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@@ -57,3 +57,7 @@ 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
+combined_dataset[[:space:]]copy.numbers filter=lfs diff=lfs merge=lfs -text
+df_prices_final.csv filter=lfs diff=lfs merge=lfs -text
+df_prices.csv filter=lfs diff=lfs merge=lfs -text
+unique_companies.csv filter=lfs diff=lfs merge=lfs -text

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
+}

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corr_beta.csv

@@ -0,0 +1,1251 @@
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de_vs_dfs_interactive.html

@@ -0,0 +1,412 @@
+
+
+<style>
+
+</style>
+
+<div id="fig_el41771fbf15ae-053b-43ae-8412-df87dbe0755a147530960"></div>
+<script>
+function mpld3_load_lib(url, callback){
+  var s = document.createElement('script');
+  s.src = url;
+  s.async = true;
+  s.onreadystatechange = s.onload = callback;
+  s.onerror = function(){console.warn("failed to load library " + url);};
+  document.getElementsByTagName("head")[0].appendChild(s);
+}
+
+if(typeof(mpld3) !== "undefined" && mpld3._mpld3IsLoaded){
+   // already loaded: just create the figure
+   !function(mpld3){
+       
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+       mpld3.draw_figure("fig_el41771fbf15ae-053b-43ae-8412-df87dbe0755a147530960", {"width": 1100.0, "height": 650.0, "axes": [{"bbox": [0.041111111111111126, 0.05957264957264963, 0.5931616161616163, 0.9173504273504273], "xlim": [-0.03549569999999999, 1.6613357], "ylim": [-7.659379425, 7.818157925], "xdomain": [-0.03549569999999999, 1.6613357], "ydomain": [-7.659379425, 7.818157925], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 11, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": true, "color": "#B0B0B0", "dasharray": "2,2", "alpha": 1.0}, "visible": true}, {"position": "left", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": true, "color": "#B0B0B0", "dasharray": "2,2", "alpha": 1.0}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": 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"color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el4177919e64ac-f14b-4239-8bb1-c4fd5c024ff2"}, {"text": "DFS < 0.40", "position": [1.0881164109463073, 0.9371098481319297], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el41770c07e42b-4bfa-4391-a602-c9cf28041584"}, {"text": "0.40\u20130.60", "position": [1.0881164109463073, 0.9019845336811703], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el4177456aa511-f099-499b-8af0-1fd02879e101"}, {"text": "\u2265 0.60", "position": [1.0881164109463073, 0.8668592192304109], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el41770fd836c6-f997-49b9-be66-12c770a27162"}], "collections": [{"offsets": 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+   }(mpld3);
+}else if(typeof define === "function" && define.amd){
+   // require.js is available: use it to load d3/mpld3
+   require.config({paths: {d3: "https://d3js.org/d3.v5"}});
+   require(["d3"], function(d3){
+      window.d3 = d3;
+      mpld3_load_lib("https://mpld3.github.io/js/mpld3.v0.5.11.js", function(){
+         
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+         mpld3.draw_figure("fig_el41771fbf15ae-053b-43ae-8412-df87dbe0755a147530960", {"width": 1100.0, "height": 650.0, "axes": [{"bbox": [0.041111111111111126, 0.05957264957264963, 0.5931616161616163, 0.9173504273504273], "xlim": [-0.03549569999999999, 1.6613357], "ylim": [-7.659379425, 7.818157925], "xdomain": [-0.03549569999999999, 1.6613357], "ydomain": [-7.659379425, 7.818157925], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 11, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": true, "color": "#B0B0B0", "dasharray": "2,2", "alpha": 1.0}, "visible": true}, {"position": "left", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": true, "color": "#B0B0B0", "dasharray": "2,2", "alpha": 1.0}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": "el4177c85f9b40-2bc2-4ec2-aa0a-25f819380b34", "lines": [{"data": "data01", "xindex": 0, "yindex": 1, "coordinates": "display", "id": "el4177144d3931-5529-4a77-9661-f627610b8f19", "color": "#1F77B4", "linewidth": 1.0, "dasharray": "6,6", "alpha": 1, "zorder": 2, "drawstyle": "default"}, {"data": "data01", "xindex": 0, "yindex": 2, "coordinates": "display", "id": "el417716aa94ee-751a-48c6-85cf-e84f67a0823c", "color": "#1F77B4", "linewidth": 1.0, "dasharray": "6,6", "alpha": 1, "zorder": 2, "drawstyle": "default"}], "paths": [{"data": "data06", "xindex": 0, "yindex": 1, "coordinates": "axes", "pathcodes": ["M", "L", "S", "L", "S", "L", "S", "L", "S", "Z"], "id": "el417709e5e552-e507-4926-ab16-5d158e5c5621", "dasharray": "none", "alpha": 0.8, "facecolor": "rgba(255, 255, 255, 0.8)", "edgecolor": "rgba(204, 204, 204, 0.8)", "edgewidth": 1.0, "zorder": 1000000.0}], "markers": [], "texts": [{"text": "Debt to Equity (latest)", "position": [0.5, -0.049100903754774994], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "hanging", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el4177a44d9804-0973-40ed-8499-2c77bf5b6c65"}, {"text": "Debt-Financed Capex Share (median of \u0394Debt / \u0394Capex)", "position": [-0.05483371081177732, 0.5], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "auto", "rotation": -90.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el417728d79063-f0a5-48d0-95e9-a87889ef7fa8"}, {"text": "Debt vs. Capex Financing \u2014 44 Copper-Linked Outperformers", "position": [0.5, 1.0139755893040157], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "auto", "rotation": -0.0, "fontsize": 12.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el417794f38e44-6eaf-47aa-b913-c2f42a46ca91"}, {"text": "DFS Buckets", "position": [1.052089215469237, 0.9722351625826889], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el4177919e64ac-f14b-4239-8bb1-c4fd5c024ff2"}, {"text": "DFS < 0.40", "position": [1.0881164109463073, 0.9371098481319297], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el41770c07e42b-4bfa-4391-a602-c9cf28041584"}, {"text": "0.40\u20130.60", "position": [1.0881164109463073, 0.9019845336811703], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el4177456aa511-f099-499b-8af0-1fd02879e101"}, {"text": "\u2265 0.60", "position": [1.0881164109463073, 0.8668592192304109], "coordinates": "axes", "h_anchor": "start", "v_baseline": "auto", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 1000003.0, "id": "el41770fd836c6-f997-49b9-be66-12c770a27162"}], "collections": [{"offsets": "data02", "xindex": 0, "yindex": 1, "paths": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], ["M", "C", "C", "C", "C", "C", "C", "C", "C", "Z"]]], "pathtransforms": [[8.333333333333334, 0.0, 0.0, 8.333333333333334, 0.0, 0.0]], "alphas": [0.85], "edgecolors": ["rgba(31, 119, 180, 0.85)"], "facecolors": ["rgba(31, 119, 180, 0.85)"], "edgewidths": [1.0], "offsetcoordinates": "data", "pathcoordinates": "display", "zorder": 1, "id": "el4177c1400a64-f331-43bc-b79e-e057add699bf"}, {"offsets": "data03", "xindex": 0, "yindex": 1, "paths": [[[[-0.5, -0.5], [0.5, -0.5], [0.5, 0.5], [-0.5, 0.5]], ["M", "L", "L", "L", "Z"]]], "pathtransforms": [[8.333333333333334, 0.0, 0.0, 8.333333333333334, 0.0, 0.0]], "alphas": [0.85], "edgecolors": ["rgba(255, 127, 14, 0.85)"], "facecolors": ["rgba(255, 127, 14, 0.85)"], "edgewidths": [1.0], "offsetcoordinates": "data", "pathcoordinates": "display", "zorder": 1, "id": "el41774d49aeff-f657-468e-9bfb-5e7e05a93b4e"}, {"offsets": "data04", "xindex": 0, "yindex": 1, "paths": [[[[0.0, 0.5], [-0.5, -0.5], [0.5, -0.5]], ["M", "L", "L", "Z"]]], "pathtransforms": [[8.333333333333334, 0.0, 0.0, 8.333333333333334, 0.0, 0.0]], "alphas": [0.85], "edgecolors": ["rgba(44, 160, 44, 0.85)"], "facecolors": ["rgba(44, 160, 44, 0.85)"], "edgewidths": [1.0], "offsetcoordinates": "data", "pathcoordinates": "display", "zorder": 1, "id": "el41772a0982d8-5440-4af2-beec-b0703d53141e"}, {"offsets": "data05", "xindex": 0, "yindex": 1, "paths": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], ["M", "C", "C", "C", "C", "C", "C", "C", "C", "Z"]]], "pathtransforms": [[8.333333333333334, 0.0, 0.0, 8.333333333333334, 0.0, 0.0]], "alphas": [0.85], "edgecolors": ["rgba(31, 119, 180, 0.85)"], "facecolors": ["rgba(31, 119, 180, 0.85)"], "edgewidths": [1.0], "offsetcoordinates": "axes", "pathcoordinates": "display", "zorder": 1000001.0, "id": "el4177c2319819-b0aa-4e0b-aef1-691610c8228a"}, {"offsets": "data05", "xindex": 0, "yindex": 2, "paths": [[[[-0.5, -0.5], [0.5, -0.5], [0.5, 0.5], [-0.5, 0.5]], ["M", "L", "L", "L", "Z"]]], "pathtransforms": [[8.333333333333334, 0.0, 0.0, 8.333333333333334, 0.0, 0.0]], "alphas": [0.85], "edgecolors": ["rgba(255, 127, 14, 0.85)"], "facecolors": ["rgba(255, 127, 14, 0.85)"], "edgewidths": [1.0], "offsetcoordinates": "axes", "pathcoordinates": "display", "zorder": 1000001.0, "id": "el41770b0f13b4-6891-4d24-9fea-14988c3d84d9"}, {"offsets": "data05", "xindex": 0, "yindex": 3, "paths": [[[[0.0, 0.5], [-0.5, -0.5], [0.5, -0.5]], ["M", "L", "L", "Z"]]], "pathtransforms": [[8.333333333333334, 0.0, 0.0, 8.333333333333334, 0.0, 0.0]], "alphas": [0.85], "edgecolors": ["rgba(44, 160, 44, 0.85)"], "facecolors": ["rgba(44, 160, 44, 0.85)"], "edgewidths": [1.0], "offsetcoordinates": "axes", "pathcoordinates": "display", "zorder": 1000001.0, "id": "el4177e45a36c7-86dc-48ea-812d-77f5f6b25470"}], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[45.222222222222236, 349.2127573206205, 356.91783034734175], [697.7000000000002, 349.2127573206205, 356.91783034734175]], "data02": [[0.199319, -3.495542], [0.949866, -0.8491345], [0.35196, -0.18051050000000002], [0.102129, -0.733371], [0.188016, 0.2604860000000002], [0.041633, -6.955855], [0.421783, -0.938834], [0.35196, -0.18051050000000002], [0.338383, 0.03131900000000004], [0.338383, 0.03131900000000004], [0.338383, 0.03131900000000004], [0.276484, -3.785816], [0.276484, -3.785816], [0.276484, -3.785816], [0.393387, 0.26781500000000014], [0.446589, -3.213031], [0.446589, -2.883438], [0.08203, 0.0570095], [0.08203, 0.0570095], [0.071123, -2.406448], [0.446589, -2.883481], [0.102129, -0.733371], [0.757474, -1.727273], [0.149349, -3.3518645]], "data03": [[0.585746, 0.5891675000000001]], "data04": [[0.365366, 7.1146335], [0.412916, 2.662484], [0.342431, 4.823058], [0.412916, 3.2088], [0.45269, 2.367807], [0.076348, 0.664401], [0.076348, 0.664401], [0.363713, 4.052453], [0.342431, 4.823058], [0.171305, 0.699751], [0.239024, 2.9986984999999997], [0.239024, 2.9986984999999997], [0.349289, 1.8894205], [0.367766, 0.912234], [0.367766, 2.1517005], [1.584207, 1.341436], [1.584207, 1.341436]], "data05": [[1.0498009297890096, 0.9432241684524365, 0.9080988540016771, 0.8729735395509177]], "data06": [[1.0242572756841444, 0.8525109475449548], [1.2123011426527937, 0.8525109475449548], [1.216558418336938, 0.8525109475449548], [1.216558418336938, 0.8571694773129601], [1.216558418336938, 0.9953414702319948], [1.216558418336938, 1.0], [1.2123011426527937, 1.0], [1.0242572756841444, 1.0], [1.02, 1.0], [1.02, 0.9953414702319948], [1.02, 0.8571694773129601], [1.02, 0.8525109475449548], [1.0242572756841444, 0.8525109475449548]]}, "id": "el41771fbf15ae-053b-43ae-8412-df87dbe0755a", "plugins": [{"type": "reset"}, {"type": "zoom", "button": true, "enabled": false}, {"type": "boxzoom", "button": true, "enabled": false}, {"type": "htmltooltip", "id": "el4177c1400a64-f331-43bc-b79e-e057add699bf", "labels": ["<b>2009.TW</b><br>D/E: 0.20<br>DFS: -3.50<br>DFS < 0.40", "<b>601168.SS</b><br>D/E: 0.95<br>DFS: -0.85<br>DFS < 0.40", "<b>603993.SS</b><br>D/E: 0.35<br>DFS: -0.18<br>DFS < 0.40", "<b>ARREF</b><br>D/E: 0.10<br>DFS: -0.73<br>DFS < 0.40", "<b>ATUSF</b><br>D/E: 0.19<br>DFS: 0.26<br>DFS < 0.40", "<b>ATYM.L</b><br>D/E: 0.04<br>DFS: -6.96<br>DFS < 0.40", "<b>BHP.AX</b><br>D/E: 0.42<br>DFS: -0.94<br>DFS < 0.40", "<b>CMCLF</b><br>D/E: 0.35<br>DFS: -0.18<br>DFS < 0.40", "<b>FCX</b><br>D/E: 0.34<br>DFS: 0.03<br>DFS < 0.40", "<b>FCXO34.SA</b><br>D/E: 0.34<br>DFS: 0.03<br>DFS < 0.40", "<b>FPMB.F</b><br>D/E: 0.34<br>DFS: 0.03<br>DFS < 0.40", "<b>FSUGY</b><br>D/E: 0.28<br>DFS: -3.79<br>DFS < 0.40", "<b>FSUMF</b><br>D/E: 0.28<br>DFS: -3.79<br>DFS < 0.40", "<b>FVJ.F</b><br>D/E: 0.28<br>DFS: -3.79<br>DFS < 0.40", "<b>GMBXF</b><br>D/E: 0.39<br>DFS: 0.27<br>DFS < 0.40", "<b>HBM</b><br>D/E: 0.45<br>DFS: -3.21<br>DFS < 0.40", "<b>HBM.TO</b><br>D/E: 0.45<br>DFS: -2.88<br>DFS < 0.40", "<b>LYSCF</b><br>D/E: 0.08<br>DFS: 0.06<br>DFS < 0.40", "<b>LYSDY</b><br>D/E: 0.08<br>DFS: 0.06<br>DFS < 0.40", "<b>MDI.TO</b><br>D/E: 0.07<br>DFS: -2.41<br>DFS < 0.40", "<b>OCKA.F</b><br>D/E: 0.45<br>DFS: -2.88<br>DFS < 0.40", "<b>RE8.F</b><br>D/E: 0.10<br>DFS: -0.73<br>DFS < 0.40", "<b>SCCO</b><br>D/E: 0.76<br>DFS: -1.73<br>DFS < 0.40", "<b>SKE</b><br>D/E: 0.15<br>DFS: -3.35<br>DFS < 0.40"], "targets": null, "hoffset": 10, "voffset": 10}, {"type": "htmltooltip", "id": "el41774d49aeff-f657-468e-9bfb-5e7e05a93b4e", "labels": ["<b>000630.SZ</b><br>D/E: 0.59<br>DFS: 0.59<br>0.40\u20130.60"], "targets": null, "hoffset": 10, "voffset": 10}, {"type": "htmltooltip", "id": "el41772a0982d8-5440-4af2-beec-b0703d53141e", "labels": ["<b>603979.SS</b><br>D/E: 0.37<br>DFS: 7.11<br>\u2265 0.60", "<b>ANFGF</b><br>D/E: 0.41<br>DFS: 2.66<br>\u2265 0.60", "<b>CS.TO</b><br>D/E: 0.34<br>DFS: 4.82<br>\u2265 0.60", "<b>FG1.F</b><br>D/E: 0.41<br>DFS: 3.21<br>\u2265 0.60", "<b>IPMLF</b><br>D/E: 0.45<br>DFS: 2.37<br>\u2265 0.60", "<b>IVPAF</b><br>D/E: 0.08<br>DFS: 0.66<br>\u2265 0.60", "<b>IYAA.F</b><br>D/E: 0.08<br>DFS: 0.66<br>\u2265 0.60", "<b>LUNMF</b><br>D/E: 0.36<br>DFS: 4.05<br>\u2265 0.60", "<b>OUW0.F</b><br>D/E: 0.34<br>DFS: 4.82<br>\u2265 0.60", "<b>PILBF</b><br>D/E: 0.17<br>DFS: 0.70<br>\u2265 0.60", "<b>RIO.AX</b><br>D/E: 0.24<br>DFS: 3.00<br>\u2265 0.60", "<b>RTNTF</b><br>D/E: 0.24<br>DFS: 3.00<br>\u2265 0.60", "<b>SFR.AX</b><br>D/E: 0.35<br>DFS: 1.89<br>\u2265 0.60", "<b>TECK</b><br>D/E: 0.37<br>DFS: 0.91<br>\u2265 0.60", "<b>TECK-A.TO</b><br>D/E: 0.37<br>DFS: 2.15<br>\u2265 0.60", "<b>TGB</b><br>D/E: 1.58<br>DFS: 1.34<br>\u2265 0.60", "<b>TKO.L</b><br>D/E: 1.58<br>DFS: 1.34<br>\u2265 0.60"], "targets": null, "hoffset": 10, "voffset": 10}]});
+      });
+    });
+}else{
+    // require.js not available: dynamically load d3 & mpld3
+    mpld3_load_lib("https://d3js.org/d3.v5.js", function(){
+         mpld3_load_lib("https://mpld3.github.io/js/mpld3.v0.5.11.js", function(){
+                 
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
+    };
+
+    HtmlTooltipPlugin.prototype.draw = function(){
+        var obj = mpld3.get_element(this.props.id);
+        var labels = this.props.labels;
+        var targets = this.props.targets;
+        var tooltip = d3.select("body").append("div")
+            .attr("class", "mpld3-tooltip")
+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
+
+        obj.elements()
+            .on("mouseover", function(d, i){
+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
+            })
+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
+    
+    mpld3.register_plugin("htmltooltip", HtmlTooltipPlugin);
+    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);
+    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;
+    HtmlTooltipPlugin.prototype.requiredProps = ["id"];
+    HtmlTooltipPlugin.prototype.defaultProps = {labels:null,
+                                                target:null,
+                                                hoffset:0,
+                                                voffset:10,
+                                                targets:null};
+    function HtmlTooltipPlugin(fig, props){
+        mpld3.Plugin.call(this, fig, props);
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+
+    HtmlTooltipPlugin.prototype.draw = function(){
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+            .style("position", "absolute")
+            .style("z-index", "10")
+            .style("visibility", "hidden");
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+        obj.elements()
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+                tooltip.html(labels[i])
+                    .style("visibility", "visible");
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+            .on("mousemove", function(d, i){
+                tooltip
+                .style("top", d3.event.pageY + this.props.voffset + "px")
+                .style("left",d3.event.pageX + this.props.hoffset + "px");
+            }.bind(this))
+            .on("mousedown.callout", function(d, i){
+                window.open(targets[i],"_blank");
+            })
+            .on("mouseout", function(d, i){
+                tooltip.style("visibility", "hidden");
+            });
+    };
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1.34<br>\u2265 0.60", "<b>TKO.L</b><br>D/E: 1.58<br>DFS: 1.34<br>\u2265 0.60"], "targets": null, "hoffset": 10, "voffset": 10}]});
+            })
+         });
+}
+</script>

de_vs_dfs_points.csv

@@ -0,0 +1,43 @@
+Ticker,Debt_to_Equity,Debt_Financed_Capex_Share,DFS_Bucket
+000630.SZ,0.585746,0.5891675000000001,0.40–0.60
+2009.TW,0.199319,-3.495542,DFS < 0.40
+601168.SS,0.949866,-0.8491345,DFS < 0.40
+603979.SS,0.365366,7.1146335,≥ 0.60
+603993.SS,0.35196,-0.18051050000000002,DFS < 0.40
+ANFGF,0.412916,2.662484,≥ 0.60
+ARREF,0.102129,-0.733371,DFS < 0.40
+ATUSF,0.188016,0.2604860000000002,DFS < 0.40
+ATYM.L,0.041633,-6.955855,DFS < 0.40
+BHP.AX,0.421783,-0.938834,DFS < 0.40
+CMCLF,0.35196,-0.18051050000000002,DFS < 0.40
+CS.TO,0.342431,4.823058,≥ 0.60
+FCX,0.338383,0.03131900000000004,DFS < 0.40
+FCXO34.SA,0.338383,0.03131900000000004,DFS < 0.40
+FG1.F,0.412916,3.2088,≥ 0.60
+FPMB.F,0.338383,0.03131900000000004,DFS < 0.40
+FSUGY,0.276484,-3.785816,DFS < 0.40
+FSUMF,0.276484,-3.785816,DFS < 0.40
+FVJ.F,0.276484,-3.785816,DFS < 0.40
+GMBXF,0.393387,0.26781500000000014,DFS < 0.40
+HBM,0.446589,-3.213031,DFS < 0.40
+HBM.TO,0.446589,-2.883438,DFS < 0.40
+IPMLF,0.45269,2.367807,≥ 0.60
+IVPAF,0.076348,0.664401,≥ 0.60
+IYAA.F,0.076348,0.664401,≥ 0.60
+LUNMF,0.363713,4.052453,≥ 0.60
+LYSCF,0.08203,0.0570095,DFS < 0.40
+LYSDY,0.08203,0.0570095,DFS < 0.40
+MDI.TO,0.071123,-2.406448,DFS < 0.40
+OCKA.F,0.446589,-2.883481,DFS < 0.40
+OUW0.F,0.342431,4.823058,≥ 0.60
+PILBF,0.171305,0.699751,≥ 0.60
+RE8.F,0.102129,-0.733371,DFS < 0.40
+RIO.AX,0.239024,2.9986984999999997,≥ 0.60
+RTNTF,0.239024,2.9986984999999997,≥ 0.60
+SCCO,0.757474,-1.727273,DFS < 0.40
+SFR.AX,0.349289,1.8894205,≥ 0.60
+SKE,0.149349,-3.3518645,DFS < 0.40
+TECK,0.367766,0.912234,≥ 0.60
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task1_aspect_contributions.csv

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task1_behavioral_shortlist.csv

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+FCXO34.SA,0.338383,0.914664,0.342491,0.003026,-0.620223,-0.033732,0.031319,-0.023918,0.024266,-0.863704,-0.228637,True
+BHP.AX,0.421783,0.782122,0.152207,0.023428,-0.048975,0.025654,-0.938834,-0.184551,0.273258,-0.412996,-0.231535,False
+RIO.AX,0.239024,0.885023,0.128688,0.013946,0.084935,0.002178,2.998698,-0.051658,0.009452,-0.914340,-0.257731,True
+RTNTF,0.239024,0.885023,0.128688,0.013946,0.084935,0.002178,2.998698,-0.051658,0.004333,-0.974151,-0.267102,True
+ANFGF,0.412916,0.742083,0.171120,0.196691,0.044466,0.043340,2.662484,0.367898,-0.095104,-0.377937,-0.343626,False
+FG1.F,0.412916,0.742083,0.108097,0.196691,0.044466,0.043340,3.208800,0.376761,-0.305606,-0.755089,-0.356913,False
+000630.SZ,0.585746,0.509301,0.372084,0.239713,0.231979,-0.004970,0.589168,0.437637,-0.034043,-0.356929,-0.369582,True
+TGB,1.584207,0.951693,0.108196,0.145849,-0.049412,0.011529,1.341436,0.671068,-0.096368,-0.089549,-0.436366,True
+TKO.L,1.584207,0.951693,0.108196,0.145849,-0.049412,0.011529,1.341436,0.671068,-0.058792,-0.233011,-0.479373,True
+CS.TO,0.342431,0.620698,0.872332,14.498742,0.399451,0.113615,4.823058,0.742305,-0.190195,-0.523307,-0.528066,False
+603979.SS,0.365366,0.359515,0.424592,0.289674,0.274110,0.025446,7.114634,0.518021,-0.047055,0.380640,-0.582258,False
+OUW0.F,0.342431,0.620698,1.007837,14.498742,0.399451,0.113615,4.823058,0.742305,-0.179373,-0.979035,-0.616115,False
+LUNMF,0.363713,0.704005,0.204200,4.081551,-0.109479,0.126667,4.052453,0.644549,0.432087,-0.206226,-0.763658,False
+PYV.F,,,-0.248329,,-0.062450,,,,,-0.996171,-0.888979,False
+IPMLF,0.452690,0.255673,0.284529,1.812626,0.036627,0.139675,2.367807,0.996162,0.391279,-0.800374,-1.112123,False

task1_top10_aspect_contributions.csv

@@ -0,0 +1,11 @@
+Ticker,prudence,capex_financing,growth_reinvest,stock_behavior,Task1_Behavior_Score
+SKE,-0.04566399109638193,0.3078587068954839,0.8276773850443488,-0.05204902881708588,1.037823072026365
+2009.TW,0.23647256877090894,0.320677196610709,0.27149592461356875,-0.022274451332974732,0.806371238662212
+ATUSF,0.34186161022717515,0.10922233943260246,0.31209920816164854,0.02686072131999513,0.7900438791414213
+HBM,0.12454001598382891,0.2708051229532755,-0.12505668670236117,0.3523341104671509,0.6226225627018942
+OCKA.F,0.12454001598382891,0.2561043754957668,-0.12480592994456077,0.336163616979835,0.59200207851487
+HBM.TO,0.12454001598382891,0.25610245732814224,-0.12497129656290626,0.33535996976029614,0.5910311465093612
+MDI.TO,0.14067525752731308,0.12188151574168966,0.14041093561650625,0.042840973094825624,0.4458086819803346
+CMCLF,0.2538209729007195,-0.23542937124760632,0.008874795297605445,0.38413636218440794,0.41140275913512653
+FVJ.F,0.0011271370221174627,0.23802769441856644,-0.06071508959235924,0.19612085635369253,0.3745605982020172
+FSUGY,0.0011271370221174627,0.23802769441856644,-0.06071508959235924,0.19591826648346095,0.3743580083317856

task2_blend_best_allocation_debtfin_vs_equityfin.csv

@@ -0,0 +1,12 @@
+Ticker,Weight
+603979.SS,0.120000
+ANFGF,0.020000
+ARREF,0.026587
+CMCLF,0.120000
+CS.TO,0.120000
+HBM,0.113413
+HBM.TO,0.120000
+IPMLF,0.120000
+OCKA.F,0.000000
+OUW0.F,0.120000
+RE8.F,0.120000

task2_blend_grid_metrics_debtfin_vs_equityfin.csv

@@ -0,0 +1,102 @@
+alpha,Ann_Mean,Ann_Vol,Sharpe
+0.000000,0.441549,0.338797,1.303286
+0.010000,0.441301,0.338761,1.302691
+0.020000,0.441053,0.338728,1.302087
+0.030000,0.440805,0.338696,1.301475
+0.040000,0.440557,0.338667,1.300855
+0.050000,0.440309,0.338640,1.300226
+0.060000,0.440061,0.338615,1.299589
+0.070000,0.439813,0.338593,1.298944
+0.080000,0.439565,0.338572,1.298290
+0.090000,0.439317,0.338554,1.297628
+0.100000,0.439069,0.338538,1.296957
+0.110000,0.438821,0.338524,1.296279
+0.120000,0.438573,0.338512,1.295591
+0.130000,0.438325,0.338502,1.294896
+0.140000,0.438077,0.338495,1.294192
+0.150000,0.437829,0.338489,1.293480
+0.160000,0.437581,0.338486,1.292760
+0.170000,0.437333,0.338485,1.292031
+0.180000,0.437085,0.338486,1.291294
+0.190000,0.436837,0.338490,1.290549
+0.200000,0.436590,0.338495,1.289796
+0.210000,0.436342,0.338503,1.289035
+0.220000,0.436094,0.338512,1.288265
+0.230000,0.435846,0.338524,1.287487
+0.240000,0.435598,0.338538,1.286701
+0.250000,0.435350,0.338555,1.285907
+0.260000,0.435102,0.338573,1.285105
+0.270000,0.434854,0.338593,1.284295
+0.280000,0.434606,0.338616,1.283476
+0.290000,0.434358,0.338641,1.282650
+0.300000,0.434110,0.338668,1.281815
+0.310000,0.433862,0.338697,1.280973
+0.320000,0.433614,0.338728,1.280122
+0.330000,0.433366,0.338762,1.279264
+0.340000,0.433118,0.338798,1.278397
+0.350000,0.432870,0.338835,1.277523
+0.360000,0.432622,0.338875,1.276641
+0.370000,0.432374,0.338917,1.275751
+0.380000,0.432126,0.338962,1.274853
+0.390000,0.431878,0.339008,1.273947
+0.400000,0.431630,0.339057,1.273033
+0.410000,0.431382,0.339107,1.272111
+0.420000,0.431134,0.339160,1.271182
+0.430000,0.430886,0.339215,1.270245
+0.440000,0.430638,0.339272,1.269300
+0.450000,0.430390,0.339332,1.268348
+0.460000,0.430142,0.339393,1.267388
+0.470000,0.429894,0.339456,1.266420
+0.480000,0.429646,0.339522,1.265444
+0.490000,0.429398,0.339590,1.264461
+0.500000,0.429151,0.339660,1.263471
+0.510000,0.428903,0.339732,1.262473
+0.520000,0.428655,0.339806,1.261467
+0.530000,0.428407,0.339883,1.260454
+0.540000,0.428159,0.339961,1.259434
+0.550000,0.427911,0.340042,1.258406
+0.560000,0.427663,0.340125,1.257370
+0.570000,0.427415,0.340210,1.256328
+0.580000,0.427167,0.340297,1.255278
+0.590000,0.426919,0.340386,1.254220
+0.600000,0.426671,0.340477,1.253156
+0.610000,0.426423,0.340571,1.252084
+0.620000,0.426175,0.340666,1.251005
+0.630000,0.425927,0.340764,1.249919
+0.640000,0.425679,0.340864,1.248825
+0.650000,0.425431,0.340965,1.247725
+0.660000,0.425183,0.341069,1.246617
+0.670000,0.424935,0.341175,1.245503
+0.680000,0.424687,0.341284,1.244381
+0.690000,0.424439,0.341394,1.243253
+0.700000,0.424191,0.341506,1.242118
+0.710000,0.423943,0.341621,1.240975
+0.720000,0.423695,0.341738,1.239826
+0.730000,0.423447,0.341856,1.238670
+0.740000,0.423199,0.341977,1.237508
+0.750000,0.422951,0.342100,1.236338
+0.760000,0.422703,0.342225,1.235162
+0.770000,0.422455,0.342352,1.233979
+0.780000,0.422207,0.342481,1.232790
+0.790000,0.421959,0.342613,1.231593
+0.800000,0.421712,0.342746,1.230391
+0.810000,0.421464,0.342881,1.229182
+0.820000,0.421216,0.343019,1.227966
+0.830000,0.420968,0.343159,1.226744
+0.840000,0.420720,0.343300,1.225515
+0.850000,0.420472,0.343444,1.224280
+0.860000,0.420224,0.343590,1.223039
+0.870000,0.419976,0.343738,1.221792
+0.880000,0.419728,0.343888,1.220538
+0.890000,0.419480,0.344039,1.219278
+0.900000,0.419232,0.344194,1.218012
+0.910000,0.418984,0.344350,1.216740
+0.920000,0.418736,0.344508,1.215461
+0.930000,0.418488,0.344668,1.214177
+0.940000,0.418240,0.344830,1.212887
+0.950000,0.417992,0.344994,1.211590
+0.960000,0.417744,0.345161,1.210288
+0.970000,0.417496,0.345329,1.208980
+0.980000,0.417248,0.345500,1.207666
+0.990000,0.417000,0.345672,1.206346
+1.000000,0.416752,0.345846,1.205021

task2_blend_metrics_debtfin_vs_equityfin.csv

@@ -0,0 +1,5 @@
+Name,Start,End,Total_Return,CAGR,Ann_Vol,Sharpe,Max_Drawdown,Obs
+Portfolio_debtfin,2020-01-21,2025-06-12,6.651612,0.458393,0.338797,1.303286,-0.537091,1358
+Portfolio_equityfin,2020-01-21,2025-06-12,5.578387,0.418092,0.345846,1.205021,-0.551080,1358
+Blend_a0.00,2020-01-21,2025-06-12,6.913539,0.467939,0.338797,1.303286,-0.525878,1358
+HG=F,2020-01-21,2025-06-12,0.679965,0.100975,0.247506,0.531863,-0.348651,1358

task2_equity_diagnostics_debtfin.txt

@@ -0,0 +1,5 @@
+OPT_DEBT_MODE: DE_CAP
+Weighted Avg D/E: 0.350 (cap: 0.350, 60th pct)
+Portfolio DFS (weighted avg): 1.509 (target >= 0.60)
+Weight in high-DFS names: 0.500 (target >= 0.50)
+Names in portfolio: 10 / 34

task2_equity_diagnostics_equityfin.txt

@@ -0,0 +1,6 @@
+OUTPUT_TAG: equityfin
+OPT_DEBT_MODE: DE_CAP
+Weighted Avg D/E: 0.350 (cap: 0.350, 60th pct)
+Portfolio DFS (weighted avg): 0.400 (target <= 0.40)
+Weight in LOW-DFS names: 0.667 (target >= 0.50)
+Names in portfolio: 9 / 34

task2_equity_inputs_snapshot_debtfin.csv

@@ -0,0 +1,35 @@
+Ticker,Total_Debt,Debt_to_Equity,Debt_Financed_Capex_Share,Capex_Growth_Pct,Debt_Growth_Pct,Exp_Return,Industry,is_high_DFS
+000630.SZ,24751709247.860001,0.585746,0.589168,0.372084,0.239713,-0.024073,Copper,0.000000
+2009.TW,1429518000.000000,0.199319,-3.495542,-0.061187,0.055142,0.013597,Copper,0.000000
+603979.SS,3309619303.320000,0.365366,7.114634,0.424592,0.289674,0.251897,Other Industrial Metals & Mining,1.000000
+603993.SS,30248216854.750000,0.351960,-0.180511,0.156568,-0.128333,0.130924,Other Industrial Metals & Mining,0.000000
+ANFGF,5349000000.000000,0.412916,2.662484,0.171120,0.196691,0.128633,Copper,1.000000
+ARREF,10702000.000000,0.102129,-0.733371,0.843645,-0.306130,0.104284,Copper,0.000000
+ATUSF,107381000.000000,0.188016,0.260486,0.217394,-0.021735,0.105991,Other Industrial Metals & Mining,0.000000
+ATYM.L,21588000.000000,0.041633,-6.955855,0.208248,-0.103383,0.042901,Copper,0.000000
+CMCLF,30248216854.750000,0.351960,-0.180511,0.156568,-0.128333,0.211560,Other Industrial Metals & Mining,0.000000
+CS.TO,1185774000.000000,0.342431,4.823058,0.872332,14.498742,0.171722,Copper,1.000000
+FCX,9738000000.000000,0.338383,0.031319,0.276501,0.003026,0.023713,Copper,0.000000
+FCXO34.SA,9738000000.000000,0.338383,0.031319,0.342491,0.003026,0.054976,Copper,0.000000
+FG1.F,5349000000.000000,0.412916,3.208800,0.108097,0.196691,0.087811,Copper,1.000000
+FPMB.F,9738000000.000000,0.338383,0.031319,0.342491,0.003026,-0.003181,Copper,0.000000
+FSUGY,5400000000.000000,0.276484,-3.785816,-0.050688,0.107346,-0.027311,Other Industrial Metals & Mining,0.000000
+FSUMF,5400000000.000000,0.276484,-3.785816,-0.050688,0.107346,0.035952,Other Industrial Metals & Mining,0.000000
+FVJ.F,5400000000.000000,0.276484,-3.785816,-0.050688,0.107346,-0.005713,Other Industrial Metals & Mining,0.000000
+HBM,1182300000.000000,0.446589,-3.213031,0.000658,-0.015265,0.211903,Copper,0.000000
+HBM.TO,1182300000.000000,0.446589,-2.883438,0.003648,-0.015265,0.237815,Copper,0.000000
+IPMLF,372851000.000000,0.452690,2.367807,0.284529,1.812626,0.184399,Copper,1.000000
+IVPAF,369192000.000000,0.076348,0.664401,1.077483,0.061661,-0.022023,Other Industrial Metals & Mining,1.000000
+IYAA.F,369192000.000000,0.076348,0.664401,1.352430,0.061661,-0.041873,Other Industrial Metals & Mining,1.000000
+LUNMF,2006157000.000000,0.363713,4.052453,0.204200,4.081551,0.107202,Copper,1.000000
+LYSCF,183978000.000000,0.082030,0.057009,1.925254,0.018469,-0.011947,Other Industrial Metals & Mining,0.000000
+LYSDY,183978000.000000,0.082030,0.057009,1.925254,0.018469,-0.006850,Other Industrial Metals & Mining,0.000000
+OCKA.F,1182300000.000000,0.446589,-2.883481,0.009436,-0.015265,0.169516,Copper,0.000000
+OUW0.F,1185774000.000000,0.342431,4.823058,1.007837,14.498742,0.118087,Copper,1.000000
+PILBF,555743000.000000,0.171305,0.699751,2.789609,0.514222,-0.146581,Other Industrial Metals & Mining,1.000000
+RE8.F,10702000.000000,0.102129,-0.733371,0.031803,-0.306130,0.128414,Copper,0.000000
+RIO.AX,13855000000.000000,0.239024,2.998698,0.128688,0.013946,0.034858,Other Industrial Metals & Mining,1.000000
+RTNTF,13855000000.000000,0.239024,2.998698,0.128688,0.013946,0.041515,Other Industrial Metals & Mining,1.000000
+SCCO,6997700000.000000,0.757474,-1.727273,0.162910,-0.021074,0.202581,Copper,0.000000
+TECK,9965000000.000000,0.367766,0.912234,-0.035022,0.026410,-0.020542,Other Industrial Metals & Mining,1.000000
+TECK-A.TO,9965000000.000000,0.367766,2.151701,-0.006912,0.026410,-0.006680,Other Industrial Metals & Mining,1.000000

task2_equity_inputs_snapshot_equityfin.csv

@@ -0,0 +1,35 @@
+Ticker,Total_Debt,Debt_to_Equity,Debt_Financed_Capex_Share,Capex_Growth_Pct,Debt_Growth_Pct,Exp_Return,Industry,is_low_DFS
+000630.SZ,24751709247.860001,0.585746,0.589168,0.372084,0.239713,-0.024073,Copper,0.000000
+2009.TW,1429518000.000000,0.199319,-3.495542,-0.061187,0.055142,0.013597,Copper,1.000000
+603979.SS,3309619303.320000,0.365366,7.114634,0.424592,0.289674,0.251897,Other Industrial Metals & Mining,0.000000
+603993.SS,30248216854.750000,0.351960,-0.180511,0.156568,-0.128333,0.130924,Other Industrial Metals & Mining,1.000000
+ANFGF,5349000000.000000,0.412916,2.662484,0.171120,0.196691,0.128633,Copper,0.000000
+ARREF,10702000.000000,0.102129,-0.733371,0.843645,-0.306130,0.104284,Copper,1.000000
+ATUSF,107381000.000000,0.188016,0.260486,0.217394,-0.021735,0.105991,Other Industrial Metals & Mining,1.000000
+ATYM.L,21588000.000000,0.041633,-6.955855,0.208248,-0.103383,0.042901,Copper,1.000000
+CMCLF,30248216854.750000,0.351960,-0.180511,0.156568,-0.128333,0.211560,Other Industrial Metals & Mining,1.000000
+CS.TO,1185774000.000000,0.342431,4.823058,0.872332,14.498742,0.171722,Copper,0.000000
+FCX,9738000000.000000,0.338383,0.031319,0.276501,0.003026,0.023713,Copper,1.000000
+FCXO34.SA,9738000000.000000,0.338383,0.031319,0.342491,0.003026,0.054976,Copper,1.000000
+FG1.F,5349000000.000000,0.412916,3.208800,0.108097,0.196691,0.087811,Copper,0.000000
+FPMB.F,9738000000.000000,0.338383,0.031319,0.342491,0.003026,-0.003181,Copper,1.000000
+FSUGY,5400000000.000000,0.276484,-3.785816,-0.050688,0.107346,-0.027311,Other Industrial Metals & Mining,1.000000
+FSUMF,5400000000.000000,0.276484,-3.785816,-0.050688,0.107346,0.035952,Other Industrial Metals & Mining,1.000000
+FVJ.F,5400000000.000000,0.276484,-3.785816,-0.050688,0.107346,-0.005713,Other Industrial Metals & Mining,1.000000
+HBM,1182300000.000000,0.446589,-3.213031,0.000658,-0.015265,0.211903,Copper,1.000000
+HBM.TO,1182300000.000000,0.446589,-2.883438,0.003648,-0.015265,0.237815,Copper,1.000000
+IPMLF,372851000.000000,0.452690,2.367807,0.284529,1.812626,0.184399,Copper,0.000000
+IVPAF,369192000.000000,0.076348,0.664401,1.077483,0.061661,-0.022023,Other Industrial Metals & Mining,0.000000
+IYAA.F,369192000.000000,0.076348,0.664401,1.352430,0.061661,-0.041873,Other Industrial Metals & Mining,0.000000
+LUNMF,2006157000.000000,0.363713,4.052453,0.204200,4.081551,0.107202,Copper,0.000000
+LYSCF,183978000.000000,0.082030,0.057009,1.925254,0.018469,-0.011947,Other Industrial Metals & Mining,1.000000
+LYSDY,183978000.000000,0.082030,0.057009,1.925254,0.018469,-0.006850,Other Industrial Metals & Mining,1.000000
+OCKA.F,1182300000.000000,0.446589,-2.883481,0.009436,-0.015265,0.169516,Copper,1.000000
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task2_equity_perf_summary.csv

@@ -0,0 +1,2 @@
+Start,End,PeriodsPerYear,Total_Return,CAGR,Ann_Vol,Sharpe,Max_Drawdown,Obs,Freq,RF_Annual
+2020-01-20,2025-06-12,252,6.651612,0.438995,0.332326,1.262110,-0.542637,1409,D,0.000000

task2_equity_portfolio_allocation_debtfin.csv

@@ -0,0 +1,35 @@
+Ticker,Weight,Exp_Return,Debt_to_Equity,Debt_Financed_Capex_Share,High_DFS_flag,Total_Debt,Industry
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task2_equity_portfolio_allocation_equityfin.csv

@@ -0,0 +1,35 @@
+Ticker,Weight,Exp_Return,Debt_to_Equity,Debt_Financed_Capex_Share,Low_DFS_flag,Total_Debt,Industry
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+TECK-A.TO,0.000000,-0.006680,0.367766,2.151701,0,9965000000.000000,Other Industrial Metals & Mining

task2_equity_rolling_sharpe_debtfin.csv

@@ -0,0 +1,1359 @@
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task2_equity_vs_HG_metrics_debtfin.csv

@@ -0,0 +1,3 @@
+Name,Start,End,Total_Return,CAGR,Ann_Vol,Sharpe,Max_Drawdown,Obs
+Portfolio,2020-01-21,2025-06-12,6.651612,0.458393,0.338797,1.303286,-0.537091,1358
+HG=F,2020-01-21,2025-06-12,0.679965,0.100975,0.247506,0.531863,-0.348651,1358

task2_equity_vs_HG_metrics_equityfin.csv

@@ -0,0 +1,3 @@
+Name,Start,End,Total_Return,CAGR,Ann_Vol,Sharpe,Max_Drawdown,Obs
+Portfolio,2020-01-21,2025-06-12,5.578387,0.418092,0.345846,1.205021,-0.551080,1358
+HG=F,2020-01-21,2025-06-12,0.679965,0.100975,0.247506,0.531863,-0.348651,1358

task2_members_HighDFS_HighDE.csv

@@ -0,0 +1,13 @@
+Ticker
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task2_members_HighDFS_LowDE.csv

@@ -0,0 +1,9 @@
+Ticker
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+RTNTF

task2_members_LowDFS_HighDE.csv

@@ -0,0 +1,9 @@
+Ticker
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task2_members_LowDFS_LowDE.csv

@@ -0,0 +1,15 @@
+Ticker
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task2_quadrant_members.csv

@@ -0,0 +1,43 @@
+Ticker,DFS,DE,Quadrant
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+RTNTF,2.9986984999999997,0.239024,HighDFS_LowDE
+SCCO,-1.727273,0.757474,LowDFS_HighDE
+SFR.AX,1.8894205,0.349289,HighDFS_HighDE
+SKE,-3.3518645,0.149349,LowDFS_LowDE
+TECK,0.912234,0.367766,HighDFS_HighDE
+TECK-A.TO,2.1517005,0.367766,HighDFS_HighDE
+TGB,1.341436,1.584207,HighDFS_HighDE
+TKO.L,1.341436,1.584207,HighDFS_HighDE

task2_quadrant_on_threshold.csv

@@ -0,0 +1,5 @@
+Ticker,value,which
+LYSCF,0.0570095,DFS
+LYSDY,0.0570095,DFS
+CS.TO,0.342431,DE
+OUW0.F,0.342431,DE

task2_quadrant_thresholds.txt

@@ -0,0 +1,2 @@
+DFS split: 0.057009  (method=percentile, value=p50)
+D/E split: 0.342431   (method=percentile, value=p50)

task2_quadrants_best_blend_weights_dfs_de_quadrants_preblend.csv

@@ -0,0 +1,5 @@
+Quadrant,Weight
+LowDFS_LowDE,0.400000
+LowDFS_HighDE,0.000000
+HighDFS_LowDE,0.600000
+HighDFS_HighDE,0.000000