Spaces:
Running
Running
File size: 3,235 Bytes
cafdd88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"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'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
} |