stock_data_test2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader.data as web\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies = ['AAPL', 'TSLA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "end = datetime.now()\n",
    "start = end - timedelta(days=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Attributes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">High</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-07</th>\n",
       "      <td>56.207317</td>\n",
       "      <td>47.543999</td>\n",
       "      <td>56.764999</td>\n",
       "      <td>47.543999</td>\n",
       "      <td>57.482498</td>\n",
       "      <td>47.712002</td>\n",
       "      <td>56.459999</td>\n",
       "      <td>45.709999</td>\n",
       "      <td>56.567501</td>\n",
       "      <td>45.959999</td>\n",
       "      <td>122306000.0</td>\n",
       "      <td>40321000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>55.548851</td>\n",
       "      <td>48.009998</td>\n",
       "      <td>56.099998</td>\n",
       "      <td>48.009998</td>\n",
       "      <td>57.014999</td>\n",
       "      <td>48.787998</td>\n",
       "      <td>56.082500</td>\n",
       "      <td>46.900002</td>\n",
       "      <td>56.455002</td>\n",
       "      <td>47.174000</td>\n",
       "      <td>111820000.0</td>\n",
       "      <td>43391000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>56.199894</td>\n",
       "      <td>48.905998</td>\n",
       "      <td>56.757500</td>\n",
       "      <td>48.905998</td>\n",
       "      <td>56.947498</td>\n",
       "      <td>49.459999</td>\n",
       "      <td>56.410000</td>\n",
       "      <td>48.130001</td>\n",
       "      <td>56.757500</td>\n",
       "      <td>48.264000</td>\n",
       "      <td>74770400.0</td>\n",
       "      <td>34472000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>56.957375</td>\n",
       "      <td>48.948002</td>\n",
       "      <td>57.522499</td>\n",
       "      <td>48.948002</td>\n",
       "      <td>57.610001</td>\n",
       "      <td>49.855999</td>\n",
       "      <td>56.825001</td>\n",
       "      <td>48.316002</td>\n",
       "      <td>56.982498</td>\n",
       "      <td>49.056000</td>\n",
       "      <td>113013600.0</td>\n",
       "      <td>31416500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>58.472343</td>\n",
       "      <td>49.577999</td>\n",
       "      <td>59.052502</td>\n",
       "      <td>49.577999</td>\n",
       "      <td>59.410000</td>\n",
       "      <td>50.216000</td>\n",
       "      <td>58.077499</td>\n",
       "      <td>49.362000</td>\n",
       "      <td>58.237499</td>\n",
       "      <td>49.430000</td>\n",
       "      <td>166795600.0</td>\n",
       "      <td>42377000.0</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>114.089996</td>\n",
       "      <td>419.070007</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>419.070007</td>\n",
       "      <td>115.309998</td>\n",
       "      <td>428.500000</td>\n",
       "      <td>113.570000</td>\n",
       "      <td>411.600006</td>\n",
       "      <td>114.550003</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>99382200.0</td>\n",
       "      <td>50219300.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>115.809998</td>\n",
       "      <td>429.010010</td>\n",
       "      <td>115.809998</td>\n",
       "      <td>429.010010</td>\n",
       "      <td>117.260002</td>\n",
       "      <td>433.929993</td>\n",
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       "      <td>420.470001</td>\n",
       "      <td>113.790001</td>\n",
       "      <td>421.320007</td>\n",
       "      <td>142675200.0</td>\n",
       "      <td>48145600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>116.790001</td>\n",
       "      <td>448.160004</td>\n",
       "      <td>116.790001</td>\n",
       "      <td>448.160004</td>\n",
       "      <td>117.720001</td>\n",
       "      <td>448.880005</td>\n",
       "      <td>115.830002</td>\n",
       "      <td>434.420013</td>\n",
       "      <td>117.639999</td>\n",
       "      <td>440.760010</td>\n",
       "      <td>116120400.0</td>\n",
       "      <td>50741500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>113.019997</td>\n",
       "      <td>415.089996</td>\n",
       "      <td>113.019997</td>\n",
       "      <td>415.089996</td>\n",
       "      <td>115.370003</td>\n",
       "      <td>439.130005</td>\n",
       "      <td>112.220001</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>112.889999</td>\n",
       "      <td>421.390015</td>\n",
       "      <td>144712000.0</td>\n",
       "      <td>71430000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>116.500000</td>\n",
       "      <td>425.679993</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>425.679993</td>\n",
       "      <td>116.650002</td>\n",
       "      <td>433.640015</td>\n",
       "      <td>113.550003</td>\n",
       "      <td>419.329987</td>\n",
       "      <td>113.910004</td>\n",
       "      <td>423.350006</td>\n",
       "      <td>105720800.0</td>\n",
       "      <td>44527400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Attributes   Adj Close                   Close                    High  \\\n",
       "Symbols           AAPL        TSLA        AAPL        TSLA        AAPL   \n",
       "Date                                                                     \n",
       "2019-10-07   56.207317   47.543999   56.764999   47.543999   57.482498   \n",
       "2019-10-08   55.548851   48.009998   56.099998   48.009998   57.014999   \n",
       "2019-10-09   56.199894   48.905998   56.757500   48.905998   56.947498   \n",
       "2019-10-10   56.957375   48.948002   57.522499   48.948002   57.610001   \n",
       "2019-10-11   58.472343   49.577999   59.052502   49.577999   59.410000   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-09-29  114.089996  419.070007  114.089996  419.070007  115.309998   \n",
       "2020-09-30  115.809998  429.010010  115.809998  429.010010  117.260002   \n",
       "2020-10-01  116.790001  448.160004  116.790001  448.160004  117.720001   \n",
       "2020-10-02  113.019997  415.089996  113.019997  415.089996  115.370003   \n",
       "2020-10-05  116.500000  425.679993  116.500000  425.679993  116.650002   \n",
       "\n",
       "Attributes                     Low                    Open              \\\n",
       "Symbols           TSLA        AAPL        TSLA        AAPL        TSLA   \n",
       "Date                                                                     \n",
       "2019-10-07   47.712002   56.459999   45.709999   56.567501   45.959999   \n",
       "2019-10-08   48.787998   56.082500   46.900002   56.455002   47.174000   \n",
       "2019-10-09   49.459999   56.410000   48.130001   56.757500   48.264000   \n",
       "2019-10-10   49.855999   56.825001   48.316002   56.982498   49.056000   \n",
       "2019-10-11   50.216000   58.077499   49.362000   58.237499   49.430000   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-09-29  428.500000  113.570000  411.600006  114.550003  416.000000   \n",
       "2020-09-30  433.929993  113.620003  420.470001  113.790001  421.320007   \n",
       "2020-10-01  448.880005  115.830002  434.420013  117.639999  440.760010   \n",
       "2020-10-02  439.130005  112.220001  415.000000  112.889999  421.390015   \n",
       "2020-10-05  433.640015  113.550003  419.329987  113.910004  423.350006   \n",
       "\n",
       "Attributes       Volume              \n",
       "Symbols            AAPL        TSLA  \n",
       "Date                                 \n",
       "2019-10-07  122306000.0  40321000.0  \n",
       "2019-10-08  111820000.0  43391000.0  \n",
       "2019-10-09   74770400.0  34472000.0  \n",
       "2019-10-10  113013600.0  31416500.0  \n",
       "2019-10-11  166795600.0  42377000.0  \n",
       "...                 ...         ...  \n",
       "2020-09-29   99382200.0  50219300.0  \n",
       "2020-09-30  142675200.0  48145600.0  \n",
       "2020-10-01  116120400.0  50741500.0  \n",
       "2020-10-02  144712000.0  71430000.0  \n",
       "2020-10-05  105720800.0  44527400.0  \n",
       "\n",
       "[252 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web.get_data_yahoo(companies, start=start, end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlalchemy.create_engine('sqlite:///stock.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engine(sqlite:///stock.db)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>company_name</th>\n",
       "      <th>now_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>55600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SK하이닉스</td>\n",
       "      <td>78700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NAVER</td>\n",
       "      <td>328500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LG화학</td>\n",
       "      <td>743000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>삼성바이오로직스</td>\n",
       "      <td>773000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>셀트리온</td>\n",
       "      <td>294000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>삼성전자우</td>\n",
       "      <td>47500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>현대차</td>\n",
       "      <td>172500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>카카오</td>\n",
       "      <td>402000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>삼성SDI</td>\n",
       "      <td>439000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>LG생활건강</td>\n",
       "      <td>1453000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>현대모비스</td>\n",
       "      <td>230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>삼성물산</td>\n",
       "      <td>107500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>237500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>엔씨소프트</td>\n",
       "      <td>813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>기아차</td>\n",
       "      <td>42150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>넷마블</td>\n",
       "      <td>197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>POSCO</td>\n",
       "      <td>186000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>KB금융</td>\n",
       "      <td>36950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>SK</td>\n",
       "      <td>212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>SK바이오팜</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>154000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>신한지주</td>\n",
       "      <td>29650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>LG</td>\n",
       "      <td>80100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>LG전자</td>\n",
       "      <td>84000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>한국전력</td>\n",
       "      <td>20700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>삼성생명</td>\n",
       "      <td>62200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>삼성에스디에스</td>\n",
       "      <td>157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>KT&amp;G</td>\n",
       "      <td>82700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>삼성전기</td>\n",
       "      <td>135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>아모레퍼시픽</td>\n",
       "      <td>169500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>삼성화재</td>\n",
       "      <td>182000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>하나금융지주</td>\n",
       "      <td>27950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>한화솔루션</td>\n",
       "      <td>48500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>신풍제약</td>\n",
       "      <td>146000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>고려아연</td>\n",
       "      <td>398000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>한온시스템</td>\n",
       "      <td>12650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>S-Oil</td>\n",
       "      <td>56900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>롯데케미칼</td>\n",
       "      <td>183000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>KT</td>\n",
       "      <td>23500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>우리금융지주</td>\n",
       "      <td>8460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>미래에셋대우</td>\n",
       "      <td>9450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>한국조선해양</td>\n",
       "      <td>86100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>기업은행</td>\n",
       "      <td>8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>CJ제일제당</td>\n",
       "      <td>396500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>코웨이</td>\n",
       "      <td>79400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>포스코케미칼</td>\n",
       "      <td>89900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>오리온</td>\n",
       "      <td>135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>LG디스플레이</td>\n",
       "      <td>14850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>KODEX 200</td>\n",
       "      <td>31175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id company_name  now_price\n",
       "0    1         삼성전자      55600\n",
       "1    2       SK하이닉스      78700\n",
       "2    3        NAVER     328500\n",
       "3    4         LG화학     743000\n",
       "4    5     삼성바이오로직스     773000\n",
       "5    6         셀트리온     294000\n",
       "6    7        삼성전자우      47500\n",
       "7    8          현대차     172500\n",
       "8    9          카카오     402000\n",
       "9   10        삼성SDI     439000\n",
       "10  11       LG생활건강    1453000\n",
       "11  12        현대모비스     230000\n",
       "12  13         삼성물산     107500\n",
       "13  14        SK텔레콤     237500\n",
       "14  15        엔씨소프트     813000\n",
       "15  16          기아차      42150\n",
       "16  17          넷마블     197500\n",
       "17  18        POSCO     186000\n",
       "18  19         KB금융      36950\n",
       "19  20           SK     212000\n",
       "20  21       SK바이오팜     190000\n",
       "21  22      SK이노베이션     154000\n",
       "22  23         신한지주      29650\n",
       "23  24           LG      80100\n",
       "24  25         LG전자      84000\n",
       "25  26         한국전력      20700\n",
       "26  27         삼성생명      62200\n",
       "27  28      삼성에스디에스     157000\n",
       "28  29         KT&G      82700\n",
       "29  30         삼성전기     135000\n",
       "30  31       아모레퍼시픽     169500\n",
       "31  32         삼성화재     182000\n",
       "32  33       하나금융지주      27950\n",
       "33  34        한화솔루션      48500\n",
       "34  35         신풍제약     146000\n",
       "35  36         고려아연     398000\n",
       "36  37        한온시스템      12650\n",
       "37  38        S-Oil      56900\n",
       "38  39        롯데케미칼     183000\n",
       "39  40           KT      23500\n",
       "40  41       우리금융지주       8460\n",
       "41  42       미래에셋대우       9450\n",
       "42  43       한국조선해양      86100\n",
       "43  44         기업은행       8200\n",
       "44  45       CJ제일제당     396500\n",
       "45  46          코웨이      79400\n",
       "46  47       포스코케미칼      89900\n",
       "47  48          오리온     135000\n",
       "48  49      LG디스플레이      14850\n",
       "49  50    KODEX 200      31175"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_table('stock', con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.result.ResultProxy at 0x18aa89bb790>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.execute('''DROP TABLE stock_hist''')\n",
    "con.execute('''\n",
    "CREATE TABLE stock_hist(\n",
    "date TIMESTAMP,\n",
    "company VARCHAR(100),\n",
    "high INTEGER,\n",
    "low INTEGER,\n",
    "open INTEGER,\n",
    "close INTEGER,\n",
    "volume INTEGER,\n",
    "adjclose INTEGER,\n",
    "PRIMARY KEY (date, company)\n",
    ") \n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "end = datetime.now()\n",
    "start = end - timedelta(days=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies = ['AAPL', 'TSLA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Attributes</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">High</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>TSLA</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>114.089996</td>\n",
       "      <td>419.070007</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>419.070007</td>\n",
       "      <td>115.309998</td>\n",
       "      <td>428.500000</td>\n",
       "      <td>113.570000</td>\n",
       "      <td>411.600006</td>\n",
       "      <td>114.550003</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>99382200</td>\n",
       "      <td>50219300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>115.809998</td>\n",
       "      <td>429.010010</td>\n",
       "      <td>115.809998</td>\n",
       "      <td>429.010010</td>\n",
       "      <td>117.260002</td>\n",
       "      <td>433.929993</td>\n",
       "      <td>113.620003</td>\n",
       "      <td>420.470001</td>\n",
       "      <td>113.790001</td>\n",
       "      <td>421.320007</td>\n",
       "      <td>142675200</td>\n",
       "      <td>48145600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>116.790001</td>\n",
       "      <td>448.160004</td>\n",
       "      <td>116.790001</td>\n",
       "      <td>448.160004</td>\n",
       "      <td>117.720001</td>\n",
       "      <td>448.880005</td>\n",
       "      <td>115.830002</td>\n",
       "      <td>434.420013</td>\n",
       "      <td>117.639999</td>\n",
       "      <td>440.760010</td>\n",
       "      <td>116120400</td>\n",
       "      <td>50741500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>113.019997</td>\n",
       "      <td>415.089996</td>\n",
       "      <td>113.019997</td>\n",
       "      <td>415.089996</td>\n",
       "      <td>115.370003</td>\n",
       "      <td>439.130005</td>\n",
       "      <td>112.220001</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>112.889999</td>\n",
       "      <td>421.390015</td>\n",
       "      <td>144712000</td>\n",
       "      <td>71430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>116.500000</td>\n",
       "      <td>425.679993</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>425.679993</td>\n",
       "      <td>116.650002</td>\n",
       "      <td>433.640015</td>\n",
       "      <td>113.550003</td>\n",
       "      <td>419.329987</td>\n",
       "      <td>113.910004</td>\n",
       "      <td>423.350006</td>\n",
       "      <td>105720800</td>\n",
       "      <td>44527400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Attributes   Adj Close                   Close                    High  \\\n",
       "Symbols           AAPL        TSLA        AAPL        TSLA        AAPL   \n",
       "Date                                                                     \n",
       "2020-09-29  114.089996  419.070007  114.089996  419.070007  115.309998   \n",
       "2020-09-30  115.809998  429.010010  115.809998  429.010010  117.260002   \n",
       "2020-10-01  116.790001  448.160004  116.790001  448.160004  117.720001   \n",
       "2020-10-02  113.019997  415.089996  113.019997  415.089996  115.370003   \n",
       "2020-10-05  116.500000  425.679993  116.500000  425.679993  116.650002   \n",
       "\n",
       "Attributes                     Low                    Open              \\\n",
       "Symbols           TSLA        AAPL        TSLA        AAPL        TSLA   \n",
       "Date                                                                     \n",
       "2020-09-29  428.500000  113.570000  411.600006  114.550003  416.000000   \n",
       "2020-09-30  433.929993  113.620003  420.470001  113.790001  421.320007   \n",
       "2020-10-01  448.880005  115.830002  434.420013  117.639999  440.760010   \n",
       "2020-10-02  439.130005  112.220001  415.000000  112.889999  421.390015   \n",
       "2020-10-05  433.640015  113.550003  419.329987  113.910004  423.350006   \n",
       "\n",
       "Attributes     Volume            \n",
       "Symbols          AAPL      TSLA  \n",
       "Date                             \n",
       "2020-09-29   99382200  50219300  \n",
       "2020-09-30  142675200  48145600  \n",
       "2020-10-01  116120400  50741500  \n",
       "2020-10-02  144712000  71430000  \n",
       "2020-10-05  105720800  44527400  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = web.get_data_yahoo(companies, start=start, end=end)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attributes  Symbols\n",
       "Adj Close   AAPL       1.140900e+02\n",
       "            TSLA       4.190700e+02\n",
       "Close       AAPL       1.140900e+02\n",
       "            TSLA       4.190700e+02\n",
       "High        AAPL       1.153100e+02\n",
       "            TSLA       4.285000e+02\n",
       "Low         AAPL       1.135700e+02\n",
       "            TSLA       4.116000e+02\n",
       "Open        AAPL       1.145500e+02\n",
       "            TSLA       4.160000e+02\n",
       "Volume      AAPL       9.938220e+07\n",
       "            TSLA       5.021930e+07\n",
       "Name: 2020-09-29 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = df.iloc[0]\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 9, 29, 0, 0)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row['Open']['TSLA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row.Open.TSLA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symbols\n",
       "AAPL    114.089996\n",
       "TSLA    419.070007\n",
       "Name: 2020-09-29 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies = ['AAPL', 'TSLA', 'MSFT']\n",
    "end = datetime.now()\n",
    "start = end - timedelta(days=365)\n",
    "df = web.get_data_yahoo(companies, start=start, end=end)\n",
    "\n",
    "for date, item in df.iterrows():\n",
    "    for company in companies:\n",
    "        high = item.High[company]\n",
    "        low = item.Low[company]\n",
    "        open_val = item.Open[company]\n",
    "        close = item.Close[company]\n",
    "        volume = item.Volume[company]\n",
    "        adjclose = item['Adj Close'][company]\n",
    "\n",
    "        try:\n",
    "            con.execute('''\n",
    "                INSERT INTO stock_hist(date, company, high, low, open, close, volume, adjclose)\n",
    "                values(:date, :company, :high, :low, :open, :close, :volume, :adjclose)\n",
    "            ''', dict(\n",
    "                date=date.to_pydatetime(),\n",
    "                company=company,\n",
    "                high=high,\n",
    "                low=low,\n",
    "                open=open_val,\n",
    "                close=close,\n",
    "                volume=volume,\n",
    "                adjclose=adjclose\n",
    "            ))\n",
    "        except sqlalchemy.exc.IntegrityError as e:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <th>low</th>\n",
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       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adjclose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>115</td>\n",
       "      <td>113</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>99382200</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>428</td>\n",
       "      <td>411</td>\n",
       "      <td>416</td>\n",
       "      <td>419</td>\n",
       "      <td>50219300</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>117</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>115</td>\n",
       "      <td>142675200</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>433</td>\n",
       "      <td>420</td>\n",
       "      <td>421</td>\n",
       "      <td>429</td>\n",
       "      <td>48145600</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>117</td>\n",
       "      <td>115</td>\n",
       "      <td>117</td>\n",
       "      <td>116</td>\n",
       "      <td>116120400</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>431</td>\n",
       "      <td>402</td>\n",
       "      <td>407</td>\n",
       "      <td>407</td>\n",
       "      <td>87596100</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>229</td>\n",
       "      <td>214</td>\n",
       "      <td>229</td>\n",
       "      <td>217</td>\n",
       "      <td>58400300</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>123</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>332607200</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>428</td>\n",
       "      <td>372</td>\n",
       "      <td>402</td>\n",
       "      <td>418</td>\n",
       "      <td>110321900</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>218</td>\n",
       "      <td>205</td>\n",
       "      <td>215</td>\n",
       "      <td>214</td>\n",
       "      <td>59664100</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date company  high  low  open  close     volume  adjclose\n",
       "0   2020-09-29    AAPL   115  113   114    114   99382200       114\n",
       "1   2020-09-29    TSLA   428  411   416    419   50219300       419\n",
       "2   2020-09-30    AAPL   117  113   113    115  142675200       115\n",
       "3   2020-09-30    TSLA   433  420   421    429   48145600       429\n",
       "4   2020-10-01    AAPL   117  115   117    116  116120400       116\n",
       "..         ...     ...   ...  ...   ...    ...        ...       ...\n",
       "751 2020-09-03    TSLA   431  402   407    407   87596100       407\n",
       "752 2020-09-03    MSFT   229  214   229    217   58400300       217\n",
       "753 2020-09-04    AAPL   123  110   120    120  332607200       120\n",
       "754 2020-09-04    TSLA   428  372   402    418  110321900       418\n",
       "755 2020-09-04    MSFT   218  205   215    214   59664100       214\n",
       "\n",
       "[756 rows x 8 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql_table('stock_hist', con)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('2019-10-07 00:00:00',)]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.execute('''\n",
    "SELECT min(date) FROM stock_hist\n",
    "''').fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('AAPL', 252), ('MSFT', 252), ('TSLA', 252)]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.execute('''\n",
    "SELECT company, count(*) FROM stock_hist\n",
    "GROUP BY company\n",
    "''').fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>AAPL</th>\n",
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       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>117</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
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       "      <td>142675200</td>\n",
       "      <td>115</td>\n",
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       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>433</td>\n",
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       "      <td>421</td>\n",
       "      <td>429</td>\n",
       "      <td>48145600</td>\n",
       "      <td>429</td>\n",
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       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>117</td>\n",
       "      <td>115</td>\n",
       "      <td>117</td>\n",
       "      <td>116</td>\n",
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       "      <th>TSLA</th>\n",
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       "      <td>87596100</td>\n",
       "      <td>407</td>\n",
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       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>229</td>\n",
       "      <td>214</td>\n",
       "      <td>229</td>\n",
       "      <td>217</td>\n",
       "      <td>58400300</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>123</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>332607200</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>428</td>\n",
       "      <td>372</td>\n",
       "      <td>402</td>\n",
       "      <td>418</td>\n",
       "      <td>110321900</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>218</td>\n",
       "      <td>205</td>\n",
       "      <td>215</td>\n",
       "      <td>214</td>\n",
       "      <td>59664100</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  high  low  open  close     volume  adjclose\n",
       "company                                                        \n",
       "AAPL    2020-09-29   115  113   114    114   99382200       114\n",
       "TSLA    2020-09-29   428  411   416    419   50219300       419\n",
       "AAPL    2020-09-30   117  113   113    115  142675200       115\n",
       "TSLA    2020-09-30   433  420   421    429   48145600       429\n",
       "AAPL    2020-10-01   117  115   117    116  116120400       116\n",
       "...            ...   ...  ...   ...    ...        ...       ...\n",
       "TSLA    2020-09-03   431  402   407    407   87596100       407\n",
       "MSFT    2020-09-03   229  214   229    217   58400300       217\n",
       "AAPL    2020-09-04   123  110   120    120  332607200       120\n",
       "TSLA    2020-09-04   428  372   402    418  110321900       418\n",
       "MSFT    2020-09-04   218  205   215    214   59664100       214\n",
       "\n",
       "[756 rows x 7 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index('company', 'date')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
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       "      <th>AAPL</th>\n",
       "      <td>2020-09-29</td>\n",
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       "      <td>114</td>\n",
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       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>117</td>\n",
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       "      <td>142675200</td>\n",
       "      <td>115</td>\n",
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       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>117</td>\n",
       "      <td>115</td>\n",
       "      <td>117</td>\n",
       "      <td>116</td>\n",
       "      <td>116120400</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>115</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>113</td>\n",
       "      <td>144712000</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>116</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>116</td>\n",
       "      <td>105720800</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>131</td>\n",
       "      <td>126</td>\n",
       "      <td>127</td>\n",
       "      <td>129</td>\n",
       "      <td>225702700</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>134</td>\n",
       "      <td>130</td>\n",
       "      <td>132</td>\n",
       "      <td>134</td>\n",
       "      <td>152470100</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>137</td>\n",
       "      <td>127</td>\n",
       "      <td>137</td>\n",
       "      <td>131</td>\n",
       "      <td>200119000</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>128</td>\n",
       "      <td>120</td>\n",
       "      <td>126</td>\n",
       "      <td>120</td>\n",
       "      <td>257599600</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>123</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>332607200</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  high  low  open  close     volume  adjclose\n",
       "company                                                        \n",
       "AAPL    2020-09-29   115  113   114    114   99382200       114\n",
       "AAPL    2020-09-30   117  113   113    115  142675200       115\n",
       "AAPL    2020-10-01   117  115   117    116  116120400       116\n",
       "AAPL    2020-10-02   115  112   112    113  144712000       113\n",
       "AAPL    2020-10-05   116  113   113    116  105720800       116\n",
       "...            ...   ...  ...   ...    ...        ...       ...\n",
       "AAPL    2020-08-31   131  126   127    129  225702700       129\n",
       "AAPL    2020-09-01   134  130   132    134  152470100       134\n",
       "AAPL    2020-09-02   137  127   137    131  200119000       131\n",
       "AAPL    2020-09-03   128  120   126    120  257599600       120\n",
       "AAPL    2020-09-04   123  110   120    120  332607200       120\n",
       "\n",
       "[252 rows x 7 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.loc['AAPL']\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-10-07 00:00:00')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.date.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.36507936507937"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.open.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.0"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.open.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adjclose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2019-10-07 00:00:00.000000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.247000e+07</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020-10-05 00:00:00.000000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>4.268848e+08</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2020-04-05 19:25:42.857142784</td>\n",
       "      <td>82.519841</td>\n",
       "      <td>80.329365</td>\n",
       "      <td>81.365079</td>\n",
       "      <td>81.511905</td>\n",
       "      <td>1.536883e+08</td>\n",
       "      <td>81.234127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaT</td>\n",
       "      <td>19.440675</td>\n",
       "      <td>18.574890</td>\n",
       "      <td>19.199354</td>\n",
       "      <td>19.025561</td>\n",
       "      <td>7.191178e+07</td>\n",
       "      <td>19.157471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>NaT</td>\n",
       "      <td>377.939844</td>\n",
       "      <td>345.026545</td>\n",
       "      <td>368.615190</td>\n",
       "      <td>361.971969</td>\n",
       "      <td>5.171304e+15</td>\n",
       "      <td>367.008711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>NaT</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.360484e+08</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                date        high         low        open  \\\n",
       "min    2019-10-07 00:00:00.000000000   56.000000   53.000000   56.000000   \n",
       "max    2020-10-05 00:00:00.000000000  137.000000  130.000000  137.000000   \n",
       "mean   2020-04-05 19:25:42.857142784   82.519841   80.329365   81.365079   \n",
       "std                              NaT   19.440675   18.574890   19.199354   \n",
       "var                              NaT  377.939844  345.026545  368.615190   \n",
       "median                           NaT   78.000000   76.500000   77.000000   \n",
       "\n",
       "             close        volume    adjclose  \n",
       "min      56.000000  3.247000e+07   55.000000  \n",
       "max     134.000000  4.268848e+08  134.000000  \n",
       "mean     81.511905  1.536883e+08   81.234127  \n",
       "std      19.025561  7.191178e+07   19.157471  \n",
       "var     361.971969  5.171304e+15  367.008711  \n",
       "median   77.000000  1.360484e+08   77.000000  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.agg(func=['min', 'max', 'std', 'var', 'mean', 'median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tradingview-taNote: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\HOJIN\\Conda\\envs\\lab\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Downloading tradingview_ta-3.1.1-py3-none-any.whl (8.6 kB)\n",
      "Requirement already satisfied: requests in c:\\users\\hojin\\conda\\envs\\lab\\lib\\site-packages (from tradingview-ta) (2.24.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\hojin\\conda\\envs\\lab\\lib\\site-packages (from requests->tradingview-ta) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\hojin\\conda\\envs\\lab\\lib\\site-packages (from requests->tradingview-ta) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\hojin\\conda\\envs\\lab\\lib\\site-packages (from requests->tradingview-ta) (2020.6.20)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\hojin\\conda\\envs\\lab\\lib\\site-packages (from requests->tradingview-ta) (1.25.9)\n",
      "Installing collected packages: tradingview-ta\n",
      "Successfully installed tradingview-ta-3.1.1\n"
     ]
    }
   ],
   "source": [
    "pip install tradingview-ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: This version of tradingview_ta is compatible with v3.0.0. You can still run your old code with this version, but consider rewriting it. Please refer to the migration guide (https://python-tradingview-ta.readthedocs.io/en/latest/migration.html).\n"
     ]
    }
   ],
   "source": [
    "from tradingview_ta import TA_Handler, Interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlalchemy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "con = sqlalchemy.create_engine('sqlite:///stock.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engine(sqlite:///stock.db)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>company_name</th>\n",
       "      <th>now_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>55600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SK하이닉스</td>\n",
       "      <td>78700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NAVER</td>\n",
       "      <td>328500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LG화학</td>\n",
       "      <td>743000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>삼성바이오로직스</td>\n",
       "      <td>773000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>셀트리온</td>\n",
       "      <td>294000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>삼성전자우</td>\n",
       "      <td>47500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>현대차</td>\n",
       "      <td>172500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>카카오</td>\n",
       "      <td>402000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>삼성SDI</td>\n",
       "      <td>439000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>LG생활건강</td>\n",
       "      <td>1453000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>현대모비스</td>\n",
       "      <td>230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>삼성물산</td>\n",
       "      <td>107500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>237500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>엔씨소프트</td>\n",
       "      <td>813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>기아차</td>\n",
       "      <td>42150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>넷마블</td>\n",
       "      <td>197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>POSCO</td>\n",
       "      <td>186000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>KB금융</td>\n",
       "      <td>36950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>SK</td>\n",
       "      <td>212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>SK바이오팜</td>\n",
       "      <td>190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>154000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>신한지주</td>\n",
       "      <td>29650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>LG</td>\n",
       "      <td>80100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>LG전자</td>\n",
       "      <td>84000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>한국전력</td>\n",
       "      <td>20700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>삼성생명</td>\n",
       "      <td>62200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>삼성에스디에스</td>\n",
       "      <td>157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>KT&amp;G</td>\n",
       "      <td>82700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>삼성전기</td>\n",
       "      <td>135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>아모레퍼시픽</td>\n",
       "      <td>169500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>삼성화재</td>\n",
       "      <td>182000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>하나금융지주</td>\n",
       "      <td>27950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>한화솔루션</td>\n",
       "      <td>48500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>신풍제약</td>\n",
       "      <td>146000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>고려아연</td>\n",
       "      <td>398000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>한온시스템</td>\n",
       "      <td>12650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>S-Oil</td>\n",
       "      <td>56900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>롯데케미칼</td>\n",
       "      <td>183000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>KT</td>\n",
       "      <td>23500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>우리금융지주</td>\n",
       "      <td>8460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>미래에셋대우</td>\n",
       "      <td>9450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>한국조선해양</td>\n",
       "      <td>86100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>기업은행</td>\n",
       "      <td>8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>CJ제일제당</td>\n",
       "      <td>396500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>코웨이</td>\n",
       "      <td>79400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>포스코케미칼</td>\n",
       "      <td>89900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>오리온</td>\n",
       "      <td>135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>LG디스플레이</td>\n",
       "      <td>14850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>KODEX 200</td>\n",
       "      <td>31175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id company_name  now_price\n",
       "0    1         삼성전자      55600\n",
       "1    2       SK하이닉스      78700\n",
       "2    3        NAVER     328500\n",
       "3    4         LG화학     743000\n",
       "4    5     삼성바이오로직스     773000\n",
       "5    6         셀트리온     294000\n",
       "6    7        삼성전자우      47500\n",
       "7    8          현대차     172500\n",
       "8    9          카카오     402000\n",
       "9   10        삼성SDI     439000\n",
       "10  11       LG생활건강    1453000\n",
       "11  12        현대모비스     230000\n",
       "12  13         삼성물산     107500\n",
       "13  14        SK텔레콤     237500\n",
       "14  15        엔씨소프트     813000\n",
       "15  16          기아차      42150\n",
       "16  17          넷마블     197500\n",
       "17  18        POSCO     186000\n",
       "18  19         KB금융      36950\n",
       "19  20           SK     212000\n",
       "20  21       SK바이오팜     190000\n",
       "21  22      SK이노베이션     154000\n",
       "22  23         신한지주      29650\n",
       "23  24           LG      80100\n",
       "24  25         LG전자      84000\n",
       "25  26         한국전력      20700\n",
       "26  27         삼성생명      62200\n",
       "27  28      삼성에스디에스     157000\n",
       "28  29         KT&G      82700\n",
       "29  30         삼성전기     135000\n",
       "30  31       아모레퍼시픽     169500\n",
       "31  32         삼성화재     182000\n",
       "32  33       하나금융지주      27950\n",
       "33  34        한화솔루션      48500\n",
       "34  35         신풍제약     146000\n",
       "35  36         고려아연     398000\n",
       "36  37        한온시스템      12650\n",
       "37  38        S-Oil      56900\n",
       "38  39        롯데케미칼     183000\n",
       "39  40           KT      23500\n",
       "40  41       우리금융지주       8460\n",
       "41  42       미래에셋대우       9450\n",
       "42  43       한국조선해양      86100\n",
       "43  44         기업은행       8200\n",
       "44  45       CJ제일제당     396500\n",
       "45  46          코웨이      79400\n",
       "46  47       포스코케미칼      89900\n",
       "47  48          오리온     135000\n",
       "48  49      LG디스플레이      14850\n",
       "49  50    KODEX 200      31175"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_table('stock', con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "con.execute(\n",
    ")"
   ]
  }
 ],
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