marius/trading_analysis
1
0
Fork 0
mirror of https://github.com/norandom/trading_analysis.git synced 2024-12-22 07:53:43 +00:00
Code Issues Packages Projects Releases Wiki Activity
43dd4f5bce
trading_analysis/COT_CFTC_DL.ipynb
Marius Ciepluch 24fc89233f Copper Analysis COT CTFC data
2024-03-09 12:29:16 +00:00

2242 lines
273 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

{
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</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>"
],
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</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>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>Swap_Positions_Long_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",
" <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",
" <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>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240305</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>198561</td>\n",
" <td>25549</td>\n",
" <td>55033</td>\n",
" <td>40929</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-27</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240227</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>189805</td>\n",
" <td>23953</td>\n",
" <td>56233</td>\n",
" <td>41435</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-20</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240220</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>229777</td>\n",
" <td>38874</td>\n",
" <td>60100</td>\n",
" <td>44483</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-13</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240213</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>257761</td>\n",
" <td>47634</td>\n",
" <td>57264</td>\n",
" <td>46061</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-06</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240206</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>245784</td>\n",
" <td>34349</td>\n",
" <td>61265</td>\n",
" <td>45534</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 190 columns</p>\n",
"</div>"
],
"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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</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>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>Swap_Positions_Long_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",
" <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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2024-01-30</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240130</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>229699</td>\n",
" <td>35413</td>\n",
" <td>64279</td>\n",
" <td>44220</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-01-23</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240123</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>233110</td>\n",
" <td>44371</td>\n",
" <td>57517</td>\n",
" <td>46296</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-01-16</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240116</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>219315</td>\n",
" <td>41761</td>\n",
" <td>58746</td>\n",
" <td>45403</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-01-09</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240109</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>206716</td>\n",
" <td>30374</td>\n",
" <td>56720</td>\n",
" <td>43049</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-01-02</th>\n",
" <td>COPPER-GRADE #1 - COMMODITY EXCHANGE INC.</td>\n",
" <td>240102</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>190752</td>\n",
" <td>17545</td>\n",
" <td>57597</td>\n",
" <td>42626</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>(CONTRACTS OF 25,000 POUNDS)</td>\n",
" <td>085692</td>\n",
" <td>CMX</td>\n",
" <td>85</td>\n",
" <td>N25</td>\n",
" <td>FutOnly</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 190 columns</p>\n",
"</div>"
],
"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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</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",
" <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": 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": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</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",
" <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
}
Reference in New Issue View Git Blame Copy Permalink