Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import 套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 讀取電子業資料、代碼&公司名分離 (E/S為EBT Growth/Sales Growth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1336</td>\n",
       "      <td>台翰</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-13.40</td>\n",
       "      <td>-15.62</td>\n",
       "      <td>0.857875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1471</td>\n",
       "      <td>首利</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>TSE</td>\n",
       "      <td>-9.16</td>\n",
       "      <td>-27.46</td>\n",
       "      <td>0.333576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1569</td>\n",
       "      <td>濱川</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>OTC</td>\n",
       "      <td>69.72</td>\n",
       "      <td>24.39</td>\n",
       "      <td>2.858549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1582</td>\n",
       "      <td>信錦</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>TSE</td>\n",
       "      <td>5.82</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.264591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1595</td>\n",
       "      <td>川寶</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-61.59</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>40.519737</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>28629</td>\n",
       "      <td>8383</td>\n",
       "      <td>千附</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>OTC</td>\n",
       "      <td>193.42</td>\n",
       "      <td>74.08</td>\n",
       "      <td>2.610961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28630</td>\n",
       "      <td>8410</td>\n",
       "      <td>森田</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-25.20</td>\n",
       "      <td>7.32</td>\n",
       "      <td>-3.442623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28631</td>\n",
       "      <td>8416</td>\n",
       "      <td>實威</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>OTC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28632</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>TSE</td>\n",
       "      <td>126.49</td>\n",
       "      <td>15.68</td>\n",
       "      <td>8.066964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28633</td>\n",
       "      <td>3713</td>\n",
       "      <td>新晶投控</td>\n",
       "      <td>NaT</td>\n",
       "      <td>OTC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28634 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Code Company       Date Class  EBT Growth  Sales Growth        E/S\n",
       "0      1336      台翰 2019-12-31   OTC      -13.40        -15.62   0.857875\n",
       "1      1471      首利 2019-12-31   TSE       -9.16        -27.46   0.333576\n",
       "2      1569      濱川 2019-12-31   OTC       69.72         24.39   2.858549\n",
       "3      1582      信錦 2019-12-31   TSE        5.82         -2.57  -2.264591\n",
       "4      1595      川寶 2019-12-31   OTC      -61.59         -1.52  40.519737\n",
       "...     ...     ...        ...   ...         ...           ...        ...\n",
       "28629  8383      千附 2011-03-31   OTC      193.42         74.08   2.610961\n",
       "28630  8410      森田 2011-03-31   OTC      -25.20          7.32  -3.442623\n",
       "28631  8416      實威 2011-03-31   OTC         NaN           NaN        NaN\n",
       "28632  9912      偉聯 2011-03-31   TSE      126.49         15.68   8.066964\n",
       "28633  3713    新晶投控        NaT   OTC         NaN           NaN        NaN\n",
       "\n",
       "[28634 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_F=pd.read_excel('data.xlsx',encoding='CP950')\n",
    "df_F.columns = ['Code','Date','Class','EBT Growth','Sales Growth','E/S']\n",
    "df_F['Code'], df_F['Company'] = df_F['Code'].str.split(' ', 1).str\n",
    "df_F = df_F[[ 'Code' , 'Company' ,'Date', 'Class','EBT Growth' , 'Sales Growth' ,'E/S']]\n",
    "df_F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 排除特定行遺漏值、轉成浮點數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_F['EBT Growth'] = df_F['EBT Growth'].replace('-',np.NaN).astype('float')\n",
    "df_F['Sales Growth'] = df_F['Sales Growth'].replace('-',np.NaN).astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 擷取年數及月分&排除遺漏值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1336</td>\n",
       "      <td>台翰</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-13.40</td>\n",
       "      <td>-15.62</td>\n",
       "      <td>0.857875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1471</td>\n",
       "      <td>首利</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>-9.16</td>\n",
       "      <td>-27.46</td>\n",
       "      <td>0.333576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1569</td>\n",
       "      <td>濱川</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>69.72</td>\n",
       "      <td>24.39</td>\n",
       "      <td>2.858549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1582</td>\n",
       "      <td>信錦</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>5.82</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.264591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1595</td>\n",
       "      <td>川寶</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-61.59</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>40.519737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28627</td>\n",
       "      <td>8299</td>\n",
       "      <td>群聯</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>80.80</td>\n",
       "      <td>-7.29</td>\n",
       "      <td>-11.083676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28628</td>\n",
       "      <td>8358</td>\n",
       "      <td>金居</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-35.31</td>\n",
       "      <td>9.58</td>\n",
       "      <td>-3.685804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28629</td>\n",
       "      <td>8383</td>\n",
       "      <td>千附</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>193.42</td>\n",
       "      <td>74.08</td>\n",
       "      <td>2.610961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28630</td>\n",
       "      <td>8410</td>\n",
       "      <td>森田</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>-25.20</td>\n",
       "      <td>7.32</td>\n",
       "      <td>-3.442623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28632</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>201103</td>\n",
       "      <td>TSE</td>\n",
       "      <td>126.49</td>\n",
       "      <td>15.68</td>\n",
       "      <td>8.066964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28103 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Code Company    Date Class  EBT Growth  Sales Growth        E/S\n",
       "0      1336      台翰  201912   OTC      -13.40        -15.62   0.857875\n",
       "1      1471      首利  201912   TSE       -9.16        -27.46   0.333576\n",
       "2      1569      濱川  201912   OTC       69.72         24.39   2.858549\n",
       "3      1582      信錦  201912   TSE        5.82         -2.57  -2.264591\n",
       "4      1595      川寶  201912   OTC      -61.59         -1.52  40.519737\n",
       "...     ...     ...     ...   ...         ...           ...        ...\n",
       "28627  8299      群聯  201103   OTC       80.80         -7.29 -11.083676\n",
       "28628  8358      金居  201103   OTC      -35.31          9.58  -3.685804\n",
       "28629  8383      千附  201103   OTC      193.42         74.08   2.610961\n",
       "28630  8410      森田  201103   OTC      -25.20          7.32  -3.442623\n",
       "28632  9912      偉聯  201103   TSE      126.49         15.68   8.066964\n",
       "\n",
       "[28103 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_F['Date'] = df_F['Date'].astype('str')\n",
    "df_F['Date'] = df_F['Date'].str.replace('-','')\n",
    "df_F['Date'] = df_F['Date'].str[0:6]\n",
    "df_F.Date = df_F.Date.astype('str')\n",
    "df_F = df_F.dropna()\n",
    "df_F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 篩選EBT growth & Sales Growth皆為正的筆數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1569</td>\n",
       "      <td>濱川</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>69.72</td>\n",
       "      <td>24.39</td>\n",
       "      <td>2.858549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1785</td>\n",
       "      <td>光洋科</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>165.70</td>\n",
       "      <td>5.26</td>\n",
       "      <td>31.501901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2308</td>\n",
       "      <td>台達電</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>27.86</td>\n",
       "      <td>13.13</td>\n",
       "      <td>2.121858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2313</td>\n",
       "      <td>華通</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>42.06</td>\n",
       "      <td>10.52</td>\n",
       "      <td>3.998099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>2329</td>\n",
       "      <td>華泰</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>303.18</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.789817</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>10155</td>\n",
       "      <td>28616</td>\n",
       "      <td>8210</td>\n",
       "      <td>勤誠</td>\n",
       "      <td>201103</td>\n",
       "      <td>TSE</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1.95</td>\n",
       "      <td>9.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10156</td>\n",
       "      <td>28619</td>\n",
       "      <td>8234</td>\n",
       "      <td>新漢</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>213.80</td>\n",
       "      <td>39.00</td>\n",
       "      <td>5.482051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10157</td>\n",
       "      <td>28620</td>\n",
       "      <td>8240</td>\n",
       "      <td>華宏</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>51.28</td>\n",
       "      <td>16.18</td>\n",
       "      <td>3.169345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10158</td>\n",
       "      <td>28629</td>\n",
       "      <td>8383</td>\n",
       "      <td>千附</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>193.42</td>\n",
       "      <td>74.08</td>\n",
       "      <td>2.610961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10159</td>\n",
       "      <td>28632</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>201103</td>\n",
       "      <td>TSE</td>\n",
       "      <td>126.49</td>\n",
       "      <td>15.68</td>\n",
       "      <td>8.066964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10160 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Code Company    Date Class  EBT Growth  Sales Growth        E/S\n",
       "0          2  1569      濱川  201912   OTC       69.72         24.39   2.858549\n",
       "1          5  1785     光洋科  201912   OTC      165.70          5.26  31.501901\n",
       "2         12  2308     台達電  201912   TSE       27.86         13.13   2.121858\n",
       "3         14  2313      華通  201912   TSE       42.06         10.52   3.998099\n",
       "4         23  2329      華泰  201912   TSE      303.18         15.32  19.789817\n",
       "...      ...   ...     ...     ...   ...         ...           ...        ...\n",
       "10155  28616  8210      勤誠  201103   TSE       19.20          1.95   9.846154\n",
       "10156  28619  8234      新漢  201103   OTC      213.80         39.00   5.482051\n",
       "10157  28620  8240      華宏  201103   OTC       51.28         16.18   3.169345\n",
       "10158  28629  8383      千附  201103   OTC      193.42         74.08   2.610961\n",
       "10159  28632  9912      偉聯  201103   TSE      126.49         15.68   8.066964\n",
       "\n",
       "[10160 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_F = df_F[df_F['EBT Growth']>0] \n",
    "df_F = df_F[df_F['Sales Growth']>0]\n",
    "df_F = df_F.reset_index()\n",
    "df_F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 排除前5%極端值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10160\n"
     ]
    }
   ],
   "source": [
    "a = df_F.groupby('Date')['E/S'].quantile(0.95)\n",
    "b = a.tolist()\n",
    "b = b[::-1]\n",
    "c = df_F['Date'].tolist()\n",
    "date_list = []\n",
    "for i in range(len(c)):\n",
    "    if c[i] not in date_list:\n",
    "        date_list.append(c[i])\n",
    "    else:\n",
    "        pass\n",
    "True_list = []\n",
    "for i in range(len(c)):\n",
    "    count = 0\n",
    "    for j in range(len(date_list)):\n",
    "        if c[i] == date_list[j]:\n",
    "            count = count\n",
    "            True_list.append(b[count])\n",
    "            break\n",
    "        else:\n",
    "            count += 1\n",
    "print(len(True_list))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 51, 56, 74, 79, 82, 85, 113, 127, 163, 176, 193, 248, 273, 299, 313, 319, 331, 340, 362, 363, 399, 402, 428, 448, 484, 502, 543, 544, 566, 580, 582, 607, 608, 618, 623, 631, 703, 727, 764, 786, 813, 815, 864, 880, 894, 928, 948, 966, 978, 997, 999, 1001, 1002, 1015, 1092, 1104, 1124, 1130, 1145, 1152, 1213, 1232, 1245, 1281, 1300, 1308, 1309, 1317, 1340, 1373, 1374, 1393, 1397, 1484, 1487, 1499, 1510, 1547, 1558, 1579, 1591, 1594, 1607, 1635, 1700, 1726, 1741, 1754, 1761, 1768, 1770, 1818, 1875, 1878, 1886, 1890, 1910, 1930, 1932, 1949, 1990, 2047, 2053, 2076, 2077, 2098, 2103, 2112, 2123, 2146, 2153, 2193, 2246, 2249, 2259, 2268, 2283, 2301, 2315, 2347, 2360, 2401, 2406, 2420, 2435, 2463, 2467, 2551, 2566, 2567, 2574, 2586, 2597, 2602, 2611, 2650, 2652, 2684, 2688, 2690, 2729, 2745, 2763, 2782, 2819, 2863, 2890, 2892, 2910, 2914, 2926, 2928, 2966, 2967, 3009, 3025, 3051, 3060, 3074, 3084, 3129, 3156, 3218, 3239, 3264, 3285, 3318, 3354, 3363, 3379, 3396, 3400, 3412, 3449, 3455, 3456, 3467, 3478, 3481, 3495, 3513, 3517, 3581, 3602, 3640, 3644, 3657, 3666, 3682, 3714, 3718, 3724, 3727, 3766, 3799, 3802, 3803, 3846, 3902, 3909, 3910, 3912, 3916, 3943, 3983, 4026, 4039, 4067, 4089, 4110, 4111, 4116, 4134, 4153, 4211, 4212, 4214, 4229, 4278, 4340, 4358, 4369, 4373, 4399, 4404, 4411, 4413, 4445, 4454, 4483, 4494, 4499, 4509, 4552, 4604, 4612, 4647, 4661, 4673, 4681, 4698, 4711, 4722, 4734, 4743, 4753, 4770, 4803, 4812, 4830, 4893, 4897, 4899, 4908, 4917, 4929, 4942, 4945, 4951, 4957, 4975, 4989, 4998, 5062, 5158, 5167, 5171, 5181, 5206, 5216, 5217, 5293, 5295, 5317, 5322, 5351, 5399, 5408, 5442, 5452, 5454, 5471, 5489, 5492, 5509, 5532, 5608, 5625, 5656, 5667, 5669, 5671, 5678, 5733, 5773, 5779, 5822, 5835, 5871, 5874, 5890, 5906, 5944, 6001, 6019, 6021, 6027, 6028, 6048, 6071, 6127, 6146, 6169, 6176, 6181, 6183, 6187, 6197, 6258, 6260, 6289, 6300, 6310, 6312, 6346, 6362, 6363, 6436, 6444, 6450, 6491, 6532, 6541, 6570, 6571, 6579, 6582, 6586, 6596, 6660, 6671, 6712, 6713, 6730, 6747, 6790, 6794, 6801, 6821, 6833, 6839, 6906, 6910, 6915, 6949, 6968, 6972, 7029, 7040, 7043, 7065, 7084, 7106, 7156, 7174, 7190, 7211, 7217, 7256, 7272, 7289, 7301, 7316, 7331, 7349, 7379, 7383, 7395, 7401, 7404, 7419, 7430, 7440, 7446, 7452, 7512, 7513, 7523, 7542, 7560, 7574, 7649, 7650, 7719, 7726, 7741, 7746, 7754, 7780, 7790, 7799, 7845, 7859, 7864, 7907, 7919, 7954, 7984, 7991, 8009, 8025, 8055, 8057, 8073, 8123, 8139, 8157, 8222, 8226, 8233, 8237, 8238, 8283, 8292, 8314, 8317, 8320, 8353, 8364, 8385, 8386, 8393, 8401, 8440, 8448, 8463, 8492, 8517, 8568, 8605, 8612, 8614, 8633, 8648, 8651, 8718, 8727, 8733, 8791, 8792, 8798, 8809, 8838, 8840, 8862, 8864, 8875, 8920, 8937, 8955, 8978, 8991, 9013, 9050, 9070, 9089, 9091, 9167, 9174, 9181, 9185, 9200, 9210, 9223, 9234, 9252, 9293, 9296, 9321, 9334, 9352, 9372, 9375, 9405, 9413, 9416, 9480, 9481, 9537, 9538, 9551, 9560, 9562, 9581, 9603, 9618, 9635, 9658, 9662, 9722, 9728, 9734, 9760, 9763, 9771, 9823, 9837, 9844, 9899, 9913, 9928, 9948, 9988, 10004, 10027, 10028, 10037, 10056, 10083, 10089, 10092, 10153]\n"
     ]
    }
   ],
   "source": [
    "count_del_list = []\n",
    "ES_list = df_F['E/S'].tolist()\n",
    "for i in range(len(True_list)):\n",
    "    if ES_list[i] > True_list[i]:\n",
    "       count_del_list.append(i)\n",
    "    else:\n",
    "        pass\n",
    "print(count_del_list) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "523"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(count_del_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index  Code Company    Date Class  EBT Growth  Sales Growth        E/S\n",
      "0          2  1569      濱川  201912   OTC       69.72         24.39   2.858549\n",
      "1          5  1785     光洋科  201912   OTC      165.70          5.26  31.501901\n",
      "2         12  2308     台達電  201912   TSE       27.86         13.13   2.121858\n",
      "3         14  2313      華通  201912   TSE       42.06         10.52   3.998099\n",
      "4         23  2329      華泰  201912   TSE      303.18         15.32  19.789817\n",
      "...      ...   ...     ...     ...   ...         ...           ...        ...\n",
      "10155  28616  8210      勤誠  201103   TSE       19.20          1.95   9.846154\n",
      "10156  28619  8234      新漢  201103   OTC      213.80         39.00   5.482051\n",
      "10157  28620  8240      華宏  201103   OTC       51.28         16.18   3.169345\n",
      "10158  28629  8383      千附  201103   OTC      193.42         74.08   2.610961\n",
      "10159  28632  9912      偉聯  201103   TSE      126.49         15.68   8.066964\n",
      "\n",
      "[10160 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "df_g = df_F.copy()\n",
    "print(df_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1569</td>\n",
       "      <td>濱川</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>69.72</td>\n",
       "      <td>24.39</td>\n",
       "      <td>2.858549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1785</td>\n",
       "      <td>光洋科</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>165.70</td>\n",
       "      <td>5.26</td>\n",
       "      <td>31.501901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2308</td>\n",
       "      <td>台達電</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>27.86</td>\n",
       "      <td>13.13</td>\n",
       "      <td>2.121858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2313</td>\n",
       "      <td>華通</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>42.06</td>\n",
       "      <td>10.52</td>\n",
       "      <td>3.998099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>2329</td>\n",
       "      <td>華泰</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>303.18</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.789817</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>10155</td>\n",
       "      <td>28616</td>\n",
       "      <td>8210</td>\n",
       "      <td>勤誠</td>\n",
       "      <td>201103</td>\n",
       "      <td>TSE</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1.95</td>\n",
       "      <td>9.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10156</td>\n",
       "      <td>28619</td>\n",
       "      <td>8234</td>\n",
       "      <td>新漢</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>213.80</td>\n",
       "      <td>39.00</td>\n",
       "      <td>5.482051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10157</td>\n",
       "      <td>28620</td>\n",
       "      <td>8240</td>\n",
       "      <td>華宏</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>51.28</td>\n",
       "      <td>16.18</td>\n",
       "      <td>3.169345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10158</td>\n",
       "      <td>28629</td>\n",
       "      <td>8383</td>\n",
       "      <td>千附</td>\n",
       "      <td>201103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>193.42</td>\n",
       "      <td>74.08</td>\n",
       "      <td>2.610961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10159</td>\n",
       "      <td>28632</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>201103</td>\n",
       "      <td>TSE</td>\n",
       "      <td>126.49</td>\n",
       "      <td>15.68</td>\n",
       "      <td>8.066964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9637 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Code Company    Date Class  EBT Growth  Sales Growth        E/S\n",
       "0          2  1569      濱川  201912   OTC       69.72         24.39   2.858549\n",
       "1          5  1785     光洋科  201912   OTC      165.70          5.26  31.501901\n",
       "2         12  2308     台達電  201912   TSE       27.86         13.13   2.121858\n",
       "3         14  2313      華通  201912   TSE       42.06         10.52   3.998099\n",
       "4         23  2329      華泰  201912   TSE      303.18         15.32  19.789817\n",
       "...      ...   ...     ...     ...   ...         ...           ...        ...\n",
       "10155  28616  8210      勤誠  201103   TSE       19.20          1.95   9.846154\n",
       "10156  28619  8234      新漢  201103   OTC      213.80         39.00   5.482051\n",
       "10157  28620  8240      華宏  201103   OTC       51.28         16.18   3.169345\n",
       "10158  28629  8383      千附  201103   OTC      193.42         74.08   2.610961\n",
       "10159  28632  9912      偉聯  201103   TSE      126.49         15.68   8.066964\n",
       "\n",
       "[9637 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g = df_g.drop(count_del_list)\n",
    "df_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 從201209開始計算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1569</td>\n",
       "      <td>濱川</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>69.72</td>\n",
       "      <td>24.39</td>\n",
       "      <td>2.858549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1785</td>\n",
       "      <td>光洋科</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>165.70</td>\n",
       "      <td>5.26</td>\n",
       "      <td>31.501901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2308</td>\n",
       "      <td>台達電</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>27.86</td>\n",
       "      <td>13.13</td>\n",
       "      <td>2.121858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2313</td>\n",
       "      <td>華通</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>42.06</td>\n",
       "      <td>10.52</td>\n",
       "      <td>3.998099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>2329</td>\n",
       "      <td>華泰</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>303.18</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.789817</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>8981</td>\n",
       "      <td>24200</td>\n",
       "      <td>8114</td>\n",
       "      <td>振樺電</td>\n",
       "      <td>201209</td>\n",
       "      <td>TSE</td>\n",
       "      <td>7.99</td>\n",
       "      <td>5.93</td>\n",
       "      <td>1.347386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8982</td>\n",
       "      <td>24203</td>\n",
       "      <td>8147</td>\n",
       "      <td>正淩</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.115538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8983</td>\n",
       "      <td>24205</td>\n",
       "      <td>8155</td>\n",
       "      <td>博智</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>11.39</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.320700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8984</td>\n",
       "      <td>24208</td>\n",
       "      <td>8176</td>\n",
       "      <td>智捷</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>16.91</td>\n",
       "      <td>14.11</td>\n",
       "      <td>1.198441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8985</td>\n",
       "      <td>24213</td>\n",
       "      <td>8213</td>\n",
       "      <td>志超</td>\n",
       "      <td>201209</td>\n",
       "      <td>TSE</td>\n",
       "      <td>252.70</td>\n",
       "      <td>38.65</td>\n",
       "      <td>6.538163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8524 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  Code Company    Date Class  EBT Growth  Sales Growth        E/S\n",
       "0         2  1569      濱川  201912   OTC       69.72         24.39   2.858549\n",
       "1         5  1785     光洋科  201912   OTC      165.70          5.26  31.501901\n",
       "2        12  2308     台達電  201912   TSE       27.86         13.13   2.121858\n",
       "3        14  2313      華通  201912   TSE       42.06         10.52   3.998099\n",
       "4        23  2329      華泰  201912   TSE      303.18         15.32  19.789817\n",
       "...     ...   ...     ...     ...   ...         ...           ...        ...\n",
       "8981  24200  8114     振樺電  201209   TSE        7.99          5.93   1.347386\n",
       "8982  24203  8147      正淩  201209   OTC        2.80          2.51   1.115538\n",
       "8983  24205  8155      博智  201209   OTC       11.39          3.43   3.320700\n",
       "8984  24208  8176      智捷  201209   OTC       16.91         14.11   1.198441\n",
       "8985  24213  8213      志超  201209   TSE      252.70         38.65   6.538163\n",
       "\n",
       "[8524 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g = df_g[df_g['Date'] >= '201209']\n",
    "df_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 將E/S排名分組排名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>index</th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "      <th>E/S_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1569</td>\n",
       "      <td>濱川</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>69.72</td>\n",
       "      <td>24.39</td>\n",
       "      <td>2.858549</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1785</td>\n",
       "      <td>光洋科</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>165.70</td>\n",
       "      <td>5.26</td>\n",
       "      <td>31.501901</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2308</td>\n",
       "      <td>台達電</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>27.86</td>\n",
       "      <td>13.13</td>\n",
       "      <td>2.121858</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2313</td>\n",
       "      <td>華通</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>42.06</td>\n",
       "      <td>10.52</td>\n",
       "      <td>3.998099</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>2329</td>\n",
       "      <td>華泰</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>303.18</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.789817</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>8981</td>\n",
       "      <td>24200</td>\n",
       "      <td>8114</td>\n",
       "      <td>振樺電</td>\n",
       "      <td>201209</td>\n",
       "      <td>TSE</td>\n",
       "      <td>7.99</td>\n",
       "      <td>5.93</td>\n",
       "      <td>1.347386</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8982</td>\n",
       "      <td>24203</td>\n",
       "      <td>8147</td>\n",
       "      <td>正淩</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.115538</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8983</td>\n",
       "      <td>24205</td>\n",
       "      <td>8155</td>\n",
       "      <td>博智</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>11.39</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.320700</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8984</td>\n",
       "      <td>24208</td>\n",
       "      <td>8176</td>\n",
       "      <td>智捷</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>16.91</td>\n",
       "      <td>14.11</td>\n",
       "      <td>1.198441</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8985</td>\n",
       "      <td>24213</td>\n",
       "      <td>8213</td>\n",
       "      <td>志超</td>\n",
       "      <td>201209</td>\n",
       "      <td>TSE</td>\n",
       "      <td>252.70</td>\n",
       "      <td>38.65</td>\n",
       "      <td>6.538163</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8524 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  Code Company    Date Class  EBT Growth  Sales Growth        E/S  \\\n",
       "0         2  1569      濱川  201912   OTC       69.72         24.39   2.858549   \n",
       "1         5  1785     光洋科  201912   OTC      165.70          5.26  31.501901   \n",
       "2        12  2308     台達電  201912   TSE       27.86         13.13   2.121858   \n",
       "3        14  2313      華通  201912   TSE       42.06         10.52   3.998099   \n",
       "4        23  2329      華泰  201912   TSE      303.18         15.32  19.789817   \n",
       "...     ...   ...     ...     ...   ...         ...           ...        ...   \n",
       "8981  24200  8114     振樺電  201209   TSE        7.99          5.93   1.347386   \n",
       "8982  24203  8147      正淩  201209   OTC        2.80          2.51   1.115538   \n",
       "8983  24205  8155      博智  201209   OTC       11.39          3.43   3.320700   \n",
       "8984  24208  8176      智捷  201209   OTC       16.91         14.11   1.198441   \n",
       "8985  24213  8213      志超  201209   TSE      252.70         38.65   6.538163   \n",
       "\n",
       "      E/S_rank  \n",
       "0        125.0  \n",
       "1          9.0  \n",
       "2        153.0  \n",
       "3         97.0  \n",
       "4         22.0  \n",
       "...        ...  \n",
       "8981     121.0  \n",
       "8982     130.0  \n",
       "8983      74.0  \n",
       "8984     128.0  \n",
       "8985      44.0  \n",
       "\n",
       "[8524 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g['E/S_rank'] = df_g.groupby('Date')['E/S'].rank(ascending=False)\n",
    "df_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 篩選每組E/S前50名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Code\n",
      "1333    201306\n",
      "1336    201703\n",
      "1471    201812\n",
      "1569    201903\n",
      "1582    201706\n",
      "         ...  \n",
      "8358    201703\n",
      "8410    201612\n",
      "8431    201409\n",
      "8455    201509\n",
      "9912    201812\n",
      "Name: Date, Length: 578, dtype: object\n"
     ]
    }
   ],
   "source": [
    "df_N = df_g.dropna()\n",
    "df_N = df_N[df_N['E/S_rank']<=50.0]\n",
    "df_N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 確認每組是否皆為50筆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "201209    50\n",
       "201212    50\n",
       "201303    50\n",
       "201306    50\n",
       "201309    50\n",
       "201312    50\n",
       "201403    50\n",
       "201406    50\n",
       "201409    50\n",
       "201412    50\n",
       "201503    50\n",
       "201506    50\n",
       "201509    50\n",
       "201512    50\n",
       "201603    50\n",
       "201606    50\n",
       "201609    50\n",
       "201612    50\n",
       "201703    50\n",
       "201706    50\n",
       "201709    50\n",
       "201712    50\n",
       "201803    50\n",
       "201806    50\n",
       "201809    50\n",
       "201812    50\n",
       "201903    50\n",
       "201906    50\n",
       "201909    50\n",
       "201912    50\n",
       "Name: Code, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_N.groupby('Date')['Code'].count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 因子值加權-計算權重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>index</th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "      <th>E/S_rank</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1785</td>\n",
       "      <td>光洋科</td>\n",
       "      <td>201912</td>\n",
       "      <td>OTC</td>\n",
       "      <td>165.70</td>\n",
       "      <td>5.26</td>\n",
       "      <td>31.501901</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.032967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>2329</td>\n",
       "      <td>華泰</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>303.18</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.789817</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.020710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>48</td>\n",
       "      <td>2367</td>\n",
       "      <td>燿華</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>211.26</td>\n",
       "      <td>14.73</td>\n",
       "      <td>14.342159</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.015009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>60</td>\n",
       "      <td>2385</td>\n",
       "      <td>群光</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>62.62</td>\n",
       "      <td>6.06</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.010814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "      <td>2392</td>\n",
       "      <td>正崴</td>\n",
       "      <td>201912</td>\n",
       "      <td>TSE</td>\n",
       "      <td>170.07</td>\n",
       "      <td>11.27</td>\n",
       "      <td>15.090506</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.015792</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>8968</td>\n",
       "      <td>24137</td>\n",
       "      <td>6282</td>\n",
       "      <td>康舒</td>\n",
       "      <td>201209</td>\n",
       "      <td>TSE</td>\n",
       "      <td>82.15</td>\n",
       "      <td>9.20</td>\n",
       "      <td>8.929348</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.007755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8972</td>\n",
       "      <td>24165</td>\n",
       "      <td>8047</td>\n",
       "      <td>星雲</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>87.54</td>\n",
       "      <td>2.47</td>\n",
       "      <td>35.441296</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.030779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8973</td>\n",
       "      <td>24166</td>\n",
       "      <td>8048</td>\n",
       "      <td>德勝</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>83.26</td>\n",
       "      <td>9.23</td>\n",
       "      <td>9.020585</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.007834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8976</td>\n",
       "      <td>24179</td>\n",
       "      <td>8076</td>\n",
       "      <td>伍豐</td>\n",
       "      <td>201209</td>\n",
       "      <td>OTC</td>\n",
       "      <td>352.19</td>\n",
       "      <td>5.93</td>\n",
       "      <td>59.391231</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.051579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8985</td>\n",
       "      <td>24213</td>\n",
       "      <td>8213</td>\n",
       "      <td>志超</td>\n",
       "      <td>201209</td>\n",
       "      <td>TSE</td>\n",
       "      <td>252.70</td>\n",
       "      <td>38.65</td>\n",
       "      <td>6.538163</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.005678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1500 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  Code Company    Date Class  EBT Growth  Sales Growth        E/S  \\\n",
       "1         5  1785     光洋科  201912   OTC      165.70          5.26  31.501901   \n",
       "4        23  2329      華泰  201912   TSE      303.18         15.32  19.789817   \n",
       "9        48  2367      燿華  201912   TSE      211.26         14.73  14.342159   \n",
       "13       60  2385      群光  201912   TSE       62.62          6.06  10.333333   \n",
       "16       64  2392      正崴  201912   TSE      170.07         11.27  15.090506   \n",
       "...     ...   ...     ...     ...   ...         ...           ...        ...   \n",
       "8968  24137  6282      康舒  201209   TSE       82.15          9.20   8.929348   \n",
       "8972  24165  8047      星雲  201209   OTC       87.54          2.47  35.441296   \n",
       "8973  24166  8048      德勝  201209   OTC       83.26          9.23   9.020585   \n",
       "8976  24179  8076      伍豐  201209   OTC      352.19          5.93  59.391231   \n",
       "8985  24213  8213      志超  201209   TSE      252.70         38.65   6.538163   \n",
       "\n",
       "      E/S_rank    Weight  \n",
       "1          9.0  0.032967  \n",
       "4         22.0  0.020710  \n",
       "9         34.0  0.015009  \n",
       "13        39.0  0.010814  \n",
       "16        32.0  0.015792  \n",
       "...        ...       ...  \n",
       "8968      40.0  0.007755  \n",
       "8972      10.0  0.030779  \n",
       "8973      39.0  0.007834  \n",
       "8976       5.0  0.051579  \n",
       "8985      44.0  0.005678  \n",
       "\n",
       "[1500 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_N['Weight'] = df_N['E/S']/(df_N.groupby('Date')['E/S'].transform('sum'))\n",
    "df_N['Date'] = df_N['Date'].replace('-','').astype('str')\n",
    "df_N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 讀取電子業每日收盤價& 代碼與公司名分離"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date_D</th>\n",
       "      <th>Class</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1471</td>\n",
       "      <td>首利</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>2.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1582</td>\n",
       "      <td>信錦</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>91.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2059</td>\n",
       "      <td>川湖</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>362.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2301</td>\n",
       "      <td>光寶科</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>49.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2302</td>\n",
       "      <td>麗正</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1654886</td>\n",
       "      <td>8277</td>\n",
       "      <td>商丞</td>\n",
       "      <td>20110103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>34.6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1654887</td>\n",
       "      <td>8289</td>\n",
       "      <td>泰藝</td>\n",
       "      <td>20110103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>15.6389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1654888</td>\n",
       "      <td>8299</td>\n",
       "      <td>群聯</td>\n",
       "      <td>20110103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>113.0809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1654889</td>\n",
       "      <td>8358</td>\n",
       "      <td>金居</td>\n",
       "      <td>20110103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>19.8187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1654890</td>\n",
       "      <td>8383</td>\n",
       "      <td>千附</td>\n",
       "      <td>20110103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>15.2079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1654891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Code Company    Date_D Class     Price\n",
       "0        1471      首利  20191231   TSE    2.6400\n",
       "1        1582      信錦  20191231   TSE   91.0000\n",
       "2        2059      川湖  20191231   TSE  362.0000\n",
       "3        2301     光寶科  20191231   TSE   49.3500\n",
       "4        2302      麗正  20191231   TSE    8.2100\n",
       "...       ...     ...       ...   ...       ...\n",
       "1654886  8277      商丞  20110103   OTC   34.6800\n",
       "1654887  8289      泰藝  20110103   OTC   15.6389\n",
       "1654888  8299      群聯  20110103   OTC  113.0809\n",
       "1654889  8358      金居  20110103   OTC   19.8187\n",
       "1654890  8383      千附  20110103   OTC   15.2079\n",
       "\n",
       "[1654891 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_P = pd.read_csv('price.txt',sep='\\t')\n",
    "df_P.columns = ['Code','Date_D','Price','Class']\n",
    "df_P['Code'], df_P['Company'] = df_P['Code'].str.split(' ', 1).str\n",
    "df_P = df_P[[ 'Code' , 'Company' ,'Date_D', 'Class','Price']]\n",
    "df_P['Price'] = df_P['Price'].replace(',','',regex=True).astype('float64')\n",
    "df_P['Date_D'] = df_P['Date_D'].replace('/','',regex=True).astype('int')\n",
    "df_P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 依照代碼&日期排序資料&計算日報酬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_P = df_P.sort_values(['Code','Date_D'])\n",
    "df_P['Return_D'] = df_P.groupby('Company')['Price'].pct_change()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 篩選回測期間&排除遺漏值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Company</th>\n",
       "      <th>Date_D</th>\n",
       "      <th>Class</th>\n",
       "      <th>Price</th>\n",
       "      <th>Return_D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1322303</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130102</td>\n",
       "      <td>OTC</td>\n",
       "      <td>6.8667</td>\n",
       "      <td>0.014778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1321607</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0.019413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1320911</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130104</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0167</td>\n",
       "      <td>0.002386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1320215</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130107</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.1000</td>\n",
       "      <td>0.011872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1319519</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130108</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0333</td>\n",
       "      <td>-0.009394</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>3771</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191225</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>-0.011125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2932</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191226</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2093</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191227</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.1000</td>\n",
       "      <td>0.012500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1254</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191230</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>415</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.5100</td>\n",
       "      <td>-0.004678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1322638 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Code Company    Date_D Class   Price  Return_D\n",
       "1322303  1333     恩得利  20130102   OTC  6.8667  0.014778\n",
       "1321607  1333     恩得利  20130103   OTC  7.0000  0.019413\n",
       "1320911  1333     恩得利  20130104   OTC  7.0167  0.002386\n",
       "1320215  1333     恩得利  20130107   OTC  7.1000  0.011872\n",
       "1319519  1333     恩得利  20130108   OTC  7.0333 -0.009394\n",
       "...       ...     ...       ...   ...     ...       ...\n",
       "3771     9912      偉聯  20191225   TSE  8.0000 -0.011125\n",
       "2932     9912      偉聯  20191226   TSE  8.0000  0.000000\n",
       "2093     9912      偉聯  20191227   TSE  8.1000  0.012500\n",
       "1254     9912      偉聯  20191230   TSE  8.5500  0.055556\n",
       "415      9912      偉聯  20191231   TSE  8.5100 -0.004678\n",
       "\n",
       "[1322638 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = df_P['Date_D'] >= 20130101\n",
    "d = df_P['Date_D'] <= 20191231\n",
    "df_P = df_P[ ( c & d ) ]\n",
    "df_P.dropna()\n",
    "df_P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 將財報接露時間納入考量:\n",
    "* 在1月2月只能拿到前一年Q3的資料\n",
    "* 在3月4月只能拿到前一年Q4的資料\n",
    "* 在5月6月7月只能拿到當年Q1的資料\n",
    "* 在8月6月7月只能拿到當年Q2的資料\n",
    "* 在11月12月只能拿到當年Q3的資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_key(date):\n",
    "    month = str(date)[4:6]\n",
    "    year = str(date)[0:4]\n",
    "\n",
    "    if (month== '05' or month== '06' or month==  '07'  ):\n",
    "        return year + '03'\n",
    "    elif (month== '08' or month== '09' or month==  '10'  ):\n",
    "        return year + '06'\n",
    "    elif (month== '11' or month== '12' ):\n",
    "        return year + '09'\n",
    "    elif (month== '01' or month== '02' ):\n",
    "        return str(int(year)-1) + '09'\n",
    "    elif (month=='03' or month=='04'):\n",
    "        return str(int(year)-1) + '12'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 將日期對應到財報發布日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_P['Date_Key'] = df_P['Date_D'].apply(make_key)\n",
    "df_P.Code     = df_P.Code.astype('str')\n",
    "df_P.Date_Key = df_P.Date_Key.astype('str')\n",
    "df_P.Date_D = df_P.Date_D.astype('str')\n",
    "df_N.Code = df_N.Code.astype('str')\n",
    "df_N.Date = df_N.Date.replace(\"-\",\"\",regex=True).astype('str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 將因子資料與日報酬資料合併"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Company_x</th>\n",
       "      <th>Date_D</th>\n",
       "      <th>Class_x</th>\n",
       "      <th>Price</th>\n",
       "      <th>Return_D</th>\n",
       "      <th>Date_Key</th>\n",
       "      <th>index</th>\n",
       "      <th>Company_y</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class_y</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "      <th>E/S_rank</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130102</td>\n",
       "      <td>OTC</td>\n",
       "      <td>6.8667</td>\n",
       "      <td>0.014778</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0.019413</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130104</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0167</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130107</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.1000</td>\n",
       "      <td>0.011872</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130108</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0333</td>\n",
       "      <td>-0.009394</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>1322633</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191225</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>-0.011125</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322634</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191226</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322635</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191227</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.1000</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322636</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191230</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322637</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.5100</td>\n",
       "      <td>-0.004678</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1322638 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Code Company_x    Date_D Class_x   Price  Return_D Date_Key  index  \\\n",
       "0        1333       恩得利  20130102     OTC  6.8667  0.014778   201209    NaN   \n",
       "1        1333       恩得利  20130103     OTC  7.0000  0.019413   201209    NaN   \n",
       "2        1333       恩得利  20130104     OTC  7.0167  0.002386   201209    NaN   \n",
       "3        1333       恩得利  20130107     OTC  7.1000  0.011872   201209    NaN   \n",
       "4        1333       恩得利  20130108     OTC  7.0333 -0.009394   201209    NaN   \n",
       "...       ...       ...       ...     ...     ...       ...      ...    ...   \n",
       "1322633  9912        偉聯  20191225     TSE  8.0000 -0.011125   201909    NaN   \n",
       "1322634  9912        偉聯  20191226     TSE  8.0000  0.000000   201909    NaN   \n",
       "1322635  9912        偉聯  20191227     TSE  8.1000  0.012500   201909    NaN   \n",
       "1322636  9912        偉聯  20191230     TSE  8.5500  0.055556   201909    NaN   \n",
       "1322637  9912        偉聯  20191231     TSE  8.5100 -0.004678   201909    NaN   \n",
       "\n",
       "        Company_y Date Class_y  EBT Growth  Sales Growth  E/S  E/S_rank  \\\n",
       "0             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "2             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "3             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "4             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "...           ...  ...     ...         ...           ...  ...       ...   \n",
       "1322633       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322634       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322635       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322636       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322637       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "\n",
       "         Weight  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "1322633     NaN  \n",
       "1322634     NaN  \n",
       "1322635     NaN  \n",
       "1322636     NaN  \n",
       "1322637     NaN  \n",
       "\n",
       "[1322638 rows x 16 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.merge(left=df_P,right=df_N,left_on=['Code','Date_Key'],right_on=['Code','Date'],how='left')\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 檢驗是否有合併成功"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_final=df_final.dropna()\n",
    "# df_final.groupby('Date_D')['Code'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 計算投組報酬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Company_x</th>\n",
       "      <th>Date_D</th>\n",
       "      <th>Class_x</th>\n",
       "      <th>Price</th>\n",
       "      <th>Return_D</th>\n",
       "      <th>Date_Key</th>\n",
       "      <th>index</th>\n",
       "      <th>Company_y</th>\n",
       "      <th>Date</th>\n",
       "      <th>Class_y</th>\n",
       "      <th>EBT Growth</th>\n",
       "      <th>Sales Growth</th>\n",
       "      <th>E/S</th>\n",
       "      <th>E/S_rank</th>\n",
       "      <th>Weight</th>\n",
       "      <th>WeightxReturn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130102</td>\n",
       "      <td>OTC</td>\n",
       "      <td>6.8667</td>\n",
       "      <td>0.014778</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130103</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0.019413</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130104</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0167</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130107</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.1000</td>\n",
       "      <td>0.011872</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1333</td>\n",
       "      <td>恩得利</td>\n",
       "      <td>20130108</td>\n",
       "      <td>OTC</td>\n",
       "      <td>7.0333</td>\n",
       "      <td>-0.009394</td>\n",
       "      <td>201209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322633</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191225</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>-0.011125</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322634</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191226</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322635</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191227</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.1000</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322636</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191230</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.5500</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1322637</td>\n",
       "      <td>9912</td>\n",
       "      <td>偉聯</td>\n",
       "      <td>20191231</td>\n",
       "      <td>TSE</td>\n",
       "      <td>8.5100</td>\n",
       "      <td>-0.004678</td>\n",
       "      <td>201909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1322638 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Code Company_x    Date_D Class_x   Price  Return_D Date_Key  index  \\\n",
       "0        1333       恩得利  20130102     OTC  6.8667  0.014778   201209    NaN   \n",
       "1        1333       恩得利  20130103     OTC  7.0000  0.019413   201209    NaN   \n",
       "2        1333       恩得利  20130104     OTC  7.0167  0.002386   201209    NaN   \n",
       "3        1333       恩得利  20130107     OTC  7.1000  0.011872   201209    NaN   \n",
       "4        1333       恩得利  20130108     OTC  7.0333 -0.009394   201209    NaN   \n",
       "...       ...       ...       ...     ...     ...       ...      ...    ...   \n",
       "1322633  9912        偉聯  20191225     TSE  8.0000 -0.011125   201909    NaN   \n",
       "1322634  9912        偉聯  20191226     TSE  8.0000  0.000000   201909    NaN   \n",
       "1322635  9912        偉聯  20191227     TSE  8.1000  0.012500   201909    NaN   \n",
       "1322636  9912        偉聯  20191230     TSE  8.5500  0.055556   201909    NaN   \n",
       "1322637  9912        偉聯  20191231     TSE  8.5100 -0.004678   201909    NaN   \n",
       "\n",
       "        Company_y Date Class_y  EBT Growth  Sales Growth  E/S  E/S_rank  \\\n",
       "0             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "2             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "3             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "4             NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "...           ...  ...     ...         ...           ...  ...       ...   \n",
       "1322633       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322634       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322635       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322636       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "1322637       NaN  NaN     NaN         NaN           NaN  NaN       NaN   \n",
       "\n",
       "         Weight  WeightxReturn  \n",
       "0           NaN            NaN  \n",
       "1           NaN            NaN  \n",
       "2           NaN            NaN  \n",
       "3           NaN            NaN  \n",
       "4           NaN            NaN  \n",
       "...         ...            ...  \n",
       "1322633     NaN            NaN  \n",
       "1322634     NaN            NaN  \n",
       "1322635     NaN            NaN  \n",
       "1322636     NaN            NaN  \n",
       "1322637     NaN            NaN  \n",
       "\n",
       "[1322638 rows x 17 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['WeightxReturn'] = df_final['Return_D']*df_final['Weight']\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按日期分組，加總加權的報酬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Port_return=df_final.groupby('Date_D')['WeightxReturn'].sum()\n",
    "Port_return.index = pd.to_datetime(Port_return.index.astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_D\n",
       "2013-01-02    0.014531\n",
       "2013-01-03    0.003315\n",
       "2013-01-04   -0.001012\n",
       "2013-01-07    0.001895\n",
       "2013-01-08   -0.004644\n",
       "                ...   \n",
       "2019-12-25    0.006901\n",
       "2019-12-26    0.000982\n",
       "2019-12-27    0.003458\n",
       "2019-12-30   -0.003225\n",
       "2019-12-31    0.001847\n",
       "Name: WeightxReturn, Length: 1717, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Port_return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 讀取電子業指數資料&計算日報酬 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index=pd.read_csv('index data.txt',encoding='CP950',sep='\\t')\n",
    "df_index.columns = ['Code','Name','Date','Close']\n",
    "df_index.index=pd.to_datetime(df_index['Date'].astype('str'))\n",
    "df_index['Return_M'] = df_index['Close'].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 篩選回測期間"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index = df_index[df_index.Date>=20130101]\n",
    "df_index = df_index[df_index.Date<=20191231]\n",
    "df_index['Return_M_Cum'] = ( df_index['Return_M']+1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_index['Return_P'] = Port_return\n",
    "df_index['Return_P_Cum'] = ( df_index['Return_P']+1).cumprod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13c22739908>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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22.75 39.890625 18.8125 39.015625 \r\nQ 14.890625 38.140625 10.796875 36.28125 \r\nz\r\n\" id=\"DejaVuSans-53\"/>\r\n      </defs>\r\n      <g transform=\"translate(119.472223 239.238437)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_4\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"175.694767\" xlink:href=\"#m3caab9a4aa\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_4\">\r\n      <!-- 2016 -->\r\n      <defs>\r\n       <path d=\"M 33.015625 40.375 \r\nQ 26.375 40.375 22.484375 35.828125 \r\nQ 18.609375 31.296875 18.609375 23.390625 \r\nQ 18.609375 15.53125 22.484375 10.953125 \r\nQ 26.375 6.390625 33.015625 6.390625 \r\nQ 39.65625 6.390625 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164.611831 188.245026 \r\nL 164.850174 186.826501 \r\nL 165.207688 185.606323 \r\nL 165.32686 185.626914 \r\nL 165.446031 184.789622 \r\nL 165.565202 185.952998 \r\nL 166.161059 186.309534 \r\nL 166.876087 185.521879 \r\nL 167.11443 184.002136 \r\nL 167.352773 182.885135 \r\nL 167.710287 182.114598 \r\nL 167.948629 182.477803 \r\nL 168.186972 183.705444 \r\nL 168.544486 183.154018 \r\nL 168.902 180.764432 \r\nL 169.378686 183.047669 \r\nL 169.497857 182.509775 \r\nL 169.617028 184.101447 \r\nL 169.7362 183.873439 \r\nL 169.855371 184.120726 \r\nL 170.212885 183.886133 \r\nL 170.332056 183.028561 \r\nL 170.451228 183.324892 \r\nL 170.689571 182.544623 \r\nL 171.047085 182.970795 \r\nL 171.166256 183.691564 \r\nL 171.285427 183.542286 \r\nL 171.404599 182.793252 \r\nL 171.52377 182.928536 \r\nL 171.881284 183.846689 \r\nL 172.238798 181.637337 \r\nL 172.715484 180.567218 \r\nL 172.834655 180.876655 \r\nL 172.953826 182.754 \r\nL 173.072998 182.323207 \r\nL 173.192169 183.92731 \r\nL 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154.510187 \r\nL 200.60158 155.74418 \r\nL 201.316608 152.85362 \r\nL 201.912465 149.038858 \r\nL 202.031636 150.578766 \r\nL 202.150808 150.224417 \r\nL 202.269979 151.23205 \r\nL 202.746665 151.203086 \r\nL 202.865836 150.279328 \r\nL 202.985007 150.249675 \r\nL 203.104179 149.5952 \r\nL 203.22335 150.15833 \r\nL 203.580864 151.050836 \r\nL 203.700035 150.29588 \r\nL 203.819207 150.339711 \r\nL 204.057549 148.84938 \r\nL 204.415063 148.512234 \r\nL 204.653406 147.22009 \r\nL 204.772578 147.935061 \r\nL 204.891749 149.384813 \r\nL 205.249263 148.362931 \r\nL 205.725948 144.755447 \r\nL 205.84512 146.178969 \r\nL 206.202634 147.927156 \r\nL 206.321805 148.758349 \r\nL 206.917662 147.346065 \r\nL 207.394347 144.762003 \r\nL 207.751861 145.240627 \r\nL 208.109375 145.31396 \r\nL 208.705232 143.283668 \r\nL 208.824404 143.54228 \r\nL 208.943575 142.994771 \r\nL 209.539432 147.54816 \r\nL 209.777774 148.138138 \r\nL 209.896946 150.513704 \r\nL 210.25446 150.070605 \r\nL 210.611974 147.844341 \r\nL 210.731145 147.911109 \r\nL 211.088659 147.19396 \r\nL 211.207831 146.499364 \r\nL 211.327002 146.527739 \r\nL 211.565345 147.213872 \r\nL 212.04203 147.096211 \r\nL 212.280373 150.462722 \r\nL 212.399544 150.352081 \r\nL 212.757058 149.405815 \r\nL 212.87623 150.012514 \r\nL 212.995401 154.753942 \r\nL 213.114572 151.11022 \r\nL 213.233744 152.146193 \r\nL 213.591258 151.842824 \r\nL 213.948772 149.445825 \r\nL 214.067943 148.255419 \r\nL 214.425457 147.795352 \r\nL 214.544629 147.004775 \r\nL 214.6638 147.224837 \r\nL 214.782971 147.163084 \r\nL 214.902143 146.420956 \r\nL 215.259657 146.465654 \r\nL 215.617171 144.770301 \r\nL 215.736342 145.180041 \r\nL 216.332199 143.490303 \r\nL 216.45137 143.248085 \r\nL 216.570542 143.503019 \r\nL 216.928056 144.906744 \r\nL 217.047227 144.211875 \r\nL 217.166398 144.354801 \r\nL 217.404741 142.829428 \r\nL 217.762255 144.289271 \r\nL 218.000598 144.226962 \r\nL 218.23894 144.531266 \r\nL 218.596455 144.596656 \r\nL 218.834797 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119.508743 \r\nL 236.472158 119.612618 \r\nL 236.591329 120.42533 \r\nL 237.187186 121.532469 \r\nL 237.5447 117.534425 \r\nL 237.783042 116.996083 \r\nL 237.902214 117.173826 \r\nL 238.021385 117.101478 \r\nL 238.140556 116.400492 \r\nL 238.259728 117.461897 \r\nL 238.617242 118.329533 \r\nL 238.736413 118.183256 \r\nL 238.855585 121.017589 \r\nL 239.093927 118.447901 \r\nL 239.451441 117.521681 \r\nL 239.570613 118.616751 \r\nL 239.689784 117.435798 \r\nL 239.928127 118.258215 \r\nL 240.285641 117.878469 \r\nL 240.404812 118.139892 \r\nL 240.643155 119.312518 \r\nL 240.762326 118.501712 \r\nL 241.11984 117.381958 \r\nL 241.239012 117.718904 \r\nL 241.477354 116.612086 \r\nL 241.596526 117.53791 \r\nL 241.95404 118.57032 \r\nL 242.073211 118.527022 \r\nL 242.430725 115.260458 \r\nL 242.788239 115.03486 \r\nL 242.907411 114.611853 \r\nL 243.026582 113.4709 \r\nL 243.264925 113.799032 \r\nL 243.622439 113.545639 \r\nL 243.860781 111.503567 \r\nL 243.979953 110.170574 \r\nL 244.099124 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210.922522 \r\nL 178.078194 210.887843 \r\nL 178.197366 209.99484 \r\nL 178.55488 209.117444 \r\nL 178.674051 209.521462 \r\nL 178.912394 208.926705 \r\nL 179.150737 207.579399 \r\nL 179.389079 207.44068 \r\nL 179.627422 207.860304 \r\nL 181.057478 207.900186 \r\nL 181.295821 206.70547 \r\nL 181.534164 205.909571 \r\nL 182.010849 205.999738 \r\nL 182.13002 206.495658 \r\nL 182.368363 205.633867 \r\nL 182.845049 204.936805 \r\nL 183.083391 204.251881 \r\nL 183.202563 204.179054 \r\nL 183.560077 204.401004 \r\nL 183.917591 204.038601 \r\nL 184.036762 203.669262 \r\nL 184.394276 203.147332 \r\nL 184.513447 204.09929 \r\nL 184.75179 203.209756 \r\nL 184.870962 202.743314 \r\nL 185.228476 202.618467 \r\nL 185.466818 203.138662 \r\nL 185.705161 203.64672 \r\nL 186.062675 203.712611 \r\nL 186.181846 204.036867 \r\nL 186.301018 203.114387 \r\nL 186.420189 203.079707 \r\nL 186.53936 203.880808 \r\nL 187.254389 204.987091 \r\nL 187.37356 204.541456 \r\nL 187.731074 204.314304 \r\nL 187.850245 204.669771 \r\nL 187.969417 203.781971 \r\nL 188.565274 204.113162 \r\nL 188.684445 204.518915 \r\nL 188.803616 205.417119 \r\nL 188.922788 204.910795 \r\nL 189.041959 205.222913 \r\nL 189.399473 205.110204 \r\nL 189.518644 204.917731 \r\nL 189.637816 205.203839 \r\nL 189.876158 206.795637 \r\nL 190.352844 207.536049 \r\nL 190.591187 208.165486 \r\nL 190.710358 208.356224 \r\nL 191.067872 208.347554 \r\nL 191.187043 208.064914 \r\nL 191.306215 208.177624 \r\nL 191.544557 208.79839 \r\nL 191.902071 208.670075 \r\nL 192.140414 208.030235 \r\nL 192.259585 208.352756 \r\nL 192.378757 208.038905 \r\nL 192.736271 206.228624 \r\nL 192.855442 206.636111 \r\nL 193.093785 205.696291 \r\nL 193.212956 205.217711 \r\nL 193.689642 204.730461 \r\nL 193.808813 204.135704 \r\nL 193.927984 204.473831 \r\nL 194.047156 204.031665 \r\nL 194.166327 204.019527 \r\nL 194.40467 203.858266 \r\nL 194.643013 202.819609 \r\nL 195.238869 204.165182 \r\nL 195.477212 203.596434 \r\nL 195.596383 204.362856 \r\nL 195.715555 203.768099 \r\nL 196.073069 203.388356 \r\nL 196.311411 202.748516 \r\nL 196.430583 203.001678 \r\nL 196.549754 204.520649 \r\nL 196.907268 204.732195 \r\nL 197.264782 202.8855 \r\nL 197.383954 202.294212 \r\nL 197.741468 201.982094 \r\nL 197.860639 202.4416 \r\nL 197.97981 203.44211 \r\nL 198.098982 202.797067 \r\nL 198.694839 201.161919 \r\nL 198.933181 201.272894 \r\nL 199.052353 200.667733 \r\nL 199.529038 200.078178 \r\nL 199.648209 200.298394 \r\nL 199.767381 199.866632 \r\nL 199.886552 200.211695 \r\nL 200.244066 200.359084 \r\nL 200.60158 199.407126 \r\nL 200.720752 200.180483 \r\nL 201.078266 199.268407 \r\nL 201.197437 199.330831 \r\nL 201.316608 199.965469 \r\nL 201.912465 198.685788 \r\nL 202.031636 198.782891 \r\nL 202.150808 198.668448 \r\nL 202.38915 199.436604 \r\nL 202.746665 199.159166 \r\nL 202.865836 199.583993 \r\nL 203.104179 199.644682 \r\nL 203.22335 200.138868 \r\nL 203.580864 200.419773 \r\nL 204.057549 199.124486 \r\nL 204.534235 199.436604 \r\nL 204.653406 199.566653 \r\nL 204.891749 200.274119 \r\nL 205.249263 199.46955 \r\nL 205.487606 198.101435 \r\nL 205.606777 198.189868 \r\nL 205.84512 199.766061 \r\nL 206.083462 200.55329 \r\nL 206.202634 200.503005 \r\nL 206.321805 200.718019 \r\nL 207.036833 198.413552 \r\nL 207.394347 197.576038 \r\nL 207.751861 198.463838 \r\nL 208.109375 197.848273 \r\nL 208.228547 198.623365 \r\nL 208.705232 197.548294 \r\nL 208.824404 197.628057 \r\nL 208.943575 197.492806 \r\nL 209.062746 197.695683 \r\nL 209.539432 197.960982 \r\nL 209.658603 197.582974 \r\nL 209.896946 198.337257 \r\nL 210.373631 198.05115 \r\nL 210.611974 197.49454 \r\nL 210.731145 197.562166 \r\nL 211.088659 197.191093 \r\nL 211.207831 196.672631 \r\nL 211.327002 196.904985 \r\nL 211.446173 197.491072 \r\nL 211.565345 197.496274 \r\nL 212.04203 197.723426 \r\nL 212.280373 199.405392 \r\nL 212.399544 199.45221 \r\nL 212.757058 198.46037 \r\nL 212.87623 198.519326 \r\nL 212.995401 200.664265 \r\nL 213.114572 199.306555 \r\nL 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.Figure(figsize=(15,7))\n",
    "plt.plot(df_index['Return_P_Cum'],label='EBT Growth/Sales Growth')\n",
    "plt.plot(df_index['Return_M_Cum'],label='Electronic Indexes' )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 計算投組績效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Geo_a_return_M = (df_index['Return_M_Cum']['2019-12-31'])**(250/len(df_index))-1\n",
    "Geo_a_return_P = (df_index['Return_P_Cum']['2019-12-31'])**(250/len(df_index))-1\n",
    "\n",
    "STD_return_M   = df_index['Return_M'].std()*np.sqrt(250)\n",
    "STD_return_P   = df_index['Return_P'].std()*np.sqrt(250)\n",
    "\n",
    "Sharpe_M = Geo_a_return_M / STD_return_M \n",
    "Sharpe_P = Geo_a_return_P / STD_return_P\n",
    "\n",
    "#計算MDD\n",
    "D = df_index['Return_M_Cum'].cummax() - df_index['Return_M_Cum']\n",
    "MDD = D.max()*1\n",
    "d = D / (D +df_index['Return_M_Cum'])\n",
    "mdd_M = d.max()\n",
    "\n",
    "D = df_index['Return_P_Cum'].cummax() - df_index['Return_P_Cum']\n",
    "MDD = D.max()*1\n",
    "d = D / (D +df_index['Return_P_Cum'])\n",
    "mdd_P = d.max()\n",
    "\n",
    "#投組勝率\n",
    "Odd_M =sum(df_index['Return_M']>0)/len(df_index)\n",
    "Odd_P =sum(df_index['Return_P']>0)/len(df_index)\n",
    "#.cummax 每一列最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_performance=pd.DataFrame([\n",
    "                  [Geo_a_return_M,STD_return_M ,Sharpe_M,mdd_M,Odd_M ],\n",
    "                  [Geo_a_return_P,STD_return_P ,Sharpe_P,mdd_P,Odd_P ]\n",
    "                 ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_performance.columns = [ 'ER','STD','Sharpe','Mdd','Odd' ]\n",
    "df_performance.index =  ['Benchmark','Portfolio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 比較兩者績效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ER</th>\n",
       "      <th>STD</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>Mdd</th>\n",
       "      <th>Odd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Benchmark</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Portfolio</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.60</td>\n",
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      "text/plain": [
       "             ER   STD  Sharpe   Mdd   Odd\n",
       "Benchmark  0.09  0.15    0.61  0.28  0.53\n",
       "Portfolio  0.26  0.17    1.58  0.37  0.60"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_performance.round(2)"
   ]
  }
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