{
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   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%pip install \"yfinance[optional]\"==\"0.2.37\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "904104edfdda9e89",
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     "start_time": "2024-03-18T09:32:44.088451Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "hg_f = yf.Ticker(\"HG=F\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8531df814e529fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:32:47.062820Z",
     "start_time": "2024-03-18T09:32:44.497587Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# get historical market data\n",
    "hist = hg_f.history(period=\"100y\")\n",
    "\n",
    "# Replace 0 values with NaN in specific columns\n",
    "hist.replace({'Volume': {0: pd.NA}, 'Open': {0: pd.NA}}, inplace=True)\n",
    "hist.replace(0, np.nan, inplace=True)  # Replace 0 with NaN\n",
    "\n",
    "columns = ['Open', 'High', 'Low', 'Close', 'Volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de2634931db1a5d6",
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     "start_time": "2024-03-18T09:32:47.066270Z"
    },
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "                            Open   High     Low   Close Volume  Dividends  \\\n",
       "Date                                                                        \n",
       "2000-08-30 00:00:00-04:00  0.879  0.887  0.8770  0.8850   2886        NaN   \n",
       "2000-08-31 00:00:00-04:00  0.885  0.888  0.8800  0.8850   1095        NaN   \n",
       "2000-09-01 00:00:00-04:00  0.878  0.889  0.8780  0.8890   3449        NaN   \n",
       "2000-09-05 00:00:00-04:00  0.896  0.907  0.8950  0.9060   1397        NaN   \n",
       "2000-09-06 00:00:00-04:00  0.905  0.906  0.8975  0.9015   1195        NaN   \n",
       "\n",
       "                           Stock Splits  \n",
       "Date                                     \n",
       "2000-08-30 00:00:00-04:00           NaN  \n",
       "2000-08-31 00:00:00-04:00           NaN  \n",
       "2000-09-01 00:00:00-04:00           NaN  \n",
       "2000-09-05 00:00:00-04:00           NaN  \n",
       "2000-09-06 00:00:00-04:00           NaN  "
      ]
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   "source": [
    "hist.head()"
   ]
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   "execution_count": 11,
   "id": "6da84391ad0032f6",
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
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       "             Open   High     Low   Close Volume  Dividends  Stock Splits\n",
       "2000-08-30  0.879  0.887  0.8770  0.8850   2886        NaN           NaN\n",
       "2000-08-31  0.885  0.888  0.8800  0.8850   1095        NaN           NaN\n",
       "2000-09-01  0.878  0.889  0.8780  0.8890   3449        NaN           NaN\n",
       "2000-09-05  0.896  0.907  0.8950  0.9060   1397        NaN           NaN\n",
       "2000-09-06  0.905  0.906  0.8975  0.9015   1195        NaN           NaN"
      ]
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   ],
   "source": [
    "# Convert the index to datetime if it's not already\n",
    "hist.index = pd.to_datetime(hist.index)\n",
    "\n",
    "# Strip the time and timezone, keeping only the date for the index\n",
    "hist.index = hist.index.date\n",
    "\n",
    "hist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46d35140195ce652",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T11:27:06.711138Z",
     "start_time": "2024-03-18T11:27:06.675173Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Get the current date in 'YYYY-MM-DD' format\n",
    "current_date = datetime.now().strftime('%Y-%m-%d')\n",
    "\n",
    "# Create the file name with the current date\n",
    "file_name = f'./Data_COPPER_{current_date}.csv'\n",
    "\n",
    "hist = hist.round(3)\n",
    "\n",
    "# Save the DataFrame to CSV with the dynamic file name\n",
    "hist[[\"Open\", \"High\", \"Low\", \"Close\"]].to_csv(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "id": "3f6096fe43e72687",
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