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"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "e2c85a90-ee2a-4c2f-b24c-394780e3a51a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reportable Positions:\n",
" Col1 Col2 \\\n",
": : Producer/Merchant/ : \n",
": Open : \n",
": Interest : Long : \n",
": :(CONTRACTS OF 100 TROY OUNCES) : \n",
": : Positions \n",
"All : 459,997: \n",
"Old : 459,997: \n",
"Other: 0: 0 \n",
": : : \n",
"\n",
" Col3 Col4 Col5 Col6 Col7 \\\n",
": : : : Positions \n",
": Processor/User : Swap Dealers : Managed Money \n",
": Interest : Short : Long : Short \n",
": \n",
": : \n",
"All 9,875 68,621 43,176 229,253 34,025 \n",
"Old 9,875 68,621 43,176 229,253 34,025 \n",
"Other: 0 0 0 0 0 \n",
": \n",
"\n",
" Col8 Col9 Col10 Col11 Col12 \\\n",
": \n",
": : Other Reportables : \n",
": Interest : :Spreading : Long : Short :Spreading : \n",
": \n",
": \n",
"All 134,155 30,068 19,999 131,761 28,779 \n",
"Old 134,155 30,068 19,999 131,761 28,779 \n",
"Other: 0 0 0 0 0: \n",
": \n",
"\n",
" Col13 Col14 Col15 Col16 Col17 Col18 Col19 \n",
": \n",
": \n",
": Interest : Long : Short :Spreading : Long : Short \n",
": \n",
": \n",
"All 30,264: 56,742 18,988 \n",
"Old 30,264: 56,742 18,988 \n",
"Other: 0 0 \n",
": \n"
]
}
],
"source": [
"import pandas as pd\n",
"import re\n",
"\n",
"raw_text = \"\"\"{GOLD - COMMODITY EXCHANGE INC. Code-088691\n",
"Disaggregated Commitments of Traders - Futures Only, November 10, 2025 \n",
"-------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
" : : Reportable Positions : Nonreportable\n",
" : : Producer/Merchant/ : : : : Positions\n",
" : Open : Processor/User : Swap Dealers : Managed Money : Other Reportables :\n",
" : Interest : Long : Short : Long : Short :Spreading : Long : Short :Spreading : Long : Short :Spreading : Long : Short\n",
"-------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
" : :(CONTRACTS OF 100 TROY OUNCES) :\n",
" : : Positions :\n",
"All : 459,997: 9,875 68,621 43,176 229,253 34,025 134,155 30,068 19,999 131,761 28,779 30,264: 56,742 18,988\n",
"Old : 459,997: 9,875 68,621 43,176 229,253 34,025 134,155 30,068 19,999 131,761 28,779 30,264: 56,742 18,988\n",
"Other: 0: 0 0 0 0 0 0 0 0 0 0 0: 0 0\n",
" : : :\n",
" : : Changes in Commitments from: November 4, 2025 :\n",
" : 9,598: 70 -635 -4,019 4,967 2,670 3,467 -75 -6,898 5,877 4,657 5,350: 3,081 -438\n",
" : : :\n",
" : : Percent of Open Interest Represented by Each Category of Trader :\n",
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" : : :\n",
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"All : 317: 13 16 21 25 29 82 22 36 109 30 39:\n",
"Old : 317: 13 16 21 25 29 82 22 36 109 30 39:\n",
"Other: 0: 0 0 0 0 0 0 0 0 0 0 0:\n",
" :-------------------------------------------------------------------------------------------------------------------------------------------------------\n",
" : Percent of Open Interest Held by the Indicated Number of the Largest Traders\n",
" : By Gross Position By Net Position\n",
" : 4 or Less Traders 8 or Less Traders 4 or Less Traders 8 or Less Traders\n",
" : Long: Short Long Short: Long Short Long Short\n",
" :----------------------------------------------------------------------------------------------------\n",
"All : 14.9 31.3 25.3 47.7 14.7 30.1 23.8 44.6\n",
"Old : 14.9 31.3 25.3 47.7 14.7 30.1 23.8 44.6\n",
"Other: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0}\"\"\" # Paste your full raw text here\n",
"\n",
"# Helper function to clean and split lines into columns\n",
"def parse_line(line):\n",
" # Remove extra spaces at start/end\n",
" line = line.strip()\n",
" # Replace multiple spaces with a single colon separator\n",
" parts = re.split(r'\\s{2,}', line)\n",
" # Remove empty strings\n",
" parts = [p for p in parts if p]\n",
" return parts\n",
"\n",
"# Split the text into lines\n",
"lines = raw_text.splitlines()\n",
"\n",
"# Sections to parse\n",
"sections = {\n",
" 'Reportable Positions': [],\n",
" 'Changes in Commitments': [],\n",
" 'Percent of Open Interest': [],\n",
" 'Number of Traders': [],\n",
" 'Percent of Largest Traders': []\n",
"}\n",
"\n",
"current_section = None\n",
"\n",
"for line in lines:\n",
" if \"Reportable Positions\" in line:\n",
" current_section = 'Reportable Positions'\n",
" continue\n",
" elif \"Changes in Commitments\" in line:\n",
" current_section = 'Changes in Commitments'\n",
" continue\n",
" elif \"Percent of Open Interest Represented\" in line:\n",
" current_section = 'Percent of Open Interest'\n",
" continue\n",
" elif \"Number of Traders in Each Category\" in line:\n",
" current_section = 'Number of Traders'\n",
" continue\n",
" elif \"Percent of Open Interest Held by the Indicated Number\" in line:\n",
" current_section = 'Percent of Largest Traders'\n",
" continue\n",
"\n",
" if current_section and line.strip() and not line.startswith('-') and not line.startswith('{'):\n",
" sections[current_section].append(parse_line(line))\n",
"\n",
"# Convert each section into a DataFrame\n",
"dfs = {}\n",
"for key, data in sections.items():\n",
" if not data:\n",
" continue\n",
" # Use first column as index if it looks like a row label\n",
" index = [row[0] for row in data]\n",
" # Take the remaining columns as data\n",
" columns_data = [row[1:] for row in data]\n",
" # Determine max number of columns\n",
" max_cols = max(len(r) for r in columns_data)\n",
" # Pad rows to have same number of columns\n",
" columns_data = [r + ['']*(max_cols - len(r)) for r in columns_data]\n",
" # Create column names\n",
" col_names = [f\"Col{i+1}\" for i in range(max_cols)]\n",
" dfs[key] = pd.DataFrame(columns_data, index=index, columns=col_names)\n",
"\n",
"# Example: Access Reportable Positions DataFrame\n",
"reportable_positions_df = dfs.get('Reportable Positions')\n",
"print(\"Reportable Positions:\")\n",
"print(reportable_positions_df)\n",
"\n",
"# You can similarly access other sections:\n",
"# dfs['Changes in Commitments']\n",
"# dfs['Percent of Open Interest']\n",
"# dfs['Number of Traders']\n",
"# dfs['Percent of Largest Traders']\n"
]
},
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"=== Positions ===\n",
"Empty DataFrame\n",
"Columns: [Type, OpenInterest, Prod_Long, Prod_Short, Swap_Long, Swap_Short, Swap_Spread, MM_Long, MM_Short, MM_Spread, OtherRep_Long, OtherRep_Short, OtherRep_Spread, NonRep_Long, NonRep_Short]\n",
"Index: []\n",
"\n",
"=== Changes in Commitments ===\n",
"Empty DataFrame\n",
"Columns: [Type, Change_OpenInterest, Prod_Long, Prod_Short, Swap_Long, Swap_Short, Swap_Spread, MM_Long, MM_Short, MM_Spread, OtherRep_Long, OtherRep_Short, OtherRep_Spread, NonRep_Long, NonRep_Short]\n",
"Index: []\n",
"\n",
"=== Percent of Open Interest ===\n",
" Type Percent_OpenInterest Prod_Long Prod_Short Swap_Long Swap_Short \\\n",
"0 All 100.0 2.1 14.9 9.4 49.8 \n",
"1 Old 100.0 2.1 14.9 9.4 49.8 \n",
"2 Other 100.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Swap_Spread MM_Long MM_Short MM_Spread OtherRep_Long OtherRep_Short \\\n",
"0 7.4 29.2 6.5 4.3 28.6 6.3 \n",
"1 7.4 29.2 6.5 4.3 28.6 6.3 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" OtherRep_Spread NonRep_Long NonRep_Short \n",
"0 6.6 12.3 4.1 \n",
"1 6.6 12.3 4.1 \n",
"2 0.0 0.0 0.0 \n",
"\n",
"=== Number of Traders ===\n",
" Type NumTraders Prod_Long Prod_Short Swap_Long Swap_Short Swap_Spread \\\n",
"0 All 317 13 16 21 25 29 \n",
"1 Old 317 13 16 21 25 29 \n",
"2 Other 0 0 0 0 0 0 \n",
"\n",
" MM_Long MM_Short MM_Spread OtherRep_Long OtherRep_Short OtherRep_Spread \n",
"0 82 22 36 109 30 39 \n",
"1 82 22 36 109 30 39 \n",
"2 0 0 0 0 0 0 \n",
"\n",
"=== Percent of Open Interest Held by Largest Traders ===\n",
"Empty DataFrame\n",
"Columns: [Type, Gross_4L_Long, Gross_4L_Short, Gross_8L_Long, Gross_8L_Short, Net_4L_Long, Net_4L_Short, Net_8L_Long, Net_8L_Short]\n",
"Index: []\n"
]
}
],
"source": [
"import pandas as pd\n",
"import re\n",
"\n",
"cot_report = \"\"\"\n",
"GOLD - COMMODITY EXCHANGE INC.\n",
"Code-088691\n",
"Disaggregated Commitments of Traders - Futures Only, November 10, 2025\n",
"-------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
" : : Reportable Positions : Nonreportable\n",
" : : Producer/Merchant/ : : : : Positions\n",
" : Open : Processor/User : Swap Dealers : Managed Money : Other Reportables :\n",
" : Interest : Long : Short : Long : Short :Spreading : Long : Short :Spreading : Long : Short :Spreading : Long : Short\n",
"-------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
" : :(CONTRACTS OF 100 TROY OUNCES) :\n",
" : : Positions :\n",
"All : 459,997: 9,875 68,621 43,176 229,253 34,025 134,155 30,068 19,999 131,761 28,779 30,264: 56,742 18,988\n",
"Old : 459,997: 9,875 68,621 43,176 229,253 34,025 134,155 30,068 19,999 131,761 28,779 30,264: 56,742 18,988\n",
"Other: 0: 0 0 0 0 0 0 0 0 0 0 0: 0 0\n",
" : : :\n",
" : : Changes in Commitments from: November 4, 2025 :\n",
" : 9,598: 70 -635 -4,019 4,967 2,670 3,467 -75 -6,898 5,877 4,657 5,350: 3,081 -438\n",
" : : :\n",
" : : Percent of Open Interest Represented by Each Category of Trader :\n",
"All : 100.0: 2.1 14.9 9.4 49.8 7.4 29.2 6.5 4.3 28.6 6.3 6.6: 12.3 4.1\n",
"Old : 100.0: 2.1 14.9 9.4 49.8 7.4 29.2 6.5 4.3 28.6 6.3 6.6: 12.3 4.1\n",
"Other: 100.0: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0: 0.0 0.0\n",
" : : :\n",
" : : Number of Traders in Each Category :\n",
"All : 317: 13 16 21 25 29 82 22 36 109 30 39:\n",
"Old : 317: 13 16 21 25 29 82 22 36 109 30 39:\n",
"Other: 0: 0 0 0 0 0 0 0 0 0 0 0:\n",
" :-------------------------------------------------------------------------------------------------------------------------------------------------------\n",
" : Percent of Open Interest Held by the Indicated Number of the Largest Traders\n",
" : By Gross Position By Net Position\n",
" : 4 or Less Traders 8 or Less Traders 4 or Less Traders 8 or Less Traders\n",
" : Long: Short Long Short: Long Short Long Short\n",
" :----------------------------------------------------------------------------------------------------\n",
"All : 14.9 31.3 25.3 47.7 14.7 30.1 23.8 44.6\n",
"Old : 14.9 31.3 25.3 47.7 14.7 30.1 23.8 44.6\n",
"Other: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
"\"\"\"\n",
"\n",
"def parse_section(lines, start_keyword, num_cols, col_names=None):\n",
" \"\"\"Extract a section of the COT report into a DataFrame.\"\"\"\n",
" data = []\n",
" capture = False\n",
" for line in lines:\n",
" if start_keyword in line:\n",
" capture = True\n",
" continue\n",
" if capture:\n",
" if not line.strip() or line.startswith(\" :\"):\n",
" break\n",
" # Split on colon or multiple spaces\n",
" parts = [p.strip() for p in re.split(r':|\\s{2,}', line) if p.strip()]\n",
" if parts:\n",
" data.append(parts)\n",
" if col_names:\n",
" df = pd.DataFrame(data, columns=col_names)\n",
" else:\n",
" df = pd.DataFrame(data)\n",
" return df\n",
"\n",
"lines = cot_report.splitlines()\n",
"\n",
"# 1. Positions\n",
"positions_cols = ['Type','OpenInterest','Prod_Long','Prod_Short','Swap_Long','Swap_Short','Swap_Spread','MM_Long','MM_Short','MM_Spread','OtherRep_Long','OtherRep_Short','OtherRep_Spread','NonRep_Long','NonRep_Short']\n",
"positions_df = parse_section(lines, \"Positions\", len(positions_cols), positions_cols)\n",
"\n",
"# 2. Changes in Commitments\n",
"changes_cols = ['Type','Change_OpenInterest','Prod_Long','Prod_Short','Swap_Long','Swap_Short','Swap_Spread','MM_Long','MM_Short','MM_Spread','OtherRep_Long','OtherRep_Short','OtherRep_Spread','NonRep_Long','NonRep_Short']\n",
"changes_df = parse_section(lines, \"Changes in Commitments\", len(changes_cols), changes_cols)\n",
"\n",
"# 3. Percent of Open Interest\n",
"percent_cols = ['Type','Percent_OpenInterest','Prod_Long','Prod_Short','Swap_Long','Swap_Short','Swap_Spread','MM_Long','MM_Short','MM_Spread','OtherRep_Long','OtherRep_Short','OtherRep_Spread','NonRep_Long','NonRep_Short']\n",
"percent_df = parse_section(lines, \"Percent of Open Interest Represented\", len(percent_cols), percent_cols)\n",
"\n",
"# 4. Number of Traders\n",
"traders_cols = ['Type','NumTraders','Prod_Long','Prod_Short','Swap_Long','Swap_Short','Swap_Spread','MM_Long','MM_Short','MM_Spread','OtherRep_Long','OtherRep_Short','OtherRep_Spread']\n",
"traders_df = parse_section(lines, \"Number of Traders\", len(traders_cols), traders_cols)\n",
"\n",
"# 5. Percent of Open Interest Held by Largest Traders\n",
"largest_cols = ['Type','Gross_4L_Long','Gross_4L_Short','Gross_8L_Long','Gross_8L_Short','Net_4L_Long','Net_4L_Short','Net_8L_Long','Net_8L_Short']\n",
"largest_df = parse_section(lines, \"Percent of Open Interest Held by the Indicated Number of the Largest Traders\", len(largest_cols), largest_cols)\n",
"\n",
"# Print all DataFrames\n",
"print(\"=== Positions ===\")\n",
"print(positions_df)\n",
"print(\"\\n=== Changes in Commitments ===\")\n",
"print(changes_df)\n",
"print(\"\\n=== Percent of Open Interest ===\")\n",
"print(percent_df)\n",
"print(\"\\n=== Number of Traders ===\")\n",
"print(traders_df)\n",
"print(\"\\n=== Percent of Open Interest Held by Largest Traders ===\")\n",
"print(largest_df)\n"
]
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"
},
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"output_type": "display_data"
},
{
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"config": {
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},
"data": [
{
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"x": {
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"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Positions:\n",
" 0 1 2 3 4 5 6 7 \\\n",
"0 All 459997.0 9875.0 68621.0 43176.0 229253.0 34025.0 134155.0 \n",
"1 Old 459997.0 9875.0 68621.0 43176.0 229253.0 34025.0 134155.0 \n",
"\n",
" 8 9 10 11 12 13 14 \n",
"0 30068.0 19999.0 131761.0 28779.0 30264.0 56742.0 18988.0 \n",
"1 30068.0 19999.0 131761.0 28779.0 30264.0 56742.0 18988.0 \n",
"\n",
"Changes:\n",
" Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"\n",
"Percent of Open Interest:\n",
" 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n",
"0 All 100.0 2.1 14.9 9.4 49.8 7.4 29.2 6.5 4.3 28.6 6.3 6.6 \n",
"1 Old 100.0 2.1 14.9 9.4 49.8 7.4 29.2 6.5 4.3 28.6 6.3 6.6 \n",
"\n",
" 13 14 \n",
"0 12.3 4.1 \n",
"1 12.3 4.1 \n",
"\n",
"Number of Traders:\n",
" 0 1 2 3 4 5 6 7 8 9 10 11 \\\n",
"0 All 317.0 13.0 16.0 21.0 25.0 29.0 82.0 22.0 36.0 109.0 30.0 \n",
"1 Old 317.0 13.0 16.0 21.0 25.0 29.0 82.0 22.0 36.0 109.0 30.0 \n",
"\n",
" 12 \n",
"0 39.0 \n",
"1 39.0 \n"
]
}
],
"source": [
"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"from io import StringIO\n",
"\n",
"# =========================\n",
"# Paste your raw COT report here as a string\n",
"# =========================\n",
"raw_data = \"\"\"\n",
"GOLD - COMMODITY EXCHANGE INC.\n",
"Code-088691\n",
"Disaggregated Commitments of Traders - Futures Only, November 10, 2025\n",
"-------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
": : Reportable Positions : Nonreportable\n",
": : Producer/Merchant/ : : : : Positions\n",
": Open : Processor/User : Swap Dealers : Managed Money : Other Reportables :\n",
": Interest : Long : Short : Long : Short :Spreading : Long : Short :Spreading : Long : Short :Spreading : Long : Short\n",
"-------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
": :(CONTRACTS OF 100 TROY OUNCES) :\n",
": : Positions :\n",
"All : 459,997: 9,875 68,621 43,176 229,253 34,025 134,155 30,068 19,999 131,761 28,779 30,264: 56,742 18,988\n",
"Old : 459,997: 9,875 68,621 43,176 229,253 34,025 134,155 30,068 19,999 131,761 28,779 30,264: 56,742 18,988\n",
"Other: 0: 0 0 0 0 0 0 0 0 0 0 0: 0 0\n",
": : :\n",
": : Changes in Commitments from: November 4, 2025 :\n",
": 9,598: 70 -635 -4,019 4,967 2,670 3,467 -75 -6,898 5,877 4,657 5,350: 3,081 -438\n",
": : :\n",
": : Percent of Open Interest Represented by Each Category of Trader :\n",
"All : 100.0: 2.1 14.9 9.4 49.8 7.4 29.2 6.5 4.3 28.6 6.3 6.6: 12.3 4.1\n",
"Old : 100.0: 2.1 14.9 9.4 49.8 7.4 29.2 6.5 4.3 28.6 6.3 6.6: 12.3 4.1\n",
"Other: 100.0: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0: 0.0 0.0\n",
": : :\n",
": : Number of Traders in Each Category :\n",
"All : 317: 13 16 21 25 29 82 22 36 109 30 39:\n",
"Old : 317: 13 16 21 25 29 82 22 36 109 30 39:\n",
"Other: 0: 0 0 0 0 0 0 0 0 0 0 0:\n",
"\"\"\"\n",
"\n",
"# =========================\n",
"# Helper function to parse sections\n",
"# =========================\n",
"def parse_section(section_name, lines):\n",
" # remove empty lines\n",
" lines = [l for l in lines if l.strip() and not l.startswith(\":\")]\n",
" \n",
" # select lines starting with All, Old, Other\n",
" data_lines = [l for l in lines if l.strip().split()[0] in [\"All\", \"Old\", \"Other\"]]\n",
" \n",
" parsed = []\n",
" for line in data_lines:\n",
" parts = line.replace(\":\", \"\").split()\n",
" row_name = parts[0]\n",
" values = [float(x.replace(\",\", \"\")) for x in parts[1:]]\n",
" parsed.append([row_name] + values)\n",
" \n",
" return pd.DataFrame(parsed)\n",
"\n",
"# =========================\n",
"# Split the raw text into relevant sections\n",
"# =========================\n",
"sections = raw_data.split(\": :\")\n",
"positions_lines = []\n",
"changes_lines = []\n",
"percent_lines = []\n",
"num_traders_lines = []\n",
"\n",
"current_section = None\n",
"for line in raw_data.splitlines():\n",
" if \"Positions\" in line and \"Contracts\" not in line:\n",
" current_section = \"positions\"\n",
" elif \"Changes in Commitments\" in line:\n",
" current_section = \"changes\"\n",
" elif \"Percent of Open Interest\" in line and \"Number of Traders\" not in line:\n",
" current_section = \"percent\"\n",
" elif \"Number of Traders in Each Category\" in line:\n",
" current_section = \"num_traders\"\n",
" \n",
" if current_section == \"positions\":\n",
" positions_lines.append(line)\n",
" elif current_section == \"changes\":\n",
" changes_lines.append(line)\n",
" elif current_section == \"percent\":\n",
" percent_lines.append(line)\n",
" elif current_section == \"num_traders\":\n",
" num_traders_lines.append(line)\n",
"\n",
"# Parse each section\n",
"df_positions = parse_section(\"Positions\", positions_lines)\n",
"df_changes = parse_section(\"Changes\", changes_lines)\n",
"df_percent = parse_section(\"Percent\", percent_lines)\n",
"df_traders = parse_section(\"NumTraders\", num_traders_lines)\n",
"\n",
"# =========================\n",
"# Plotting using Plotly\n",
"# =========================\n",
"def plot_cot(df, title):\n",
" fig = go.Figure()\n",
" categories = df.columns[1:]\n",
" for i, row in df.iterrows():\n",
" fig.add_trace(go.Bar(\n",
" x=categories,\n",
" y=row[1:],\n",
" name=row[0]\n",
" ))\n",
" fig.update_layout(\n",
" barmode='group',\n",
" title=title,\n",
" template='plotly_white',\n",
" xaxis_title='Categories',\n",
" yaxis_title='Contracts / Percent',\n",
" legend_title='Data Row'\n",
" )\n",
" fig.show()\n",
"\n",
"# =========================\n",
"# Display plots\n",
"# =========================\n",
"plot_cot(df_positions, \"COT Positions\")\n",
"plot_cot(df_changes, \"COT Changes from Previous Week\")\n",
"plot_cot(df_percent, \"Percent of Open Interest\")\n",
"plot_cot(df_traders, \"Number of Traders\")\n",
"\n",
"# =========================\n",
"# Optional: print DataFrames\n",
"# =========================\n",
"print(\"Positions:\\n\", df_positions)\n",
"print(\"\\nChanges:\\n\", df_changes)\n",
"print(\"\\nPercent of Open Interest:\\n\", df_percent)\n",
"print(\"\\nNumber of Traders:\\n\", df_traders)\n"
]
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