data_ref.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 투자 참고용 데이터 수집\n",
    "\n",
    "이번 장에서는 주식과 재무제표 같은 금융 데이터 외에 투자에 도움이 될 수 있는 기업의 공시내용 및 각종 투자 지표를 수집하는 방법에 대해 알아보도록 하겠다. \n",
    "\n",
    "## DART의 Open API를 이용한 데이터 수집하기\n",
    "\n",
    "DART(Data Analysis, Retrieval and Transfer System)는 금융감독원 전자공시시스템으로써, 상장법인 등이 공시서류를 인터넷으로 제출하고, 투자자 등 이용자는 제출 즉시 인터넷을 통해 조회할 수 있도록 하는 종합적 기업공시 시스템이다. 홈페이지에서도 각종 공시내역을 확인할 수 있지만, 해당 사이트에서 제공하는 API를 이용할 경우 더욱 쉽게 공시 내용을 수집할 수 있다.\n",
    "\n",
    "### API Key 발급 및 추가하기\n",
    "\n",
    "먼저 https://opendart.fss.or.kr/ 에서 회원가입을 한 후 [인증키 신청/관리] → [인증키 신청]을 통해 API Key를 발급받아야 한다.\n",
    "\n",
    "```{figure} image/data_ref/dart_api_key.png\n",
    "---\n",
    "name: dart_api_key\n",
    "---\n",
    "OpenAPI 인증키 신청\n",
    "```\n",
    "\n",
    "계정을 생성하고 이메일을 통해 이용자 등록을 한 후 로그인을 한다. 그 후 [오픈API 이용현황]을 살펴보면 **API Key** 부분에 발급받은 Key가 있으며, 금일 몇번의 API를 요청했는지가 일일이용현황에 나온다. 하루 총 10,000번까지 데이터를 요청할 수 있다.\n",
    "\n",
    "\n",
    "```{figure} image/data_ref/dart_api_status.png\n",
    "---\n",
    "name: dart_api_status\n",
    "---\n",
    "OpenAPI 이용현황\n",
    "```\n",
    "\n",
    "다음으로 발급받은 API Key를 keyring 패키지를 이용해 저장한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "import keyring\n",
    "\n",
    "keyring.set_password('dart_api_key', 'User Name', 'Password')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 고유번호 다운로드\n",
    "\n",
    "Open API에서 각 기업의 데이터를 받기 위해서는 종목에 해당하는 고유번호를 알아야한다. 이에 대한 자세한 내용은 홈페이지(https://opendart.fss.or.kr/)에서 [개발가이드 → 공시정보 → 고유번호] 화면에 설명되어 있다.\n",
    "\n",
    "```{figure} image/data_ref/dart_guide.png\n",
    "---\n",
    "name: dart_guide\n",
    "---\n",
    "OPEN API 개발가이드\n",
    "```\n",
    "\n",
    "위 페이지의 내용을 코드로 나타내보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cache-Control': 'no-cache, no-store', 'Connection': 'keep-alive', 'Set-Cookie': 'WMONID=VFGuqP5ftuv; Expires=Tue, 11-Feb-2025 17:13:28 GMT; Path=/', 'Pragma': 'no-cache', 'Expires': '0', 'Content-Transfer-Encoding': 'binary', 'Content-Disposition': ': attachment; filename=CORPCODE.zip', 'Date': 'Mon, 12 Feb 2024 08:13:28 GMT', 'Content-Type': 'application/x-msdownload;charset=UTF-8', 'Content-Length': '1765078'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keyring\n",
    "import requests as rq\n",
    "from io import BytesIO\n",
    "import zipfile\n",
    "\n",
    "api_key = keyring.get_password('dart_api_key', 'Henry')\n",
    "codezip_url = f'''https://opendart.fss.or.kr/api/corpCode.xml?crtfc_key={api_key}'''\n",
    "codezip_data = rq.get(codezip_url)\n",
    "codezip_data.headers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `get_password()` 함수를 통해 API Key를 불러온다.\n",
    "2. https://opendart.fss.or.kr/api/corpCode.xml?crtfc_key= 뒤에 본인의 API 키를 입력한다.\n",
    "3. `get()` 함수를 통해 해당 페이지 내용을 받는다.\n",
    "4. 헤더를 확인해보면 'attachment; filename=CORPCODE.zip', 즉 파일이 첨부되어 있다. 이에 대해 좀더 자세히 알아보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "': attachment; filename=CORPCODE.zip'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codezip_data.headers['Content-Disposition']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "headers의 'content-disposition' 부분을 확인해보면 'CORPCODE.zip' 파일이 첨부되어 있다. 해당 파일의 압축을 풀어 첨부된 내용을 확인해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CORPCODE.xml']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codezip_file = zipfile.ZipFile(BytesIO(codezip_data.content))\n",
    "codezip_file.namelist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`BytesIO()`를 통해 바이너리스트림 형태로 만든 후, `ZipFile()` 함수를 통해 압축을 풀어준다. `namelist()`를 통해 파일명을 확인해보면 'CORPCODE.xml'라는 파일이 존재하며, 이를 불러오도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>corp_code</th>\n",
       "      <th>corp_name</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>modify_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00434003</td>\n",
       "      <td>다코</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00434456</td>\n",
       "      <td>일산약품</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00430964</td>\n",
       "      <td>굿앤엘에스</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00432403</td>\n",
       "      <td>한라판지</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00388953</td>\n",
       "      <td>크레디피아제이십오차유동화전문회사</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  corp_code          corp_name stock_code modify_date\n",
       "0  00434003                 다코       None    20170630\n",
       "1  00434456               일산약품       None    20170630\n",
       "2  00430964              굿앤엘에스       None    20170630\n",
       "3  00432403               한라판지       None    20170630\n",
       "4  00388953  크레디피아제이십오차유동화전문회사       None    20170630"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xmltodict\n",
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "code_data = codezip_file.read('CORPCODE.xml').decode('utf-8')\n",
    "data_odict = xmltodict.parse(code_data)\n",
    "data_dict = json.loads(json.dumps(data_odict))\n",
    "data = data_dict.get('result').get('list')\n",
    "corp_list = pd.DataFrame(data)\n",
    "\n",
    "corp_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. `read()` 메서드를 통해 'CORPCODE.xml' 파일을 불러온 후, `decode()` 메서드를 통해 UTF-8 형태로 변경한다.\n",
    "2. xmltodict 패키지의 `parse()` 함수를 이용해 딕셔너리 형태로 변경한다.\n",
    "3. 위 데이터를 `dumps()` 함수를 통해 JSON 형태로 바꿔준 후, `loads()` 함수를 통해 불러온다.\n",
    "4. `get()` 함수를 통해 result 내에서 list 부분만 불러온다.\n",
    "5. 데이터프레임 형태로 변경해준다.\n",
    "\n",
    "해당 데이터의 길이를 확인해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104025"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corp_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>corp_code</th>\n",
       "      <th>corp_name</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>modify_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00434003</td>\n",
       "      <td>다코</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00434456</td>\n",
       "      <td>일산약품</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00430964</td>\n",
       "      <td>굿앤엘에스</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00432403</td>\n",
       "      <td>한라판지</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00388953</td>\n",
       "      <td>크레디피아제이십오차유동화전문회사</td>\n",
       "      <td>None</td>\n",
       "      <td>20170630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  corp_code          corp_name stock_code modify_date\n",
       "0  00434003                 다코       None    20170630\n",
       "1  00434456               일산약품       None    20170630\n",
       "2  00430964              굿앤엘에스       None    20170630\n",
       "3  00432403               한라판지       None    20170630\n",
       "4  00388953  크레디피아제이십오차유동화전문회사       None    20170630"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corp_list[corp_list['stock_code'].isin([None])].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`len()` 함수를 통해 종목수를 확인해보면 거래소의 상장 종목수보다 훨씬 많으며, 이는 stock_code 열이 빈 종목, 즉 거래소에 상장되지 않은 종목도 포함되어 있기 때문이다. 따라서 해당 데이터는 삭제하여 거래소 상장 종목만을 남긴 후 DB에 저장한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3697"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pymysql\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "corp_list = corp_list[~corp_list.stock_code.isin(\n",
    "    [None])].reset_index(drop=True)\n",
    "\n",
    "engine = create_engine('mysql+pymysql://root:1234@127.0.0.1:3306/stock_db')\n",
    "corp_list.to_sql(name='dart_code', con=engine, index=True, if_exists='replace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} image/data_ref/dart.png\n",
    "---\n",
    "name: dart\n",
    "---\n",
    "고유번호 테이블\n",
    "```\n",
    "\n",
    "### 공시 데이터\n",
    "\n",
    "#### 전체 종목의 공시 데이터 수집\n",
    "\n",
    "먼저 전체 종목의 공시를 수집하도록 하며, 개발가이드는 [개발가이드 → 공시정보 → 공시검색]에 나와있다. 각종 요청인자를 통해 URL을 생성 후 전송하여, 요청에 맞는 데이터를 받을 수 있다. 공시 검색에 해당하는 인자는 {numref}`dart_api_input`와 같다.\n",
    "\n",
    "```{figure} image/data_ref/dart_api_input.png\n",
    "---\n",
    "name: dart_api_input\n",
    "---\n",
    "OpenAPI 요청 인자 예시\n",
    "```\n",
    "\n",
    "페이지 하단에서 인자를 입력 후 [검색]을 누르면 {numref}`dart_api_exam`와 같이 각 인자에 맞게 생성된 URL과 그 결과를 볼 수 있다.\n",
    "\n",
    "```{figure} image/data_ref/dart_api_exam.png\n",
    "---\n",
    "name: dart_api_exam\n",
    "---\n",
    "OpenAPI 테스트 예시\n",
    "```\n",
    "\n",
    "이를 참조하여 시작일과 종료일을 토대로 최근 공시 100건에 해당하는 URL을 생성한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>corp_code</th>\n",
       "      <th>corp_name</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>corp_cls</th>\n",
       "      <th>report_nm</th>\n",
       "      <th>rcept_no</th>\n",
       "      <th>flr_nm</th>\n",
       "      <th>rcept_dt</th>\n",
       "      <th>rm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>00372688</td>\n",
       "      <td>티사이언티픽</td>\n",
       "      <td>057680</td>\n",
       "      <td>K</td>\n",
       "      <td>증권발행결과(자율공시)(제3자배정 유상증자)</td>\n",
       "      <td>20220805900490</td>\n",
       "      <td>티사이언티픽</td>\n",
       "      <td>20220805</td>\n",
       "      <td>코</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>00113465</td>\n",
       "      <td>하나증권</td>\n",
       "      <td></td>\n",
       "      <td>E</td>\n",
       "      <td>증권발행실적보고서</td>\n",
       "      <td>20220805000463</td>\n",
       "      <td>하나증권</td>\n",
       "      <td>20220805</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>00113465</td>\n",
       "      <td>하나증권</td>\n",
       "      <td></td>\n",
       "      <td>E</td>\n",
       "      <td>증권발행실적보고서</td>\n",
       "      <td>20220805000462</td>\n",
       "      <td>하나증권</td>\n",
       "      <td>20220805</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>00355548</td>\n",
       "      <td>한국테크놀로지</td>\n",
       "      <td>053590</td>\n",
       "      <td>K</td>\n",
       "      <td>주식등의대량보유상황보고서(일반)</td>\n",
       "      <td>20220805000460</td>\n",
       "      <td>한국이노베이션</td>\n",
       "      <td>20220805</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>00132868</td>\n",
       "      <td>성안</td>\n",
       "      <td>011300</td>\n",
       "      <td>Y</td>\n",
       "      <td>최대주주등소유주식변동신고서</td>\n",
       "      <td>20220805800507</td>\n",
       "      <td>성안</td>\n",
       "      <td>20220805</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   corp_code corp_name stock_code corp_cls                 report_nm  \\\n",
       "95  00372688    티사이언티픽     057680        K  증권발행결과(자율공시)(제3자배정 유상증자)   \n",
       "96  00113465      하나증권                   E                 증권발행실적보고서   \n",
       "97  00113465      하나증권                   E                 증권발행실적보고서   \n",
       "98  00355548   한국테크놀로지     053590        K         주식등의대량보유상황보고서(일반)   \n",
       "99  00132868        성안     011300        Y            최대주주등소유주식변동신고서   \n",
       "\n",
       "          rcept_no   flr_nm  rcept_dt rm  \n",
       "95  20220805900490   티사이언티픽  20220805  코  \n",
       "96  20220805000463     하나증권  20220805     \n",
       "97  20220805000462     하나증권  20220805     \n",
       "98  20220805000460  한국이노베이션  20220805     \n",
       "99  20220805800507       성안  20220805  유  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "bgn_date = (date.today() + relativedelta(days=-7)).strftime(\"%Y%m%d\")\n",
    "end_date = (date.today()).strftime(\"%Y%m%d\")\n",
    "\n",
    "notice_url = f'''https://opendart.fss.or.kr/api/list.json?crtfc_key={api_key}\n",
    "&bgn_de={bgn_date}&end_de={end_date}&page_no=1&page_count=100'''\n",
    "\n",
    "notice_data = rq.get(notice_url)\n",
    "notice_data_df = notice_data.json().get('list')\n",
    "notice_data_df = pd.DataFrame(notice_data_df)\n",
    "\n",
    "notice_data_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 'bgn_date'에는 현재로부터 일주일 전 날짜룰, 'end_date'는 오늘 날짜를, 페이지별 건수에 해당하는 'page_count'에는 100을 입력한다.\n",
    "2. 홈페이지에 나와있는 예시에 맞게 URL을 작성해준다.\n",
    "3. `get()` 함수를 통해 해당 페이지 내용을 받는다.\n",
    "4. `json()` 함수를 통해 JSON 데이터를 불러온 후, list 부분만을 선택한다.\n",
    "5. 데이터프레임 형태로 변경한다.\n",
    "\n",
    "데이터를 확인해보면 우리가 원하는 공시정보, 즉 일주일 전부터 오늘까지 전체 종목에 대한 100건의 공시 정보를 받을 수 있다.\n",
    "\n",
    "#### 특정 기업의 공시 데이터 수집\n",
    "\n",
    "이번에는 고유번호를 추가하여 전체 종목이 아닌 원하는 기업의 공시만 받도록 한다. 예시로써 삼성전자의 공시 데이터를 수집하겠으며, 삼성전자의 고유번호는 '00126380' 다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>corp_code</th>\n",
       "      <th>corp_name</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>modify_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>20220509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     corp_code corp_name stock_code modify_date\n",
       "2432  00126380      삼성전자     005930    20220509"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corp_list[corp_list['corp_name'] == '삼성전자']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>corp_code</th>\n",
       "      <th>corp_name</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>corp_cls</th>\n",
       "      <th>report_nm</th>\n",
       "      <th>rcept_no</th>\n",
       "      <th>flr_nm</th>\n",
       "      <th>rcept_dt</th>\n",
       "      <th>rm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>Y</td>\n",
       "      <td>임원ㆍ주요주주특정증권등소유상황보고서</td>\n",
       "      <td>20220707000377</td>\n",
       "      <td>김도형</td>\n",
       "      <td>20220707</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>Y</td>\n",
       "      <td>기업설명회(IR)개최(안내공시)</td>\n",
       "      <td>20220707800073</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>20220707</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>Y</td>\n",
       "      <td>연결재무제표기준영업(잠정)실적(공정공시)</td>\n",
       "      <td>20220707800057</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>20220707</td>\n",
       "      <td>유정</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>Y</td>\n",
       "      <td>임원ㆍ주요주주특정증권등소유상황보고서</td>\n",
       "      <td>20220706000249</td>\n",
       "      <td>박종만</td>\n",
       "      <td>20220706</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>Y</td>\n",
       "      <td>임원ㆍ주요주주특정증권등소유상황보고서</td>\n",
       "      <td>20220706000082</td>\n",
       "      <td>홍형선</td>\n",
       "      <td>20220706</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   corp_code corp_name stock_code corp_cls               report_nm  \\\n",
       "18  00126380      삼성전자     005930        Y     임원ㆍ주요주주특정증권등소유상황보고서   \n",
       "19  00126380      삼성전자     005930        Y       기업설명회(IR)개최(안내공시)   \n",
       "20  00126380      삼성전자     005930        Y  연결재무제표기준영업(잠정)실적(공정공시)   \n",
       "21  00126380      삼성전자     005930        Y     임원ㆍ주요주주특정증권등소유상황보고서   \n",
       "22  00126380      삼성전자     005930        Y     임원ㆍ주요주주특정증권등소유상황보고서   \n",
       "\n",
       "          rcept_no flr_nm  rcept_dt  rm  \n",
       "18  20220707000377    김도형  20220707      \n",
       "19  20220707800073   삼성전자  20220707   유  \n",
       "20  20220707800057   삼성전자  20220707  유정  \n",
       "21  20220706000249    박종만  20220706      \n",
       "22  20220706000082    홍형선  20220706      "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgn_date = (date.today() + relativedelta(days=-30)).strftime(\"%Y%m%d\")\n",
    "end_date = (date.today()).strftime(\"%Y%m%d\")\n",
    "corp_code = '00126380'\n",
    "\n",
    "notice_url_ss = f'''https://opendart.fss.or.kr/api/list.json?crtfc_key={api_key}\n",
    "&corp_code={corp_code}&bgn_de={bgn_date}&end_de={end_date}&page_no=1&page_count=100'''\n",
    "\n",
    "notice_data_ss = rq.get(notice_url_ss)\n",
    "notice_data_ss_df = notice_data_ss.json().get('list')\n",
    "notice_data_ss_df = pd.DataFrame(notice_data_ss_df)\n",
    "\n",
    "notice_data_ss_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 시작일을 과거 30일로 수정하였으며, 기존 URL에 종목코드에 해당하는 '&corp_code=' 부분을 추가한다.\n",
    "2. 그 이후 진행과정은 이전과 동일하다.\n",
    "\n",
    "데이터 중 'rcept_no'는 공시번호에 해당하며, 해당 데이터를 이용해 공시에 해당하는 URL에 접속하여 내역을 확인할 수도 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://dart.fss.or.kr/dsaf001/main.do?rcpNo=20220803000274\n"
     ]
    }
   ],
   "source": [
    "notice_url_exam = notice_data_ss_df.loc[0, 'rcept_no']\n",
    "notice_dart_url = f'http://dart.fss.or.kr/dsaf001/main.do?rcpNo={notice_url_exam}'\n",
    "\n",
    "print(notice_dart_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dart 홈페이지의 공시에 해당하는 URL과 공시번호를 합쳐준다. 위 URL에 접속하여 해당 공시를 좀 더 자세하게 확인할 수 있다.\n",
    "\n",
    "```{figure} image/data_ref/dart_web.png\n",
    "---\n",
    "name: dart_web\n",
    "---\n",
    "공시 정보의 확인\n",
    "```\n",
    "\n",
    "### 사업보고서 주요 정보\n",
    "\n",
    "API를 이용하여 사업보고서의 주요 정보 역시 다운로드 받을 수 있으며, 제공하는 목록은 다음과 같다.\n",
    "\n",
    "```{figure} image/data_ref/dart_report.png\n",
    "---\n",
    "name: dart_report\n",
    "---\n",
    "사업보고서 주요정보 목록\n",
    "```\n",
    "\n",
    "이 중 예시로써 [배당에 관한 사항]을 다운로드 받도록 하자. URL 생성에 필요한 요청 인자는 다음과 같다.\n",
    "\n",
    "```{table} 배당에 관한 사항 주요 인자\n",
    ":name: div_input\n",
    "| 키\t| 명칭 | 설명 |\n",
    "| --- | --- | --- |\n",
    "| crtfc_key\t| API 인증키 |\t발급받은 인증키\n",
    "| corp_code\t| 고유번호 | 공시대상회사의 고유번호(8자리) |\n",
    "| bsns_year\t| 사업년도 | 사업연도(4자리) |\n",
    "| reprt_code | 보고서 코드 | 1분기보고서 : 11013 <br> 반기보고서 : 11012 <br> 3분기보고서 : 11014 <br> 사업보고서 : 11011 |\n",
    "```\n",
    "\n",
    "이를 바탕으로 삼성전자의 2021년 사업보고서를 통해 배당에 관한 사항을 살펴보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>rcept_no</th>\n",
       "      <th>corp_cls</th>\n",
       "      <th>corp_code</th>\n",
       "      <th>corp_name</th>\n",
       "      <th>se</th>\n",
       "      <th>thstrm</th>\n",
       "      <th>frmtrm</th>\n",
       "      <th>lwfr</th>\n",
       "      <th>stock_knd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20220308000798</td>\n",
       "      <td>Y</td>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>주당액면가액(원)</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20220308000798</td>\n",
       "      <td>Y</td>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>(연결)당기순이익(백만원)</td>\n",
       "      <td>39,243,791</td>\n",
       "      <td>26,090,846</td>\n",
       "      <td>21,505,054</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20220308000798</td>\n",
       "      <td>Y</td>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>(별도)당기순이익(백만원)</td>\n",
       "      <td>30,970,954</td>\n",
       "      <td>15,615,018</td>\n",
       "      <td>15,353,323</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20220308000798</td>\n",
       "      <td>Y</td>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>(연결)주당순이익(원)</td>\n",
       "      <td>5,777</td>\n",
       "      <td>3,841</td>\n",
       "      <td>3,166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20220308000798</td>\n",
       "      <td>Y</td>\n",
       "      <td>00126380</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>현금배당금총액(백만원)</td>\n",
       "      <td>9,809,438</td>\n",
       "      <td>20,338,075</td>\n",
       "      <td>9,619,243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rcept_no corp_cls corp_code corp_name              se      thstrm  \\\n",
       "0  20220308000798        Y  00126380      삼성전자       주당액면가액(원)         100   \n",
       "1  20220308000798        Y  00126380      삼성전자  (연결)당기순이익(백만원)  39,243,791   \n",
       "2  20220308000798        Y  00126380      삼성전자  (별도)당기순이익(백만원)  30,970,954   \n",
       "3  20220308000798        Y  00126380      삼성전자    (연결)주당순이익(원)       5,777   \n",
       "4  20220308000798        Y  00126380      삼성전자    현금배당금총액(백만원)   9,809,438   \n",
       "\n",
       "       frmtrm        lwfr stock_knd  \n",
       "0         100         100       NaN  \n",
       "1  26,090,846  21,505,054       NaN  \n",
       "2  15,615,018  15,353,323       NaN  \n",
       "3       3,841       3,166       NaN  \n",
       "4  20,338,075   9,619,243       NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corp_code = '00126380'\n",
    "bsns_year = '2021'\n",
    "reprt_code = '11011'\n",
    "\n",
    "url_div = f'''https://opendart.fss.or.kr/api/alotMatter.json?crtfc_key={api_key}\n",
    "&corp_code={corp_code}&bsns_year={bsns_year}&reprt_code={reprt_code}'''\n",
    "\n",
    "div_data_ss = rq.get(url_div)\n",
    "div_data_ss_df = div_data_ss.json().get('list')\n",
    "div_data_ss_df = pd.DataFrame(div_data_ss_df)\n",
    "\n",
    "div_data_ss_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "API 인증키, 고유번호, 사업년도, 보고서 코드에 해당하는 데이터를 입력하여 URL을 생성하고, 앞에서 했던것과 동일한 방식으로 데이터를 불러온다. 데이터를 확인해보면, 사업보고서 중 배당에 관한 사항만이 나타나 있다. 위 URL의 'alotMatter' 부분을 각 사업보고서에 해당하는 값으로 변경해주면 다른 정보 역시 동일한 방법으로 수집이 가능하다.\n",
    "\n",
    "## FRED 데이터 다운로드\n",
    "\n",
    "미국 연방준비은행에서 관리하는 Federal Reserve Economic Data(FRED)는 미국 및 각국의 중요 경제지표 데이터를 살펴볼 때 가장 많이 참조되는 곳 중 하나이다. 509,000여 개의 방대한 데이터를 API 형태로 제공하고 있으며, pandas_datareader 패키지의 `DataReader()` 함수를 이용하면 이곳에서 제공하는 여러 데이터를 쉽게 받을 수 있다.\n",
    "\n",
    "### 장단기 금리차\n",
    "\n",
    "장기로 돈을 빌리는 것이 단기로 빌리는 것보다 위험하므로 금리 역시 높아야 하는 것이 당연하며, 이러한 장기금리와 단기금리간의 차이를 기간 프리미엄(Term Premium)이라고 한다. 그러나 가끔씩 장기금리가 단기금리보다 낮아지는 현상이 발생하며, 이는 주요 금융시장 및 경제지표 가운데 경기침체에 대한 예측력이 가장 정확한 것으로 알려져 있다. 실제로 미국에서는 1960년 이후 발생한 모든 경기침체에 앞서 장단기 금리가 역전되어 음수가 되었다.\n",
    "\n",
    "FRED에서 각 항목별 코드를 찾는 방법은 매우 간단하다. 먼저 FRED 웹사이트(https://fred.stlouisfed.org/) 에서 원하는 데이터를 검색한다. 만일 '10년-2년 장단기 금리차'에 해당하는 코드를 찾고자 한다면 [10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity]를 검색하여 해당 페이지로 이동한다. 이 중 {numref}`fred_code`에서 네모로 표시한 [T10Y2Y]가 10년 금리와 2년 금리차의 코드에 해당한다. 동일한 방법으로 10년 금리와 3개월 금리 차이에 해당하는 [10-Year Treasury Constant Maturity Minus 3-Month Treasury Constant Maturity]를 검색해보면 해당 코드가 [T10Y3M]임을 알 수 있다.\n",
    "\n",
    "```{figure} image/data_ref/fred_code.png\n",
    "---\n",
    "name: fred_code\n",
    "---\n",
    "FRED 코드 확인\n",
    "```\n",
    "\n",
    "해당 데이터를 다운로드 받아보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>10Y - 2Y</th>\n",
       "      <th>10Y - 3M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-29</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-02</th>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-03</th>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-04</th>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            10Y - 2Y  10Y - 3M\n",
       "DATE                          \n",
       "2022-07-29     -0.22      0.26\n",
       "2022-08-01     -0.30      0.04\n",
       "2022-08-02     -0.31      0.19\n",
       "2022-08-03     -0.37      0.21\n",
       "2022-08-04     -0.35      0.18"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_datareader as web\n",
    "import pandas as pd\n",
    "\n",
    "t10y2y = web.DataReader('T10Y2Y', 'fred', start='1990-01-01')\n",
    "t10y3m = web.DataReader('T10Y3M', 'fred', start='1990-01-01')\n",
    "\n",
    "rate_diff = pd.concat([t10y2y, t10y3m], axis=1)\n",
    "rate_diff.columns = ['10Y - 2Y', '10Y - 3M']\n",
    "\n",
    "rate_diff.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터 출처에 해당하는 부분에 'fred'를 입력하면 FRED의 API를 통해 데이터를 받아올 수 있다. 장단기 금리차를 주가 지수와 비교해 그려보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 주가지수 다운로드\n",
    "sp = web.DataReader('^GSPC', 'yahoo', start='1990-01-01')\n",
    "\n",
    "plt.rc('font', family='Malgun Gothic')\n",
    "plt.rc('axes', unicode_minus=False)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax1.plot(t10y2y, color = 'black', linewidth = 0.5, label = '10Y-2Y')\n",
    "ax1.plot(t10y3m, color = 'gray', linewidth = 0.5, label = '10Y-3M')\n",
    "ax1.axhline(y=0, color='r', linestyle='dashed')\n",
    "ax1.set_ylabel('장단기 금리차')\n",
    "ax1.legend(loc = 'lower right')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(np.log(sp['Close']), label = 'S&P500')\n",
    "ax2.set_ylabel('S&P500 지수(로그)')\n",
    "ax2.legend(loc = 'upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2000년 IT 버블, 20008년 금융위기, 2020년 코로나로 인한 하락 직전에는 항상 장단기 금리차가 음수를 기록했다.\n",
    "\n",
    "### 기대 인플레이션\n",
    "\n",
    "기대 인플레이션이란 현재 정보를 바탕으로 미래에 예상되는 인플레이션이다. 이는 다음과 같이 계산된다.\n",
    "\n",
    "$$기대\\ 인플레이션 = 10년물\\ 미국\\ 국채금리 - 10년물\\ 물가연동국채(TIPS)\\ 금리$$\n",
    "\n",
    "만일 향후 인플레이션이 올 것이라고 사람들이 전망한다면, 물가연동국채를 매수하므로 물가연동국채의 가격이 상승하고 해당 금리는 하락하게 될 것이다. 따라서 물가연동국채의 금리가 하락할 수록 기대 인플레이션은 상승한다. 기대 인플레이션 역시 자산의 가격에 매우 크게 영향을 미치는 요소이며, FED의 목표 중 하나가 인플레이션을 2%로 유지하는 것이므로, 해당 지표의 위치를 통해 FED의 향후 움직임 및 자산 가격의 변동을 예측할 수 있다. FRED에서 기대 인플레이션에 해당하는 코드는 [T10YIE] 이다. 해당 데이터를 받아보자."
   ]
  },
  {
   "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>T10YIE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-29</th>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-02</th>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-03</th>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-04</th>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            T10YIE\n",
       "DATE              \n",
       "2022-07-29    2.53\n",
       "2022-08-01    2.51\n",
       "2022-08-02    2.48\n",
       "2022-08-03    2.50\n",
       "2022-08-04    2.46"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_datareader as web\n",
    "import pandas as pd\n",
    "\n",
    "bei = web.DataReader('T10YIE', 'fred', start='1990-01-01')\n",
    "\n",
    "bei.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이번에는 그래프로 나타내보도록 하자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7vMjPtoNF8NO48a2VAbeJX5zH5zMcLnz+ynGa7YL56+yN+P37P2luc3sCR6AO1Lfi5v+sxENfbAQA7K5twdZDTfD4OEb0NH5DiPyoe84aiiaXR5PknurO/MciXPHv74Nu0+L2Ii+DRtyi1eB044OVe5XVo1edcAwA60t6k2nd/nrD2/fVteLVxTsN7yOEtH8bD2g/t95cuguf/rQfLS6RxuK/79st1YYFgtUf02LGpqYpdN9YCqAg5UXkZNk0UWhrFDWRRKuUL9YexFGDVUxiGi3Ua2w91IhXDE7+bQZTeBMfXoAv1h7U3PbUvC0AtJVWxXTMPy4bgx/+eCqqHpmO6aO6Y4k8mmO0v6ksWL5aa5s3o6Yso3XPB2vx+/d/UqaLzx7ZHZ0LcywXlUskfR019TS12nnPLsanYYwoE0KSa/3+epTPmo3DQVabq+lbsXy0ah8++2m/8lnX7PafLzw+blgjSvTIO2tEN+W2ulbz/GaBAihIZeCz7TZNscl3lu/GntoWfLX+YJBHGtt8sFGZ/qgxCEhy5Q/1lhBTeK0m9wfLgVLbVSNF2p0Ls1W3SdMZ6/YZX7GL5PP2ojHIqqqWNg/ys2idhFUij0hM4eVn25GXbYPLnXoBlD5u/t/KvYbBv9Hfn7DpoHHQRQhJnunPSCV+JvxtvqXt9akw3YpzUdvcpvl8XR6iOHavjvmoemQ6nv/FOOU2zoFVu4PnQ4cMoBhjJYyxdxljlYyxbxljfVX39WaM7Zfvq2SMDQv1fKlkf10rZq85IAVQDptStRsAHvh8A059ciFu+L+VplXDzXKYrntzRUDDXjUxKuIMMQKl7+8T6na97dVSAFVa4B+BmjGpHABwyfhemm0HdS0EYG1aMZW4TH4HnHN8v6MWy6tqDRP9SSDxLhcBU162HTkOu5LTl0r0V51tHh/GPvg1XEEuAM4e2U3z/Y3/Fzj9TghpX/QjSjlZNqzcdRTbVPnJYtamU0E2rpTrPYbi9XFc+K8lQbexMgKVD+B2znkFgEcB3KG6rwTAfznnFfK/dtWl9oLnvsMtb/+IVrcXnEtL/p+6bLRyv/jgMJoyA4JXE7/9vdWm94n+dKFGe8yeP1T7iorBZZrvO6gaGf/lvOGoemQ6hnTT5kXdecYQAFDmjdsL/SjdN5sPY/PBRqzc5b9yqA9SaoKoyEGJuHLLy7Ijx2ELGpQki1EHdQB4vlJadfn4V5vw5Fxt8vsVE6QTpxiRrappiailEiEkftSfV1a06D4nxcItkQ8MQGkg7PZaa+UCWOuHF/KZOOf7OeciieAoAHUGVol8W7t0WLVy52U510gEN2pmuUrPzN9q+tzfbTNfISZqQ4UKhCo3VxveHmoK7xp5lEmwUveiIEc6blGAs73Qj45c+9oPOOMf3+Kd5XuU26ysdiT+aTGxokUagbKl5AiUWdyzfGct9tS24LlvtuOZBds094kaaUNV9dgOB1m9RwhJvJE9OwAA+nYusLT9/32/S/O90cedWPkuVSK3VgfKyuIZywkijLGekEaffq26OR/AxYyxMwD8AOBOzrlb97iZAGYCQFlZGSorK62+ZEw0NTUZvub+psAPhcrKSnhcgT+0+QsXo1OeNtbknOPNpf7l0QVZwCUDs/HGhsCcC/3rH3VKr71h4yZUNpnXKXp9SeBqAQDYumMnKu3+auKNjU0A/G+KHRu1FcWt/Mw31UpBxu/+uxol9eaBYSjLDnjQr4MNZfmxTa9T/x7VVWeXLf8B+4sCX+uDH/cqXz/32VJcMCCCiqUJZvZeTZSGRikHqmrfIQDAyu+XYH9NKw4285jtV6yO0eUxPrkt2V6Dj+YbD7s37V4PBwMmlTRh5Ogc/OsnF+YuXILeBu+faCT795gIdIzpIRWPsbVRSh7vke2ytG9Ltkufk8XZDKce40BVdWAfux9XrUJTlR1tHi8O7NuLysrDAdsIA0ts2Frnw/IVoaf4LQVQjLFzAJwL4HrOuTK0wjn/CsBXjDEbgPsBXA/gX+rHcs5fAvASAAwePJhXVFRYecmYqayshHhNzjneXr4bZ4/ojhkPfh2wrdiuz7A6TZ2Y0eOOw4Au2hpJ2w43AV8tVL5f/6BUIHPd80s000e/OqkvKiq0qWHVjS6gch6acrugomI0TM2ZbXhzj169UVExVPl+3oJvAPiDuamTJuDB778FII0+WfmZ52yvAZZLZQEi/R1xzjHjni9QmOPAuvvPiOg5zKh/j/UtbmDuXADAsePGYXgP6YrF5fECc+YEPPbjbW7847rTY7o/8aA+xmTI/2kR0NAA5BQCaMBpJ1fghnlfAAAmTDoJ+dnRJ+TH6hgbnW5g3lzD+/oPHg6s/FFz29J7Tkb3Dnk4V34bLNl+BP/6aRn6DxuFSf07R70/asn+PSYCHWN6SMVjfLPqB+DQYXTo1BkVFeNCP0D+nFx27xnIy7bj+jdXAIcPaTYZMWo0OuZnwztnEfr1LUdFxSDTp3P0PIJfvLIMo8eMBb5farodYC2JfBSAcznnN6iDJ/k+BwBwzn0AUr5t++o9dfjjR+tw36frA+67YUo/5WtRgVnQ15kAtMN7IrcCAD64aZJmu7vOHBzwWDGE+L+VewPuC+bL304GALy5RDtkKRZJHd+3FJ0Lc9CzxN8r77UZx1l67mFhFA8zI6Z64t1vTFSYBbRTOY1O89elXJfQxNSwKAlgszGUFkgjd84UW4kXbHS9Ubdo4LGLR6F7hzzNbR3lHkqfrKISB4SkkgWbpNEhq6t/J/brhAnlpUrNP6P6TR4vx1lPLwIAZIeYwhMpL54QKTaAtSTyMwFMVq20e5Mx9ihjLBvAzxhjixljCwGMBfCKhedLGtFZvslgVdaZqvoPRbnaAOrWd1YFLHlWBwkPX2TeWyvbIGEt0l48BdkOTBlUhkG6iuEiJerMEd2w4t5TkZdtV+aRR/XqYOm51Yl7Vssk6KlH3l77bqcStKzfX495Gw6ZPSxszapEd3Ug2xCkbodR7Q+iZbS68275AkCfrN/m8eHlRTuSlmAeLCDWB/Aeg2hLBFD/XbEn4D5CSPLN33QY7/0Q+u/T5fEiJ8v/Ofvj7rqAbd5S5UmFSiIXlcrNFqqoWUkif4xzPky10u5qzvndnPM2zvk7nPOTOOdTOefXcM7bRUamUTsTdfK4fgQKkCpfqwUrU3DfOf4pO6PXUk+FmCU4izpNt548AOeN7oE+pfno0ykf+Vl2tLYZf0Co3xif3XoSqh6ZjpIwuhU/cP5wAMEbJAdz5cvLlK/v/2wDvlovBU3Tn1msNHMNpa6lDW8urQr6Abm71p8bpn6TNwQZgWpvBUKNLN9ZG7J9TTSMAufcLHnFqPw+3XigAX/5dD1+884q/HX2Rry8KDlVvvUxUb/OBThvdA+Ud8rHKt0JdNoQ7apUACjJD2+lDyEk8eZauPD+cXcdDjcEDz3Uz+MIEUCJepBGF14B24bcIkOoR52sVK8WAdT0kd0D7vvlSX0DblNTj0CZTXed86xUTKy+1Y0eJXng4OCcIz/bHlCrSeTTqutYRUIEWz+oGhdH448faZPZjUro6836YC3u+2Q9ftprXOgTAH75uj8Y0wRQQUag6i1UlU11l764NGT7mmgYrQpVSm7IAdS2w014fUkV5sgFZg/UmzfxjSf91eGCOypQmOtAk8uj1P266oRjMKRbUcD0HeAPDAkhqStUI3Nxob35kHEbsskDA/MbQy3CE6vkvT6f0u7MTNoEUBsPNGDOugNBtwm2LFEdQBmNGumJwGfWWUMM779lWn+cO7pH6OcJMmoi3H3mYCy662QwxpBnEEDVOcUIVGRTg0JH+ar85v/8GGJL6Y376uKdyrSox2D0Ql8F+nwLDVxFFexQfziC+leqD5JyVAGleN5UV9vchtdV05+JpL4wE6OwIq9ABFD6gN9tshou3oyG1/fXteJIUxt2VDdjyqAyPHD+cMz53ZQk7B0hJFLqtJcjqnwmt9eHFxdu16QNGI0SqWeQjPKYzhvTM+jrq3OgjD7X1NImgDrr6UW48a3gH/xmBTEBoEA3bWc0svTcN/66MmIESv844c4zhuDZK8YG3R8A2HO0Jej9V088RhPQ5WfbUdvcpvmAfXmd9CZbvy+61hThFDBbv78BD3y+AXfKjY5rDQKUs0d2wyFVPyMro0Bi/jlYAp/6qkKTA6XLbVv7F/9KQCuBajLVt7rxVZUbv313Ff7y2QbTK6pY8fo4fv32j/hk9T7Vbf77r5ooNRIWQahI6NQH71mO6IJ2QMqXW7O3LqzHGNW4u2x8bwBSS5qiHEfIC6HfnToQQPtolkxIJmhwujWf01tU1cTf+n4XHv5yE47/23wliBJ/u8WqAZCPbvYv5FLXfBOCpd8A/s8gr4+HTCRPmwDKCpEka3T1qk8su2ZSOQZ0KdTc9sVa/wiXSEoWBSjDderQLgCkztHB6At7imk60dMOAI60SMdz4bHBI+tQyi0WLgP8K+72HpWmcIwClM6FOQEfuKFyeJhcz2rhFuMiooC2wJpPk0Su3Ydshw3zfz8VQPDgORX88aO1eGdTGxZtDaxhEg+Vmw/j8zUH8Nt3VwOQEjHVV3viyku831zy9/orsqLc6HKJ2jw+3P/ZBtwQZlsVo7/hPNXUu83CQg0xjZeKldYJyUR3vb/G9D5xfq9rceMVufC1GIG69eSBynYDu/oXWa3YVYuBJdrP9lDtysQUnsfHQ+ZBpV0Adcvb5qNQIoBaURW6ePqEvqWYd/tULJA/gAFp1EVEvE0uD7LszLByuRX3nz8CQGCTwx3VTXjq6y3K9zm6vKbx5aUAgLs+WIPqRhcanG4lB6p/mTbgC1dxbhamDirDaAsr97bIIySiHc1aOeldPaXZ0OoOyEsKlcMjpgTFH4gR9Sq8XbX+QLLB6Q6YxhTDwaGqvifbEd3SW0eEKzWtUgcYmw824t3l2tUu4m9F+fnJ31tJrAyHWN13oN5a53VBHUC9dJVUK0a9OMPKj69YDv5eX1IV1msTQuJjYFfzz7DNh/wzLDVNbZiz7qCy8ttsZTsDsLVOe9EXapW5SCL3cR6ynUvaBVCz15jnQYlRiHDqFOk/eMVjX/+uKqoPZfEBqf/A/8e8rXha1SJGH6DVNkkBxvKdtTjuoXmaVXz6YCsSxXlZQVezKfshBzp58lW8GMno17kAw+WaUo1Oj5J7pJ7XXr/fPEFcDLkGCyBaVXWg7vnQn6je0OpWPhQFMYJitQFzsuiHimMdqOjlqH4fO480B+Rctcn7o/z85L+dww3aQCfa6a9I2+yI3X3iZ6Nx+nCpBIl68YfNQh5jJ7kn3mNzNofYkhCSCGIU+evbAnMXv1h7UPn6o1X7cONbK3HNq8sB+KfdzJ5PmHvbFIzoGXyAQPTNW7W7LuRnfNoFUMGYfYiK5qJG+pTma77/w0dr8e7y3QF1ccJVWpCNQV0L8ejFozS365Od1fUtAED/uaCeOrOS/B5KhzyHpVylOnk/G50ePPLlJqXg4tg+HTH7N5MxpFsR5m86jBmv/QBAO4U2/ZnFps8rAkqPj+O17wJHoTjnmj8kweP14T/LdqOmuQ0Vg8tw7/Sh8vP5m0imshW7tKOiVoq4xcrna/Zj8yFtD0Tx89L//PTxUrT7adZnMhQxAqWOs9VBk5Vaa+qVeJQHRUjyNTk9cNgYBnQpxO9Pk6qFz98YWMqgVrdAyezvvSRP+9l+TKd8w+00zyV/BlkZmU7LAGrLoUa8b1Agz6wparCr1bxsO+5RTUvNXnMAsz5ca7q9VVl2G04e0hUPzd6IZ+dvVRLb9Dkw+kKcUwdpa9rsq4vtMvLi3CzUNrfhm02BvYK+Wn8QK+QSB0flWlH76lrxwsLtqG1uw9g+Jcqyz00HtUnQM1WV3oNRF7y8/7MNAfdvrzYuhTBftb+vXzsB102WXs/fRDJ1AyijFXf6/f1qfWDQGA23KmD4fM0BvLN8t+b+6yZLpTjECJRYbZevy/nzWuhYHkyofAQz4m9ZnbvYt3OBUt/pqhOOCfkcuaoR22gviAghkVu9pw6fr9mPJpcHhbnSApB8eYHWr96QytbkBSk9YjZj8fvTtS1bjApbW30uI2kZQJ35j29x5/8Ck9HMRqBC/cA+WR2fdg8vLNyOHUea8fevt+CZ+cYNfPXJsJ0Kc3DasK7K9wfqpCmVaEsYCMXySrxrX/8h4L4b/m8lLnlB6g1kVHOpc6F5zYwLQiwdBaSE8M9+8v+se5bkoa6lDf9csFUZcTAbKagzKVMgPmD/OntjyNdPFn25ByBwCi/cJOtQzJbnnju6h1w/SZpKFe+rxduOyI+L7VSjOnD5fof1IqGiXYu6/Eheth2r7zsdVY9Mx+jeJSGfQ103zWrZDEJI7F3w3Hf49dur8ObSXciXA6UC1fTb5oONQS9yTh/WTfP9k5eOxoTyUk1C+bI/nGJpliacTiFpGUCJc/rCLdX48ZAHHq8PT8/bippmbaLuorumYUi3Ijysm0bTu+ds41pPseT28oAeXicO6GS47UMXjFC+vusDKVD8v18dH5P9UJcy+GbTYfzxo7V4+IuNASMiRiMH6gBK3VtwQt9SDOtRjLdU+6guOfDW97uw7XCT1ARSpSjXgTEPfI0n5m7Bplrp9Y3e/+v21eNPH0v9DY/tU6K5Tz1Ckap5UFt102eAP2kbiM/0ktnc/v3nDceDqvdXjl06iX0qB7b690Esc6Auf8l6kVCRp1ccRukNPfWJ8vE5mzUrOgkhybFfXlCSryoRdMY/vjXdvrxTPjoWaKfqLjq2F967caLmNqs5wvYwUmGib6+eAsxOfCLBLK/rTjw1b0vA/SX5WZYK7Z3YvzNO6FeK73doV8y9O/OECPbWWG6WTUnEFv587nDDbbsU5wbcZqV6uhXqJGz1KJS+ImuzwRW7epsbp/bHi9/uAAAcI+eRjVUFNxv2N+CEfp3g9XHc+/E6w31RTwPaGbBq91F8bVDa/5xnF+PqicfgzaW78PjPRmvuU39Ibj3ciOE9rPUGTCR9/SpAO8X2QZhNp60wW12iP8no6zzpA6+oR6AinMITjaOLcyM/hamn7t9fuRfnj+mJkwwqFxNCEi9YZwm1I03WiiRbXTFvD2M2Jy1GoBZtC14759E5mwxvVy97DsZmY3h35kTNdMGgroU4oZ/xCJFVIkkOAApzHUoXav9rFOkfYirY/HA4zKL0N5ZWab7XFyPr17kAJ/QrVb5Xj2T1lgOoghwHepdKbTXEaIPRishpgwN7l62p9uLCfy3Bvyq3AwC66AK6t5dJOTydC8ynEffUJqftSCh2xnB831LNbeopNjHKKEQ76nO4wYlfv73K8D59OyB1zoDL4w2Y+ot2XyLNPRIn12jqUOmH6kMVtSWExN4h3cpe8Xdptd2S1Rm3eIxApUUAFelS6HDmOgFtA9KnLhsT0WuqqVu9VDf6pxf7lxXgCd1Iip4+KTtY/lE4zEYURPAxSq4R1ezy4iJV4c4Fd1RgUn//1bvNxnDe6B548tLR+M0p/iJnV0zoo3leowDqhqn9A26bvdN/NdKrYx4u1z2P2G+bwTv6FydI26ZqwcTr3lyBZTtrceEA//tLPdKjD66iTYj/cNU+w9vLO+UHFJRV/428vWx3wPsjljlQVtW1tOHBz6UFBvqyFeHQLx6JNKGdEBI5feqGWPluNa831Dno+pHZOHN4N0vFdYHwBiPSIoASkeW4YzrG9XUKc6ST9clDusRkKqhHib/J6WvfVSlfv/mr43HJuF5BH/uHs4fijOH+ZHL9HHCkJpnkXQk1TW24/KWl2FfXquk5ZOSZK8biomO1x3GJ/L0ICvR5X4D0+zQahRK+vXMaThpgPNVitKLypooBACIPtBPFrYqLHpuzSfnZHNMpH92Kc5XSDNFWVX/kS+MR2SZX4M9HnXTZ5vEFvHa0q/DC/Z28ubQKV7+6XFmFl5sV+SlMvyAnFv0Ha5pc+GhrG+VTEWLRnlrtyO/fL5UGDyoGdTHcXl92KFQplRN7ZuEFudiuFTYbU1q59egQmC6j2dbys6YwsUJLvToN8I889DVoUVIUQe5EthwRxyrfKNthMwyUuhnkOBnpmC+9kX41IjbBEyBd0f/8+D6m9++ra1VywfKzHah6ZDqqHplu+fm7FOdiRM9i5Wdo1AIm22HD9ZPNyx68vzKwRIVgNKoorigizbdJlGm9/e/JHUea8cRXUoFHsbRXjA7Fq0ZUqB5RdhuDSxfwfLH2YFSBqf53sqvGuEwFIAU4932yHmv2+guxRlP7TP9YszIn4fjLZxvwyXa3smqREBKcKIkDAH+9YASO7SMNhHTIz8KUQYEX0uLv9uGLRgIA3FFexBmZLbdt+/ulY4JulxYBlJjuMAtsanRtMgDgjtMHh/066/c3BH2dSIg3gZCbZbM8tThSnk5ri/H758Yp/THYQv5VpFfsHfKylGKdjQYf2k63T9Ok2WFjKFDN1Nz9gXkdLqMRKDFK4UzRVXh9SvNxwZge6JRnw6K7pim3O+UhqUanB4U5jrgXBQ01nWZjDE63D0W5DmUqF9COnobjg5V78e1WqeehaBA99fFK0+1jXZ1dn7/1+FebsWBT4CKFcIhgMtypyWaXVJB29Z46wwKyhKS7P5w9BL/Q1W8z+owRH49di3PkbeK3TxP7B5+RSYsASlQq1c9dvvW9lFjc4PTgrBHaOhGjLPR70xMncKvJ51boE9acbusfjvvlIpqNbbF9B/XplI+vbpuCW08eoLld//MVq+zCVZKXjTo5gBIjUEO7F+PzW0/ChL6lGN6jWNOk+fop/dCsm+krV1WUVU8lGgWfuY7UHoFye31K8rZ6nl68NZpcHhTlOpScAHVT62hce2J5WNt/s/kwFm87ghE9OuDTX5+k3G60itCK37//E77bVmM5uTPWZSiMAtEtupISq/fUKQsUrBC/o5W6yvLBrKiqxfA/f4UXFm7HBc99h/s/20CV0UlGUL/P9ekeZsRFcpciazM1kXj8klG4YkLv0PsStz1IoPdWSMu89X1v1DoVZqPqkelKKXd9smyyWE1sM3LNpHJMKC9FRa/4VKNo1uXETNJF4w+eb1xmIZQO+VnKKiqRRP7qjPEY0bMD3rthInKz7JoRKJ/BJYbDbsPALlLjyQ9umqTcbvTjtNkYsh02pfFxKnF5vDhQ71SCA3XujBJAOUUAJb1njSq0R8KsTIYZUSVfn3cUyRSe+jF52XbNtORa1RSdWqwDKKOp0A37GzTfX/Dcd/jDR9Y7D4g+Wi+FcXHxoEGRV6PcQELSjRiBBqwvhBIXzIU5Dpw1ohteuWZ8zPfrZ+N74+GLgteHBNIkgBKCTa2JzyURvUYTQKVK9/YuRbl478aJKMmNz69xxqRyTBlUht+eMhBj+5Rokt4B4ESTRO5QOuRl4UhTG7YealRGoPTL0dWjfPpRurNGdENpQbaSs6P+vZvlxOQ6bHCFMbqXKCL4/1iudt9dlbS4q6YFv3t3FbYebkJBtiNmQf+ALoU4e2S30Bua0C8vDmfUVFCPWjlsNk1dqnP/adwrMdrkeb0h3YtwfN9SfHLLicptB3VLqgUR8L23Yg+W76w13AbwFxwFrE9x6/PKAFjqR0lIexfuYou8LDvuO3cYJvXvhG4dcvH8L8bhlKFdQz8wTtIqgMrLMh+JESMTYsgwktYnoj3E3WfGtjJ51SPT8emvpZP4oK6FMX3uaPTplI83fzkBt502CB/dfCJuquiPkT07YEi3IlTeUYF+ZZHtq/jJn/bUt2h0ecAYlPL9QoEmKNI+XkzTPX3FWJw6tCt6lOTh5avH4/wxPWAmN8ueklN4+lEVhypIWrK9RgmsXB6facfxcLm9vrCCMbH6T9BPuUUyAqUefj/S5ArZ9RxAQAA866zo/g5zHHb894aJGN27RFl1M8qkU7toXnrX/9bg0heXmj7nceX+lcANrdbaw3QwqKb+1NeBhX8JSTdipkFd6iaYkvwsTB5YhrevP8Fynah4ancBVKPTjb9+vsHwpB1sBErUixEfWDkR/PAfvXgkJg/sHHbuiBUiByaeCXHR6lGSh89uPQlzfjcF5QYrG626qUKq83TKkC5ocnpQmO0ImMoUgcTwHsVg0N535xnSAoDjykvx8jXjYbcxnDqsK56+fKzpa+Zl21NyCs/qKIWXc0uNMK3weHlAAHXl8X3w9OVjDLe/TrciMnAEKvyfq36azkpivL6OV6gyGuF47spjkeMwX8Cx5VCj4e1q9S1udFB1f7eaG9bo9ODUodol2x/Hqf8mIanA7fXhprdWKiO2k006AOgveo0uNpKp3QVQry6uwsuLd+INg2m0nCA1YQrlsgXPXTkWpw3rarlUgNqQbsX4v18dH5fIV1xdp0puVjyJ6br5mw5j3b565XejV/XIdMz+zWTNCNRtpw7CMZ3CD95yHXZ8sno/5hm0gkkmq9NSbTEagdp6qBH76lqVEdiS/Cz0LMnDQxeOxPkWGj4DgQFUJMUw9+qqfuvf90ZB2fZqf4L3F7+ZHLBiJ1rZdpvp7+NwgytksHu40Yl5Gw9hVGfp5yNy9/6zbJdho+SqI814cu5m1Le6o+rpR0h7Y2cMX647qFxImS0kefCCEZqLC1EjKlW0u09rMcr0Q5WUh6A+qYkvjWo8XX6cVNto3DGl+PfV48OuQh5vQ7oX4bRhXfHkZan1Bom35VW1IatJXzOpXPk60vhSfMhfp6t6mwxurw9//XwDappcSg6Y+v14y7TASuxtnvCm3cxcJrfQEQnUq+87Hd/NOjms59Cf7BoNankFwzlXKokL+uk4o+e88a0fla+H9SgO6zWtcNiZJrH8k9X+iu1HW9pC1omqkaf5inOk36VI7/jjR+sMGyVXPFGJZxZsw766VnTIy8JDF0pNnE8b1hVFMRxdIyTViLZla/eJAMp4UGJo92K8fM1xyvep1su03QVQXeTaD+Jkpj6pDehSiDOGd8X//er4gMeVxqhSd7zkOOz499XjMaRb7D8YUl2o303nwhyc3VcKsiItnLi7NnX6nM3feBgvL96J+z/boOSRqafP7jxjSMBFwJ/PHRaTAEqM7IR7ASGmXdXPMVouBWJUDDWY/fVOpeO6oO8rGWz6K9LVn6EcbXHjgx/9jZvVzb3rWt2a6YTyWbOxRFcsU+RJdcj2l5v4fI12Ku6+T9ahfNZsHKjX9mX0+TiuPP4YVD0yHaN6dkCjy5OyrYcIiZb+QjbUaf26k/riqRQcXGh3AZQYFl+09QiONrcpJzXxAfPiVeMxRk72Ju2DlQ8KMS5gVCizvRHvYZfHq4ygju5VotlGP8rTr6wwooUPeuIZwp0OvF3V+PqNpbsA+PP2wl1yb7a9QxXUBevEHs9pbrN+eI1ON1p004rLdKvx9CNQj3+1OaBp85vyz27iwws0t6svDERbproWWolHMsMxqrp+Ru49ZxguHGutTlQitbsA6vOf/EUEn/tmmzI1oy/yeM9ZQ/CIrso3SR3/nXmC8rWVziRnlmehYnCZpeJmocSrkrdVIk7wcX8V/WxdwGQ00qYOHCLttSZez2HUdTkIo6BFVNEf2j28UVOzwMCrmo43WsYvRs2mj+oe1uvFQmubD61t2pG2p+dvxclPVKJ81mzsqG5S8jKLs8MPdNXBo2jRdLSlLfIdJqQdMZvCS3XtLoCaL8+dAtKHDgcwsmcHZWpPuGFqf1w+wbynG0mu4/t1Uqat1KMbZjrkMLx+7QSU5Ec2FateJrtA9R5KBn8l3RwlmNMHKGI6qGtxjnIhoP6QjbT/k0iSdkSQAyhqR90wRVqVN6BLEUb27AB9KOfx+nD/Z+tx2KSmUp0cGAzTBV7vXH8CxvYpASB1D9ATJUj0NcNizeP1BSSMt7o9hqNTO45Ivfsue+l7bDvchN6leTBby7KrplmZ9tS7UTVF2jFfOr6j+vL7JGXsqW1B+azZ6HvPbDwQo8K2mUJfZf+qGC8GSaR2F0CpFedJK4g+u/UknDwkecW0SGTOH9MTVY9Mx1SDhpGxdsZw//sj1hWtwyUCqB3VzdgsL4/XT8/1k8tE3DJtgOGFQDgNhasbXcooiWCPYDrwT+cMw1kjuuFWVTCa47AFTMEu2V6D176rwqwPjSt4i+ahL+o6pJ/QrxNe/IV020OzN2DN3jq8v2IP5qw7GPa+RkIskXZ6fAGr8VrbvKbTe4D0MwaAPbWthtXwAeAPH601LTqqrsLcq6M0nfGbd1dhzrrYtO0hsXX+c98BkBYuvWqxd+GummbMeG15WG1+0pH+/KsffW9P2u+eExIG9RBxsvuMiYUPS3fUKH3W9CNQR+QG2OqK7OpZvXACqKteWQYAOPnvC5XbIhmB6t4hD8//Ypym/lJOVmCFdzHVZla4VExNdS7MwVUnHKOZzhXL+Q81uHDeP7/Dnf9bgxvfWqmMWsXTHadLI6GtbV44dR26W9q8yvF8rKpcbsQsTe+48tKAYPO0YV3x/o0TNbf1LpUq/lc3ujQrD0nqGHdMR833+kUBenUtbTjvn9+hcnM1lu0MLGmRSfQB1OaDoWuspap2HUC1tHmxdHsNrn51OfbVGb+Bz+mXhddmHGd4H8kc6qRsHjDplFht3sDAQl8kU4x2jDSpjB3OCq1NBieocHOgzDhstoCcMhEMmuWaNTo9yLIz5GXb8eAFI3C8agWeWT2YFxZG1rg6HKK4rtPtRYtbO4W4bGctPpbLGujzLfXMQtO3l+2Gy+NDxWBpxPUPZw/Bv68ej+PKS7WPT4OFEulOfxF23yfrg27/i1eWKXl9NU2Zndvm0p3/ukZQkzFVtO8AyuXBnqMt+HZLtWlS7SWDsjFtSBfD+0jm6KbqMReq7lS8GfXk01diF6NU6hIPfTv7W+dE0n9OLZIRKCM2hoBwVIxArdh11DCIam3zmgYhjDHDICoRQa+YWt1ztMVwuu4TuTp4XpZd0z9P3Vfw/RsnwmyG+HCjCwfqneiQl4WqR6Zj5pTAel8ktazbV4+/z90ccHt9qxuT+nfCL06Qptd/3HUUi7ZW44WF2w2Lpq7b529SrW/GnWn0I1CdC1O7xFAw7eo3qQ+SXl68U2koG8u2DiT9ZNltqHpkOqoemZ7U5pMAQhZkVBMJxYAUmPzz51K7mmjb0lytKk4aDcaYUpZBUMdmy3Zol/p7fByvL6kyTBIXjCr9v5iAEahvNkuLCx79cpMyXfeSLk8LkD4AR6tKpVyhylE7rrwUNU7tz0O/RPsTC21aeqoad6/ZWxdyexIfl7ywBM8u2IYPVu5F+azZ+GbTYbg8XqzcdRS7a1tw/3kjwJhUwuKqV5bjkS834fKXvkf5rNmavEP1SPJz32xP1uGkhP110uIS8TNR15hrb9pVACVWHvVV9WETRfwKKIAi7YSVJPZ3Z56AG6b20zQXBvxTfdEkwo/q1SFmPaVsLLB/o/o6R71acM66g3h5rSvkcxqVMEiEm6ZKJ/JzR/fwl0fJtisrAwUx1ffqjPH47SkDA4qSntjDgRE9izFBnpo70ujChWP9bXKe+/mxIfflT+cMU76+n1Z5JY14+/7+/Z8AANe+/gN2VEsrL/cebYXdxoL+LZ365EJc+fL3SsVt//OmcNPTOPL6uNKMu2dJHqoemR7xyupU0K6iDpE4e/lxvfHwl5sAAE1tHmTbbe06k59kFiv5Syf06xRQnRvwT49ZTYQXoypqhxtCBzFWSSNQ2tvUI1I5qgDwxrdWxux146G3vPrNxpgyApWfbcfEfp2wanedsp2Yfjx5SFecPKQr3luxR/M8hdkMn986GQDwty824tShXTGhbym+XHcATrcPJ5k0TlUb0MU/XbvfJL+TxJ9Rb0QRXP/qpL4ApL5uZnwc+G5b4JTe3qOt6BOieGQ6inYlcKppV1GHCKDUV+VNTg8KctpnES6SmVweHxiLLBdC5Ep5QzS2Fa597YeA26Kd/tPbeKABWw75E9WtrBA8bZj5NGqyLoby5D6brW5/yYK8LAeum9xPs52+5MS5o3oAAP51ZeDI0h/OHooJfaWRqPdumIgZk8pRbNI8W21Al0KcIudu6qdISXKJ4Pp0+T1sdTS4tCBbCYxfWpSZ03hfrfeXJLnj9MFJ3JPYaFcBlJgOUJ/AdlQ30/QdaVfaPD7kOGy49eSBoTfWEVe70UwBxPLz+KDc004dqKlHx8wCvZuD5D2cMbyb6X3qkgexlu2wwWFjaHZ5lH5/uVk2lBZkK3W5gMBVcnnZdlQ9Mh1njwxeIX1UrxL85bzhllfZ3S/3/BuvW6VHkuuLtVJtLlFixCWPUt0wtZ/pYwDg75eOxl/OlX6ng7sWxXEPU5PPx/HE3C3K9+o8v/aq3QRQ++paMf6v8wBIS6cvOlbKKWhu81ACOWlXXB4fsu025UP6zjOsX4k5wpzCMxLLEQ2R/qMuI6IOmswSpo0SxYVg4cXxBtOasZSXbUdLm1f53YhRKfE7Gp3APpu9OuajX1lB0J9HJvl8zX787t1VCavj1urRvs6pQ6URwf/ItdvEe6NcnoqbOqgMVY9Mx4tXjcOVx/cJKEEybXAXjO4t3RbtKtr2aO9R7VR0OqTdtJsj+PMn65SvHXaGifKJtK7FTQEUSbq/z92M5brmsmZcHi9ysqQP6oJsO26ZNsDy69giDKDuOtMfpMWyIfPu2hbla9H+xKtKHD9Q34o/fLQWt/xHWxAyaACl2r2h3Yvxq5P64szh3fBuHEefhIJsB1rbvEoV9Vy5AKv4sFyvSwaOtyybLayiqensTx+vw8er92NXTXNCXq+6RRvkNLRqV46WyCtkRSsqkUx+xvBueOjCkZq/uWly7a8CedTqoS82KkV0M4W6FInIH2vv2k0ApR72zrIzJXrdXdsSdmd5QmLt2QXblNUlobjkKbxGpzvsvm72MHOglNd0+5SaUm9ff3xYjw3mqKoxsAjq1Hm3Ww814e1luzF7rbYlSbBilOq/5oJsO/50zjC8cNU4w6T6WDvY4MR/VUnhInAS1dM9CV49leVgSW9+nSrEz/5Xb6zAT3vq4v56G2q0P/cHLxih+b6T/PckAiv9aryJ/Trh58f3wYLfT8Vr104AoK339oePjFsdpSt1bTX1KtP2rN0EUOpKzQ6bTdP6Ql2kjJBgfvn6D3hxYXITOF0eH7IdNpR3LsDE/uEFBWL06M+frsePu6331Hp6/lbUNrdh+qjuGN7DuLp5tDy+wBGow43GK/6CJdCLRSKFOQ48eemY2O1gBERRz7NGSPlNL/wisC5UPGXZbXBn6JJ3ve5yMdydR5px8fNL4v56NU5tADW4WxH+ffV4AMDkgZ2Vi3pRdkMfQDnsNvztwpHoV1YI4l+9mE7azdyXepQpy8400xBdinKMHkJIgLX76pP+fnG5fchx2HFzhfWpO0HkQO2obsYlzy/Bjoenh/X47YebQm8UIY/BCJSZYFN495w1BFl2hj+fOzzodokgPiRzs6RE8UTLstngTnLz61Sx5ZD/vevxcbz+3U7MODF+U0EHmzmG9yhGm8enJPKLIG5IN38SeH2rGzZGxZxDEasXEzEVnyjtZgRKXazOYbMhS5WAlg7JaCQxsu02uGOcU8LDmE57ZfFOzNt4CBsPRDZqqi/aGMp4uenpa9dK/SDj2WfNI0dOHl/oD3yznncA0KkwBw9fNCopwZN6tZ26FlOyLK+qxdIdNahvSU5x0VRhlPP3l882hPW3F47dNS1Ye8SLniV5+Pr2qXj4opEAgBE9O6Dqken443T/FFTPjnk4eUgXy39bZ8qrTIsslLNIJ+raaukiZOTBGCthjL3LGKtkjH3LGOuruq+QMfaOfPvHjLHiuO2o6s3psDNkqZqhUg4UscphZ5Y+4MMRTkL3g59HV1Va/Xfg48C/vw3e4sRhZ5jQtxSdC3Lkx0f18kGJEajtcqVmdR8/vVRtmPuX84YrX7/xywlJ3BOtV77bmexdSKqWNuPWP/+qjM90/K/fkRY9WBlVumJCH7x8jfWG9aLsTqPTo6mflu5a3BkYQAHIB3A757wCwKMA7lDddxuAzzjnUwB8DeCmcHfg6Xlb8fGqfSG3K1D90DnXrtQJVgmWEDWHjcV8VZNRQvfOI824+T8rLVUdD4d+BOqhLzYG3zcfh8Pm71cX7giWVRP6lip/h+IV9D3g2gP1xVh+kqcP1eI10tJeGDV3BoDHvwps9BsLa/ZKqy3jUS38D2cPUb6++pXlMX/+VOVsE7XVUufvKlosnD9MxtgkABdwzu+Sv/8GwOmcczdjrBuAFzjnF5g9fmx+Pl81QXtVJzpXh1phs6O6GYcbpaJ9PTvmIdtux84j0px4cV4WhnU3Hvyqq6tDSUmJhaNrv+gYrVuztx45WbaYFrLzco4f5BIG4n284UADGlrdGNq9WJNcqu7Urn/PWznGVrc3YAVSsL+ddfvrYWcMvUrzsX5fPQpyHAH1aaIhjqdrcS5KC7LRIS8Lu2pacKjRiaIcR0Bfuyw7Q4+SfCWXJNU0OD3YsF/68JzQtzSikg+x/HsUP99eHfPRq2PqFB5M9DnH6H0vxHp1po9zpSRJj5I89CmNfRC152gr9h1tgY0xpVJ9MiTy93i0xY2DDU4M7FKo5HImQrTHyBYuXMk5H290n+VJWMZYT0ijT79W3ZzDORdnyBoAHQ0eNxPATAAYkZWFuro6AICHAweb/VMp4nYzTpd/21zeBrt8QZJjZyjL8Zk+3uv1hnzu9o6OMYzn8fnQ1hbbn5d6QEt5f8uJv01NTeAu6WShv1TR74OVYzS6EA/2GI/HB86AxsZGy68RiUMNThxqcGJIqR2tTh8YAI8ncNqlay5HHneiTu7InmrUxRPr6+sjKmIZj5+xy+VEXV3sehhGK5HnnO11voCViLkOBqf8uzpaVxfTYqPqVypgbaira4vhs0tESNYhJ/RnXzwl8vdY08rh8XA0NSS2llo8j9HSCBRj7BwA5wL4A+e8RnX7YgBTOOc+xlgXAP/inF9i9jyDBw/mmzdLQ65Pzt2MZxZsU+4LtcLlpEcXKJVMNz5wplKfJZTKykpUVFRY2ra9omO0rnzWbABSYbsXrhqHHEf0w8n1LW6MfmAuAP/7+JpXl2Phlmq8NuM4TJN7mjndXgz50xzlcfr3vJVj3F3TgimPf6O5LdjfzjnPLkLXolzcPK0/Ln5+KU7oV4p3Z060fGyhfL+jBuv3Nyi5XV/9bgrO+Me3AIAzhnfFV+sPabb//bgc3PqzU2P2+rG2bl89znl2MYDQ5yQzsfx7XLnrKC5+fgnuOWsIbphq3v4m0RJxzmlt8+K3767C3A2HAu5bf/8ZOO3Jhdhf78TUQWUxz1cT54l4rrzsd89s+Dgw7/YpGNAl9Ij4x6v2YU9tC249JfwWUGYS+dnxu3dX4cfddfj2rmkJeT0h2mNkjJmOQFlJIh8F4FzO+Q3q4Em2DMD58tcXA5hndadywpwHFcl8t582yHLwRIiZbzZXY/Xuupg8l36aCjBuuRKLgoi2MBecerwcdhvD2N4dccOUfnjqsjFR74PaCf064bLjeivfn/vPxcrX6lptQluKr8hPtQUpo3tJ060Pf7kpIcUjU8kpf680DJ4AqRDrzClS77mFW6pxoL7VcLtI3X/ecNwzIb7TzOLU8Pv3frK0/e/+uxp//3pL6A1TUNWRZny8er+mc0E6sHI6PhPAZHkVXiVj7E3G2KOMsWwADwOYyRirBDAOwGtWXzjYMmYjTrcX543ugd/EMPommS1Wabn/931VwG2i4rC6crU6eT1YIclgHGFEUE0uDzYdbMTcDYdgszHcc/ZQdO8Q+zwal6pAnrozvViNp9k2xWvphfPzTQSHKgj95es/BNky/eyvN5/mtdmY5kL6r58HX0wRjs0HG/H2st1I1NqkRqfxCsMHPtuA4/82D+WzZmPlLn/R3GgaiSeL0UVmOgiZA8U5fwzAYyZ3HwFwViQvHG4mvtPti/hDhxAjsVrY9O9FgUvMv5avnNWNe92q8gn/jXAazejz3efjmhYRyuslqACjy+R1jGpdleSk1giPXrbBqFmqGNg1+XWpku2jmydhlTxyrP4MEU16Y2Hv0RZsPtQIR9/4jkDlOGxweXyobgrMbfP6OF5Vla5QV16va3UHLRGSir5cdxAAMGNSeXJ3JMaSdrYI97PL6fGm1fJHknw8ZmNQ5oxGoHqX5mF075KIns+oZEebydSgeO3yOJcTMAug9K6ZeAyGdUrtv+FUm8JTu3piOZ6ZvxXXvRHeSNSRJhfKZ83GY3M2xWnPEuOfPx+LsX064pdyI9qzRnTHtSeWAzAvcxCJX72xAgDQMTe+74VThkq5kUYjUEcMgiqhtjl1FhNY9YLcPuuCsT2TvCexlZQAqrrRhT99vC6sx7S2eYM2ICXECs0IQ4zjJ6MaSz6DAOq3pwyK6WuYBTAi/2rmlPgmH7cZvP7pw7oG3NYeJh5EADUohUZ7zh4pVa6uaW7Dk19vwbyNh8N6/HfbjgCIX9HJRPjNKQNxzqgemtuyHTb8+dzhKMp1oC4OldrjPVoqBqSNyoocCDJ9WdMU+1WB8SYq/MeyhEoqSEoA9db3u8La3ufjUgd7CqBIlNQrQGL9gS5im89+2q/cph6BEiNFWVGMchgHUMZX36LAZ7xrrhhVFh7WI7AuW+qO7fiVFebgupP64omfjU72rii6FElTSeFedAr6qu9urw/ls2bjlv/8GPW+xcuz87dqvr/k2F6m2zY6PXh9SRWuemUZDjdEXx6jJD8LV088Ju7V8sXf5y3TAntiHjRIihcLCmI52pYoBTkOnDykS9wK+SZLUgKocKvqiitsyoEi0VI3Eo51cWdRdPHWd1YZ3i9ayBitTrPK6AR0uMF4SN8rj3gZ5UfFUm+DQoNihZRae5iCZ4zh3nOGYVSvkmTviuL20wNHLM1amxj5cu0Bzfe7aqSVULPXHsDWFG0lol9tZuXcv2jrEby/cm9Ur+vyeFHX4kZZYfwbjouFVNmOwL9PoxGobnLx2Q9+jO4YE23zwUas3VefdsETkKQA6pXF4fV1csqrfGgKj0RLfVEZ6xwooxOEunSBmMKLZkTIqDK22fRFokagAOCiY/25DeOP6Yj8bP/6lLNHdsMNU/vFtH5NJinOzQq4rTWMUQiRwNtZDgoWb61W7msvH2q5FkvXqC+QIiGmx8qifB4rHjh/BADg6fnbAu6rbgy8KBJT5Z+vORBwXyq77xNp5HTb4aYk70nsJSWAatb98Yfqeu70pF8PHZIc6mH5WI9AGSZ4q/KD3N74jEA53cYfpnvkmiu1zfHPmVC3UhL7ePeZUs+vXIcd95w11FJjVmJNVY21ejo1qmRkMdX7l8/8Da0Lc9vH7yQ3SMHbt68/XvnaF8Yf9aaDDTj9qYU475+Llam/ZxdIU4eJCKBKC7Jxc0V/rN9XH1CawOhv9nqDUd32YJncFkfd0ipdpMScWKcQSzKdbprCI7EXi/jJoxphanR5sKNae5WlDqBEPlQ0K73UQdpv5REdp0kO1NvLdgMA5sgjEPGkzk8Ux3fd5L64uaI//nzu8Li/fqZ5fUmVpe1+qKpVvm50enDPh2s19xflpN6H2nPfBI7IBMsbVC+iuPuDtWhyWZvevO2/P2HLoSas2VuPCX+bjw9W7sU7y/cASEwABUhBlMfH0SRPybo8Xlz24lIs2HQYQ7oV4fNbT1K2PaGvv+eftx3Wgkpk/7tESUpEIiLRV66RqqOHatgprpxi0XaDECEWHe71V4rPfaNd6fTQFxuV4fiO+Vk4f0wPdC2OvL6MOp/pknFSYu2v316Fx7/ahPJZs7Fgk79y8zmjuwMwzqGJNXWSrzi5Z9ltuOvMIeiQn3of0u3Vx7ecCACYPLCz6Tb/XLAVf/1cjDJpz63vLN+t+f60pxbGdP9iwWgJfzgJ3aKxt5kauayDvk7Z79/3VwTvmJ+YOktiVLZJLmWwo7oZy3bW4nCjC6UF2ZpRW/XffqOz/RWm7FTYvmpXWZGUAGpItyJM6FuKU4Z2xYS+pSFzUVzyCFS41csJCSYW13D6KTWvL3BJ/5Nfb8GpTy5Ep4IcPH35WAzqGrrvlRXqKW0RuP3y9RXKbSI+DDXCG5t98f9tWq0LRcLXq6NUSf6u/60x3eaJuVvwspJnGvxdLvqLppJwR1fCDXY+WrUv5Dbi5xxvYgpVjJqpzydLtteYTnul4u/NzFB5ev9vF45M8p7EXsIjksMNTizbWYuD8iqD5Ttr8f2OWjz8pXkpfnFCphEoElNxGAUX081q7yzfjW2HmzB/U3j1e0IxmtI+Y7i//pJY9edIQHVt9QhBO5xdSHkf3jwJ/7hsDIpUOUueEL0Vjza34ca3pFIF904farpdQ4qNZrhUf0OTB3bG30OUlOioG+EMdUH+zebgf4eL7poW9xIGghhhEsU01fmMJflZmt+32nsr9sR/52LE5fFi+qju6JSAlY2JlvAA6kM5+tc3FXxx4Q7TxyhTeJQDRWIoFqvwvLppQLOaTADw3x9246RHF4S1BN3IqzPG481fTjBcVPHVev8UntsjptISm3sQi6lRonVsn464YGxPzUWkfjGO3saD/imqif07mW63J8UavDa6pICuINuOl64aj4vHmdeAAoDeHbVlNOwh+hmqe1IaPp9BWY54KZJXWIoRKPUF2JOXjg64+Jl1lrQwIxaNyRMlnYtgJzwiCbZKwmy1EE3hkXiIxee8fsbOrK0KADS0erD3aGvInL9QTh7SFVMGlQVdzbdg0yHc9YE0zRPNqr9IhLMSikTOZbD60qjyPRA8WTzVAqgv1kqLHq6eVK5pGGzGZmN46rLReOhCqSzANa8u1/wcNuxvQPms2Tjhb/Ph9XE4dVPMdhvDazOOi+ERWCdGmNbsqUP5rNm49MWlyn09SwIDuQl9SwFASXZvD1rdXsNiu+kg4RHJ4q1HNN+LNwQAHPvg14ZtIcQUXjYFUCSGYvE5HzACZTCFJyhTajFcjWIWi932X39CbKJXvxikgZEYEgnkrQYBVIvqNvVIZ6fCbKUlDAB8+usT8eAFUsDxzSZ/XahUEk5j5wvH9sLxqlVq6v5yovDkwQYn6lraUNciXaifMkTqRVdWmINpQ7rg2SvG4v0bI2vyHSnR4qS2JXDwoESemnzu58fig5uk/RqTQgVerWpp81oKhNujhEckS7bXaL7v3kG7ImlfXWByHBXSJPEQi3ESff0Ws5ICALC9uhlAbIsXmgWB6guRRORAqdEIVHzdftogXDa+t+H5UJ0XJfKfAGnBwY1T/T0RR/TogKtOOAZA6k0HnTlcCvRumBpe3aM+qqm3MQ/OVb5W/73VNrehtqkNMyaV45UZx+HB84fjvzecAAA4d3QPHFfuv6BPBIfdhmHdiwNWBF42vrdSFHT6qO4Yd4y0X2ZdBe79eG1KNov2+jjaPL60/exO6JnVpfpsOXGAdLXwwHkjNNsYFQWkQpokHmKRq6MPFkL1qcqys7gmqA7tXowGp1szOpHoHCgKoOJrbJ+OePSSUehiUA7DbZLfY7cxpRUI4P8gHtKtyHLdpESx2xgGdCnUVLO3Qj1DoX4Lqt/9BxucaHR5UCqvTL1qYjmO6VQQze5GrW/nAmzYrw2gbqrob3qeKNCN5qzbV4+3vt8dVrPoNo8Pkx6ej6+qol9AsL+uFRMemoeqI80B96X74EdCA6g6l/9d/czlYwEAHfKzcP3kvsrtRgGUaFtAARSJpXkbD4XeKAT9kusmZ/API0eIBNdoub0+rNx1NKGvqcfaRdvg9mtPbQvKZ83G9GcWBdxnNJr0l3OHAZCmqu44fZCmcndBjiPlmtN6fL6op53VQYb6+O58X8oLLEmh2mQdC7LQoDtvBFswddbI7uhZ4i+z8MTczcrXVi8Ks+wM1U0uNLiiv9j5ZPV+HG50BdQYA/yf5+n62Z3QM2urx//LUi9p/OP0YcrXRn/M1EyYxMN7K6JvyqkfbRHvVXVvOLV45yMZLW1P9AhUglaAZyyRT7JeN2oBGK8wu3piOQCp1MSvTx6ISf39RTjzs+0pNwLl9fGoF1o0t3mxSO75py7TcFBu2RJtz7xYyrYHBhfBSvZk2ZlmsYo632vjAWvNoRljKMxxoDXEikQrxDnQaHqxlUagEqvR4Are6faCsfCSCglJhOLcLPz8+D7K9+KKa2I/42Xj8U6mNJrCSVRNGyHaDz8SXOcg9XTcBhn8ZnkzgPTht3pPXcCoZTJ5fTzidkdPXz4G/cqkKbk3l+7Ceyv24JPV+wO2Ky1InQDq262BSfylQYrfZtltmgsl9e/OaBRIz+fjGHzvlzja4saC3R6s3lMX3g4bPB8AGL3NRFmGdC1BlDJHdflxvQEAN761Ekd15Qxa27zIddgT/kFASChdinPxtwtHYlBXqSG2GIEy+wCId0Ndt9enmUALdiKOlwTPGGYksxXJoWoc6YlRLKP+c8ni8fGIF1qcP6Yn3p0pJYUP6VZkWrG9d2liKo1bse2wv3/mzCn9NFOsRr5afxBHW9yG5SeOPaYk5OtV1TRrugX85/td1nfWgMhiEH06XR4vpj1RiUkPz8fZ8jQzTeHF2R9VlXJv+L+VmvvcXh+VMCDtilkxv0PyFEI8ZNtt8Pi45kIjGVMVlAMVf784/piAZGIA+Pci84LERsQMdEGcA/tweH08qqnuLkW5yM2yaap1P3bxKM023TukTgAlctQAoGJQmWaK1UizvBpr8mPfBOQ8GZUB0jvcqO01+P7K6FIZRCkXcd45WO/EziPN2F/vVPZHv1o5XaRMVCIqsgLAclUHcUC6IknHTs4kuS4dH7zCcTTsJqOloapHR6M4zwG3V5uA+/q1E+L2emboTzX+SvKz0NzmDUga/1+YH4biA7gwJ3VGCDwxyIHKy7LjUIM/ULj0uN745o6KKPcsPiYPKlO+tlJyRJ2z1veeL5TRb8B8FaaaUZpMNMR7SPzOjNpZDYxR/89Uk9AAKtJp0GjmxAkxE+4yaSOfrN6H0ffPDbjyi2WtJ6uK87Lg9vqUD9UPbpqkWbqeKMFydEhsiCaz9a3RLUPvIa/mSpUplvkbD2H5ztqwGwrrGf1t9+1cgFdnjMcnt5wY1XPHWv8yfwBkZcGH/tSy5VCTEkSF6o8IAE2u2PY+VJLImZQDesY/vtXcX5Btx4AuhUYPbfdSZgRKbWyfEs33bi9P+FJskv5iUQeqpc2L+lY3/vnzYzW36wOoeK2EO6Gfv/BfUW4WPF6u5MEkevWd8PdLgzd/JdETy/BDBVAiH8jMa9dKLUxinZt3/Zsr8LcvzBvEm/n9+1IF/aMGlbnDYbZi++QhXTG6d0lUzx1PVtoufXhzYABYki/lOnosBJ5//mQ9AOCO0wfh/P7S+yiaKTYx6uWw23CwPjBFIZ6j7smW8KikrCgH/zMply8+cwp0Vw9eny8pV/QkvcViWl5c8XXVFTXUTzn/7cKR0b+YgcvkxRcAUJzrgMfHlSXOie6BJ4iTOYkfMQL17293YMSfv0J1owvls2YHbHeCyWpQQeQCPbsgtknkX284hJe+DS8fCwDqWqSA0EogEIx6tWui27NEw8rf7JjeJdj+t7M1t5XKf3NmvTiXbq/BiD9/hZW7apWaU9dN7oc8h3Seao6iwbnoyfjonE1RB77tTULPsJwDJ/bvhPEm5fK/vn0qAGDxtiPKB9PDX2zEx6v30xQeiTkeg2YubfLVl77Ehn7puAiw1AXwYsGtqq3WTX4NUUoh0SNQr84Yjzm/m5zQ18xUYpr03R/2oMnl0Ux5JasxbiwF6ylphag7NGNSecLbs0TD6uecfkBBTNWbrcI81OBEk8uD/yzzlznIcdiQJ49VRJMX1aZ6TaMRqHSW8KUXZquTAO1c8JfrDuLc0T3wonwVQ0nkJNZiOQKlP/Hp36+5WXY8ffkY04uHSNXIJT9uO3WQUmtFFKNN9LT3yUO6JvT1MllxrraStkdV/0mfAmFVXUtb0kcPcxw2uDy+qIt7ilXbHdvJaGi/zgXYcaQ54gbnvTpKF2ZmOVBiZEqdq8kYU0agovl5u1TdQ462xDa/KtUldgQKgNVZhRcWavv6BAu8CIlELHKgxFSDfujdxhg+unmS8r3dJtWoifUI1IiexfjVSX3x21MHKvsgWh9lUemPtKUv66JugfXGksjq+qzZWx/VPkXria82K/WJOhVGF/iIEbnC3NQpzxBMb1Uj5EiM6V2CLDuD2+Sq8KmvtwDw55ZNHiiVSsiNcgRqX10rPly1T/n+Dx+tBSA1ZhY+VJ0H003Cz7BWA6EjTdpaFclKiCXpKxY9b8UVnf796bAzjO3TUUnOjVd17skDy/Cnc4Zp9kG0T8iiUdu0pQ+g6lv9H4AuT2RJu7FqAm3Uj8+Kf6qKef7nuuDFJEMRA3Lt5U/g6cvH4LGLR0W8Wm1g1yJk2W1wy+ejFxZuxznP+nslHpCn1sTF1ZVy9wRxjrj4+SU4/5+Lw37dq19ZZnj7acP8o9HH9ukY9vO2Fyk3AiVWjRxqcGlWBlCDdxJrsfjAEI1PGWOagngiYBIn8ERMp4kRKGUKj1ofpS19APXYnE3K15FOW0VbOkCIRSJxr47RjciIaUyjmkSpqCQ/G5eqFoSEq8nlQUubFy8v3om6ljY88uUmrNvXgEandkqtVf55iMKp6rfRTxGMQG6vbja8PTdDRr8Te5TcvMCgoF410qoalrZSYZWQcMTi88Lt9dcoG9Gzg3K7yIESyZ6JmIEWr9kqr6ihhRfpS79oYdlOqfjwCf1KMePE8rCe65VrxgOIPoCqa2lD+azZ+Fg1pRPO8viKwVJByUV3TYtqPwDgrjOH4MHzh2PGpPKon6s9aFJNwW1VtYZxun1Ysv2I8v3GA1LrHjEyrj9DxKpTwqA0LZypl9AAygep6JdV6sQ2s+WZhEQqFqOabq8PWXJ0pF4ZY1cCqMD74kU/ApVFeYNpyyyl4Yap/cMuX9G3s9R8tyXKej1jHvgaAPC3L/yjYeqL4GA27G9A5WapqW60+UCA9Pd21cTyuDfvTgU3Tu2vlLUAgHzVMXt9HM9Xbg94TI5D2sapW7VXdcR4RMnMsO7FQe/vkYRCvomU8DPs0h01lrdVR8Mui3+IhFgViyRyt9enJGt365CLfvKHkT+AkrZLxCpSMeK0ak8d8rPtpsUESftn1lhdLN//5JYT8cFN1uofiemc3/13NWa8tjw2Oyg7UN9qaTvRdJaEb9ZZQzQdB77b5h9x8vh8St6Tmjg36M9L4azGa23zYoM8ogUAN0zpp3xd3rkAT146Gl/+dorl52uPUvoM+4uX/QlqXkqCIjEWkxwor79PY/cOebj99EEA/AGUyH2KVxK5WnknKXjbdrgJLW1e0w9Zkr7E6NPo3iUYd4y1khnqRsJiFEjN6fai3z2z8cnqfQH3hfLg5+FVI79xav+wX4NovbJ4p/K118fR4AwsLdBPLhk0uKMNN0zth7vPHAIgvACqWtWUuEeHXKUd0PF9pffdRcf2Qof8LMPHpouEBlD5DhZWQ8cG1bwuzeCRWItFSN7m9WmmTEQeiV2fA5WAYKZLkb8HXaS1gEj78dqM4/DwRdoK92UR9CEM1calutEFHwd+++5q7KoJb4pn4ZZqS8UVp8oNdWedNSSs5yeBSgv874F3lu8J2u6HMYZ7zhqKi8f1BBBeOQN17bEPbp6kTJeOVOWCpruEBlBd8pky3x7MRwZ1I2Ix3UKI2ier90f9HB4v1+Sj/Pbd1QD8iyVEAJWIEVT1qruNqqF1kp6mDemijDoKfTpFlj/Uv8z4vLx2bz0mP/aN8v2X6w4abhesie2OI6HzXnOzbBicIYnHsTLv9ql426Dcw6Xjeylfv7BwOxpaPab3C0U50khRWFN4clrNnWcMRvcOeSiQA6h07n2nl5JTeGMN6kacOKBzEvaEpKPKMEZBQ/H4fIZJu/oRqGiadVqlXnUnhtNJelP/zqNpkluoqmx+laq2z4er9mq2M0s0dwVZJX3r26tw1tPBc5zcXh5QmoEEN6BLISapPhdFY+j7P9ug2U6dyP/05WPw2CWBzb5zs2yw21hA2YNgRImI4T2kRPJ8uYdtaxR99dqb9lGmFcBJFECRGCm3MApqVZuHG9ZbEoFTead8bDvclJDGvupVd7kOCqAygTow/+0pAyJ+niLVNN6irUdwyfNL0Oj0YPOhRs12ZkU6Nx3UbndMp3zsqmkBILUbEi2HzLi9PiqWHKWJIRpHA1I3BCOMMWTbbXCb9NIz8uGPUnAtHiP6fXYsaB/tc2IhZUP+7qpVBdNHdcfQEMslCQlHl6IcxCItyePzIdvgxC8Sy5+8bAye+/mxMQ3azKhLJdAKvMzgVI38TOwX+UVmWZE2d2rFrqMBwRMAOE1GoFbtPqr5fldNC16+eryl1163rx6Lth5BlRxwkchEe5HmsLGwqsi/v0IKoJpc0qjViQM64anLRuOuMzInjy1lR6Dm3jYFn/10ACX5WTh7ZPdk7w5JM8f364R1+6Lv/eX2+gxHoGxyMFOcm4XpoxLz/s2iKbyMoy5MHE3QrA+gzDS6PDj32cXI8ThRUeG/fXiPwMThERaTiT9fcwAAUBtilIoEF22tOYedwRPGCNSNFf3xzPytOGuEdH5jjOHCsYH5VeksZQOootws/Fzu10NIrGWFebVl5MHPN+C7bTXKsl21RNR90lOXLcihACojnDjAP20TTdkKq+/XjvnZWGtw4SFKgkzq3wk+znHv9GGWG/lStY3U4LDbNCvrQvH5OGwMyMng3LXMPXKS0cK92jIi6q0YJb/aktzFNBkBHEm8WNX6svKX0LkwG1sMpvUA4OEvpXpPvz99EN6dOREjenZAvi6IN1upR+/UxAg1Qlnd6MI7y/dYfj6n24vcLHtG15ujAIpkJIfdFtUI1Ib9/jIBRkPn+g+PRAvVc5Kkj8o7KixXHTdjVlRW3SIkN8uO1bvrArbZXt2Edfukvwd1vTObjeG3pwxUvnearNSjIsnxcck4/3TaO9efgPm/r4jp87+8eGfU7X/au5ABFGOsjDH2EGPsQd3tvRlj+xljlfK/YWbPQUiqyY4ygJq38ZDytVETVqO8qEQ6JsJ6QKT9Ke9cYLnqeLjURRjzs+1oNKgTtETVOkRfzuDWk/0rA1vbvFi9pw7ls2Zj0L1fAgBeXbwTLy7cAYCKv8bCorumKXWe1H3oJvbvhJ4leUEfW07njLBZOcv/HYALgL4mewmA/3LOK+R/GwIeSUiKctgYPFHUZlJPR+inAlPhg+CB80ckexdIe2LhTyHPZFRVPV2tbx7ssNtw3ugeAKQpn0VbpFYxbR4fnpy7GQ987v/YePWa48Lda6LTuzRfqQdmdWGAMKFvKboVh9f8N5OqjhsJGUBxzq8G8K3BXSUAjhrcTkjKc9htUeVAPbNgm/K1eiTLbmOY1D90PZZ4y4Qu9CR2xBReZ4NWMCcN6IzZvznJdGWnetrOKMg6fXhXAMDkx75BV9WoiPpvCMis+kHxdKRRWs0o+t1ZFU4SuSi4magVxqkqmlV4+QAuZoydAeAHAHdyzgPKmDLGZgKYCQBlZWWorKyM4iXD19TUlPDXTDQ6xvDt29sGt9eHb775JuokyNq6emXffD6OPbt3o7LSuOVFMLE8xlR9P9B7NTU1VUun7ov7AZ9tZ9jf7L+4cDjrUL1lFVobtT3txN/Olr3+075z91pU7tY+97bD/mm/u/63xnQfUu1n1h5/jwCQ3+jFZYOzsWX9T8ptZsehPsZDB1xodXksHfP+JinQOrpvJyorrSeeJ0M8f48RB1Cc868AfMUYswG4H8D1AP5lsN1LAF4CgMGDB/MKdfGQBKisrESiXzPR6BjDt9a7FXz7Fpw0ZWpkBejmzFa+zM0vREXFZADAH+07MKZ3CcaXh5+TEotjrBzRjEanByN7pebQOr1XU9NkH8eU9Qdx5ohuWPDUt0Czv39dSecuqKgYixmq9zwATDxpCnKz7Ni/bDewbi0AGB539rYjwI/LAm4XBnYpxK9PHoAKkyrZydIef48AUCH/v+1wE7B0oXSbyXGoj/Hbxg1YdmiPpWNetLUaWLwcJ58wFsdbqICeTPH8PUYcQDHGHJxzD+fcxxirieVOERJvovRAm8e4l1041MPe103uF9VzRSsRFc9J+rHbGM4aKQoiau/rapIX09ImLWN3uoOvxMoNMZ289XCTaYsRErkBXQpx5xmDMWVgmaXts+zWa+MdqJdGI3uESExPd2F/cjDGHmWMZQP4GWNsMWNsIYCxAF6J+d4REifqACocg+79Er96/QeMVo3wRFtPipBUwnSVmW47bZDhdi1y01h94rieWfI5ib9bpg2wPBq940gzXB6fZlWlEafbi00HpHpgXYrDS1RPN5YCKM55Jed8lvz13ZzzNs75O5zzkzjnUznn13DOXfHdVUJiRwmgwixl0ObxYf6mwyhW1ccJ9zkIaU9E8ngnXZJ3q1wDSIxAfTfrZMPHhwqgRvSkPqep4Psd0kTS3A2Hgm73l0/X49XvdiI3y4acDG9anrKtXAiJJ1Fo0qyAYCjqxHMagSLp6OJje+Gc0f5VVnN+NwXvrdiDsqIc3PW/NUoRxdY2L/Kz7aZ1hoKtCL315AH41Ul9Y7vjJCI+uaxLqJ56hTlS2JCfTeED/QRIRrIpAVRkj/9WrmcDIOqeeoSkEi4Xhbp0fC9NgnBZUQ5umTZAGalYuKUao3p1gNPjDTrKpC9/MLhrEU4e2gU7q5tx+2mDMroVSCoR58JQXaBEj8PcDO6BJ1AARTKSOGf7oiimKQzpXhT1cxCSKsSgrFlD6jFyocYnv96CvUdb4PUFBklq+uDqxAGdcfeZQ2KyryR2REudUH08G51S7tv+emfQ7TIBhZAkI4kRqFAzeM/M34ryWbODrjT615XjYrlrhCSV+JPIMRlhUN/+3oq9cLq9Qafpsh02XHSsf5Xd3WcNjsl+ktga2k26ECzKCT6uIpqoEwqgSIayye/8UDlQry+pAgA0G/QAA4Cf/ny6puEqIe2daFNkFkDpp9xa3cGn8ADg5CFdlK8zPfE4VT3xs9EAgC4W27lMH5nZVcgBCqBIhrJFmUQOSH2gKHgi6WZod2lVnMh1CaW1LXQApS+NQFJPUa50LjNqji5w1flStOjJZJQDRTISiyCJnOuCrVC5AoS0R0/8bDSuPbEvuhRZG4locXtDXki4PNIU+AVjekS9fyQ+xOq7YE3Wf/PuagDAoK6FSpPoTEYjUCQjidhHHxQFoz+xOCiAImmoIMeBCX2ttyKqb2lDXlbwjxKnW5oWDJZsTpJLBFDeIKuKP/tpPwDgrBHdafUkKIAiGSqSMgb6ek92OoEQgprmtpCBkViEQQFU6lICKAvnxGCLBjIJBVAkI4nBI6s5UBzanneAPxGdkEzW6PQgN0RiuFOewssJMVJFkkeMqHt9oeva0eC7hN7NJCMxi0nk4jzh4zxgBMpBERQhAIDcEIFR9w5SPtXALlQzLVVZyYESbDT6DoCSyEmGsloHSuA88MRCSeSESMyKbgoXjOmJrsW5mKiqbE5Siz8HKvRJMYrFy2mFAiiSkcKdwvNxHrCtneInkqEY/AU3gdBtPRhjmNS/c1z3iURH5HR6LZwTu3WwtkIz3VEARTJSuEnkPh7Y9oV6CJNMxZh2FCLUCBRJfTYbg40Fb47ev6wA9a1unDOKimgClANFMhSzOAIl7vX5OP5VuV1zn4eaCJMMpR98NataTtoXxpjSTLq1zRtw0djS5sW0wV2ohIGM3vUkI/lzoCyuwuPAhz/u1dzm8lAARTJTQABFI1BpgUE61zndXgy9bw4e/WqTcp/L48XRljYUhOiVl0kogCIZKdwpPA4e0OJg5a6jsd4tQtqFIZ20AZNZr0jSvoiBpYZWNwDgwx/3KfcNu+8rON0+FFIApaAAimQkJYncYgTlM1iFR0im+vWYHDx9+Rjle/GBS9o/DqBNTk/ItvtDBHEBSSNQfhRAkYxktReeug6UcPXEY+K0V4S0D7kOhuE9ipXv86kydVpgYOAcaJPTE7INctsKc+h3LVAARTJSuL3w1NvRFRgh2kKy+dn0N5EWmJSuUFaUAwAY27skYBNqx+NHARTJSKIIZjhlDARacUQI4FAVQhvQpTCJe0JihQEA90/XlRZkB2yTZafzn0A/CZKRIimkKeTRFRghmhGoKYPKkrgnJFYYk3KgFmw6DADYe7Q1YBs7dWBQUABFMpLVXnji3kMNLnSRh7UHd5P6eZ04gNpSkMzloFL8aUfKgeJodUvNn7Pk0Xb1CmQKoPwogCIZyWovPFEs893lu3HZcb0BABWDu2D+76fi31ePj+s+EpLKaCQ2/YgK8063dhXekSaXsk3vjvlJ2bdURJl/JCNZncITV142xuD1cTjkB/Yvo5wPktloMUX6ET0OnfIIVLZDOt+JMhW3TOuPkb06JGnvUg+NQJGMZLWQpqj9xJjUZNNGw9eEkDQlUhtEACXOk7XNbQCA8eWlydmxFEUBFMlIVnvhiXYtNsbg83GlYzkhRFKUSyNR6US0cgGA6kYX3F4fLnvpewBAEY06atBPg2SkcHvh2Rjg9VECJSFq399zCuVCpRFpCo/D6ZbOi3M3HMLAP36p3E/Ttlr00yAZSQRQB+qdlrf3cQ6Knwjx69YhN9m7QGJJTiJvM2mUTjXwtOinQTKSCITu/2wD3luxJ+T2jDF4fD44qIgcISRNietDt9c4gCrJDyysmcno04BkJKbKZfp2S7XhNnuPtihf223SFJ6NcqAIIWmKMakOVJtJAGVUmTyTUQBFMpJ6Kk4kTOodVE3vtbR5pSRy+oshhKQpUYncbAqPaNHHAclI4Y4kfb7mADy0Co8QksYYpBwooym8Sf2p84IeBVAkI6kDKLOFePpYqcnlpk7khJC0JVIb3N7Ak+IjF41K9O6kPAqgSEaKZCDpaAsFUISQ9MZhnAPVpxO1cNGjAIpkJHVFcfNgSntHfYsbOVn0J0MISU/BpvBIIPo0IBlJnURusZYmmlweZFEWOSEkTTEGtLZ5sflgY7J3pV2gQpokI1lJBtdv4nR7lWbChBCSfhg+XLUv2TvRbtDlNMlIzEIAJdq8XHxsLwBAq9tLhTQJIWmLFhmHh0agSEbSTOGZbCPSAESz1FYagSKEpDGjs9uss4bgsvG9E74v7QFdTpOM5LWQ+OTxSRGUSBznHBRAEULSltEI1EXH9kRHqkBuiAIokpFyHKHLEcjxk2Zbh50CKEJIemIGY1BUPNhcyCk8xlgZgN8B8HHO/6S6vRDAvwH0BFAL4GrOeUOc9pOQmOqQlxVyGzEClasqXeCw0TUHISRzUP9Pc1Y+Df4OwAVA/4lzG4DPOOdTAHwN4KYY7xshCcFNpvN88u2aESiawiOEpKmaZlfAbTY655liZh8emo0YqwBwJud8luq2bwCczjl3M8a6AXiBc36BwWNnApgJAGVlZePee++92Oy5RU1NTSgsLEzoayYaHWNkZsxpBgAM72TDncflBdz/4yEPnlnlwjXDsvHGhjYAwOSeDvxqZE5M90Og32N6oGNMD5l4jOKcqPb8qfnIc7TfICra3+O0adNWcs7HG90XzSq8HM65W/66BkBHo4045y8BeAkABg8ezCsqKqJ4yfBVVlYi0a+ZaHSMEZozGwCwv9UO3n0Ypg3uornbue4AsOpHjBw+FNjwEwCgV88eqKgYGdv9kNHvMT3QMaaHjDxG+ZyoVjFlCvKy228Lq3j+HqNJ6PAxxsTjOwKojsH+EJJwR1vcuPa1HwJu9/ik0Vl1DlQWJZETQtLUhPLSgNso7dNcND+aZQDOl7++GMC86HeHkORpcnk033vlAKoo15/+Z6d8AEJImupcFFiugJLIzYUdQDHGHmWMZQN4GMBMxlglgHEAXovxvhESV92KczXfH6hr1XwvAqg+pfkokIewqRceISRdGZV3oTIG5izlQHHOKwFUyl/fLd98BMBZcdkrQhJAP5rk9moXVIgAymFjKM7LQnObl0agCCFpS52uINAqPHN0OU0yln5uX9R9EkQAZbcxJYkyi04mhJA0ZaXAMPGjAIpkLP3cvturC6C4fwTK5Zbus1NGJSEkTeWoRqCGdi/GhgfOSOLepD76NCAZKzCAMp7Cs9sY9sn5UdTKhRCSrtQjUFl2hvzsaCodpT8KoEjGenXGcZrvPUECKIEqkRNC0lWOg0KCcNBPi2Ssvp0LNN+7g+RAnT+mBwDAQavwCCFpSh1A0YKZ0OjTgBBZsBEoUb6ARqAIIelKHTRR+YLQKIAiRObRJZF7jKbwKAeKEJKm1HmhNAIVGgVQhMjcPu0IlE8EUIxBnEpoBIoQkq7Up7dzR/dI3o60ExRAESJze0KPQBFCSLpi8ghUt+JcXHl8nyTvTeqjAIoQWZtuCs/HOWxMOqlQOgAhJN2JKbzcLJsSTBFzFEARImvWNRP2+DgccuFMEVv5J/MIISS90GB7eCiAIkTW7PJqvvf5uNLuRQRX+TnU6oAQkp70xYVJcBRAESLj0CaRq0egmtukAKowhyrzEkLSk4ifaPrOGgqgCJFxbfwEr48rQ9rnjOoOABjSrTjBe0UIIYlBI1DhoctpQmS6+AleH1cqj186vjcuGdebVuQRQtKWSFng+qtJYogCKEIE3UnDy7lyRcYYA9XQJISkM/X5joRGU3iEQJr7DxiB8nIqnEkIyRgicKIRKGsogCIE0pWXz2AEiqbsCCGZgk534aEAihAADMZJ5BRAEUIyBSWRh4cCKEJgMoVHARQhJIPQ6S48lEROMlrfzgVgDNhb20ojUISQjEbJ4+GhAIpktG/uqAAADLr3y4BCml4fh51OKISQDCGm8CiF3BqawiMEUg6U/qzhoREoQkgGsVNEEBb6cREC4xwoH63CI4RkEJrCCw8FUIQAYGABtU9oBIoQkkmUKTyaw7OEAihCII9A6U4aPgqgCCEZhE534aEAihDIdaB0t3l8PgqgCCEZg+pAhYcCKEIgzf0HjkCBVuERQjIGne7CQwEUIRAjUPocKB8c1EGYEJIhaAQqPBRAEQIABjlQXg6awiOEZAwKoMJDARQhkOtA6Xh9PprCI4RkDHG9WN/qTu6OtBMUQBECwGYLLGPg9dEIFCEkc+Rm2QFQAGUVBVCEQBqB8gX0wqNVeISQzNG5MAcAkEW5n5ZQAEUI5FV4Rr3wKIAihGQIOt2FhwIoQiCvwlPFT/vrWrG9upkCKEJIxmBUiTwsFEARgsBeeOc8uxgA5UARQjIHne/CQwEUIQAAhl01zVi0tRoAUNvcBgAY1LUomTtFCCEJQ/FTeCiAIgTSCNR322pw1SvLNbfPnNwvSXtECCGJxahsS1gogCIExnWgAGDL4caE7gchhCQLjUCFhwIoQmDeA2pHdXNid4QQQpKEKpGHhwIoQgAwkzEoWo1CCMkUIoCi0541lgIoxtiDjLGFjLHvGGPDVbf3ZoztZ4xVyv+GxW9XCYkfswsvfW0oQghJVzY5ItB3ZSDGHKE2YIxNBtCVcz6VMTYCwOMAzpbvLgHwX875bfHbRULiz2zgWl+dnBBC0hVN4YXHygjU6QDeAQDO+ToApar7SgAcjf1uEZJYZqtP6EqMEJIpKHwKDwv1AcEYexHAs3LwBMbYYgBTOOc+xtgZAB4D0ATgBwB3cs7dusfPBDATAMrKysa99957sT+KIJqamlBYWJjQ10w0Osbo/b6yBTVO6W/h9TMLMGOOlDx+w6gcTOwRcqA2Juj3mB7oGNNDJh4j5xw3z2/BpYOyMa1PVhL3LHai/T1OmzZtJed8vNF9Vj4Z6gF0VH3v45z7AIBz/hWArxhjNgD3A7gewL/UD+acvwTgJQAYPHgwr6ioCPsAolFZWYlEv2ai0TFGL2/ZAsDZCgDS68yZDQAYOnQoKsb2jNvrqtHvMT3QMaaHTD3GjdOSsy/xEs/fo5UpvEUALgEAOUl8r7iDMeYAADmgqonHDhKSCOq5f58q8YmSyAkhhBixEkDNBpDNGFsE4AkAdzPGHmWMZQP4GWNsMWNsIYCxAF6J474SEjfqFCivalrb50vCzhBCCEl5Iafw5NGlm3Q33y3//478j5B2TZ086dWMQBFCCCGBqJAmIdCuwlOvq5gysHMS9oYQQkiqS8zyIkJSnGYEinMwBtw6bQC6FOcmbZ8IIYSkLhqBIgTQRFBeHwfn1JmcEEKIOQqgCIFxDhRV5SWEEGKGAihCoB1t8nh98m3J2htCCCGpjgIoQqAdgfIoI1DJ2RdCCCGpjwIoQqAdbfJ4uXwbRVCEEEKMUQBFCACmGoPyyNUzKQeKEEKIGQqgCIFuBMrHA24jhBBC1CiAIkRHTOFRDhQhhBAzFEARAm2+0wc/Sv2yaQqPEEKIGQqgCIF2Fd4ri3dKt1EARQghxAQFUIQA2FfXGnAbTeERQggxQwEUIQDqW90Bt1H8RAghxAwFUISYsNEQFCGEEBMUQBFignKgCCGEmKEAihATNABFCCHEDAVQhJhglAVFCCHEBAVQhJigEShCCCFmKIAixAQV0iSEEGKGAihCTFD8RAghxAwFUISYoFV4hBBCzFAARYgJyoEihBBihgIoQkxQDhQhhBAzFEARYoLiJ0IIIWYogCLEBOVAEUIIMUMBFCEmKAeKEEKIGQqgCDGxp7Y12btACCEkRVEARYiJ2mZXsneBEEJIiqIAihATTS5vsneBEEJIiqIAihATt506MNm7QAghJEVRAEWIgW7FuehSnJvs3SCEEJKiKIAixMDBBmeyd4EQQkgKowCKEBM+H0/2LhBCCElRFEARYsJGhaAIIYSYoACKEJX7zxue7F0ghBDSDjiSvQOEpJJrJpWjd2keth5qSvauEEIISWEUQBGic/KQrjh5SNdk7wYhhJAURlN4hBBCCCFhogCKEEIIISRMFEARQgghhISJcqAIAfDSVeNwpKkt2btBCCGknbAUQDHGHgQwRd5+Jud8vXx7IYB/A+gJoBbA1ZzzhjjtKyFxc/rwbsneBUIIIe1IyCk8xthkAF0551MB3ADgcdXdtwH4jHM+BcDXAG6Ky14SQgghhKQQKzlQpwN4BwA45+sAlKruOxnA+/LXHwCYGNO9I4QQQghJQYzz4P2+GGMvAnhWDp7AGFsMYArn3McYW8I5nyTfngVgnjxSpX78TAAzAaCsrGzce++9F4fDMNfU1ITCwsKEvmai0TGmBzrG9EDHmB7oGNNDtMc4bdq0lZzz8Ub3WcmBqgfQUfW9j3PuE18zxmzy9x0BVOsfzDl/CcBLADB48GBeUVERzr5HrbKyEol+zUSjY0wPdIzpgY4xPdAxpod4HqOVKbxFAC4BAMbYMAB7VfctA3C+/PXFAObFdO8IIYQQQlKQlQBqNoBsxtgiAE8AuJsx9ihjLBvAwwBmMsYqAYwD8Frc9pQQQgghJEWEnMKTp+f0q+vulv8/AuCsWO8UIYQQQkgqo0rkhBBCCCFhogCKEEIIISRMFEARQgghhISJAihCCCGEkDBRAEUIIYQQEiYKoAghhBBCwhSylUtMX4yxRgCbE/aCks6Qyi0kSgdI1dsTiY4x9ugY44OOMfboGOODjjH22uMxDuacFxndYaWVSyxtNuspEy+MsRWJfE3G2Euc85mJej35NekYY/96dIzxeU06xti/Hh1jfF6TjjH2r9fujpExtsLsPprCi73Pkr0DCUDHmB7oGNMDHWN6oGNsZyiAijHOeVq9QYzQMaYHOsb0QMeYHugY259EB1AvJfj1kvWaiUbHmB7oGNMDHWN6oGNMD9Eeo+njE5pETgghhBCSDmgKjxBCCCEkTO0ygGKMlTDG3mWMVTLGvmWM9WWMDWaMzWeMfccYe1y17YOMsYXy7cN1z3M+Y+z7xB9BaNEeI2PMxhj7B2NsqXx7p+QdjbEYHGMvxtgcxtgixtgzyTsSc2EeYxlj7CHG2IOq2wy3TSUxOMbL5ceuYIzdk5yjCC7aY1Tdly7nHKPfY7qdc4yOMW3OOUbbybenzTknyDHG5pzDOW93/wD0ANBD/no6gOcAfAmgXL7tfQDHA5gM4CX5thEAvlA9hx3ABwC+T/bxxOMYAdwE4JfJPo44H+OTAE6Vv34LwLhkH1Okxyh//SaA+wA8onq84bap9C8Gxzhe/t8G4HsAZck+plgfo3x7Wpxzgvwe0+acE+QY0+acY7Sd/HXanHOCHGNMzjntcgSKc76fc75f/vYoABeAXM55lXzbBwAmAjgdwDvyY9YBKFU9za8B/CchOxyBGBzj2QAGy1H344wxlrCdtygGx9gIoJQxZgNQJD9HSgnjGME5vxrAt+KxjDGH2bapJJpjlG9bIf/vA1ADoC0Bux2WaI9Rli7nHLNjTKdzjtkxps05x2C75nQ75xgdo/z4mJxz2mUAJTDGegK4A8DfIf0QhBoAHQF0AVCtut0jDzOPgPTD/TBhOxuhSI8RwAQA/+OcTwGQB+CixOxx+KI4xhcBPAFgI4B6zvmOxOxx+Cwco5GyMLZNugiPUf34mwEs4pwnulKxZZEeY5qdc8yk0znHTNqdc1Tb/QNpes7RHaP68VGdcxJdiTxmGGPnADgXwPUAWgCUqO7uCOkDNw/aX74PQDaApwH8PCE7GoVIj5Fz7mOMHeSc/yDfNhvAeEhReUqJ8hhfBXAi53wPY+xWxtjNnPN/JWjXLbN4jEbqwtg2qaI4RjDGigA8DmBeKv7+hEiPkTGWi/Q655hJp3OOmbQ656i345zXMMbyzLZNNZEeo3xbTM457XIEijE2CsC5nPMbOOc1nPNWADlylAlIVz7zASwCcIn8mGEA9gI4BVLg+DRj7F0AAxhjf0z4QYQQ5TECwG7G2Ej56woAaxK28xbF4Bi7A6iVvz4AoDxR+25VGMcYIJxtkymaY5T9E8CTnPP/xXtfIxXlMabbOcdMOp1zzKTNOUe/HZB+5xyjY5TF5JzTXkegzgQwmTFWKX+/G8DtAP7HGHMB+JRzvpExthnA2YyxRZDmrm/gnO+BdHUEAGCMfc85fyixu29JxMcob38HgJfkNISfAHyayJ23KNpjvBfAXMaYG9IVyIxE7rxFlo4xyOPD2TZZoj3GcwAco0qZeYBzviBeOxuhiI+Rcz4baXTOCfL4tDnnBHl82pxzGGN36beT877S5pwT5Bhjcs6hQpqEEEIIIWFql1N4hBBCCCHJRAEUIYQQQkiYKIAihBBCCAkTBVCEEEIIIWGiAIoQQgghJEzttYwBISQDMMYaAPwof+sB8JRcFkDcfyyABQB6c84bGWMvAxgAYAykOkQ+AD+D1BvLJn+/j3N+ZcIOghCSlqiMASEkZck1k06Qv+4K4F0Af+KcL5Zvex5AA4CdnPMXVI+rBHAm59xp9D0hhESLpvAIIe0C5/wQpGKNVwMAYywfwDEAHgBweRJ3jRCSgSiAIoS0J1UA+shfXwrgfc55M4AtjLFxIR47lzFWyRi7L547SAjJDJQDRQhpT0YC2CZ/fRUAF2PsfEiNRGfC3+bHyOk0hUcIiRUKoAgh7QJjrD+ABwHMYIwNhdTX6lrV/UsYY4Wc86ak7SQhJGNQAEUISWXDGGPfAPACOAxgBud8O2PsSUgr69Q+BnAFgH+bPNdcxphP/vp0znlbPHaYEJIZaBUeIYQQQkiYKImcEEIIISRMFEARQgghhISJAihCCCGEkDBRAEUIIYQQEiYKoAghhBBCwkQBFCGEEEJImCiAIoQQQggJEwVQhBBCCCFh+n9CeXU8X8wcnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "bei.plot(figsize=(10, 6), grid=True)\n",
    "plt.axhline(y=2, color='r', linestyle='-')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fear & Greed Index\n",
    "\n",
    "CNN에서는 발표하는 Fear & Greed Index(공포와 탐욕 지수)는 투자 심리를 반영하는 대표적인 지수다. 해당 지수는 총 7개 지표를 이용해 계산된다.\n",
    "\n",
    "1. 주가의 강도\n",
    "2. 시장의 모멘텀\n",
    "3. 주가의 확산정도\n",
    "4. 시장 변동성\n",
    "5. 안전 자산에 대한 수요\n",
    "6. 풋옵션과 콜옵션 비율\n",
    "7. 투기 등급에 대한 수요\n",
    "\n",
    "위 7개 지표를 합산하여 0점에서 100점 사이의 점수를 구하며, 점수가 낮을 수록 시장이 공포에 휩싸여 있음을, 점수가 높을 수록 시장이 탐욕에 물들어 있음을 나타낸다.\n",
    "\n",
    "```{figure} image/data_ref/fear_greed.png\n",
    "---\n",
    "name: fear_greed\n",
    "---\n",
    "Fear & Greed Index\n",
    "```\n",
    "\n",
    "해당 지표는 아래 사이트에서 확인할 수 있다.\n",
    "\n",
    "```\n",
    "https://money.cnn.com/data/fear-and-greed/\n",
    "```\n",
    "\n",
    "개발자도구 화면을 통해 현재 점수가 위치하는 곳을 찾아보면, 클래스 이름이 [market-fng-gauge__dial-number-value]이며, 내용이 동적으로 계속 변하기에 정적 크롤링이 아닌 동적 크롤링을 이용해야 한다. 셀레니움을 사용해 해당 지표를 크롤링 해보자.\n",
    "\n",
    "```{figure} image/data_ref/fear_greed_html.png\n",
    "---\n",
    "name: fear_greed_html\n",
    "---\n",
    "해당 지표의 태그 확인\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "47\n"
     ]
    }
   ],
   "source": [
    "import selenium\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.common.by import By\n",
    "\n",
    "driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))\n",
    "driver.get(url='https://edition.cnn.com/markets/fear-and-greed')\n",
    "idx = driver.find_element(By.CLASS_NAME,\n",
    "                          value='market-fng-gauge__dial-number-value').text\n",
    "driver.close()\n",
    "idx = int(idx)\n",
    "\n",
    "print(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 크롬 드라이버를 로드한다.\n",
    "2. 해당 URL을 브라우저에 띄운다.\n",
    "3. 클래스명이 'market-fng-gauge__dial-number-value'인 곳을 찾은 후, 텍스트를 추출한다.\n",
    "4. 브라우저를 닫는다.\n",
    "5. 지표가 문자형이므로 정수형으로 변경한다.\n",
    "\n",
    "해당 지표를 통해 투자 심리를 객관적으로 판단할 수 있다. **'공포에 사서 탐욕에 팔아라'** 라는 투자 격언이 있듯이, 지표가 지나치게 낮은 공포 구간에서는 오히려 매수를, 지표가 지나치게 높은 탐욕 구간에서는 투자 비중을 줄이는 선택을 할 수 있다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "pygments_lexer": "ipython3",
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     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
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