trading_analysis/Silver_Fundamentals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf16a6eb-44a2-4dd7-8d4d-598e6f2c9b24",
   "metadata": {},
   "source": [
    "# Silver "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4405f172-43b6-4245-bcd8-2c6e01fada3a",
   "metadata": {},
   "source": [
    "We can download the data from the CFTC via Python.\n",
    "\n",
    "Many data APIs become commercial for recent prices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3704b41-1622-4596-ae99-3201b9bd8e14",
   "metadata": {},
   "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": 2,
   "id": "f4f90eb0-dca4-4589-a385-408c66c6e0a0",
   "metadata": {},
   "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": "markdown",
   "id": "414b7248-5acc-46a9-916a-cbed7228331f",
   "metadata": {},
   "source": [
    "At this point, we have the CFTC Disaggregated Futures report from 2022 to 2024 (until today)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084eb98b-1914-49e0-b57a-4e7717ea159c",
   "metadata": {},
   "source": [
    "## Load the data, and prepare the dataset\n",
    "\n",
    "The CFTC report contains data on all futures, not just silver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb167036-4f9b-4405-a942-71fae7d7aec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1629470/490941526.py:1: DtypeWarning: Columns (120,121,132,133,134,135,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('COT_CFTC_2024.csv')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('COT_CFTC_2024.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f554d5a-e6da-4a6c-a16e-f2b3294b4824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8344, 191)"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "id": "4bd367af-8385-406a-87ee-5aa68b3a8b7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8345\n"
     ]
    }
   ],
   "source": [
    "!cat COT_CFTC_2024.csv | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc275bed-93a3-4c66-87e0-7312aa712f6a",
   "metadata": {},
   "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>240813</td>\n",
       "      <td>2024-08-13</td>\n",
       "      <td>001602</td>\n",
       "      <td>CBT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>401604</td>\n",
       "      <td>68899</td>\n",
       "      <td>85867</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>54.2</td>\n",
       "      <td>42.6</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>1</th>\n",
       "      <td>WHEAT-SRW - CHICAGO BOARD OF TRADE</td>\n",
       "      <td>240806</td>\n",
       "      <td>2024-08-06</td>\n",
       "      <td>001602</td>\n",
       "      <td>CBT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>431547</td>\n",
       "      <td>73401</td>\n",
       "      <td>85750</td>\n",
       "      <td>...</td>\n",
       "      <td>41.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>57.3</td>\n",
       "      <td>41.7</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>240730</td>\n",
       "      <td>2024-07-30</td>\n",
       "      <td>001602</td>\n",
       "      <td>CBT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>432863</td>\n",
       "      <td>73086</td>\n",
       "      <td>83875</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>60.8</td>\n",
       "      <td>38.4</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>240723</td>\n",
       "      <td>2024-07-23</td>\n",
       "      <td>001602</td>\n",
       "      <td>CBT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>421227</td>\n",
       "      <td>63636</td>\n",
       "      <td>81374</td>\n",
       "      <td>...</td>\n",
       "      <td>48.3</td>\n",
       "      <td>27.9</td>\n",
       "      <td>63.5</td>\n",
       "      <td>37.7</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>240716</td>\n",
       "      <td>2024-07-16</td>\n",
       "      <td>001602</td>\n",
       "      <td>CBT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>413885</td>\n",
       "      <td>64297</td>\n",
       "      <td>81558</td>\n",
       "      <td>...</td>\n",
       "      <td>50.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>65.9</td>\n",
       "      <td>37.3</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                     240813   \n",
       "1  WHEAT-SRW - CHICAGO BOARD OF TRADE                     240806   \n",
       "2  WHEAT-SRW - CHICAGO BOARD OF TRADE                     240730   \n",
       "3  WHEAT-SRW - CHICAGO BOARD OF TRADE                     240723   \n",
       "4  WHEAT-SRW - CHICAGO BOARD OF TRADE                     240716   \n",
       "\n",
       "  Report_Date_as_YYYY-MM-DD CFTC_Contract_Market_Code CFTC_Market_Code  \\\n",
       "0                2024-08-13                    001602             CBT    \n",
       "1                2024-08-06                    001602             CBT    \n",
       "2                2024-07-30                    001602             CBT    \n",
       "3                2024-07-23                    001602             CBT    \n",
       "4                2024-07-16                    001602             CBT    \n",
       "\n",
       "   CFTC_Region_Code  CFTC_Commodity_Code  Open_Interest_All  \\\n",
       "0                 0                    1             401604   \n",
       "1                 0                    1             431547   \n",
       "2                 0                    1             432863   \n",
       "3                 0                    1             421227   \n",
       "4                 0                    1             413885   \n",
       "\n",
       "   Prod_Merc_Positions_Long_All  Prod_Merc_Positions_Short_All  ...  \\\n",
       "0                         68899                          85867  ...   \n",
       "1                         73401                          85750  ...   \n",
       "2                         73086                          83875  ...   \n",
       "3                         63636                          81374  ...   \n",
       "4                         64297                          81558  ...   \n",
       "\n",
       "   Conc_Net_LE_4_TDR_Long_Other  Conc_Net_LE_4_TDR_Short_Other  \\\n",
       "0                          39.0                           31.7   \n",
       "1                          41.6                           32.3   \n",
       "2                          46.0                           28.5   \n",
       "3                          48.3                           27.9   \n",
       "4                          50.4                           28.0   \n",
       "\n",
       "   Conc_Net_LE_8_TDR_Long_Other  Conc_Net_LE_8_TDR_Short_Other  \\\n",
       "0                          54.2                           42.6   \n",
       "1                          57.3                           41.7   \n",
       "2                          60.8                           38.4   \n",
       "3                          63.5                           37.7   \n",
       "4                          65.9                           37.3   \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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38311a58-d12d-49ae-9abb-a17bcc7f7a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Filter for rows where \"Market_and_Exchange_Names\" matches either of the specified values\n",
    "silver_df = df[df[\"Market_and_Exchange_Names\"].isin([\"SILVER - 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",
    "silver_df[\"Market_and_Exchange_Names\"] = \"COPPER-GRADE #1 - COMMODITY EXCHANGE INC.\"\n",
    "\n",
    "silver_df[\"Report_Date_as_YYYY-MM-DD\"] = pd.to_datetime(df[\"Report_Date_as_YYYY-MM-DD\"], format='%Y-%m-%d')\n",
    "silver_df = silver_df.set_index(\"Report_Date_as_YYYY-MM-DD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af7ce715-7958-48bd-bb4e-2dbb4ec50171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 190)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silver_df.shape # we have calendar week 33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c1b24aa-db20-4935-828b-c7ca9f4959e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Market_and_Exchange_Names', 'As_of_Date_In_Form_YYMMDD', 'CFTC_Contract_Market_Code', 'CFTC_Market_Code', 'CFTC_Region_Code', 'CFTC_Commodity_Code', 'Open_Interest_All', 'Prod_Merc_Positions_Long_All', 'Prod_Merc_Positions_Short_All', 'Swap_Positions_Long_All', 'Swap__Positions_Short_All', 'Swap__Positions_Spread_All', 'M_Money_Positions_Long_All', 'M_Money_Positions_Short_All', 'M_Money_Positions_Spread_All', 'Other_Rept_Positions_Long_All', 'Other_Rept_Positions_Short_All', 'Other_Rept_Positions_Spread_All', 'Tot_Rept_Positions_Long_All', 'Tot_Rept_Positions_Short_All', 'NonRept_Positions_Long_All', 'NonRept_Positions_Short_All', 'Open_Interest_Old', 'Prod_Merc_Positions_Long_Old', 'Prod_Merc_Positions_Short_Old', 'Swap_Positions_Long_Old', 'Swap__Positions_Short_Old', 'Swap__Positions_Spread_Old', 'M_Money_Positions_Long_Old', 'M_Money_Positions_Short_Old', 'M_Money_Positions_Spread_Old', 'Other_Rept_Positions_Long_Old', 'Other_Rept_Positions_Short_Old', 'Other_Rept_Positions_Spread_Old', 'Tot_Rept_Positions_Long_Old', 'Tot_Rept_Positions_Short_Old', 'NonRept_Positions_Long_Old', 'NonRept_Positions_Short_Old', 'Open_Interest_Other', 'Prod_Merc_Positions_Long_Other', 'Prod_Merc_Positions_Short_Other', 'Swap_Positions_Long_Other', 'Swap__Positions_Short_Other', 'Swap__Positions_Spread_Other', 'M_Money_Positions_Long_Other', 'M_Money_Positions_Short_Other', 'M_Money_Positions_Spread_Other', 'Other_Rept_Positions_Long_Other', 'Other_Rept_Positions_Short_Other', 'Other_Rept_Positions_Spread_Other', 'Tot_Rept_Positions_Long_Other', 'Tot_Rept_Positions_Short_Other', 'NonRept_Positions_Long_Other', 'NonRept_Positions_Short_Other', 'Change_in_Open_Interest_All', 'Change_in_Prod_Merc_Long_All', 'Change_in_Prod_Merc_Short_All', 'Change_in_Swap_Long_All', 'Change_in_Swap_Short_All', 'Change_in_Swap_Spread_All', 'Change_in_M_Money_Long_All', 'Change_in_M_Money_Short_All', 'Change_in_M_Money_Spread_All', 'Change_in_Other_Rept_Long_All', 'Change_in_Other_Rept_Short_All', 'Change_in_Other_Rept_Spread_All', 'Change_in_Tot_Rept_Long_All', 'Change_in_Tot_Rept_Short_All', 'Change_in_NonRept_Long_All', 'Change_in_NonRept_Short_All', 'Pct_of_Open_Interest_All', 'Pct_of_OI_Prod_Merc_Long_All', 'Pct_of_OI_Prod_Merc_Short_All', 'Pct_of_OI_Swap_Long_All', 'Pct_of_OI_Swap_Short_All', 'Pct_of_OI_Swap_Spread_All', 'Pct_of_OI_M_Money_Long_All', 'Pct_of_OI_M_Money_Short_All', 'Pct_of_OI_M_Money_Spread_All', 'Pct_of_OI_Other_Rept_Long_All', 'Pct_of_OI_Other_Rept_Short_All', 'Pct_of_OI_Other_Rept_Spread_All', 'Pct_of_OI_Tot_Rept_Long_All', 'Pct_of_OI_Tot_Rept_Short_All', 'Pct_of_OI_NonRept_Long_All', 'Pct_of_OI_NonRept_Short_All', 'Pct_of_Open_Interest_Old', 'Pct_of_OI_Prod_Merc_Long_Old', 'Pct_of_OI_Prod_Merc_Short_Old', 'Pct_of_OI_Swap_Long_Old', 'Pct_of_OI_Swap_Short_Old', 'Pct_of_OI_Swap_Spread_Old', 'Pct_of_OI_M_Money_Long_Old', 'Pct_of_OI_M_Money_Short_Old', 'Pct_of_OI_M_Money_Spread_Old', 'Pct_of_OI_Other_Rept_Long_Old', 'Pct_of_OI_Other_Rept_Short_Old', 'Pct_of_OI_Other_Rept_Spread_Old', 'Pct_of_OI_Tot_Rept_Long_Old', 'Pct_of_OI_Tot_Rept_Short_Old', 'Pct_of_OI_NonRept_Long_Old', 'Pct_of_OI_NonRept_Short_Old', 'Pct_of_Open_Interest_Other', 'Pct_of_OI_Prod_Merc_Long_Other', 'Pct_of_OI_Prod_Merc_Short_Other', 'Pct_of_OI_Swap_Long_Other', 'Pct_of_OI_Swap_Short_Other', 'Pct_of_OI_Swap_Spread_Other', 'Pct_of_OI_M_Money_Long_Other', 'Pct_of_OI_M_Money_Short_Other', 'Pct_of_OI_M_Money_Spread_Other', 'Pct_of_OI_Other_Rept_Long_Other', 'Pct_of_OI_Other_Rept_Short_Other', 'Pct_of_OI_Other_Rept_Spread_Other', 'Pct_of_OI_Tot_Rept_Long_Other', 'Pct_of_OI_Tot_Rept_Short_Other', 'Pct_of_OI_NonRept_Long_Other', 'Pct_of_OI_NonRept_Short_Other', 'Traders_Tot_All', 'Traders_Prod_Merc_Long_All', 'Traders_Prod_Merc_Short_All', 'Traders_Swap_Long_All', 'Traders_Swap_Short_All', 'Traders_Swap_Spread_All', 'Traders_M_Money_Long_All', 'Traders_M_Money_Short_All', 'Traders_M_Money_Spread_All', 'Traders_Other_Rept_Long_All', 'Traders_Other_Rept_Short_All', 'Traders_Other_Rept_Spread_All', 'Traders_Tot_Rept_Long_All', 'Traders_Tot_Rept_Short_All', 'Traders_Tot_Old', 'Traders_Prod_Merc_Long_Old', 'Traders_Prod_Merc_Short_Old', 'Traders_Swap_Long_Old', 'Traders_Swap_Short_Old', 'Traders_Swap_Spread_Old', 'Traders_M_Money_Long_Old', 'Traders_M_Money_Short_Old', 'Traders_M_Money_Spread_Old', 'Traders_Other_Rept_Long_Old', 'Traders_Other_Rept_Short_Old', 'Traders_Other_Rept_Spread_Old', 'Traders_Tot_Rept_Long_Old', 'Traders_Tot_Rept_Short_Old', 'Traders_Tot_Other', 'Traders_Prod_Merc_Long_Other', 'Traders_Prod_Merc_Short_Other', 'Traders_Swap_Long_Other', 'Traders_Swap_Short_Other', 'Traders_Swap_Spread_Other', 'Traders_M_Money_Long_Other', 'Traders_M_Money_Short_Other', 'Traders_M_Money_Spread_Other', 'Traders_Other_Rept_Long_Other', 'Traders_Other_Rept_Short_Other', 'Traders_Other_Rept_Spread_Other', 'Traders_Tot_Rept_Long_Other', 'Traders_Tot_Rept_Short_Other', 'Conc_Gross_LE_4_TDR_Long_All', 'Conc_Gross_LE_4_TDR_Short_All', 'Conc_Gross_LE_8_TDR_Long_All', 'Conc_Gross_LE_8_TDR_Short_All', 'Conc_Net_LE_4_TDR_Long_All', 'Conc_Net_LE_4_TDR_Short_All', 'Conc_Net_LE_8_TDR_Long_All', 'Conc_Net_LE_8_TDR_Short_All', 'Conc_Gross_LE_4_TDR_Long_Old', 'Conc_Gross_LE_4_TDR_Short_Old', 'Conc_Gross_LE_8_TDR_Long_Old', 'Conc_Gross_LE_8_TDR_Short_Old', 'Conc_Net_LE_4_TDR_Long_Old', 'Conc_Net_LE_4_TDR_Short_Old', 'Conc_Net_LE_8_TDR_Long_Old', 'Conc_Net_LE_8_TDR_Short_Old', 'Conc_Gross_LE_4_TDR_Long_Other', 'Conc_Gross_LE_4_TDR_Short_Other', 'Conc_Gross_LE_8_TDR_Long_Other', 'Conc_Gross_LE_8_TDR_Short_Other', 'Conc_Net_LE_4_TDR_Long_Other', 'Conc_Net_LE_4_TDR_Short_Other', 'Conc_Net_LE_8_TDR_Long_Other', 'Conc_Net_LE_8_TDR_Short_Other', 'Contract_Units', 'CFTC_Contract_Market_Code_Quotes', 'CFTC_Market_Code_Quotes', 'CFTC_Commodity_Code_Quotes', 'CFTC_SubGroup_Code', 'FutOnly_or_Combined']\n"
     ]
    }
   ],
   "source": [
    "print(list(silver_df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b30a8b94-6388-4476-a008-8e69959cfbb9",
   "metadata": {},
   "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": 14,
     "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": "code",
   "execution_count": 21,
   "id": "7f04ca58-7f5b-4cef-9941-620067cd8751",
   "metadata": {},
   "outputs": [],
   "source": [
    "needed_columns = [\"Open_Interest_All\", \"Pct_of_OI_Prod_Merc_Long_Old\", \"Pct_of_OI_Prod_Merc_Short_Old\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a862cbe7-49c8-43b1-ba34-a87f37b40daa",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Open_Interest_All</th>\n",
       "      <th>Pct_of_OI_Prod_Merc_Long_Old</th>\n",
       "      <th>Pct_of_OI_Prod_Merc_Short_Old</th>\n",
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       "                           Open_Interest_All  Pct_of_OI_Prod_Merc_Long_Old  \\\n",
       "Report_Date_as_YYYY-MM-DD                                                    \n",
       "2024-08-13                            147859                           3.1   \n",
       "2024-08-06                            147537                           3.0   \n",
       "2024-07-30                            151437                           2.7   \n",
       "2024-07-23                            157106                           2.2   \n",
       "2024-07-16                            166641                           1.7   \n",
       "\n",
       "                           Pct_of_OI_Prod_Merc_Short_Old  \n",
       "Report_Date_as_YYYY-MM-DD                                 \n",
       "2024-08-13                                          25.3  \n",
       "2024-08-06                                          25.4  \n",
       "2024-07-30                                          25.4  \n",
       "2024-07-23                                          25.8  \n",
       "2024-07-16                                          26.0  "
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     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "filtered_silver_df = silver_df[needed_columns].copy()\n",
    "filtered_silver_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "106e2cd2-8c1a-407e-9c1d-94301b3261e7",
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   "source": []
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   "cell_type": "markdown",
   "id": "ac29c5fd-e220-4c6c-9366-ce3349a71385",
   "metadata": {},
   "source": [
    "## Open Interest (COT) CFTC"
   ]
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  {
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   "execution_count": 29,
   "id": "6c517b33-fae5-44f2-be07-00b7e9a65cac",
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Data: Open Interest Over Time (Silver)\"},\"yaxis\":{\"title\":{\"text\":\"Open Interest\"}},\"xaxis\":{\"title\":{\"text\":\"Date\"},\"tickformat\":\"%d %b %Y\",\"tickangle\":45,\"dtick\":\"M1\",\"ticklabelmode\":\"period\"},\"height\":600,\"width\":800,\"hovermode\":\"x unified\"},                        {\"responsive\": true}                    ).then(function(){\n",
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     "text": [
      "\n",
      "Metrics for Open Interest:\n",
      "Volatility (Standard Deviation): 15889.35\n",
      "Average: 158040.03\n",
      "Median: 157106.00\n",
      "Minimum: 131956.00\n",
      "Maximum: 186945.00\n",
      "Typical Range: 146584.00 to 171371.00\n",
      "\n",
      "Overall trend: Downward\n",
      "\n",
      "Significant points:\n",
      "Peak on 4: 166641.00\n",
      "Peak on 12: 186945.00\n",
      "Peak on 17: 175784.00\n",
      "Peak on 26: 152544.00\n",
      "Peak on 29: 138617.00\n",
      "Trough on 6: 154724.00\n",
      "Trough on 14: 161868.00\n",
      "Trough on 23: 141026.00\n",
      "Trough on 28: 136544.00\n",
      "Trough on 30: 131956.00\n",
      "\n",
      "Sudden changes (>5% day-to-day):\n",
      "2024-07-16 00:00:00: 6.07% change\n",
      "2024-07-02 00:00:00: -5.22% change\n",
      "2024-06-25 00:00:00: 7.59% change\n",
      "2024-06-18 00:00:00: 6.09% change\n",
      "2024-05-14 00:00:00: -8.33% change\n",
      "2024-05-07 00:00:00: -5.55% change\n",
      "2024-04-23 00:00:00: 5.67% change\n",
      "2024-03-12 00:00:00: -5.63% change\n",
      "2024-01-30 00:00:00: -7.32% change\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "\n",
    "# Assuming df is your DataFrame with the COT data\n",
    "# If not, you'll need to load your data here\n",
    "\n",
    "# Create the main figure\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add trace for Open Interest\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=filtered_silver_df.index, \n",
    "    y=filtered_silver_df['Open_Interest_All'], \n",
    "    mode='lines',\n",
    "    name='Open Interest',\n",
    "    line=dict(width=2, color='blue')\n",
    "))\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(\n",
    "    title='COT Data: Open Interest Over Time (Silver)',\n",
    "    yaxis_title='Open Interest',\n",
    "    xaxis_title='Date',\n",
    "    height=600,\n",
    "    width=800,\n",
    "    hovermode='x unified'\n",
    ")\n",
    "\n",
    "# Improve date labeling for better readability\n",
    "fig.update_xaxes(\n",
    "    tickformat=\"%d %b %Y\",\n",
    "    tickangle=45,\n",
    "    dtick=\"M1\",\n",
    "    ticklabelmode=\"period\"\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n",
    "\n",
    "# Calculate and print metrics\n",
    "print(\"\\nMetrics for Open Interest:\")\n",
    "data = filtered_silver_df['Open_Interest_All']\n",
    "\n",
    "print(f\"Volatility (Standard Deviation): {data.std():.2f}\")\n",
    "print(f\"Average: {data.mean():.2f}\")\n",
    "print(f\"Median: {data.median():.2f}\")\n",
    "print(f\"Minimum: {data.min():.2f}\")\n",
    "print(f\"Maximum: {data.max():.2f}\")\n",
    "\n",
    "# Calculate typical range\n",
    "typical_low = np.percentile(data, 25)\n",
    "typical_high = np.percentile(data, 75)\n",
    "print(f\"Typical Range: {typical_low:.2f} to {typical_high:.2f}\")\n",
    "\n",
    "# Identify trends\n",
    "def identify_trend(series):\n",
    "    # Calculate the overall trend\n",
    "    trend = np.polyfit(range(len(series)), series, 1)[0]\n",
    "    if trend > 0:\n",
    "        return \"Upward\"\n",
    "    elif trend < 0:\n",
    "        return \"Downward\"\n",
    "    else:\n",
    "        return \"Stable\"\n",
    "\n",
    "trend = identify_trend(data)\n",
    "print(f\"\\nOverall trend: {trend}\")\n",
    "\n",
    "# Identify significant points\n",
    "def find_peaks(series, prominence=1000):\n",
    "    peaks, _ = signal.find_peaks(series, prominence=prominence)\n",
    "    troughs, _ = signal.find_peaks(-series, prominence=prominence)\n",
    "    return peaks, troughs\n",
    "\n",
    "peaks, troughs = find_peaks(data)\n",
    "\n",
    "print(\"\\nSignificant points:\")\n",
    "for peak in peaks:\n",
    "    print(f\"Peak on {df.index[peak]}: {data.iloc[peak]:.2f}\")\n",
    "for trough in troughs:\n",
    "    print(f\"Trough on {df.index[trough]}: {data.iloc[trough]:.2f}\")\n",
    "\n",
    "# Identify sudden changes\n",
    "pct_change = data.pct_change()\n",
    "sudden_changes = pct_change[abs(pct_change) > 0.05]  # 5% threshold\n",
    "if not sudden_changes.empty:\n",
    "    print(\"\\nSudden changes (>5% day-to-day):\")\n",
    "    for date, change in sudden_changes.items():\n",
    "        print(f\"{date}: {change*100:.2f}% change\")\n",
    "else:\n",
    "    print(\"\\nNo sudden changes above 5% threshold detected.\")"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Data Export for AmiBroker\n",
    "\n",
    "(You have to tick the box \"No quotation data\" in the Import wizard and select the fields according to the CSV header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d7d3aae5-7043-409e-b5ae-0aed4a3b4de4",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data exported to silver_data_for_amibroker.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def export_to_amibroker_csv(df, output_file='amibroker_data.csv'):\n",
    "    # Create a copy of the DataFrame to avoid modifying the original\n",
    "    export_df = df.copy()\n",
    "    \n",
    "    # Reset the index to make the date a column\n",
    "    export_df = export_df.reset_index()\n",
    "    \n",
    "    # Rename columns to match AmiBroker format\n",
    "    export_df['Date'] = pd.to_datetime(export_df['Report_Date_as_YYYY-MM-DD'])\n",
    "    export_df['Close'] = export_df['Open_Interest_All']\n",
    "    export_df['Volume'] = export_df['Pct_of_OI_Prod_Merc_Long_Old']\n",
    "    \n",
    "    # Create Open, High, Low columns with the same value as Close\n",
    "    export_df['Open'] = export_df['Close']\n",
    "    export_df['High'] = export_df['Close']\n",
    "    export_df['Low'] = export_df['Close']\n",
    "    \n",
    "    # Select and order columns for AmiBroker\n",
    "    amibroker_df = export_df[['Date', 'Open', 'High', 'Low', 'Close', 'Volume']]\n",
    "    \n",
    "    # Sort by date in descending order (most recent first)\n",
    "    amibroker_df = amibroker_df.sort_values('Date', ascending=False)\n",
    "    \n",
    "    # Export to CSV\n",
    "    amibroker_df.to_csv(output_file, index=False, date_format='%Y-%m-%d')\n",
    "    print(f\"Data exported to {output_file}\")\n",
    "\n",
    "# Usage\n",
    "export_to_amibroker_csv(filtered_silver_df, 'silver_data_for_amibroker.csv')"
   ]
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   "source": []
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