Update GDELT mean sentiment alpha/Alpha Implementation/Alpha Implementation.ipynb

GDELT mean sentiment alpha/Alpha Implementation/Alpha Implementation.ipynb

@@ -38,21 +38,7 @@
     "The first step involves importing and sanitizing time‐series data for thirteen composite futures (six energy and seven metals) and the S&P GSCI Energy & Metals Index benchmark. We load each CSV, parse the “Date” column into datetime, and strip non‐numeric characters (commas, percent signs) from columns AvgTone, Price, Open, High, Low, Vol., and Change % before converting them to floats to ensure consistent numeric types."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f55219fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "unmatched ')' (100150859.py, line 38)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[2], line 38\u001b[0;36m\u001b[0m\n\u001b[0;31m    contracts[f.replace(\"_data.csv\",\"\")] = df\u001b[0m\n\u001b[0m                                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unmatched ')'\n"
-     ]
-    }
-   ],
+
    "source": [
     "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n",
     "from pathlib import Path\n",
@@ -91,7 +77,7 @@
     "for f in contracts_csv:\n",
     "    df = load_and_clean_csv(f).sort_values(\"Date\").reset_index(drop=True)\n",
     "    df[\"Return\"] = df[\"Price\"].pct_change()\n",
-    "    contracts[f.replace(\"_data.csv\",\"\")] = df)"
+    "    contracts[f.replace(\"_data.csv\",\"\")] = df"
    ]
   },
   {