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    "---\n",
    "title: Multi-Factor Long/Short Equity\n",
    "summary: The classic dollar-neutral combination of value, quality, and momentum — the flagship long-short format.\n",
    "tags: [multifactor, long-short, value, quality, momentum]\n",
    "---\n",
    "\n",
    "# Multi-Factor Long/Short Equity\n",
    "\n",
    "The bread-and-butter of systematic equity: don't bet on one signal, **combine several**. We z-score\n",
    "value, quality, and momentum, add them into a single composite score, and build a **dollar-neutral**\n",
    "long/short book — long the best names, short the worst. Diversifying across factors smooths the ride\n",
    "when any one of them is out of favor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73297dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import convexpi.lab  # noqa\n",
    "except ImportError:\n",
    "    import subprocess, sys\n",
    "    subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"convexpi-lab\"])\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"ready\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cace286",
   "metadata": {},
   "source": [
    "## The idea\n",
    "\n",
    "Each factor is already cross-sectionally standardized each day, so we can simply **sum** them into a\n",
    "composite and sort on it. Value (`val_bm`) buys cheap, quality (`qual_roe`) buys profitable, momentum\n",
    "(`mom_12m`) buys winners — three largely independent edges in one book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bee9cc0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from convexpi.lab import Strategy\n",
    "\n",
    "def _long_short(signal, frac=0.2):\n",
    "    \"\"\"Dollar-neutral long/short: long the top `frac`, short the bottom `frac`, equal-weighted.\"\"\"\n",
    "    s = np.nan_to_num(np.asarray(signal, dtype=float))\n",
    "    n = len(s); k = max(1, int(n * frac))\n",
    "    order = np.argsort(s)\n",
    "    w = np.zeros(n); w[order[-k:]] = 1.0 / k; w[order[:k]] = -1.0 / k\n",
    "    return w\n",
    "\n",
    "class MyStrategy(Strategy):\n",
    "    \"\"\"Composite of value + quality + momentum, dollar-neutral top/bottom quintile.\"\"\"\n",
    "    weights = {\"val_bm\": 1.0, \"qual_roe\": 1.0, \"mom_12m\": 1.0}\n",
    "\n",
    "    def on_day(self, day, features, prices, portfolio):\n",
    "        n = len(prices)\n",
    "        combo = np.zeros(n)\n",
    "        for name, wt in self.weights.items():\n",
    "            combo += wt * np.nan_to_num(features.get(name, np.zeros(n)))\n",
    "        return _long_short(combo, frac=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8aca91e",
   "metadata": {},
   "source": [
    "## Out-of-sample evaluation\n",
    "\n",
    "Train on the first half of a synthetic market, evaluate on the held-out second half — the same discipline as the leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b2225ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from convexpi.lab import SyntheticMarket, Grader\n",
    "market = SyntheticMarket(n_stocks=80, n_days=1800, seed=1)\n",
    "report = Grader(market).evaluate(MyStrategy())\n",
    "print(f\"in-sample Sharpe    : {report.is_sharpe:+.2f}\")\n",
    "print(f\"out-of-sample Sharpe: {report.oos_sharpe:+.2f}\")\n",
    "print(f\"overfitting ratio   : {report.overfitting_ratio:+.2f}\")\n",
    "oos = report.oos_result.daily_returns\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(np.cumprod(1 + oos)); ax.set_title(\"Out-of-sample equity curve\"); ax.set_ylabel(\"growth of $1\")\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# How much does each factor contribute? Compare the composite to each factor alone.\n",
    "single = {}\n",
    "for f in [\"val_bm\", \"qual_roe\", \"mom_12m\"]:\n",
    "    class _S(MyStrategy):\n",
    "        weights = {f: 1.0}\n",
    "    single[f] = Grader(market).evaluate(_S()).oos_sharpe\n",
    "print(\"OOS Sharpe — single factors:\", {k: round(v, 2) for k, v in single.items()})\n",
    "print(\"OOS Sharpe — composite     :\", round(report.oos_sharpe, 2))\n",
    "print(\"Combining factors smooths in-sample — but beating OOS is hard; watch the overfitting ratio.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf4e3c8",
   "metadata": {},
   "source": [
    "## What I'd try next\n",
    "\n",
    "- **Weight the factors** by recent IC instead of equally.\n",
    "- **Neutralize** the book to size or sector before sorting.\n",
    "- Add a **volatility scale** so the book targets constant risk.\n",
    "- Watch the overfitting ratio as you add factors — more knobs = more ways to fool yourself."
   ]
  }
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