get_coprice_corpice10yr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('prices_stocksLT10yr.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>dividends</th>\n",
       "      <th>splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2011-05-04</td>\n",
       "      <td>685.200012</td>\n",
       "      <td>678.466675</td>\n",
       "      <td>3056598.0</td>\n",
       "      <td>689.955566</td>\n",
       "      <td>685.288879</td>\n",
       "      <td>584.130493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2011-05-05</td>\n",
       "      <td>688.444458</td>\n",
       "      <td>677.799988</td>\n",
       "      <td>2940534.0</td>\n",
       "      <td>696.888916</td>\n",
       "      <td>681.133362</td>\n",
       "      <td>580.588257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>688.311096</td>\n",
       "      <td>683.555542</td>\n",
       "      <td>2961454.0</td>\n",
       "      <td>697.288879</td>\n",
       "      <td>687.155579</td>\n",
       "      <td>585.721680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>688.888916</td>\n",
       "      <td>680.222229</td>\n",
       "      <td>2071185.0</td>\n",
       "      <td>693.622192</td>\n",
       "      <td>689.266663</td>\n",
       "      <td>587.521057</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2011-05-10</td>\n",
       "      <td>688.444458</td>\n",
       "      <td>681.777771</td>\n",
       "      <td>1498772.0</td>\n",
       "      <td>695.511108</td>\n",
       "      <td>685.311096</td>\n",
       "      <td>584.149414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol        date        open         low     volume        high  \\\n",
       "0  LT.NS  2011-05-04  685.200012  678.466675  3056598.0  689.955566   \n",
       "1  LT.NS  2011-05-05  688.444458  677.799988  2940534.0  696.888916   \n",
       "2  LT.NS  2011-05-06  688.311096  683.555542  2961454.0  697.288879   \n",
       "3  LT.NS  2011-05-09  688.888916  680.222229  2071185.0  693.622192   \n",
       "4  LT.NS  2011-05-10  688.444458  681.777771  1498772.0  695.511108   \n",
       "\n",
       "        close    adjclose  dividends  splits  \n",
       "0  685.288879  584.130493        0.0     0.0  \n",
       "1  681.133362  580.588257        0.0     0.0  \n",
       "2  687.155579  585.721680        0.0     0.0  \n",
       "3  689.266663  587.521057        0.0     0.0  \n",
       "4  685.311096  584.149414        0.0     0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime \n",
    "dt = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-05-04'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = datetime.datetime.now()\n",
    "dt_yb = dt - datetime.timedelta(days=364)\n",
    "dtt = dt.strftime('%Y-%m-%d')\n",
    "dtt_yb = dt_yb.strftime('%Y-%m-%d')\n",
    "mask = (df['date'] > dtt_yb) & (df['date'] <= dtt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>dividends</th>\n",
       "      <th>splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>838.000000</td>\n",
       "      <td>817.599976</td>\n",
       "      <td>5081524.0</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>837.299988</td>\n",
       "      <td>814.287964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>833.000000</td>\n",
       "      <td>820.099976</td>\n",
       "      <td>3529134.0</td>\n",
       "      <td>836.049988</td>\n",
       "      <td>822.849976</td>\n",
       "      <td>800.235046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>833.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>4394509.0</td>\n",
       "      <td>834.849976</td>\n",
       "      <td>815.950012</td>\n",
       "      <td>793.524719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>821.200012</td>\n",
       "      <td>816.349976</td>\n",
       "      <td>2882920.0</td>\n",
       "      <td>827.500000</td>\n",
       "      <td>821.599976</td>\n",
       "      <td>799.019409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>804.000000</td>\n",
       "      <td>3024381.0</td>\n",
       "      <td>819.250000</td>\n",
       "      <td>814.799988</td>\n",
       "      <td>792.406311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>1384.849976</td>\n",
       "      <td>1372.500000</td>\n",
       "      <td>3220346.0</td>\n",
       "      <td>1399.949951</td>\n",
       "      <td>1375.900024</td>\n",
       "      <td>1375.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>1383.949951</td>\n",
       "      <td>1353.500000</td>\n",
       "      <td>2221346.0</td>\n",
       "      <td>1389.550049</td>\n",
       "      <td>1358.650024</td>\n",
       "      <td>1358.650024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>1350.000000</td>\n",
       "      <td>1336.099976</td>\n",
       "      <td>1968093.0</td>\n",
       "      <td>1365.000000</td>\n",
       "      <td>1340.449951</td>\n",
       "      <td>1340.449951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-05-03</td>\n",
       "      <td>1325.500000</td>\n",
       "      <td>1319.150024</td>\n",
       "      <td>1653245.0</td>\n",
       "      <td>1351.300049</td>\n",
       "      <td>1342.500000</td>\n",
       "      <td>1342.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>1350.000000</td>\n",
       "      <td>1331.000000</td>\n",
       "      <td>2029280.0</td>\n",
       "      <td>1350.900024</td>\n",
       "      <td>1337.599976</td>\n",
       "      <td>1337.599976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symbol        date         open          low     volume         high  \\\n",
       "2212  LT.NS  2020-05-06   838.000000   817.599976  5081524.0   848.000000   \n",
       "2213  LT.NS  2020-05-07   833.000000   820.099976  3529134.0   836.049988   \n",
       "2214  LT.NS  2020-05-08   833.000000   810.000000  4394509.0   834.849976   \n",
       "2215  LT.NS  2020-05-11   821.200012   816.349976  2882920.0   827.500000   \n",
       "2216  LT.NS  2020-05-12   817.000000   804.000000  3024381.0   819.250000   \n",
       "...     ...         ...          ...          ...        ...          ...   \n",
       "2456  LT.NS  2021-04-28  1384.849976  1372.500000  3220346.0  1399.949951   \n",
       "2457  LT.NS  2021-04-29  1383.949951  1353.500000  2221346.0  1389.550049   \n",
       "2458  LT.NS  2021-04-30  1350.000000  1336.099976  1968093.0  1365.000000   \n",
       "2459  LT.NS  2021-05-03  1325.500000  1319.150024  1653245.0  1351.300049   \n",
       "2460  LT.NS  2021-05-04  1350.000000  1331.000000  2029280.0  1350.900024   \n",
       "\n",
       "            close     adjclose  dividends  splits  \n",
       "2212   837.299988   814.287964        0.0     0.0  \n",
       "2213   822.849976   800.235046        0.0     0.0  \n",
       "2214   815.950012   793.524719        0.0     0.0  \n",
       "2215   821.599976   799.019409        0.0     0.0  \n",
       "2216   814.799988   792.406311        0.0     0.0  \n",
       "...           ...          ...        ...     ...  \n",
       "2456  1375.900024  1375.900024        0.0     0.0  \n",
       "2457  1358.650024  1358.650024        0.0     0.0  \n",
       "2458  1340.449951  1340.449951        0.0     0.0  \n",
       "2459  1342.500000  1342.500000        0.0     0.0  \n",
       "2460  1337.599976  1337.599976        0.0     0.0  \n",
       "\n",
       "[249 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>dividends</th>\n",
       "      <th>splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [symbol, date, open, low, volume, high, close, adjclose, dividends, splits]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (df['date']== '2000-01-01')\n",
    "df.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good\n"
     ]
    }
   ],
   "source": [
    "if df.loc[mask].empty:\n",
    "    print('good')\n",
    "else:\n",
    "    print('bad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'[-1] not in index'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-34ab21f3f443>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'close'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3028\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3029\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3030\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3031\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3032\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1263\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1265\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1266\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1313\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1314\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1315\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1317\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '[-1] not in index'"
     ]
    }
   ],
   "source": [
    "df[['close',-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "685.2888793945312"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['close'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-5",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    350\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    352\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: -5 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-3bac86e9ab22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    822\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    933\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    351\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -5"
     ]
    }
   ],
   "source": [
    "df['close'][-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfc = df['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1337.5999755859375"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfc.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "    dt = datetime.datetime.now()\n",
    "\n",
    "    dt_yb = dt - datetime.timedelta(days=364)\n",
    "    dt_yb = dt_yb.strftime('%Y-%m-%d')\n",
    "    yb_mask = (df['date'] == dt_yb)\n",
    "    df_yb = df.loc[yb_mask]\n",
    "    p_yb = df_yb['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "836.9000244140625"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_yb.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "    dt_mb = dt - datetime.timedelta(days=32)\n",
    "    dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "    mb_mask = (df['date'] == dt_mb)\n",
    "    df_mb = df.loc[mb_mask]\n",
    "    p_mb = df_mb['close']\n",
    "    if df_mb.empty:\n",
    "        try:\n",
    "            dt_mb = dt - datetime.timedelta(days=30)\n",
    "            dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "            mb_mask = (df['date'] == dt_mb)\n",
    "            df_mb = df.loc[mb_mask]\n",
    "            p_mb = df_mb['close']\n",
    "        except df.loc[mask].empty:\n",
    "            try:\n",
    "                dt_mb = dt - datetime.timedelta(days=32)\n",
    "                dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "                mb_mask = (df['date'] == dt_mb)\n",
    "                df_mb = df.loc[mb_mask]\n",
    "                p_mb = df_mb['close']\n",
    "            except:\n",
    "                dt_mb = dt - datetime.timedelta(days=33)\n",
    "                dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "                mb_mask = (df['date'] == dt_mb)\n",
    "                df_mb = df.loc[mb_mask]\n",
    "                p_mb = df_mb['close']              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: close, dtype: float64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "    dt_mb = dt - datetime.timedelta(days=31)\n",
    "    dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "    mb_mask = (df['date'] == dt_mb)\n",
    "    df_mb = df.loc[mb_mask]\n",
    "    p_mb = df_mb['close']\n",
    "    if df_mb.empty:\n",
    "        try:\n",
    "            dt_mb = dt - datetime.timedelta(days=30)\n",
    "            dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "            mb_mask = (df['date'] == dt_mb)\n",
    "            df_mb = df.loc[mb_mask]\n",
    "            p_mb = df_mb['close']\n",
    "            if df_mb.empty:\n",
    "                try:\n",
    "                    dt_mb = dt - datetime.timedelta(days=32)\n",
    "                    dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "                    mb_mask = (df['date'] == dt_mb)\n",
    "                    df_mb = df.loc[mb_mask]\n",
    "                    p_mb = df_mb['close']\n",
    "                except:\n",
    "                    dt_mb = dt - datetime.timedelta(days=33)\n",
    "                    dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "                    mb_mask = (df['date'] == dt_mb)\n",
    "                    df_mb = df.loc[mb_mask]\n",
    "                    p_mb = df_mb['close']      \n",
    "        except:\n",
    "            dt_mb = dt - datetime.timedelta(days=29)\n",
    "            dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "            mb_mask = (df['date'] == dt_mb)\n",
    "            df_mb = df.loc[mb_mask]\n",
    "            p_mb = df_mb['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mb.empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dt_mb = dt - datetime.timedelta(days=30)\n",
    "dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "mb_mask = (df['date'] == dt_mb)\n",
    "df_mb = df.loc[mb_mask]\n",
    "p_mb = df_mb['close']\n",
    "if df_mb.empty:\n",
    "    dt_mb = dt - datetime.timedelta(days=34)\n",
    "    dt_mb = dt_mb.strftime('%Y-%m-%d')\n",
    "    mb_mask = (df['date'] == dt_mb)\n",
    "    df_mb = df.loc[mb_mask]\n",
    "    p_mb = df_mb['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2439    1418.900024\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>dividends</th>\n",
       "      <th>splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>1384.849976</td>\n",
       "      <td>1372.500000</td>\n",
       "      <td>3220346.0</td>\n",
       "      <td>1399.949951</td>\n",
       "      <td>1375.900024</td>\n",
       "      <td>1375.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>1383.949951</td>\n",
       "      <td>1353.500000</td>\n",
       "      <td>2221346.0</td>\n",
       "      <td>1389.550049</td>\n",
       "      <td>1358.650024</td>\n",
       "      <td>1358.650024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>1350.000000</td>\n",
       "      <td>1336.099976</td>\n",
       "      <td>1968093.0</td>\n",
       "      <td>1365.000000</td>\n",
       "      <td>1340.449951</td>\n",
       "      <td>1340.449951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-05-03</td>\n",
       "      <td>1325.500000</td>\n",
       "      <td>1319.150024</td>\n",
       "      <td>1653245.0</td>\n",
       "      <td>1351.300049</td>\n",
       "      <td>1342.500000</td>\n",
       "      <td>1342.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>1350.000000</td>\n",
       "      <td>1331.000000</td>\n",
       "      <td>2029280.0</td>\n",
       "      <td>1350.900024</td>\n",
       "      <td>1337.599976</td>\n",
       "      <td>1337.599976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     symbol        date         open          low     volume         high  \\\n",
       "2456  LT.NS  2021-04-28  1384.849976  1372.500000  3220346.0  1399.949951   \n",
       "2457  LT.NS  2021-04-29  1383.949951  1353.500000  2221346.0  1389.550049   \n",
       "2458  LT.NS  2021-04-30  1350.000000  1336.099976  1968093.0  1365.000000   \n",
       "2459  LT.NS  2021-05-03  1325.500000  1319.150024  1653245.0  1351.300049   \n",
       "2460  LT.NS  2021-05-04  1350.000000  1331.000000  2029280.0  1350.900024   \n",
       "\n",
       "            close     adjclose  dividends  splits  \n",
       "2456  1375.900024  1375.900024        0.0     0.0  \n",
       "2457  1358.650024  1358.650024        0.0     0.0  \n",
       "2458  1340.449951  1340.449951        0.0     0.0  \n",
       "2459  1342.500000  1342.500000        0.0     0.0  \n",
       "2460  1337.599976  1337.599976        0.0     0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_list = [datetime.datetime.now() - datetime.timedelta(days=x) for x in range(2460)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfd = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd.iloc[-3].weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'][0].weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_array = []\n",
    "for index,row in df.iterrows():\n",
    "    if (row['date'].weekday() == 0):\n",
    "        date_array.append(row['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2011-05-09 00:00:00'),\n",
       " Timestamp('2011-05-16 00:00:00'),\n",
       " Timestamp('2011-05-23 00:00:00'),\n",
       " Timestamp('2011-05-30 00:00:00'),\n",
       " Timestamp('2011-06-06 00:00:00'),\n",
       " Timestamp('2011-06-13 00:00:00'),\n",
       " Timestamp('2011-06-20 00:00:00'),\n",
       " Timestamp('2011-06-27 00:00:00'),\n",
       " Timestamp('2011-07-04 00:00:00'),\n",
       " Timestamp('2011-07-11 00:00:00'),\n",
       " Timestamp('2011-07-18 00:00:00'),\n",
       " Timestamp('2011-07-25 00:00:00'),\n",
       " Timestamp('2011-08-01 00:00:00'),\n",
       " Timestamp('2011-08-08 00:00:00'),\n",
       " Timestamp('2011-08-22 00:00:00'),\n",
       " Timestamp('2011-08-29 00:00:00'),\n",
       " Timestamp('2011-09-05 00:00:00'),\n",
       " Timestamp('2011-09-12 00:00:00'),\n",
       " Timestamp('2011-09-19 00:00:00'),\n",
       " Timestamp('2011-09-26 00:00:00'),\n",
       " Timestamp('2011-10-03 00:00:00'),\n",
       " Timestamp('2011-10-10 00:00:00'),\n",
       " Timestamp('2011-10-17 00:00:00'),\n",
       " Timestamp('2011-10-24 00:00:00'),\n",
       " Timestamp('2011-10-31 00:00:00'),\n",
       " Timestamp('2011-11-14 00:00:00'),\n",
       " Timestamp('2011-11-21 00:00:00'),\n",
       " Timestamp('2011-11-28 00:00:00'),\n",
       " Timestamp('2011-12-05 00:00:00'),\n",
       " Timestamp('2011-12-12 00:00:00'),\n",
       " Timestamp('2011-12-19 00:00:00'),\n",
       " Timestamp('2011-12-26 00:00:00'),\n",
       " Timestamp('2012-01-02 00:00:00'),\n",
       " Timestamp('2012-01-09 00:00:00'),\n",
       " Timestamp('2012-01-16 00:00:00'),\n",
       " Timestamp('2012-01-23 00:00:00'),\n",
       " Timestamp('2012-01-30 00:00:00'),\n",
       " Timestamp('2012-02-06 00:00:00'),\n",
       " Timestamp('2012-02-13 00:00:00'),\n",
       " Timestamp('2012-02-27 00:00:00'),\n",
       " Timestamp('2012-03-05 00:00:00'),\n",
       " Timestamp('2012-03-12 00:00:00'),\n",
       " Timestamp('2012-03-19 00:00:00'),\n",
       " Timestamp('2012-03-26 00:00:00'),\n",
       " Timestamp('2012-04-02 00:00:00'),\n",
       " Timestamp('2012-04-09 00:00:00'),\n",
       " Timestamp('2012-04-16 00:00:00'),\n",
       " Timestamp('2012-04-23 00:00:00'),\n",
       " Timestamp('2012-04-30 00:00:00'),\n",
       " Timestamp('2012-05-07 00:00:00'),\n",
       " Timestamp('2012-05-14 00:00:00'),\n",
       " Timestamp('2012-05-21 00:00:00'),\n",
       " Timestamp('2012-05-28 00:00:00'),\n",
       " Timestamp('2012-06-04 00:00:00'),\n",
       " Timestamp('2012-06-11 00:00:00'),\n",
       " Timestamp('2012-06-18 00:00:00'),\n",
       " Timestamp('2012-06-25 00:00:00'),\n",
       " Timestamp('2012-07-02 00:00:00'),\n",
       " Timestamp('2012-07-09 00:00:00'),\n",
       " Timestamp('2012-07-16 00:00:00'),\n",
       " Timestamp('2012-07-23 00:00:00'),\n",
       " Timestamp('2012-07-30 00:00:00'),\n",
       " Timestamp('2012-08-06 00:00:00'),\n",
       " Timestamp('2012-08-13 00:00:00'),\n",
       " Timestamp('2012-08-27 00:00:00'),\n",
       " Timestamp('2012-09-03 00:00:00'),\n",
       " Timestamp('2012-09-10 00:00:00'),\n",
       " Timestamp('2012-09-17 00:00:00'),\n",
       " Timestamp('2012-09-24 00:00:00'),\n",
       " Timestamp('2012-10-01 00:00:00'),\n",
       " Timestamp('2012-10-08 00:00:00'),\n",
       " Timestamp('2012-10-15 00:00:00'),\n",
       " Timestamp('2012-10-22 00:00:00'),\n",
       " Timestamp('2012-10-29 00:00:00'),\n",
       " Timestamp('2012-11-05 00:00:00'),\n",
       " Timestamp('2012-11-12 00:00:00'),\n",
       " Timestamp('2012-11-19 00:00:00'),\n",
       " Timestamp('2012-11-26 00:00:00'),\n",
       " Timestamp('2012-12-03 00:00:00'),\n",
       " Timestamp('2012-12-10 00:00:00'),\n",
       " Timestamp('2012-12-17 00:00:00'),\n",
       " Timestamp('2012-12-24 00:00:00'),\n",
       " Timestamp('2012-12-31 00:00:00'),\n",
       " Timestamp('2013-01-07 00:00:00'),\n",
       " Timestamp('2013-01-14 00:00:00'),\n",
       " Timestamp('2013-01-21 00:00:00'),\n",
       " Timestamp('2013-01-28 00:00:00'),\n",
       " Timestamp('2013-02-04 00:00:00'),\n",
       " Timestamp('2013-02-11 00:00:00'),\n",
       " Timestamp('2013-02-18 00:00:00'),\n",
       " Timestamp('2013-02-25 00:00:00'),\n",
       " Timestamp('2013-03-04 00:00:00'),\n",
       " Timestamp('2013-03-11 00:00:00'),\n",
       " Timestamp('2013-03-18 00:00:00'),\n",
       " Timestamp('2013-03-25 00:00:00'),\n",
       " Timestamp('2013-04-01 00:00:00'),\n",
       " Timestamp('2013-04-08 00:00:00'),\n",
       " Timestamp('2013-04-15 00:00:00'),\n",
       " Timestamp('2013-04-22 00:00:00'),\n",
       " Timestamp('2013-04-29 00:00:00'),\n",
       " Timestamp('2013-05-06 00:00:00'),\n",
       " Timestamp('2013-05-13 00:00:00'),\n",
       " Timestamp('2013-05-20 00:00:00'),\n",
       " Timestamp('2013-05-27 00:00:00'),\n",
       " Timestamp('2013-06-03 00:00:00'),\n",
       " Timestamp('2013-06-10 00:00:00'),\n",
       " Timestamp('2013-06-17 00:00:00'),\n",
       " Timestamp('2013-06-24 00:00:00'),\n",
       " Timestamp('2013-07-01 00:00:00'),\n",
       " Timestamp('2013-07-08 00:00:00'),\n",
       " Timestamp('2013-07-15 00:00:00'),\n",
       " Timestamp('2013-07-22 00:00:00'),\n",
       " Timestamp('2013-07-29 00:00:00'),\n",
       " Timestamp('2013-08-05 00:00:00'),\n",
       " Timestamp('2013-08-12 00:00:00'),\n",
       " Timestamp('2013-08-19 00:00:00'),\n",
       " Timestamp('2013-08-26 00:00:00'),\n",
       " Timestamp('2013-09-02 00:00:00'),\n",
       " Timestamp('2013-09-16 00:00:00'),\n",
       " Timestamp('2013-09-23 00:00:00'),\n",
       " Timestamp('2013-09-30 00:00:00'),\n",
       " Timestamp('2013-10-07 00:00:00'),\n",
       " Timestamp('2013-10-14 00:00:00'),\n",
       " Timestamp('2013-10-21 00:00:00'),\n",
       " Timestamp('2013-10-28 00:00:00'),\n",
       " Timestamp('2013-11-11 00:00:00'),\n",
       " Timestamp('2013-11-18 00:00:00'),\n",
       " Timestamp('2013-11-25 00:00:00'),\n",
       " Timestamp('2013-12-02 00:00:00'),\n",
       " Timestamp('2013-12-09 00:00:00'),\n",
       " Timestamp('2013-12-16 00:00:00'),\n",
       " Timestamp('2013-12-23 00:00:00'),\n",
       " Timestamp('2013-12-30 00:00:00'),\n",
       " Timestamp('2014-01-06 00:00:00'),\n",
       " Timestamp('2014-01-13 00:00:00'),\n",
       " Timestamp('2014-01-20 00:00:00'),\n",
       " Timestamp('2014-01-27 00:00:00'),\n",
       " Timestamp('2014-02-03 00:00:00'),\n",
       " Timestamp('2014-02-10 00:00:00'),\n",
       " Timestamp('2014-02-17 00:00:00'),\n",
       " Timestamp('2014-02-24 00:00:00'),\n",
       " Timestamp('2014-03-03 00:00:00'),\n",
       " Timestamp('2014-03-10 00:00:00'),\n",
       " Timestamp('2014-03-24 00:00:00'),\n",
       " Timestamp('2014-03-31 00:00:00'),\n",
       " Timestamp('2014-04-07 00:00:00'),\n",
       " Timestamp('2014-04-21 00:00:00'),\n",
       " Timestamp('2014-04-28 00:00:00'),\n",
       " Timestamp('2014-05-05 00:00:00'),\n",
       " Timestamp('2014-05-12 00:00:00'),\n",
       " Timestamp('2014-05-19 00:00:00'),\n",
       " Timestamp('2014-05-26 00:00:00'),\n",
       " Timestamp('2014-06-02 00:00:00'),\n",
       " Timestamp('2014-06-09 00:00:00'),\n",
       " Timestamp('2014-06-16 00:00:00'),\n",
       " Timestamp('2014-06-23 00:00:00'),\n",
       " Timestamp('2014-06-30 00:00:00'),\n",
       " Timestamp('2014-07-07 00:00:00'),\n",
       " Timestamp('2014-07-14 00:00:00'),\n",
       " Timestamp('2014-07-21 00:00:00'),\n",
       " Timestamp('2014-07-28 00:00:00'),\n",
       " Timestamp('2014-08-04 00:00:00'),\n",
       " Timestamp('2014-08-11 00:00:00'),\n",
       " Timestamp('2014-08-18 00:00:00'),\n",
       " Timestamp('2014-08-25 00:00:00'),\n",
       " Timestamp('2014-09-01 00:00:00'),\n",
       " Timestamp('2014-09-08 00:00:00'),\n",
       " Timestamp('2014-09-15 00:00:00'),\n",
       " Timestamp('2014-09-22 00:00:00'),\n",
       " Timestamp('2014-09-29 00:00:00'),\n",
       " Timestamp('2014-10-13 00:00:00'),\n",
       " Timestamp('2014-10-20 00:00:00'),\n",
       " Timestamp('2014-10-27 00:00:00'),\n",
       " Timestamp('2014-11-03 00:00:00'),\n",
       " Timestamp('2014-11-10 00:00:00'),\n",
       " Timestamp('2014-11-17 00:00:00'),\n",
       " Timestamp('2014-11-24 00:00:00'),\n",
       " Timestamp('2014-12-01 00:00:00'),\n",
       " Timestamp('2014-12-08 00:00:00'),\n",
       " Timestamp('2014-12-15 00:00:00'),\n",
       " Timestamp('2014-12-22 00:00:00'),\n",
       " Timestamp('2014-12-29 00:00:00'),\n",
       " Timestamp('2015-01-05 00:00:00'),\n",
       " Timestamp('2015-01-12 00:00:00'),\n",
       " Timestamp('2015-01-19 00:00:00'),\n",
       " Timestamp('2015-02-02 00:00:00'),\n",
       " Timestamp('2015-02-09 00:00:00'),\n",
       " Timestamp('2015-02-16 00:00:00'),\n",
       " Timestamp('2015-02-23 00:00:00'),\n",
       " Timestamp('2015-03-02 00:00:00'),\n",
       " Timestamp('2015-03-09 00:00:00'),\n",
       " Timestamp('2015-03-16 00:00:00'),\n",
       " Timestamp('2015-03-23 00:00:00'),\n",
       " Timestamp('2015-03-30 00:00:00'),\n",
       " Timestamp('2015-04-06 00:00:00'),\n",
       " Timestamp('2015-04-13 00:00:00'),\n",
       " Timestamp('2015-04-20 00:00:00'),\n",
       " Timestamp('2015-04-27 00:00:00'),\n",
       " Timestamp('2015-05-04 00:00:00'),\n",
       " Timestamp('2015-05-11 00:00:00'),\n",
       " Timestamp('2015-05-18 00:00:00'),\n",
       " Timestamp('2015-05-25 00:00:00'),\n",
       " Timestamp('2015-06-01 00:00:00'),\n",
       " Timestamp('2015-06-08 00:00:00'),\n",
       " Timestamp('2015-06-15 00:00:00'),\n",
       " Timestamp('2015-06-22 00:00:00'),\n",
       " Timestamp('2015-06-29 00:00:00'),\n",
       " Timestamp('2015-07-06 00:00:00'),\n",
       " Timestamp('2015-07-13 00:00:00'),\n",
       " Timestamp('2015-07-20 00:00:00'),\n",
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       " Timestamp('2018-12-03 00:00:00'),\n",
       " Timestamp('2018-12-10 00:00:00'),\n",
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       " Timestamp('2018-12-24 00:00:00'),\n",
       " Timestamp('2018-12-31 00:00:00'),\n",
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       " Timestamp('2019-01-14 00:00:00'),\n",
       " Timestamp('2019-01-21 00:00:00'),\n",
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       " Timestamp('2019-02-18 00:00:00'),\n",
       " Timestamp('2019-02-25 00:00:00'),\n",
       " Timestamp('2019-03-11 00:00:00'),\n",
       " Timestamp('2019-03-18 00:00:00'),\n",
       " Timestamp('2019-03-25 00:00:00'),\n",
       " Timestamp('2019-04-01 00:00:00'),\n",
       " Timestamp('2019-04-08 00:00:00'),\n",
       " Timestamp('2019-04-15 00:00:00'),\n",
       " Timestamp('2019-04-22 00:00:00'),\n",
       " Timestamp('2019-05-06 00:00:00'),\n",
       " Timestamp('2019-05-13 00:00:00'),\n",
       " Timestamp('2019-05-20 00:00:00'),\n",
       " Timestamp('2019-05-27 00:00:00'),\n",
       " Timestamp('2019-06-03 00:00:00'),\n",
       " Timestamp('2019-06-10 00:00:00'),\n",
       " Timestamp('2019-06-17 00:00:00'),\n",
       " Timestamp('2019-06-24 00:00:00'),\n",
       " Timestamp('2019-07-01 00:00:00'),\n",
       " Timestamp('2019-07-08 00:00:00'),\n",
       " Timestamp('2019-07-15 00:00:00'),\n",
       " Timestamp('2019-07-22 00:00:00'),\n",
       " Timestamp('2019-07-29 00:00:00'),\n",
       " Timestamp('2019-08-05 00:00:00'),\n",
       " Timestamp('2019-08-19 00:00:00'),\n",
       " Timestamp('2019-08-26 00:00:00'),\n",
       " Timestamp('2019-09-09 00:00:00'),\n",
       " Timestamp('2019-09-16 00:00:00'),\n",
       " Timestamp('2019-09-23 00:00:00'),\n",
       " Timestamp('2019-09-30 00:00:00'),\n",
       " Timestamp('2019-10-07 00:00:00'),\n",
       " Timestamp('2019-10-14 00:00:00'),\n",
       " Timestamp('2019-11-04 00:00:00'),\n",
       " Timestamp('2019-11-11 00:00:00'),\n",
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       " Timestamp('2019-12-16 00:00:00'),\n",
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       " Timestamp('2020-01-27 00:00:00'),\n",
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       " Timestamp('2020-04-27 00:00:00'),\n",
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       " Timestamp('2020-06-08 00:00:00'),\n",
       " Timestamp('2020-06-15 00:00:00'),\n",
       " Timestamp('2020-06-22 00:00:00'),\n",
       " Timestamp('2020-06-29 00:00:00'),\n",
       " Timestamp('2020-07-06 00:00:00'),\n",
       " Timestamp('2020-07-13 00:00:00'),\n",
       " Timestamp('2020-07-20 00:00:00'),\n",
       " Timestamp('2020-07-27 00:00:00'),\n",
       " Timestamp('2020-08-03 00:00:00'),\n",
       " Timestamp('2020-08-10 00:00:00'),\n",
       " Timestamp('2020-08-17 00:00:00'),\n",
       " Timestamp('2020-08-24 00:00:00'),\n",
       " Timestamp('2020-08-31 00:00:00'),\n",
       " Timestamp('2020-09-07 00:00:00'),\n",
       " Timestamp('2020-09-14 00:00:00'),\n",
       " Timestamp('2020-09-21 00:00:00'),\n",
       " Timestamp('2020-09-28 00:00:00'),\n",
       " Timestamp('2020-10-05 00:00:00'),\n",
       " Timestamp('2020-10-12 00:00:00'),\n",
       " Timestamp('2020-10-19 00:00:00'),\n",
       " Timestamp('2020-10-26 00:00:00'),\n",
       " Timestamp('2020-11-02 00:00:00'),\n",
       " Timestamp('2020-11-09 00:00:00'),\n",
       " Timestamp('2020-11-23 00:00:00'),\n",
       " Timestamp('2020-12-07 00:00:00'),\n",
       " Timestamp('2020-12-14 00:00:00'),\n",
       " Timestamp('2020-12-21 00:00:00'),\n",
       " Timestamp('2020-12-28 00:00:00'),\n",
       " Timestamp('2021-01-04 00:00:00'),\n",
       " Timestamp('2021-01-11 00:00:00'),\n",
       " Timestamp('2021-01-18 00:00:00'),\n",
       " Timestamp('2021-01-25 00:00:00'),\n",
       " Timestamp('2021-02-01 00:00:00'),\n",
       " Timestamp('2021-02-08 00:00:00'),\n",
       " Timestamp('2021-02-15 00:00:00'),\n",
       " Timestamp('2021-02-22 00:00:00'),\n",
       " Timestamp('2021-03-01 00:00:00'),\n",
       " Timestamp('2021-03-08 00:00:00'),\n",
       " Timestamp('2021-03-15 00:00:00'),\n",
       " Timestamp('2021-03-22 00:00:00'),\n",
       " Timestamp('2021-04-05 00:00:00'),\n",
       " Timestamp('2021-04-12 00:00:00'),\n",
       " Timestamp('2021-04-19 00:00:00'),\n",
       " Timestamp('2021-04-26 00:00:00'),\n",
       " Timestamp('2021-05-03 00:00:00')]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfm = df[df.date.isin(date_array)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>1340.000000</td>\n",
       "      <td>1306.000000</td>\n",
       "      <td>4157065.0</td>\n",
       "      <td>1346.199951</td>\n",
       "      <td>1310.449951</td>\n",
       "      <td>1310.449951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>1330.050049</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>1431562.0</td>\n",
       "      <td>1341.199951</td>\n",
       "      <td>1335.900024</td>\n",
       "      <td>1335.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>2021-05-03</td>\n",
       "      <td>1325.500000</td>\n",
       "      <td>1319.150024</td>\n",
       "      <td>1653245.0</td>\n",
       "      <td>1351.300049</td>\n",
       "      <td>1342.500000</td>\n",
       "      <td>1342.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>491 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symbol       date         open          low     volume         high  \\\n",
       "3     LT.NS 2011-05-09   688.888916   680.222229  2071185.0   693.622192   \n",
       "8     LT.NS 2011-05-16   679.555542   671.644470  2306061.0   683.955566   \n",
       "13    LT.NS 2011-05-23   731.599976   715.599976  3812436.0   731.599976   \n",
       "18    LT.NS 2011-05-30   733.311096   721.555542  2061364.0   735.422241   \n",
       "23    LT.NS 2011-06-06   755.555542   755.155579  2770548.0   771.577759   \n",
       "...     ...        ...          ...          ...        ...          ...   \n",
       "2441  LT.NS 2021-04-05  1439.000000  1381.400024  2548649.0  1439.000000   \n",
       "2446  LT.NS 2021-04-12  1393.949951  1332.000000  3755497.0  1393.949951   \n",
       "2450  LT.NS 2021-04-19  1340.000000  1306.000000  4157065.0  1346.199951   \n",
       "2454  LT.NS 2021-04-26  1330.050049  1323.000000  1431562.0  1341.199951   \n",
       "2459  LT.NS 2021-05-03  1325.500000  1319.150024  1653245.0  1351.300049   \n",
       "\n",
       "            close     adjclose  dividends  splits  \n",
       "3      689.266663   587.521057        0.0     0.0  \n",
       "8      674.155579   574.640564        0.0     0.0  \n",
       "13     716.666687   610.876465        0.0     0.0  \n",
       "18     724.866638   617.865967        0.0     0.0  \n",
       "23     767.911133   654.556519        0.0     0.0  \n",
       "...           ...          ...        ...     ...  \n",
       "2441  1403.800049  1403.800049        0.0     0.0  \n",
       "2446  1345.150024  1345.150024        0.0     0.0  \n",
       "2450  1310.449951  1310.449951        0.0     0.0  \n",
       "2454  1335.900024  1335.900024        0.0     0.0  \n",
       "2459  1342.500000  1342.500000        0.0     0.0  \n",
       "\n",
       "[491 rows x 10 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dfm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-05-09 00:00:00')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmm  = dfm['date']\n",
    "dfmm[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = \"2019-1-1\"\n",
    "end_date = \"2021-1-31\"\n",
    "\n",
    "after_start_date = df[\"date\"] >= start_date\n",
    "before_end_date = df[\"date\"] <= end_date\n",
    "between_two_dates = after_start_date & before_end_date\n",
    "filtered_dates = df.loc[between_two_dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>dividends</th>\n",
       "      <th>splits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [symbol, date, open, low, volume, high, close, adjclose, dividends, splits]\n",
       "Index: []"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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