07.2 K10 지수 산출.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bs4\n",
    "from urllib.request import urlopen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 크롤링 차단 장치 우회를 위해 웹사이트 호출 시 headers 값을 추가\n",
    "import urllib.request\n",
    "headers = {\n",
    "    'User-Agent': 'Mozilla/5.0',\n",
    "    'X-Requested-With': 'XMLHttpRequest',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# url_float = 'http://companyinfo.stock.naver.com/v1/company/c1010001.aspx?cmp_cd=035420'  \n",
    "url_float = 'https://navercomp.wisereport.co.kr/v2/company/c1010001.aspx?cmp_cd=005930'\n",
    "# Floating ratio from Naver\n",
    "url_float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = urlopen(url_float).read()   \n",
    "soup = bs4.BeautifulSoup(source, 'lxml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "//*[@id=\"cTB11\"]/tbody/tr[7]/td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup.find(id='cTB11').find_all('tr')[6].td.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = soup.find(id='cTB11').find_all('tr')[6].td.text\n",
    "tmp = tmp.replace('\\r', '')\n",
    "tmp = tmp.replace('\\n', '')\n",
    "tmp = tmp.replace('\\t', '')\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = re.split('/', tmp)\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outstanding = tmp[0].replace(',', '')\n",
    "outstanding = outstanding.replace('주', '')\n",
    "outstanding = outstanding.replace(' ', '')\n",
    "outstanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "floating = tmp[1].replace(' ', '')\n",
    "floating = floating.replace('%', '')\n",
    "floating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outstanding = int(outstanding)\n",
    "outstanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "floating = float(floating)\n",
    "floating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구성종목 기본정보\n",
    "def stock_info(stock_cd):\n",
    "    url_float = 'http://companyinfo.stock.naver.com/v1/company/c1010001.aspx?cmp_cd=' + stock_cd\n",
    "    source = urlopen(url_float).read()\n",
    "    soup = bs4.BeautifulSoup(source, 'lxml')\n",
    "    \n",
    "    tmp = soup.find(id='cTB11').find_all('tr')[6].td.text\n",
    "    tmp = tmp.replace('\\r', '')\n",
    "    tmp = tmp.replace('\\n', '')\n",
    "    tmp = tmp.replace('\\t', '')\n",
    "    \n",
    "    tmp = re.split('/', tmp)\n",
    "\n",
    "    outstanding = tmp[0].replace(',', '')\n",
    "    outstanding = outstanding.replace('주', '')\n",
    "    outstanding = outstanding.replace(' ', '')\n",
    "    outstanding = int(outstanding)\n",
    "\n",
    "    floating = tmp[1].replace(' ', '')\n",
    "    floating = floating.replace('%', '')\n",
    "    floating = float(floating)\n",
    "    \n",
    "    name = soup.find(id='pArea').find('div').find('div').find('tr').find('td').find('span').text\n",
    "    \n",
    "    k10_outstanding[stock_cd] = outstanding\n",
    "    k10_floating[stock_cd] = floating\n",
    "    k10_name[stock_cd] = name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "한국거래소 시가총액 상위 10종목 (2019년1월 기준)\n",
    "005930\t삼성전자\n",
    "000660\tSK하이닉스\n",
    "068270\t셀트리온\n",
    "005380\t현대차\n",
    "207940\t삼성바이오로직스\n",
    "051910\tLG화학\n",
    "005490\tPOSCO\n",
    "035420\tNAVER\n",
    "017670\tSK텔레콤\n",
    "015760\t한국전력\n",
    "'''\n",
    "k10_component = ['005930', '000660', '068270', '005380', '207940',\\\n",
    "                 '051910', '005490', '035420', '017670', '015760']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10_outstanding = dict()\n",
    "k10_floating = dict()\n",
    "k10_name = dict()\n",
    "for stock_cd in k10_component:\n",
    "    print(stock_cd)\n",
    "    stock_info(stock_cd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10_outstanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10_floating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "과거 주가 구하기 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_format(d):\n",
    "    d = str(d).replace('-', '.')\n",
    "    \n",
    "    yyyy = int(d.split('.')[0]) \n",
    "    mm = int(d.split('.')[1])\n",
    "    dd = int(d.split('.')[2])\n",
    "\n",
    "    this_date= dt.date(yyyy, mm, dd)\n",
    "    return this_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def historical_stock_naver(stock_cd, start_date='', end_date='', page_n=1, last_page=0):\n",
    "    \n",
    "    if start_date:   # start_date가 있으면\n",
    "        start_date = date_format(start_date)   # date 포맷으로 변환\n",
    "    else:    # 없으면\n",
    "        start_date = dt.date.today()   # 오늘 날짜를 지정\n",
    "    if end_date:   # end_date가 없으면 \n",
    "        end_date = date_format(end_date)   # date 포맷으로 변환\n",
    "    else:   # end_date가 있으면\n",
    "        end_date = dt.date.today()   # 오늘 날짜를 end_date로 지정\n",
    "        \n",
    "    naver_stock = 'http://finance.naver.com/item/sise_day.nhn?code=' + stock_cd + '&page=' + str(page_n)\n",
    "    \n",
    "    # 기존 코드\n",
    "    # source = urlopen(naver_stock).read()\n",
    "    \n",
    "    # 개정 코드 (1줄에서 2줄로 늘어남)\n",
    "    url = urllib.request.Request(naver_stock, headers=headers)   # headers 정보 보내기\n",
    "    source = urlopen(url).read()\n",
    "    \n",
    "    source = bs4.BeautifulSoup(source, 'lxml')\n",
    "    \n",
    "    dates = source.find_all('span', class_='tah p10 gray03')   # 날짜 수집   \n",
    "    prices = source.find_all('td', class_='num')   # 종가 수집\n",
    "    \n",
    "    for n in range(len(dates)):\n",
    "    \n",
    "        if len(dates) > 0:\n",
    "            \n",
    "            # 날짜 처리\n",
    "            this_date = dates[n].text\n",
    "            this_date = date_format(this_date)\n",
    "            \n",
    "            if this_date <= end_date and this_date >= start_date:   \n",
    "            # start_date와 end_date 사이에서 데이터 저장\n",
    "                # 종가 처리\n",
    "                this_close = prices[n*6].text\n",
    "                this_close = this_close.replace(',', '')\n",
    "                this_close = float(this_close)\n",
    "\n",
    "                # 딕셔너리에 저장\n",
    "                historical_prices[this_date] = this_close\n",
    "                              \n",
    "            elif this_date < start_date:   \n",
    "            # start_date 이전이면 함수 종료\n",
    "                return historical_prices              \n",
    "            \n",
    "    # 페이지 네비게이션\n",
    "    if last_page == 0:\n",
    "        last_page = source.find_all('table')[1].find('td', class_='pgRR').find('a')['href']\n",
    "        last_page = last_page.split('&')[1]\n",
    "        last_page = last_page.split('=')[1]\n",
    "        last_page = float(last_page)\n",
    "        \n",
    "    # 다음 페이지 호출\n",
    "    if page_n < last_page:\n",
    "        page_n = page_n + 1\n",
    "        historical_stock_naver(stock_cd, start_date, end_date, page_n, last_page)   \n",
    "        \n",
    "    return historical_prices  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10_historical_prices = dict()\n",
    "\n",
    "for stock_cd in k10_component:\n",
    "    \n",
    "    historical_prices = dict()\n",
    "    start_date = '2020-1-1'\n",
    "    end_date = '2020-12-31'\n",
    "    historical_stock_naver(stock_cd, start_date, end_date)\n",
    "    \n",
    "    k10_historical_prices[stock_cd] = historical_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "k10_historical_price = pd.DataFrame(k10_historical_prices)\n",
    "k10_historical_price.sort_index(axis=1, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "k10_historical_price = k10_historical_price.fillna(method='ffill')   # ffill로 구멍을 채우고\n",
    "if k10_historical_price.isnull().values.any():   # 그래도 구멍이 남아 있으면\n",
    "    k10_historical_price = k10_historical_price.fillna(method='bfill')   # bfill로 채워라\n",
    "k10_historical_price.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10_historical_price['005930'] = k10_historical_price['005930'] / 50   # 삼성전자 액면분할에 따른 수정주가 계산\n",
    "k10_historical_price.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지수 산출을 위한 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tmp = {'Outstanding' : k10_outstanding,\\\n",
    "       'Floating' : k10_floating,\\\n",
    "       'Price' : k10_historical_price.iloc[0],\\\n",
    "       'Name' : k10_name}\n",
    "k10_info = pd.DataFrame(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10_info['f Market Cap'] = k10_info['Outstanding'] * k10_info['Floating'] * k10_info['Price'] * 0.01\n",
    "k10_info['Market Cap'] = k10_info['Outstanding'] * k10_info['Price'] * 0.01\n",
    "k10_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "k10_historical_mc = k10_historical_price * k10_info['Outstanding'] * k10_info['Floating'] * 0.01\n",
    "k10_historical_mc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "<데이터프레임>.sum() 은 각 열의 합 (세로방향)\n",
    "<데이터프레임>.sum(axis=1) 은 각 행의 합 (가로방향)\n",
    "'''\n",
    "k10_historical_mc.sum(axis=1)     # 일자별 시가총액 합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "k10 = pd.DataFrame()\n",
    "k10['K10 Market Cap'] = k10_historical_mc.sum(axis=1)\n",
    "k10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "k10['K10'] = k10['K10 Market Cap'] / k10['K10 Market Cap'][0] * 100\n",
    "k10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K10 지수 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(k10['K10'], label='K10')\n",
    "plt.legend(loc=0)\n",
    "plt.grid(True, color='0.7', linestyle=':', linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KOSPI200과의 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def historical_index_naver(index_cd, start_date='', end_date='', page_n=1, last_page=0):\n",
    "    \n",
    "    index_cd = index_cd   # 인덱스 코드 \n",
    "    page_n = page_n   # 페이지 번호 \n",
    "    \n",
    "    if start_date:   # start_date가 있으면\n",
    "        start_date = date_format(start_date)   # date 포맷으로 변환\n",
    "    else:    # 없으면\n",
    "        start_date = dt.date.today()   # 오늘 날짜를 지정\n",
    "    if not end_date:   # end_date가 없으면 \n",
    "        end_date = dt.date.today()   # 오늘 날짜를 end_date로 지정\n",
    "    else:   # end_date가 있으면\n",
    "        end_date = date_format(end_date)   # date 포맷으로 변환\n",
    "        \n",
    "    naver_index = 'http://finance.naver.com/sise/sise_index_day.nhn?code=' + index_cd + '&page=' + str(page_n)\n",
    "    \n",
    "    # source = urlopen(naver_index).read()   # 지정한 페이지에서 코드 읽기\n",
    "    \n",
    "    url = urllib.request.Request(naver_index, headers=headers)   # headers 정보 보내기\n",
    "    source = urlopen(url).read()\n",
    "    \n",
    "    source = bs4.BeautifulSoup(source, 'lxml')   # 뷰티풀 스프로 태그별로 코드 분류\n",
    "    \n",
    "    dates = source.find_all('td', class_='date')   # <td class=\"date\">태그에서 날짜 수집   \n",
    "    prices = source.find_all('td', class_='number_1')   # <td class=\"number_1\">태그에서 지수 수집\n",
    "    \n",
    "    for n in range(len(dates)):\n",
    "    \n",
    "        if dates[n].text.split('.')[0].isdigit():\n",
    "            \n",
    "            # 날짜 처리\n",
    "            this_date = dates[n].text\n",
    "            this_date= date_format(this_date)\n",
    "            \n",
    "            if this_date <= end_date and this_date >= start_date:   \n",
    "            # start_date와 end_date 사이에서 데이터 저장\n",
    "                # 종가 처리\n",
    "                this_close = prices[n*4].text   # prices 중 종가지수인 0,4,8,...번째 데이터 추출\n",
    "                this_close = this_close.replace(',', '')\n",
    "                this_close = float(this_close)\n",
    "\n",
    "                # 딕셔너리에 저장\n",
    "                historical_prices[this_date] = this_close\n",
    "                \n",
    "            elif this_date < start_date:   \n",
    "            # start_date 이전이면 함수 종료\n",
    "                return historical_prices           \n",
    "            \n",
    "    # 페이지 네비게이션\n",
    "    if last_page == 0:\n",
    "        last_page = source.find('td', class_='pgRR').find('a')['href']\n",
    "        # 마지막페이지 주소 추출\n",
    "        last_page = last_page.split('&')[1]   # & 뒤의 page=506 부분 추출\n",
    "        last_page = last_page.split('=')[1]   # = 뒤의 페이지번호만 추출\n",
    "        last_page = int(last_page)   # 숫자형 변수로 변환\n",
    "        \n",
    "    # 다음 페이지 호출\n",
    "    if page_n < last_page:   \n",
    "        page_n = page_n + 1   \n",
    "        historical_index_naver(index_cd, start_date, end_date, page_n, last_page)   \n",
    "        \n",
    "    return historical_prices  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "historical_prices = dict()     # 딕셔너리 초기화\n",
    "kospi200 = historical_index_naver('KPI200', '2020-1-1', '2020-12-31')\n",
    "kospi200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "k200 = pd.DataFrame({'K200' : kospi200})\n",
    "k200.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(k10['K10'] / k10['K10'][0] * 100, label='K10')\n",
    "plt.plot(k200['K200'] / k200['K200'][0] * 100, label='K200')\n",
    "plt.legend(loc=0)\n",
    "plt.grid(True, color='0.7', linestyle=':', linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K10 선물가격 산출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F = Se^(r-d)t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def futures_price (S, r, d, T, t0):\n",
    "    t = (T - t0).days / 252\n",
    "    F = S * math.exp((r-d)*t)\n",
    "    return F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = dt.date(2018, 12, 14)     # 만기일\n",
    "t0 = dt.date(2018, 6, 15)     # 현재일\n",
    "\n",
    "futures_price (100.0, 0.02, 0.015, T, t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = dt.date(2018, 12, 14)     # 만기일\n",
    "t0 = dt.date(2018, 12, 10)     # 현재일\n",
    "\n",
    "futures_price (100.0, 0.02, 0.015, T, t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}