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            "source": [
                "# QuantScale AI: Automated Direct Indexing & Attribution\n",
                "## Goldman Sachs Quant Prep Project\n",
                "\n",
                "This notebook demonstrates the end-to-end workflow:\n",
                "1. **Data Ingestion**: Scraping S&P 500 & fetching market data.\n",
                "2. **Risk Modeling**: Computing Ledoit-Wolf Shrinkage Covariance.\n",
                "3. **Optimization**: Minimizing Tracking Error with Sector Exclusion Constraints.\n",
                "4. **AI Reporting**: Using Hugging Face to generate professional commentary."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "!pip install -r requirements.txt"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "from main import QuantScaleSystem\n",
                "from core.schema import OptimizationRequest\n",
                "import matplotlib.pyplot as plt\n",
                "\n",
                "# Initialize System\n",
                "system = QuantScaleSystem()\n",
                "\n",
                "# Test Case: Optimization with Energy Exclusion\n",
                "req = OptimizationRequest(client_id=\"COLAB_USER\", excluded_sectors=[\"Energy\"])\n",
                "result = system.run_pipeline(req)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Visualization of Weights\n",
                "if result:\n",
                "    weights = result['optimization'].weights\n",
                "    plt.figure(figsize=(12, 6))\n",
                "    plt.bar(range(len(weights)), list(weights.values()), align='center')\n",
                "    plt.title('Optimized Portfolio Weights (Energy Excluded)')\n",
                "    plt.xlabel('Assets')\n",
                "    plt.ylabel('Weight')\n",
                "    plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# AI Commentary\n",
                "print(result['commentary'])"
            ]
        }
    ],
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