{ "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 }