{
"cells": [
{
"cell_type": "markdown",
"id": "c1ed63c850198a27",
"metadata": {
"collapsed": false
},
"source": [
"# DL of CTFC COT\n",
"\n",
"\n",
"* go to [CFTC legacy reports](https://www.cftc.gov/MarketReports/CommitmentsofTraders/index.htm) for the respective commodity\n",
"* [Data source](https://www.cftc.gov/MarketReports/CommitmentsofTraders/HistoricalCompressed/index.htm)\n",
"\n",
"## Example \"Metals and other\" \n",
"\n",
"-> Short Format -> [Report](https://www.cftc.gov/dea/futures/other_sf.htm)\n",
"\n",
"```\n",
"Disaggregated Commitments of Traders-All Futures Combined Positions as of March 5, 2024 \n",
": Reportable Positions :\n",
":------------------------------------------------------------------------------------------------------------- :\n",
": Producer/Merchant : : : :\n",
": Processor/User : Swap Dealers : Managed Money : Other Reportables :\n",
": Long : Short : Long : Short :Spreading: Long : Short :Spreading: Long : Short :Spreading :\n",
"----------------------------------------------------------------------------------------------------------------\n",
"COPPER- #1 - COMMODITY EXCHANGE INC. (CONTRACTS OF 25,000 POUNDS) :\n",
"CFTC Code #085692 Open Interest is 198,561 :\n",
": Positions :\n",
": 25,549 55,033 40,929 8,737 4,060 51,724 60,287 29,969 19,246 16,010 11,212 :\n",
": :\n",
"`: Changes from: February 27, 2024 :`\n",
": 1,596 -1,200 -506 -138 -166 -2,788 10,821 5,613 4,085 -5,800 1,264 :\n",
": :\n",
": Percent of Open Interest Represented by Each Category of Trader :\n",
": 12.9 27.7 20.6 4.4 2.0 26.0 30.4 15.1 9.7 8.1 5.6 :\n",
": :\n",
": Number of Traders in Each Category Total Traders: 310 :\n",
": 33 42 20 12 16 66 58 55 54 45 35 :\n",
"----------------------------------------------------------------------------------------------------------------\n",
"```\n",
"\n",
"## Long / Short positions most notably per \n",
"\n",
"* Processor (who produces the commodity, knows the market in adv.)\n",
"* Swap Dealers (retail investors)\n",
"* Managed Money (investment banks, market makers)\n",
"\n",
"### Here for March 5th '24: \n",
"\n",
"Analyse-Compass (basis Ray Dalio)\n",
"\n",
"Our target:\n",
"% Open Interest \n",
"Bullish / Bearish ranges\n",
"\n",
"* Copper\t25-20%\t/ 40-50%\n",
"\n",
"-> we want 25-20% Producers long for Bullish\n",
"-> we want 40-50% Producers short for Bearish\n",
"\n",
"Both conditions are not met.\n",
"\n",
"12,9\n",
"27,7%.\n",
"\n",
"Therefore, this signal indiction: no trade to set for March '24.\n",
"\n",
"### Signal interpretation\n",
"\n",
"* Bullish (=rising expectation), means go long (IF at all)\n",
"* Bearish (=falling expectation), means go short \n",
"\n",
"* Copper COT signal interpretation for 5th of March '24\n",
"\n",
"12,9% of Producers are long\n",
"27,7% are short\n",
"\n",
"Most producers expect the market to fall.\n",
"Swap dealers are positioned differently.\n",
"Market Makers are balanced."
]
},
{
"cell_type": "markdown",
"id": "17e978b957c34cc",
"metadata": {
"collapsed": false
},
"source": [
"## Data-driven analysis\n",
"\n",
"The COT data as shown above is pasted in a format, which looks like from the Mainframe era of computing."
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "afa222d7d5329711",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T10:49:10.777655Z",
"start_time": "2024-03-09T10:49:07.411783Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: cot_reports==0.1.3 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (0.1.3)\r\n",
"Requirement already satisfied: pandas in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from cot_reports==0.1.3) (2.2.1)\r\n",
"Requirement already satisfied: requests in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from cot_reports==0.1.3) (2.31.0)\r\n",
"Requirement already satisfied: beautifulsoup4 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from cot_reports==0.1.3) (4.12.3)\r\n",
"Requirement already satisfied: soupsieve>1.2 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from beautifulsoup4->cot_reports==0.1.3) (2.5)\r\n",
"Requirement already satisfied: numpy<2,>=1.23.2 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from pandas->cot_reports==0.1.3) (1.26.4)\r\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from pandas->cot_reports==0.1.3) (2.9.0)\r\n",
"Requirement already satisfied: pytz>=2020.1 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from pandas->cot_reports==0.1.3) (2024.1)\r\n",
"Requirement already satisfied: tzdata>=2022.7 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from pandas->cot_reports==0.1.3) (2024.1)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from requests->cot_reports==0.1.3) (3.3.2)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from requests->cot_reports==0.1.3) (3.6)\r\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from requests->cot_reports==0.1.3) (2.2.1)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from requests->cot_reports==0.1.3) (2024.2.2)\r\n",
"Requirement already satisfied: six>=1.5 in /home/marius/miniconda3/envs/lang_chain/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->cot_reports==0.1.3) (1.16.0)\r\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install cot_reports==\"0.1.3\""
]
},
{
"cell_type": "markdown",
"id": "dc4d9aade9f5a019",
"metadata": {
"collapsed": false
},
"source": [
"## Analysis of the initial data\n",
"\n",
"* different commodities etc. in the disaggregated Futures report\n",
"* we need to filter this down intelligently"
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "59275665b54f456",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:06:49.260108Z",
"start_time": "2024-03-09T11:06:40.871193Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected: disaggregated_fut\n",
"Downloaded single year data from: 2020\n",
"Stored the file f_year.txt in the working directory.\n",
"Selected: disaggregated_fut\n",
"Downloaded single year data from: 2021\n",
"Stored the file f_year.txt in the working directory.\n",
"Selected: disaggregated_fut\n",
"Downloaded single year data from: 2022\n",
"Stored the file f_year.txt in the working directory.\n",
"Selected: disaggregated_fut\n",
"Downloaded single year data from: 2023\n",
"Stored the file f_year.txt in the working directory.\n",
"Selected: disaggregated_fut\n",
"Downloaded single year data from: 2024\n",
"Stored the file f_year.txt in the working directory.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import cot_reports as cot # Ensure cot_reports is correctly imported and cot.cot_year() works as expected\n",
"\n",
"df = pd.DataFrame()\n",
"begin_year = 2020\n",
"end_year = 2024\n",
"\n",
"for i in range(begin_year, end_year + 1):\n",
" # Assuming cot.cot_year returns a DataFrame\n",
" single_year = pd.DataFrame(cot.cot_year(i, cot_report_type='disaggregated_fut'))\n",
" single_year.to_csv(f'./COT_CFTC_{i}.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "e6d4d271e1d99abb",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:08:44.322381Z",
"start_time": "2024-03-09T11:08:44.318455Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['COT_CFTC_2022.csv', 'COT_CFTC_2023.csv', 'COT_CFTC_2021.csv', 'COT_CFTC_2020.csv', 'COT_CFTC_2024.csv']\n"
]
}
],
"source": [
"import glob\n",
"\n",
"# Adjust the path and pattern according to your CSV files location and naming convention\n",
"csv_files = glob.glob('COT_CFTC_20*.csv')\n",
"print(csv_files)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"id": "b284835ac41d4a4f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:22:19.817094Z",
"start_time": "2024-03-09T11:22:19.742368Z"
},
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv('COT_CFTC_2024.csv')"
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "7946ebc889c6f8ae",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:22:30.274312Z",
"start_time": "2024-03-09T11:22:30.218822Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2481, 191)"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df[\"Report_Date_as_YYYY-MM-DD\"] = pd.to_datetime(df[\"Report_Date_as_YYYY-MM-DD\"], format='%Y-%m-%d')\n",
"df.set_index(\"Report_Date_as_YYYY-MM-DD\")\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 138,
"id": "f81761725b000258",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:22:54.071128Z",
"start_time": "2024-03-09T11:22:53.921142Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2482\r\n"
]
}
],
"source": [
"!cat COT_CFTC_2024.csv | wc -l"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "80c7995f47756134",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:23:33.531638Z",
"start_time": "2024-03-09T11:23:33.499054Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Market_and_Exchange_Names | \n",
" As_of_Date_In_Form_YYMMDD | \n",
" Report_Date_as_YYYY-MM-DD | \n",
" CFTC_Contract_Market_Code | \n",
" CFTC_Market_Code | \n",
" CFTC_Region_Code | \n",
" CFTC_Commodity_Code | \n",
" Open_Interest_All | \n",
" Prod_Merc_Positions_Long_All | \n",
" Prod_Merc_Positions_Short_All | \n",
" ... | \n",
" Conc_Net_LE_4_TDR_Long_Other | \n",
" Conc_Net_LE_4_TDR_Short_Other | \n",
" Conc_Net_LE_8_TDR_Long_Other | \n",
" Conc_Net_LE_8_TDR_Short_Other | \n",
" Contract_Units | \n",
" CFTC_Contract_Market_Code_Quotes | \n",
" CFTC_Market_Code_Quotes | \n",
" CFTC_Commodity_Code_Quotes | \n",
" CFTC_SubGroup_Code | \n",
" FutOnly_or_Combined | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" WHEAT-SRW - CHICAGO BOARD OF TRADE | \n",
" 240305 | \n",
" 2024-03-05 | \n",
" 001602 | \n",
" CBT | \n",
" 0 | \n",
" 1 | \n",
" 401311 | \n",
" 50034 | \n",
" 74289 | \n",
" ... | \n",
" 20.5 | \n",
" 18.7 | \n",
" 32.4 | \n",
" 28.2 | \n",
" (CONTRACTS OF 5,000 BUSHELS) | \n",
" 001602 | \n",
" CBT | \n",
" 1 | \n",
" A10 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 1 | \n",
" WHEAT-SRW - CHICAGO BOARD OF TRADE | \n",
" 240227 | \n",
" 2024-02-27 | \n",
" 001602 | \n",
" CBT | \n",
" 0 | \n",
" 1 | \n",
" 373389 | \n",
" 43686 | \n",
" 75295 | \n",
" ... | \n",
" 21.9 | \n",
" 20.7 | \n",
" 33.8 | \n",
" 30.1 | \n",
" (CONTRACTS OF 5,000 BUSHELS) | \n",
" 001602 | \n",
" CBT | \n",
" 1 | \n",
" A10 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2 | \n",
" WHEAT-SRW - CHICAGO BOARD OF TRADE | \n",
" 240220 | \n",
" 2024-02-20 | \n",
" 001602 | \n",
" CBT | \n",
" 0 | \n",
" 1 | \n",
" 396470 | \n",
" 51309 | \n",
" 73366 | \n",
" ... | \n",
" 23.2 | \n",
" 25.0 | \n",
" 35.4 | \n",
" 35.5 | \n",
" (CONTRACTS OF 5,000 BUSHELS) | \n",
" 001602 | \n",
" CBT | \n",
" 1 | \n",
" A10 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 3 | \n",
" WHEAT-SRW - CHICAGO BOARD OF TRADE | \n",
" 240213 | \n",
" 2024-02-13 | \n",
" 001602 | \n",
" CBT | \n",
" 0 | \n",
" 1 | \n",
" 396009 | \n",
" 48951 | \n",
" 86037 | \n",
" ... | \n",
" 24.1 | \n",
" 26.7 | \n",
" 38.3 | \n",
" 38.6 | \n",
" (CONTRACTS OF 5,000 BUSHELS) | \n",
" 001602 | \n",
" CBT | \n",
" 1 | \n",
" A10 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 4 | \n",
" WHEAT-SRW - CHICAGO BOARD OF TRADE | \n",
" 240206 | \n",
" 2024-02-06 | \n",
" 001602 | \n",
" CBT | \n",
" 0 | \n",
" 1 | \n",
" 419546 | \n",
" 57414 | \n",
" 84652 | \n",
" ... | \n",
" 24.2 | \n",
" 27.0 | \n",
" 38.6 | \n",
" 36.6 | \n",
" (CONTRACTS OF 5,000 BUSHELS) | \n",
" 001602 | \n",
" CBT | \n",
" 1 | \n",
" A10 | \n",
" FutOnly | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 191 columns
\n",
"
"
],
"text/plain": [
" Market_and_Exchange_Names As_of_Date_In_Form_YYMMDD \\\n",
"0 WHEAT-SRW - CHICAGO BOARD OF TRADE 240305 \n",
"1 WHEAT-SRW - CHICAGO BOARD OF TRADE 240227 \n",
"2 WHEAT-SRW - CHICAGO BOARD OF TRADE 240220 \n",
"3 WHEAT-SRW - CHICAGO BOARD OF TRADE 240213 \n",
"4 WHEAT-SRW - CHICAGO BOARD OF TRADE 240206 \n",
"\n",
" Report_Date_as_YYYY-MM-DD CFTC_Contract_Market_Code CFTC_Market_Code \\\n",
"0 2024-03-05 001602 CBT \n",
"1 2024-02-27 001602 CBT \n",
"2 2024-02-20 001602 CBT \n",
"3 2024-02-13 001602 CBT \n",
"4 2024-02-06 001602 CBT \n",
"\n",
" CFTC_Region_Code CFTC_Commodity_Code Open_Interest_All \\\n",
"0 0 1 401311 \n",
"1 0 1 373389 \n",
"2 0 1 396470 \n",
"3 0 1 396009 \n",
"4 0 1 419546 \n",
"\n",
" Prod_Merc_Positions_Long_All Prod_Merc_Positions_Short_All ... \\\n",
"0 50034 74289 ... \n",
"1 43686 75295 ... \n",
"2 51309 73366 ... \n",
"3 48951 86037 ... \n",
"4 57414 84652 ... \n",
"\n",
" Conc_Net_LE_4_TDR_Long_Other Conc_Net_LE_4_TDR_Short_Other \\\n",
"0 20.5 18.7 \n",
"1 21.9 20.7 \n",
"2 23.2 25.0 \n",
"3 24.1 26.7 \n",
"4 24.2 27.0 \n",
"\n",
" Conc_Net_LE_8_TDR_Long_Other Conc_Net_LE_8_TDR_Short_Other \\\n",
"0 32.4 28.2 \n",
"1 33.8 30.1 \n",
"2 35.4 35.5 \n",
"3 38.3 38.6 \n",
"4 38.6 36.6 \n",
"\n",
" Contract_Units CFTC_Contract_Market_Code_Quotes \\\n",
"0 (CONTRACTS OF 5,000 BUSHELS) 001602 \n",
"1 (CONTRACTS OF 5,000 BUSHELS) 001602 \n",
"2 (CONTRACTS OF 5,000 BUSHELS) 001602 \n",
"3 (CONTRACTS OF 5,000 BUSHELS) 001602 \n",
"4 (CONTRACTS OF 5,000 BUSHELS) 001602 \n",
"\n",
" CFTC_Market_Code_Quotes CFTC_Commodity_Code_Quotes CFTC_SubGroup_Code \\\n",
"0 CBT 1 A10 \n",
"1 CBT 1 A10 \n",
"2 CBT 1 A10 \n",
"3 CBT 1 A10 \n",
"4 CBT 1 A10 \n",
"\n",
" FutOnly_or_Combined \n",
"0 FutOnly \n",
"1 FutOnly \n",
"2 FutOnly \n",
"3 FutOnly \n",
"4 FutOnly \n",
"\n",
"[5 rows x 191 columns]"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "e8cae63ae3b3b510",
"metadata": {
"collapsed": false
},
"source": [
"### For Copper\n",
"\n",
"* This is not the final version (!)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"id": "813a190783166944",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:27:04.071662Z",
"start_time": "2024-03-09T11:27:04.056034Z"
},
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Filter for rows where \"Market_and_Exchange_Names\" matches either of the specified values\n",
"copper_df = df[df[\"Market_and_Exchange_Names\"].isin([\"COPPER-GRADE #1 - COMMODITY EXCHANGE INC.\", \"COPPER- #1 - COMMODITY EXCHANGE INC.\"])].copy()\n",
"\n",
"\n",
"# After filtering, you can standardize the \"Market_and_Exchange_Names\" if you want all of them to have the same name\n",
"# This is optional and based on your specific requirement to 'merge' under a unified label\n",
"copper_df[\"Market_and_Exchange_Names\"] = \"COPPER-GRADE #1 - COMMODITY EXCHANGE INC.\"\n",
"\n",
"copper_df[\"Report_Date_as_YYYY-MM-DD\"] = pd.to_datetime(df[\"Report_Date_as_YYYY-MM-DD\"], format='%Y-%m-%d')\n",
"copper_df = copper_df.set_index(\"Report_Date_as_YYYY-MM-DD\")"
]
},
{
"cell_type": "code",
"execution_count": 155,
"id": "e884267fcd65fb7c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:27:08.228288Z",
"start_time": "2024-03-09T11:27:08.181704Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Market_and_Exchange_Names | \n",
" As_of_Date_In_Form_YYMMDD | \n",
" CFTC_Contract_Market_Code | \n",
" CFTC_Market_Code | \n",
" CFTC_Region_Code | \n",
" CFTC_Commodity_Code | \n",
" Open_Interest_All | \n",
" Prod_Merc_Positions_Long_All | \n",
" Prod_Merc_Positions_Short_All | \n",
" Swap_Positions_Long_All | \n",
" ... | \n",
" Conc_Net_LE_4_TDR_Long_Other | \n",
" Conc_Net_LE_4_TDR_Short_Other | \n",
" Conc_Net_LE_8_TDR_Long_Other | \n",
" Conc_Net_LE_8_TDR_Short_Other | \n",
" Contract_Units | \n",
" CFTC_Contract_Market_Code_Quotes | \n",
" CFTC_Market_Code_Quotes | \n",
" CFTC_Commodity_Code_Quotes | \n",
" CFTC_SubGroup_Code | \n",
" FutOnly_or_Combined | \n",
"
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" \n",
" Report_Date_as_YYYY-MM-DD | \n",
" | \n",
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" \n",
" 2024-03-05 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240305 | \n",
" 085692 | \n",
" CMX | \n",
" 1 | \n",
" 85 | \n",
" 198561 | \n",
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" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2024-02-27 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240227 | \n",
" 085692 | \n",
" CMX | \n",
" 1 | \n",
" 85 | \n",
" 189805 | \n",
" 23953 | \n",
" 56233 | \n",
" 41435 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2024-02-20 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240220 | \n",
" 085692 | \n",
" CMX | \n",
" 1 | \n",
" 85 | \n",
" 229777 | \n",
" 38874 | \n",
" 60100 | \n",
" 44483 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2024-02-13 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240213 | \n",
" 085692 | \n",
" CMX | \n",
" 1 | \n",
" 85 | \n",
" 257761 | \n",
" 47634 | \n",
" 57264 | \n",
" 46061 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2024-02-06 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240206 | \n",
" 085692 | \n",
" CMX | \n",
" 1 | \n",
" 85 | \n",
" 245784 | \n",
" 34349 | \n",
" 61265 | \n",
" 45534 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 190 columns
\n",
"
"
],
"text/plain": [
" Market_and_Exchange_Names \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-02-27 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-02-20 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-02-13 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-02-06 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"\n",
" As_of_Date_In_Form_YYMMDD \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 240305 \n",
"2024-02-27 240227 \n",
"2024-02-20 240220 \n",
"2024-02-13 240213 \n",
"2024-02-06 240206 \n",
"\n",
" CFTC_Contract_Market_Code CFTC_Market_Code \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 085692 CMX \n",
"2024-02-27 085692 CMX \n",
"2024-02-20 085692 CMX \n",
"2024-02-13 085692 CMX \n",
"2024-02-06 085692 CMX \n",
"\n",
" CFTC_Region_Code CFTC_Commodity_Code \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 1 85 \n",
"2024-02-27 1 85 \n",
"2024-02-20 1 85 \n",
"2024-02-13 1 85 \n",
"2024-02-06 1 85 \n",
"\n",
" Open_Interest_All Prod_Merc_Positions_Long_All \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 198561 25549 \n",
"2024-02-27 189805 23953 \n",
"2024-02-20 229777 38874 \n",
"2024-02-13 257761 47634 \n",
"2024-02-06 245784 34349 \n",
"\n",
" Prod_Merc_Positions_Short_All \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 55033 \n",
"2024-02-27 56233 \n",
"2024-02-20 60100 \n",
"2024-02-13 57264 \n",
"2024-02-06 61265 \n",
"\n",
" Swap_Positions_Long_All ... \\\n",
"Report_Date_as_YYYY-MM-DD ... \n",
"2024-03-05 40929 ... \n",
"2024-02-27 41435 ... \n",
"2024-02-20 44483 ... \n",
"2024-02-13 46061 ... \n",
"2024-02-06 45534 ... \n",
"\n",
" Conc_Net_LE_4_TDR_Long_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 0.0 \n",
"2024-02-27 0.0 \n",
"2024-02-20 0.0 \n",
"2024-02-13 0.0 \n",
"2024-02-06 0.0 \n",
"\n",
" Conc_Net_LE_4_TDR_Short_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 0.0 \n",
"2024-02-27 0.0 \n",
"2024-02-20 0.0 \n",
"2024-02-13 0.0 \n",
"2024-02-06 0.0 \n",
"\n",
" Conc_Net_LE_8_TDR_Long_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 0.0 \n",
"2024-02-27 0.0 \n",
"2024-02-20 0.0 \n",
"2024-02-13 0.0 \n",
"2024-02-06 0.0 \n",
"\n",
" Conc_Net_LE_8_TDR_Short_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 0.0 \n",
"2024-02-27 0.0 \n",
"2024-02-20 0.0 \n",
"2024-02-13 0.0 \n",
"2024-02-06 0.0 \n",
"\n",
" Contract_Units \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-02-27 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-02-20 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-02-13 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-02-06 (CONTRACTS OF 25,000 POUNDS) \n",
"\n",
" CFTC_Contract_Market_Code_Quotes \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 085692 \n",
"2024-02-27 085692 \n",
"2024-02-20 085692 \n",
"2024-02-13 085692 \n",
"2024-02-06 085692 \n",
"\n",
" CFTC_Market_Code_Quotes \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 CMX \n",
"2024-02-27 CMX \n",
"2024-02-20 CMX \n",
"2024-02-13 CMX \n",
"2024-02-06 CMX \n",
"\n",
" CFTC_Commodity_Code_Quotes CFTC_SubGroup_Code \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 85 N25 \n",
"2024-02-27 85 N25 \n",
"2024-02-20 85 N25 \n",
"2024-02-13 85 N25 \n",
"2024-02-06 85 N25 \n",
"\n",
" FutOnly_or_Combined \n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 FutOnly \n",
"2024-02-27 FutOnly \n",
"2024-02-20 FutOnly \n",
"2024-02-13 FutOnly \n",
"2024-02-06 FutOnly \n",
"\n",
"[5 rows x 190 columns]"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copper_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 156,
"id": "c175c46af208157b",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:27:12.043766Z",
"start_time": "2024-03-09T11:27:12.003480Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
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" CFTC_Contract_Market_Code | \n",
" CFTC_Market_Code | \n",
" CFTC_Region_Code | \n",
" CFTC_Commodity_Code | \n",
" Open_Interest_All | \n",
" Prod_Merc_Positions_Long_All | \n",
" Prod_Merc_Positions_Short_All | \n",
" Swap_Positions_Long_All | \n",
" ... | \n",
" Conc_Net_LE_4_TDR_Long_Other | \n",
" Conc_Net_LE_4_TDR_Short_Other | \n",
" Conc_Net_LE_8_TDR_Long_Other | \n",
" Conc_Net_LE_8_TDR_Short_Other | \n",
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" CMX | \n",
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" N25 | \n",
" FutOnly | \n",
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\n",
" \n",
" 2024-01-23 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240123 | \n",
" 085692 | \n",
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" 1 | \n",
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" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2024-01-16 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240116 | \n",
" 085692 | \n",
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" 85 | \n",
" N25 | \n",
" FutOnly | \n",
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\n",
" \n",
" 2024-01-09 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240109 | \n",
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" CMX | \n",
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" 85 | \n",
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" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
" 2024-01-02 | \n",
" COPPER-GRADE #1 - COMMODITY EXCHANGE INC. | \n",
" 240102 | \n",
" 085692 | \n",
" CMX | \n",
" 1 | \n",
" 85 | \n",
" 190752 | \n",
" 17545 | \n",
" 57597 | \n",
" 42626 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" (CONTRACTS OF 25,000 POUNDS) | \n",
" 085692 | \n",
" CMX | \n",
" 85 | \n",
" N25 | \n",
" FutOnly | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 190 columns
\n",
"
"
],
"text/plain": [
" Market_and_Exchange_Names \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-01-23 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-01-16 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-01-09 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"2024-01-02 COPPER-GRADE #1 - COMMODITY EXCHANGE INC. \n",
"\n",
" As_of_Date_In_Form_YYMMDD \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 240130 \n",
"2024-01-23 240123 \n",
"2024-01-16 240116 \n",
"2024-01-09 240109 \n",
"2024-01-02 240102 \n",
"\n",
" CFTC_Contract_Market_Code CFTC_Market_Code \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 085692 CMX \n",
"2024-01-23 085692 CMX \n",
"2024-01-16 085692 CMX \n",
"2024-01-09 085692 CMX \n",
"2024-01-02 085692 CMX \n",
"\n",
" CFTC_Region_Code CFTC_Commodity_Code \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 1 85 \n",
"2024-01-23 1 85 \n",
"2024-01-16 1 85 \n",
"2024-01-09 1 85 \n",
"2024-01-02 1 85 \n",
"\n",
" Open_Interest_All Prod_Merc_Positions_Long_All \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 229699 35413 \n",
"2024-01-23 233110 44371 \n",
"2024-01-16 219315 41761 \n",
"2024-01-09 206716 30374 \n",
"2024-01-02 190752 17545 \n",
"\n",
" Prod_Merc_Positions_Short_All \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 64279 \n",
"2024-01-23 57517 \n",
"2024-01-16 58746 \n",
"2024-01-09 56720 \n",
"2024-01-02 57597 \n",
"\n",
" Swap_Positions_Long_All ... \\\n",
"Report_Date_as_YYYY-MM-DD ... \n",
"2024-01-30 44220 ... \n",
"2024-01-23 46296 ... \n",
"2024-01-16 45403 ... \n",
"2024-01-09 43049 ... \n",
"2024-01-02 42626 ... \n",
"\n",
" Conc_Net_LE_4_TDR_Long_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 0.0 \n",
"2024-01-23 0.0 \n",
"2024-01-16 0.0 \n",
"2024-01-09 0.0 \n",
"2024-01-02 0.0 \n",
"\n",
" Conc_Net_LE_4_TDR_Short_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 0.0 \n",
"2024-01-23 0.0 \n",
"2024-01-16 0.0 \n",
"2024-01-09 0.0 \n",
"2024-01-02 0.0 \n",
"\n",
" Conc_Net_LE_8_TDR_Long_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 0.0 \n",
"2024-01-23 0.0 \n",
"2024-01-16 0.0 \n",
"2024-01-09 0.0 \n",
"2024-01-02 0.0 \n",
"\n",
" Conc_Net_LE_8_TDR_Short_Other \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 0.0 \n",
"2024-01-23 0.0 \n",
"2024-01-16 0.0 \n",
"2024-01-09 0.0 \n",
"2024-01-02 0.0 \n",
"\n",
" Contract_Units \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-01-23 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-01-16 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-01-09 (CONTRACTS OF 25,000 POUNDS) \n",
"2024-01-02 (CONTRACTS OF 25,000 POUNDS) \n",
"\n",
" CFTC_Contract_Market_Code_Quotes \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 085692 \n",
"2024-01-23 085692 \n",
"2024-01-16 085692 \n",
"2024-01-09 085692 \n",
"2024-01-02 085692 \n",
"\n",
" CFTC_Market_Code_Quotes \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 CMX \n",
"2024-01-23 CMX \n",
"2024-01-16 CMX \n",
"2024-01-09 CMX \n",
"2024-01-02 CMX \n",
"\n",
" CFTC_Commodity_Code_Quotes CFTC_SubGroup_Code \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 85 N25 \n",
"2024-01-23 85 N25 \n",
"2024-01-16 85 N25 \n",
"2024-01-09 85 N25 \n",
"2024-01-02 85 N25 \n",
"\n",
" FutOnly_or_Combined \n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-01-30 FutOnly \n",
"2024-01-23 FutOnly \n",
"2024-01-16 FutOnly \n",
"2024-01-09 FutOnly \n",
"2024-01-02 FutOnly \n",
"\n",
"[5 rows x 190 columns]"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copper_df.tail()"
]
},
{
"cell_type": "markdown",
"id": "edb063af2e9080f9",
"metadata": {
"collapsed": false
},
"source": [
"The data is filtered down for the commodity in scope."
]
},
{
"cell_type": "code",
"execution_count": 157,
"id": "b9a15b47ce6f7702",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:27:23.901870Z",
"start_time": "2024-03-09T11:27:23.863022Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Pct_of_Open_Interest_All',\n",
" 'Pct_of_OI_Prod_Merc_Long_All',\n",
" 'Pct_of_OI_Prod_Merc_Short_All',\n",
" 'Pct_of_OI_Swap_Long_All',\n",
" 'Pct_of_OI_Swap_Short_All',\n",
" 'Pct_of_OI_Swap_Spread_All',\n",
" 'Pct_of_OI_M_Money_Long_All',\n",
" 'Pct_of_OI_M_Money_Short_All',\n",
" 'Pct_of_OI_M_Money_Spread_All',\n",
" 'Pct_of_OI_Other_Rept_Long_All',\n",
" 'Pct_of_OI_Other_Rept_Short_All',\n",
" 'Pct_of_OI_Other_Rept_Spread_All',\n",
" 'Pct_of_OI_Tot_Rept_Long_All',\n",
" 'Pct_of_OI_Tot_Rept_Short_All',\n",
" 'Pct_of_OI_NonRept_Long_All',\n",
" 'Pct_of_OI_NonRept_Short_All',\n",
" 'Pct_of_Open_Interest_Old',\n",
" 'Pct_of_OI_Prod_Merc_Long_Old',\n",
" 'Pct_of_OI_Prod_Merc_Short_Old',\n",
" 'Pct_of_OI_Swap_Long_Old',\n",
" 'Pct_of_OI_Swap_Short_Old',\n",
" 'Pct_of_OI_Swap_Spread_Old',\n",
" 'Pct_of_OI_M_Money_Long_Old',\n",
" 'Pct_of_OI_M_Money_Short_Old',\n",
" 'Pct_of_OI_M_Money_Spread_Old',\n",
" 'Pct_of_OI_Other_Rept_Long_Old',\n",
" 'Pct_of_OI_Other_Rept_Short_Old',\n",
" 'Pct_of_OI_Other_Rept_Spread_Old',\n",
" 'Pct_of_OI_Tot_Rept_Long_Old',\n",
" 'Pct_of_OI_Tot_Rept_Short_Old',\n",
" 'Pct_of_OI_NonRept_Long_Old',\n",
" 'Pct_of_OI_NonRept_Short_Old',\n",
" 'Pct_of_Open_Interest_Other',\n",
" 'Pct_of_OI_Prod_Merc_Long_Other',\n",
" 'Pct_of_OI_Prod_Merc_Short_Other',\n",
" 'Pct_of_OI_Swap_Long_Other',\n",
" 'Pct_of_OI_Swap_Short_Other',\n",
" 'Pct_of_OI_Swap_Spread_Other',\n",
" 'Pct_of_OI_M_Money_Long_Other',\n",
" 'Pct_of_OI_M_Money_Short_Other',\n",
" 'Pct_of_OI_M_Money_Spread_Other',\n",
" 'Pct_of_OI_Other_Rept_Long_Other',\n",
" 'Pct_of_OI_Other_Rept_Short_Other',\n",
" 'Pct_of_OI_Other_Rept_Spread_Other',\n",
" 'Pct_of_OI_Tot_Rept_Long_Other',\n",
" 'Pct_of_OI_Tot_Rept_Short_Other',\n",
" 'Pct_of_OI_NonRept_Long_Other',\n",
" 'Pct_of_OI_NonRept_Short_Other']"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open_interest_pct_columns = [col for col in copper_df.columns if 'pct' in col.lower()]\n",
"open_interest_pct_columns"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "a1de9add52a1b195",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:27:24.707306Z",
"start_time": "2024-03-09T11:27:24.666095Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Pct_of_Open_Interest_Old',\n",
" 'Pct_of_OI_Prod_Merc_Long_Old',\n",
" 'Pct_of_OI_Prod_Merc_Short_Old',\n",
" 'Pct_of_OI_Swap_Long_Old',\n",
" 'Pct_of_OI_Swap_Short_Old',\n",
" 'Pct_of_OI_Swap_Spread_Old',\n",
" 'Pct_of_OI_M_Money_Long_Old',\n",
" 'Pct_of_OI_M_Money_Short_Old',\n",
" 'Pct_of_OI_M_Money_Spread_Old',\n",
" 'Pct_of_OI_Other_Rept_Long_Old',\n",
" 'Pct_of_OI_Other_Rept_Short_Old',\n",
" 'Pct_of_OI_Other_Rept_Spread_Old',\n",
" 'Pct_of_OI_Tot_Rept_Long_Old',\n",
" 'Pct_of_OI_Tot_Rept_Short_Old',\n",
" 'Pct_of_OI_NonRept_Long_Old',\n",
" 'Pct_of_OI_NonRept_Short_Old']"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open_interest_pct_old_columns = [col for col in df.columns if 'pct' in col.lower() and col.endswith('_Old')]\n",
"open_interest_pct_old_columns"
]
},
{
"cell_type": "markdown",
"id": "ee4e807283d4829f",
"metadata": {
"collapsed": false
},
"source": [
"Columns of interest filtered down from the data set."
]
},
{
"cell_type": "code",
"execution_count": 160,
"id": "c1683c4a417cca92",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:28:18.890283Z",
"start_time": "2024-03-09T11:28:18.822733Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
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" Pct_of_Open_Interest_Old | \n",
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" Pct_of_OI_Prod_Merc_Short_Old | \n",
" Pct_of_OI_Swap_Long_Old | \n",
" Pct_of_OI_Swap_Short_Old | \n",
" Pct_of_OI_Swap_Spread_Old | \n",
" Pct_of_OI_M_Money_Long_Old | \n",
" Pct_of_OI_M_Money_Short_Old | \n",
" Pct_of_OI_M_Money_Spread_Old | \n",
" Pct_of_OI_Other_Rept_Long_Old | \n",
" Pct_of_OI_Other_Rept_Short_Old | \n",
" Pct_of_OI_Other_Rept_Spread_Old | \n",
" Pct_of_OI_Tot_Rept_Long_Old | \n",
" Pct_of_OI_Tot_Rept_Short_Old | \n",
" Pct_of_OI_NonRept_Long_Old | \n",
" Pct_of_OI_NonRept_Short_Old | \n",
"
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" \n",
" Report_Date_as_YYYY-MM-DD | \n",
" | \n",
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" \n",
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" 100.0 | \n",
" 12.6 | \n",
" 29.6 | \n",
" 21.8 | \n",
" 4.7 | \n",
" 2.2 | \n",
" 28.7 | \n",
" 26.1 | \n",
" 12.8 | \n",
" 8.0 | \n",
" 11.5 | \n",
" 5.2 | \n",
" 91.5 | \n",
" 92.2 | \n",
" 8.5 | \n",
" 7.8 | \n",
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" 2024-02-20 | \n",
" 100.0 | \n",
" 16.9 | \n",
" 26.2 | \n",
" 19.4 | \n",
" 3.1 | \n",
" 2.8 | \n",
" 27.5 | \n",
" 32.2 | \n",
" 11.6 | \n",
" 7.3 | \n",
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" 7.4 | \n",
" 92.9 | \n",
" 92.8 | \n",
" 7.1 | \n",
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" 2024-02-13 | \n",
" 100.0 | \n",
" 18.5 | \n",
" 22.2 | \n",
" 17.9 | \n",
" 2.3 | \n",
" 2.6 | \n",
" 20.3 | \n",
" 37.3 | \n",
" 14.2 | \n",
" 10.1 | \n",
" 5.7 | \n",
" 9.8 | \n",
" 93.3 | \n",
" 94.2 | \n",
" 6.7 | \n",
" 5.8 | \n",
"
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" 2024-02-06 | \n",
" 100.0 | \n",
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" 18.5 | \n",
" 3.1 | \n",
" 4.7 | \n",
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" 17.5 | \n",
" 8.5 | \n",
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" 9.7 | \n",
" 93.4 | \n",
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" 6.6 | \n",
" 5.4 | \n",
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" \n",
"
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],
"text/plain": [
" Pct_of_Open_Interest_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 100.0 \n",
"2024-02-27 100.0 \n",
"2024-02-20 100.0 \n",
"2024-02-13 100.0 \n",
"2024-02-06 100.0 \n",
"\n",
" Pct_of_OI_Prod_Merc_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 12.9 \n",
"2024-02-27 12.6 \n",
"2024-02-20 16.9 \n",
"2024-02-13 18.5 \n",
"2024-02-06 14.0 \n",
"\n",
" Pct_of_OI_Prod_Merc_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 27.7 \n",
"2024-02-27 29.6 \n",
"2024-02-20 26.2 \n",
"2024-02-13 22.2 \n",
"2024-02-06 24.9 \n",
"\n",
" Pct_of_OI_Swap_Long_Old Pct_of_OI_Swap_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 20.6 4.4 \n",
"2024-02-27 21.8 4.7 \n",
"2024-02-20 19.4 3.1 \n",
"2024-02-13 17.9 2.3 \n",
"2024-02-06 18.5 3.1 \n",
"\n",
" Pct_of_OI_Swap_Spread_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 2.0 \n",
"2024-02-27 2.2 \n",
"2024-02-20 2.8 \n",
"2024-02-13 2.6 \n",
"2024-02-06 4.7 \n",
"\n",
" Pct_of_OI_M_Money_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 26.0 \n",
"2024-02-27 28.7 \n",
"2024-02-20 27.5 \n",
"2024-02-13 20.3 \n",
"2024-02-06 20.5 \n",
"\n",
" Pct_of_OI_M_Money_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 30.4 \n",
"2024-02-27 26.1 \n",
"2024-02-20 32.2 \n",
"2024-02-13 37.3 \n",
"2024-02-06 29.5 \n",
"\n",
" Pct_of_OI_M_Money_Spread_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 15.1 \n",
"2024-02-27 12.8 \n",
"2024-02-20 11.6 \n",
"2024-02-13 14.2 \n",
"2024-02-06 17.5 \n",
"\n",
" Pct_of_OI_Other_Rept_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 9.7 \n",
"2024-02-27 8.0 \n",
"2024-02-20 7.3 \n",
"2024-02-13 10.1 \n",
"2024-02-06 8.5 \n",
"\n",
" Pct_of_OI_Other_Rept_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 8.1 \n",
"2024-02-27 11.5 \n",
"2024-02-20 9.5 \n",
"2024-02-13 5.7 \n",
"2024-02-06 5.0 \n",
"\n",
" Pct_of_OI_Other_Rept_Spread_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 5.6 \n",
"2024-02-27 5.2 \n",
"2024-02-20 7.4 \n",
"2024-02-13 9.8 \n",
"2024-02-06 9.7 \n",
"\n",
" Pct_of_OI_Tot_Rept_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 92.0 \n",
"2024-02-27 91.5 \n",
"2024-02-20 92.9 \n",
"2024-02-13 93.3 \n",
"2024-02-06 93.4 \n",
"\n",
" Pct_of_OI_Tot_Rept_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 93.3 \n",
"2024-02-27 92.2 \n",
"2024-02-20 92.8 \n",
"2024-02-13 94.2 \n",
"2024-02-06 94.6 \n",
"\n",
" Pct_of_OI_NonRept_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 8.0 \n",
"2024-02-27 8.5 \n",
"2024-02-20 7.1 \n",
"2024-02-13 6.7 \n",
"2024-02-06 6.6 \n",
"\n",
" Pct_of_OI_NonRept_Short_Old \n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 6.7 \n",
"2024-02-27 7.8 \n",
"2024-02-20 7.2 \n",
"2024-02-13 5.8 \n",
"2024-02-06 5.4 "
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"columns = open_interest_pct_old_columns\n",
"filtered_copper_df = copper_df[columns].copy()\n",
"filtered_copper_df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 162,
"id": "eb7bed9ce96a6571",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:28:57.288352Z",
"start_time": "2024-03-09T11:28:57.240551Z"
},
"collapsed": false
},
"outputs": [
{
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" Pct_of_Open_Interest_Old | \n",
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" Pct_of_OI_Prod_Merc_Short_Old | \n",
" Pct_of_OI_Swap_Long_Old | \n",
" Pct_of_OI_Swap_Short_Old | \n",
" Pct_of_OI_Swap_Spread_Old | \n",
" Pct_of_OI_M_Money_Long_Old | \n",
" Pct_of_OI_M_Money_Short_Old | \n",
" Pct_of_OI_M_Money_Spread_Old | \n",
" Pct_of_OI_Other_Rept_Long_Old | \n",
" Pct_of_OI_Other_Rept_Short_Old | \n",
" Pct_of_OI_Other_Rept_Spread_Old | \n",
" Pct_of_OI_Tot_Rept_Long_Old | \n",
" Pct_of_OI_Tot_Rept_Short_Old | \n",
" Pct_of_OI_NonRept_Long_Old | \n",
" Pct_of_OI_NonRept_Short_Old | \n",
"
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" Report_Date_as_YYYY-MM-DD | \n",
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" 20.6 | \n",
" 4.4 | \n",
" 2.0 | \n",
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" 30.4 | \n",
" 15.1 | \n",
" 9.7 | \n",
" 8.1 | \n",
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" 2.2 | \n",
" 28.7 | \n",
" 26.1 | \n",
" 12.8 | \n",
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" 11.5 | \n",
" 5.2 | \n",
" 91.5 | \n",
" 92.2 | \n",
" 8.5 | \n",
" 7.8 | \n",
"
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" 2024-02-20 | \n",
" 100.0 | \n",
" 16.9 | \n",
" 26.2 | \n",
" 19.4 | \n",
" 3.1 | \n",
" 2.8 | \n",
" 27.5 | \n",
" 32.2 | \n",
" 11.6 | \n",
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" 7.4 | \n",
" 92.9 | \n",
" 92.8 | \n",
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"
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" 2024-02-13 | \n",
" 100.0 | \n",
" 18.5 | \n",
" 22.2 | \n",
" 17.9 | \n",
" 2.3 | \n",
" 2.6 | \n",
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" 18.5 | \n",
" 3.1 | \n",
" 4.7 | \n",
" 20.5 | \n",
" 29.5 | \n",
" 17.5 | \n",
" 8.5 | \n",
" 5.0 | \n",
" 9.7 | \n",
" 93.4 | \n",
" 94.6 | \n",
" 6.6 | \n",
" 5.4 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Pct_of_Open_Interest_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 100.0 \n",
"2024-02-27 100.0 \n",
"2024-02-20 100.0 \n",
"2024-02-13 100.0 \n",
"2024-02-06 100.0 \n",
"\n",
" Pct_of_OI_Prod_Merc_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 12.9 \n",
"2024-02-27 12.6 \n",
"2024-02-20 16.9 \n",
"2024-02-13 18.5 \n",
"2024-02-06 14.0 \n",
"\n",
" Pct_of_OI_Prod_Merc_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 27.7 \n",
"2024-02-27 29.6 \n",
"2024-02-20 26.2 \n",
"2024-02-13 22.2 \n",
"2024-02-06 24.9 \n",
"\n",
" Pct_of_OI_Swap_Long_Old Pct_of_OI_Swap_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 20.6 4.4 \n",
"2024-02-27 21.8 4.7 \n",
"2024-02-20 19.4 3.1 \n",
"2024-02-13 17.9 2.3 \n",
"2024-02-06 18.5 3.1 \n",
"\n",
" Pct_of_OI_Swap_Spread_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 2.0 \n",
"2024-02-27 2.2 \n",
"2024-02-20 2.8 \n",
"2024-02-13 2.6 \n",
"2024-02-06 4.7 \n",
"\n",
" Pct_of_OI_M_Money_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 26.0 \n",
"2024-02-27 28.7 \n",
"2024-02-20 27.5 \n",
"2024-02-13 20.3 \n",
"2024-02-06 20.5 \n",
"\n",
" Pct_of_OI_M_Money_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 30.4 \n",
"2024-02-27 26.1 \n",
"2024-02-20 32.2 \n",
"2024-02-13 37.3 \n",
"2024-02-06 29.5 \n",
"\n",
" Pct_of_OI_M_Money_Spread_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 15.1 \n",
"2024-02-27 12.8 \n",
"2024-02-20 11.6 \n",
"2024-02-13 14.2 \n",
"2024-02-06 17.5 \n",
"\n",
" Pct_of_OI_Other_Rept_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 9.7 \n",
"2024-02-27 8.0 \n",
"2024-02-20 7.3 \n",
"2024-02-13 10.1 \n",
"2024-02-06 8.5 \n",
"\n",
" Pct_of_OI_Other_Rept_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 8.1 \n",
"2024-02-27 11.5 \n",
"2024-02-20 9.5 \n",
"2024-02-13 5.7 \n",
"2024-02-06 5.0 \n",
"\n",
" Pct_of_OI_Other_Rept_Spread_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 5.6 \n",
"2024-02-27 5.2 \n",
"2024-02-20 7.4 \n",
"2024-02-13 9.8 \n",
"2024-02-06 9.7 \n",
"\n",
" Pct_of_OI_Tot_Rept_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 92.0 \n",
"2024-02-27 91.5 \n",
"2024-02-20 92.9 \n",
"2024-02-13 93.3 \n",
"2024-02-06 93.4 \n",
"\n",
" Pct_of_OI_Tot_Rept_Short_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 93.3 \n",
"2024-02-27 92.2 \n",
"2024-02-20 92.8 \n",
"2024-02-13 94.2 \n",
"2024-02-06 94.6 \n",
"\n",
" Pct_of_OI_NonRept_Long_Old \\\n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 8.0 \n",
"2024-02-27 8.5 \n",
"2024-02-20 7.1 \n",
"2024-02-13 6.7 \n",
"2024-02-06 6.6 \n",
"\n",
" Pct_of_OI_NonRept_Short_Old \n",
"Report_Date_as_YYYY-MM-DD \n",
"2024-03-05 6.7 \n",
"2024-02-27 7.8 \n",
"2024-02-20 7.2 \n",
"2024-02-13 5.8 \n",
"2024-02-06 5.4 "
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"columns = [\"Pct_of_OI_Prod_Merc_Long_Old\", \"Pct_of_OI_Prod_Merc_Short_Old\"]\n",
"filtered_copper_df_producers = copper_df[columns].copy()\n",
"filtered_copper_df.head()"
]
},
{
"cell_type": "markdown",
"id": "3d3ff0847b3496e8",
"metadata": {
"collapsed": false
},
"source": [
"## Automated Compass Analysis\n",
"\n",
"* 5th of March vs. our target %"
]
},
{
"cell_type": "code",
"execution_count": 181,
"id": "cb03cf7bb2c92a61",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T12:02:45.277418Z",
"start_time": "2024-03-09T12:02:45.200217Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pct_of_Open_Interest_Old 100.0\n",
"Pct_of_OI_Prod_Merc_Long_Old 12.9\n",
"Pct_of_OI_Prod_Merc_Short_Old 27.7\n",
"Pct_of_OI_Swap_Long_Old 20.6\n",
"Pct_of_OI_Swap_Short_Old 4.4\n",
"Pct_of_OI_Swap_Spread_Old 2.0\n",
"Pct_of_OI_M_Money_Long_Old 26.0\n",
"Pct_of_OI_M_Money_Short_Old 30.4\n",
"Pct_of_OI_M_Money_Spread_Old 15.1\n",
"Pct_of_OI_Other_Rept_Long_Old 9.7\n",
"Pct_of_OI_Other_Rept_Short_Old 8.1\n",
"Pct_of_OI_Other_Rept_Spread_Old 5.6\n",
"Pct_of_OI_Tot_Rept_Long_Old 92.0\n",
"Pct_of_OI_Tot_Rept_Short_Old 93.3\n",
"Pct_of_OI_NonRept_Long_Old 8.0\n",
"Pct_of_OI_NonRept_Short_Old 6.7\n",
"Short_MA NaN\n",
"Long_MA NaN\n",
"Name: 2024-03-05 00:00:00, dtype: float64\n",
"False\n",
"False\n",
"\n",
"Long OI % -- Producer Signal as qualified market indicator : 12.9\n",
"Market likelihood -- Bullish: False\n",
"Market likelihood -- Bearish: False\n"
]
}
],
"source": [
"compare_data = filtered_copper_df.loc['2024-03-05']\n",
"print(compare_data)\n",
"\n",
"\"\"\"\n",
"Analyse-Compass (Ray Dalio)\n",
"% Open Interest \n",
"Bullish / Bearish ranges\n",
"\n",
"25-20%\t/ 40-50%\n",
"\"\"\"\n",
"\n",
"Pct_of_OI_Prod_Merc_Long_Old = compare_data[\"Pct_of_OI_Prod_Merc_Long_Old\"] \n",
"in_range_bull = 20 <= Pct_of_OI_Prod_Merc_Long_Old <= 25\n",
"print(in_range_bull)\n",
"\n",
"Pct_of_OI_Prod_Merc_Short_Old = compare_data[\"Pct_of_OI_Prod_Merc_Short_Old\"] \n",
"in_range_bear = 40 <= Pct_of_OI_Prod_Merc_Short_Old <= 50\n",
"print(in_range_bear)\n",
"\n",
"print()\n",
"print(\"Long OI % -- Producer Signal as qualified market indicator : {}\".format(compare_data[\"Pct_of_OI_Prod_Merc_Long_Old\"]))\n",
"print(\"Market likelihood -- Bullish: {}\".format(in_range_bull))\n",
"print(\"Market likelihood -- Bearish: {}\".format(in_range_bear))"
]
},
{
"cell_type": "markdown",
"id": "5da29ce146ef71f2",
"metadata": {
"collapsed": false
},
"source": [
"## COT Trend Analysis for signal changes"
]
},
{
"cell_type": "code",
"execution_count": 166,
"id": "596c61dcbf4ac7a2",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:35:01.969254Z",
"start_time": "2024-03-09T11:35:01.531144Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
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",
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