投資：分類-ディープ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "import copy\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6302</th>\n",
       "      <td>2019-04-10</td>\n",
       "      <td>111.147</td>\n",
       "      <td>111.282</td>\n",
       "      <td>110.845</td>\n",
       "      <td>111.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6303</th>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>111.012</td>\n",
       "      <td>110.69</td>\n",
       "      <td>110.908</td>\n",
       "      <td>111.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6304</th>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>111.665</td>\n",
       "      <td>112.094</td>\n",
       "      <td>111.586</td>\n",
       "      <td>112.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6305</th>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>112.020</td>\n",
       "      <td>112.096</td>\n",
       "      <td>111.890</td>\n",
       "      <td>112.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6306</th>\n",
       "      <td>2019-04-16</td>\n",
       "      <td>112.038</td>\n",
       "      <td>112.048</td>\n",
       "      <td>111.847</td>\n",
       "      <td>112.014</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            Date     Open     High      Low    Close\n",
       "6302  2019-04-10  111.147  111.282  110.845  111.012\n",
       "6303  2019-04-11  111.012   110.69  110.908  111.012\n",
       "6304  2019-04-12  111.665  112.094  111.586  112.020\n",
       "6305  2019-04-15  112.020  112.096  111.890  112.038\n",
       "6306  2019-04-16  112.038  112.048  111.847  112.014"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = \"./\"\n",
    "data = pd.read_csv(data_dir + \"USDJPY_d2.csv\")\n",
    "data.loc['6300'] = ['2019-04-08','111.721','111.760','111.284','111.489']\n",
    "data.loc['6301'] = ['2019-04-09','111.489','111.580','110.980','111.147']\n",
    "data.loc['6302'] = ['2019-04-10','111.147','111.282','110.845','111.012']\n",
    "data.loc['6303'] = ['2019-04-11','111.012','110.69','110.908','111.012']\n",
    "data.loc['6304'] = ['2019-04-12','111.665','112.094','111.586','112.020']\n",
    "data.loc['6305'] = ['2019-04-15','112.020','112.096','111.890','112.038']\n",
    "data.loc['6306'] = ['2019-04-16','112.038','112.048','111.847','112.014']\n",
    "data.tail() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5日移動平均線の追加\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))] # 列の追加\n",
    "ave_day = 5\n",
    "for i in range(ave_day, len(data2)):\n",
    "    tmp =data2[i-ave_day+1:i+1,4].astype(np.float) \n",
    "    data2[i,5] = np.mean(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 25日移動平均線の追加\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))]\n",
    "ave_day = 25\n",
    "for i in range(ave_day, len(data2)):\n",
    "    tmp =data2[i-ave_day+1:i+1,4].astype(np.float)\n",
    "    data2[i,6] = np.mean(tmp)\n",
    "\n",
    "# 75日移動平均線の追加\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))] \n",
    "ave_day = 75\n",
    "for i in range(ave_day, len(data2)):\n",
    "    tmp =data2[i-ave_day+1:i+1,4].astype(np.float)\n",
    "    data2[i,7] = np.mean(tmp)\n",
    "    \n",
    "# 200日移動平均線の追加\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))]\n",
    "ave_day = 200\n",
    "for i in range(ave_day, len(data2)):\n",
    "    tmp =data2[i-ave_day+1:i+1,4].astype(np.float)\n",
    "    data2[i,8] = np.mean(tmp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一目均衡表の追加\n",
    "para1 =9\n",
    "para2 = 26\n",
    "para3 = 52\n",
    "\n",
    "# 転換線 = （過去(para1)日間の高値 + 安値） ÷ 2\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))] # 列の追加\n",
    "for i in range(para1, len(data2)):\n",
    "    tmp_high =data2[i-para1+1:i+1,2].astype(np.float)\n",
    "    tmp_low =data2[i-para1+1:i+1,3].astype(np.float)\n",
    "    data2[i,9] = (np.max(tmp_high) + np.min(tmp_low)) / 2 \n",
    "    \n",
    "# 基準線 = （過去(para2)日間の高値 + 安値） ÷ 2\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))]\n",
    "for i in range(para2, len(data2)):\n",
    "    tmp_high =data2[i-para2+1:i+1,2].astype(np.float)\n",
    "    tmp_low =data2[i-para2+1:i+1,3].astype(np.float)\n",
    "    data2[i,10] = (np.max(tmp_high) + np.min(tmp_low)) / 2 \n",
    "\n",
    "# 先行スパン1 = ｛ （転換値+基準値） ÷ 2 ｝を(para2)日先にずらしたもの\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))]\n",
    "for i in range(0, len(data2)-para2):\n",
    "    tmp =(data2[i,9] + data2[i,10]) / 2 \n",
    "    data2[i+para2,11] = tmp\n",
    "\n",
    "\n",
    "# 先行スパン2 = ｛ （過去(para3)日間の高値+安値） ÷ 2 ｝を(para2)日先にずらしたもの\n",
    "data2 = np.c_[data2, np.zeros((len(data2),1))]\n",
    "for i in range(para3, len(data2)-para2):\n",
    "    tmp_high =data2[i-para3+1:i+1,2].astype(np.float)\n",
    "    tmp_low =data2[i-para3+1:i+1,3].astype(np.float)\n",
    "    data2[i+para2,12] = (np.max(tmp_high) + np.min(tmp_low)) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 25日ボリンジャーバンド（±1, 2シグマ）の追加\n",
    "parab = 25\n",
    "data2 = np.c_[data2, np.zeros((len(data2),4))] \n",
    "for i in range(parab, len(data2)):\n",
    "    tmp = data2[i-parab+1:i+1,4].astype(np.float)\n",
    "    data2[i,13] = np.mean(tmp) + 1.0* np.std(tmp) \n",
    "    data2[i,14] = np.mean(tmp) - 1.0* np.std(tmp) \n",
    "    data2[i,15] = np.mean(tmp) + 2.0* np.std(tmp) \n",
    "    data2[i,16] = np.mean(tmp) - 2.0* np.std(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995-01-02</td>\n",
       "      <td>99.55</td>\n",
       "      <td>100.4</td>\n",
       "      <td>99.55</td>\n",
       "      <td>100.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-01-03</td>\n",
       "      <td>100.21</td>\n",
       "      <td>100.68</td>\n",
       "      <td>100</td>\n",
       "      <td>100.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995-01-04</td>\n",
       "      <td>100.65</td>\n",
       "      <td>101.45</td>\n",
       "      <td>100.55</td>\n",
       "      <td>101.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-01-05</td>\n",
       "      <td>101.31</td>\n",
       "      <td>101.35</td>\n",
       "      <td>100.75</td>\n",
       "      <td>100.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-01-06</td>\n",
       "      <td>100.85</td>\n",
       "      <td>101.39</td>\n",
       "      <td>100.8</td>\n",
       "      <td>101.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995-01-09</td>\n",
       "      <td>101.25</td>\n",
       "      <td>101.35</td>\n",
       "      <td>99.6</td>\n",
       "      <td>99.92</td>\n",
       "      <td>100.816</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1995-01-10</td>\n",
       "      <td>99.96</td>\n",
       "      <td>100.43</td>\n",
       "      <td>99.8</td>\n",
       "      <td>100.06</td>\n",
       "      <td>100.698</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1995-01-11</td>\n",
       "      <td>100</td>\n",
       "      <td>100.2</td>\n",
       "      <td>99.45</td>\n",
       "      <td>99.99</td>\n",
       "      <td>100.434</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1995-01-12</td>\n",
       "      <td>100.06</td>\n",
       "      <td>100.18</td>\n",
       "      <td>98.57</td>\n",
       "      <td>98.62</td>\n",
       "      <td>99.978</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1995-01-13</td>\n",
       "      <td>98.7</td>\n",
       "      <td>99.07</td>\n",
       "      <td>98.25</td>\n",
       "      <td>98.55</td>\n",
       "      <td>99.428</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1995-01-16</td>\n",
       "      <td>98.56</td>\n",
       "      <td>98.8</td>\n",
       "      <td>97.97</td>\n",
       "      <td>98.4</td>\n",
       "      <td>99.124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1995-01-17</td>\n",
       "      <td>98.39</td>\n",
       "      <td>99.28</td>\n",
       "      <td>98.39</td>\n",
       "      <td>99.11</td>\n",
       "      <td>98.934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1995-01-18</td>\n",
       "      <td>99.05</td>\n",
       "      <td>99.75</td>\n",
       "      <td>98.75</td>\n",
       "      <td>99.69</td>\n",
       "      <td>98.874</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1995-01-19</td>\n",
       "      <td>99.55</td>\n",
       "      <td>100.02</td>\n",
       "      <td>98.6</td>\n",
       "      <td>99.06</td>\n",
       "      <td>98.962</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1995-01-20</td>\n",
       "      <td>99.1</td>\n",
       "      <td>99.65</td>\n",
       "      <td>98.94</td>\n",
       "      <td>99.37</td>\n",
       "      <td>99.126</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995-01-23</td>\n",
       "      <td>99.38</td>\n",
       "      <td>100.21</td>\n",
       "      <td>99.3</td>\n",
       "      <td>99.73</td>\n",
       "      <td>99.392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1995-01-24</td>\n",
       "      <td>99.78</td>\n",
       "      <td>99.87</td>\n",
       "      <td>99.15</td>\n",
       "      <td>99.73</td>\n",
       "      <td>99.516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1995-01-25</td>\n",
       "      <td>99.68</td>\n",
       "      <td>99.78</td>\n",
       "      <td>99.12</td>\n",
       "      <td>99.5</td>\n",
       "      <td>99.478</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1995-01-26</td>\n",
       "      <td>99.49</td>\n",
       "      <td>99.68</td>\n",
       "      <td>99.15</td>\n",
       "      <td>99.38</td>\n",
       "      <td>99.542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1995-01-27</td>\n",
       "      <td>99.39</td>\n",
       "      <td>99.63</td>\n",
       "      <td>99</td>\n",
       "      <td>99.37</td>\n",
       "      <td>99.542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1995-01-30</td>\n",
       "      <td>99.34</td>\n",
       "      <td>99.35</td>\n",
       "      <td>98.1</td>\n",
       "      <td>98.4</td>\n",
       "      <td>99.276</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1995-01-31</td>\n",
       "      <td>98.41</td>\n",
       "      <td>99.75</td>\n",
       "      <td>98.35</td>\n",
       "      <td>99.55</td>\n",
       "      <td>99.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>99.53</td>\n",
       "      <td>99.63</td>\n",
       "      <td>98.8</td>\n",
       "      <td>99.19</td>\n",
       "      <td>99.178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1995-02-02</td>\n",
       "      <td>99.21</td>\n",
       "      <td>99.75</td>\n",
       "      <td>99</td>\n",
       "      <td>99.4</td>\n",
       "      <td>99.182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1995-02-03</td>\n",
       "      <td>99.4</td>\n",
       "      <td>99.85</td>\n",
       "      <td>99.17</td>\n",
       "      <td>99.8</td>\n",
       "      <td>99.268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1995-02-06</td>\n",
       "      <td>99.72</td>\n",
       "      <td>99.72</td>\n",
       "      <td>99.05</td>\n",
       "      <td>99.32</td>\n",
       "      <td>99.452</td>\n",
       "      <td>99.612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.975</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.389</td>\n",
       "      <td>98.8354</td>\n",
       "      <td>101.165</td>\n",
       "      <td>98.0587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1995-02-07</td>\n",
       "      <td>99.35</td>\n",
       "      <td>99.5</td>\n",
       "      <td>99.03</td>\n",
       "      <td>99.46</td>\n",
       "      <td>99.434</td>\n",
       "      <td>99.5644</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.975</td>\n",
       "      <td>99.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.312</td>\n",
       "      <td>98.8169</td>\n",
       "      <td>101.059</td>\n",
       "      <td>98.0695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1995-02-08</td>\n",
       "      <td>99.43</td>\n",
       "      <td>99.46</td>\n",
       "      <td>98.65</td>\n",
       "      <td>98.91</td>\n",
       "      <td>99.378</td>\n",
       "      <td>99.4684</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.975</td>\n",
       "      <td>99.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.135</td>\n",
       "      <td>98.8015</td>\n",
       "      <td>100.802</td>\n",
       "      <td>98.1346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1995-02-09</td>\n",
       "      <td>98.91</td>\n",
       "      <td>99.13</td>\n",
       "      <td>98.6</td>\n",
       "      <td>98.88</td>\n",
       "      <td>99.274</td>\n",
       "      <td>99.3876</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.975</td>\n",
       "      <td>99.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.9959</td>\n",
       "      <td>98.7793</td>\n",
       "      <td>100.604</td>\n",
       "      <td>98.1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>98.85</td>\n",
       "      <td>99.35</td>\n",
       "      <td>98.6</td>\n",
       "      <td>98.91</td>\n",
       "      <td>99.096</td>\n",
       "      <td>99.292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.1</td>\n",
       "      <td>99.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.765</td>\n",
       "      <td>98.819</td>\n",
       "      <td>100.238</td>\n",
       "      <td>98.3459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6277</th>\n",
       "      <td>2019-03-06</td>\n",
       "      <td>111.895</td>\n",
       "      <td>111.919</td>\n",
       "      <td>111.619</td>\n",
       "      <td>111.712</td>\n",
       "      <td>111.743</td>\n",
       "      <td>110.606</td>\n",
       "      <td>110.869</td>\n",
       "      <td>111.341</td>\n",
       "      <td>111.246</td>\n",
       "      <td>110.317</td>\n",
       "      <td>108.75</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.365</td>\n",
       "      <td>109.846</td>\n",
       "      <td>112.125</td>\n",
       "      <td>109.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6278</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>111.703</td>\n",
       "      <td>111.857</td>\n",
       "      <td>111.484</td>\n",
       "      <td>111.611</td>\n",
       "      <td>111.79</td>\n",
       "      <td>110.717</td>\n",
       "      <td>110.854</td>\n",
       "      <td>111.356</td>\n",
       "      <td>111.246</td>\n",
       "      <td>110.317</td>\n",
       "      <td>108.769</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.409</td>\n",
       "      <td>110.024</td>\n",
       "      <td>112.102</td>\n",
       "      <td>109.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6279</th>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>111.61</td>\n",
       "      <td>111.651</td>\n",
       "      <td>110.799</td>\n",
       "      <td>111.119</td>\n",
       "      <td>111.618</td>\n",
       "      <td>110.781</td>\n",
       "      <td>110.827</td>\n",
       "      <td>111.367</td>\n",
       "      <td>111.246</td>\n",
       "      <td>110.431</td>\n",
       "      <td>108.691</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.432</td>\n",
       "      <td>110.13</td>\n",
       "      <td>112.083</td>\n",
       "      <td>109.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6280</th>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>111.089</td>\n",
       "      <td>111.344</td>\n",
       "      <td>110.88</td>\n",
       "      <td>111.325</td>\n",
       "      <td>111.532</td>\n",
       "      <td>110.838</td>\n",
       "      <td>110.805</td>\n",
       "      <td>111.38</td>\n",
       "      <td>111.246</td>\n",
       "      <td>110.785</td>\n",
       "      <td>108.665</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.472</td>\n",
       "      <td>110.203</td>\n",
       "      <td>112.106</td>\n",
       "      <td>109.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6281</th>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>111.324</td>\n",
       "      <td>111.466</td>\n",
       "      <td>111.112</td>\n",
       "      <td>111.316</td>\n",
       "      <td>111.417</td>\n",
       "      <td>110.892</td>\n",
       "      <td>110.784</td>\n",
       "      <td>111.388</td>\n",
       "      <td>111.4</td>\n",
       "      <td>110.85</td>\n",
       "      <td>108.615</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.506</td>\n",
       "      <td>110.278</td>\n",
       "      <td>112.12</td>\n",
       "      <td>109.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6282</th>\n",
       "      <td>2019-03-13</td>\n",
       "      <td>111.316</td>\n",
       "      <td>111.464</td>\n",
       "      <td>111.008</td>\n",
       "      <td>111.249</td>\n",
       "      <td>111.324</td>\n",
       "      <td>110.945</td>\n",
       "      <td>110.753</td>\n",
       "      <td>111.396</td>\n",
       "      <td>111.468</td>\n",
       "      <td>110.85</td>\n",
       "      <td>108.499</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.53</td>\n",
       "      <td>110.359</td>\n",
       "      <td>112.116</td>\n",
       "      <td>109.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6283</th>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>111.249</td>\n",
       "      <td>111.834</td>\n",
       "      <td>111.147</td>\n",
       "      <td>111.699</td>\n",
       "      <td>111.342</td>\n",
       "      <td>111.021</td>\n",
       "      <td>110.725</td>\n",
       "      <td>111.405</td>\n",
       "      <td>111.468</td>\n",
       "      <td>110.871</td>\n",
       "      <td>108.42</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.574</td>\n",
       "      <td>110.469</td>\n",
       "      <td>112.126</td>\n",
       "      <td>109.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6284</th>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>111.698</td>\n",
       "      <td>111.902</td>\n",
       "      <td>111.385</td>\n",
       "      <td>111.476</td>\n",
       "      <td>111.413</td>\n",
       "      <td>111.089</td>\n",
       "      <td>110.697</td>\n",
       "      <td>111.412</td>\n",
       "      <td>111.468</td>\n",
       "      <td>110.893</td>\n",
       "      <td>108.863</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.586</td>\n",
       "      <td>110.592</td>\n",
       "      <td>112.083</td>\n",
       "      <td>110.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6285</th>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>111.491</td>\n",
       "      <td>111.63</td>\n",
       "      <td>111.3</td>\n",
       "      <td>111.401</td>\n",
       "      <td>111.428</td>\n",
       "      <td>111.13</td>\n",
       "      <td>110.669</td>\n",
       "      <td>111.42</td>\n",
       "      <td>111.359</td>\n",
       "      <td>110.956</td>\n",
       "      <td>109.085</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.609</td>\n",
       "      <td>110.65</td>\n",
       "      <td>112.088</td>\n",
       "      <td>110.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6286</th>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>111.4</td>\n",
       "      <td>111.476</td>\n",
       "      <td>111.159</td>\n",
       "      <td>111.368</td>\n",
       "      <td>111.439</td>\n",
       "      <td>111.166</td>\n",
       "      <td>110.641</td>\n",
       "      <td>111.43</td>\n",
       "      <td>111.35</td>\n",
       "      <td>111.197</td>\n",
       "      <td>109.302</td>\n",
       "      <td>109.447</td>\n",
       "      <td>111.628</td>\n",
       "      <td>110.704</td>\n",
       "      <td>112.089</td>\n",
       "      <td>110.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6287</th>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>111.366</td>\n",
       "      <td>111.691</td>\n",
       "      <td>110.536</td>\n",
       "      <td>110.727</td>\n",
       "      <td>111.334</td>\n",
       "      <td>111.153</td>\n",
       "      <td>110.602</td>\n",
       "      <td>111.433</td>\n",
       "      <td>111.219</td>\n",
       "      <td>111.197</td>\n",
       "      <td>109.394</td>\n",
       "      <td>109.338</td>\n",
       "      <td>111.623</td>\n",
       "      <td>110.684</td>\n",
       "      <td>112.092</td>\n",
       "      <td>110.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6288</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>110.727</td>\n",
       "      <td>110.96</td>\n",
       "      <td>110.3</td>\n",
       "      <td>110.791</td>\n",
       "      <td>111.153</td>\n",
       "      <td>111.165</td>\n",
       "      <td>110.576</td>\n",
       "      <td>111.435</td>\n",
       "      <td>111.101</td>\n",
       "      <td>111.197</td>\n",
       "      <td>109.652</td>\n",
       "      <td>109.338</td>\n",
       "      <td>111.621</td>\n",
       "      <td>110.71</td>\n",
       "      <td>112.077</td>\n",
       "      <td>110.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6289</th>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>110.787</td>\n",
       "      <td>110.897</td>\n",
       "      <td>109.742</td>\n",
       "      <td>109.954</td>\n",
       "      <td>110.848</td>\n",
       "      <td>111.147</td>\n",
       "      <td>110.533</td>\n",
       "      <td>111.433</td>\n",
       "      <td>110.822</td>\n",
       "      <td>110.939</td>\n",
       "      <td>109.864</td>\n",
       "      <td>109.338</td>\n",
       "      <td>111.64</td>\n",
       "      <td>110.654</td>\n",
       "      <td>112.133</td>\n",
       "      <td>110.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6290</th>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>110.013</td>\n",
       "      <td>110.24</td>\n",
       "      <td>109.724</td>\n",
       "      <td>110.043</td>\n",
       "      <td>110.577</td>\n",
       "      <td>111.124</td>\n",
       "      <td>110.497</td>\n",
       "      <td>111.43</td>\n",
       "      <td>110.813</td>\n",
       "      <td>110.93</td>\n",
       "      <td>109.951</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.653</td>\n",
       "      <td>110.595</td>\n",
       "      <td>112.183</td>\n",
       "      <td>110.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6291</th>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>110.043</td>\n",
       "      <td>110.691</td>\n",
       "      <td>110.002</td>\n",
       "      <td>110.529</td>\n",
       "      <td>110.409</td>\n",
       "      <td>111.122</td>\n",
       "      <td>110.469</td>\n",
       "      <td>111.43</td>\n",
       "      <td>110.813</td>\n",
       "      <td>110.93</td>\n",
       "      <td>109.951</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.654</td>\n",
       "      <td>110.591</td>\n",
       "      <td>112.185</td>\n",
       "      <td>110.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6292</th>\n",
       "      <td>2019-03-27</td>\n",
       "      <td>110.529</td>\n",
       "      <td>110.71</td>\n",
       "      <td>110.236</td>\n",
       "      <td>110.492</td>\n",
       "      <td>110.362</td>\n",
       "      <td>111.11</td>\n",
       "      <td>110.432</td>\n",
       "      <td>111.43</td>\n",
       "      <td>110.813</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.652</td>\n",
       "      <td>110.567</td>\n",
       "      <td>112.194</td>\n",
       "      <td>110.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6293</th>\n",
       "      <td>2019-03-28</td>\n",
       "      <td>110.491</td>\n",
       "      <td>110.832</td>\n",
       "      <td>110.021</td>\n",
       "      <td>110.657</td>\n",
       "      <td>110.335</td>\n",
       "      <td>111.109</td>\n",
       "      <td>110.396</td>\n",
       "      <td>111.433</td>\n",
       "      <td>110.707</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.069</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.652</td>\n",
       "      <td>110.566</td>\n",
       "      <td>112.195</td>\n",
       "      <td>110.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6294</th>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>110.658</td>\n",
       "      <td>110.948</td>\n",
       "      <td>110.54</td>\n",
       "      <td>110.852</td>\n",
       "      <td>110.515</td>\n",
       "      <td>111.116</td>\n",
       "      <td>110.363</td>\n",
       "      <td>111.435</td>\n",
       "      <td>110.707</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.132</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.654</td>\n",
       "      <td>110.578</td>\n",
       "      <td>112.193</td>\n",
       "      <td>110.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6295</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>110.947</td>\n",
       "      <td>111.444</td>\n",
       "      <td>110.816</td>\n",
       "      <td>111.41</td>\n",
       "      <td>110.788</td>\n",
       "      <td>111.13</td>\n",
       "      <td>110.334</td>\n",
       "      <td>111.443</td>\n",
       "      <td>110.707</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.252</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.671</td>\n",
       "      <td>110.589</td>\n",
       "      <td>112.212</td>\n",
       "      <td>110.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6296</th>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>111.412</td>\n",
       "      <td>111.456</td>\n",
       "      <td>111.251</td>\n",
       "      <td>111.334</td>\n",
       "      <td>110.949</td>\n",
       "      <td>111.161</td>\n",
       "      <td>110.307</td>\n",
       "      <td>111.45</td>\n",
       "      <td>110.59</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.307</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.691</td>\n",
       "      <td>110.631</td>\n",
       "      <td>112.221</td>\n",
       "      <td>110.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6297</th>\n",
       "      <td>2019-04-03</td>\n",
       "      <td>111.332</td>\n",
       "      <td>111.575</td>\n",
       "      <td>111.209</td>\n",
       "      <td>111.464</td>\n",
       "      <td>111.143</td>\n",
       "      <td>111.181</td>\n",
       "      <td>110.288</td>\n",
       "      <td>111.458</td>\n",
       "      <td>110.649</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.307</td>\n",
       "      <td>109.282</td>\n",
       "      <td>111.713</td>\n",
       "      <td>110.65</td>\n",
       "      <td>112.244</td>\n",
       "      <td>110.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6298</th>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>111.456</td>\n",
       "      <td>111.678</td>\n",
       "      <td>111.336</td>\n",
       "      <td>111.616</td>\n",
       "      <td>111.335</td>\n",
       "      <td>111.191</td>\n",
       "      <td>110.277</td>\n",
       "      <td>111.466</td>\n",
       "      <td>110.701</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.307</td>\n",
       "      <td>109.266</td>\n",
       "      <td>111.728</td>\n",
       "      <td>110.654</td>\n",
       "      <td>112.265</td>\n",
       "      <td>110.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6299</th>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>111.617</td>\n",
       "      <td>111.82</td>\n",
       "      <td>111.564</td>\n",
       "      <td>111.678</td>\n",
       "      <td>111.5</td>\n",
       "      <td>111.179</td>\n",
       "      <td>110.267</td>\n",
       "      <td>111.473</td>\n",
       "      <td>110.911</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.46</td>\n",
       "      <td>109.189</td>\n",
       "      <td>111.701</td>\n",
       "      <td>110.656</td>\n",
       "      <td>112.224</td>\n",
       "      <td>110.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6300</th>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>111.721</td>\n",
       "      <td>111.760</td>\n",
       "      <td>111.284</td>\n",
       "      <td>111.489</td>\n",
       "      <td>111.516</td>\n",
       "      <td>111.168</td>\n",
       "      <td>110.27</td>\n",
       "      <td>111.479</td>\n",
       "      <td>110.921</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.752</td>\n",
       "      <td>108.853</td>\n",
       "      <td>111.681</td>\n",
       "      <td>110.655</td>\n",
       "      <td>112.195</td>\n",
       "      <td>110.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6301</th>\n",
       "      <td>2019-04-09</td>\n",
       "      <td>111.489</td>\n",
       "      <td>111.580</td>\n",
       "      <td>110.980</td>\n",
       "      <td>111.147</td>\n",
       "      <td>111.479</td>\n",
       "      <td>111.138</td>\n",
       "      <td>110.267</td>\n",
       "      <td>111.481</td>\n",
       "      <td>110.921</td>\n",
       "      <td>110.93</td>\n",
       "      <td>110.752</td>\n",
       "      <td>108.762</td>\n",
       "      <td>111.63</td>\n",
       "      <td>110.647</td>\n",
       "      <td>112.121</td>\n",
       "      <td>110.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6302</th>\n",
       "      <td>2019-04-10</td>\n",
       "      <td>111.147</td>\n",
       "      <td>111.282</td>\n",
       "      <td>110.845</td>\n",
       "      <td>111.012</td>\n",
       "      <td>111.388</td>\n",
       "      <td>111.11</td>\n",
       "      <td>110.277</td>\n",
       "      <td>111.482</td>\n",
       "      <td>111.18</td>\n",
       "      <td>110.821</td>\n",
       "      <td>110.782</td>\n",
       "      <td>108.732</td>\n",
       "      <td>111.588</td>\n",
       "      <td>110.633</td>\n",
       "      <td>112.066</td>\n",
       "      <td>110.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6303</th>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>111.012</td>\n",
       "      <td>110.69</td>\n",
       "      <td>110.908</td>\n",
       "      <td>111.012</td>\n",
       "      <td>111.268</td>\n",
       "      <td>111.086</td>\n",
       "      <td>110.273</td>\n",
       "      <td>111.484</td>\n",
       "      <td>111.318</td>\n",
       "      <td>110.813</td>\n",
       "      <td>110.782</td>\n",
       "      <td>108.497</td>\n",
       "      <td>111.553</td>\n",
       "      <td>110.62</td>\n",
       "      <td>112.02</td>\n",
       "      <td>110.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6304</th>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>111.665</td>\n",
       "      <td>112.094</td>\n",
       "      <td>111.586</td>\n",
       "      <td>112.020</td>\n",
       "      <td>111.336</td>\n",
       "      <td>111.122</td>\n",
       "      <td>110.287</td>\n",
       "      <td>111.492</td>\n",
       "      <td>111.469</td>\n",
       "      <td>110.909</td>\n",
       "      <td>110.782</td>\n",
       "      <td>108.497</td>\n",
       "      <td>111.624</td>\n",
       "      <td>110.621</td>\n",
       "      <td>112.125</td>\n",
       "      <td>110.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6305</th>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>112.020</td>\n",
       "      <td>112.096</td>\n",
       "      <td>111.890</td>\n",
       "      <td>112.038</td>\n",
       "      <td>111.446</td>\n",
       "      <td>111.151</td>\n",
       "      <td>110.312</td>\n",
       "      <td>111.498</td>\n",
       "      <td>111.471</td>\n",
       "      <td>110.91</td>\n",
       "      <td>110.839</td>\n",
       "      <td>108.497</td>\n",
       "      <td>111.683</td>\n",
       "      <td>110.619</td>\n",
       "      <td>112.214</td>\n",
       "      <td>110.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6306</th>\n",
       "      <td>2019-04-16</td>\n",
       "      <td>112.038</td>\n",
       "      <td>112.048</td>\n",
       "      <td>111.847</td>\n",
       "      <td>112.014</td>\n",
       "      <td>111.619</td>\n",
       "      <td>111.179</td>\n",
       "      <td>110.343</td>\n",
       "      <td>111.506</td>\n",
       "      <td>111.471</td>\n",
       "      <td>110.91</td>\n",
       "      <td>111.016</td>\n",
       "      <td>108.497</td>\n",
       "      <td>111.736</td>\n",
       "      <td>110.622</td>\n",
       "      <td>112.293</td>\n",
       "      <td>110.064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6307 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0        1        2        3        4        5        6   \\\n",
       "0     1995-01-02    99.55    100.4    99.55   100.14        0        0   \n",
       "1     1995-01-03   100.21   100.68      100   100.65        0        0   \n",
       "2     1995-01-04   100.65   101.45   100.55   101.31        0        0   \n",
       "3     1995-01-05   101.31   101.35   100.75    100.9        0        0   \n",
       "4     1995-01-06   100.85   101.39    100.8    101.3        0        0   \n",
       "5     1995-01-09   101.25   101.35     99.6    99.92  100.816        0   \n",
       "6     1995-01-10    99.96   100.43     99.8   100.06  100.698        0   \n",
       "7     1995-01-11      100    100.2    99.45    99.99  100.434        0   \n",
       "8     1995-01-12   100.06   100.18    98.57    98.62   99.978        0   \n",
       "9     1995-01-13     98.7    99.07    98.25    98.55   99.428        0   \n",
       "10    1995-01-16    98.56     98.8    97.97     98.4   99.124        0   \n",
       "11    1995-01-17    98.39    99.28    98.39    99.11   98.934        0   \n",
       "12    1995-01-18    99.05    99.75    98.75    99.69   98.874        0   \n",
       "13    1995-01-19    99.55   100.02     98.6    99.06   98.962        0   \n",
       "14    1995-01-20     99.1    99.65    98.94    99.37   99.126        0   \n",
       "15    1995-01-23    99.38   100.21     99.3    99.73   99.392        0   \n",
       "16    1995-01-24    99.78    99.87    99.15    99.73   99.516        0   \n",
       "17    1995-01-25    99.68    99.78    99.12     99.5   99.478        0   \n",
       "18    1995-01-26    99.49    99.68    99.15    99.38   99.542        0   \n",
       "19    1995-01-27    99.39    99.63       99    99.37   99.542        0   \n",
       "20    1995-01-30    99.34    99.35     98.1     98.4   99.276        0   \n",
       "21    1995-01-31    98.41    99.75    98.35    99.55    99.24        0   \n",
       "22    1995-02-01    99.53    99.63     98.8    99.19   99.178        0   \n",
       "23    1995-02-02    99.21    99.75       99     99.4   99.182        0   \n",
       "24    1995-02-03     99.4    99.85    99.17     99.8   99.268        0   \n",
       "25    1995-02-06    99.72    99.72    99.05    99.32   99.452   99.612   \n",
       "26    1995-02-07    99.35     99.5    99.03    99.46   99.434  99.5644   \n",
       "27    1995-02-08    99.43    99.46    98.65    98.91   99.378  99.4684   \n",
       "28    1995-02-09    98.91    99.13     98.6    98.88   99.274  99.3876   \n",
       "29    1995-02-10    98.85    99.35     98.6    98.91   99.096   99.292   \n",
       "...          ...      ...      ...      ...      ...      ...      ...   \n",
       "6277  2019-03-06  111.895  111.919  111.619  111.712  111.743  110.606   \n",
       "6278  2019-03-07  111.703  111.857  111.484  111.611   111.79  110.717   \n",
       "6279  2019-03-08   111.61  111.651  110.799  111.119  111.618  110.781   \n",
       "6280  2019-03-11  111.089  111.344   110.88  111.325  111.532  110.838   \n",
       "6281  2019-03-12  111.324  111.466  111.112  111.316  111.417  110.892   \n",
       "6282  2019-03-13  111.316  111.464  111.008  111.249  111.324  110.945   \n",
       "6283  2019-03-14  111.249  111.834  111.147  111.699  111.342  111.021   \n",
       "6284  2019-03-15  111.698  111.902  111.385  111.476  111.413  111.089   \n",
       "6285  2019-03-18  111.491   111.63    111.3  111.401  111.428   111.13   \n",
       "6286  2019-03-19    111.4  111.476  111.159  111.368  111.439  111.166   \n",
       "6287  2019-03-20  111.366  111.691  110.536  110.727  111.334  111.153   \n",
       "6288  2019-03-21  110.727   110.96    110.3  110.791  111.153  111.165   \n",
       "6289  2019-03-22  110.787  110.897  109.742  109.954  110.848  111.147   \n",
       "6290  2019-03-25  110.013   110.24  109.724  110.043  110.577  111.124   \n",
       "6291  2019-03-26  110.043  110.691  110.002  110.529  110.409  111.122   \n",
       "6292  2019-03-27  110.529   110.71  110.236  110.492  110.362   111.11   \n",
       "6293  2019-03-28  110.491  110.832  110.021  110.657  110.335  111.109   \n",
       "6294  2019-03-29  110.658  110.948   110.54  110.852  110.515  111.116   \n",
       "6295  2019-04-01  110.947  111.444  110.816   111.41  110.788   111.13   \n",
       "6296  2019-04-02  111.412  111.456  111.251  111.334  110.949  111.161   \n",
       "6297  2019-04-03  111.332  111.575  111.209  111.464  111.143  111.181   \n",
       "6298  2019-04-04  111.456  111.678  111.336  111.616  111.335  111.191   \n",
       "6299  2019-04-05  111.617   111.82  111.564  111.678    111.5  111.179   \n",
       "6300  2019-04-08  111.721  111.760  111.284  111.489  111.516  111.168   \n",
       "6301  2019-04-09  111.489  111.580  110.980  111.147  111.479  111.138   \n",
       "6302  2019-04-10  111.147  111.282  110.845  111.012  111.388   111.11   \n",
       "6303  2019-04-11  111.012   110.69  110.908  111.012  111.268  111.086   \n",
       "6304  2019-04-12  111.665  112.094  111.586  112.020  111.336  111.122   \n",
       "6305  2019-04-15  112.020  112.096  111.890  112.038  111.446  111.151   \n",
       "6306  2019-04-16  112.038  112.048  111.847  112.014  111.619  111.179   \n",
       "\n",
       "           7        8        9        10       11       12       13       14  \\\n",
       "0           0        0        0        0        0        0        0        0   \n",
       "1           0        0        0        0        0        0        0        0   \n",
       "2           0        0        0        0        0        0        0        0   \n",
       "3           0        0        0        0        0        0        0        0   \n",
       "4           0        0        0        0        0        0        0        0   \n",
       "5           0        0        0        0        0        0        0        0   \n",
       "6           0        0        0        0        0        0        0        0   \n",
       "7           0        0        0        0        0        0        0        0   \n",
       "8           0        0        0        0        0        0        0        0   \n",
       "9           0        0    99.85        0        0        0        0        0   \n",
       "10          0        0    99.71        0        0        0        0        0   \n",
       "11          0        0    99.68        0        0        0        0        0   \n",
       "12          0        0    99.68        0        0        0        0        0   \n",
       "13          0        0    99.66        0        0        0        0        0   \n",
       "14          0        0     99.2        0        0        0        0        0   \n",
       "15          0        0    99.09        0        0        0        0        0   \n",
       "16          0        0    99.09        0        0        0        0        0   \n",
       "17          0        0    99.09        0        0        0        0        0   \n",
       "18          0        0    99.09        0        0        0        0        0   \n",
       "19          0        0     99.3        0        0        0        0        0   \n",
       "20          0        0   99.155        0        0        0        0        0   \n",
       "21          0        0   99.155        0        0        0        0        0   \n",
       "22          0        0   99.155        0        0        0        0        0   \n",
       "23          0        0   99.155        0        0        0        0        0   \n",
       "24          0        0   98.985        0        0        0        0        0   \n",
       "25          0        0   98.975        0        0        0  100.389  98.8354   \n",
       "26          0        0   98.975    99.71        0        0  100.312  98.8169   \n",
       "27          0        0   98.975    99.71        0        0  100.135  98.8015   \n",
       "28          0        0   98.975    99.68        0        0  99.9959  98.7793   \n",
       "29          0        0     99.1    99.68        0        0   99.765   98.819   \n",
       "...       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "6277  110.869  111.341  111.246  110.317   108.75  109.447  111.365  109.846   \n",
       "6278  110.854  111.356  111.246  110.317  108.769  109.447  111.409  110.024   \n",
       "6279  110.827  111.367  111.246  110.431  108.691  109.447  111.432   110.13   \n",
       "6280  110.805   111.38  111.246  110.785  108.665  109.447  111.472  110.203   \n",
       "6281  110.784  111.388    111.4   110.85  108.615  109.447  111.506  110.278   \n",
       "6282  110.753  111.396  111.468   110.85  108.499  109.447   111.53  110.359   \n",
       "6283  110.725  111.405  111.468  110.871   108.42  109.447  111.574  110.469   \n",
       "6284  110.697  111.412  111.468  110.893  108.863  109.447  111.586  110.592   \n",
       "6285  110.669   111.42  111.359  110.956  109.085  109.447  111.609   110.65   \n",
       "6286  110.641   111.43   111.35  111.197  109.302  109.447  111.628  110.704   \n",
       "6287  110.602  111.433  111.219  111.197  109.394  109.338  111.623  110.684   \n",
       "6288  110.576  111.435  111.101  111.197  109.652  109.338  111.621   110.71   \n",
       "6289  110.533  111.433  110.822  110.939  109.864  109.338   111.64  110.654   \n",
       "6290  110.497   111.43  110.813   110.93  109.951  109.282  111.653  110.595   \n",
       "6291  110.469   111.43  110.813   110.93  109.951  109.282  111.654  110.591   \n",
       "6292  110.432   111.43  110.813   110.93      110  109.282  111.652  110.567   \n",
       "6293  110.396  111.433  110.707   110.93  110.069  109.282  111.652  110.566   \n",
       "6294  110.363  111.435  110.707   110.93  110.132  109.282  111.654  110.578   \n",
       "6295  110.334  111.443  110.707   110.93  110.252  109.282  111.671  110.589   \n",
       "6296  110.307   111.45   110.59   110.93  110.307  109.282  111.691  110.631   \n",
       "6297  110.288  111.458  110.649   110.93  110.307  109.282  111.713   110.65   \n",
       "6298  110.277  111.466  110.701   110.93  110.307  109.266  111.728  110.654   \n",
       "6299  110.267  111.473  110.911   110.93   110.46  109.189  111.701  110.656   \n",
       "6300   110.27  111.479  110.921   110.93  110.752  108.853  111.681  110.655   \n",
       "6301  110.267  111.481  110.921   110.93  110.752  108.762   111.63  110.647   \n",
       "6302  110.277  111.482   111.18  110.821  110.782  108.732  111.588  110.633   \n",
       "6303  110.273  111.484  111.318  110.813  110.782  108.497  111.553   110.62   \n",
       "6304  110.287  111.492  111.469  110.909  110.782  108.497  111.624  110.621   \n",
       "6305  110.312  111.498  111.471   110.91  110.839  108.497  111.683  110.619   \n",
       "6306  110.343  111.506  111.471   110.91  111.016  108.497  111.736  110.622   \n",
       "\n",
       "           15       16  \n",
       "0           0        0  \n",
       "1           0        0  \n",
       "2           0        0  \n",
       "3           0        0  \n",
       "4           0        0  \n",
       "5           0        0  \n",
       "6           0        0  \n",
       "7           0        0  \n",
       "8           0        0  \n",
       "9           0        0  \n",
       "10          0        0  \n",
       "11          0        0  \n",
       "12          0        0  \n",
       "13          0        0  \n",
       "14          0        0  \n",
       "15          0        0  \n",
       "16          0        0  \n",
       "17          0        0  \n",
       "18          0        0  \n",
       "19          0        0  \n",
       "20          0        0  \n",
       "21          0        0  \n",
       "22          0        0  \n",
       "23          0        0  \n",
       "24          0        0  \n",
       "25    101.165  98.0587  \n",
       "26    101.059  98.0695  \n",
       "27    100.802  98.1346  \n",
       "28    100.604  98.1709  \n",
       "29    100.238  98.3459  \n",
       "...       ...      ...  \n",
       "6277  112.125  109.087  \n",
       "6278  112.102  109.331  \n",
       "6279  112.083  109.479  \n",
       "6280  112.106  109.569  \n",
       "6281   112.12  109.664  \n",
       "6282  112.116  109.773  \n",
       "6283  112.126  109.917  \n",
       "6284  112.083  110.095  \n",
       "6285  112.088  110.171  \n",
       "6286  112.089  110.242  \n",
       "6287  112.092  110.215  \n",
       "6288  112.077  110.254  \n",
       "6289  112.133  110.161  \n",
       "6290  112.183  110.066  \n",
       "6291  112.185  110.059  \n",
       "6292  112.194  110.025  \n",
       "6293  112.195  110.023  \n",
       "6294  112.193   110.04  \n",
       "6295  112.212  110.048  \n",
       "6296  112.221  110.101  \n",
       "6297  112.244  110.118  \n",
       "6298  112.265  110.117  \n",
       "6299  112.224  110.134  \n",
       "6300  112.195  110.142  \n",
       "6301  112.121  110.156  \n",
       "6302  112.066  110.155  \n",
       "6303   112.02  110.153  \n",
       "6304  112.125  110.119  \n",
       "6305  112.214  110.088  \n",
       "6306  112.293  110.064  \n",
       "\n",
       "[6307 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_show=pd.DataFrame(data2)\n",
    "data_show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 説明変数\n",
    "day_ago = 25 # 何日前までのデータを使用するのか\n",
    "num_sihyou = 1 + 4 + 4 +4 # 終値1本、MVave4本、itimoku4本、ボリンジャー4本\n",
    "\n",
    "X = np.zeros((len(data2), day_ago*num_sihyou)) \n",
    "\n",
    "for s in range(0, num_sihyou): # 日にちごとに横向きに並べる\n",
    "    for i in range(0, day_ago):\n",
    "        X[i:len(data2),day_ago*s+i] = data2[0:len(data2)-i,s+4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 被説明変数となる Y = pre_day後の終値-当日終値 を作成\n",
    "Y = np.zeros(len(data2))\n",
    "\n",
    "# 何日後の値段の差を予測するのか決定\n",
    "pre_day = 1\n",
    "for i in range(len(Y)-pre_day):\n",
    "    if X[pre_day + i,0] - X[i,0] >= 0:\n",
    "        Y[i] = 1\n",
    "    else:\n",
    "        Y[i] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X, Yを正規化\n",
    "original_X = np.copy(X) \n",
    "tmp_mean = np.zeros(len(X))\n",
    "\n",
    "for i in range(day_ago,len(X)):\n",
    "    tmp_mean[i] = np.mean(original_X[i-day_ago+1:i+1,0]) \n",
    "    for j in range(0, X.shape[1]): \n",
    "        X[i,j] = (X[i,j] - tmp_mean[i]) # Xを正規化\n",
    "    Y[i] =  Y[i] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習データとテストデータへ分割\n",
    "X_train = X[200:6100,:] # 200日平均を使うので、それ以降を学習データに使用\n",
    "Y_train = Y[200:6100] \n",
    "\n",
    "X_test = X[6100:len(X),:]\n",
    "Y_test = Y[6100:len(Y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def result():\n",
    "    test_data = original_X[6100:len(X),0]\n",
    "    test_data_result = np.c_[test_data, np.zeros((len(test_data),4))]\n",
    "\n",
    "    for i in range(len(test_data)):\n",
    "        test_data_result[i, 1] = Y_pred[i]\n",
    "\n",
    "    for i in range(len(test_data)-1):\n",
    "        if Y_pred[i] >= 0:\n",
    "            test_data_result[i, 2] = test_data_result[i+1 , 0] - test_data_result[i, 0]\n",
    "        else:\n",
    "            test_data_result[i, 2] = test_data_result[i, 0] - test_data_result[i + 1, 0]\n",
    "        \n",
    "    for i in range(len(Y_pred)):\n",
    "        test_data_result[i, 3] = sum(test_data_result[0:i+1, 2])\n",
    "    \n",
    "    test_data_result[0, 4] = test_data_result[0,0]*(1 + test_data_result[0,2]/test_data_result[0,0]) \n",
    "    for i in range(1, len(Y_pred)):\n",
    "        test_data_result[i, 4] = (test_data_result[i-1, 4]/test_data_result[i, 0])*(test_data_result[i, 0] + test_data_result[i, 2])\n",
    "\n",
    "\n",
    "    print(\"Benefit is {:.3f} yen\".format(test_data_result[-1, 3])) \n",
    "    print(\"Benefit2 is {:.3f} yen\".format(test_data_result[-1, 4]-test_data_result[0, 4]))\n",
    "    data_show=pd.DataFrame(test_data_result)\n",
    "    plt.plot(test_data_result[:, 3])\n",
    "    return data_show#.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def result_proba(model):\n",
    "    test_data = original_X[6100:len(X),0]\n",
    "    test_data_result = np.c_[test_data, np.zeros((len(test_data),4))]\n",
    "\n",
    "    for i in range(len(test_data)):\n",
    "        test_data_result[i, 1] = Y_pred[i]\n",
    "\n",
    "    for i in range(len(test_data)-1):\n",
    "        if model.predict_proba(X_test)[i][0] >= 0.6:\n",
    "            test_data_result[i, 2] = test_data_result[i+1 , 0] - test_data_result[i, 0]\n",
    "        elif model.predict_proba(X_test)[i][0] <= 0.4:\n",
    "            test_data_result[i, 2] = test_data_result[i, 0] - test_data_result[i + 1, 0]\n",
    "        else:\n",
    "            continue\n",
    "        \n",
    "    for i in range(len(Y_pred)):\n",
    "        test_data_result[i, 3] = sum(test_data_result[0:i+1, 2])\n",
    "        \n",
    "    test_data_result[0, 4] = test_data_result[0,0]*(1 + test_data_result[0,2]/test_data_result[0,0])\n",
    "    for i in range(1, len(Y_pred)):\n",
    "        test_data_result[i, 4] = (test_data_result[i-1, 4]/test_data_result[i, 0])*(test_data_result[i, 0] + test_data_result[i, 2])\n",
    "\n",
    "\n",
    "    print(\"Benefit is {:.3f} yen\".format(test_data_result[-1, 3])) \n",
    "    print(\"Benefit2 is {:.3f} yen\".format(test_data_result[-1, 4]-test_data_result[0, 4]))\n",
    "    data_show=pd.DataFrame(test_data_result)\n",
    "    plt.plot(test_data_result[:, 3])\n",
    "    return data_show#.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "svm_model = SVC(kernel = 'rbf', C = 10, gamma = 0.1).fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "予測日数：207、正解日数：102、正解率：49.275362318840585\n",
      "Training score: 1.000\n",
      "Test score: 0.488\n"
     ]
    }
   ],
   "source": [
    "Y_pred = svm_model.predict(X_test) # 予測\n",
    "\n",
    "# 正答率を計算\n",
    "success_num = 0\n",
    "for i in range(len(Y_pred)):\n",
    "    if Y_pred[i] * Y_test[i] >=0:\n",
    "        success_num+=1\n",
    "\n",
    "print(\"予測日数：\"+ str(len(Y_pred))+\"、正解日数：\"+str(success_num)+\"、正解率：\"+str(success_num/len(Y_pred)*100))\n",
    "\n",
    "print(\"Training score: {:.3f}\".format(svm_model.score(X_train, Y_train)))\n",
    "print(\"Test score: {:.3f}\".format(svm_model.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benefit is 0.182 yen\n",
      "Benefit2 is -0.035 yen\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110.466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.231</td>\n",
       "      <td>110.697000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.396</td>\n",
       "      <td>110.862000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.277</td>\n",
       "      <td>0.119</td>\n",
       "      <td>110.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110.585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.134</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>110.451000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.201</td>\n",
       "      <td>110.667000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>110.423000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.423</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.382</td>\n",
       "      <td>110.848000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110.848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.525</td>\n",
       "      <td>110.991000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110.991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.944</td>\n",
       "      <td>1.469</td>\n",
       "      <td>111.935000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>111.935</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.592</td>\n",
       "      <td>2.061</td>\n",
       "      <td>112.527000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>112.527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>1.865</td>\n",
       "      <td>112.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112.331</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>1.809</td>\n",
       "      <td>112.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112.275</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.630</td>\n",
       "      <td>2.439</td>\n",
       "      <td>112.905000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112.905</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>2.375</td>\n",
       "      <td>112.841000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>112.841</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.403</td>\n",
       "      <td>1.972</td>\n",
       "      <td>112.438000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>112.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.934</td>\n",
       "      <td>1.038</td>\n",
       "      <td>111.504000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>111.504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.841</td>\n",
       "      <td>111.307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>111.307</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>0.738</td>\n",
       "      <td>111.204000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>111.204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>0.393</td>\n",
       "      <td>110.859000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>110.859</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.779</td>\n",
       "      <td>111.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>111.245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>0.525</td>\n",
       "      <td>110.991000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>110.991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.589</td>\n",
       "      <td>111.055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>111.055</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788</td>\n",
       "      <td>1.377</td>\n",
       "      <td>111.843000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>111.843</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>1.229</td>\n",
       "      <td>111.695000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>111.695</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>1.175</td>\n",
       "      <td>111.641000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>111.641</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.419</td>\n",
       "      <td>0.756</td>\n",
       "      <td>111.222000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>111.222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.934</td>\n",
       "      <td>111.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>111.400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.916</td>\n",
       "      <td>111.382000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>111.382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.456</td>\n",
       "      <td>0.460</td>\n",
       "      <td>110.926000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>110.926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.636</td>\n",
       "      <td>111.102000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>111.712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>110.264198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>111.611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>109.778135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>111.119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.206</td>\n",
       "      <td>-0.507</td>\n",
       "      <td>109.981649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>111.325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.516</td>\n",
       "      <td>109.972758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>111.316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>109.906566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>111.249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.450</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>110.351136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>111.699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.223</td>\n",
       "      <td>-0.356</td>\n",
       "      <td>110.130827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>111.476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>-0.431</td>\n",
       "      <td>110.056732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>111.401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>-0.464</td>\n",
       "      <td>110.024130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>111.368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.641</td>\n",
       "      <td>-1.105</td>\n",
       "      <td>109.390865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>110.727</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-1.041</td>\n",
       "      <td>109.454093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>110.791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.837</td>\n",
       "      <td>-1.878</td>\n",
       "      <td>108.627193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>109.954</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-1.789</td>\n",
       "      <td>108.715119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>110.043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.486</td>\n",
       "      <td>-1.303</td>\n",
       "      <td>109.195255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>110.529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>-1.340</td>\n",
       "      <td>109.158701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>110.492</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.165</td>\n",
       "      <td>-1.175</td>\n",
       "      <td>109.321710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>110.657</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.195</td>\n",
       "      <td>-0.980</td>\n",
       "      <td>109.514357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>110.852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.558</td>\n",
       "      <td>-0.422</td>\n",
       "      <td>110.065624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>111.410</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>-0.498</td>\n",
       "      <td>109.990541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>111.334</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.130</td>\n",
       "      <td>-0.368</td>\n",
       "      <td>110.118972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>111.464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.152</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>110.269138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>111.616</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>110.330390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>111.678</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.189</td>\n",
       "      <td>-0.343</td>\n",
       "      <td>110.143670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>111.489</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.342</td>\n",
       "      <td>-0.685</td>\n",
       "      <td>109.805797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>111.147</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>-0.820</td>\n",
       "      <td>109.672426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>111.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.820</td>\n",
       "      <td>109.672426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>111.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.008</td>\n",
       "      <td>0.188</td>\n",
       "      <td>110.668263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>112.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.206</td>\n",
       "      <td>110.686046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>112.038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.182</td>\n",
       "      <td>110.662335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>112.014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.182</td>\n",
       "      <td>110.662335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1      2      3           4\n",
       "0    110.466  1.0  0.231  0.231  110.697000\n",
       "1    110.697  1.0  0.165  0.396  110.862000\n",
       "2    110.862  1.0 -0.277  0.119  110.585000\n",
       "3    110.585  1.0 -0.134 -0.015  110.451000\n",
       "4    110.451  1.0  0.216  0.201  110.667000\n",
       "5    110.667  1.0 -0.244 -0.043  110.423000\n",
       "6    110.423  1.0  0.425  0.382  110.848000\n",
       "7    110.848  1.0  0.143  0.525  110.991000\n",
       "8    110.991  1.0  0.944  1.469  111.935000\n",
       "9    111.935  1.0  0.592  2.061  112.527000\n",
       "10   112.527  1.0 -0.196  1.865  112.331000\n",
       "11   112.331  1.0 -0.056  1.809  112.275000\n",
       "12   112.275  1.0  0.630  2.439  112.905000\n",
       "13   112.905  1.0 -0.064  2.375  112.841000\n",
       "14   112.841  1.0 -0.403  1.972  112.438000\n",
       "15   112.438  1.0 -0.934  1.038  111.504000\n",
       "16   111.504  1.0 -0.197  0.841  111.307000\n",
       "17   111.307  1.0 -0.103  0.738  111.204000\n",
       "18   111.204  1.0 -0.345  0.393  110.859000\n",
       "19   110.859  1.0  0.386  0.779  111.245000\n",
       "20   111.245  1.0 -0.254  0.525  110.991000\n",
       "21   110.991  1.0  0.064  0.589  111.055000\n",
       "22   111.055  1.0  0.788  1.377  111.843000\n",
       "23   111.843  1.0 -0.148  1.229  111.695000\n",
       "24   111.695  1.0 -0.054  1.175  111.641000\n",
       "25   111.641  1.0 -0.419  0.756  111.222000\n",
       "26   111.222  1.0  0.178  0.934  111.400000\n",
       "27   111.400  1.0 -0.018  0.916  111.382000\n",
       "28   111.382  1.0 -0.456  0.460  110.926000\n",
       "29   110.926  1.0  0.176  0.636  111.102000\n",
       "..       ...  ...    ...    ...         ...\n",
       "177  111.712  1.0 -0.101 -0.221  110.264198\n",
       "178  111.611  1.0 -0.492 -0.713  109.778135\n",
       "179  111.119  1.0  0.206 -0.507  109.981649\n",
       "180  111.325  1.0 -0.009 -0.516  109.972758\n",
       "181  111.316  1.0 -0.067 -0.583  109.906566\n",
       "182  111.249  1.0  0.450 -0.133  110.351136\n",
       "183  111.699  1.0 -0.223 -0.356  110.130827\n",
       "184  111.476  1.0 -0.075 -0.431  110.056732\n",
       "185  111.401  1.0 -0.033 -0.464  110.024130\n",
       "186  111.368  1.0 -0.641 -1.105  109.390865\n",
       "187  110.727  1.0  0.064 -1.041  109.454093\n",
       "188  110.791  1.0 -0.837 -1.878  108.627193\n",
       "189  109.954  1.0  0.089 -1.789  108.715119\n",
       "190  110.043  1.0  0.486 -1.303  109.195255\n",
       "191  110.529  1.0 -0.037 -1.340  109.158701\n",
       "192  110.492  1.0  0.165 -1.175  109.321710\n",
       "193  110.657  1.0  0.195 -0.980  109.514357\n",
       "194  110.852  1.0  0.558 -0.422  110.065624\n",
       "195  111.410  1.0 -0.076 -0.498  109.990541\n",
       "196  111.334  1.0  0.130 -0.368  110.118972\n",
       "197  111.464  1.0  0.152 -0.216  110.269138\n",
       "198  111.616  1.0  0.062 -0.154  110.330390\n",
       "199  111.678  1.0 -0.189 -0.343  110.143670\n",
       "200  111.489  1.0 -0.342 -0.685  109.805797\n",
       "201  111.147  1.0 -0.135 -0.820  109.672426\n",
       "202  111.012  1.0  0.000 -0.820  109.672426\n",
       "203  111.012  1.0  1.008  0.188  110.668263\n",
       "204  112.020  1.0  0.018  0.206  110.686046\n",
       "205  112.038  1.0 -0.024  0.182  110.662335\n",
       "206  112.014  1.0  0.000  0.182  110.662335\n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0c7dddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.1, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier \n",
    "mlpclassifier_model = MLPClassifier(max_iter=1000, alpha=0.1,hidden_layer_sizes=(100,100))\n",
    "\n",
    "mlpclassifier_model.fit(X_train, Y_train) # 学習データをフィット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "予測日数：207、正解日数：115、正解率：55.55555555555556\n",
      "Training score: 0.592\n",
      "Test score: 0.551\n"
     ]
    }
   ],
   "source": [
    "Y_pred = mlpclassifier_model.predict(X_test) # 予測\n",
    "\n",
    "# 正答率を計算\n",
    "success_num = 0\n",
    "for i in range(len(Y_pred)):\n",
    "    if Y_pred[i] * Y_test[i] >=0:\n",
    "        success_num+=1\n",
    "\n",
    "print(\"予測日数：\"+ str(len(Y_pred))+\"、正解日数：\"+str(success_num)+\"、正解率：\"+str(success_num/len(Y_pred)*100))\n",
    "\n",
    "print(\"Training score: {:.3f}\".format(mlpclassifier_model.score(X_train, Y_train)))\n",
    "print(\"Test score: {:.3f}\".format(mlpclassifier_model.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benefit is 11.286 yen\n",
      "Benefit2 is 11.901 yen\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110.466</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>110.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.697</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.165</td>\n",
       "      <td>-0.396</td>\n",
       "      <td>110.070689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.862</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.277</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>110.345711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110.585</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.015</td>\n",
       "      <td>110.479422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.451</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>-0.201</td>\n",
       "      <td>110.263366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110.667</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.043</td>\n",
       "      <td>110.506476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.423</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>-0.382</td>\n",
       "      <td>110.081155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110.848</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>-0.525</td>\n",
       "      <td>109.939144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110.991</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.944</td>\n",
       "      <td>-1.469</td>\n",
       "      <td>109.004090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>111.935</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.592</td>\n",
       "      <td>-2.061</td>\n",
       "      <td>108.427591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>112.527</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-1.865</td>\n",
       "      <td>108.616451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112.331</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-1.809</td>\n",
       "      <td>108.670599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112.275</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.630</td>\n",
       "      <td>-2.439</td>\n",
       "      <td>108.060824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112.905</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-2.375</td>\n",
       "      <td>108.122078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>112.841</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>-1.972</td>\n",
       "      <td>108.508225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>112.438</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.934</td>\n",
       "      <td>-1.038</td>\n",
       "      <td>109.409581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>111.504</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-0.841</td>\n",
       "      <td>109.602881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>111.307</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>-0.944</td>\n",
       "      <td>109.501458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>111.204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>-1.289</td>\n",
       "      <td>109.161740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>110.859</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.386</td>\n",
       "      <td>-0.903</td>\n",
       "      <td>109.541830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>111.245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>-1.157</td>\n",
       "      <td>109.291719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>110.991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-1.093</td>\n",
       "      <td>109.354739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>111.055</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788</td>\n",
       "      <td>-0.305</td>\n",
       "      <td>110.130675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>111.843</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.148</td>\n",
       "      <td>-0.157</td>\n",
       "      <td>110.276409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>111.695</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>110.329723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>111.641</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.316</td>\n",
       "      <td>110.743802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>111.222</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>0.138</td>\n",
       "      <td>110.566567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>111.400</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.156</td>\n",
       "      <td>110.584432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>111.382</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.612</td>\n",
       "      <td>111.037167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>110.926</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.436</td>\n",
       "      <td>110.860991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>111.712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>12.243</td>\n",
       "      <td>123.199746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>111.611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>11.751</td>\n",
       "      <td>122.656661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>111.119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.206</td>\n",
       "      <td>11.957</td>\n",
       "      <td>122.884050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>111.325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>11.948</td>\n",
       "      <td>122.874116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>111.316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>11.881</td>\n",
       "      <td>122.800159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>111.249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.450</td>\n",
       "      <td>12.331</td>\n",
       "      <td>123.296884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>111.699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.223</td>\n",
       "      <td>12.108</td>\n",
       "      <td>123.050729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>111.476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>12.033</td>\n",
       "      <td>122.967942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>111.401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>12.000</td>\n",
       "      <td>122.931515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>111.368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.641</td>\n",
       "      <td>11.359</td>\n",
       "      <td>122.223959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>110.727</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>11.423</td>\n",
       "      <td>122.294604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>110.791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.837</td>\n",
       "      <td>10.586</td>\n",
       "      <td>121.370697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>109.954</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.089</td>\n",
       "      <td>10.675</td>\n",
       "      <td>121.468938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>110.043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.486</td>\n",
       "      <td>11.161</td>\n",
       "      <td>122.005401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>110.529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>11.124</td>\n",
       "      <td>121.964559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>110.492</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.165</td>\n",
       "      <td>11.289</td>\n",
       "      <td>122.146691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>110.657</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.195</td>\n",
       "      <td>11.484</td>\n",
       "      <td>122.361938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>110.852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.558</td>\n",
       "      <td>12.042</td>\n",
       "      <td>122.977876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>111.410</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>11.966</td>\n",
       "      <td>122.893985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>111.334</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>11.836</td>\n",
       "      <td>122.750487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>111.464</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.152</td>\n",
       "      <td>11.684</td>\n",
       "      <td>122.583096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>111.616</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>11.622</td>\n",
       "      <td>122.515004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>111.678</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.189</td>\n",
       "      <td>11.811</td>\n",
       "      <td>122.722344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>111.489</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.342</td>\n",
       "      <td>12.153</td>\n",
       "      <td>123.098803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>111.147</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.135</td>\n",
       "      <td>12.288</td>\n",
       "      <td>123.248320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>111.012</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.288</td>\n",
       "      <td>123.248320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>111.012</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.008</td>\n",
       "      <td>11.280</td>\n",
       "      <td>122.129213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>112.020</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>11.262</td>\n",
       "      <td>122.109588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>112.038</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>11.286</td>\n",
       "      <td>122.135746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>112.014</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.286</td>\n",
       "      <td>122.135746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1      2       3           4\n",
       "0    110.466 -1.0 -0.231  -0.231  110.235000\n",
       "1    110.697 -1.0 -0.165  -0.396  110.070689\n",
       "2    110.862 -1.0  0.277  -0.119  110.345711\n",
       "3    110.585 -1.0  0.134   0.015  110.479422\n",
       "4    110.451 -1.0 -0.216  -0.201  110.263366\n",
       "5    110.667 -1.0  0.244   0.043  110.506476\n",
       "6    110.423 -1.0 -0.425  -0.382  110.081155\n",
       "7    110.848 -1.0 -0.143  -0.525  109.939144\n",
       "8    110.991 -1.0 -0.944  -1.469  109.004090\n",
       "9    111.935 -1.0 -0.592  -2.061  108.427591\n",
       "10   112.527 -1.0  0.196  -1.865  108.616451\n",
       "11   112.331 -1.0  0.056  -1.809  108.670599\n",
       "12   112.275 -1.0 -0.630  -2.439  108.060824\n",
       "13   112.905 -1.0  0.064  -2.375  108.122078\n",
       "14   112.841 -1.0  0.403  -1.972  108.508225\n",
       "15   112.438 -1.0  0.934  -1.038  109.409581\n",
       "16   111.504 -1.0  0.197  -0.841  109.602881\n",
       "17   111.307  1.0 -0.103  -0.944  109.501458\n",
       "18   111.204  1.0 -0.345  -1.289  109.161740\n",
       "19   110.859  1.0  0.386  -0.903  109.541830\n",
       "20   111.245  1.0 -0.254  -1.157  109.291719\n",
       "21   110.991  1.0  0.064  -1.093  109.354739\n",
       "22   111.055  1.0  0.788  -0.305  110.130675\n",
       "23   111.843 -1.0  0.148  -0.157  110.276409\n",
       "24   111.695 -1.0  0.054  -0.103  110.329723\n",
       "25   111.641 -1.0  0.419   0.316  110.743802\n",
       "26   111.222 -1.0 -0.178   0.138  110.566567\n",
       "27   111.400 -1.0  0.018   0.156  110.584432\n",
       "28   111.382 -1.0  0.456   0.612  111.037167\n",
       "29   110.926 -1.0 -0.176   0.436  110.860991\n",
       "..       ...  ...    ...     ...         ...\n",
       "177  111.712  1.0 -0.101  12.243  123.199746\n",
       "178  111.611  1.0 -0.492  11.751  122.656661\n",
       "179  111.119  1.0  0.206  11.957  122.884050\n",
       "180  111.325  1.0 -0.009  11.948  122.874116\n",
       "181  111.316  1.0 -0.067  11.881  122.800159\n",
       "182  111.249  1.0  0.450  12.331  123.296884\n",
       "183  111.699  1.0 -0.223  12.108  123.050729\n",
       "184  111.476  1.0 -0.075  12.033  122.967942\n",
       "185  111.401  1.0 -0.033  12.000  122.931515\n",
       "186  111.368  1.0 -0.641  11.359  122.223959\n",
       "187  110.727  1.0  0.064  11.423  122.294604\n",
       "188  110.791  1.0 -0.837  10.586  121.370697\n",
       "189  109.954  1.0  0.089  10.675  121.468938\n",
       "190  110.043  1.0  0.486  11.161  122.005401\n",
       "191  110.529  1.0 -0.037  11.124  121.964559\n",
       "192  110.492  1.0  0.165  11.289  122.146691\n",
       "193  110.657  1.0  0.195  11.484  122.361938\n",
       "194  110.852  1.0  0.558  12.042  122.977876\n",
       "195  111.410  1.0 -0.076  11.966  122.893985\n",
       "196  111.334 -1.0 -0.130  11.836  122.750487\n",
       "197  111.464 -1.0 -0.152  11.684  122.583096\n",
       "198  111.616 -1.0 -0.062  11.622  122.515004\n",
       "199  111.678 -1.0  0.189  11.811  122.722344\n",
       "200  111.489 -1.0  0.342  12.153  123.098803\n",
       "201  111.147 -1.0  0.135  12.288  123.248320\n",
       "202  111.012 -1.0  0.000  12.288  123.248320\n",
       "203  111.012 -1.0 -1.008  11.280  122.129213\n",
       "204  112.020 -1.0 -0.018  11.262  122.109588\n",
       "205  112.038 -1.0  0.024  11.286  122.135746\n",
       "206  112.014 -1.0  0.000  11.286  122.135746\n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0c7dd9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benefit is -3.144 yen\n",
      "Benefit2 is -3.175 yen\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110.466</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.697</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.862</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110.585</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.451</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110.667</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.423</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110.848</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110.991</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>111.935</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>112.527</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112.331</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112.275</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112.905</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>112.841</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>112.438</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>111.504</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>111.307</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>111.204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>110.859</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>111.245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>110.991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>111.055</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>111.843</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>111.695</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>111.641</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>111.222</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>111.400</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>111.382</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>110.926</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>110.466000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>111.712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.798</td>\n",
       "      <td>107.626274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>111.611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.798</td>\n",
       "      <td>107.626274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>111.119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.798</td>\n",
       "      <td>107.626274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>111.325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.798</td>\n",
       "      <td>107.626274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>111.316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-2.731</td>\n",
       "      <td>107.691053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>111.249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.450</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>111.699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>111.476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>111.401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>111.368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>110.727</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>110.791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>109.954</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>110.043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.181</td>\n",
       "      <td>107.255445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>110.529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>110.492</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>110.657</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>110.852</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>111.410</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>111.334</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>111.464</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>111.616</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>111.678</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>111.489</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>111.147</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>111.012</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>111.012</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>112.020</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>112.038</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>112.014</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.144</td>\n",
       "      <td>107.291349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1      2      3           4\n",
       "0    110.466 -1.0  0.000  0.000  110.466000\n",
       "1    110.697 -1.0  0.000  0.000  110.466000\n",
       "2    110.862 -1.0  0.000  0.000  110.466000\n",
       "3    110.585 -1.0  0.000  0.000  110.466000\n",
       "4    110.451 -1.0  0.000  0.000  110.466000\n",
       "5    110.667 -1.0  0.000  0.000  110.466000\n",
       "6    110.423 -1.0  0.000  0.000  110.466000\n",
       "7    110.848 -1.0  0.000  0.000  110.466000\n",
       "8    110.991 -1.0  0.000  0.000  110.466000\n",
       "9    111.935 -1.0  0.000  0.000  110.466000\n",
       "10   112.527 -1.0  0.000  0.000  110.466000\n",
       "11   112.331 -1.0  0.000  0.000  110.466000\n",
       "12   112.275 -1.0  0.000  0.000  110.466000\n",
       "13   112.905 -1.0  0.000  0.000  110.466000\n",
       "14   112.841 -1.0  0.000  0.000  110.466000\n",
       "15   112.438 -1.0  0.000  0.000  110.466000\n",
       "16   111.504 -1.0  0.000  0.000  110.466000\n",
       "17   111.307  1.0  0.000  0.000  110.466000\n",
       "18   111.204  1.0  0.000  0.000  110.466000\n",
       "19   110.859  1.0  0.000  0.000  110.466000\n",
       "20   111.245  1.0  0.000  0.000  110.466000\n",
       "21   110.991  1.0  0.000  0.000  110.466000\n",
       "22   111.055  1.0  0.000  0.000  110.466000\n",
       "23   111.843 -1.0  0.000  0.000  110.466000\n",
       "24   111.695 -1.0  0.000  0.000  110.466000\n",
       "25   111.641 -1.0  0.000  0.000  110.466000\n",
       "26   111.222 -1.0  0.000  0.000  110.466000\n",
       "27   111.400 -1.0  0.000  0.000  110.466000\n",
       "28   111.382 -1.0  0.000  0.000  110.466000\n",
       "29   110.926 -1.0  0.000  0.000  110.466000\n",
       "..       ...  ...    ...    ...         ...\n",
       "177  111.712  1.0  0.000 -2.798  107.626274\n",
       "178  111.611  1.0  0.000 -2.798  107.626274\n",
       "179  111.119  1.0  0.000 -2.798  107.626274\n",
       "180  111.325  1.0  0.000 -2.798  107.626274\n",
       "181  111.316  1.0  0.067 -2.731  107.691053\n",
       "182  111.249  1.0 -0.450 -3.181  107.255445\n",
       "183  111.699  1.0  0.000 -3.181  107.255445\n",
       "184  111.476  1.0  0.000 -3.181  107.255445\n",
       "185  111.401  1.0  0.000 -3.181  107.255445\n",
       "186  111.368  1.0  0.000 -3.181  107.255445\n",
       "187  110.727  1.0  0.000 -3.181  107.255445\n",
       "188  110.791  1.0  0.000 -3.181  107.255445\n",
       "189  109.954  1.0  0.000 -3.181  107.255445\n",
       "190  110.043  1.0  0.000 -3.181  107.255445\n",
       "191  110.529  1.0  0.037 -3.144  107.291349\n",
       "192  110.492  1.0  0.000 -3.144  107.291349\n",
       "193  110.657  1.0  0.000 -3.144  107.291349\n",
       "194  110.852  1.0  0.000 -3.144  107.291349\n",
       "195  111.410  1.0  0.000 -3.144  107.291349\n",
       "196  111.334 -1.0  0.000 -3.144  107.291349\n",
       "197  111.464 -1.0  0.000 -3.144  107.291349\n",
       "198  111.616 -1.0  0.000 -3.144  107.291349\n",
       "199  111.678 -1.0  0.000 -3.144  107.291349\n",
       "200  111.489 -1.0  0.000 -3.144  107.291349\n",
       "201  111.147 -1.0  0.000 -3.144  107.291349\n",
       "202  111.012 -1.0  0.000 -3.144  107.291349\n",
       "203  111.012 -1.0  0.000 -3.144  107.291349\n",
       "204  112.020 -1.0  0.000 -3.144  107.291349\n",
       "205  112.038 -1.0  0.000 -3.144  107.291349\n",
       "206  112.014 -1.0  0.000 -3.144  107.291349\n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0cc22198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_proba(mlpclassifier_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56248357, 0.43751643],\n",
       "       [0.57870899, 0.42129101],\n",
       "       [0.57959326, 0.42040674],\n",
       "       [0.57065484, 0.42934516],\n",
       "       [0.56209286, 0.43790714],\n",
       "       [0.56404804, 0.43595196],\n",
       "       [0.55176105, 0.44823895],\n",
       "       [0.53535898, 0.46464102],\n",
       "       [0.53473045, 0.46526955],\n",
       "       [0.55560239, 0.44439761],\n",
       "       [0.55269003, 0.44730997],\n",
       "       [0.54126165, 0.45873835],\n",
       "       [0.53203665, 0.46796335],\n",
       "       [0.56624993, 0.43375007],\n",
       "       [0.57822833, 0.42177167],\n",
       "       [0.55562111, 0.44437889],\n",
       "       [0.51240804, 0.48759196],\n",
       "       [0.49021818, 0.50978182],\n",
       "       [0.47995312, 0.52004688],\n",
       "       [0.48538049, 0.51461951],\n",
       "       [0.49195956, 0.50804044],\n",
       "       [0.48883171, 0.51116829],\n",
       "       [0.49325277, 0.50674723],\n",
       "       [0.51060346, 0.48939654],\n",
       "       [0.52963479, 0.47036521],\n",
       "       [0.52723154, 0.47276846],\n",
       "       [0.50920606, 0.49079394],\n",
       "       [0.50862334, 0.49137666],\n",
       "       [0.50498852, 0.49501148],\n",
       "       [0.50220592, 0.49779408],\n",
       "       [0.50228441, 0.49771559],\n",
       "       [0.49894163, 0.50105837],\n",
       "       [0.50749528, 0.49250472],\n",
       "       [0.52117996, 0.47882004],\n",
       "       [0.52068329, 0.47931671],\n",
       "       [0.54023564, 0.45976436],\n",
       "       [0.54246319, 0.45753681],\n",
       "       [0.52581749, 0.47418251],\n",
       "       [0.50024822, 0.49975178],\n",
       "       [0.46245668, 0.53754332],\n",
       "       [0.48162599, 0.51837401],\n",
       "       [0.46937349, 0.53062651],\n",
       "       [0.49024058, 0.50975942],\n",
       "       [0.51513587, 0.48486413],\n",
       "       [0.52755678, 0.47244322],\n",
       "       [0.55641799, 0.44358201],\n",
       "       [0.54004209, 0.45995791],\n",
       "       [0.51509977, 0.48490023],\n",
       "       [0.51127443, 0.48872557],\n",
       "       [0.50088576, 0.49911424],\n",
       "       [0.46425155, 0.53574845],\n",
       "       [0.44796723, 0.55203277],\n",
       "       [0.45551873, 0.54448127],\n",
       "       [0.5045053 , 0.4954947 ],\n",
       "       [0.51800357, 0.48199643],\n",
       "       [0.50354475, 0.49645525],\n",
       "       [0.51901971, 0.48098029],\n",
       "       [0.54679884, 0.45320116],\n",
       "       [0.54975804, 0.45024196],\n",
       "       [0.5103211 , 0.4896789 ],\n",
       "       [0.47950475, 0.52049525],\n",
       "       [0.47561934, 0.52438066],\n",
       "       [0.48449202, 0.51550798],\n",
       "       [0.46956691, 0.53043309],\n",
       "       [0.44992325, 0.55007675],\n",
       "       [0.46635339, 0.53364661],\n",
       "       [0.49531552, 0.50468448],\n",
       "       [0.49859935, 0.50140065],\n",
       "       [0.48664177, 0.51335823],\n",
       "       [0.50943527, 0.49056473],\n",
       "       [0.50797739, 0.49202261],\n",
       "       [0.52015593, 0.47984407],\n",
       "       [0.48672603, 0.51327397],\n",
       "       [0.4765777 , 0.5234223 ],\n",
       "       [0.4780705 , 0.5219295 ],\n",
       "       [0.48289618, 0.51710382],\n",
       "       [0.47675377, 0.52324623],\n",
       "       [0.4778891 , 0.5221109 ],\n",
       "       [0.52420088, 0.47579912],\n",
       "       [0.56589825, 0.43410175],\n",
       "       [0.59027564, 0.40972436],\n",
       "       [0.58675331, 0.41324669],\n",
       "       [0.57476304, 0.42523696],\n",
       "       [0.56960935, 0.43039065],\n",
       "       [0.55224288, 0.44775712],\n",
       "       [0.54036047, 0.45963953],\n",
       "       [0.52030543, 0.47969457],\n",
       "       [0.5410215 , 0.4589785 ],\n",
       "       [0.54665815, 0.45334185],\n",
       "       [0.56000689, 0.43999311],\n",
       "       [0.54384755, 0.45615245],\n",
       "       [0.55548152, 0.44451848],\n",
       "       [0.56373614, 0.43626386],\n",
       "       [0.56544784, 0.43455216],\n",
       "       [0.54473961, 0.45526039],\n",
       "       [0.52073024, 0.47926976],\n",
       "       [0.48956421, 0.51043579],\n",
       "       [0.47558075, 0.52441925],\n",
       "       [0.46396281, 0.53603719],\n",
       "       [0.45927602, 0.54072398],\n",
       "       [0.4603283 , 0.5396717 ],\n",
       "       [0.46444363, 0.53555637],\n",
       "       [0.47731699, 0.52268301],\n",
       "       [0.48719203, 0.51280797],\n",
       "       [0.5069473 , 0.4930527 ],\n",
       "       [0.52079629, 0.47920371],\n",
       "       [0.50492126, 0.49507874],\n",
       "       [0.52876575, 0.47123425],\n",
       "       [0.5536967 , 0.4463033 ],\n",
       "       [0.56390978, 0.43609022],\n",
       "       [0.55800193, 0.44199807],\n",
       "       [0.5393727 , 0.4606273 ],\n",
       "       [0.52679712, 0.47320288],\n",
       "       [0.52725505, 0.47274495],\n",
       "       [0.51938803, 0.48061197],\n",
       "       [0.50149739, 0.49850261],\n",
       "       [0.51146993, 0.48853007],\n",
       "       [0.53529274, 0.46470726],\n",
       "       [0.5605427 , 0.4394573 ],\n",
       "       [0.5658442 , 0.4341558 ],\n",
       "       [0.56907401, 0.43092599],\n",
       "       [0.57127925, 0.42872075],\n",
       "       [0.56283905, 0.43716095],\n",
       "       [0.52598787, 0.47401213],\n",
       "       [0.50939508, 0.49060492],\n",
       "       [0.47010754, 0.52989246],\n",
       "       [0.45102788, 0.54897212],\n",
       "       [0.42482031, 0.57517969],\n",
       "       [0.45589703, 0.54410297],\n",
       "       [0.53791034, 0.46208966],\n",
       "       [0.57502361, 0.42497639],\n",
       "       [0.51011798, 0.48988202],\n",
       "       [0.3742478 , 0.6257522 ],\n",
       "       [0.36434117, 0.63565883],\n",
       "       [0.4901951 , 0.5098049 ],\n",
       "       [0.52605114, 0.47394886],\n",
       "       [0.57242179, 0.42757821],\n",
       "       [0.54128453, 0.45871547],\n",
       "       [0.5447516 , 0.4552484 ],\n",
       "       [0.57872341, 0.42127659],\n",
       "       [0.55744305, 0.44255695],\n",
       "       [0.55504059, 0.44495941],\n",
       "       [0.55367591, 0.44632409],\n",
       "       [0.57444374, 0.42555626],\n",
       "       [0.57316775, 0.42683225],\n",
       "       [0.5517468 , 0.4482532 ],\n",
       "       [0.60575255, 0.39424745],\n",
       "       [0.64719047, 0.35280953],\n",
       "       [0.60804676, 0.39195324],\n",
       "       [0.54607182, 0.45392818],\n",
       "       [0.51030069, 0.48969931],\n",
       "       [0.45838154, 0.54161846],\n",
       "       [0.41852227, 0.58147773],\n",
       "       [0.39978434, 0.60021566],\n",
       "       [0.44535319, 0.55464681],\n",
       "       [0.45836013, 0.54163987],\n",
       "       [0.44883719, 0.55116281],\n",
       "       [0.38094749, 0.61905251],\n",
       "       [0.34211767, 0.65788233],\n",
       "       [0.35601641, 0.64398359],\n",
       "       [0.38647227, 0.61352773],\n",
       "       [0.4624768 , 0.5375232 ],\n",
       "       [0.48287664, 0.51712336],\n",
       "       [0.50890176, 0.49109824],\n",
       "       [0.49341644, 0.50658356],\n",
       "       [0.50681268, 0.49318732],\n",
       "       [0.49781028, 0.50218972],\n",
       "       [0.51181468, 0.48818532],\n",
       "       [0.50981823, 0.49018177],\n",
       "       [0.53493571, 0.46506429],\n",
       "       [0.55616011, 0.44383989],\n",
       "       [0.48919291, 0.51080709],\n",
       "       [0.44383111, 0.55616889],\n",
       "       [0.41243106, 0.58756894],\n",
       "       [0.46645494, 0.53354506],\n",
       "       [0.47628012, 0.52371988],\n",
       "       [0.46249063, 0.53750937],\n",
       "       [0.46395955, 0.53604045],\n",
       "       [0.44413652, 0.55586348],\n",
       "       [0.4147789 , 0.5852211 ],\n",
       "       [0.42740986, 0.57259014],\n",
       "       [0.37085161, 0.62914839],\n",
       "       [0.38510537, 0.61489463],\n",
       "       [0.40709257, 0.59290743],\n",
       "       [0.42585871, 0.57414129],\n",
       "       [0.42429804, 0.57570196],\n",
       "       [0.43488108, 0.56511892],\n",
       "       [0.42732633, 0.57267367],\n",
       "       [0.42972811, 0.57027189],\n",
       "       [0.42431068, 0.57568932],\n",
       "       [0.40631993, 0.59368007],\n",
       "       [0.39900748, 0.60099252],\n",
       "       [0.40226805, 0.59773195],\n",
       "       [0.4257244 , 0.5742756 ],\n",
       "       [0.447095  , 0.552905  ],\n",
       "       [0.47795201, 0.52204799],\n",
       "       [0.52391727, 0.47608273],\n",
       "       [0.52686074, 0.47313926],\n",
       "       [0.53856449, 0.46143551],\n",
       "       [0.52868241, 0.47131759],\n",
       "       [0.52195609, 0.47804391],\n",
       "       [0.51481752, 0.48518248],\n",
       "       [0.51574492, 0.48425508],\n",
       "       [0.52339217, 0.47660783],\n",
       "       [0.56394467, 0.43605533],\n",
       "       [0.59843513, 0.40156487],\n",
       "       [0.58371929, 0.41628071]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpclassifier_model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,\n",
       "       with_scaling=True)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#　データの前処理\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transformed shape: (5900, 325)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1.67568442e-02, -1.69245727e-02, -1.68113434e-02, -2.14865580e-02,\n",
       "       -2.71541140e-02, -2.71600268e-02, -2.99601659e-02, -2.97430106e-02,\n",
       "       -2.47816697e-02, -2.25343421e-02, -1.25171650e-02, -5.71799914e-03,\n",
       "        1.21626659e-02,  1.28108186e-02,  1.95402940e-02,  2.50466513e-02,\n",
       "        2.58828220e-02,  2.33108747e-02,  2.13204650e-02,  2.32525912e-02,\n",
       "        2.84483369e-02,  2.95509794e-02,  3.14203902e-02,  2.78920979e-02,\n",
       "        2.79312674e-02, -1.50587232e-02, -9.69647118e-03, -9.88127176e-03,\n",
       "       -1.47484649e-02, -1.49506817e-02, -2.32127685e-02, -2.53812019e-02,\n",
       "       -2.17055314e-02, -1.64313793e-02, -2.17381954e-03,  1.25724000e-02,\n",
       "        1.89653704e-02,  2.48476042e-02,  2.16302942e-02,  1.49637976e-02,\n",
       "        1.84477898e-02,  2.80614595e-02,  3.05140330e-02,  3.60355593e-02,\n",
       "        3.61028784e-02,  3.07143886e-02,  2.51222343e-02,  2.53407387e-02,\n",
       "        2.19873199e-02,  2.70537864e-02,  0.00000000e+00,  2.58555710e-02,\n",
       "        2.47838521e-02,  2.28164999e-02,  2.01273306e-02,  1.92002409e-02,\n",
       "        1.68308061e-02,  1.53784352e-02,  1.24466302e-02,  1.47026673e-02,\n",
       "        1.49138934e-02,  1.35589474e-02,  1.71947063e-02,  1.52151626e-02,\n",
       "        1.50653469e-02,  1.43230330e-02,  1.63075706e-02,  1.75786462e-02,\n",
       "        1.74745622e-02,  1.62201682e-02,  1.54444050e-02,  1.18200401e-02,\n",
       "        1.02247188e-02,  3.38447291e-03,  1.58888012e-03,  5.20961720e-03,\n",
       "        9.09920332e-03,  1.29073841e-02,  1.77187752e-02,  1.86868094e-02,\n",
       "        1.79224534e-02,  1.80486514e-02,  1.62201326e-02,  1.59739450e-02,\n",
       "        1.54721101e-02,  1.63555576e-02,  1.63240555e-02,  1.76178354e-02,\n",
       "        2.09128577e-02,  2.14575272e-02,  2.45666595e-02,  2.56018874e-02,\n",
       "        2.95645158e-02,  3.04121388e-02,  3.10001036e-02,  3.06689005e-02,\n",
       "        3.19826900e-02,  3.32508590e-02,  3.47793374e-02,  3.36526565e-02,\n",
       "        5.12429159e-03,  2.73492716e-03,  2.07721651e-04, -1.45543241e-03,\n",
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       "       -5.47341600e-02, -4.38592658e-02, -5.00784690e-02, -5.09535844e-02,\n",
       "       -5.34926729e-02, -6.02295279e-02, -6.40922470e-02, -5.61213144e-02,\n",
       "       -4.54231481e-02, -3.67274031e-02, -2.36095358e-02, -1.23793116e-02,\n",
       "       -3.69702398e-03,  3.05112881e-03,  1.29733115e-02,  1.49052044e-02,\n",
       "        1.19428568e-02,  1.09619120e-02,  6.36409529e-03,  6.28795407e-03,\n",
       "        8.34972696e-03,  1.02903689e-02,  1.14382046e-02,  1.23498695e-02,\n",
       "        1.06674153e-02,  1.10639536e-02, -8.33132267e-02, -6.78711114e-02,\n",
       "       -4.45326011e-02, -1.72896017e-02,  1.74965918e-03,  5.04516057e-03,\n",
       "       -2.79089418e-03, -8.17838935e-03, -1.34196492e-02, -1.67647560e-02,\n",
       "       -1.60555738e-02, -1.59689739e-02, -1.73691696e-02, -1.92205040e-02,\n",
       "       -1.77135698e-02, -1.39546444e-02, -1.40039729e-02, -1.53247434e-02,\n",
       "       -1.81930229e-02, -1.88894001e-02, -2.04646031e-02, -2.11668745e-02,\n",
       "       -2.16651770e-02, -2.40647197e-02, -2.50278661e-02, -1.28589486e-02,\n",
       "       -1.42803882e-02, -1.19436340e-02, -1.16555698e-02, -1.21462601e-02,\n",
       "       -1.39090550e-02, -1.43371279e-02, -1.84539398e-02, -1.81058442e-02,\n",
       "       -1.79692120e-02, -1.72569558e-02, -1.49523274e-02, -1.59931592e-02,\n",
       "       -1.43166839e-02, -1.48576825e-02, -1.47033597e-02, -9.38709497e-03,\n",
       "       -8.34402465e-03, -7.41682678e-03, -4.75059173e-03, -7.70163638e-04,\n",
       "        1.46129677e-03,  5.09704368e-03,  8.49884672e-03,  9.53611826e-03,\n",
       "        7.08482583e-03,  1.18134753e-02,  1.34843911e-02,  1.67859240e-02,\n",
       "        2.02477470e-02,  2.18320874e-02,  2.42256631e-02,  2.43834621e-02,\n",
       "        2.75279168e-02,  3.05349530e-02,  3.36306552e-02,  3.55352820e-02,\n",
       "        3.91749520e-02,  4.06874152e-02,  4.13707883e-02,  4.16587629e-02,\n",
       "        4.18078209e-02,  4.10667194e-02,  4.16023377e-02,  4.04730070e-02,\n",
       "        4.02252669e-02,  4.11150919e-02,  4.15667383e-02,  4.05992622e-02,\n",
       "        4.11645841e-02,  2.04592111e-01,  2.22997077e-01,  2.50991351e-01,\n",
       "        2.79789890e-01,  2.93550026e-01,  2.98786439e-01,  2.89851537e-01,\n",
       "        2.72055965e-01,  2.55518200e-01,  2.38738539e-01,  2.21862674e-01,\n",
       "        2.05307886e-01,  1.92082781e-01,  1.70988729e-01,  1.59818812e-01,\n",
       "        1.46053056e-01,  1.36082110e-01,  1.22878057e-01,  1.14137761e-01,\n",
       "        1.06362959e-01,  9.80709917e-02,  9.31574893e-02,  8.30001029e-02,\n",
       "        7.86099937e-02,  7.18056215e-02, -2.04592111e-01, -1.81158169e-01,\n",
       "       -1.92583979e-01, -2.13253644e-01, -2.15179714e-01, -2.10904418e-01,\n",
       "       -1.99188461e-01, -1.91688111e-01, -1.81089639e-01, -1.67991190e-01,\n",
       "       -1.52617118e-01, -1.40104267e-01, -1.26767444e-01, -1.14689968e-01,\n",
       "       -1.04695534e-01, -9.30123084e-02, -8.49414653e-02, -7.94463981e-02,\n",
       "       -7.48384180e-02, -6.98544085e-02, -6.27268050e-02, -6.29564294e-02,\n",
       "       -5.69852904e-02, -5.20734537e-02, -5.12714010e-02,  2.04592111e-01,\n",
       "        2.07585444e-01,  2.22861493e-01,  2.37000561e-01,  2.50202945e-01,\n",
       "        2.68223219e-01,  2.77203201e-01,  2.83038711e-01,  2.81557296e-01,\n",
       "        2.75021075e-01,  2.69886831e-01,  2.59031358e-01,  2.49892023e-01,\n",
       "        2.41526877e-01,  2.29525283e-01,  2.16932384e-01,  2.04020576e-01,\n",
       "        1.91792503e-01,  1.83246590e-01,  1.75143354e-01,  1.64559961e-01,\n",
       "        1.53133972e-01,  1.46009693e-01,  1.37897266e-01,  1.29879891e-01,\n",
       "       -2.04592111e-01, -1.83128277e-01, -1.86068566e-01, -1.88577128e-01,\n",
       "       -1.97461738e-01, -1.97150828e-01, -2.01208177e-01, -2.06359153e-01,\n",
       "       -2.03873332e-01, -1.94076648e-01, -1.86319834e-01, -1.78680830e-01,\n",
       "       -1.70290581e-01, -1.60973550e-01, -1.52194780e-01, -1.38889348e-01,\n",
       "       -1.31137405e-01, -1.20220912e-01, -1.11652927e-01, -1.07285864e-01,\n",
       "       -1.02547444e-01, -9.09656354e-02, -8.39701443e-02, -7.73381580e-02,\n",
       "       -7.13100034e-02])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled = scaler.transform(X_train)\n",
    "print(\"transformed shape: {}\".format(X_train_scaled.shape))\n",
    "X_train_scaled.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.1, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier # scikit-learnライブラリの関数を使用します\n",
    "mlpclassifier_model_d = MLPClassifier(max_iter=1000, alpha=0.1,hidden_layer_sizes=(100,100))\n",
    "\n",
    "mlpclassifier_model_d.fit(X_train_scaled, Y_train) # モデルに対して、学習データをフィットさせ係数を学習させます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "予測日数：207、正解日数：115、正解率：55.55555555555556\n",
      "Training score: 0.718\n",
      "Test score: 0.512\n"
     ]
    }
   ],
   "source": [
    "Y_pred = mlpclassifier_model_d.predict(X_test_scaled) # 予測する\n",
    "\n",
    "# 正答率を計算\n",
    "success_num = 0\n",
    "for i in range(len(Y_pred)):\n",
    "    if Y_pred[i] * Y_test[i] >=0:\n",
    "        success_num+=1\n",
    "\n",
    "print(\"予測日数：\"+ str(len(Y_pred))+\"、正解日数：\"+str(success_num)+\"、正解率：\"+str(success_num/len(Y_pred)*100))\n",
    "\n",
    "print(\"Training score: {:.3f}\".format(mlpclassifier_model_d.score(X_train_scaled, Y_train)))\n",
    "print(\"Test score: {:.3f}\".format(mlpclassifier_model.score(X_test_scaled, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benefit is 6.042 yen\n",
      "Benefit2 is 5.879 yen\n"
     ]
    },
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110.466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.231</td>\n",
       "      <td>110.697000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.396</td>\n",
       "      <td>110.862000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.277</td>\n",
       "      <td>0.119</td>\n",
       "      <td>110.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110.585</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.253</td>\n",
       "      <td>110.719000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.451</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>0.037</td>\n",
       "      <td>110.502476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110.667</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.281</td>\n",
       "      <td>110.746113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.423</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>-0.144</td>\n",
       "      <td>110.319870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110.848</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>-0.287</td>\n",
       "      <td>110.177551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110.991</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.944</td>\n",
       "      <td>-1.231</td>\n",
       "      <td>109.240469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>111.935</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.592</td>\n",
       "      <td>-1.823</td>\n",
       "      <td>108.662720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>112.527</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.196</td>\n",
       "      <td>-1.627</td>\n",
       "      <td>108.851989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>112.331</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-1.571</td>\n",
       "      <td>108.906255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112.275</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.630</td>\n",
       "      <td>-0.941</td>\n",
       "      <td>109.517352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112.905</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.877</td>\n",
       "      <td>109.579432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>112.841</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>-0.474</td>\n",
       "      <td>109.970784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>112.438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.934</td>\n",
       "      <td>-1.408</td>\n",
       "      <td>109.057278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>111.504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.197</td>\n",
       "      <td>-1.605</td>\n",
       "      <td>108.864601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>111.307</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>-1.708</td>\n",
       "      <td>108.763861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>111.204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>-2.053</td>\n",
       "      <td>108.426431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>110.859</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.386</td>\n",
       "      <td>-1.667</td>\n",
       "      <td>108.803961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>111.245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.254</td>\n",
       "      <td>-1.921</td>\n",
       "      <td>108.555535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>110.991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-1.857</td>\n",
       "      <td>108.618131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>111.055</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788</td>\n",
       "      <td>-1.069</td>\n",
       "      <td>109.388840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>111.843</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>-1.217</td>\n",
       "      <td>109.244087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>111.695</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-1.163</td>\n",
       "      <td>109.296902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>111.641</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>-0.744</td>\n",
       "      <td>109.707105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>111.222</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>-0.922</td>\n",
       "      <td>109.531529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>111.400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-0.940</td>\n",
       "      <td>109.513831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>111.382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.456</td>\n",
       "      <td>-1.396</td>\n",
       "      <td>109.065479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>110.926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.176</td>\n",
       "      <td>-1.220</td>\n",
       "      <td>109.238527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>111.712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>6.349</td>\n",
       "      <td>116.908324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>111.611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.492</td>\n",
       "      <td>5.857</td>\n",
       "      <td>116.392972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>111.119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.206</td>\n",
       "      <td>6.063</td>\n",
       "      <td>116.608750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>111.325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>6.054</td>\n",
       "      <td>116.599322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>111.316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>5.987</td>\n",
       "      <td>116.529142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>111.249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.450</td>\n",
       "      <td>6.437</td>\n",
       "      <td>117.000501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>111.699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.223</td>\n",
       "      <td>6.214</td>\n",
       "      <td>116.766916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>111.476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>6.139</td>\n",
       "      <td>116.688357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>111.401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>6.106</td>\n",
       "      <td>116.653790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>111.368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.641</td>\n",
       "      <td>5.465</td>\n",
       "      <td>115.982367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>110.727</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>5.529</td>\n",
       "      <td>116.049405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>110.791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.837</td>\n",
       "      <td>4.692</td>\n",
       "      <td>115.172679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>109.954</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.089</td>\n",
       "      <td>4.781</td>\n",
       "      <td>115.265903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>110.043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.486</td>\n",
       "      <td>5.267</td>\n",
       "      <td>115.774970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>110.529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>5.230</td>\n",
       "      <td>115.736213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>110.492</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.165</td>\n",
       "      <td>5.395</td>\n",
       "      <td>115.909045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>110.657</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>5.200</td>\n",
       "      <td>115.704790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>110.852</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.558</td>\n",
       "      <td>4.642</td>\n",
       "      <td>115.122362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>111.410</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.076</td>\n",
       "      <td>4.718</td>\n",
       "      <td>115.200894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>111.334</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>4.588</td>\n",
       "      <td>115.066379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>111.464</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.152</td>\n",
       "      <td>4.436</td>\n",
       "      <td>114.909467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>111.616</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>4.374</td>\n",
       "      <td>114.845637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>111.678</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.189</td>\n",
       "      <td>4.563</td>\n",
       "      <td>115.039998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>111.489</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.342</td>\n",
       "      <td>4.905</td>\n",
       "      <td>115.392891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>111.147</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.135</td>\n",
       "      <td>5.040</td>\n",
       "      <td>115.533048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>111.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.040</td>\n",
       "      <td>115.533048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>111.012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.008</td>\n",
       "      <td>6.048</td>\n",
       "      <td>116.582100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>112.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>6.066</td>\n",
       "      <td>116.600833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>112.038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>6.042</td>\n",
       "      <td>116.575855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>112.014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.042</td>\n",
       "      <td>116.575855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1      2      3           4\n",
       "0    110.466  1.0  0.231  0.231  110.697000\n",
       "1    110.697  1.0  0.165  0.396  110.862000\n",
       "2    110.862  1.0 -0.277  0.119  110.585000\n",
       "3    110.585 -1.0  0.134  0.253  110.719000\n",
       "4    110.451 -1.0 -0.216  0.037  110.502476\n",
       "5    110.667 -1.0  0.244  0.281  110.746113\n",
       "6    110.423 -1.0 -0.425 -0.144  110.319870\n",
       "7    110.848 -1.0 -0.143 -0.287  110.177551\n",
       "8    110.991 -1.0 -0.944 -1.231  109.240469\n",
       "9    111.935 -1.0 -0.592 -1.823  108.662720\n",
       "10   112.527 -1.0  0.196 -1.627  108.851989\n",
       "11   112.331 -1.0  0.056 -1.571  108.906255\n",
       "12   112.275  1.0  0.630 -0.941  109.517352\n",
       "13   112.905 -1.0  0.064 -0.877  109.579432\n",
       "14   112.841 -1.0  0.403 -0.474  109.970784\n",
       "15   112.438  1.0 -0.934 -1.408  109.057278\n",
       "16   111.504  1.0 -0.197 -1.605  108.864601\n",
       "17   111.307  1.0 -0.103 -1.708  108.763861\n",
       "18   111.204  1.0 -0.345 -2.053  108.426431\n",
       "19   110.859  1.0  0.386 -1.667  108.803961\n",
       "20   111.245  1.0 -0.254 -1.921  108.555535\n",
       "21   110.991  1.0  0.064 -1.857  108.618131\n",
       "22   111.055  1.0  0.788 -1.069  109.388840\n",
       "23   111.843  1.0 -0.148 -1.217  109.244087\n",
       "24   111.695 -1.0  0.054 -1.163  109.296902\n",
       "25   111.641 -1.0  0.419 -0.744  109.707105\n",
       "26   111.222 -1.0 -0.178 -0.922  109.531529\n",
       "27   111.400  1.0 -0.018 -0.940  109.513831\n",
       "28   111.382  1.0 -0.456 -1.396  109.065479\n",
       "29   110.926  1.0  0.176 -1.220  109.238527\n",
       "..       ...  ...    ...    ...         ...\n",
       "177  111.712  1.0 -0.101  6.349  116.908324\n",
       "178  111.611  1.0 -0.492  5.857  116.392972\n",
       "179  111.119  1.0  0.206  6.063  116.608750\n",
       "180  111.325  1.0 -0.009  6.054  116.599322\n",
       "181  111.316  1.0 -0.067  5.987  116.529142\n",
       "182  111.249  1.0  0.450  6.437  117.000501\n",
       "183  111.699  1.0 -0.223  6.214  116.766916\n",
       "184  111.476  1.0 -0.075  6.139  116.688357\n",
       "185  111.401  1.0 -0.033  6.106  116.653790\n",
       "186  111.368  1.0 -0.641  5.465  115.982367\n",
       "187  110.727  1.0  0.064  5.529  116.049405\n",
       "188  110.791  1.0 -0.837  4.692  115.172679\n",
       "189  109.954  1.0  0.089  4.781  115.265903\n",
       "190  110.043  1.0  0.486  5.267  115.774970\n",
       "191  110.529  1.0 -0.037  5.230  115.736213\n",
       "192  110.492  1.0  0.165  5.395  115.909045\n",
       "193  110.657 -1.0 -0.195  5.200  115.704790\n",
       "194  110.852 -1.0 -0.558  4.642  115.122362\n",
       "195  111.410 -1.0  0.076  4.718  115.200894\n",
       "196  111.334 -1.0 -0.130  4.588  115.066379\n",
       "197  111.464 -1.0 -0.152  4.436  114.909467\n",
       "198  111.616 -1.0 -0.062  4.374  114.845637\n",
       "199  111.678 -1.0  0.189  4.563  115.039998\n",
       "200  111.489 -1.0  0.342  4.905  115.392891\n",
       "201  111.147 -1.0  0.135  5.040  115.533048\n",
       "202  111.012  1.0  0.000  5.040  115.533048\n",
       "203  111.012  1.0  1.008  6.048  116.582100\n",
       "204  112.020  1.0  0.018  6.066  116.600833\n",
       "205  112.038  1.0 -0.024  6.042  116.575855\n",
       "206  112.014  1.0  0.000  6.042  116.575855\n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a517320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result()"
   ]
  }
 ],
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