{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case 1-5: 日銀のREIT, ETF買い入れとREIT, 株価指数の関係を見てみる\n",
    "Correlation between ETF/J-REIT purchases and stock prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:29.276327",
     "start_time": "2016-09-17T17:59:22.444340"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import requests\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "from io import BytesIO\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 日本銀行のサイトからETF、REITの買入結果を取得する\n",
    "download ETF/J-REIT purchases data from BOJ site\n",
    "\n",
    "> 日本銀行  \n",
    "> 指数連動型上場投資信託受益権（ETF）および不動産投資法人投資口（J-REIT）の買入結果  \n",
    "> http://www3.boj.or.jp/market/jp/menu_etf.htm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "元のファイルはzipファイルなので、展開する関数を用意  \n",
    "make function for unzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:29.289326",
     "start_time": "2016-09-17T17:59:29.279326"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_zip(input_zip):\n",
    "    input_zip=zipfile.ZipFile(input_zip)\n",
    "    return {name: input_zip.read(name) for name in input_zip.namelist()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandasからExcelファイルを読み込む場合はpandas.read_excel()を使う  \n",
    "リンク先のzipファイルをメモリ上で処理する場合にはBytesIOを使う  \n",
    "use pandas.read_excel() for reading Excel file in pandas  \n",
    "use BytesIO for opening zip file on memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:29.320331",
     "start_time": "2016-09-17T17:59:29.294329"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_excel2df(url, filename):\n",
    "    zipdata = BytesIO()\n",
    "    res = requests.get(url, stream=True)\n",
    "    zipdata.write(res.content)\n",
    "    return pd.read_excel(BytesIO(extract_zip(zipdata)[filename]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ExcelファイルからETFとREITの買い入れ額を取得\n",
    "get ETF/J-REIT purchases data from Excel files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:29.965361",
     "start_time": "2016-09-17T17:59:29.324332"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    etfreit_df = load_excel2df('http://www3.boj.or.jp/market/jp/etfreit.zip', '2016.xls')\n",
    "except Exception:\n",
    "    etfreit_df = pd.read_excel('data/2016.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.010328",
     "start_time": "2016-09-17T17:59:29.969329"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>指数連動型上場投資信託受益権（ETF）および不動産投資法人投資口（J-REIT）の買入結果</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Purchases of ETFs and J-REITs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>（億円)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>（100 million yen)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>約定日\\nTrade Date</td>\n",
       "      <td>指数連動型上場投資信託受益権</td>\n",
       "      <td>NaN</td>\n",
       "      <td>不動産投資法人投資口\\nJ-REIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ETF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(1) 右記以外</td>\n",
       "      <td>(2) 設備投資および人材投資に積極的に取り組んでいる企業を支援するためのETF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Purchases of ETFs other than those mentioned i...</td>\n",
       "      <td>Purchases of ETFs to support investment in phy...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 指数連動型上場投資信託受益権（ETF）および不動産投資法人投資口（J-REIT）の買入結果\\n\\n  \\\n",
       "0         NaN                     Purchases of ETFs and J-REITs   \n",
       "1         NaN                                               NaN   \n",
       "2         NaN                                               NaN   \n",
       "3         NaN                                               NaN   \n",
       "4         NaN                                   約定日\\nTrade Date   \n",
       "5         NaN                                               NaN   \n",
       "6         NaN                                               NaN   \n",
       "7         NaN                                               NaN   \n",
       "8         NaN                               2016-04-01 00:00:00   \n",
       "9         NaN                               2016-04-02 00:00:00   \n",
       "\n",
       "                                          Unnamed: 2  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                     指数連動型上場投資信託受益権   \n",
       "5                                                ETF   \n",
       "6                                           (1) 右記以外   \n",
       "7  Purchases of ETFs other than those mentioned i...   \n",
       "8                                                336   \n",
       "9                                                NaN   \n",
       "\n",
       "                                          Unnamed: 3          Unnamed: 4  \n",
       "0                                                NaN                 NaN  \n",
       "1                                                NaN                 NaN  \n",
       "2                                                NaN                （億円)  \n",
       "3                                                NaN   （100 million yen)  \n",
       "4                                                NaN  不動産投資法人投資口\\nJ-REIT  \n",
       "5                                                NaN                 NaN  \n",
       "6           (2) 設備投資および人材投資に積極的に取り組んでいる企業を支援するためのETF                 NaN  \n",
       "7  Purchases of ETFs to support investment in phy...                 NaN  \n",
       "8                                                NaN                  12  \n",
       "9                                                NaN                 NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etfreit_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "データを整形(クレンジング)  \n",
    "cleansing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.036327",
     "start_time": "2016-09-17T17:59:30.014327"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "etfreit_df = etfreit_df.iloc[8:, 1:].copy()\n",
    "etfreit_df.columns = ['Date', 'ETF', 'Ohter ETF', 'REIT']\n",
    "etfreit_df.set_index('Date', inplace=True)\n",
    "etfreit_df = etfreit_df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.072334",
     "start_time": "2016-09-17T17:59:30.041328"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETF</th>\n",
       "      <th>Ohter ETF</th>\n",
       "      <th>REIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>336.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-05</th>\n",
       "      <td>333.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-07</th>\n",
       "      <td>333.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ETF  Ohter ETF  REIT\n",
       "Date                              \n",
       "2016-04-01  336.0        NaN  12.0\n",
       "2016-04-02    NaN        NaN   NaN\n",
       "2016-04-03    NaN        NaN   NaN\n",
       "2016-04-04    NaN       12.0   NaN\n",
       "2016-04-05  333.0       12.0   NaN\n",
       "2016-04-06    NaN       12.0  12.0\n",
       "2016-04-07  333.0       12.0   NaN\n",
       "2016-04-08    NaN       12.0  12.0\n",
       "2016-04-09    NaN        NaN   NaN\n",
       "2016-04-10    NaN        NaN   NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etfreit_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-02T10:26:31.046868",
     "start_time": "2016-09-02T10:26:31.042874"
    }
   },
   "source": [
    "## ExcelファイルからREITと平均株価を取得\n",
    "get TSE REIT index and stock indices data from Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "データが複数あるので整形用の関数を用意  \n",
    "make funcation for shaping multiple data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.122329",
     "start_time": "2016-09-17T17:59:30.080329"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cleansing_df(input_df):\n",
    "    df = input_df.copy()\n",
    "    df.columns = ['Date', 'Open', 'High', 'Low', 'Close']\n",
    "    df['Date'] = pd.to_datetime(df['Date'])\n",
    "    df.set_index('Date', inplace=True)\n",
    "    df = df.astype(float)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.143329",
     "start_time": "2016-09-17T17:59:30.126327"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_df(key):\n",
    "    data_dict = ohlc_data_dict[key]\n",
    "    try:\n",
    "        df = cleansing_df(pd.read_csv(data_dict['link']))\n",
    "    except Exception:\n",
    "        df = cleansing_df(pd.read_csv(data_dict['data'], encoding='sjis'))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当日(終値 - 始値)の騰落率を算出  \n",
    "get daily return (close price - open price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.160329",
     "start_time": "2016-09-17T17:59:30.147328"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pct_change(data):\n",
    "    return ((data['Close'] - data['Open']) / data['Close']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:30.177324",
     "start_time": "2016-09-17T17:59:30.166328"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ohlc_data_dict = {'tse_reit_index': {'link': 'http://k-db.com/indices/I133/1d/2016?download=csv',\n",
    "                                'data': 'data/indices_I133_1d_2016.csv'},\n",
    "             'topix': {'link': 'http://k-db.com/indices/I102/1d/2016',\n",
    "                      'data': 'data/indices_I102_1d_2016.csv'},\n",
    "             'jpx400': {'link': 'http://k-db.com/indices/I103/1d/2016?download=csv',\n",
    "                       'data': 'data/indices_I103_1d_2016.csv'},\n",
    "             'nikkei225': {'link': 'http://k-db.com/indices/I101/1d/2016?download=csv',\n",
    "                          'data': 'data/indices_I101_1d_2016.csv'}\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:31.174406",
     "start_time": "2016-09-17T17:59:30.181327"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = dict(zip(ohlc_data_dict.keys(), map(load_df, ohlc_data_dict.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:31.226332",
     "start_time": "2016-09-17T17:59:31.177325"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reit_df = pd.concat([etfreit_df['REIT'],\n",
    "                     df['tse_reit_index'].apply(pct_change, axis=1)], axis=1).dropna()\n",
    "reit_df.columns = ['BOJ', 'TSE REIT INDEX']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 日銀のREITの買い入れ額と東証REIT指数  \n",
    "Purchases of ETFs and J-REITs and TSE REIT index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:31.258336",
     "start_time": "2016-09-17T17:59:31.230330"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BOJ</th>\n",
       "      <th>TSE REIT INDEX</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>2016-04-01</th>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.533002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-06</th>\n",
       "      <td>12.0</td>\n",
       "      <td>-0.399714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-08</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.784616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.003160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-15</th>\n",
       "      <td>12.0</td>\n",
       "      <td>-0.330606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BOJ  TSE REIT INDEX\n",
       "Date                            \n",
       "2016-04-01  12.0       -1.533002\n",
       "2016-04-06  12.0       -0.399714\n",
       "2016-04-08  12.0        0.784616\n",
       "2016-04-11  12.0        0.003160\n",
       "2016-04-15  12.0       -0.330606"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:31.851328",
     "start_time": "2016-09-17T17:59:31.263327"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda304ccc50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l7zoAAAAAR6jVJ+kBAAAAbkdABgAAACwIyAAAAIAFARkAAACwICADAAAAFgRk\nAAAAwMKngPzCCy9o79693tcej0efffaZTpw4UWlcRkaGxo8fX7sVAgAAABeQTwH51Vdf1b59+7yv\nT506pd/97nf6+uuvK437/vvvtXr16tqtEAAAALiAqj3FwjTN2qwDAAAAcATmIAMAAAAWBGQAAADA\nwueAbBiGT9sAAAAANwvydeDYsWPP2HbffffVajEAAACA3XwKyCkpKf6uAwAAAHAEAjIAAABgUeOb\n9EpKSvSf//xHR48erY16AAAAAFv5FJA3bdqkl156SR6Pp9L2efPmqUuXLrr99tvVs2dPjR8/XiUl\nJX4pFAAAALgQfJpisWjRIhUUFCgwMNC7bcOGDZo2bZo6duyoAQMGKCsrS4sXL9aVV16p3//+934r\nGAAAAPAnnwJyZmamRowYUWnbe++9p0aNGun1119XWFiYJKm0tFSrV68mIAMAAMC1fJpikZeXp6io\nqErb1q9fr549e3rDsSQlJCQoOzu7disEAAAALiCfriBfeumlOnLkiPf1N998o+PHj6tz586VxtWr\nV09BQT4vrVxlpmlq3LhxysjIUG5ursLDw9WtWzeNHz9erVq18ttxAQAAcPHw6QpyfHy80tPTdezY\nMUnS/PnzFRAQoMTExErj9u7d6/egesUVV+jJJ59Uenq6Jk+erL1792r06NF+PSYAAAAuHj5d7n3w\nwQd11113qVevXgoJCdGPP/6ooUOHqmXLlpXGrV69WgkJCX4pVDr9aOs//vGP3tcdOnSQYRhKSUlR\nfn5+pekeAAAAQHX4FJB3796tV199VevWrdOPP/6ouLg4DRgwoNKYgwcPKiEhQXfeeadfCj2bw4cP\na/ny5WrXrh3hGAAAALXCME3TPN+gdu3aacaMGerbt++FqOm8pk2bpgULFqi8vFwdOnTQ7Nmz1aRJ\nkyrv5+TJQnk85X6oEDURGBig8PAQ+uNA9Ma56I1z0Rtnoz/OVdEbO/gUkGNiYvTyyy/7LSDPmDFD\nc+fOPef7hmFoxYoVio6OlnR6VY28vDzl5ORo1qxZCg0N1RtvvOGX2gAAAHBx8d+SE1UwbNgw9evX\n72fHWG/+a9KkiZo0aaKrr75aV199tRITE7Vp0yZ17dq1Ssflp0Vn4qd556I3zkVvnIveOBv9cS47\nryD7HJDXrVtXaam3czEMQ/fcc0+VioiMjFRkZGSVPlOhqKhIkqr1iGuPp1xlZXwZnIr+OBe9cS56\n41z0xtnWVwAAAAAZzElEQVToD6x8DsjvvfeeT+OqE5B9tXXrVm3YsEEdO3ZUeHi4Dhw4oDlz5igq\nKkpdunTxyzEBAABwcfE5IKempurGG2/0Yynnd+mll2rnzp16++23lZ+fr6ZNm6pnz54aPXq06tev\nb2ttAAAAqBt8Dsjh4eHVngZRW6688kq99tprttYAAACAus2nJ+kBAAAAFwsCMgAAAGDh0xSLbdu2\nqUGDBv6uBQAAALCdTwG5YcOG/q4DAAAAcASmWAAAAAAWBGQAAADAgoAMAAAAWBCQAQAAAAsCMgAA\nAGBBQAYAAAAsCMgAAACABQEZAAAAsCAgAwAAABYEZAAAAMCCgAwAAABYEJABAAAACwIyAAAAYEFA\nBgAAACwIyAAAAIAFARkAAACwICADAAAAFgRkAAAAwIKADAAAAFgQkAEAAAALAjIAAABgQUAGAAAA\nLAjIAAAAgAUBGQAAALAgIAMAAAAWBGQAAADAgoAMAAAAWBCQAQAAAAsCMgAAAGBBQAYAAAAsCMgA\nAACABQEZAAAAsCAgAwAAABYEZAAAAMCCgAwAAABYEJABAAAACwIyAAAAYEFABgAAACwIyAAAAICF\nawNySUmJ7rjjDsXExGjbtm12lwMAAIA6wrUB+bnnnlPLli1lGIbdpQAAAKAOcWVA/vTTT/X555/r\n4YcflmmadpcDAACAOiTI7gKq6siRI3ryySc1e/ZsNWjQwO5yAAAAUMe4LiA/+uijGjZsmK699lp9\n9913NdpXYKArL6DXeRV9oT/OQ2+ci944F71xNvrjXHb2xBEBecaMGZo7d+453zcMQytWrNDatWtV\nWFioUaNGSVKNp1eEh4fU6PPwL/rjXPTGueiNc9EbZ6M/sDJMB0zizc3NVW5u7s+OadOmjcaOHat1\n69ZV2u7xeBQUFKS+ffvqhRdeqNJxT54slMdTXuV64V+BgQEKDw+hPw5Eb5yL3jgXvXE2+uNcFb2x\ngyOuIEdGRioyMvK84yZPnqz8/Hzv68OHD+v+++/X1KlT1bVr1yof1+MpV1kZXwanoj/ORW+ci944\nF71xNvoDK0cEZF+1aNGi0uuQkNM/VURFRal58+Z2lAQAAIA6xvUz0lkHGQAAALXJVVeQfyoqKkpf\nffWV3WUAAACgDnH9FWQAAACgNhGQAQAAAAsCMgAAAGBBQAYAAAAsCMgAAACABQEZAAAAsCAgAwAA\nABYEZAAAAMCCgAwAAABYEJABAAAACwIyAAAAYEFABgAAACwIyAAAAIAFARkAAACwICADAAAAFgRk\nAAAAwIKADAAAAFgQkAEAAAALAjIAAABgQUAGAAAALAjIAAAAgAUBGQAAALAgIAMAAAAWBGQAAADA\ngoAMAAAAWBCQAQAAAAsCMgAAAGBBQAYAAAAsCMgAAACABQEZAAAAsCAgAwAAABYEZAAAAMCCgAwA\nAABYEJABAAAACwIyAAAAYEFABgAAACwIyAAAAIAFARkAAACwICADAAAAFgRkAAAAwIKADAAAAFgQ\nkAEAAACLILsLqKrExETl5OR4XxuGoTFjxiglJcXGqgAAAFBXuC4gS9KDDz6oxMREmaYpSWratKnN\nFQEAAKCucGVAbtmypa655hq7ywAAAEAd5Mo5yM8995w6deqk2267TWlpaSopKbG7JAAAANQRrruC\nPHToUF177bVq1KiRduzYoRkzZujQoUNKTU2t8r4CA13580GdV9EX+uM89Ma56I1z0Rtnoz/OZWdP\nDLNiIq+NZsyYoblz557zfcMwtGLFCkVHR5/x3tKlSzVp0iRt3rxZYWFh/iwTAAAAFwFHXEEeNmyY\n+vXr97NjWrVqddbt8fHxkqQDBw7o2muvrdJxT54slMdTXqXPwP8CAwMUHh5CfxyI3jgXvXEueuNs\n9Me5KnpjB0cE5MjISEVGRlbrszt27JBUvZUsPJ5ylZXxZXAq+uNc9Ma56I1z0Rtnoz+wckRA9tXW\nrVv12WefqWvXrgoLC9OOHTuUlpamfv36sdQbAAAAaoWrAnJ4eLi2b9+uxYsXq6ioSFFRUbr//vv1\nm9/8xu7SAAAAUEe4KiBfddVVWrhwod1lAAAAoA5jTRMAAADAgoAMAAAAWBCQAQAAAAsCMgAAAGBB\nQAYAAAAsCMgAAACAhauWeQMAnCkjM1NvvrNMRZ4ANQgs1/ChSYqLjbW7LABwLa4gA4CLZWRmKjVt\nifJCE3SqUXflhSYoNW2JMjIz7S4NAFyLgAwALrbg7WUKje4lwzAkSYZhKDS6lxa8s8zmygDAvQjI\nAOBi+cWmNxxXMAxD+UWmTRUBgPsRkAHAxcLqGzLNymHYNE2FNTDO8QkAwPkQkAHAxZLvTlLB/nXe\nkGyapgr2r1Py0CSbKwMA92IVCwBwsbjYWE1KkRYuXq7CUkMhQeUal3Inq1gAQA0QkAHA5eJiY/Vi\nxzg1bhyqY8cKVFZWbndJAOBqTLEAAAAALAjIAAAAgAUBGQAAALAgIAMAAAAWBGQAAADAgoAMAAAA\nWBCQAQAAAAsCMgAAAGBBQAYAAAAsCMgAAACABQEZAAAAsCAgAwAAABYEZAAAAMCCgAwAAABYEJAB\nAAAACwIyAAAAYEFABgAAACwIyAAAAIAFARkAAACwICADAAAAFgRkAAAAwIKADAAAAFgQkAEAAAAL\nAjIAAABgQUAGAAAALAjIAAAAgAUBGQAAALAgIAMAAAAWrgzIH3/8se68807Fx8crISFB48ePt7sk\nAAAA1BFBdhdQVatWrdJjjz2mUaNG6dFHH5Uk7du3z+aqAAAAUFe4KiCXlpZq6tSpGjdunEaMGOHd\nfv3119tXFAAAAOoUV02x2Llzp/Ly8hQSEqIBAwaoZ8+eeuCBB7R37167SwMAAEAd4aoryNnZ2ZKk\nOXPm6PHHH1fz5s316quv6t5779Xf/vY3NWrUqEr7Cwx01c8HF42KvtAf56E3zkVvnIveOBv9cS47\ne+KIgDxjxgzNnTv3nO8bhqEVK1aovLxcpmlqzJgxuvnmmyVJ06ZNU48ePbRq1Srdc889VTpueHhI\njeqGf9Ef56I3zkVvnIveOBv9gZUjAvKwYcPUr1+/nx3TqlUrNW3aVIZhqHXr1t7tISEhatWqlb7/\n/nt/lwkAAICLgCMCcmRkpCIjI887Li4uToGBgfr222/VuXNnSVJhYaG+//57tWrVyt9lAgAA4CLg\niIDsq0svvVSDBw/WK6+8oqZNm6p58+aaPXu26tevr1tvvdXu8gAAAFAHGKZpmnYXURWlpaV6/vnn\ntXLlSpWUlCguLk4TJ05U27Zt7S4NAAAAdYDrAjIAAADgT6xpAgAAAFgQkAEAAAALAjIAAABgQUAG\nAAAALAjIAAAAgAUBGQAAALBwZUBeunSp+vbtqw4dOmjgwIHauHHjz47fsWOHhgwZori4OCUmJmrh\nwoVnjPn000/Vv39/xcXF6Ve/+pVWr159zv3NmTNHMTExeuKJJ2p8LnWNXb05dOiQxo8fr27duqlT\np04aPHiw9uzZU2vnVVfY0Z+SkhJNmzZNN910k+Lj49WvXz+9/vrrtXpedUFt9yYrK0spKSm66aab\nFBMTozfffLNWjnsxsqM3Cxcu1N13360uXbqoe/fueuCBB5SVlVWr51VX2PXdqUAmODe7elMrmcB0\nmU8++cRs166dOX/+fPPrr782n376aTM+Pt48cODAWccfPnzYvO6668xHH33U3LNnj/nuu++a7du3\nN1euXOkds3v3brN9+/bm888/b3799dfmrFmzzHbt2plbtmw5Y387d+40ExMTzQEDBpiTJk3y23m6\nkV29OXr0qHnjjTeao0aNMj/99FNz586d5l//+lfzm2++8fcpu4pd/Zk2bZqZkJBgfvbZZ+aBAwfM\npUuXmu3btzeXLl3q93N2C3/0Ztu2beb06dPNNWvWmF26dDEXLFhQ4+NejOzqzZAhQ8xXX33V3LBh\ng/n555+b99xzj9m9e3czLy/Pb+fqRnb1pwKZ4Nzs6k1tZQLXBeTk5GRzzJgxlbbdcsst5tSpU886\nPi0tzezevbvp8Xi82x577DFz4MCB3tcTJ040k5KSzjhOSkpKpW0//vij2bdvX3P9+vXmvffey5fh\nJ+zqzf/+7/+aAwYMqI1TqNPs6s/dd99tPvXUU5XGDB061Jw8eXJ1T6XO8UdvrLp163bW/5FU9bgX\nI7t681N5eXnmNddcY/7jH/+oQvV1n539IRP8PLt6U1uZwHVTLHbu3KmEhIRK2xISErR9+/Zzju/a\ntasCAv7/qfbo0UNZWVkqLi4+5z5vuOGGM/Y5ZcoUJSYmnjEWp9nVm48//ljXX3+9UlJSlJCQoEGD\nBum9996rrdOqM+zqT6dOnbRhwwZlZ2dLkrZt26Z///vfuvHGG2vjtOoEf/TGH8e9GNnVm5+q+Gzj\nxo2rvY+6yM7+kAl+nl29qa1M4KqAnJ+fr8LCQkVGRlba3qRJEx05cuSsn8nNzT1jfEREhEzTVG5u\n7jnHNGnSREePHvW+XrFihfbs2aOHHnqoNk6lzrGzNzk5OVq8eLGuueYazZs3T4MHD9aUKVO0bNmy\n2ji1OsHO/jz00EO64YYbdMsttyg2NlbDhw/X+PHj1bt379o4NdfzV2/8cdyLjV29OZvU1FTFxcXp\nuuuuq/Y+6ho7+0Mm+Hl29qa2MkFQlUY7mGEYfvvMwYMHNXXqVM2fP1/16tWr8nEudv7sjWmaKi8v\nV1xcnP7whz9IkmJiYrRr1y6lp6crKSmpyse+2PizP5L07rvvau3atXr55ZfVunVrbd++XVOnTlXT\npk3Vp0+fKh/7YuLv3vhzH3XdhezNlClTtGfPHqWnp1fr8xcjf/bnu+++IxPUgFsygasCclhYmEJC\nQs74SSIvL++MnzoqREZGnjH+6NGjMgxDERER5xyTl5fnfX/Xrl06fvy4Bg8eLNM0JUkej0dbtmzR\nBx98oC1btqhBgwa1co5uZVdvDMNQkyZN1Lp160pjrrzySv3zn/+s0TnVJXb1R5Kef/55/elPf1Lf\nvn0lSVdddZV2796t119/nYAs//XGH8e92NjVG6snn3xS69ev16JFi9S8efMqf74us6s/mZmZZILz\nsKs3tZkJXDXFQpLi4+O1YcOGSts2bNigzp07n3V8x44dtWnTJpWXl3u3/etf/1JMTIz3P+COHTue\nsc/169erU6dOkqSePXtq5cqVWrZsmZYvX67ly5crNjZWt9xyi5YvX37RfxEq2NEbSercubO+/fbb\nSmP27dunVq1a1eR06hw7+uPxeFRSUnLGvgMCAirt92Lnj97447gXI7t6Y5qmJk6cqA0bNmjRokVq\n0aJF9U6gjrOjP2QC39j13amtTBA4efLkyVX6hM0aN26stLQ0hYaGqmHDhvrLX/6iLVu2aOrUqWrU\nqJFmzpypFStWKDExUZJ0xRVXaOHChfrmm2902WWX6eOPP9Zrr72mhx9+WFdffbUkKSoqSrNnz9ap\nU6fUtGlTLV68WMuWLdOUKVPUqlUrBQcHKyIiotI/K1euVIsWLTR48GA7/zgcxY7eSFKLFi00a9Ys\nBQQEKCIiQh999JFee+01Pfjgg4qJibHtz8Np7OhPQECAdu3apTVr1uiKK66QYRj65JNPNHv2bA0d\nOpT5lP/HH70pKSnRv//9bx05ckQffPCBoqOj1aRJExUXFys8PNyn48K+3kyYMEFr165VamqqgoKC\nlJubq9zcXAUEBCgkJMS2Pw+nsaM/ZALf2PXdqbVMUON1MGywdOlS85ZbbjE7dOhgJiUlmRs3bvS+\nN2HCBHPw4MGVxu/YscO86667zA4dOpg33XSTuWjRojP2+emnn5q333672aFDB/OXv/yluWbNmp+t\n4b777mNJl7OwqzcfffSRefvtt5txcXHmrbfeai5evLj2T64OsKM/J06cMJ966inzxhtvNOPj482+\nffuac+bMMcvLy/1zki5V2735z3/+Y15zzTVmTExMpX/Gjh3r83Fxmh29ufbaa894PyYmxqcl4S42\ndn13rMgEZ2dXb2ojExim+X8TaAAAAAC4bw4yAAAA4E8EZAAAAMCCgAwAAABYEJABAAAACwIyAAAA\nYEFABgAAACwIyAAAAIAFARkAAMClXnnlFQ0ZMkTx8fHq2rWrT59ZvXq1Ro4cqa5duyomJkZZWVln\njMnOzlZKSooSEhJ03XXX6aGHHtLRo0crjdm1a5dGjhypLl26qHv37nrqqadUWFhYpfo3bNig3/72\nt+rRo4diYmL08ccfV+nz/kJABgAAcLBRo0Zp2bJlZ33P4/Gof//+VXrMdVFRkbp166YHH3xQhmGc\n8X5hYaFGjhyp4uJizZs3T4sWLdKJEyf0+9//3jvm8OHDuv/++9WiRQu98847mjVrlr788ks9/vjj\nVTq3wsJCtW/fXo8//vhZa7FLkN0FAADOLi0tTWlpad7XwcHBatWqlZKSkjRq1CgFBgZ63/vhhx80\nc+ZM/fOf/1ReXp4iIiL0i1/8QikpKWrevHml/cbExCglJUUpKSkX7FwAVF9RUZHKysrO+t4f//hH\nSVJ6errP+xs0aJAk6d///vdZ39+2bZu+++47vfXWW2rWrJkk6c9//rNuvPFGbd68WV26dNEnn3yi\nkpISpaamev8ueuyxx3Tvvfdq/PjxioqKkiTt3btXzz77rDZv3qyGDRvqhhtu0MSJExURESFJSkxM\nVGJiok6dOiUnPdyZgAwADmYYhmbPni3p9P8kN23apJdeekklJSUaO3asJOk///mP7rvvPgUHB2vE\niBG6/PLLdeDAAS1YsEBr167VwoUL1aZNGztPA4CLlJSUSJIaNGjg3Vbx71u3blWXLl1UWlqqoKCg\nSj+oh4SESDodsKOiopSfn68RI0YoKSlJEydOVFlZmV588UU9+OCDWrhw4QU8o6pjigUAOFzv3r3V\nu3dv9evXT0888YR69Oihv//97973x48fr8DAQL3//vsaMWKEEhMTNWLECC1ZskSGYWjChAk2Vg/A\nbeLj4xUWFqYXXnhBhYWFOnXqlJ5//nkZhqEjR45Ikrp3767CwkLNmjVLpaWlOn78uF555ZVKY956\n6y21bt1aEyZMUOvWrXXVVVdp6tSp2rJli/bv32/nKZ4XARkAXCY8PNz769aNGzcqKytLo0aN8v7K\nskKTJk30m9/8Rl999ZU2b95sR6kAquHFF19U+/btvf9s3rxZTz75pPd1bGysvv32W78dPyIiQjNm\nzND69evVuXNnde3aVWVlZYqKilJAwOno2LZtWz3zzDNKT09Xx44d9Ytf/EJt27ZVcHCwd8yePXu0\nefNmxcTEqF27dmrXrp26desmwzCUnZ3tt/prA1MsAMDhTp06JUkqLi7Wxo0btXbtWv3mN7+RJG3a\ntEmGYeiWW24562f79u2rZ555Rps2bVKXLl0uWM0Aqi85OVn9+/f3vn700UeVmJiovn37erf913/9\nl19ruOGGG/T3v/9dx48fV1BQkOrVq6frrruu0nFvv/123X777crLy1PDhg2Vm5ur119/XZdddpkk\nqaCgQLfddpuef/55v9bqDwRkAHAw0zTVuXNn72vDMHTrrbd6b7A7fPiwAgMD1aJFi7N+vkWLFgoK\nCtIPP/xwQeoFUHMRERGVfiMUEhKiZs2aqW3bthe8lksvvVTS6ZsATdNUYmLiGWOaNGki6fSUitDQ\nUHXv3l2S1L59e7377rsqLCz0zk92CwIyADiYYRh66623ZJqmysrKtGfPHqWlpWnSpEn685//7NM+\nnHRnOIDalZOTo5MnT+rQoUPyeDzeNY3btGmj4OBglZeXa9iwYfr1r3+tPn36SJKOHz+uQ4cOaf/+\n/TJNU/v27ZMkNW/eXI0bN5Z0OhBXvN68ebNmzpypX//6196rw5I0Z84cxcTEqGHDhlq7dq0WLFig\nJ598UqGhoZKke+65R++++67GjBmjP/zhD4qMjNT+/fv1wQcfeK8qFxQUKDs727t+cnZ2trKyshQR\nEeFdQcMOBGQAcLhOnTp5/71r164KDg7W5MmTNXz4cDVv3lwej0eHDh1Sy5Ytz/hsxf80f7rUGwD3\n+Ln1gadPn641a9Z4Xw8cOFCStHLlSrVt21amaWrHjh2Vfov04YcfavLkyTIMQ4ZhaPz48ZKkSZMm\nadiwYZJOh+jZs2fr+PHjioqK0sMPP6z77ruv0rFzcnI0f/58FRQUqE2bNnruued02223ed9v0qSJ\n3n77bT333HP63e9+p5KSErVq1Uq9evXyjtm2bZtGjRrlrWXatGmSpJEjR+rhhx+u7h9ZjRkmlxYA\nwJHS0tI0c+ZMffXVV5W2b9myRffee6+mT5+upk2bKjk5WRMnTlRycvIZ+5g3b56effZZLVy4UNdf\nf70k1kEGgPNhFQsAcJndu3fLMAxFRESoW7duateunebOnavc3NxK444cOaK5c+eqffv23nAMADg/\nplgAgMN9+umnkqSysjJlZWVp3rx5atOmjbp27Srp9K9Yk5OTNWjQIA0fPlzR0dHav3+/dyH+5557\n7ox9OumRrgDgNARkAHC40aNHS5ICAwPVsmVLJSUlacyYMQoKOv1X+JVXXqn3339fM2fOVHp6unJz\ncxUREaHevXtrzJgxleYfV9wIExwcfOFPBABcgjnIAHARycjI0JAhQ/TCCy/o1ltvtbscAHAkriAD\nwEXg8OHD+uSTT5Senq6wsDD16NHD7pIAwLG4SQ8ALgK7d+/WM888o9DQUL3xxhtq1KiR3SUBgGMx\nxQIAAACw4AoyAAAAYEFABgAAACwIyAAAAIAFARkAAACwICADAAAAFgRkAAAAwIKADAAAAFgQkAEA\nAACL/wetiUPeb7ykdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda3041ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reit_df.plot(kind='scatter', x='BOJ', y='TSE REIT INDEX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:32.300325",
     "start_time": "2016-09-17T17:59:31.854325"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda303eff98>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZBgCA2lbolcv/Nn/+/Bg/fnxMnDix8H3r632WqFy7d2VnxdhbcXZWGXsrLnNnRc5RXz8g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\nJQAAacQlAABpxCUAAGnEJQAAaf4/biRoW5GdHb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda3014abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reit_df['TSE REIT INDEX'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "↑REITの買い入れがあった当日に必ずしも上昇しているわけではない  \n",
    "TSE REIT index does not always rize when BOJ purchases amount of REIT."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 日銀のETFの買い入れ額と平均株価  \n",
    "Purchases of ETFs and stock average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:32.389361",
     "start_time": "2016-09-17T17:59:32.303332"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stock_index_df = pd.concat([df['topix'].apply(pct_change, axis=1),\n",
    "                            df['jpx400'].apply(pct_change, axis=1),\n",
    "                            df['nikkei225'].apply(pct_change, axis=1)], axis=1)\n",
    "stock_index_df = pd.concat([etfreit_df['ETF'], stock_index_df], axis=1).dropna()\n",
    "stock_index_df.columns = ['BOJ', 'topix', 'jpx400', 'nikkei225']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:32.415329",
     "start_time": "2016-09-17T17:59:32.392331"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BOJ</th>\n",
       "      <th>topix</th>\n",
       "      <th>jpx400</th>\n",
       "      <th>nikkei225</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>336.0</td>\n",
       "      <td>-3.214999</td>\n",
       "      <td>-3.239965</td>\n",
       "      <td>-3.435997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-05</th>\n",
       "      <td>333.0</td>\n",
       "      <td>-2.116102</td>\n",
       "      <td>-2.086293</td>\n",
       "      <td>-1.979365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-07</th>\n",
       "      <td>333.0</td>\n",
       "      <td>0.417243</td>\n",
       "      <td>0.467328</td>\n",
       "      <td>0.067175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-11</th>\n",
       "      <td>333.0</td>\n",
       "      <td>-0.144555</td>\n",
       "      <td>-0.144684</td>\n",
       "      <td>-0.066281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-15</th>\n",
       "      <td>333.0</td>\n",
       "      <td>0.581754</td>\n",
       "      <td>0.610833</td>\n",
       "      <td>0.757596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              BOJ     topix    jpx400  nikkei225\n",
       "Date                                            \n",
       "2016-04-01  336.0 -3.214999 -3.239965  -3.435997\n",
       "2016-04-05  333.0 -2.116102 -2.086293  -1.979365\n",
       "2016-04-07  333.0  0.417243  0.467328   0.067175\n",
       "2016-04-11  333.0 -0.144555 -0.144684  -0.066281\n",
       "2016-04-15  333.0  0.581754  0.610833   0.757596"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_index_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-17T17:59:36.465327",
     "start_time": "2016-09-17T17:59:32.418329"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fda2fbe2d30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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/vqQr9+c+fvy4Kisr/cempqbK7XbryJEjkqS2tjb19/cHHFNQUKDi4mL/MQAA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\n1OHDh3XTTTfpu9/9rumB3nHHHbr33nunfN3lcumxxx7Tc889J5/Ppyu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MAAAg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\nZfXtUf0nK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jqjj7s7ZrUz9Zlfn52PrWt9sc7u7U991quPvhqYme1hdptH4jO1+jHQRsGX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\n3LhxQ+s6bW1t8d///hf+/v4qcbUd25MnT6Jbt26YMmUKevTog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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda2fbe2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(stock_index_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "↑REITと同様にETFの買い入れがあった当日に必ずしも上昇しているわけではない  \n",
    "stock average does not always rize when BOJ purchases amount of ETF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
   },
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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