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@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+Stock_Benchmark[[:space:]]Analysis/unique_companies.csv filter=lfs diff=lfs merge=lfs -text

Stock_Benchmark Analysis/Stock_Benchmark Analysis copy.ipynb

@@ -0,0 +1,682 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b48026f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "# Load the CSV file\n",
+    "df = pd.read_csv('unique_companies.csv')\n",
+    "# Filter rows where 'Industry' is 'Copper' or 'Diversified Metals & Mining'\n",
+    "filtered_df = df[df['Industry'].isin(['Copper', 'Diversified Metals & Mining'])]\n",
+    "# Save the result to a new CSV\n",
+    "filtered_df.to_csv('unique_companies_copper_diversified.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "261ce11e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 150 tickers (including HG=F).\n",
+      "Downloading batch 1: 50 tickers\n",
+      "Downloading batch 2: 50 tickers\n",
+      "Downloading batch 3: 50 tickers\n",
+      "df_prices shape: (1307, 150)\n",
+      "Columns: ['HG=F', '000630.SZ', '000737.SZ', '000878.SZ', '002203.SZ', '005810.KS', '08W.F', '2009.TW', '2IK.F', '300618.SZ', '300697.SZ', '301511.SZ', '381.F', '3N4.SG', '4989.TW', '5PMA.F', '600255.SS', '600362.SS', '600490.SS', '601137.SS', '601609.SS', '603124.SS', '688102.SS', '688388.SS', '7GI.F', '7LY0.F', '97E0.F', '9CM0.F', 'ACMDY', 'ALM.AX', 'ANFGF', 'AR1.AX', 'ARJN.V', 'ARJNF', 'ARREF', 'ASCU.TO', 'ASCUF', 'ATCUF', 'ATYM.L', 'AXO.V', 'BCU.V', 'BCUFF', 'BFGFF', 'BHAGYANGR.NS', 'BP60.F', 'BRVRF', 'BZDLF', 'C730.F', 'CAEN', 'CAML.L', 'CAMLF', 'CFV0.F', 'COPR', 'CPCPF', 'CPER.V', 'CPFXF', 'CPO.AX', 'CPORF', 'CPPKF', 'CPPMF', 'CPR.JO', 'CS.TO', 'CSC.AX', 'CSCCF', 'CUBEXTUB.NS', 'CUU.V', 'CVV.AX', 'CYM.AX', 'CYPMF', 'E2E1.F', 'E9E.F', 'EMTRF', 'ERO', 'FCX', 'FCXO34.SA', 'FDY.TO', 'FG1.F', 'FPMB.F', 'FQVLF', 'GCUMF', 'GRX.AX', 'GRX.L', 'GSCU.L', 'H6F.F', 'HBM', 'HBM.TO', 'HCH.V', 'HDRSF', 'HGO.AX', 'HHLKF', 'HI.V', 'HIN.MU', 'HINDCOPPER.NS', 'HLGVF', 'HNCUF', 'IE', 'IE.TO', 'INUMF', 'IPMLF', 'JGRRF', 'JIX.F', 'KCC.V', 'KGH.WA', 'KGHPF', 'LA.V', 'LSANF', 'LUNMF', 'MAC.AX', 'MARI.TO', 'MARIF', 'MCL.NS', 'MMLTF', 'MTAL', 'MTJ3.F', 'NFM.AX', 'NRX.AX', 'NTM.AX', 'NU0.F', 'OCKA.F', 'OUW0.F', 'PMAM3.SA', 'PNTZF', 'PSGR', 'PUCOBRE.SN', 'Q.V', 'QCCUF', 'RAJMET.NS', 'RDS.AX', 'RE8.F', 'RRR.AX', 'SAGARDEEP.NS', 'SARKY.IS', 'SCCO', 'SFR.AX', 'SFRRF', 'SLMFF', 'TFM.V', 'TGB', 'TKO.L', 'TNC.AX', 'TRRCF', 'TVCCF', 'TWO.V', 'TWOSF', 'USCUF', 'VCUFF', 'WA1.AX', 'WAORF', 'WCUFF', 'XXIX.V']\n",
+      "Saved to 'df_prices.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------------------------------------------------------------\n",
+    "# Build df_prices.csv for HG=F + tickers in unique_companies_copper.csv\n",
+    "# • period=\"5y\" (more reliable than start/end for some venues)\n",
+    "# • Prefer 'Adj Close', fallback to 'Close'\n",
+    "# • Re-download single tickers that are all-NaN in batch (e.g., 2IK.F)\n",
+    "# ------------------------------------------------------------\n",
+    "# pip install yfinance pandas\n",
+    "\n",
+    "import pandas as pd\n",
+    "import yfinance as yf\n",
+    "\n",
+    "CSV_PATH = \"unique_companies_copper.csv\"\n",
+    "TICKER_COL = \"PrimaryTicker\"\n",
+    "UNDERLYING = \"HG=F\"\n",
+    "BATCH_SIZE = 50\n",
+    "OUT_CSV   = \"df_prices.csv\"\n",
+    "\n",
+    "# --- Read tickers ---\n",
+    "tickers = (\n",
+    "    pd.read_csv(CSV_PATH, usecols=[TICKER_COL])[TICKER_COL]\n",
+    "      .dropna().astype(str).str.strip().str.upper().tolist()\n",
+    ")\n",
+    "tickers = sorted(set(tickers))\n",
+    "if UNDERLYING not in tickers:\n",
+    "    tickers = [UNDERLYING] + tickers\n",
+    "\n",
+    "print(f\"Found {len(tickers)} tickers (including {UNDERLYING}).\")\n",
+    "\n",
+    "def _extract_adj_or_close(df_multi: pd.DataFrame) -> tuple[pd.DataFrame, list[str]]:\n",
+    "    \"\"\"From yfinance multi-ticker frame, prefer 'Adj Close', else 'Close' per ticker.\"\"\"\n",
+    "    if not isinstance(df_multi.columns, pd.MultiIndex):\n",
+    "        raise ValueError(\"Expected MultiIndex columns for multi-ticker download.\")\n",
+    "    fields = set(df_multi.columns.get_level_values(-1))\n",
+    "    adj = df_multi.xs(\"Adj Close\", axis=1, level=-1, drop_level=True) if \"Adj Close\" in fields else pd.DataFrame(index=df_multi.index)\n",
+    "    clo = df_multi.xs(\"Close\",      axis=1, level=-1, drop_level=True) if \"Close\"      in fields else pd.DataFrame(index=df_multi.index)\n",
+    "\n",
+    "    cols = sorted(set(adj.columns).union(clo.columns))\n",
+    "    out  = pd.DataFrame(index=df_multi.index, columns=cols, dtype=\"float64\")\n",
+    "    used_close = []\n",
+    "\n",
+    "    for t in cols:\n",
+    "        a = adj[t] if t in adj.columns else None\n",
+    "        c = clo[t] if t in clo.columns else None\n",
+    "        if a is not None and not a.dropna().empty:\n",
+    "            out[t] = a\n",
+    "        elif c is not None and not c.dropna().empty:\n",
+    "            out[t] = c\n",
+    "            used_close.append(t)\n",
+    "    return out, used_close\n",
+    "\n",
+    "def _download_batch(batch):\n",
+    "    df = yf.download(\n",
+    "        tickers=batch,\n",
+    "        period=\"5y\",\n",
+    "        interval=\"1d\",\n",
+    "        auto_adjust=False,\n",
+    "        actions=False,\n",
+    "        progress=False,\n",
+    "        group_by=\"ticker\",\n",
+    "        threads=True\n",
+    "    )\n",
+    "    if isinstance(df.columns, pd.MultiIndex):\n",
+    "        return _extract_adj_or_close(df)\n",
+    "    else:\n",
+    "        # Single-ticker shape\n",
+    "        tkr = batch[0]\n",
+    "        adj = df.get(\"Adj Close\")\n",
+    "        clo = df.get(\"Close\")\n",
+    "        used_close = []\n",
+    "        if adj is not None and not adj.dropna().empty:\n",
+    "            out = adj.rename(tkr).to_frame()\n",
+    "        elif clo is not None and not clo.dropna().empty:\n",
+    "            out = clo.rename(tkr).to_frame()\n",
+    "            used_close.append(tkr)\n",
+    "        else:\n",
+    "            out = pd.DataFrame(index=df.index, columns=[tkr], dtype=\"float64\")\n",
+    "        return out, used_close\n",
+    "\n",
+    "def _download_single(tkr: str) -> pd.Series:\n",
+    "    \"\"\"Single-ticker repair path; prefer Adj Close, else Close.\"\"\"\n",
+    "    df = yf.download(\n",
+    "        tickers=tkr,\n",
+    "        period=\"5y\",\n",
+    "        interval=\"1d\",\n",
+    "        auto_adjust=False,\n",
+    "        actions=False,\n",
+    "        progress=False\n",
+    "    )\n",
+    "    s = df.get(\"Adj Close\")\n",
+    "    if s is None or s.dropna().empty:\n",
+    "        s = df.get(\"Close\")\n",
+    "    if s is None:\n",
+    "        return pd.Series(dtype=\"float64\", name=tkr)\n",
+    "    return s.rename(tkr)\n",
+    "\n",
+    "# --- Batch download + merge ---\n",
+    "frames, used_close_all = [], []\n",
+    "for i in range(0, len(tickers), BATCH_SIZE):\n",
+    "    batch = tickers[i:i+BATCH_SIZE]\n",
+    "    print(f\"Downloading batch {i//BATCH_SIZE + 1}: {len(batch)} tickers\")\n",
+    "    part, used_close = _download_batch(batch)\n",
+    "    frames.append(part)\n",
+    "    used_close_all.extend(used_close)\n",
+    "\n",
+    "df_prices = pd.concat(frames, axis=1)\n",
+    "df_prices = df_prices.loc[:, ~df_prices.columns.duplicated()].sort_index()\n",
+    "\n",
+    "# --- Repair tickers that are NaN-only or missing after batch ---\n",
+    "to_repair = [t for t in tickers if (t in df_prices.columns and df_prices[t].dropna().empty) or (t not in df_prices.columns)]\n",
+    "to_repair = sorted(set(to_repair))\n",
+    "if to_repair:\n",
+    "    print(f\"Repairing via single-ticker fetch: {to_repair}\")\n",
+    "    for t in to_repair:\n",
+    "        s = _download_single(t)\n",
+    "        if not s.dropna().empty:\n",
+    "            df_prices = df_prices.reindex(df_prices.index.union(s.index)).sort_index()\n",
+    "            df_prices[t] = s.reindex(df_prices.index)\n",
+    "\n",
+    "# --- Order columns; drop all-NaN tickers ---\n",
+    "ordered_cols = [UNDERLYING] + [t for t in tickers if t != UNDERLYING and t in df_prices.columns]\n",
+    "df_prices = df_prices.reindex(columns=ordered_cols)\n",
+    "all_nan_cols = [c for c in df_prices.columns if df_prices[c].dropna().empty]\n",
+    "if all_nan_cols:\n",
+    "    print(f\"Dropping tickers with no usable data: {all_nan_cols}\")\n",
+    "    df_prices = df_prices.drop(columns=all_nan_cols)\n",
+    "\n",
+    "# --- Report fallback usage ---\n",
+    "used_close_all = sorted(set([t for t in used_close_all if t in df_prices.columns]))\n",
+    "if used_close_all:\n",
+    "    print(f\"Used 'Close' fallback for: {used_close_all}\")\n",
+    "\n",
+    "print(\"df_prices shape:\", df_prices.shape)\n",
+    "print(\"Columns:\", list(df_prices.columns))\n",
+    "\n",
+    "# --- Save ---\n",
+    "df_prices.to_csv(OUT_CSV, index_label=\"Date\", float_format=\"%.6f\")\n",
+    "print(f\"Saved to '{OUT_CSV}'.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "11079562",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Missing % by ticker ===\n",
+      "           missing_pct\n",
+      "AXO.V        97.016067\n",
+      "603124.SS    93.037490\n",
+      "NFM.AX       87.299158\n",
+      "WAORF        85.233359\n",
+      "ASCUF        81.637337\n",
+      "...                ...\n",
+      "OUW0.F        2.371844\n",
+      "9CM0.F        2.371844\n",
+      "5PMA.F        2.371844\n",
+      "3N4.SG        2.371844\n",
+      "E2E1.F        2.371844\n",
+      "\n",
+      "[150 rows x 1 columns]\n",
+      "\n",
+      "Dropping 45 tickers (> 10% missing): ['AXO.V', '603124.SS', 'NFM.AX', 'WAORF', 'ASCUF', 'CSC.AX', 'MAC.AX', 'CPPMF', 'JGRRF', '301511.SZ', 'HNCUF', 'TRRCF', 'CPR.JO', 'CYPMF', '381.F', 'CPORF', 'IE', 'IE.TO', 'CPCPF', '688102.SS', 'INUMF', 'CSCCF', 'WA1.AX', '7LY0.F', 'CPER.V', 'NU0.F', 'HCH.V', 'GSCU.L', 'E9E.F', 'Q.V', 'ASCU.TO', 'H6F.F', 'AR1.AX', 'VCUFF', '97E0.F', 'MTAL', 'RRR.AX', 'CAMLF', 'CPO.AX', 'WCUFF', 'EMTRF', '7GI.F', 'QCCUF', '2IK.F', 'CPPKF']\n",
+      "\n",
+      "Shapes:\n",
+      "Before: (1307, 150) After: (1307, 105)\n",
+      "Saved to 'df_prices_final.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------------------------------------------------------------\n",
+    "# Load df_prices.csv, compute missing % per ticker,\n",
+    "# drop columns with >50% missing, save df_prices_final.csv\n",
+    "# ------------------------------------------------------------\n",
+    "import pandas as pd\n",
+    "\n",
+    "IN_CSV  = \"df_prices.csv\"\n",
+    "OUT_CSV = \"df_prices_final.csv\"\n",
+    "THRESH  = 10.0  # percent\n",
+    "\n",
+    "df_prices = pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "\n",
+    "# Missing % over the full DataFrame index\n",
+    "missing_pct = df_prices.isna().mean() * 100.0\n",
+    "report = (\n",
+    "    pd.DataFrame({\"missing_pct\": missing_pct})\n",
+    "    .sort_values(\"missing_pct\", ascending=False)\n",
+    ")\n",
+    "print(\"=== Missing % by ticker ===\")\n",
+    "print(report)\n",
+    "\n",
+    "# Drop tickers with >50% missing\n",
+    "to_drop = report.index[report[\"missing_pct\"] > THRESH].tolist()\n",
+    "print(f\"\\nDropping {len(to_drop)} tickers (> {THRESH:.0f}% missing): {to_drop}\")\n",
+    "\n",
+    "df_prices_final = df_prices.drop(columns=to_drop, errors=\"ignore\")\n",
+    "\n",
+    "print(\"\\nShapes:\")\n",
+    "print(\"Before:\", df_prices.shape, \"After:\", df_prices_final.shape)\n",
+    "\n",
+    "df_prices_final.to_csv(OUT_CSV, index_label=\"Date\", float_format=\"%.6f\")\n",
+    "print(f\"Saved to '{OUT_CSV}'.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8dc7673c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved clean, rectangular prices to 'df_prices_final.csv' with shape (1208, 105).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------------------------------------------------------------\n",
+    "# Clean df_prices_final: common window + bfill→ffill + final NA drop\n",
+    "# Input : df_prices_final.csv (your current file with some missing)\n",
+    "# Output: df_prices_final.csv (overwritten, rectangular, NA-free)\n",
+    "# ------------------------------------------------------------\n",
+    "import pandas as pd\n",
+    "\n",
+    "IN_CSV  = \"df_prices_final.csv\"\n",
+    "OUT_CSV = \"df_prices_final.csv\"   # overwrite in place\n",
+    "\n",
+    "df = pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\").sort_index()\n",
+    "\n",
+    "# 1) Common window (everyone has started and not yet delisted)\n",
+    "first_valid = df.apply(pd.Series.first_valid_index)\n",
+    "last_valid  = df.apply(pd.Series.last_valid_index)\n",
+    "\n",
+    "common_start = max(first_valid.dropna())\n",
+    "common_end   = min(last_valid.dropna())\n",
+    "\n",
+    "df = df.loc[common_start:common_end].copy()\n",
+    "\n",
+    "# 2) Business-day index to harmonize calendars\n",
+    "bidx = pd.date_range(df.index.min(), df.index.max(), freq=\"B\")\n",
+    "df = df.reindex(bidx)\n",
+    "\n",
+    "# 3) Fill:\n",
+    "#    - Backfill once to seed the first business day for tickers closed on common_start\n",
+    "#    - Forward-fill for holiday gaps etc.\n",
+    "df = df.bfill(limit=None).ffill(limit=None)\n",
+    "\n",
+    "# 4) Final sanity check: drop any rare rows still containing NA\n",
+    "before_rows = df.shape[0]\n",
+    "df = df.dropna(how=\"any\")\n",
+    "after_rows = df.shape[0]\n",
+    "if before_rows != after_rows:\n",
+    "    print(f\"Dropped {before_rows - after_rows} rows that still had NAs after filling.\")\n",
+    "\n",
+    "# 5) Save\n",
+    "df.to_csv(OUT_CSV, index_label=\"Date\", float_format=\"%.6f\")\n",
+    "print(f\"Saved clean, rectangular prices to '{OUT_CSV}' with shape {df.shape}.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8cdec19b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Saved  corr_beta.csv,  excess_summary.csv (with corr_to_copper)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================\n",
+    "# Copper-linked stocks vs COMEX copper (HG=F)\n",
+    "#   • Correlation & beta\n",
+    "#   • Rolling correlation\n",
+    "#   • β-hedged alpha / excess-return summary\n",
+    "# Outputs:\n",
+    "#   corr_beta.csv, rolling_corr_long.csv, excess_summary.csv\n",
+    "#   (excess_summary.csv now includes corr_to_copper)\n",
+    "# ============================================================\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# ---------- PARAMETERS ----------\n",
+    "IN_CSV       = \"df_prices_final.csv\"  # prices, Date index\n",
+    "BENCH        = \"HG=F\"                 # underlying metal\n",
+    "FREQ         = \"W-FRI\"                # 'D' for daily or 'W-FRI'\n",
+    "ROLL_WINDOW  = None                     # 26 weeks ≈ 6 months; set None to skip\n",
+    "USE_LOG_RET  = True                   # True = log-returns, False = pct-change\n",
+    "ALPHA_T_CUT  = 0.9                    # |t| threshold for alpha significance\n",
+    "SHARPE_CUT   = 0.0                    # >0 required for outperform label\n",
+    "\n",
+    "# Annualisation factor\n",
+    "PER_YEAR = 52 if FREQ.startswith(\"W\") else 252\n",
+    "\n",
+    "# ---------- LOAD PRICES ----------\n",
+    "px = (pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "         .sort_index())\n",
+    "assert BENCH in px.columns, f\"{BENCH} not found in columns.\"\n",
+    "\n",
+    "# Optional resample (weekly smooths roll/holiday noise)\n",
+    "if FREQ != \"D\":\n",
+    "    px = px.resample(FREQ).last()\n",
+    "\n",
+    "# ---------- RETURNS ----------\n",
+    "rets = np.log(px).diff() if USE_LOG_RET else px.pct_change()\n",
+    "rets = rets.dropna(how=\"all\")\n",
+    "\n",
+    "bench = rets[BENCH]\n",
+    "stocks = [c for c in rets.columns if c != BENCH]\n",
+    "\n",
+    "# ---------- 1) CORRELATION & BETA ----------\n",
+    "corr = rets[stocks].corrwith(bench)                 # Series: index=ticker\n",
+    "var_b = bench.var(ddof=1)\n",
+    "beta = rets[stocks].apply(lambda s: s.cov(bench)) / var_b\n",
+    "\n",
+    "corr_beta = (pd.DataFrame({\"corr_to_copper\": corr,\n",
+    "                           \"beta_to_copper\": beta})\n",
+    "             .sort_values(\"corr_to_copper\", ascending=False))\n",
+    "corr_beta.index.name = \"ticker\"\n",
+    "corr_beta.to_csv(\"corr_beta.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "# Keep maps for reuse in excess_summary\n",
+    "corr_map = corr.to_dict()\n",
+    "beta_map = beta.to_dict()\n",
+    "\n",
+    "# ---------- OPTIONAL ROLLING CORRELATION ----------\n",
+    "if ROLL_WINDOW:\n",
+    "    rolling_long = (\n",
+    "        rets[stocks]\n",
+    "        .rolling(ROLL_WINDOW)\n",
+    "        .corr(bench)               # wide: Date × tickers\n",
+    "        .stack()                   # -> Series with MultiIndex (Date, ticker)\n",
+    "        .rename(\"rolling_corr\")\n",
+    "        .rename_axis([\"Date\", \"ticker\"])\n",
+    "        .reset_index()\n",
+    "        .dropna(subset=[\"rolling_corr\"])\n",
+    "    )\n",
+    "    rolling_long.to_csv(\"rolling_corr_long.csv\",\n",
+    "                        index=False, float_format=\"%.6f\")\n",
+    "\n",
+    "# ---------- 2) β-HEDGED EXCESS & SELECTION ----------\n",
+    "def _nw_lags(freq: str) -> int:\n",
+    "    return 4 if freq.startswith(\"W\") else 21   # ≈1 month of obs\n",
+    "\n",
+    "hac_lags = _nw_lags(FREQ)\n",
+    "\n",
+    "rows = []\n",
+    "for s in stocks:\n",
+    "    # align & drop NA\n",
+    "    df_xy = rets[[s, BENCH]].dropna()\n",
+    "    if df_xy.empty:\n",
+    "        continue\n",
+    "\n",
+    "    y = df_xy[s]\n",
+    "    X = sm.add_constant(df_xy[BENCH])\n",
+    "    model = sm.OLS(y, X).fit(\n",
+    "        cov_type=\"HAC\",\n",
+    "        cov_kwds={\"maxlags\": hac_lags}\n",
+    "    )\n",
+    "\n",
+    "    alpha     = model.params[\"const\"]\n",
+    "    beta_i    = model.params[BENCH]\n",
+    "    t_alpha   = model.tvalues[\"const\"]\n",
+    "    alpha_ann = alpha * PER_YEAR\n",
+    "\n",
+    "    # β-hedged excess series\n",
+    "    r_excess = y - beta_i * df_xy[BENCH]\n",
+    "    mu, sd   = r_excess.mean(), r_excess.std(ddof=1)\n",
+    "    hedged_sharpe = (mu / sd) * np.sqrt(PER_YEAR) if sd > 0 else np.nan\n",
+    "    cum_excess_beta_log = r_excess.cumsum().iloc[-1]\n",
+    "\n",
+    "    # Simple 1× copper cumulative log excess\n",
+    "    cum_excess_1x_log = y.cumsum().iloc[-1] - df_xy[BENCH].cumsum().iloc[-1]\n",
+    "\n",
+    "    # Label\n",
+    "    if (alpha_ann > 0) and (t_alpha > ALPHA_T_CUT) and (hedged_sharpe > SHARPE_CUT):\n",
+    "        label = \"Outperform\"\n",
+    "    elif (alpha_ann < 0) and (t_alpha < -ALPHA_T_CUT) and (hedged_sharpe < -SHARPE_CUT):\n",
+    "        label = \"Underperform\"\n",
+    "    else:\n",
+    "        label = \"Neutral\"\n",
+    "\n",
+    "    rows.append({\n",
+    "        \"ticker\": s,\n",
+    "        \"corr_to_copper\": corr_map.get(s, np.nan),  # <-- added\n",
+    "        \"beta_to_copper\": beta_map.get(s, np.nan),  # keep for consistency\n",
+    "        \"alpha_ann\": alpha_ann,\n",
+    "        \"t_alpha\": t_alpha,\n",
+    "        \"hedged_sharpe\": hedged_sharpe,\n",
+    "        \"cum_excess_beta_log\": cum_excess_beta_log,\n",
+    "        \"cum_excess_1x_log\": cum_excess_1x_log,\n",
+    "        \"label\": label\n",
+    "    })\n",
+    "\n",
+    "excess_summary = (pd.DataFrame(rows)\n",
+    "                    .set_index(\"ticker\")\n",
+    "                    .sort_values([\"label\", \"alpha_ann\", \"hedged_sharpe\"],\n",
+    "                                 ascending=[True, False, False]))\n",
+    "excess_summary.to_csv(\"excess_summary.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "print(\"✓ Saved  corr_beta.csv,\",\n",
+    "      \"rolling_corr_long.csv,\" if ROLL_WINDOW else \"\",\n",
+    "      \"excess_summary.csv (with corr_to_copper)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a651e4d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Saved corr_beta.csv,  excess_summary.csv (with corr_to_copper, vol_ann, bench_vol_ann)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================\n",
+    "# Copper-linked stocks vs COMEX copper (HG=F)\n",
+    "#   • Correlation & beta\n",
+    "#   • Rolling correlation\n",
+    "#   • β-hedged alpha / excess-return summary\n",
+    "#   • Annualized volatility (each stock + HG=F)\n",
+    "# Outputs:\n",
+    "#   corr_beta.csv, rolling_corr_long.csv, excess_summary.csv\n",
+    "# ============================================================\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# ---------- PARAMETERS ----------\n",
+    "IN_CSV       = \"df_prices_final.csv\"  # prices, Date index\n",
+    "BENCH        = \"HG=F\"                 # underlying metal\n",
+    "FREQ         = \"W-FRI\"                # 'D' for daily or 'W-FRI' (recommended)\n",
+    "ROLL_WINDOW  = None                     # 26 weeks ≈ 6 months; set None to skip\n",
+    "USE_LOG_RET  = True                   # True = log-returns, False = pct-change\n",
+    "ALPHA_T_CUT  = 0.8                    # |t| threshold for alpha significance\n",
+    "SHARPE_CUT   = 0.0                    # >0 required for outperform label\n",
+    "\n",
+    "# Annualisation factor\n",
+    "PER_YEAR = 52 if FREQ.startswith(\"W\") else 252\n",
+    "\n",
+    "# ---------- LOAD PRICES ----------\n",
+    "px = (pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "         .sort_index())\n",
+    "assert BENCH in px.columns, f\"{BENCH} not found in columns.\"\n",
+    "\n",
+    "# Optional resample (weekly smooths roll/holiday noise)\n",
+    "if FREQ != \"D\":\n",
+    "    px = px.resample(FREQ).last()\n",
+    "\n",
+    "# ---------- RETURNS ----------\n",
+    "rets = np.log(px).diff() if USE_LOG_RET else px.pct_change()\n",
+    "rets = rets.dropna(how=\"all\")\n",
+    "\n",
+    "bench = rets[BENCH]\n",
+    "stocks = [c for c in rets.columns if c != BENCH]\n",
+    "\n",
+    "# ---------- 1) CORRELATION & BETA ----------\n",
+    "corr = rets[stocks].corrwith(bench)\n",
+    "var_b = bench.var(ddof=1)\n",
+    "beta = rets[stocks].apply(lambda s: s.cov(bench)) / var_b\n",
+    "\n",
+    "corr_beta = (pd.DataFrame({\"corr_to_copper\": corr,\n",
+    "                           \"beta_to_copper\": beta})\n",
+    "             .sort_values(\"corr_to_copper\", ascending=False))\n",
+    "corr_beta.index.name = \"ticker\"\n",
+    "corr_beta.to_csv(\"corr_beta.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "# Maps to reuse\n",
+    "corr_map = corr.to_dict()\n",
+    "beta_map = beta.to_dict()\n",
+    "\n",
+    "# ---------- OPTIONAL ROLLING CORRELATION ----------\n",
+    "if ROLL_WINDOW:\n",
+    "    rolling_long = (\n",
+    "        rets[stocks]\n",
+    "        .rolling(ROLL_WINDOW)\n",
+    "        .corr(bench)               # wide: Date × tickers\n",
+    "        .stack()                   # -> Series with MultiIndex (Date, ticker)\n",
+    "        .rename(\"rolling_corr\")\n",
+    "        .rename_axis([\"Date\", \"ticker\"])\n",
+    "        .reset_index()\n",
+    "        .dropna(subset=[\"rolling_corr\"])\n",
+    "    )\n",
+    "    rolling_long.to_csv(\"rolling_corr_long.csv\",\n",
+    "                        index=False, float_format=\"%.6f\")\n",
+    "\n",
+    "# ---------- 2) β-HEDGED EXCESS, VOLATILITY & SELECTION ----------\n",
+    "def _nw_lags(freq: str) -> int:\n",
+    "    return 4 if freq.startswith(\"W\") else 21   # ≈1 month of obs\n",
+    "\n",
+    "hac_lags = _nw_lags(FREQ)\n",
+    "\n",
+    "# Benchmark annualized volatility (on full return series at chosen frequency)\n",
+    "bench_vol_ann = bench.std(ddof=1) * np.sqrt(PER_YEAR)\n",
+    "\n",
+    "rows = []\n",
+    "for s in stocks:\n",
+    "    # Align series to common non-NA dates for regression & stats\n",
+    "    df_xy = rets[[s, BENCH]].dropna()\n",
+    "    if df_xy.empty:\n",
+    "        continue\n",
+    "\n",
+    "    y = df_xy[s]\n",
+    "    X = sm.add_constant(df_xy[BENCH])\n",
+    "    model = sm.OLS(y, X).fit(\n",
+    "        cov_type=\"HAC\",\n",
+    "        cov_kwds={\"maxlags\": hac_lags}\n",
+    "    )\n",
+    "\n",
+    "    alpha     = model.params[\"const\"]\n",
+    "    beta_i    = model.params[BENCH]\n",
+    "    t_alpha   = model.tvalues[\"const\"]\n",
+    "    alpha_ann = alpha * PER_YEAR\n",
+    "\n",
+    "    # β-hedged excess series\n",
+    "    r_excess = y - beta_i * df_xy[BENCH]\n",
+    "    mu, sd   = r_excess.mean(), r_excess.std(ddof=1)\n",
+    "    hedged_sharpe = (mu / sd) * np.sqrt(PER_YEAR) if sd > 0 else np.nan\n",
+    "    cum_excess_beta_log = r_excess.cumsum().iloc[-1]\n",
+    "\n",
+    "    # Simple 1× copper cumulative log excess\n",
+    "    cum_excess_1x_log = y.cumsum().iloc[-1] - df_xy[BENCH].cumsum().iloc[-1]\n",
+    "\n",
+    "    # Annualized volatility for this stock (aligned sample)\n",
+    "    vol_ann = y.std(ddof=1) * np.sqrt(PER_YEAR)\n",
+    "\n",
+    "    # Label\n",
+    "    if (alpha_ann > 0) and (t_alpha > ALPHA_T_CUT) and (hedged_sharpe > SHARPE_CUT):\n",
+    "        label = \"Outperform\"\n",
+    "    elif (alpha_ann < 0) and (t_alpha < -ALPHA_T_CUT) and (hedged_sharpe < -SHARPE_CUT):\n",
+    "        label = \"Underperform\"\n",
+    "    else:\n",
+    "        label = \"Neutral\"\n",
+    "\n",
+    "    rows.append({\n",
+    "        \"ticker\": s,\n",
+    "        \"corr_to_copper\": corr_map.get(s, np.nan),\n",
+    "        \"beta_to_copper\": beta_map.get(s, np.nan),\n",
+    "        \"alpha_ann\": alpha_ann,\n",
+    "        \"t_alpha\": t_alpha,\n",
+    "        \"hedged_sharpe\": hedged_sharpe,\n",
+    "        \"cum_excess_beta_log\": cum_excess_beta_log,\n",
+    "        \"cum_excess_1x_log\": cum_excess_1x_log,\n",
+    "        \"vol_ann\": vol_ann,                 # stock annualized volatility\n",
+    "        \"bench_vol_ann\": bench_vol_ann,     # HG=F annualized volatility (same for all rows)\n",
+    "        \"label\": label\n",
+    "    })\n",
+    "\n",
+    "excess_summary = (pd.DataFrame(rows)\n",
+    "                    .set_index(\"ticker\")\n",
+    "                    .sort_values([\"label\", \"alpha_ann\", \"hedged_sharpe\"],\n",
+    "                                 ascending=[True, False, False]))\n",
+    "excess_summary.to_csv(\"excess_summary.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "print(\"✓ Saved corr_beta.csv,\",\n",
+    "      \"rolling_corr_long.csv,\" if ROLL_WINDOW else \"\",\n",
+    "      \"excess_summary.csv (with corr_to_copper, vol_ann, bench_vol_ann)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "13fb4bda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 20 outperformers; price matrix shape: (1208, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load prices and labels\n",
+    "prices = pd.read_csv(\"df_prices_final.csv\", parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "labels = pd.read_csv(\"excess_summary.csv\", index_col=0)\n",
+    "\n",
+    "# Select tickers labeled as OUTPERFORM (case-insensitive)\n",
+    "op_tickers = (\n",
+    "    labels.assign(_lab=labels[\"label\"].astype(str).str.upper())\n",
+    "          .query(\"_lab == 'OUTPERFORM'\")\n",
+    "          .index.tolist()\n",
+    ")\n",
+    "\n",
+    "# Keep only those tickers that exist in the price matrix\n",
+    "op_tickers = [t for t in op_tickers if t in prices.columns]\n",
+    "\n",
+    "# Slice prices → new DataFrame\n",
+    "outperforming_stocks = prices.loc[:, op_tickers].copy()\n",
+    "\n",
+    "print(f\"Found {len(op_tickers)} outperformers; price matrix shape: {outperforming_stocks.shape}\")\n",
+    "\n",
+    "# Optional: include the benchmark column too\n",
+    "# outperforming_with_bench = prices.loc[:, ['HG=F'] + op_tickers].copy()\n",
+    "\n",
+    "# Save to CSV (optional)\n",
+    "outperforming_stocks.to_csv(\"outperforming_stocks.csv\", index_label=\"Date\", float_format=\"%.6f\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Stock_Benchmark Analysis/Stock_Benchmark Analysis.ipynb

@@ -0,0 +1,694 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "811bdabd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/6b/p92_dgbd07ldpbpq29vbpd_m0000gn/T/ipykernel_75834/244845463.py:4: DtypeWarning: Columns (11,13,23,24,75,76,79,80,83,84,90) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  df = pd.read_csv('unique_companies.csv')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the CSV file\n",
+    "df = pd.read_csv('unique_companies.csv')\n",
+    "\n",
+    "# Filter rows where 'Industry' is 'Copper'\n",
+    "filtered_df = df[df['Industry'] == 'Copper']\n",
+    "\n",
+    "# If you want to save the result to a new CSV\n",
+    "filtered_df.to_csv('unique_companies_copper.csv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "261ce11e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 150 tickers (including HG=F).\n",
+      "Downloading batch 1: 50 tickers\n",
+      "Downloading batch 2: 50 tickers\n",
+      "Downloading batch 3: 50 tickers\n",
+      "df_prices shape: (1307, 150)\n",
+      "Columns: ['HG=F', '000630.SZ', '000737.SZ', '000878.SZ', '002203.SZ', '005810.KS', '08W.F', '2009.TW', '2IK.F', '300618.SZ', '300697.SZ', '301511.SZ', '381.F', '3N4.SG', '4989.TW', '5PMA.F', '600255.SS', '600362.SS', '600490.SS', '601137.SS', '601609.SS', '603124.SS', '688102.SS', '688388.SS', '7GI.F', '7LY0.F', '97E0.F', '9CM0.F', 'ACMDY', 'ALM.AX', 'ANFGF', 'AR1.AX', 'ARJN.V', 'ARJNF', 'ARREF', 'ASCU.TO', 'ASCUF', 'ATCUF', 'ATYM.L', 'AXO.V', 'BCU.V', 'BCUFF', 'BFGFF', 'BHAGYANGR.NS', 'BP60.F', 'BRVRF', 'BZDLF', 'C730.F', 'CAEN', 'CAML.L', 'CAMLF', 'CFV0.F', 'COPR', 'CPCPF', 'CPER.V', 'CPFXF', 'CPO.AX', 'CPORF', 'CPPKF', 'CPPMF', 'CPR.JO', 'CS.TO', 'CSC.AX', 'CSCCF', 'CUBEXTUB.NS', 'CUU.V', 'CVV.AX', 'CYM.AX', 'CYPMF', 'E2E1.F', 'E9E.F', 'EMTRF', 'ERO', 'FCX', 'FCXO34.SA', 'FDY.TO', 'FG1.F', 'FPMB.F', 'FQVLF', 'GCUMF', 'GRX.AX', 'GRX.L', 'GSCU.L', 'H6F.F', 'HBM', 'HBM.TO', 'HCH.V', 'HDRSF', 'HGO.AX', 'HHLKF', 'HI.V', 'HIN.MU', 'HINDCOPPER.NS', 'HLGVF', 'HNCUF', 'IE', 'IE.TO', 'INUMF', 'IPMLF', 'JGRRF', 'JIX.F', 'KCC.V', 'KGH.WA', 'KGHPF', 'LA.V', 'LSANF', 'LUNMF', 'MAC.AX', 'MARI.TO', 'MARIF', 'MCL.NS', 'MMLTF', 'MTAL', 'MTJ3.F', 'NFM.AX', 'NRX.AX', 'NTM.AX', 'NU0.F', 'OCKA.F', 'OUW0.F', 'PMAM3.SA', 'PNTZF', 'PSGR', 'PUCOBRE.SN', 'Q.V', 'QCCUF', 'RAJMET.NS', 'RDS.AX', 'RE8.F', 'RRR.AX', 'SAGARDEEP.NS', 'SARKY.IS', 'SCCO', 'SFR.AX', 'SFRRF', 'SLMFF', 'TFM.V', 'TGB', 'TKO.L', 'TNC.AX', 'TRRCF', 'TVCCF', 'TWO.V', 'TWOSF', 'USCUF', 'VCUFF', 'WA1.AX', 'WAORF', 'WCUFF', 'XXIX.V']\n",
+      "Saved to 'df_prices.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------------------------------------------------------------\n",
+    "# Build df_prices.csv for HG=F + tickers in unique_companies_copper.csv\n",
+    "# • period=\"5y\" (more reliable than start/end for some venues)\n",
+    "# • Prefer 'Adj Close', fallback to 'Close'\n",
+    "# • Re-download single tickers that are all-NaN in batch (e.g., 2IK.F)\n",
+    "# ------------------------------------------------------------\n",
+    "# pip install yfinance pandas\n",
+    "\n",
+    "import pandas as pd\n",
+    "import yfinance as yf\n",
+    "\n",
+    "CSV_PATH = \"unique_companies_copper.csv\"\n",
+    "TICKER_COL = \"PrimaryTicker\"\n",
+    "UNDERLYING = \"HG=F\"\n",
+    "BATCH_SIZE = 50\n",
+    "OUT_CSV   = \"df_prices.csv\"\n",
+    "\n",
+    "# --- Read tickers ---\n",
+    "tickers = (\n",
+    "    pd.read_csv(CSV_PATH, usecols=[TICKER_COL])[TICKER_COL]\n",
+    "      .dropna().astype(str).str.strip().str.upper().tolist()\n",
+    ")\n",
+    "tickers = sorted(set(tickers))\n",
+    "if UNDERLYING not in tickers:\n",
+    "    tickers = [UNDERLYING] + tickers\n",
+    "\n",
+    "print(f\"Found {len(tickers)} tickers (including {UNDERLYING}).\")\n",
+    "\n",
+    "def _extract_adj_or_close(df_multi: pd.DataFrame) -> tuple[pd.DataFrame, list[str]]:\n",
+    "    \"\"\"From yfinance multi-ticker frame, prefer 'Adj Close', else 'Close' per ticker.\"\"\"\n",
+    "    if not isinstance(df_multi.columns, pd.MultiIndex):\n",
+    "        raise ValueError(\"Expected MultiIndex columns for multi-ticker download.\")\n",
+    "    fields = set(df_multi.columns.get_level_values(-1))\n",
+    "    adj = df_multi.xs(\"Adj Close\", axis=1, level=-1, drop_level=True) if \"Adj Close\" in fields else pd.DataFrame(index=df_multi.index)\n",
+    "    clo = df_multi.xs(\"Close\",      axis=1, level=-1, drop_level=True) if \"Close\"      in fields else pd.DataFrame(index=df_multi.index)\n",
+    "\n",
+    "    cols = sorted(set(adj.columns).union(clo.columns))\n",
+    "    out  = pd.DataFrame(index=df_multi.index, columns=cols, dtype=\"float64\")\n",
+    "    used_close = []\n",
+    "\n",
+    "    for t in cols:\n",
+    "        a = adj[t] if t in adj.columns else None\n",
+    "        c = clo[t] if t in clo.columns else None\n",
+    "        if a is not None and not a.dropna().empty:\n",
+    "            out[t] = a\n",
+    "        elif c is not None and not c.dropna().empty:\n",
+    "            out[t] = c\n",
+    "            used_close.append(t)\n",
+    "    return out, used_close\n",
+    "\n",
+    "def _download_batch(batch):\n",
+    "    df = yf.download(\n",
+    "        tickers=batch,\n",
+    "        period=\"5y\",\n",
+    "        interval=\"1d\",\n",
+    "        auto_adjust=False,\n",
+    "        actions=False,\n",
+    "        progress=False,\n",
+    "        group_by=\"ticker\",\n",
+    "        threads=True\n",
+    "    )\n",
+    "    if isinstance(df.columns, pd.MultiIndex):\n",
+    "        return _extract_adj_or_close(df)\n",
+    "    else:\n",
+    "        # Single-ticker shape\n",
+    "        tkr = batch[0]\n",
+    "        adj = df.get(\"Adj Close\")\n",
+    "        clo = df.get(\"Close\")\n",
+    "        used_close = []\n",
+    "        if adj is not None and not adj.dropna().empty:\n",
+    "            out = adj.rename(tkr).to_frame()\n",
+    "        elif clo is not None and not clo.dropna().empty:\n",
+    "            out = clo.rename(tkr).to_frame()\n",
+    "            used_close.append(tkr)\n",
+    "        else:\n",
+    "            out = pd.DataFrame(index=df.index, columns=[tkr], dtype=\"float64\")\n",
+    "        return out, used_close\n",
+    "\n",
+    "def _download_single(tkr: str) -> pd.Series:\n",
+    "    \"\"\"Single-ticker repair path; prefer Adj Close, else Close.\"\"\"\n",
+    "    df = yf.download(\n",
+    "        tickers=tkr,\n",
+    "        period=\"5y\",\n",
+    "        interval=\"1d\",\n",
+    "        auto_adjust=False,\n",
+    "        actions=False,\n",
+    "        progress=False\n",
+    "    )\n",
+    "    s = df.get(\"Adj Close\")\n",
+    "    if s is None or s.dropna().empty:\n",
+    "        s = df.get(\"Close\")\n",
+    "    if s is None:\n",
+    "        return pd.Series(dtype=\"float64\", name=tkr)\n",
+    "    return s.rename(tkr)\n",
+    "\n",
+    "# --- Batch download + merge ---\n",
+    "frames, used_close_all = [], []\n",
+    "for i in range(0, len(tickers), BATCH_SIZE):\n",
+    "    batch = tickers[i:i+BATCH_SIZE]\n",
+    "    print(f\"Downloading batch {i//BATCH_SIZE + 1}: {len(batch)} tickers\")\n",
+    "    part, used_close = _download_batch(batch)\n",
+    "    frames.append(part)\n",
+    "    used_close_all.extend(used_close)\n",
+    "\n",
+    "df_prices = pd.concat(frames, axis=1)\n",
+    "df_prices = df_prices.loc[:, ~df_prices.columns.duplicated()].sort_index()\n",
+    "\n",
+    "# --- Repair tickers that are NaN-only or missing after batch ---\n",
+    "to_repair = [t for t in tickers if (t in df_prices.columns and df_prices[t].dropna().empty) or (t not in df_prices.columns)]\n",
+    "to_repair = sorted(set(to_repair))\n",
+    "if to_repair:\n",
+    "    print(f\"Repairing via single-ticker fetch: {to_repair}\")\n",
+    "    for t in to_repair:\n",
+    "        s = _download_single(t)\n",
+    "        if not s.dropna().empty:\n",
+    "            df_prices = df_prices.reindex(df_prices.index.union(s.index)).sort_index()\n",
+    "            df_prices[t] = s.reindex(df_prices.index)\n",
+    "\n",
+    "# --- Order columns; drop all-NaN tickers ---\n",
+    "ordered_cols = [UNDERLYING] + [t for t in tickers if t != UNDERLYING and t in df_prices.columns]\n",
+    "df_prices = df_prices.reindex(columns=ordered_cols)\n",
+    "all_nan_cols = [c for c in df_prices.columns if df_prices[c].dropna().empty]\n",
+    "if all_nan_cols:\n",
+    "    print(f\"Dropping tickers with no usable data: {all_nan_cols}\")\n",
+    "    df_prices = df_prices.drop(columns=all_nan_cols)\n",
+    "\n",
+    "# --- Report fallback usage ---\n",
+    "used_close_all = sorted(set([t for t in used_close_all if t in df_prices.columns]))\n",
+    "if used_close_all:\n",
+    "    print(f\"Used 'Close' fallback for: {used_close_all}\")\n",
+    "\n",
+    "print(\"df_prices shape:\", df_prices.shape)\n",
+    "print(\"Columns:\", list(df_prices.columns))\n",
+    "\n",
+    "# --- Save ---\n",
+    "df_prices.to_csv(OUT_CSV, index_label=\"Date\", float_format=\"%.6f\")\n",
+    "print(f\"Saved to '{OUT_CSV}'.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "11079562",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Missing % by ticker ===\n",
+      "           missing_pct\n",
+      "AXO.V        97.016067\n",
+      "603124.SS    93.037490\n",
+      "NFM.AX       87.299158\n",
+      "WAORF        85.233359\n",
+      "ASCUF        81.637337\n",
+      "...                ...\n",
+      "OUW0.F        2.371844\n",
+      "9CM0.F        2.371844\n",
+      "5PMA.F        2.371844\n",
+      "3N4.SG        2.371844\n",
+      "E2E1.F        2.371844\n",
+      "\n",
+      "[150 rows x 1 columns]\n",
+      "\n",
+      "Dropping 45 tickers (> 10% missing): ['AXO.V', '603124.SS', 'NFM.AX', 'WAORF', 'ASCUF', 'CSC.AX', 'MAC.AX', 'CPPMF', 'JGRRF', '301511.SZ', 'HNCUF', 'TRRCF', 'CPR.JO', 'CYPMF', '381.F', 'CPORF', 'IE', 'IE.TO', 'CPCPF', '688102.SS', 'INUMF', 'CSCCF', 'WA1.AX', '7LY0.F', 'CPER.V', 'NU0.F', 'HCH.V', 'GSCU.L', 'E9E.F', 'Q.V', 'ASCU.TO', 'H6F.F', 'AR1.AX', 'VCUFF', '97E0.F', 'MTAL', 'RRR.AX', 'CAMLF', 'CPO.AX', 'WCUFF', 'EMTRF', '7GI.F', 'QCCUF', '2IK.F', 'CPPKF']\n",
+      "\n",
+      "Shapes:\n",
+      "Before: (1307, 150) After: (1307, 105)\n",
+      "Saved to 'df_prices_final.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------------------------------------------------------------\n",
+    "# Load df_prices.csv, compute missing % per ticker,\n",
+    "# drop columns with >50% missing, save df_prices_final.csv\n",
+    "# ------------------------------------------------------------\n",
+    "import pandas as pd\n",
+    "\n",
+    "IN_CSV  = \"df_prices.csv\"\n",
+    "OUT_CSV = \"df_prices_final.csv\"\n",
+    "THRESH  = 10.0  # percent\n",
+    "\n",
+    "df_prices = pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "\n",
+    "# Missing % over the full DataFrame index\n",
+    "missing_pct = df_prices.isna().mean() * 100.0\n",
+    "report = (\n",
+    "    pd.DataFrame({\"missing_pct\": missing_pct})\n",
+    "    .sort_values(\"missing_pct\", ascending=False)\n",
+    ")\n",
+    "print(\"=== Missing % by ticker ===\")\n",
+    "print(report)\n",
+    "\n",
+    "# Drop tickers with >50% missing\n",
+    "to_drop = report.index[report[\"missing_pct\"] > THRESH].tolist()\n",
+    "print(f\"\\nDropping {len(to_drop)} tickers (> {THRESH:.0f}% missing): {to_drop}\")\n",
+    "\n",
+    "df_prices_final = df_prices.drop(columns=to_drop, errors=\"ignore\")\n",
+    "\n",
+    "print(\"\\nShapes:\")\n",
+    "print(\"Before:\", df_prices.shape, \"After:\", df_prices_final.shape)\n",
+    "\n",
+    "df_prices_final.to_csv(OUT_CSV, index_label=\"Date\", float_format=\"%.6f\")\n",
+    "print(f\"Saved to '{OUT_CSV}'.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8dc7673c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved clean, rectangular prices to 'df_prices_final.csv' with shape (1208, 105).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ------------------------------------------------------------\n",
+    "# Clean df_prices_final: common window + bfill→ffill + final NA drop\n",
+    "# Input : df_prices_final.csv (your current file with some missing)\n",
+    "# Output: df_prices_final.csv (overwritten, rectangular, NA-free)\n",
+    "# ------------------------------------------------------------\n",
+    "import pandas as pd\n",
+    "\n",
+    "IN_CSV  = \"df_prices_final.csv\"\n",
+    "OUT_CSV = \"df_prices_final.csv\"   # overwrite in place\n",
+    "\n",
+    "df = pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\").sort_index()\n",
+    "\n",
+    "# 1) Common window (everyone has started and not yet delisted)\n",
+    "first_valid = df.apply(pd.Series.first_valid_index)\n",
+    "last_valid  = df.apply(pd.Series.last_valid_index)\n",
+    "\n",
+    "common_start = max(first_valid.dropna())\n",
+    "common_end   = min(last_valid.dropna())\n",
+    "\n",
+    "df = df.loc[common_start:common_end].copy()\n",
+    "\n",
+    "# 2) Business-day index to harmonize calendars\n",
+    "bidx = pd.date_range(df.index.min(), df.index.max(), freq=\"B\")\n",
+    "df = df.reindex(bidx)\n",
+    "\n",
+    "# 3) Fill:\n",
+    "#    - Backfill once to seed the first business day for tickers closed on common_start\n",
+    "#    - Forward-fill for holiday gaps etc.\n",
+    "df = df.bfill(limit=None).ffill(limit=None)\n",
+    "\n",
+    "# 4) Final sanity check: drop any rare rows still containing NA\n",
+    "before_rows = df.shape[0]\n",
+    "df = df.dropna(how=\"any\")\n",
+    "after_rows = df.shape[0]\n",
+    "if before_rows != after_rows:\n",
+    "    print(f\"Dropped {before_rows - after_rows} rows that still had NAs after filling.\")\n",
+    "\n",
+    "# 5) Save\n",
+    "df.to_csv(OUT_CSV, index_label=\"Date\", float_format=\"%.6f\")\n",
+    "print(f\"Saved clean, rectangular prices to '{OUT_CSV}' with shape {df.shape}.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8cdec19b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Saved  corr_beta.csv,  excess_summary.csv (with corr_to_copper)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================\n",
+    "# Copper-linked stocks vs COMEX copper (HG=F)\n",
+    "#   • Correlation & beta\n",
+    "#   • Rolling correlation\n",
+    "#   • β-hedged alpha / excess-return summary\n",
+    "# Outputs:\n",
+    "#   corr_beta.csv, rolling_corr_long.csv, excess_summary.csv\n",
+    "#   (excess_summary.csv now includes corr_to_copper)\n",
+    "# ============================================================\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# ---------- PARAMETERS ----------\n",
+    "IN_CSV       = \"df_prices_final.csv\"  # prices, Date index\n",
+    "BENCH        = \"HG=F\"                 # underlying metal\n",
+    "FREQ         = \"W-FRI\"                # 'D' for daily or 'W-FRI'\n",
+    "ROLL_WINDOW  = None                     # 26 weeks ≈ 6 months; set None to skip\n",
+    "USE_LOG_RET  = True                   # True = log-returns, False = pct-change\n",
+    "ALPHA_T_CUT  = 0.9                    # |t| threshold for alpha significance\n",
+    "SHARPE_CUT   = 0.0                    # >0 required for outperform label\n",
+    "\n",
+    "# Annualisation factor\n",
+    "PER_YEAR = 52 if FREQ.startswith(\"W\") else 252\n",
+    "\n",
+    "# ---------- LOAD PRICES ----------\n",
+    "px = (pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "         .sort_index())\n",
+    "assert BENCH in px.columns, f\"{BENCH} not found in columns.\"\n",
+    "\n",
+    "# Optional resample (weekly smooths roll/holiday noise)\n",
+    "if FREQ != \"D\":\n",
+    "    px = px.resample(FREQ).last()\n",
+    "\n",
+    "# ---------- RETURNS ----------\n",
+    "rets = np.log(px).diff() if USE_LOG_RET else px.pct_change()\n",
+    "rets = rets.dropna(how=\"all\")\n",
+    "\n",
+    "bench = rets[BENCH]\n",
+    "stocks = [c for c in rets.columns if c != BENCH]\n",
+    "\n",
+    "# ---------- 1) CORRELATION & BETA ----------\n",
+    "corr = rets[stocks].corrwith(bench)                 # Series: index=ticker\n",
+    "var_b = bench.var(ddof=1)\n",
+    "beta = rets[stocks].apply(lambda s: s.cov(bench)) / var_b\n",
+    "\n",
+    "corr_beta = (pd.DataFrame({\"corr_to_copper\": corr,\n",
+    "                           \"beta_to_copper\": beta})\n",
+    "             .sort_values(\"corr_to_copper\", ascending=False))\n",
+    "corr_beta.index.name = \"ticker\"\n",
+    "corr_beta.to_csv(\"corr_beta.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "# Keep maps for reuse in excess_summary\n",
+    "corr_map = corr.to_dict()\n",
+    "beta_map = beta.to_dict()\n",
+    "\n",
+    "# ---------- OPTIONAL ROLLING CORRELATION ----------\n",
+    "if ROLL_WINDOW:\n",
+    "    rolling_long = (\n",
+    "        rets[stocks]\n",
+    "        .rolling(ROLL_WINDOW)\n",
+    "        .corr(bench)               # wide: Date × tickers\n",
+    "        .stack()                   # -> Series with MultiIndex (Date, ticker)\n",
+    "        .rename(\"rolling_corr\")\n",
+    "        .rename_axis([\"Date\", \"ticker\"])\n",
+    "        .reset_index()\n",
+    "        .dropna(subset=[\"rolling_corr\"])\n",
+    "    )\n",
+    "    rolling_long.to_csv(\"rolling_corr_long.csv\",\n",
+    "                        index=False, float_format=\"%.6f\")\n",
+    "\n",
+    "# ---------- 2) β-HEDGED EXCESS & SELECTION ----------\n",
+    "def _nw_lags(freq: str) -> int:\n",
+    "    return 4 if freq.startswith(\"W\") else 21   # ≈1 month of obs\n",
+    "\n",
+    "hac_lags = _nw_lags(FREQ)\n",
+    "\n",
+    "rows = []\n",
+    "for s in stocks:\n",
+    "    # align & drop NA\n",
+    "    df_xy = rets[[s, BENCH]].dropna()\n",
+    "    if df_xy.empty:\n",
+    "        continue\n",
+    "\n",
+    "    y = df_xy[s]\n",
+    "    X = sm.add_constant(df_xy[BENCH])\n",
+    "    model = sm.OLS(y, X).fit(\n",
+    "        cov_type=\"HAC\",\n",
+    "        cov_kwds={\"maxlags\": hac_lags}\n",
+    "    )\n",
+    "\n",
+    "    alpha     = model.params[\"const\"]\n",
+    "    beta_i    = model.params[BENCH]\n",
+    "    t_alpha   = model.tvalues[\"const\"]\n",
+    "    alpha_ann = alpha * PER_YEAR\n",
+    "\n",
+    "    # β-hedged excess series\n",
+    "    r_excess = y - beta_i * df_xy[BENCH]\n",
+    "    mu, sd   = r_excess.mean(), r_excess.std(ddof=1)\n",
+    "    hedged_sharpe = (mu / sd) * np.sqrt(PER_YEAR) if sd > 0 else np.nan\n",
+    "    cum_excess_beta_log = r_excess.cumsum().iloc[-1]\n",
+    "\n",
+    "    # Simple 1× copper cumulative log excess\n",
+    "    cum_excess_1x_log = y.cumsum().iloc[-1] - df_xy[BENCH].cumsum().iloc[-1]\n",
+    "\n",
+    "    # Label\n",
+    "    if (alpha_ann > 0) and (t_alpha > ALPHA_T_CUT) and (hedged_sharpe > SHARPE_CUT):\n",
+    "        label = \"Outperform\"\n",
+    "    elif (alpha_ann < 0) and (t_alpha < -ALPHA_T_CUT) and (hedged_sharpe < -SHARPE_CUT):\n",
+    "        label = \"Underperform\"\n",
+    "    else:\n",
+    "        label = \"Neutral\"\n",
+    "\n",
+    "    rows.append({\n",
+    "        \"ticker\": s,\n",
+    "        \"corr_to_copper\": corr_map.get(s, np.nan),  # <-- added\n",
+    "        \"beta_to_copper\": beta_map.get(s, np.nan),  # keep for consistency\n",
+    "        \"alpha_ann\": alpha_ann,\n",
+    "        \"t_alpha\": t_alpha,\n",
+    "        \"hedged_sharpe\": hedged_sharpe,\n",
+    "        \"cum_excess_beta_log\": cum_excess_beta_log,\n",
+    "        \"cum_excess_1x_log\": cum_excess_1x_log,\n",
+    "        \"label\": label\n",
+    "    })\n",
+    "\n",
+    "excess_summary = (pd.DataFrame(rows)\n",
+    "                    .set_index(\"ticker\")\n",
+    "                    .sort_values([\"label\", \"alpha_ann\", \"hedged_sharpe\"],\n",
+    "                                 ascending=[True, False, False]))\n",
+    "excess_summary.to_csv(\"excess_summary.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "print(\"✓ Saved  corr_beta.csv,\",\n",
+    "      \"rolling_corr_long.csv,\" if ROLL_WINDOW else \"\",\n",
+    "      \"excess_summary.csv (with corr_to_copper)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a651e4d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Saved corr_beta.csv,  excess_summary.csv (with corr_to_copper, vol_ann, bench_vol_ann)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ============================================================\n",
+    "# Copper-linked stocks vs COMEX copper (HG=F)\n",
+    "#   • Correlation & beta\n",
+    "#   • Rolling correlation\n",
+    "#   • β-hedged alpha / excess-return summary\n",
+    "#   • Annualized volatility (each stock + HG=F)\n",
+    "# Outputs:\n",
+    "#   corr_beta.csv, rolling_corr_long.csv, excess_summary.csv\n",
+    "# ============================================================\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# ---------- PARAMETERS ----------\n",
+    "IN_CSV       = \"df_prices_final.csv\"  # prices, Date index\n",
+    "BENCH        = \"HG=F\"                 # underlying metal\n",
+    "FREQ         = \"W-FRI\"                # 'D' for daily or 'W-FRI' (recommended)\n",
+    "ROLL_WINDOW  = None                     # 26 weeks ≈ 6 months; set None to skip\n",
+    "USE_LOG_RET  = True                   # True = log-returns, False = pct-change\n",
+    "ALPHA_T_CUT  = 0.8                    # |t| threshold for alpha significance\n",
+    "SHARPE_CUT   = 0.0                    # >0 required for outperform label\n",
+    "\n",
+    "# Annualisation factor\n",
+    "PER_YEAR = 52 if FREQ.startswith(\"W\") else 252\n",
+    "\n",
+    "# ---------- LOAD PRICES ----------\n",
+    "px = (pd.read_csv(IN_CSV, parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "         .sort_index())\n",
+    "assert BENCH in px.columns, f\"{BENCH} not found in columns.\"\n",
+    "\n",
+    "# Optional resample (weekly smooths roll/holiday noise)\n",
+    "if FREQ != \"D\":\n",
+    "    px = px.resample(FREQ).last()\n",
+    "\n",
+    "# ---------- RETURNS ----------\n",
+    "rets = np.log(px).diff() if USE_LOG_RET else px.pct_change()\n",
+    "rets = rets.dropna(how=\"all\")\n",
+    "\n",
+    "bench = rets[BENCH]\n",
+    "stocks = [c for c in rets.columns if c != BENCH]\n",
+    "\n",
+    "# ---------- 1) CORRELATION & BETA ----------\n",
+    "corr = rets[stocks].corrwith(bench)\n",
+    "var_b = bench.var(ddof=1)\n",
+    "beta = rets[stocks].apply(lambda s: s.cov(bench)) / var_b\n",
+    "\n",
+    "corr_beta = (pd.DataFrame({\"corr_to_copper\": corr,\n",
+    "                           \"beta_to_copper\": beta})\n",
+    "             .sort_values(\"corr_to_copper\", ascending=False))\n",
+    "corr_beta.index.name = \"ticker\"\n",
+    "corr_beta.to_csv(\"corr_beta.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "# Maps to reuse\n",
+    "corr_map = corr.to_dict()\n",
+    "beta_map = beta.to_dict()\n",
+    "\n",
+    "# ---------- OPTIONAL ROLLING CORRELATION ----------\n",
+    "if ROLL_WINDOW:\n",
+    "    rolling_long = (\n",
+    "        rets[stocks]\n",
+    "        .rolling(ROLL_WINDOW)\n",
+    "        .corr(bench)               # wide: Date × tickers\n",
+    "        .stack()                   # -> Series with MultiIndex (Date, ticker)\n",
+    "        .rename(\"rolling_corr\")\n",
+    "        .rename_axis([\"Date\", \"ticker\"])\n",
+    "        .reset_index()\n",
+    "        .dropna(subset=[\"rolling_corr\"])\n",
+    "    )\n",
+    "    rolling_long.to_csv(\"rolling_corr_long.csv\",\n",
+    "                        index=False, float_format=\"%.6f\")\n",
+    "\n",
+    "# ---------- 2) β-HEDGED EXCESS, VOLATILITY & SELECTION ----------\n",
+    "def _nw_lags(freq: str) -> int:\n",
+    "    return 4 if freq.startswith(\"W\") else 21   # ≈1 month of obs\n",
+    "\n",
+    "hac_lags = _nw_lags(FREQ)\n",
+    "\n",
+    "# Benchmark annualized volatility (on full return series at chosen frequency)\n",
+    "bench_vol_ann = bench.std(ddof=1) * np.sqrt(PER_YEAR)\n",
+    "\n",
+    "rows = []\n",
+    "for s in stocks:\n",
+    "    # Align series to common non-NA dates for regression & stats\n",
+    "    df_xy = rets[[s, BENCH]].dropna()\n",
+    "    if df_xy.empty:\n",
+    "        continue\n",
+    "\n",
+    "    y = df_xy[s]\n",
+    "    X = sm.add_constant(df_xy[BENCH])\n",
+    "    model = sm.OLS(y, X).fit(\n",
+    "        cov_type=\"HAC\",\n",
+    "        cov_kwds={\"maxlags\": hac_lags}\n",
+    "    )\n",
+    "\n",
+    "    alpha     = model.params[\"const\"]\n",
+    "    beta_i    = model.params[BENCH]\n",
+    "    t_alpha   = model.tvalues[\"const\"]\n",
+    "    alpha_ann = alpha * PER_YEAR\n",
+    "\n",
+    "    # β-hedged excess series\n",
+    "    r_excess = y - beta_i * df_xy[BENCH]\n",
+    "    mu, sd   = r_excess.mean(), r_excess.std(ddof=1)\n",
+    "    hedged_sharpe = (mu / sd) * np.sqrt(PER_YEAR) if sd > 0 else np.nan\n",
+    "    cum_excess_beta_log = r_excess.cumsum().iloc[-1]\n",
+    "\n",
+    "    # Simple 1× copper cumulative log excess\n",
+    "    cum_excess_1x_log = y.cumsum().iloc[-1] - df_xy[BENCH].cumsum().iloc[-1]\n",
+    "\n",
+    "    # Annualized volatility for this stock (aligned sample)\n",
+    "    vol_ann = y.std(ddof=1) * np.sqrt(PER_YEAR)\n",
+    "\n",
+    "    # Label\n",
+    "    if (alpha_ann > 0) and (t_alpha > ALPHA_T_CUT) and (hedged_sharpe > SHARPE_CUT):\n",
+    "        label = \"Outperform\"\n",
+    "    elif (alpha_ann < 0) and (t_alpha < -ALPHA_T_CUT) and (hedged_sharpe < -SHARPE_CUT):\n",
+    "        label = \"Underperform\"\n",
+    "    else:\n",
+    "        label = \"Neutral\"\n",
+    "\n",
+    "    rows.append({\n",
+    "        \"ticker\": s,\n",
+    "        \"corr_to_copper\": corr_map.get(s, np.nan),\n",
+    "        \"beta_to_copper\": beta_map.get(s, np.nan),\n",
+    "        \"alpha_ann\": alpha_ann,\n",
+    "        \"t_alpha\": t_alpha,\n",
+    "        \"hedged_sharpe\": hedged_sharpe,\n",
+    "        \"cum_excess_beta_log\": cum_excess_beta_log,\n",
+    "        \"cum_excess_1x_log\": cum_excess_1x_log,\n",
+    "        \"vol_ann\": vol_ann,                 # stock annualized volatility\n",
+    "        \"bench_vol_ann\": bench_vol_ann,     # HG=F annualized volatility (same for all rows)\n",
+    "        \"label\": label\n",
+    "    })\n",
+    "\n",
+    "excess_summary = (pd.DataFrame(rows)\n",
+    "                    .set_index(\"ticker\")\n",
+    "                    .sort_values([\"label\", \"alpha_ann\", \"hedged_sharpe\"],\n",
+    "                                 ascending=[True, False, False]))\n",
+    "excess_summary.to_csv(\"excess_summary.csv\", float_format=\"%.6f\")\n",
+    "\n",
+    "print(\"✓ Saved corr_beta.csv,\",\n",
+    "      \"rolling_corr_long.csv,\" if ROLL_WINDOW else \"\",\n",
+    "      \"excess_summary.csv (with corr_to_copper, vol_ann, bench_vol_ann)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "13fb4bda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 20 outperformers; price matrix shape: (1208, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load prices and labels\n",
+    "prices = pd.read_csv(\"df_prices_final.csv\", parse_dates=[\"Date\"], index_col=\"Date\")\n",
+    "labels = pd.read_csv(\"excess_summary.csv\", index_col=0)\n",
+    "\n",
+    "# Select tickers labeled as OUTPERFORM (case-insensitive)\n",
+    "op_tickers = (\n",
+    "    labels.assign(_lab=labels[\"label\"].astype(str).str.upper())\n",
+    "          .query(\"_lab == 'OUTPERFORM'\")\n",
+    "          .index.tolist()\n",
+    ")\n",
+    "\n",
+    "# Keep only those tickers that exist in the price matrix\n",
+    "op_tickers = [t for t in op_tickers if t in prices.columns]\n",
+    "\n",
+    "# Slice prices → new DataFrame\n",
+    "outperforming_stocks = prices.loc[:, op_tickers].copy()\n",
+    "\n",
+    "print(f\"Found {len(op_tickers)} outperformers; price matrix shape: {outperforming_stocks.shape}\")\n",
+    "\n",
+    "# Optional: include the benchmark column too\n",
+    "# outperforming_with_bench = prices.loc[:, ['HG=F'] + op_tickers].copy()\n",
+    "\n",
+    "# Save to CSV (optional)\n",
+    "outperforming_stocks.to_csv(\"outperforming_stocks.csv\", index_label=\"Date\", float_format=\"%.6f\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Stock_Benchmark Analysis/corr_beta.csv

@@ -0,0 +1,105 @@
+ticker,corr_to_copper,beta_to_copper
+SCCO,0.751106,1.169829
+FCX,0.744135,1.426671
+KGH.WA,0.732039,1.197883
+CS.TO,0.731330,1.730688
+FPMB.F,0.715914,1.326046
+HBM,0.699505,1.612239
+HBM.TO,0.694932,1.508307
+FCXO34.SA,0.690445,1.266068
+TGB,0.682023,1.598263
+LUNMF,0.668351,1.278764
+ERO,0.667417,1.451800
+ARREF,0.663149,1.258081
+FQVLF,0.631128,1.608453
+OCKA.F,0.607377,1.362314
+ANFGF,0.604987,1.130759
+OUW0.F,0.587710,1.451457
+FG1.F,0.586101,0.937223
+600362.SS,0.578169,0.859542
+RE8.F,0.558737,0.930441
+000878.SZ,0.551521,0.736491
+000630.SZ,0.514417,0.750244
+SFR.AX,0.512234,0.893583
+CAML.L,0.508801,0.666887
+TKO.L,0.486790,0.908630
+ATYM.L,0.470796,0.713631
+2009.TW,0.459276,0.934166
+IPMLF,0.394290,0.790985
+000737.SZ,0.382355,0.744536
+HINDCOPPER.NS,0.380606,0.851744
+HGO.AX,0.365913,0.891063
+MARI.TO,0.364093,0.608576
+JIX.F,0.359048,0.845307
+601609.SS,0.357557,0.417161
+FDY.TO,0.332434,0.867706
+9CM0.F,0.321625,0.854389
+600490.SS,0.319510,0.648428
+SFRRF,0.309463,0.617418
+3N4.SG,0.308499,0.985685
+300697.SZ,0.301865,0.581731
+002203.SZ,0.290713,0.322780
+USCUF,0.290002,1.475519
+CVV.AX,0.288097,0.897164
+XXIX.V,0.287216,0.782133
+CPFXF,0.284255,1.126958
+PUCOBRE.SN,0.275522,0.266430
+300618.SZ,0.263318,0.526491
+CUU.V,0.258176,0.975104
+GCUMF,0.241195,0.706705
+BCU.V,0.238980,0.954954
+CYM.AX,0.237302,0.894108
+E2E1.F,0.235437,0.396147
+005810.KS,0.233232,0.308425
+MARIF,0.233169,0.419449
+NRX.AX,0.201323,0.821103
+4989.TW,0.198282,0.334545
+GRX.AX,0.192572,0.500298
+HDRSF,0.182560,0.582387
+GRX.L,0.181102,0.358387
+601137.SS,0.178591,0.352066
+BCUFF,0.169038,0.735712
+HI.V,0.168562,0.481719
+600255.SS,0.165257,0.313489
+PNTZF,0.157047,0.715470
+CUBEXTUB.NS,0.141331,0.384223
+MCL.NS,0.137765,0.342360
+ALM.AX,0.131943,0.506486
+BHAGYANGR.NS,0.131430,0.266450
+688388.SS,0.129130,0.283810
+5PMA.F,0.111442,0.332867
+PMAM3.SA,0.101593,0.236565
+HHLKF,0.094041,0.329713
+HIN.MU,0.090223,0.097573
+BFGFF,0.083922,0.685144
+C730.F,0.081191,0.517667
+RDS.AX,0.079513,0.302310
+LA.V,0.079408,0.135596
+NTM.AX,0.070476,0.282849
+MTJ3.F,0.069175,0.098631
+KCC.V,0.059441,0.239155
+ATCUF,0.055618,0.189407
+SARKY.IS,0.054537,0.108979
+LSANF,0.051172,0.096407
+SAGARDEEP.NS,0.050789,0.118154
+BRVRF,0.049157,0.282762
+TWOSF,0.040633,0.220326
+TFM.V,0.033252,0.216380
+08W.F,0.032241,0.289057
+CFV0.F,0.030216,0.478409
+MMLTF,0.023101,0.055416
+TVCCF,0.016415,0.386299
+TNC.AX,0.015794,0.153874
+ACMDY,0.012986,0.031652
+TWO.V,0.012275,0.047806
+HLGVF,0.010366,0.060247
+KGHPF,0.010355,0.018364
+SLMFF,0.009290,0.142938
+BZDLF,0.002504,0.016546
+ARJN.V,-0.010778,-0.056995
+BP60.F,-0.017995,-0.174410
+RAJMET.NS,-0.020866,-0.054317
+PSGR,-0.044813,-0.319164
+CAEN,-0.050007,-0.123762
+COPR,-0.071854,-1.133700
+ARJNF,-0.203482,-0.983979

Stock_Benchmark Analysis/excess_summary.csv

@@ -0,0 +1,105 @@
+ticker,corr_to_copper,beta_to_copper,alpha_ann,t_alpha,hedged_sharpe,cum_excess_beta_log,cum_excess_1x_log,vol_ann,bench_vol_ann,label
+COPR,-0.071854,-1.133700,0.517289,0.449278,0.131217,2.397436,1.401024,3.952470,0.250506,Neutral
+RAJMET.NS,-0.020866,-0.054317,0.275785,0.785795,0.423001,1.278157,0.785804,0.652115,0.250506,Neutral
+5PMA.F,0.111442,0.332867,0.207392,0.799005,0.278909,0.961181,0.649638,0.748242,0.250506,Neutral
+PSGR,-0.044813,-0.319164,0.140210,0.253978,0.078666,0.649821,0.033788,1.784135,0.250506,Neutral
+TKO.L,0.486790,0.908630,0.136683,0.797193,0.334639,0.633471,0.590803,0.467589,0.250506,Neutral
+SFRRF,0.309463,0.617418,0.128597,0.633124,0.270583,0.595996,0.417335,0.499792,0.250506,Neutral
+TGB,0.682023,1.598263,0.126734,0.775979,0.295197,0.587363,0.866744,0.587040,0.250506,Neutral
+000737.SZ,0.382355,0.744536,0.121576,0.597471,0.269730,0.563456,0.444157,0.487795,0.250506,Neutral
+600255.SS,0.165257,0.313489,0.117316,0.578922,0.250317,0.543717,0.223125,0.475205,0.250506,Neutral
+HI.V,0.168562,0.481719,0.112686,0.406562,0.159690,0.522257,0.280227,0.715901,0.250506,Neutral
+ATCUF,0.055618,0.189407,0.101662,0.339563,0.119353,0.471165,0.092628,0.853097,0.250506,Neutral
+MARIF,0.233169,0.419449,0.096767,0.626419,0.220821,0.448479,0.177369,0.450637,0.250506,Neutral
+MMLTF,0.023101,0.055416,0.091871,0.348528,0.152923,0.425788,-0.015321,0.600930,0.250506,Neutral
+E2E1.F,0.235437,0.396147,0.087337,0.581520,0.213197,0.404773,0.122781,0.421502,0.250506,Neutral
+MARI.TO,0.364093,0.608576,0.086713,0.596685,0.222354,0.401882,0.219092,0.418718,0.250506,Neutral
+HIN.MU,0.090223,0.097573,0.078546,0.637151,0.291117,0.364032,-0.057391,0.270914,0.250506,Neutral
+HDRSF,0.182560,0.582387,0.073606,0.265038,0.093681,0.341137,0.146117,0.799142,0.250506,Neutral
+SCCO,0.751106,1.169829,0.070963,0.662415,0.275507,0.328888,0.408197,0.390157,0.250506,Neutral
+ACMDY,0.012986,0.031652,0.070372,0.263062,0.115258,0.326145,-0.126061,0.610606,0.250506,Neutral
+601137.SS,0.178591,0.352066,0.067975,0.301107,0.139895,0.315037,0.012460,0.493837,0.250506,Neutral
+CUU.V,0.258176,0.975104,0.060114,0.190617,0.065766,0.278607,0.266981,0.946136,0.250506,Neutral
+FPMB.F,0.715914,1.326046,0.055928,0.464070,0.172639,0.259204,0.411463,0.463998,0.250506,Neutral
+C730.F,0.081191,0.517667,0.054750,0.093825,0.034392,0.253747,0.028504,1.597207,0.250506,Neutral
+FCXO34.SA,0.690445,1.266068,0.054267,0.422590,0.163313,0.251508,0.375759,0.459353,0.250506,Neutral
+FG1.F,0.586101,0.937223,0.049986,0.404243,0.154009,0.231665,0.202350,0.400580,0.250506,Neutral
+KGHPF,0.010355,0.018364,0.049922,0.245796,0.112378,0.231370,-0.227042,0.444257,0.250506,Neutral
+FCX,0.744135,1.426671,0.039917,0.334655,0.124413,0.184998,0.384248,0.480276,0.250506,Neutral
+CPFXF,0.284255,1.126958,0.036005,0.110617,0.037813,0.166872,0.226159,0.993156,0.250506,Neutral
+002203.SZ,0.290713,0.322780,0.032944,0.268439,0.123793,0.152684,-0.163569,0.278137,0.250506,Neutral
+ANFGF,0.604987,1.130759,0.029458,0.223128,0.079016,0.136525,0.197588,0.468211,0.250506,Neutral
+OCKA.F,0.607377,1.362314,0.012027,0.074320,0.026946,0.055742,0.224938,0.561872,0.250506,Neutral
+000630.SZ,0.514417,0.750244,0.010539,0.083426,0.033639,0.048844,-0.067789,0.365347,0.250506,Neutral
+HBM.TO,0.694932,1.508307,0.009032,0.059625,0.023101,0.041859,0.279232,0.543708,0.250506,Neutral
+LUNMF,0.668351,1.278764,0.005465,0.035919,0.015328,0.025327,0.155506,0.479296,0.250506,Neutral
+600362.SS,0.578169,0.859542,0.004120,0.036685,0.013559,0.019095,-0.046497,0.372418,0.250506,Neutral
+CVV.AX,0.288097,0.897164,0.003916,0.011738,0.005243,0.018151,-0.029872,0.780101,0.250506,Neutral
+300697.SZ,0.301865,0.581731,-0.002608,-0.013438,-0.005666,-0.012085,-0.207411,0.482757,0.250506,Neutral
+IPMLF,0.394290,0.790985,-0.006833,-0.032261,-0.014795,-0.031668,-0.129275,0.502541,0.250506,Neutral
+HBM,0.699505,1.612239,-0.009536,-0.061583,-0.023113,-0.044198,0.241711,0.577374,0.250506,Neutral
+LSANF,0.051172,0.096407,-0.012109,-0.056337,-0.025691,-0.056120,-0.478087,0.471953,0.250506,Neutral
+SAGARDEEP.NS,0.050789,0.118154,-0.018762,-0.065170,-0.032236,-0.086954,-0.498766,0.582763,0.250506,Neutral
+LA.V,0.079408,0.135596,-0.032739,-0.175741,-0.076778,-0.151733,-0.555399,0.427760,0.250506,Neutral
+600490.SS,0.319510,0.648428,-0.040850,-0.178563,-0.084797,-0.189325,-0.353504,0.508388,0.250506,Neutral
+MCL.NS,0.137765,0.342360,-0.044606,-0.168211,-0.072342,-0.206732,-0.513841,0.622532,0.250506,Neutral
+4989.TW,0.198282,0.334545,-0.060966,-0.335245,-0.147167,-0.282556,-0.593315,0.422658,0.250506,Neutral
+USCUF,0.290002,1.475519,-0.075437,-0.176058,-0.061844,-0.349621,-0.127559,1.274568,0.250506,Neutral
+CAEN,-0.050007,-0.123762,-0.076369,-0.282749,-0.123334,-0.353939,-0.878722,0.619978,0.250506,Neutral
+FQVLF,0.631128,1.608453,-0.082618,-0.339056,-0.166833,-0.382901,-0.098760,0.638424,0.250506,Neutral
+PNTZF,0.157047,0.715470,-0.108809,-0.222571,-0.096540,-0.504289,-0.637161,1.141246,0.250506,Neutral
+HGO.AX,0.365913,0.891063,-0.116983,-0.476673,-0.206058,-0.542173,-0.593046,0.610027,0.250506,Neutral
+ERO,0.667417,1.451800,-0.130054,-0.716827,-0.320497,-0.602749,-0.391764,0.544914,0.250506,Neutral
+TNC.AX,0.015794,0.153874,-0.176595,-0.160029,-0.072367,-0.818449,-1.213580,2.440585,0.250506,Neutral
+XXIX.V,0.287216,0.782133,-0.185069,-0.690849,-0.283230,-0.857723,-0.959464,0.682166,0.250506,Neutral
+TFM.V,0.033252,0.216380,-0.219508,-0.398677,-0.134731,-1.017337,-1.383278,1.630131,0.250506,Neutral
+BRVRF,0.049157,0.282762,-0.265536,-0.556172,-0.184499,-1.230659,-1.565600,1.440969,0.250506,Neutral
+TWOSF,0.040633,0.220326,-0.267789,-0.648638,-0.197306,-1.241101,-1.605199,1.358348,0.250506,Neutral
+TWO.V,0.012275,0.047806,-0.272738,-0.774504,-0.279577,-1.264038,-1.708701,0.975615,0.250506,Neutral
+CFV0.F,0.030216,0.478409,-0.277935,-0.220005,-0.070107,-1.288122,-1.531698,3.966237,0.250506,Neutral
+08W.F,0.032241,0.289057,-0.300310,-0.377124,-0.133782,-1.391822,-1.723824,2.245939,0.250506,Neutral
+ARJNF,-0.203482,-0.983979,-0.300538,-0.729700,-0.253399,-1.392878,-2.319372,1.211372,0.250506,Neutral
+BZDLF,0.002504,0.016546,-0.388271,-0.585466,-0.234512,-1.799486,-2.258747,1.655661,0.250506,Neutral
+BP60.F,-0.017995,-0.174410,-0.431102,-0.551721,-0.177583,-1.997994,-2.546429,2.428006,0.250506,Neutral
+HLGVF,0.010366,0.060247,-0.440024,-0.640713,-0.302238,-2.039340,-2.478193,1.455962,0.250506,Neutral
+BFGFF,0.083922,0.685144,-0.638709,-0.724760,-0.313411,-2.960170,-3.107204,2.045144,0.250506,Neutral
+SLMFF,0.009290,0.142938,-0.722036,-0.510444,-0.187347,-3.346360,-3.746597,3.854169,0.250506,Neutral
+3N4.SG,0.308499,0.985685,0.771863,2.347572,1.013805,3.577288,3.570603,0.800392,0.250506,Outperform
+CUBEXTUB.NS,0.141331,0.384223,0.449954,1.727216,0.667396,2.085361,1.797801,0.681029,0.250506,Outperform
+JIX.F,0.359048,0.845307,0.445271,1.866527,0.808936,2.063658,1.991418,0.589766,0.250506,Outperform
+HINDCOPPER.NS,0.380606,0.851744,0.364786,1.531268,0.703668,1.690643,1.621409,0.560599,0.250506,Outperform
+SARKY.IS,0.054537,0.108979,0.359428,1.683551,0.719093,1.665813,1.249717,0.500581,0.250506,Outperform
+RE8.F,0.558737,0.930441,0.294804,1.992390,0.852114,1.366304,1.333821,0.417158,0.250506,Outperform
+BHAGYANGR.NS,0.131430,0.266450,0.293131,1.419577,0.582246,1.358548,1.015989,0.507854,0.250506,Outperform
+FDY.TO,0.332434,0.867706,0.259834,1.047747,0.421347,1.204230,1.142450,0.653861,0.250506,Outperform
+9CM0.F,0.321625,0.854389,0.246569,0.958671,0.391314,1.142754,1.074755,0.665463,0.250506,Outperform
+GRX.AX,0.192572,0.500298,0.227764,0.884677,0.356647,1.055600,0.822245,0.650809,0.250506,Outperform
+ARREF,0.663149,1.258081,0.225933,1.501482,0.635153,1.047110,1.167631,0.475243,0.250506,Outperform
+MTJ3.F,0.069175,0.098631,0.211207,1.454970,0.592745,0.978865,0.557937,0.357176,0.250506,Outperform
+GRX.L,0.181102,0.358387,0.198418,0.913302,0.406982,0.919593,0.619967,0.495733,0.250506,Outperform
+005810.KS,0.233232,0.308425,0.191605,1.338640,0.594803,0.888016,0.565059,0.331268,0.250506,Outperform
+2009.TW,0.459276,0.934166,0.189113,0.806763,0.417827,0.876468,0.845724,0.509529,0.250506,Outperform
+OUW0.F,0.587710,1.451457,0.163790,0.869680,0.327220,0.759102,0.969927,0.618671,0.250506,Outperform
+CS.TO,0.731330,1.730688,0.162900,0.981190,0.402899,0.754977,1.096199,0.592822,0.250506,Outperform
+SFR.AX,0.512234,0.893583,0.142969,0.921112,0.380927,0.662608,0.612912,0.437004,0.250506,Outperform
+PUCOBRE.SN,0.275522,0.266430,0.126627,1.279036,0.543780,0.586865,0.244297,0.242240,0.250506,Outperform
+ATYM.L,0.470796,0.713631,0.125949,0.849829,0.375963,0.583723,0.449992,0.379717,0.250506,Outperform
+000878.SZ,0.551521,0.736491,-0.096890,-0.817530,-0.347221,-0.449048,-0.572104,0.334521,0.250506,Underperform
+CAML.L,0.508801,0.666887,-0.102868,-0.826752,-0.363926,-0.476754,-0.632314,0.328339,0.250506,Underperform
+KGH.WA,0.732039,1.197883,-0.115414,-0.942098,-0.413282,-0.534901,-0.442492,0.409920,0.250506,Underperform
+601609.SS,0.357557,0.417161,-0.142637,-1.261420,-0.522587,-0.661067,-0.933246,0.292265,0.250506,Underperform
+688388.SS,0.129130,0.283810,-0.196729,-0.831598,-0.360332,-0.911763,-1.246215,0.550576,0.250506,Underperform
+300618.SZ,0.263318,0.526491,-0.207924,-0.937295,-0.430308,-0.963648,-1.184771,0.500874,0.250506,Underperform
+GCUMF,0.241195,0.706705,-0.310318,-1.136020,-0.435646,-1.438204,-1.575169,0.733986,0.250506,Underperform
+NRX.AX,0.201323,0.821103,-0.340847,-0.971002,-0.340582,-1.579696,-1.663239,1.021698,0.250506,Underperform
+RDS.AX,0.079513,0.302310,-0.362839,-1.117052,-0.382171,-1.681620,-2.007433,0.952431,0.250506,Underperform
+BCU.V,0.238980,0.954954,-0.395339,-1.025668,-0.406724,-1.832246,-1.853282,1.001014,0.250506,Underperform
+PMAM3.SA,0.101593,0.236565,-0.398886,-1.309932,-0.687379,-1.848682,-2.205197,0.583318,0.250506,Underperform
+HHLKF,0.094041,0.329713,-0.403220,-1.393698,-0.461143,-1.868770,-2.181786,0.878285,0.250506,Underperform
+KCC.V,0.059441,0.239155,-0.410701,-1.280651,-0.408206,-1.903442,-2.258747,1.007894,0.250506,Underperform
+BCUFF,0.169038,0.735712,-0.413650,-1.033868,-0.384933,-1.917108,-2.040527,1.090292,0.250506,Underperform
+ALM.AX,0.131943,0.506486,-0.418863,-1.372710,-0.439427,-1.941271,-2.171736,0.961611,0.250506,Underperform
+CYM.AX,0.237302,0.894108,-0.516695,-1.284740,-0.563525,-2.394685,-2.444135,0.943860,0.250506,Underperform
+ARJN.V,-0.010778,-0.056995,-0.728125,-1.492287,-0.549668,-3.374582,-3.868185,1.324742,0.250506,Underperform
+NTM.AX,0.070476,0.282849,-0.786178,-2.409327,-0.783916,-3.643633,-3.978533,1.005386,0.250506,Underperform
+TVCCF,0.016415,0.386299,-1.610456,-1.012340,-0.273220,-7.463845,-7.750436,5.895146,0.250506,Underperform

Stock_Benchmark Analysis/unique_companies.csv

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