f55cR5+r7XEd/6tbhx6y7nAq/xdpMG+nNFheGo1Woi7seyY88RVDm5pKSmkZubR5cOzenYppmxwyt2bt6OYOn3W/h67sccOXmeE2cv8fnMj3B2ckSpyiEqOhYLCwv9BEn4vfuYm5tTwbuskSMvWtLSM1m1fjvDB76Hk6MDEVExrN60k6TkVCaM7E/N6vl9ycKl6/Fwd6Vfj05AfmI/efZihg3oLgm6KPJ0SYquzouZqSljh/ehWuXyxg6t2Lh9N4p9v5ymfcsm2NuVYO/hUzyMf8zHowfqE3VVTq4khwZQcNn0lys20qNzG1xKlWTPwZNERsfyVv1a+kS94KTrjPnLGdCrs6xEewkuXglmasBStq5Z+Jen04hXQ5J08VJlZmax75czbN11iMa+dbl4JZhpE4dJkbiXLCtbiUqVw8Zte7l89Sajh/SSyvkGUnBgEXIznIQnTzE1NWXrzsPMmz4G9zKuXLl2k0oVPHFylIGcoSQ8eYpancetOxG0bdFYv1/Uwd6ON6pWpIKXBzMWrODTcYOpVyd/8Hbw6Fm8y3nok3ghXmcF+xbd3cP/VTBRnzz7G7w83Zkwsv/vji8Vf99/q7inM2nmVzg62PNVwCQAomPi2P/LGcIjopk+aZjUcjGwK1dDuX3vPuH37jN+ZH9cSznx5Okzjp8O5NadCBq/VVd/g0FWRb0a5y5dw9LCAt96NY0dyr+eJOnilQi/d59nKWmUdLSXWf9XLC09E3s7Wyn2YUCBQSHcCLtLWffS/LT3KHa2tvoj7a5cu8nKdduYN22sLIc0AF1SotVqOXDkDLsPnqRvj460frsREVExbN6xn7DwKKpW8ubdDi3wrVfzTxMcIV5XWdlKoqIfUrN6Ja5cDSUzKxv/xg3+MlF/mpTMZ1+sZO7UMTg7Ob76oIuBgkfbRT54iL2dLSvXbGfkkF6809YfgKjoh+w9fIp2LRvzRtWKRo64+Ai5Gc7Cpeup9UZl7kU9oGb1ygzu04WSjvY8SUziyKkLNGpQh8oVvVAqVUyfv4y+PTrJVrFXRMaMxidJuhDFlHSwL8fD+Mes3rSTwX26UN7LgzGTF2BhYc6Qfl1JepbKms27GD7oPZo2rGfsUIuNazduEx0TT7dOLflp71HOXAji3fYtaNvCT39H3cXZiY5tmuJdzsPY4Qrxjz1LSWXul99ja2PNvagHTJ84nNp/cs62LknXPU9W7BTOuUvXWLN5Fx8O6oGfrw9HTp7np71H6f5Oa/1d3GylUmroGFBEVAxrNu9ixKAeVPAuS2BQCOcCr2FpaU6/Hp1wdnIkJycXCwtzAOIePSE3V413OXcjRy7EqyO3GoQopiRBN7yU1HR6DfkYS0sLynvlJ4PfzPuEErY2/LzvGMfPBDJqSC+aNqwnlWcL4dHjpxw7fVH//ulzV7D8z2CtZ5e2NG/iy6Fjv3Lo+K94ebrzfrf2xDx8xPEzl1Dl5PzZlxXitaTVanFydKBdyyZcuBxMrTeqULtGlT+s2K5L0NMzMhk0egapqRlGiLj4iI6JY/XGnSyaPQE/Xx/CI6KJeZhAxzbN2LxjP3sPnwKQBN2AspVKAoNC+C34FjfvRADQ6M06NG1Uj5TUDDZs3Udurhpz8/8WLPNwc5UEXfzrSMk+IYT4mxwd7Pgq4GOmzV3GgF7v4OlRBgsLc+ZMGYWJiYn+PHSQSZIXpdVqCQuP5Nv1O1Dl5PJOW3+SnqXorytAj3fboFar2XvoNL71alG1cnk+6NcVp5IOWFpYGDF6If4Z3Yqn+IQn2NhYMXvKSL5d/xNrf9hF3x4dsbayIjklDRsbK8zNzPQJ+vR5yxn/YT/9ZKF4Maampri6lOTE2UukpKaTmpbB1eAw6tSswrAB7+FSysnYIRYLBVf2WVtZ0bVTS7KVKk6fu0IpJ0f8fH1o9GYdtFotpV2dn0vQhfi3kuXuQgjxD/3vUSUF901Lcm4YJ85eYuvOwwzu8y4PHj6iRrVKVPT2JCMzExMTBU4l7UlLz8TFuaSxQxXihej6i/OXrrN5x34mj/uASuU9uX03illfrKJjm2Z4epRh267DBEwdg1vpUqRnZDJlzhI5scBAlEoV+4+c4eSvl+nXsxNNG9bj1p1Idu4/zsdjBmJrY23sEIs8XTu/FhJG0PVbYAItmzXEu5w7P+w4wO3wKDq19aeZnxS3FaIgSdKFEOIFyFElhqcr+BZ0/SZR0Q9xLunIhm17uf8gDt96NVEoFKhUOajz8vhoRF8p4iSKvOuhd/hm5WYmjR6AT61q+r+B8Hv3Wb15J2p1Hl06tKB5U19ycnL5Yuk6OrX1l+JZBqY74uvajdssXvWD1BUxsAuXr/Pdhp9p0cyXpGepHDhyhhWLplGtcnk2bttHWHgkn33yIY4OdjLRLcR/SJIuhBAvSI4qMbz/LeJ04uwlduw5gp+vD4P7dCEvT0NySiql5A66KAYOHD1LVlY2vbq2Q5WTg5mpGQqFSf72GVUOKlUODvYlgPxE8llyKq6yBNvg1Hl5hN+LZvG3mxnYuzNNG9WXlVEGosrJ4fPFa+nasSV1alYFYNeB42zffYR1ywLIyMxEo9FS1r20kSMV4vUiheOEEOIFNW1YD996NaVInIH8rojTvftERMXQvmUTDh8/x97DpzA1VeBUUqpZi+Ih4fFTjp0OBMDSwgJTUwU3bt1lz6GTmJkq9Al6Xp4GM1NTSdBfEjNTUyp4ezBnymhJ0A3MVKHg8ZMkbt+9D+SvmGrbojGVK3qhzlPjXsZVEnQh/oAk6UIIUUgymDOMgkWcVqzdxs4DJzh2OpDQ2/cYMagH5b3KAsgZ6KJI0k3mPUlM4kFsPADdO7fCy9ONr1ZsIjUtg2s3bvP5knW4lXbBzOy/xbN0R66Jl8faykq/dUn69Bena+eqnByUqhzMzMzo1Naf8IhoLv12A4VCwYPYRzxKSCQ7W2XkaIV4fclydyGEEK8FKeIkiruzF3/juw0/YW5ujotzSTq1aYatjTW7DpwgJS0dgP4936FJw7pGjlSIF3cu8Cq7DpyghK0Nndu9jU+tamzesZ8TZy9TrbI3t+9GMXZYX2nnQvwFSdKFEEK8VqSIkyiOYuMSWL9lLz27tKFKRW++3/QzKalpDOz9Lh5urjxLScVUYYqDfQlZbi2KrOiYeBYuW0/ndm+TmpbBuh93M3/GOHzr1eRu5AMSnz7DtZQTlSt6STsX4i9Iki6EEOK1IkWcRHGTkppOh16jaNvCj1mTRwL57Xz81IVUqlCO8R/2M3KEQrw4Xf/8LCWVW3ciSU5Jo3O7t4H84ogr1mxj8rjBtGz2lnEDFaIIkSRdCCHEaydbqeRZchoebq6SoItiITAohCkBS9i8aj5enu4AHD5+jofxjxk+8D0jRydE4fx68SrLVm/B0cEOjUbL+uUB+s/tPniSpd//yJ7NS3B0sJO6IkL8DZKkCyGEEEK8AhcuX2fe16sZMagHrqWcWLRsA5PHDcbP18fYoQnxwh7GP2bLz4do6f8WHmVcGTV5PnVqVGH2p6P0j3mSmISri7MRoxSiaJEkXQghhBDiFbn02w0mzviS3t3a06lNMyp4l5XVIqLISklNp1Pv0XTv3JoJI/sDkJqWzpBxs6hY3pOFsyY893hp60L8PbLeRAghhBDiFWnYoDbfzPuEIyfOY29na+xwhCgURwc7Fs2ZyMGjZ3kY/xgAB3s71i6dQ9idSO5GPtAfywZyvJ0Qf5fcSRdCCCGEeMXOnA9i5brtbFuz8Lkz0YUoii5cvs78r9ewdtls3MvknzevVqulbQvxgiRJF0IIIYQwgqxsJTbWVsYOQwiDuHglmKkBS9m6ZiEebq7GDkeIIk2SdCGEEEIIIUShnbt0DUsLC3zr1TR2KEIUaZKkCyGEEEIIIQxGCsQJUThSOE4IIYQQQghhMJKgC1E4kqQLIYQQQgghhBCvCUnShRBCCCGEEEKI14Qk6UIIIYQQQgghxGtCknQhhBBCCCGEEOI1IUm6EEIIIYQQQgjxmpAkXQghhBBCCCGEeE2YGTsAIYQQQrx6oz6Zx83bEZiZmuo/Nnpob7q/0/qFv2bXAeMZ/2E//P0aGCJEIYQQ4l9JknQhhBDiX2rUB73p3a2dscPQU6vVmJnJ0EQIIcS/m7wSCiGEEELvyrWbfLfhJ2LjEnBxLsnID3rStFF9AC5fDdV/zsrKAn+/Bowd3hcrSwumzVvG4ydJfPb5SkwVCtq2aMyAXu/QbeAEju36HrsStgAs/vYHMjKzmPnxCB4lJNJt4ASmTxzGxm37yMpWcnjHKsLv3WfZ6q3ci4rB3s6W/j3f4d0OzY15WYQQQohXRpJ0IYQQQgAQERXDjPnLWDDzI+rVrk5o2D0mzfyKdcvm4OXpjqWFOVPGD6FS+XIkPHnKpJlfsX3XLwzq8y4LZoz73XL3RwmJf+v7nrt0jQ0r5mJuZkbSsxTGTV3IJ2MH0byJL9GxcYyfuhB3NxferFvzZf74QgghxGtBCscJIYQQ/1LfbthB627D9f+27/6FDq2b0cCnBgqFgjo1q9L4rbqc/PUyAD61qlG1kjempgo83Fzp0qEF127cLnQcQ/p1w66ELVZWlvxy8jw+tarSyr8hpqYKKnp70rFNM46dDiz09xFCCCGKArmTLoQQQvxLjRzc67k96ZNmfsnxs5c4dOxX/cfy8vKwtWkCQFh4JN+u/4nI6FhUOTnk5WkoV9at0HGUdnHW///R46cEBoXQuttw/cc0Gg11alYt9PcRQgghigJJ0oUQQggBgGspZ3p1acuoIb3/8POffb6STm38WTRnAtZWVmzffYTDx/+b0CtMTJ57vLW1FQBKVY5+T3rSsxQsLS2ee5xC8d/nlXZxwt+vAXOnjTHIzySEEEIUNbLcXQghhBAAdOnYgoPHfuVqcBh5eRpycnIJDbtHdEwcAFlZSkqUsMHayoromDj2HDrx3POdSjoQF/9E/76jgx1lXJ05fPwcGo2Gq8FhBAaF/GUM7Vo24WpwGKfPXUGtVqNWq7kb+YCw8EjD/8BCCCHEa0jupAshhBACgKqVvAmYOprvN/1MdEw8CoUJlSt4MXZ4HwAmfzSYZd9vZdW67VStXJ5W/o04F3hV//wBvTuzeNUPbNi6lzbNG/HJ2MFMmziML5dvZPP2/fj5+tDKvyHqvLw/jcG1lBOLF0xm1brtLFy2Ho1Gi3c5d4YN6P7Sf34hhBDidWCSk6PSGjsIIYQQQgghhBBCyHJ3IYQQQgghhBDitSFJuhBCCCGEEEII8ZqQJF0IIYQQQgghhHhNSJIuhBBCCCGEEEK8JiRJF0IIIYQQQgghXhOSpAshhBBCCCGEEK+J/wPSBT1ZEaGUYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "stab_pd = stability.to_pandas()\n",
    "ax.errorbar(\n",
    "    stab_pd[\"feature\"],\n",
    "    stab_pd[\"ic_mean\"],\n",
    "    yerr=stab_pd[\"ic_std\"],\n",
    "    fmt=\"o\",\n",
    "    capsize=5,\n",
    "    capthick=2,\n",
    "    markersize=8,\n",
    ")\n",
    "ax.axhline(0, color=\"black\", linewidth=0.5)\n",
    "ax.set_xlabel(\"Feature\")\n",
    "ax.set_ylabel(\"Mean IC ± Std\")\n",
    "ax.set_title(\"Feature IC Stability (Bootstrap)\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1f88d7",
   "metadata": {
    "papermill": {
     "duration": 0.002569,
     "end_time": "2026-06-13T03:32:27.065359+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:27.062790+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 8. ML-Based Feature Importance\n",
    "\n",
    "Beyond IC ranking, ML models identify features with non-linear predictive\n",
    "power. We fit a quick LightGBM model and compare its feature importance\n",
    "with the IC rankings above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76711d7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:32:27.071039Z",
     "iopub.status.busy": "2026-06-13T03:32:27.070951Z",
     "iopub.status.idle": "2026-06-13T03:32:38.370561Z",
     "shell.execute_reply": "2026-06-13T03:32:38.370105Z"
    },
    "papermill": {
     "duration": 11.303242,
     "end_time": "2026-06-13T03:32:38.371182+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:27.067940+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== ML Feature Importance (LightGBM) ===\n",
      "\n",
      "Consensus top features: ['dist_52w_low', 'natr_14', 'vol_63d_rank', 'ret_189d', 'max_dd_63d', 'skip_recent_6_1', 'ret_126d_rank', 'sma_ratio_200', 'mom_accel_short', 'pct_positive_63d']\n",
      "Methods run: ['mdi', 'pfi']\n",
      "Method agreement: {'mdi_vs_pfi': 0.49090909090909085}\n",
      "\n",
      "Strong consensus: 10 features rank in top 10 across all methods\n",
      "  Top consensus features: dist_52w_low, natr_14, vol_63d_rank, ret_189d, max_dd_63d\n",
      "Low agreement between methods (avg correlation: 0.49) - investigate further\n",
      "\n",
      "Potential Issues:\n",
      "  - Features {'vol_63d_rank', 'ret_126d_rank'} rank high in MDI but not PFI - possible overfitting to tree structure\n",
      "  - Low agreement between methods (min correlation: 0.49) - results may be unreliable\n"
     ]
    }
   ],
   "source": [
    "from ml4t.diagnostic.metrics import analyze_ml_importance\n",
    "\n",
    "ml_data = analysis.select([\"timestamp\", \"symbol\"] + final_features + [\"fwd_return_1m\"]).drop_nulls()\n",
    "X = ml_data.select(final_features).to_numpy()\n",
    "y = ml_data[\"fwd_return_1m\"].to_numpy()\n",
    "\n",
    "if len(X) > 100:\n",
    "    from lightgbm import LGBMRegressor\n",
    "\n",
    "    lgbm = LGBMRegressor(n_estimators=100, max_depth=5, verbose=-1, random_state=SEED)\n",
    "    lgbm.fit(X, y)\n",
    "\n",
    "    importance_result = analyze_ml_importance(\n",
    "        model=lgbm,\n",
    "        X=X,\n",
    "        y=y,\n",
    "        feature_names=final_features,\n",
    "        methods=[\"mdi\", \"pfi\"],\n",
    "    )\n",
    "\n",
    "    print(\"=== ML Feature Importance (LightGBM) ===\\n\")\n",
    "    print(f\"Consensus top features: {importance_result['consensus_ranking'][:10]}\")\n",
    "    print(f\"Methods run: {importance_result['methods_run']}\")\n",
    "    if importance_result.get(\"method_agreement\"):\n",
    "        print(f\"Method agreement: {importance_result['method_agreement']}\")\n",
    "    print(f\"\\n{importance_result['interpretation']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165bd1a1",
   "metadata": {
    "papermill": {
     "duration": 0.002727,
     "end_time": "2026-06-13T03:32:38.376842+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.374115+00:00",
     "status": "completed"
    }
   },
   "source": [
    "**Interpretation**: MDI (Mean Decrease in Impurity) measures how much each\n",
    "feature reduces prediction error in the tree ensemble. PFI (Permutation\n",
    "Feature Importance) measures how much shuffling a feature degrades\n",
    "predictions. Features ranking high in both IC and ML importance are the\n",
    "strongest candidates for production."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06dc7092",
   "metadata": {
    "papermill": {
     "duration": 0.002592,
     "end_time": "2026-06-13T03:32:38.382153+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.379561+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 9. Post-Selection Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4127baa4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:32:38.388246Z",
     "iopub.status.busy": "2026-06-13T03:32:38.388144Z",
     "iopub.status.idle": "2026-06-13T03:32:38.564518Z",
     "shell.execute_reply": "2026-06-13T03:32:38.563967Z"
    },
    "papermill": {
     "duration": 0.180097,
     "end_time": "2026-06-13T03:32:38.564865+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.384768+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max remaining correlation: 0.878\n"
     ]
    }
   ],
   "source": [
    "# Verify low inter-correlation among selected features\n",
    "selected_matrix = features_df.select(final_features).drop_nulls()\n",
    "corr_after = selected_matrix.corr().to_numpy()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "mask = np.triu(np.ones_like(corr_after, dtype=bool), k=1)\n",
    "sns.heatmap(\n",
    "    corr_after,\n",
    "    mask=mask,\n",
    "    annot=True,\n",
    "    fmt=\".2f\",\n",
    "    cmap=\"RdBu_r\",\n",
    "    center=0,\n",
    "    vmin=-1,\n",
    "    vmax=1,\n",
    "    ax=ax,\n",
    "    xticklabels=final_features,\n",
    "    yticklabels=final_features,\n",
    "    cbar_kws={\"label\": \"Correlation\"},\n",
    ")\n",
    "ax.set_title(\"Selected Features — Residual Correlation\")\n",
    "plt.show()\n",
    "\n",
    "np.fill_diagonal(corr_after, 0)\n",
    "max_corr = np.abs(corr_after).max()\n",
    "print(f\"Max remaining correlation: {max_corr:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ccdb262",
   "metadata": {
    "papermill": {
     "duration": 0.002985,
     "end_time": "2026-06-13T03:32:38.571184+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.568199+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 10. Selection Summary and Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c740a13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:32:38.577970Z",
     "iopub.status.busy": "2026-06-13T03:32:38.577856Z",
     "iopub.status.idle": "2026-06-13T03:32:38.582146Z",
     "shell.execute_reply": "2026-06-13T03:32:38.581802Z"
    },
    "papermill": {
     "duration": 0.008258,
     "end_time": "2026-06-13T03:32:38.582411+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.574153+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "FEATURE SELECTION REPORT\n",
      "============================================================\n",
      "\n",
      "Initial Features:           57\n",
      "After Correlation Filter:   43\n",
      "After IC Filter:            18\n",
      "Final Selected:             10\n",
      "Removal Rate:               82.5%\n",
      "\n",
      "------------------------------------------------------------\n",
      "SELECTED FEATURES FOR CHAPTER 9\n",
      "------------------------------------------------------------\n",
      " 1. dist_52w_low                   IC=+0.0764  IC_IR=45.19\n",
      " 2. natr_14                        IC=+0.0572  IC_IR=62.70\n",
      " 3. vol_63d_rank                   IC=+0.0509  IC_IR=25.12\n",
      " 4. ret_189d                       IC=+0.0411  IC_IR=0.34\n",
      " 5. sma_ratio_200                  IC=+0.0335  IC_IR=-1.68\n",
      " 6. pct_positive_63d               IC=+0.0294  IC_IR=-1.12\n",
      " 7. mom_accel_short                IC=-0.0261  IC_IR=-6.91\n",
      " 8. skip_recent_6_1                IC=+0.0241  IC_IR=7.59\n",
      " 9. ret_126d_rank                  IC=+0.0234  IC_IR=8.19\n",
      "10. max_dd_63d                     IC=-0.0203  IC_IR=-34.96\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"=\" * 60)\n",
    "print(\"FEATURE SELECTION REPORT\")\n",
    "print(\"=\" * 60)\n",
    "print(f\"\\nInitial Features:           {len(all_feature_cols)}\")\n",
    "print(f\"After Correlation Filter:   {len(kept_after_corr)}\")\n",
    "print(f\"After IC Filter:            {len(kept_after_ic)}\")\n",
    "print(f\"Final Selected:             {len(final_features)}\")\n",
    "print(f\"Removal Rate:               {100 * (1 - len(final_features) / len(all_feature_cols)):.1f}%\")\n",
    "print(\"\\n\" + \"-\" * 60)\n",
    "print(\"SELECTED FEATURES FOR CHAPTER 9\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for i, f in enumerate(final_features, 1):\n",
    "    ic = ic_scores[f]\n",
    "    stab_row = stability.filter(pl.col(\"feature\") == f)\n",
    "    ic_ir = stab_row[\"ic_ir\"][0] if len(stab_row) > 0 else np.nan\n",
    "    print(f\"{i:2d}. {f:30s} IC={ic:+.4f}  IC_IR={ic_ir:.2f}\")\n",
    "\n",
    "print(\"=\" * 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f8616122",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-13T03:32:38.589619Z",
     "iopub.status.busy": "2026-06-13T03:32:38.589516Z",
     "iopub.status.idle": "2026-06-13T03:32:38.631600Z",
     "shell.execute_reply": "2026-06-13T03:32:38.631145Z"
    },
    "papermill": {
     "duration": 0.046221,
     "end_time": "2026-06-13T03:32:38.632015+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.585794+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved selected features to 08_financial_features/output/feature_selection\n",
      "  - selected_features.parquet: 10 features\n",
      "  - features_selected.parquet: (396186, 12)\n"
     ]
    }
   ],
   "source": [
    "# Save selected features for Chapter 9\n",
    "OUTPUT_DIR = get_output_dir(8, \"feature_selection\")\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "selected_df = pl.DataFrame(\n",
    "    {\"feature\": final_features, \"ic\": [ic_scores[f] for f in final_features]}\n",
    ")\n",
    "selected_df.write_parquet(OUTPUT_DIR / \"selected_features.parquet\")\n",
    "\n",
    "filtered_features = features_df.select([\"timestamp\", \"symbol\"] + final_features)\n",
    "filtered_features.write_parquet(OUTPUT_DIR / \"features_selected.parquet\")\n",
    "\n",
    "print(f\"Saved selected features to {OUTPUT_DIR}\")\n",
    "print(f\"  - selected_features.parquet: {len(final_features)} features\")\n",
    "print(f\"  - features_selected.parquet: {filtered_features.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3973d358",
   "metadata": {
    "papermill": {
     "duration": 0.002856,
     "end_time": "2026-06-13T03:32:38.638004+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:38.635148+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## Key Takeaways\n",
    "\n",
    "1. **Cross-sectional IC** is the correct method for factor evaluation —\n",
    "   pooled IC conflates time-series drift with predictive power\n",
    "2. **Correlation filtering** (|r| > 0.9) removes obvious redundancy;\n",
    "   **clustering** catches subtler near-duplicates within feature families\n",
    "3. **Use average or complete linkage** (not Ward) for correlation distances —\n",
    "   Ward assumes Euclidean geometry\n",
    "4. **BH-FDR with HAC-adjusted p-values** controls false discovery when\n",
    "   screening many candidates. The p-values fed into BH-FDR come from the\n",
    "   Newey-West t-statistic on each feature's daily IC series, not the\n",
    "   i.i.d. t-stat, because daily ICs are serially correlated. Without\n",
    "   multiple-testing correction, ~5% of null features appear significant\n",
    "   at the 5% level by chance alone\n",
    "5. **Bootstrap stability** separates features with robust IC from those\n",
    "   that depend on a few outlier periods\n",
    "6. Features ranking high in both IC and ML importance are the strongest\n",
    "   production candidates\n",
    "\n",
    "**Next**: `06_robustness_sensitivity` — parameter sensitivity and\n",
    "regime-conditional analysis"
   ]
  }
 ],
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   "input_path": "08_financial_features/05_feature_selection.ipynb",
   "output_path": "08_financial_features/05_feature_selection.ipynb",
   "parameters": {},
   "start_time": "2026-06-13T03:31:45.048100+00:00",
   "version": "2.7.0"
  },
  "ml4t_provenance": {
   "source_py_blob": "fa870a052739f32d5022432b3b4250ef1bb9c6ff",
   "executed_at": "2026-06-13T03:32:40.088731+00:00",
   "executor": "cpu-local",
   "production": true,
   "parameters": {},
   "notes": "executed-state finalization batch (2026-06-13 run)"
  }
 },
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}