{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-08T19:24:48.711659Z",
     "start_time": "2024-03-08T19:24:48.709724Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0                   Name     Ticker\n0           0        20 Microns Ltd.  20MICRONS\n1           1       360 One Wam Ltd.     360ONE\n2           2          3M India Ltd.    3MINDIA\n3           3  3P Land Holdings Ltd.     3PLAND\n4           4       3i Infotech Ltd.  3IINFOLTD",
      "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>Unnamed: 0</th>\n      <th>Name</th>\n      <th>Ticker</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>20 Microns Ltd.</td>\n      <td>20MICRONS</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>360 One Wam Ltd.</td>\n      <td>360ONE</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>3M India Ltd.</td>\n      <td>3MINDIA</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>3P Land Holdings Ltd.</td>\n      <td>3PLAND</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>3i Infotech Ltd.</td>\n      <td>3IINFOLTD</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/Company List.csv\")\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T19:24:48.887880Z",
     "start_time": "2024-03-08T19:24:48.881294Z"
    }
   },
   "id": "ff8275fdbab66ba3",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "l = (df[\"Ticker\"]+'.NS')[:5].to_list()\n",
    "l.append(\"BHUSANSTL.NS\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T19:24:49.073847Z",
     "start_time": "2024-03-08T19:24:49.071352Z"
    }
   },
   "id": "8c0fc5687a75e887",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "['20MICRONS.NS',\n '360ONE.NS',\n '3MINDIA.NS',\n '3PLAND.NS',\n '3IINFOLTD.NS',\n 'BHUSANSTL.NS']"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T19:24:49.617566Z",
     "start_time": "2024-03-08T19:24:49.614957Z"
    }
   },
   "id": "f564268b1875dee3",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "['BHUSANSTL.NS']: Exception('%ticker%: 1d data not available for startTime=-2208971040 and endTime=1709874000. Only 100 years worth of day granularity data are allowed to be fetched per request.')\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "com_df = pd.DataFrame()\n",
    "\n",
    "for i in l:\n",
    "    com_data = yf.download(i, start=\"1900-01-01\", end=\"2024-03-08\")\n",
    "    \n",
    "    if (com_data.empty == False):\n",
    "        com_df[i] = pd.Series(com_data['Adj Close'], index=com_data.index)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T19:24:53.664589Z",
     "start_time": "2024-03-08T19:24:51.930859Z"
    }
   },
   "id": "6f4b522412812a96",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "            20MICRONS.NS  360ONE.NS   3MINDIA.NS  3PLAND.NS  3IINFOLTD.NS\nDate                                                                     \n2008-10-06     14.736081        NaN  1210.735474  14.001053    521.733643\n2008-10-07     13.198785        NaN  1143.373535  14.646491    481.067078\n2008-10-08     11.639526        NaN  1053.750000  15.391228    432.082306\n2008-10-10     10.190077        NaN  1002.206726  12.312982    408.052063\n2008-10-13     10.826957        NaN   980.954834  10.922807    478.294373",
      "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>20MICRONS.NS</th>\n      <th>360ONE.NS</th>\n      <th>3MINDIA.NS</th>\n      <th>3PLAND.NS</th>\n      <th>3IINFOLTD.NS</th>\n    </tr>\n    <tr>\n      <th>Date</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>2008-10-06</th>\n      <td>14.736081</td>\n      <td>NaN</td>\n      <td>1210.735474</td>\n      <td>14.001053</td>\n      <td>521.733643</td>\n    </tr>\n    <tr>\n      <th>2008-10-07</th>\n      <td>13.198785</td>\n      <td>NaN</td>\n      <td>1143.373535</td>\n      <td>14.646491</td>\n      <td>481.067078</td>\n    </tr>\n    <tr>\n      <th>2008-10-08</th>\n      <td>11.639526</td>\n      <td>NaN</td>\n      <td>1053.750000</td>\n      <td>15.391228</td>\n      <td>432.082306</td>\n    </tr>\n    <tr>\n      <th>2008-10-10</th>\n      <td>10.190077</td>\n      <td>NaN</td>\n      <td>1002.206726</td>\n      <td>12.312982</td>\n      <td>408.052063</td>\n    </tr>\n    <tr>\n      <th>2008-10-13</th>\n      <td>10.826957</td>\n      <td>NaN</td>\n      <td>980.954834</td>\n      <td>10.922807</td>\n      <td>478.294373</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T19:23:54.775082Z",
     "start_time": "2024-03-08T19:23:54.770174Z"
    }
   },
   "id": "ff14463f49d5d8f4",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "162b263160a53be2"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "['BHUSANSTL.NS']: Exception('%ticker%: No price data found, symbol may be delisted (1d 2023-01-01 -> 2024-01-01)')\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           AAPL       GOOG        MSFT\n",
      "Date                                                  \n",
      "2023-01-03 00:00:00  124.216301  89.699997  237.036026\n",
      "2023-01-04 00:00:00  125.497498  88.709999  226.667297\n",
      "2023-01-05 00:00:00  124.166634  86.769997  219.949371\n",
      "2023-01-06 00:00:00  128.735229  88.160004  222.541565\n",
      "2023-01-09 00:00:00  129.261612  88.800003  224.708313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "\n",
    "def fetch_adj_close(ticker):\n",
    "    try:\n",
    "        data = yf.download(ticker, start=\"2023-01-01\", end=\"2024-01-01\")\n",
    "        adj_close = data['Adj Close']  # Extracting adjusted close prices\n",
    "        adj_close = pd.DataFrame(adj_close)  # Convert Series to DataFrame\n",
    "        adj_close.columns = [ticker]  # Rename column to ticker symbol\n",
    "        return adj_close\n",
    "    except Exception as e:\n",
    "        print(f\"Error fetching data for {ticker}: {e}\")\n",
    "        return None\n",
    "\n",
    "# List of tickers you want to fetch data for\n",
    "tickers = [\"AAPL\", \"GOOG\", \"BHUSANSTL.NS\", \"MSFT\"]\n",
    "\n",
    "# Create an empty DataFrame to store the adjusted close prices\n",
    "adj_close_df = pd.DataFrame()\n",
    "\n",
    "for ticker in tickers:\n",
    "    data = fetch_adj_close(ticker)\n",
    "    if data is not None:\n",
    "        adj_close_df = pd.concat([adj_close_df, data], axis=1)  # Concatenate the data to the DataFrame\n",
    "\n",
    "# Set the date as the index\n",
    "adj_close_df.index.name = 'Date'\n",
    "\n",
    "# Drop columns with any NaN values (i.e., for invalid tickers)\n",
    "adj_close_df.dropna(axis=1, how='any', inplace=True)\n",
    "\n",
    "print(adj_close_df.head())\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-08T23:14:00.320054Z",
     "start_time": "2024-03-08T23:14:00.259300Z"
    }
   },
   "id": "fdb2a5f6391b0ca2",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "b34afac23ee07c5e"
  }
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