Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "efa25343",
   "metadata": {},
   "source": [
    "# วิธีดูข้อมูลตลาดโดยรวม\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b883976",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader.data as pdr\n",
    "import datetime as dt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "58a95c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>Saint Paul, Minnesota</td>\n",
       "      <td>1976-08-09</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AOS</td>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Building Products</td>\n",
       "      <td>Milwaukee, Wisconsin</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>91142</td>\n",
       "      <td>1916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABMD</td>\n",
       "      <td>Abiomed</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Danvers, Massachusetts</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>815094</td>\n",
       "      <td>1981</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>YUM</td>\n",
       "      <td>Yum! Brands</td>\n",
       "      <td>reports</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>Louisville, Kentucky</td>\n",
       "      <td>1997-10-06</td>\n",
       "      <td>1041061</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ZBRA</td>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Electronic Equipment &amp; Instruments</td>\n",
       "      <td>Lincolnshire, Illinois</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>877212</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>ZBH</td>\n",
       "      <td>Zimmer Biomet</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Warsaw, Indiana</td>\n",
       "      <td>2001-08-07</td>\n",
       "      <td>1136869</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ZION</td>\n",
       "      <td>Zions Bancorporation</td>\n",
       "      <td>reports</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Regional Banks</td>\n",
       "      <td>Salt Lake City, Utah</td>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>109380</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>ZTS</td>\n",
       "      <td>Zoetis</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>Parsippany, New Jersey</td>\n",
       "      <td>2013-06-21</td>\n",
       "      <td>1555280</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol              Security SEC filings             GICS Sector  \\\n",
       "0      MMM                    3M     reports             Industrials   \n",
       "1      AOS           A. O. Smith     reports             Industrials   \n",
       "2      ABT                Abbott     reports             Health Care   \n",
       "3     ABBV                AbbVie     reports             Health Care   \n",
       "4     ABMD               Abiomed     reports             Health Care   \n",
       "..     ...                   ...         ...                     ...   \n",
       "498    YUM           Yum! Brands     reports  Consumer Discretionary   \n",
       "499   ZBRA    Zebra Technologies     reports  Information Technology   \n",
       "500    ZBH         Zimmer Biomet     reports             Health Care   \n",
       "501   ZION  Zions Bancorporation     reports              Financials   \n",
       "502    ZTS                Zoetis     reports             Health Care   \n",
       "\n",
       "                      GICS Sub-Industry    Headquarters Location  \\\n",
       "0              Industrial Conglomerates    Saint Paul, Minnesota   \n",
       "1                     Building Products     Milwaukee, Wisconsin   \n",
       "2                 Health Care Equipment  North Chicago, Illinois   \n",
       "3                       Pharmaceuticals  North Chicago, Illinois   \n",
       "4                 Health Care Equipment   Danvers, Massachusetts   \n",
       "..                                  ...                      ...   \n",
       "498                         Restaurants     Louisville, Kentucky   \n",
       "499  Electronic Equipment & Instruments   Lincolnshire, Illinois   \n",
       "500               Health Care Equipment          Warsaw, Indiana   \n",
       "501                      Regional Banks     Salt Lake City, Utah   \n",
       "502                     Pharmaceuticals   Parsippany, New Jersey   \n",
       "\n",
       "    Date first added      CIK      Founded  \n",
       "0         1976-08-09    66740         1902  \n",
       "1         2017-07-26    91142         1916  \n",
       "2         1964-03-31     1800         1888  \n",
       "3         2012-12-31  1551152  2013 (1888)  \n",
       "4         2018-05-31   815094         1981  \n",
       "..               ...      ...          ...  \n",
       "498       1997-10-06  1041061         1997  \n",
       "499       2019-12-23   877212         1969  \n",
       "500       2001-08-07  1136869         1927  \n",
       "501       2001-06-22   109380         1873  \n",
       "502       2013-06-21  1555280         1952  \n",
       "\n",
       "[503 rows x 9 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
    "df = table[0]\n",
    "df.to_csv('S&P500-Info.csv')\n",
    "df.to_csv(\"S&P500-Symbols.csv\", columns=['Symbol'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63f98b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       MMM\n",
       "1       AOS\n",
       "2       ABT\n",
       "3      ABBV\n",
       "4      ABMD\n",
       "       ... \n",
       "498     YUM\n",
       "499    ZBRA\n",
       "500     ZBH\n",
       "501    ZION\n",
       "502     ZTS\n",
       "Name: Symbol, Length: 503, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_S=df[\"Symbol\"]\n",
    "all_S"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ec8e12",
   "metadata": {},
   "source": [
    "# วิธี import data เเบบที่ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7010ff7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1 usa stock\n",
    "import pandas_datareader.data as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "55f142de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas_datareader in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: lxml in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas_datareader) (4.8.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas_datareader) (1.4.2)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas_datareader) (2.27.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas>=0.23->pandas_datareader) (1.21.5)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (1.26.9)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pandas_datareader) (2021.10.8)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas_datareader) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas_datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "92e00e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "71da9634",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-03</th>\n",
       "      <td>36.325001</td>\n",
       "      <td>35.775002</td>\n",
       "      <td>36.220001</td>\n",
       "      <td>35.875000</td>\n",
       "      <td>57111200.0</td>\n",
       "      <td>33.961647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-05</th>\n",
       "      <td>36.197498</td>\n",
       "      <td>35.680000</td>\n",
       "      <td>35.922501</td>\n",
       "      <td>36.022499</td>\n",
       "      <td>86278400.0</td>\n",
       "      <td>34.101284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>35.875000</td>\n",
       "      <td>35.602501</td>\n",
       "      <td>35.755001</td>\n",
       "      <td>35.682499</td>\n",
       "      <td>96515200.0</td>\n",
       "      <td>33.779408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>36.187500</td>\n",
       "      <td>35.724998</td>\n",
       "      <td>35.724998</td>\n",
       "      <td>36.044998</td>\n",
       "      <td>76806800.0</td>\n",
       "      <td>34.122574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>36.487499</td>\n",
       "      <td>35.842499</td>\n",
       "      <td>36.027500</td>\n",
       "      <td>36.264999</td>\n",
       "      <td>84362400.0</td>\n",
       "      <td>34.330841</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-27</th>\n",
       "      <td>143.490005</td>\n",
       "      <td>140.970001</td>\n",
       "      <td>142.699997</td>\n",
       "      <td>141.660004</td>\n",
       "      <td>70207900.0</td>\n",
       "      <td>141.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-28</th>\n",
       "      <td>143.419998</td>\n",
       "      <td>137.320007</td>\n",
       "      <td>142.130005</td>\n",
       "      <td>137.440002</td>\n",
       "      <td>67083400.0</td>\n",
       "      <td>137.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-29</th>\n",
       "      <td>140.669998</td>\n",
       "      <td>136.669998</td>\n",
       "      <td>137.460007</td>\n",
       "      <td>139.229996</td>\n",
       "      <td>66242400.0</td>\n",
       "      <td>139.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>138.369995</td>\n",
       "      <td>133.770004</td>\n",
       "      <td>137.250000</td>\n",
       "      <td>136.720001</td>\n",
       "      <td>98964500.0</td>\n",
       "      <td>136.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>139.039993</td>\n",
       "      <td>135.660004</td>\n",
       "      <td>136.039993</td>\n",
       "      <td>138.929993</td>\n",
       "      <td>71007500.0</td>\n",
       "      <td>138.929993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2017-07-03   36.325001   35.775002   36.220001   35.875000  57111200.0   \n",
       "2017-07-05   36.197498   35.680000   35.922501   36.022499  86278400.0   \n",
       "2017-07-06   35.875000   35.602501   35.755001   35.682499  96515200.0   \n",
       "2017-07-07   36.187500   35.724998   35.724998   36.044998  76806800.0   \n",
       "2017-07-10   36.487499   35.842499   36.027500   36.264999  84362400.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-06-27  143.490005  140.970001  142.699997  141.660004  70207900.0   \n",
       "2022-06-28  143.419998  137.320007  142.130005  137.440002  67083400.0   \n",
       "2022-06-29  140.669998  136.669998  137.460007  139.229996  66242400.0   \n",
       "2022-06-30  138.369995  133.770004  137.250000  136.720001  98964500.0   \n",
       "2022-07-01  139.039993  135.660004  136.039993  138.929993  71007500.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2017-07-03   33.961647  \n",
       "2017-07-05   34.101284  \n",
       "2017-07-06   33.779408  \n",
       "2017-07-07   34.122574  \n",
       "2017-07-10   34.330841  \n",
       "...                ...  \n",
       "2022-06-27  141.660004  \n",
       "2022-06-28  137.440002  \n",
       "2022-06-29  139.229996  \n",
       "2022-06-30  136.720001  \n",
       "2022-07-01  138.929993  \n",
       "\n",
       "[1259 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symb=\"AAPL\"\n",
    "start=dt.datetime(2019,1,1)\n",
    "end=dt.datetime(2020,1,1)\n",
    "\n",
    "data=pdr.get_data_yahoo(symb)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3739bd8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>Saint Paul, Minnesota</td>\n",
       "      <td>1976-08-09</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AOS</td>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Building Products</td>\n",
       "      <td>Milwaukee, Wisconsin</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>91142</td>\n",
       "      <td>1916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABMD</td>\n",
       "      <td>Abiomed</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Danvers, Massachusetts</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>815094</td>\n",
       "      <td>1981</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>YUM</td>\n",
       "      <td>Yum! Brands</td>\n",
       "      <td>reports</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>Louisville, Kentucky</td>\n",
       "      <td>1997-10-06</td>\n",
       "      <td>1041061</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ZBRA</td>\n",
       "      <td>Zebra Technologies</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Electronic Equipment &amp; Instruments</td>\n",
       "      <td>Lincolnshire, Illinois</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>877212</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>ZBH</td>\n",
       "      <td>Zimmer Biomet</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Warsaw, Indiana</td>\n",
       "      <td>2001-08-07</td>\n",
       "      <td>1136869</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ZION</td>\n",
       "      <td>Zions Bancorporation</td>\n",
       "      <td>reports</td>\n",
       "      <td>Financials</td>\n",
       "      <td>Regional Banks</td>\n",
       "      <td>Salt Lake City, Utah</td>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>109380</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>ZTS</td>\n",
       "      <td>Zoetis</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>Parsippany, New Jersey</td>\n",
       "      <td>2013-06-21</td>\n",
       "      <td>1555280</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol              Security SEC filings             GICS Sector  \\\n",
       "0      MMM                    3M     reports             Industrials   \n",
       "1      AOS           A. O. Smith     reports             Industrials   \n",
       "2      ABT                Abbott     reports             Health Care   \n",
       "3     ABBV                AbbVie     reports             Health Care   \n",
       "4     ABMD               Abiomed     reports             Health Care   \n",
       "..     ...                   ...         ...                     ...   \n",
       "498    YUM           Yum! Brands     reports  Consumer Discretionary   \n",
       "499   ZBRA    Zebra Technologies     reports  Information Technology   \n",
       "500    ZBH         Zimmer Biomet     reports             Health Care   \n",
       "501   ZION  Zions Bancorporation     reports              Financials   \n",
       "502    ZTS                Zoetis     reports             Health Care   \n",
       "\n",
       "                      GICS Sub-Industry    Headquarters Location  \\\n",
       "0              Industrial Conglomerates    Saint Paul, Minnesota   \n",
       "1                     Building Products     Milwaukee, Wisconsin   \n",
       "2                 Health Care Equipment  North Chicago, Illinois   \n",
       "3                       Pharmaceuticals  North Chicago, Illinois   \n",
       "4                 Health Care Equipment   Danvers, Massachusetts   \n",
       "..                                  ...                      ...   \n",
       "498                         Restaurants     Louisville, Kentucky   \n",
       "499  Electronic Equipment & Instruments   Lincolnshire, Illinois   \n",
       "500               Health Care Equipment          Warsaw, Indiana   \n",
       "501                      Regional Banks     Salt Lake City, Utah   \n",
       "502                     Pharmaceuticals   Parsippany, New Jersey   \n",
       "\n",
       "    Date first added      CIK      Founded  \n",
       "0         1976-08-09    66740         1902  \n",
       "1         2017-07-26    91142         1916  \n",
       "2         1964-03-31     1800         1888  \n",
       "3         2012-12-31  1551152  2013 (1888)  \n",
       "4         2018-05-31   815094         1981  \n",
       "..               ...      ...          ...  \n",
       "498       1997-10-06  1041061         1997  \n",
       "499       2019-12-23   877212         1969  \n",
       "500       2001-08-07  1136869         1927  \n",
       "501       2001-06-22   109380         1873  \n",
       "502       2013-06-21  1555280         1952  \n",
       "\n",
       "[503 rows x 9 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "table=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
    "df = table[0]\n",
    "df.to_csv('S&P500-Info.csv')\n",
    "df.to_csv(\"S&P500-Symbols.csv\", columns=['Symbol'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "95411637",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=df[\"Symbol\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0339c414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       MMM\n",
       "1       AOS\n",
       "2       ABT\n",
       "3      ABBV\n",
       "4      ABMD\n",
       "       ... \n",
       "498     YUM\n",
       "499    ZBRA\n",
       "500     ZBH\n",
       "501    ZION\n",
       "502     ZTS\n",
       "Name: Symbol, Length: 503, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "44eaefc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2 thai stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c56c8461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "data=yf.download(\"PTT.BK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b46f13f2",
   "metadata": {
    "scrolled": true
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-12-06</th>\n",
       "      <td>3.800</td>\n",
       "      <td>3.825</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.575</td>\n",
       "      <td>1.521888</td>\n",
       "      <td>1736808000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-07</th>\n",
       "      <td>3.625</td>\n",
       "      <td>3.625</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.550</td>\n",
       "      <td>1.511245</td>\n",
       "      <td>412533000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-10</th>\n",
       "      <td>3.550</td>\n",
       "      <td>3.550</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.550</td>\n",
       "      <td>1.511245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-11</th>\n",
       "      <td>3.550</td>\n",
       "      <td>3.550</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1.447389</td>\n",
       "      <td>409158000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-12</th>\n",
       "      <td>3.425</td>\n",
       "      <td>3.525</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1.489960</td>\n",
       "      <td>307291000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-27</th>\n",
       "      <td>33.500</td>\n",
       "      <td>34.250</td>\n",
       "      <td>33.25</td>\n",
       "      <td>34.000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>60386500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-28</th>\n",
       "      <td>34.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>33.75</td>\n",
       "      <td>35.000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>103017000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-29</th>\n",
       "      <td>34.750</td>\n",
       "      <td>35.250</td>\n",
       "      <td>34.50</td>\n",
       "      <td>35.000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>87925800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>35.250</td>\n",
       "      <td>35.250</td>\n",
       "      <td>34.00</td>\n",
       "      <td>34.000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>73497500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>34.250</td>\n",
       "      <td>34.500</td>\n",
       "      <td>34.00</td>\n",
       "      <td>34.500</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>26783100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5090 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High    Low   Close  Adj Close      Volume\n",
       "Date                                                            \n",
       "2001-12-06   3.800   3.825   3.55   3.575   1.521888  1736808000\n",
       "2001-12-07   3.625   3.625   3.55   3.550   1.511245   412533000\n",
       "2001-12-10   3.550   3.550   3.55   3.550   1.511245           0\n",
       "2001-12-11   3.550   3.550   3.40   3.400   1.447389   409158000\n",
       "2001-12-12   3.425   3.525   3.40   3.500   1.489960   307291000\n",
       "...            ...     ...    ...     ...        ...         ...\n",
       "2022-06-27  33.500  34.250  33.25  34.000  34.000000    60386500\n",
       "2022-06-28  34.000  35.000  33.75  35.000  35.000000   103017000\n",
       "2022-06-29  34.750  35.250  34.50  35.000  35.000000    87925800\n",
       "2022-06-30  35.250  35.250  34.00  34.000  34.000000    73497500\n",
       "2022-07-01  34.250  34.500  34.00  34.500  34.500000    26783100\n",
       "\n",
       "[5090 rows x 6 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cbb60752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "data=yf.download(\"BTC-USD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68489cf4",
   "metadata": {},
   "source": [
    "# วิธี import data เเบบที่ 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "345c9309",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uncleengineer\n",
    "from thaistock import SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "89e3d0d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ชื่อหุ้น', 'ราคาล่าสุด', 'เปลี่ยนแปลง', '%เปลี่ยนแปลง', 'อัพเดต วัน-เวลา']\n",
      "['TEAMG ', 6.5, 0.15, 2.36, '02/07/2022 03:19:59']\n",
      "--------index for select--------\n",
      "วันที่ [0]\n",
      "ราคาเปิด [1]\n",
      "ราคาสูงสุด [2]\n",
      "ราคาต่ำสุด [3]\n",
      "ราคาเฉลี่ย [4]\n",
      "ราคาปิด [5]\n",
      "เปลี่ยนแปลง [6]\n",
      "%เปลี่ยนแปลง [7]\n",
      "ปริมาณ(พันหุ้น) [8]\n",
      "มูลค่า(ล้านบาท) [9]\n",
      "SET Index [10]\n",
      "%เปลี่ยนแปลง [11]\n",
      "--------------------------------\n"
     ]
    }
   ],
   "source": [
    "#สร้างตัวแปร stock (object)\n",
    "stock = SET()\n",
    "\n",
    "#เช็คราคา ณ ปัจจุบัน (ใช้ขณะตลาดเปิดได้)\n",
    "current = stock.current('TEAMG',show=True,header=True)\n",
    "\n",
    "#โชว์หัวข้อตารางราคาย้อนหลังเพื่ออ้างอิง index\n",
    "stock.show_header()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0036eeb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['วันที่', 'ราคาปิด', 'เปลี่ยนแปลง', '%เปลี่ยนแปลง']\n",
      "['01/07/22', 6.5, 0.15, 2.36]\n",
      "['30/06/22', 6.35, -0.3, -4.51]\n",
      "['29/06/22', 6.65, -0.05, -0.75]\n",
      "['28/06/22', 6.7, 0.0, 0.0]\n",
      "['27/06/22', 6.7, 0.1, 1.52]\n",
      "['24/06/22', 6.6, -0.05, -0.75]\n",
      "['23/06/22', 6.65, 0.05, 0.76]\n",
      "['22/06/22', 6.6, -0.3, -4.35]\n",
      "['21/06/22', 6.9, 0.5, 7.81]\n",
      "['20/06/22', 6.4, -0.25, -3.76]\n",
      "['17/06/22', 6.65, 0.35, 5.56]\n",
      "['16/06/22', 6.3, -0.5, -7.35]\n",
      "['15/06/22', 6.8, -0.65, -8.72]\n",
      "['14/06/22', 7.45, -0.8, -9.7]\n",
      "['13/06/22', 8.25, -1.35, -14.06]\n",
      "['10/06/22', 9.6, 0.05, 0.52]\n",
      "['09/06/22', 9.55, -0.4, -4.02]\n",
      "['08/06/22', 9.95, 0.15, 1.53]\n",
      "['07/06/22', 9.8, -0.2, -2.0]\n",
      "['06/06/22', 10.0, 0.2, 2.04]\n",
      "['02/06/22', 9.8, -0.1, -1.01]\n",
      "['01/06/22', 9.9, -0.5, -4.81]\n",
      "['31/05/22', 10.4, 0.4, 4.0]\n",
      "['30/05/22', 10.0, 0.05, 0.5]\n",
      "['27/05/22', 9.95, -0.35, -3.4]\n",
      "['26/05/22', 10.3, -0.4, -3.74]\n",
      "['25/05/22', 10.7, 0.5, 4.9]\n",
      "['24/05/22', 10.2, 1.1, 12.09]\n",
      "['23/05/22', 9.1, -0.15, -1.62]\n",
      "['20/05/22', 9.25, -0.05, -0.54]\n"
     ]
    }
   ],
   "source": [
    "# เช็คราคาย้อนหลัง 30 วัน แสดงวันที่ ,ราคาปิด, เปลี่ยนแปลง, %เปลี่ยนแปลง\n",
    "historical_price = stock.historical('TEAMG',days=30,show=True,select=[0,5,6,7],header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1c154c58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['01/07/22', 6.5], ['30/06/22', 6.35], ['29/06/22', 6.65], ['28/06/22', 6.7], ['27/06/22', 6.7], ['24/06/22', 6.6], ['23/06/22', 6.65], ['22/06/22', 6.6], ['21/06/22', 6.9], ['20/06/22', 6.4], ['17/06/22', 6.65], ['16/06/22', 6.3], ['15/06/22', 6.8], ['14/06/22', 7.45], ['13/06/22', 8.25], ['10/06/22', 9.6], ['09/06/22', 9.55], ['08/06/22', 9.95], ['07/06/22', 9.8], ['06/06/22', 10.0], ['02/06/22', 9.8], ['01/06/22', 9.9], ['31/05/22', 10.4], ['30/05/22', 10.0], ['27/05/22', 9.95], ['26/05/22', 10.3], ['25/05/22', 10.7], ['24/05/22', 10.2], ['23/05/22', 9.1], ['20/05/22', 9.25]]\n",
      "[9.25, 9.1, 10.2, 10.7, 10.3, 9.95, 10.0, 10.4, 9.9, 9.8, 10.0, 9.8, 9.95, 9.55, 9.6, 8.25, 7.45, 6.8, 6.3, 6.65, 6.4, 6.9, 6.6, 6.65, 6.6, 6.7, 6.7, 6.65, 6.35, 6.5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# พล็อตราคาปิด 30 วันย้อนหลัง\n",
    "stock.plot('TEAMG',days=30)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}