StockDataCollection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data\n",
    "import fix_yahoo_finance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 downloaded\n",
      "[*********************100%***********************]  1 of 1 downloaded\n"
     ]
    }
   ],
   "source": [
    "yf.pdr_override()\n",
    "\n",
    "start_date = '1996-05-06' #startdate를 1996년으로 설정해두면 가장 오래된 데이터부터 전부 가져올 수 있다.\n",
    "tickers = ['067160.KQ', '035420.KS'] #1 아프리카tv와 네이버의 ticker(종목코드)\n",
    "afreeca = data.get_data_yahoo(tickers[0], start_date)\n",
    "naver = data.get_data_yahoo(tickers[1], start_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#1\n",
    "주목해야 할 점은 아프리카tv는 코스닥 종목이고, 네이버는 코스피 종목이라는 것.\n",
    "코스닥은 ticker에 `.KQ`, 코스피는 `.KS`가 붙는다.\n",
    "#2\n",
    "data객체의 get_data_yahoo 를 이용해서 얻은 데이터들(아프리카, 네이버)은 DataFrame형의 객체이다.\n",
    "(정확히는 pandas.core.frame.DataFrame)\n",
    "data객체가 pandas-datareader에서 가져온 것임을 잊지말자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>2003-12-22</th>\n",
       "      <td>3485.0</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>2924.239502</td>\n",
       "      <td>18600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-23</th>\n",
       "      <td>3070.0</td>\n",
       "      <td>3290.0</td>\n",
       "      <td>3070.0</td>\n",
       "      <td>3070.0</td>\n",
       "      <td>2576.016113</td>\n",
       "      <td>1911800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-24</th>\n",
       "      <td>3065.0</td>\n",
       "      <td>3270.0</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2517.279297</td>\n",
       "      <td>666100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-26</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>2433.370117</td>\n",
       "      <td>342200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-29</th>\n",
       "      <td>2720.0</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2349.460693</td>\n",
       "      <td>707000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Adj Close   Volume\n",
       "Date                                                            \n",
       "2003-12-22  3485.0  3485.0  3485.0  3485.0  2924.239502    18600\n",
       "2003-12-23  3070.0  3290.0  3070.0  3070.0  2576.016113  1911800\n",
       "2003-12-24  3065.0  3270.0  2830.0  3000.0  2517.279297   666100\n",
       "2003-12-26  3000.0  3140.0  2900.0  2900.0  2433.370117   342200\n",
       "2003-12-29  2720.0  3030.0  2625.0  2800.0  2349.460693   707000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afreeca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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>2002-10-29</th>\n",
       "      <td>8988.620117</td>\n",
       "      <td>8988.620117</td>\n",
       "      <td>8988.620117</td>\n",
       "      <td>8988.620117</td>\n",
       "      <td>8888.736328</td>\n",
       "      <td>100349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-10-30</th>\n",
       "      <td>10061.099609</td>\n",
       "      <td>10061.099609</td>\n",
       "      <td>9948.769531</td>\n",
       "      <td>10061.099609</td>\n",
       "      <td>9949.297852</td>\n",
       "      <td>4178437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-10-31</th>\n",
       "      <td>10214.299805</td>\n",
       "      <td>10459.500000</td>\n",
       "      <td>9325.700195</td>\n",
       "      <td>9560.629883</td>\n",
       "      <td>9454.389648</td>\n",
       "      <td>6465417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-01</th>\n",
       "      <td>9805.769531</td>\n",
       "      <td>10112.200195</td>\n",
       "      <td>8620.910156</td>\n",
       "      <td>8968.200195</td>\n",
       "      <td>8868.543945</td>\n",
       "      <td>3674734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-11-04</th>\n",
       "      <td>8886.480469</td>\n",
       "      <td>8947.769531</td>\n",
       "      <td>8304.259766</td>\n",
       "      <td>8518.759766</td>\n",
       "      <td>8424.097656</td>\n",
       "      <td>3387882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High          Low         Close  \\\n",
       "Date                                                                \n",
       "2002-10-29   8988.620117   8988.620117  8988.620117   8988.620117   \n",
       "2002-10-30  10061.099609  10061.099609  9948.769531  10061.099609   \n",
       "2002-10-31  10214.299805  10459.500000  9325.700195   9560.629883   \n",
       "2002-11-01   9805.769531  10112.200195  8620.910156   8968.200195   \n",
       "2002-11-04   8886.480469   8947.769531  8304.259766   8518.759766   \n",
       "\n",
       "              Adj Close   Volume  \n",
       "Date                              \n",
       "2002-10-29  8888.736328   100349  \n",
       "2002-10-30  9949.297852  4178437  \n",
       "2002-10-31  9454.389648  6465417  \n",
       "2002-11-01  8868.543945  3674734  \n",
       "2002-11-04  8424.097656  3387882  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naver.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "naver.to_csv('./naver.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "afreeca.to_pickle('./afreeca.pickle')\n",
    "naver.to_excel('./naver.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "kosdaq = pd.read_pickle('./kosdaq.pickle')\n",
    "kospi = pd.read_pickle('./kospi.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kor_name</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>제일홀딩스</td>\n",
       "      <td>003380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>하나금융9호스팩</td>\n",
       "      <td>261200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>교보7호스팩</td>\n",
       "      <td>267320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>보라티알</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>한화수성스팩</td>\n",
       "      <td>265920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   kor_name  ticker\n",
       "0     제일홀딩스  003380\n",
       "1  하나금융9호스팩  261200\n",
       "2    교보7호스팩  267320\n",
       "3      보라티알  250000\n",
       "4    한화수성스팩  265920"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kosdaq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kor_name</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>하이록코리아</td>\n",
       "      <td>013030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>SBI인베스트먼트</td>\n",
       "      <td>019550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>엠벤처투자</td>\n",
       "      <td>019590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>제미니투자</td>\n",
       "      <td>019570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>모헨즈</td>\n",
       "      <td>006920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       kor_name  ticker\n",
       "1226     하이록코리아  013030\n",
       "1227  SBI인베스트먼트  019550\n",
       "1228      엠벤처투자  019590\n",
       "1229      제미니투자  019570\n",
       "1230        모헨즈  006920"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kosdaq.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "def save_kosdaq():\n",
    "    for stock in kosdaq.values:\n",
    "        kor_name = stock[0]\n",
    "        ticker = stock[1]\n",
    "        df = data.get_data_yahoo(ticker + '.KQ', '1996-05-06', thread=20)\n",
    "        df.to_csv('./kosdaq/{}.csv'.format(ticker))\n",
    "        print('{}.csv is saved'.format(ticker))\n",
    "\n",
    "def save_kospi():\n",
    "    for stock in kospi.values:\n",
    "        kor_name = stock[0]\n",
    "        ticker = stock[1]\n",
    "        df = data.get_data_yahoo(ticker + '.KS', '1996-05-06', thread=20)\n",
    "        df.to_csv('./kospi/{}.csv'.format(ticker))\n",
    "        print('{}.csv is saved'.format(ticker))\n",
    "\n",
    "save_kosdaq()\n",
    "save_kospi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empty file ./kosdaq/036010.csv is updated\n",
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     ]
    }
   ],
   "source": [
    "import re\n",
    "import glob\n",
    "from pandas_datareader import data\n",
    "\n",
    "def reload_empty(market):\n",
    "    file_list = glob.glob('./{}/*.csv'.format(market))\n",
    "    six_digit = re.compile('\\d{6}')\n",
    "    for file_name in file_list:\n",
    "        file = pd.read_csv(file_name)\n",
    "        if file.empty:\n",
    "            print(\"empty file {} is updated\".format(file_name))\n",
    "            ticker = six_digit.findall(file_name)[0]\n",
    "            tmp_df = data.get_data_yahoo(ticker+'.KQ', start='1996-05-06', thread=20)\n",
    "            tmp_df.to_csv(file_name)\n",
    "\n",
    "reload_empty('kosdaq')\n",
    "reload_empty('kospi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
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