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Copper Analysis COT CTFC data
2024-03-09 12:26:33 +00:00
{
"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": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Market_and_Exchange_Names</th>\n",
" <th>As_of_Date_In_Form_YYMMDD</th>\n",
" <th>Report_Date_as_YYYY-MM-DD</th>\n",
" <th>CFTC_Contract_Market_Code</th>\n",
" <th>CFTC_Market_Code</th>\n",
" <th>CFTC_Region_Code</th>\n",
" <th>CFTC_Commodity_Code</th>\n",
" <th>Open_Interest_All</th>\n",
" <th>Prod_Merc_Positions_Long_All</th>\n",
" <th>Prod_Merc_Positions_Short_All</th>\n",
" <th>...</th>\n",
" <th>Conc_Net_LE_4_TDR_Long_Other</th>\n",
" <th>Conc_Net_LE_4_TDR_Short_Other</th>\n",
" <th>Conc_Net_LE_8_TDR_Long_Other</th>\n",
" <th>Conc_Net_LE_8_TDR_Short_Other</th>\n",
" <th>Contract_Units</th>\n",
" <th>CFTC_Contract_Market_Code_Quotes</th>\n",
" <th>CFTC_Market_Code_Quotes</th>\n",
" <th>CFTC_Commodity_Code_Quotes</th>\n",
" <th>CFTC_SubGroup_Code</th>\n",
" <th>FutOnly_or_Combined</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>WHEAT-SRW - CHICAGO BOARD OF TRADE</td>\n",
" <td>240305</td>\n",
" <td>2024-03-05</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>401311</td>\n",
" <td>50034</td>\n",
" <td>74289</td>\n",
" <td>...</td>\n",
" <td>20.5</td>\n",
" <td>18.7</td>\n",
" <td>32.4</td>\n",
" <td>28.2</td>\n",
" <td>(CONTRACTS OF 5,000 BUSHELS)</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>1</td>\n",
" <td>A10</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>WHEAT-SRW - CHICAGO BOARD OF TRADE</td>\n",
" <td>240227</td>\n",
" <td>2024-02-27</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>373389</td>\n",
" <td>43686</td>\n",
" <td>75295</td>\n",
" <td>...</td>\n",
" <td>21.9</td>\n",
" <td>20.7</td>\n",
" <td>33.8</td>\n",
" <td>30.1</td>\n",
" <td>(CONTRACTS OF 5,000 BUSHELS)</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>1</td>\n",
" <td>A10</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>WHEAT-SRW - CHICAGO BOARD OF TRADE</td>\n",
" <td>240220</td>\n",
" <td>2024-02-20</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>396470</td>\n",
" <td>51309</td>\n",
" <td>73366</td>\n",
" <td>...</td>\n",
" <td>23.2</td>\n",
" <td>25.0</td>\n",
" <td>35.4</td>\n",
" <td>35.5</td>\n",
" <td>(CONTRACTS OF 5,000 BUSHELS)</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>1</td>\n",
" <td>A10</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>WHEAT-SRW - CHICAGO BOARD OF TRADE</td>\n",
" <td>240213</td>\n",
" <td>2024-02-13</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>396009</td>\n",
" <td>48951</td>\n",
" <td>86037</td>\n",
" <td>...</td>\n",
" <td>24.1</td>\n",
" <td>26.7</td>\n",
" <td>38.3</td>\n",
" <td>38.6</td>\n",
" <td>(CONTRACTS OF 5,000 BUSHELS)</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>1</td>\n",
" <td>A10</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>WHEAT-SRW - CHICAGO BOARD OF TRADE</td>\n",
" <td>240206</td>\n",
" <td>2024-02-06</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>419546</td>\n",
" <td>57414</td>\n",
" <td>84652</td>\n",
" <td>...</td>\n",
" <td>24.2</td>\n",
" <td>27.0</td>\n",
" <td>38.6</td>\n",
" <td>36.6</td>\n",
" <td>(CONTRACTS OF 5,000 BUSHELS)</td>\n",
" <td>001602</td>\n",
" <td>CBT</td>\n",
" <td>1</td>\n",
" <td>A10</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 191 columns</p>\n",
"</div>"
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" 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\")"
]
},
{
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"execution_count": 155,
"id": "e884267fcd65fb7c",
"metadata": {
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"end_time": "2024-03-09T11:27:08.228288Z",
"start_time": "2024-03-09T11:27:08.181704Z"
},
"collapsed": false
},
"outputs": [
{
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" 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",
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"2024-02-27 240227 \n",
"2024-02-20 240220 \n",
"2024-02-13 240213 \n",
"2024-02-06 240206 \n",
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"2024-02-27 085692 CMX \n",
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"2024-02-13 085692 CMX \n",
"2024-02-06 085692 CMX \n",
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"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",
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"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",
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"2024-02-27 085692 \n",
"2024-02-20 085692 \n",
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"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",
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"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"
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"outputs": [
{
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],
"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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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pct_of_Open_Interest_Old</th>\n",
" <th>Pct_of_OI_Prod_Merc_Long_Old</th>\n",
" <th>Pct_of_OI_Prod_Merc_Short_Old</th>\n",
" <th>Pct_of_OI_Swap_Long_Old</th>\n",
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" <th>Pct_of_OI_M_Money_Short_Old</th>\n",
" <th>Pct_of_OI_M_Money_Spread_Old</th>\n",
" <th>Pct_of_OI_Other_Rept_Long_Old</th>\n",
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" <th>Pct_of_OI_Other_Rept_Spread_Old</th>\n",
" <th>Pct_of_OI_Tot_Rept_Long_Old</th>\n",
" <th>Pct_of_OI_Tot_Rept_Short_Old</th>\n",
" <th>Pct_of_OI_NonRept_Long_Old</th>\n",
" <th>Pct_of_OI_NonRept_Short_Old</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Report_Date_as_YYYY-MM-DD</th>\n",
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" <th>2024-03-05</th>\n",
" <td>100.0</td>\n",
" <td>12.9</td>\n",
" <td>27.7</td>\n",
" <td>20.6</td>\n",
" <td>4.4</td>\n",
" <td>2.0</td>\n",
" <td>26.0</td>\n",
" <td>30.4</td>\n",
" <td>15.1</td>\n",
" <td>9.7</td>\n",
" <td>8.1</td>\n",
" <td>5.6</td>\n",
" <td>92.0</td>\n",
" <td>93.3</td>\n",
" <td>8.0</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-27</th>\n",
" <td>100.0</td>\n",
" <td>12.6</td>\n",
" <td>29.6</td>\n",
" <td>21.8</td>\n",
" <td>4.7</td>\n",
" <td>2.2</td>\n",
" <td>28.7</td>\n",
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" <td>12.8</td>\n",
" <td>8.0</td>\n",
" <td>11.5</td>\n",
" <td>5.2</td>\n",
" <td>91.5</td>\n",
" <td>92.2</td>\n",
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" <td>16.9</td>\n",
" <td>26.2</td>\n",
" <td>19.4</td>\n",
" <td>3.1</td>\n",
" <td>2.8</td>\n",
" <td>27.5</td>\n",
" <td>32.2</td>\n",
" <td>11.6</td>\n",
" <td>7.3</td>\n",
" <td>9.5</td>\n",
" <td>7.4</td>\n",
" <td>92.9</td>\n",
" <td>92.8</td>\n",
" <td>7.1</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-13</th>\n",
" <td>100.0</td>\n",
" <td>18.5</td>\n",
" <td>22.2</td>\n",
" <td>17.9</td>\n",
" <td>2.3</td>\n",
" <td>2.6</td>\n",
" <td>20.3</td>\n",
" <td>37.3</td>\n",
" <td>14.2</td>\n",
" <td>10.1</td>\n",
" <td>5.7</td>\n",
" <td>9.8</td>\n",
" <td>93.3</td>\n",
" <td>94.2</td>\n",
" <td>6.7</td>\n",
" <td>5.8</td>\n",
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" <tr>\n",
" <th>2024-02-06</th>\n",
" <td>100.0</td>\n",
" <td>14.0</td>\n",
" <td>24.9</td>\n",
" <td>18.5</td>\n",
" <td>3.1</td>\n",
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" <td>20.5</td>\n",
" <td>29.5</td>\n",
" <td>17.5</td>\n",
" <td>8.5</td>\n",
" <td>5.0</td>\n",
" <td>9.7</td>\n",
" <td>93.4</td>\n",
" <td>94.6</td>\n",
" <td>6.6</td>\n",
" <td>5.4</td>\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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" <th>Pct_of_Open_Interest_Old</th>\n",
" <th>Pct_of_OI_Prod_Merc_Long_Old</th>\n",
" <th>Pct_of_OI_Prod_Merc_Short_Old</th>\n",
" <th>Pct_of_OI_Swap_Long_Old</th>\n",
" <th>Pct_of_OI_Swap_Short_Old</th>\n",
" <th>Pct_of_OI_Swap_Spread_Old</th>\n",
" <th>Pct_of_OI_M_Money_Long_Old</th>\n",
" <th>Pct_of_OI_M_Money_Short_Old</th>\n",
" <th>Pct_of_OI_M_Money_Spread_Old</th>\n",
" <th>Pct_of_OI_Other_Rept_Long_Old</th>\n",
" <th>Pct_of_OI_Other_Rept_Short_Old</th>\n",
" <th>Pct_of_OI_Other_Rept_Spread_Old</th>\n",
" <th>Pct_of_OI_Tot_Rept_Long_Old</th>\n",
" <th>Pct_of_OI_Tot_Rept_Short_Old</th>\n",
" <th>Pct_of_OI_NonRept_Long_Old</th>\n",
" <th>Pct_of_OI_NonRept_Short_Old</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Report_Date_as_YYYY-MM-DD</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2024-03-05</th>\n",
" <td>100.0</td>\n",
" <td>12.9</td>\n",
" <td>27.7</td>\n",
" <td>20.6</td>\n",
" <td>4.4</td>\n",
" <td>2.0</td>\n",
" <td>26.0</td>\n",
" <td>30.4</td>\n",
" <td>15.1</td>\n",
" <td>9.7</td>\n",
" <td>8.1</td>\n",
" <td>5.6</td>\n",
" <td>92.0</td>\n",
" <td>93.3</td>\n",
" <td>8.0</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-27</th>\n",
" <td>100.0</td>\n",
" <td>12.6</td>\n",
" <td>29.6</td>\n",
" <td>21.8</td>\n",
" <td>4.7</td>\n",
" <td>2.2</td>\n",
" <td>28.7</td>\n",
" <td>26.1</td>\n",
" <td>12.8</td>\n",
" <td>8.0</td>\n",
" <td>11.5</td>\n",
" <td>5.2</td>\n",
" <td>91.5</td>\n",
" <td>92.2</td>\n",
" <td>8.5</td>\n",
" <td>7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-20</th>\n",
" <td>100.0</td>\n",
" <td>16.9</td>\n",
" <td>26.2</td>\n",
" <td>19.4</td>\n",
" <td>3.1</td>\n",
" <td>2.8</td>\n",
" <td>27.5</td>\n",
" <td>32.2</td>\n",
" <td>11.6</td>\n",
" <td>7.3</td>\n",
" <td>9.5</td>\n",
" <td>7.4</td>\n",
" <td>92.9</td>\n",
" <td>92.8</td>\n",
" <td>7.1</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-13</th>\n",
" <td>100.0</td>\n",
" <td>18.5</td>\n",
" <td>22.2</td>\n",
" <td>17.9</td>\n",
" <td>2.3</td>\n",
" <td>2.6</td>\n",
" <td>20.3</td>\n",
" <td>37.3</td>\n",
" <td>14.2</td>\n",
" <td>10.1</td>\n",
" <td>5.7</td>\n",
" <td>9.8</td>\n",
" <td>93.3</td>\n",
" <td>94.2</td>\n",
" <td>6.7</td>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-06</th>\n",
" <td>100.0</td>\n",
" <td>14.0</td>\n",
" <td>24.9</td>\n",
" <td>18.5</td>\n",
" <td>3.1</td>\n",
" <td>4.7</td>\n",
" <td>20.5</td>\n",
" <td>29.5</td>\n",
" <td>17.5</td>\n",
" <td>8.5</td>\n",
" <td>5.0</td>\n",
" <td>9.7</td>\n",
" <td>93.4</td>\n",
" <td>94.6</td>\n",
" <td>6.6</td>\n",
" <td>5.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": {
"image/png": "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
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Assuming 'df' is your DataFrame and it's already indexed by \"Report_Date_as_YYYY-MM-DD\" with datetime format\n",
"columns_to_plot = [\n",
" 'Pct_of_OI_Prod_Merc_Long_Old', \n",
" 'Pct_of_OI_Prod_Merc_Short_Old',\n",
" 'Pct_of_OI_Swap_Short_Old', \n",
" 'Pct_of_OI_Swap_Long_Old',\n",
" 'Pct_of_OI_M_Money_Long_Old', \n",
" 'Pct_of_OI_M_Money_Short_Old'\n",
"]\n",
"\n",
"# Plotting\n",
"ax = filtered_copper_df[columns_to_plot].plot(kind='line', figsize=(10, 6), title='Percentage of Open Interest by Category Over Time')\n",
"ax.set_xlabel('Date')\n",
"ax.set_ylabel('Percentage of Open Interest')\n",
"\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 170,
"id": "7f0f0826f62e9212",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-09T11:42:25.972848Z",
"start_time": "2024-03-09T11:42:25.657720Z"
},
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Example column\n",
"column = 'Pct_of_OI_Prod_Merc_Long_Old'\n",
"\n",
"# Calculate moving averages\n",
"filtered_copper_df['Short_MA'] = filtered_copper_df[column].rolling(window=5).mean() # Short-term moving average (e.g., 5 days)\n",
"filtered_copper_df['Long_MA'] = filtered_copper_df[column].rolling(window=20).mean() # Long-term moving average (e.g., 20 days)\n",
"\n",
"# Plot the original data and moving averages\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(filtered_copper_df.index, filtered_copper_df[column], label='Original Data', alpha=0.5)\n",
"plt.plot(filtered_copper_df.index, filtered_copper_df['Short_MA'], label='Short-term MA', alpha=0.75)\n",
"plt.plot(filtered_copper_df.index, filtered_copper_df['Long_MA'], label='Long-term MA', alpha=0.75)\n",
"\n",
"# Identifying the crossing points\n",
"crossings = np.where(np.diff(np.sign(filtered_copper_df['Short_MA'] - filtered_copper_df['Long_MA'])))[0]\n",
"plt.scatter(filtered_copper_df.index[crossings], filtered_copper_df[column].iloc[crossings], color='red', label='Trend Change')\n",
"\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Percentage of Open Interest')\n",
"plt.title(f'Trend Changes in {column}')\n",
"plt.legend()\n",
"plt.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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