{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_excel('3-B_EXAM4.xlsx')\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data=data.iloc[:,[0,7,8]]\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.columns=['date','rf','mkt']\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# root='./datastk/'\n",
    "# file_name = os.listdir(root)\n",
    "# print(file_name)\n",
    "# stock = pd.DataFrame()\n",
    "# for i in file_name:  # 将文件夹数据整合成一个大的DataFrame\n",
    "#     tmp_df = pd.read_excel(root+i)\n",
    "#     stock = stock.append(tmp_df)\n",
    "# stock.dropna(how='any',inplace=True)\n",
    "# # stock\n",
    "# stock=pd.read_excel('2008-2023.xlsx')\n",
    "# stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.columns=['stkcode','date','stkclose']\n",
    "# stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock=stock.pivot(index='date',columns='stkcode',values='stkclose')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.dropna(inplace=True)\n",
    "# stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.reset_index(inplace=True)\n",
    "# # # stock.drop(columns='stkcode')\n",
    "# stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.reset_index(inplace=True)\n",
    "# stock\n",
    "#\n",
    "# # stock['YYYYmm']=stock['date'].dt.strftime('%Y-%m')\n",
    "# # stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.reindex()\n",
    "# stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.drop(columns='index',inplace=True)\n",
    "# stock.to_excel('stock.xlsx')\n",
    "# stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>661</th>\n",
       "      <th>858</th>\n",
       "      <th>600196</th>\n",
       "      <th>600276</th>\n",
       "      <th>600519</th>\n",
       "      <th>600900</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-01-31</td>\n",
       "      <td>12.53</td>\n",
       "      <td>37.75</td>\n",
       "      <td>15.71</td>\n",
       "      <td>54.81</td>\n",
       "      <td>206.03</td>\n",
       "      <td>16.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02-29</td>\n",
       "      <td>13.63</td>\n",
       "      <td>36.20</td>\n",
       "      <td>16.54</td>\n",
       "      <td>51.80</td>\n",
       "      <td>195.00</td>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>12.03</td>\n",
       "      <td>24.20</td>\n",
       "      <td>14.78</td>\n",
       "      <td>42.60</td>\n",
       "      <td>187.71</td>\n",
       "      <td>14.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-04-30</td>\n",
       "      <td>10.45</td>\n",
       "      <td>28.45</td>\n",
       "      <td>15.94</td>\n",
       "      <td>38.81</td>\n",
       "      <td>183.13</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.66</td>\n",
       "      <td>18.22</td>\n",
       "      <td>12.09</td>\n",
       "      <td>34.90</td>\n",
       "      <td>138.58</td>\n",
       "      <td>14.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>169.10</td>\n",
       "      <td>156.86</td>\n",
       "      <td>37.10</td>\n",
       "      <td>39.83</td>\n",
       "      <td>1599.00</td>\n",
       "      <td>21.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>166.45</td>\n",
       "      <td>180.69</td>\n",
       "      <td>35.24</td>\n",
       "      <td>38.53</td>\n",
       "      <td>1727.00</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>201.10</td>\n",
       "      <td>209.00</td>\n",
       "      <td>34.02</td>\n",
       "      <td>42.21</td>\n",
       "      <td>1845.76</td>\n",
       "      <td>20.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>197.29</td>\n",
       "      <td>204.42</td>\n",
       "      <td>33.70</td>\n",
       "      <td>43.50</td>\n",
       "      <td>1813.74</td>\n",
       "      <td>21.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>163.30</td>\n",
       "      <td>197.00</td>\n",
       "      <td>32.39</td>\n",
       "      <td>42.82</td>\n",
       "      <td>1820.00</td>\n",
       "      <td>21.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     661     858  600196  600276   600519  600900\n",
       "0    2008-01-31   12.53   37.75   15.71   54.81   206.03   16.94\n",
       "1    2008-02-29   13.63   36.20   16.54   51.80   195.00   17.42\n",
       "2    2008-03-31   12.03   24.20   14.78   42.60   187.71   14.05\n",
       "3    2008-04-30   10.45   28.45   15.94   38.81   183.13   13.99\n",
       "4    2008-06-30    6.66   18.22   12.09   34.90   138.58   14.65\n",
       "..          ...     ...     ...     ...     ...      ...     ...\n",
       "163  2022-11-30  169.10  156.86   37.10   39.83  1599.00   21.22\n",
       "164  2022-12-30  166.45  180.69   35.24   38.53  1727.00   21.00\n",
       "165  2023-01-31  201.10  209.00   34.02   42.21  1845.76   20.79\n",
       "166  2023-02-28  197.29  204.42   33.70   43.50  1813.74   21.16\n",
       "167  2023-03-31  163.30  197.00   32.39   42.82  1820.00   21.25\n",
       "\n",
       "[168 rows x 7 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock=pd.read_excel('stock.xlsx')\n",
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mkt</th>\n",
       "      <th>rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-01-31</td>\n",
       "      <td>-0.1161</td>\n",
       "      <td>0.003731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02-29</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>0.003753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>-0.1963</td>\n",
       "      <td>0.003751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-04-30</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.003745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-05-30</td>\n",
       "      <td>-0.0805</td>\n",
       "      <td>0.003743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>0.0863</td>\n",
       "      <td>0.001676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>0.001931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.001973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.001986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>-0.0072</td>\n",
       "      <td>0.002042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     mkt        rf\n",
       "0    2008-01-31 -0.1161  0.003731\n",
       "1    2008-02-29  0.0314  0.003753\n",
       "2    2008-03-31 -0.1963  0.003751\n",
       "3    2008-04-30  0.0243  0.003745\n",
       "4    2008-05-30 -0.0805  0.003743\n",
       "..          ...     ...       ...\n",
       "178  2022-11-30  0.0863  0.001676\n",
       "179  2022-12-30 -0.0189  0.001931\n",
       "180  2023-01-31  0.0637  0.001973\n",
       "181  2023-02-28  0.0015  0.001986\n",
       "182  2023-03-31 -0.0072  0.002042\n",
       "\n",
       "[183 rows x 3 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('mkt.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2008-01-31</td>\n",
       "      <td>-0.1161</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>12.53</td>\n",
       "      <td>37.75</td>\n",
       "      <td>15.71</td>\n",
       "      <td>54.81</td>\n",
       "      <td>206.03</td>\n",
       "      <td>16.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02-29</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>0.003753</td>\n",
       "      <td>13.63</td>\n",
       "      <td>36.20</td>\n",
       "      <td>16.54</td>\n",
       "      <td>51.80</td>\n",
       "      <td>195.00</td>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>-0.1963</td>\n",
       "      <td>0.003751</td>\n",
       "      <td>12.03</td>\n",
       "      <td>24.20</td>\n",
       "      <td>14.78</td>\n",
       "      <td>42.60</td>\n",
       "      <td>187.71</td>\n",
       "      <td>14.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-04-30</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.003745</td>\n",
       "      <td>10.45</td>\n",
       "      <td>28.45</td>\n",
       "      <td>15.94</td>\n",
       "      <td>38.81</td>\n",
       "      <td>183.13</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>-0.2319</td>\n",
       "      <td>0.003732</td>\n",
       "      <td>6.66</td>\n",
       "      <td>18.22</td>\n",
       "      <td>12.09</td>\n",
       "      <td>34.90</td>\n",
       "      <td>138.58</td>\n",
       "      <td>14.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>0.0863</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>169.10</td>\n",
       "      <td>156.86</td>\n",
       "      <td>37.10</td>\n",
       "      <td>39.83</td>\n",
       "      <td>1599.00</td>\n",
       "      <td>21.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>0.001931</td>\n",
       "      <td>166.45</td>\n",
       "      <td>180.69</td>\n",
       "      <td>35.24</td>\n",
       "      <td>38.53</td>\n",
       "      <td>1727.00</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>201.10</td>\n",
       "      <td>209.00</td>\n",
       "      <td>34.02</td>\n",
       "      <td>42.21</td>\n",
       "      <td>1845.76</td>\n",
       "      <td>20.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>197.29</td>\n",
       "      <td>204.42</td>\n",
       "      <td>33.70</td>\n",
       "      <td>43.50</td>\n",
       "      <td>1813.74</td>\n",
       "      <td>21.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>-0.0072</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>163.30</td>\n",
       "      <td>197.00</td>\n",
       "      <td>32.39</td>\n",
       "      <td>42.82</td>\n",
       "      <td>1820.00</td>\n",
       "      <td>21.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date     mkt        rf     661     858  600196  600276   600519  \\\n",
       "0    2008-01-31 -0.1161  0.003731   12.53   37.75   15.71   54.81   206.03   \n",
       "1    2008-02-29  0.0314  0.003753   13.63   36.20   16.54   51.80   195.00   \n",
       "2    2008-03-31 -0.1963  0.003751   12.03   24.20   14.78   42.60   187.71   \n",
       "3    2008-04-30  0.0243  0.003745   10.45   28.45   15.94   38.81   183.13   \n",
       "4    2008-06-30 -0.2319  0.003732    6.66   18.22   12.09   34.90   138.58   \n",
       "..          ...     ...       ...     ...     ...     ...     ...      ...   \n",
       "163  2022-11-30  0.0863  0.001676  169.10  156.86   37.10   39.83  1599.00   \n",
       "164  2022-12-30 -0.0189  0.001931  166.45  180.69   35.24   38.53  1727.00   \n",
       "165  2023-01-31  0.0637  0.001973  201.10  209.00   34.02   42.21  1845.76   \n",
       "166  2023-02-28  0.0015  0.001986  197.29  204.42   33.70   43.50  1813.74   \n",
       "167  2023-03-31 -0.0072  0.002042  163.30  197.00   32.39   42.82  1820.00   \n",
       "\n",
       "     600900  \n",
       "0     16.94  \n",
       "1     17.42  \n",
       "2     14.05  \n",
       "3     13.99  \n",
       "4     14.65  \n",
       "..      ...  \n",
       "163   21.22  \n",
       "164   21.00  \n",
       "165   20.79  \n",
       "166   21.16  \n",
       "167   21.25  \n",
       "\n",
       "[168 rows x 9 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.merge(left=data,right=stock,on='date',how='inner')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mkt</th>\n",
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       "      <th>0</th>\n",
       "      <td>2008-01-31</td>\n",
       "      <td>-0.1161</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>12.53</td>\n",
       "      <td>37.75</td>\n",
       "      <td>15.71</td>\n",
       "      <td>54.81</td>\n",
       "      <td>206.03</td>\n",
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       "      <td>200801</td>\n",
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       "      <th>1</th>\n",
       "      <td>2008-02-29</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>0.003753</td>\n",
       "      <td>13.63</td>\n",
       "      <td>36.20</td>\n",
       "      <td>16.54</td>\n",
       "      <td>51.80</td>\n",
       "      <td>195.00</td>\n",
       "      <td>17.42</td>\n",
       "      <td>200802</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>-0.1963</td>\n",
       "      <td>0.003751</td>\n",
       "      <td>12.03</td>\n",
       "      <td>24.20</td>\n",
       "      <td>14.78</td>\n",
       "      <td>42.60</td>\n",
       "      <td>187.71</td>\n",
       "      <td>14.05</td>\n",
       "      <td>200803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-04-30</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>0.003745</td>\n",
       "      <td>10.45</td>\n",
       "      <td>28.45</td>\n",
       "      <td>15.94</td>\n",
       "      <td>38.81</td>\n",
       "      <td>183.13</td>\n",
       "      <td>13.99</td>\n",
       "      <td>200804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>-0.2319</td>\n",
       "      <td>0.003732</td>\n",
       "      <td>6.66</td>\n",
       "      <td>18.22</td>\n",
       "      <td>12.09</td>\n",
       "      <td>34.90</td>\n",
       "      <td>138.58</td>\n",
       "      <td>14.65</td>\n",
       "      <td>200806</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>0.0863</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>169.10</td>\n",
       "      <td>156.86</td>\n",
       "      <td>37.10</td>\n",
       "      <td>39.83</td>\n",
       "      <td>1599.00</td>\n",
       "      <td>21.22</td>\n",
       "      <td>202211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>0.001931</td>\n",
       "      <td>166.45</td>\n",
       "      <td>180.69</td>\n",
       "      <td>35.24</td>\n",
       "      <td>38.53</td>\n",
       "      <td>1727.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>202212</td>\n",
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       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>201.10</td>\n",
       "      <td>209.00</td>\n",
       "      <td>34.02</td>\n",
       "      <td>42.21</td>\n",
       "      <td>1845.76</td>\n",
       "      <td>20.79</td>\n",
       "      <td>202301</td>\n",
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       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>197.29</td>\n",
       "      <td>204.42</td>\n",
       "      <td>33.70</td>\n",
       "      <td>43.50</td>\n",
       "      <td>1813.74</td>\n",
       "      <td>21.16</td>\n",
       "      <td>202302</td>\n",
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       "      <th>167</th>\n",
       "      <td>2023-03-31</td>\n",
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       "      <td>0.002042</td>\n",
       "      <td>163.30</td>\n",
       "      <td>197.00</td>\n",
       "      <td>32.39</td>\n",
       "      <td>42.82</td>\n",
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       "          date     mkt        rf     661     858  600196  600276   600519  \\\n",
       "0   2008-01-31 -0.1161  0.003731   12.53   37.75   15.71   54.81   206.03   \n",
       "1   2008-02-29  0.0314  0.003753   13.63   36.20   16.54   51.80   195.00   \n",
       "2   2008-03-31 -0.1963  0.003751   12.03   24.20   14.78   42.60   187.71   \n",
       "3   2008-04-30  0.0243  0.003745   10.45   28.45   15.94   38.81   183.13   \n",
       "4   2008-06-30 -0.2319  0.003732    6.66   18.22   12.09   34.90   138.58   \n",
       "..         ...     ...       ...     ...     ...     ...     ...      ...   \n",
       "163 2022-11-30  0.0863  0.001676  169.10  156.86   37.10   39.83  1599.00   \n",
       "164 2022-12-30 -0.0189  0.001931  166.45  180.69   35.24   38.53  1727.00   \n",
       "165 2023-01-31  0.0637  0.001973  201.10  209.00   34.02   42.21  1845.76   \n",
       "166 2023-02-28  0.0015  0.001986  197.29  204.42   33.70   43.50  1813.74   \n",
       "167 2023-03-31 -0.0072  0.002042  163.30  197.00   32.39   42.82  1820.00   \n",
       "\n",
       "     600900  YYYYmm  \n",
       "0     16.94  200801  \n",
       "1     17.42  200802  \n",
       "2     14.05  200803  \n",
       "3     13.99  200804  \n",
       "4     14.65  200806  \n",
       "..      ...     ...  \n",
       "163   21.22  202211  \n",
       "164   21.00  202212  \n",
       "165   20.79  202301  \n",
       "166   21.16  202302  \n",
       "167   21.25  202303  \n",
       "\n",
       "[168 rows x 10 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['date']=pd.to_datetime(data['date'],format='%Y-%m-%d')\n",
    "data['YYYYmm']=data['date'].dt.strftime('%Y%m')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>0.0314</td>\n",
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       "      <td>36.20</td>\n",
       "      <td>16.54</td>\n",
       "      <td>51.80</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.1963</td>\n",
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       "      <td>12.03</td>\n",
       "      <td>24.20</td>\n",
       "      <td>14.78</td>\n",
       "      <td>42.60</td>\n",
       "      <td>187.71</td>\n",
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       "      <td>200803</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0243</td>\n",
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       "      <td>28.45</td>\n",
       "      <td>15.94</td>\n",
       "      <td>38.81</td>\n",
       "      <td>183.13</td>\n",
       "      <td>13.99</td>\n",
       "      <td>200804</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.2319</td>\n",
       "      <td>0.003732</td>\n",
       "      <td>6.66</td>\n",
       "      <td>18.22</td>\n",
       "      <td>12.09</td>\n",
       "      <td>34.90</td>\n",
       "      <td>138.58</td>\n",
       "      <td>14.65</td>\n",
       "      <td>200806</td>\n",
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       "      <td>0.0863</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>169.10</td>\n",
       "      <td>156.86</td>\n",
       "      <td>37.10</td>\n",
       "      <td>39.83</td>\n",
       "      <td>1599.00</td>\n",
       "      <td>21.22</td>\n",
       "      <td>202211</td>\n",
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       "    <tr>\n",
       "      <th>164</th>\n",
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       "      <td>166.45</td>\n",
       "      <td>180.69</td>\n",
       "      <td>35.24</td>\n",
       "      <td>38.53</td>\n",
       "      <td>1727.00</td>\n",
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       "      <th>165</th>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.001973</td>\n",
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       "      <td>209.00</td>\n",
       "      <td>34.02</td>\n",
       "      <td>42.21</td>\n",
       "      <td>1845.76</td>\n",
       "      <td>20.79</td>\n",
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       "      <td>0.0015</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>197.29</td>\n",
       "      <td>204.42</td>\n",
       "      <td>33.70</td>\n",
       "      <td>43.50</td>\n",
       "      <td>1813.74</td>\n",
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       "      <td>-0.0072</td>\n",
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       "      <td>163.30</td>\n",
       "      <td>197.00</td>\n",
       "      <td>32.39</td>\n",
       "      <td>42.82</td>\n",
       "      <td>1820.00</td>\n",
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       "      <td>202303</td>\n",
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      "text/plain": [
       "        mkt        rf     661     858  600196  600276   600519  600900  YYYYmm\n",
       "0   -0.1161  0.003731   12.53   37.75   15.71   54.81   206.03   16.94  200801\n",
       "1    0.0314  0.003753   13.63   36.20   16.54   51.80   195.00   17.42  200802\n",
       "2   -0.1963  0.003751   12.03   24.20   14.78   42.60   187.71   14.05  200803\n",
       "3    0.0243  0.003745   10.45   28.45   15.94   38.81   183.13   13.99  200804\n",
       "4   -0.2319  0.003732    6.66   18.22   12.09   34.90   138.58   14.65  200806\n",
       "..      ...       ...     ...     ...     ...     ...      ...     ...     ...\n",
       "163  0.0863  0.001676  169.10  156.86   37.10   39.83  1599.00   21.22  202211\n",
       "164 -0.0189  0.001931  166.45  180.69   35.24   38.53  1727.00   21.00  202212\n",
       "165  0.0637  0.001973  201.10  209.00   34.02   42.21  1845.76   20.79  202301\n",
       "166  0.0015  0.001986  197.29  204.42   33.70   43.50  1813.74   21.16  202302\n",
       "167 -0.0072  0.002042  163.30  197.00   32.39   42.82  1820.00   21.25  202303\n",
       "\n",
       "[168 rows x 9 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=['date'],inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 12.53,  13.63,  12.03,  10.45,   6.66,   5.3 ,   6.1 ,   9.08,\n",
       "        12.9 ,  13.5 ,  16.52,  15.23,  17.09,  16.25,  24.5 ,  21.12,\n",
       "        24.83,  25.37,  26.72,  33.81,  34.6 ,  37.92,  43.5 ,  46.07,\n",
       "        36.  ,  42.  ,  52.15,  51.9 ,  57.19,  68.65,  60.8 ,  52.5 ,\n",
       "        55.7 ,  48.77,  44.82,  45.38,  47.5 ,  48.26,  45.42,  39.1 ,\n",
       "        38.2 ,  42.48,  35.49,  34.12,  43.72,  47.26,  48.55,  51.3 ,\n",
       "        50.  ,  62.53,  53.45,  61.1 ,  76.34,  88.3 ,  85.95,  90.81,\n",
       "        96.38,  97.91, 106.63, 113.5 , 110.31,  97.25, 110.85, 108.7 ,\n",
       "       121.5 , 110.6 ,  87.19,  96.2 ,  81.3 ,  81.96,  94.  , 102.28,\n",
       "        98.79,  91.99,  86.7 ,  92.9 ,  97.7 , 127.6 , 158.28,  99.88,\n",
       "        97.1 , 114.77, 120.45,  96.04,  91.  , 101.9 ,  91.18,  91.6 ,\n",
       "       106.68, 105.89, 101.6 , 103.88, 117.66, 115.9 , 111.85, 110.3 ,\n",
       "       108.88, 110.  , 113.95, 114.6 , 129.11, 121.73, 135.4 , 148.55,\n",
       "       169.7 , 170.22, 183.  , 172.21, 162.4 , 183.58, 201.7 , 214.61,\n",
       "       227.7 , 206.  , 230.38, 237.  , 177.2 , 196.02, 175.  , 185.87,\n",
       "       316.99, 304.01, 315.85, 338.  , 332.  , 351.65, 394.36, 455.21,\n",
       "       439.  , 447.  , 498.  , 484.  , 548.  , 595.88, 666.  , 435.3 ,\n",
       "       497.  , 467.6 , 369.64, 374.52, 366.55, 448.91, 454.62, 420.92,\n",
       "       452.73, 497.6 , 410.61, 387.  , 305.76, 255.24, 274.66, 272.76,\n",
       "       271.4 , 174.55, 182.05, 167.85, 157.02, 165.88, 233.42, 193.83,\n",
       "       177.08, 170.35, 161.9 , 169.1 , 166.45, 201.1 , 197.29, 163.3 ])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = data.iloc[:, 2].values\n",
    "p2 = data.iloc[:, 3].values\n",
    "p3 = data.iloc[:, 4].values\n",
    "p4 = data.iloc[:, 5].values\n",
    "p5 = data.iloc[:, 6].values\n",
    "p6 = data.iloc[:, 7].values\n",
    "rf = data.iloc[:, 1].values\n",
    "mkt = data.iloc[:, 0].values\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.08414748, -0.12486972, -0.14080155, -0.45048249, -0.22841266,\n",
       "        0.14058195,  0.39778542,  0.35115312,  0.04546237,  0.20188208,\n",
       "       -0.0813046 ,  0.11522633, -0.05040059,  0.41058021, -0.14845266,\n",
       "        0.16183214,  0.02151477,  0.05184498,  0.23534427,  0.02309707,\n",
       "        0.091625  ,  0.13728226,  0.05740104, -0.24664304,  0.15415068,\n",
       "        0.21645456, -0.00480539,  0.09706027,  0.18264207, -0.12143134,\n",
       "       -0.14677662,  0.05916698, -0.13286478, -0.0844609 ,  0.01241701,\n",
       "        0.04565823,  0.01587335, -0.06065052, -0.14983007, -0.02328695,\n",
       "        0.10619786, -0.17978241, -0.03936724,  0.24792194,  0.07785861,\n",
       "        0.02692992,  0.05509656, -0.02566775,  0.22362344, -0.1568998 ,\n",
       "        0.13376523,  0.22268518,  0.14554306, -0.02697438,  0.05500368,\n",
       "        0.0595293 ,  0.01574998,  0.08531621,  0.06243794, -0.02850825,\n",
       "       -0.1260096 ,  0.13089295, -0.01958614,  0.11132247, -0.09399417,\n",
       "       -0.23783044,  0.09833971, -0.16828334,  0.00808531,  0.13706346,\n",
       "        0.08441937, -0.03471777, -0.07131651, -0.05922599,  0.06906976,\n",
       "        0.05037791,  0.26699881,  0.21546525, -0.46039615, -0.02822809,\n",
       "        0.16718875,  0.0483046 , -0.22646996, -0.05390526,  0.11313243,\n",
       "       -0.11115637,  0.0045957 ,  0.15240243, -0.00743288, -0.04135728,\n",
       "        0.02219285,  0.12456272, -0.01507136, -0.03556906, -0.01395476,\n",
       "       -0.01295757,  0.01023401,  0.03527939,  0.00568805,  0.11921695,\n",
       "       -0.05885928,  0.10642788,  0.09268824,  0.13311057,  0.00305955,\n",
       "        0.07239443, -0.06077149, -0.05865223,  0.12258811,  0.09413091,\n",
       "        0.06204098,  0.05920655, -0.1001528 ,  0.11185395,  0.02833002,\n",
       "       -0.2907811 ,  0.10093766, -0.11343072,  0.06026153,  0.53382272,\n",
       "       -0.04180963,  0.03820682,  0.06777848, -0.01791093,  0.05750139,\n",
       "        0.11462783,  0.14349465, -0.03625944,  0.01805918,  0.10804148,\n",
       "       -0.02851517,  0.12419038,  0.08376402,  0.11125037, -0.42525422,\n",
       "        0.13255458, -0.0609768 , -0.23508367,  0.01311565, -0.02151027,\n",
       "        0.20268748,  0.01263948, -0.07701911,  0.07285313,  0.09450062,\n",
       "       -0.19215268, -0.05921917, -0.23562421, -0.1805962 ,  0.07332969,\n",
       "       -0.00694168, -0.00499854, -0.44138251,  0.04207014, -0.08121065,\n",
       "       -0.06669754,  0.05489145,  0.34157477, -0.18585792, -0.09037988,\n",
       "       -0.03874646, -0.05087628,  0.0435114 , -0.01579529,  0.18910733,\n",
       "       -0.01912757, -0.18908573])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1 = np.log(p1[1:]) - np.log(p1[:-1])\n",
    "r2 = np.log(p2[1:]) - np.log(p2[:-1])\n",
    "r3 = np.log(p3[1:]) - np.log(p3[:-1])\n",
    "r4 = np.log(p4[1:]) - np.log(p4[:-1])\n",
    "r5 = np.log(p5[1:]) - np.log(p5[:-1])\n",
    "r6 = np.log(p6[1:]) - np.log(p6[:-1])\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.08039448, -0.12862072, -0.14454655, -0.45421449, -0.23193466,\n",
       "        0.13751895,  0.39587742,  0.34989712,  0.04437437,  0.20084108,\n",
       "       -0.0823176 ,  0.11421633, -0.05144359,  0.40935321, -0.14991166,\n",
       "        0.16035114,  0.02001577,  0.05032898,  0.23379127,  0.02149307,\n",
       "        0.090006  ,  0.13566626,  0.05575504, -0.24864704,  0.15201668,\n",
       "        0.21439956, -0.00693539,  0.09481427,  0.18019607, -0.12466134,\n",
       "       -0.15070662,  0.05484698, -0.13644578, -0.0880529 ,  0.00862201,\n",
       "        0.04114423,  0.01076535, -0.06529252, -0.15451707, -0.02798695,\n",
       "        0.10146686, -0.18422841, -0.04357524,  0.24418294,  0.07444361,\n",
       "        0.02370492,  0.05206556, -0.02871475,  0.22054044, -0.1600398 ,\n",
       "        0.13054523,  0.21943918,  0.14230306, -0.03021038,  0.05176868,\n",
       "        0.0562943 ,  0.01150898,  0.08134421,  0.05855794, -0.03239225,\n",
       "       -0.1299066 ,  0.12697295, -0.02400314,  0.10666847, -0.09865117,\n",
       "       -0.24241244,  0.09397071, -0.17228134,  0.00413031,  0.13315846,\n",
       "        0.08057037, -0.03847577, -0.07487551, -0.06310999,  0.06494376,\n",
       "        0.04629091,  0.26316081,  0.21265025, -0.46298815, -0.03083009,\n",
       "        0.16465475,  0.0457556 , -0.22901396, -0.05640926,  0.11079143,\n",
       "       -0.11353737,  0.0021657 ,  0.14994143, -0.00984588, -0.04369528,\n",
       "        0.01986585,  0.12222372, -0.01750436, -0.03821206, -0.01700176,\n",
       "       -0.01642357,  0.00661101,  0.03170039,  0.00198905,  0.11532195,\n",
       "       -0.06245528,  0.10282588,  0.08903824,  0.12946957, -0.00073845,\n",
       "        0.06836443, -0.06469549, -0.06257623,  0.11868911,  0.09066691,\n",
       "        0.05861598,  0.05560955, -0.1031548 ,  0.10945195,  0.02596202,\n",
       "       -0.2931671 ,  0.09841166, -0.11609472,  0.05775053,  0.53149772,\n",
       "       -0.04414863,  0.03578382,  0.06537448, -0.02009593,  0.05528339,\n",
       "        0.11236683,  0.14119065, -0.03874944,  0.01553518,  0.10563848,\n",
       "       -0.03069317,  0.12239438,  0.08248502,  0.11007837, -0.42684322,\n",
       "        0.13062258, -0.0631478 , -0.23731467,  0.01078365, -0.02403427,\n",
       "        0.20024848,  0.01043448, -0.07936111,  0.07059213,  0.09232762,\n",
       "       -0.19425468, -0.06125917, -0.23763921, -0.1825652 ,  0.07134969,\n",
       "       -0.00896868, -0.00707754, -0.44345251,  0.04005514, -0.08318165,\n",
       "       -0.06863254,  0.05313345,  0.33990777, -0.18747792, -0.09174588,\n",
       "       -0.04008846, -0.05228928,  0.0418354 , -0.01772629,  0.18713433,\n",
       "       -0.02111357, -0.19112773])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rexc1 = r1 - rf[1:]\n",
    "rexc2 = r2 - rf[1:]\n",
    "rexc3 = r3 - rf[1:]\n",
    "rexc4 = r4 - rf[1:]\n",
    "rexc5 = r5 - rf[1:]\n",
    "rexc6 = r6 - rf[1:]\n",
    "rexc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.08039448, -0.04567936,  0.04773124, -0.06023551, -0.05877523,\n",
       "         0.02418828],\n",
       "       [-0.12862072, -0.40645749, -0.11625777, -0.1992869 , -0.04185234,\n",
       "        -0.21874758],\n",
       "       [-0.14454655,  0.15805053,  0.07181176, -0.09692131, -0.02844694,\n",
       "        -0.00802461],\n",
       "       ...,\n",
       "       [ 0.18713433,  0.1435784 , -0.03720621,  0.089247  ,  0.06453232,\n",
       "        -0.01202334],\n",
       "       [-0.02111357, -0.02414355, -0.01143675,  0.02811778, -0.01948611,\n",
       "         0.01565451],\n",
       "       [-0.19112773, -0.03901497, -0.0416901 , -0.01779765,  0.00140349,\n",
       "         0.00220229]])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = np.concatenate([rexc1[:, None],\n",
    "                    rexc2[:, None],\n",
    "                    rexc3[:, None],\n",
    "                    rexc4[:, None],\n",
    "                    rexc5[:, None],\n",
    "                    rexc6[:, None]],\n",
    "                   axis=1)\n",
    "R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.02225903, 0.00803892, 0.00886106, 0.00738788, 0.00632493,\n",
       "         0.00018128],\n",
       "        [0.00803892, 0.01465269, 0.00642426, 0.00447938, 0.00927125,\n",
       "         0.0019935 ],\n",
       "        [0.00886106, 0.00642426, 0.01794523, 0.00554715, 0.00513087,\n",
       "         0.00079735],\n",
       "        [0.00738788, 0.00447938, 0.00554715, 0.01116744, 0.00382483,\n",
       "         0.00019026],\n",
       "        [0.00632493, 0.00927125, 0.00513087, 0.00382483, 0.01081704,\n",
       "         0.00091163],\n",
       "        [0.00018128, 0.0019935 , 0.00079735, 0.00019026, 0.00091163,\n",
       "         0.00446373]])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cov_Sample = np.mat(np.cov(R, rowvar=False))\n",
    "Cov_Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.000e+00,  3.140e-02],\n",
       "        [ 1.000e+00, -1.963e-01],\n",
       "        [ 1.000e+00,  2.430e-02],\n",
       "        [ 1.000e+00, -2.319e-01],\n",
       "        [ 1.000e+00, -2.685e-01],\n",
       "        [ 1.000e+00,  1.294e-01],\n",
       "        [ 1.000e+00,  1.220e-02],\n",
       "        [ 1.000e+00,  1.328e-01],\n",
       "        [ 1.000e+00,  5.740e-02],\n",
       "        [ 1.000e+00,  1.857e-01],\n",
       "        [ 1.000e+00,  4.980e-02],\n",
       "        [ 1.000e+00,  5.350e-02],\n",
       "        [ 1.000e+00,  1.147e-01],\n",
       "        [ 1.000e+00,  1.703e-01],\n",
       "        [ 1.000e+00,  5.020e-02],\n",
       "        [ 1.000e+00,  9.460e-02],\n",
       "        [ 1.000e+00,  8.350e-02],\n",
       "        [ 1.000e+00,  2.860e-02],\n",
       "        [ 1.000e+00, -8.580e-02],\n",
       "        [ 1.000e+00,  2.950e-02],\n",
       "        [ 1.000e+00,  2.370e-02],\n",
       "        [ 1.000e+00, -7.860e-02],\n",
       "        [ 1.000e+00, -8.460e-02],\n",
       "        [ 1.000e+00, -7.810e-02],\n",
       "        [ 1.000e+00,  1.213e-01],\n",
       "        [ 1.000e+00,  2.430e-02],\n",
       "        [ 1.000e+00,  8.600e-03],\n",
       "        [ 1.000e+00,  1.176e-01],\n",
       "        [ 1.000e+00, -4.560e-02],\n",
       "        [ 1.000e+00, -1.200e-02],\n",
       "        [ 1.000e+00, -2.500e-02],\n",
       "        [ 1.000e+00,  4.930e-02],\n",
       "        [ 1.000e+00, -1.800e-03],\n",
       "        [ 1.000e+00, -1.750e-02],\n",
       "        [ 1.000e+00, -6.390e-02],\n",
       "        [ 1.000e+00,  1.940e-02],\n",
       "        [ 1.000e+00, -1.400e-02],\n",
       "        [ 1.000e+00, -5.060e-02],\n",
       "        [ 1.000e+00, -9.390e-02],\n",
       "        [ 1.000e+00,  3.880e-02],\n",
       "        [ 1.000e+00, -5.520e-02],\n",
       "        [ 1.000e+00,  6.750e-02],\n",
       "        [ 1.000e+00, -7.280e-02],\n",
       "        [ 1.000e+00, -3.800e-03],\n",
       "        [ 1.000e+00, -5.380e-02],\n",
       "        [ 1.000e+00, -5.860e-02],\n",
       "        [ 1.000e+00, -2.680e-02],\n",
       "        [ 1.000e+00,  2.120e-02],\n",
       "        [ 1.000e+00, -1.230e-02],\n",
       "        [ 1.000e+00, -6.360e-02],\n",
       "        [ 1.000e+00,  1.475e-01],\n",
       "        [ 1.000e+00,  4.790e-02],\n",
       "        [ 1.000e+00, -2.100e-03],\n",
       "        [ 1.000e+00, -5.300e-02],\n",
       "        [ 1.000e+00, -2.690e-02],\n",
       "        [ 1.000e+00,  7.510e-02],\n",
       "        [ 1.000e+00, -1.376e-01],\n",
       "        [ 1.000e+00,  2.510e-02],\n",
       "        [ 1.000e+00,  4.930e-02],\n",
       "        [ 1.000e+00,  4.030e-02],\n",
       "        [ 1.000e+00, -2.460e-02],\n",
       "        [ 1.000e+00,  4.100e-02],\n",
       "        [ 1.000e+00, -4.500e-02],\n",
       "        [ 1.000e+00, -2.580e-02],\n",
       "        [ 1.000e+00,  6.500e-03],\n",
       "        [ 1.000e+00, -1.070e-02],\n",
       "        [ 1.000e+00,  1.010e-02],\n",
       "        [ 1.000e+00,  2.250e-02],\n",
       "        [ 1.000e+00,  7.690e-02],\n",
       "        [ 1.000e+00,  1.490e-02],\n",
       "        [ 1.000e+00,  7.680e-02],\n",
       "        [ 1.000e+00,  1.750e-02],\n",
       "        [ 1.000e+00,  8.740e-02],\n",
       "        [ 1.000e+00,  1.387e-01],\n",
       "        [ 1.000e+00,  1.190e-02],\n",
       "        [ 1.000e+00,  4.320e-02],\n",
       "        [ 1.000e+00,  1.753e-01],\n",
       "        [ 1.000e+00,  9.080e-02],\n",
       "        [ 1.000e+00, -1.469e-01],\n",
       "        [ 1.000e+00, -5.340e-02],\n",
       "        [ 1.000e+00,  3.950e-02],\n",
       "        [ 1.000e+00,  3.810e-02],\n",
       "        [ 1.000e+00, -2.547e-01],\n",
       "        [ 1.000e+00, -2.510e-02],\n",
       "        [ 1.000e+00,  1.415e-01],\n",
       "        [ 1.000e+00, -2.240e-02],\n",
       "        [ 1.000e+00, -6.200e-03],\n",
       "        [ 1.000e+00,  2.360e-02],\n",
       "        [ 1.000e+00,  8.700e-03],\n",
       "        [ 1.000e+00,  3.940e-02],\n",
       "        [ 1.000e+00, -2.190e-02],\n",
       "        [ 1.000e+00,  2.700e-02],\n",
       "        [ 1.000e+00,  3.910e-02],\n",
       "        [ 1.000e+00, -5.190e-02],\n",
       "        [ 1.000e+00,  4.700e-03],\n",
       "        [ 1.000e+00,  2.390e-02],\n",
       "        [ 1.000e+00, -9.400e-03],\n",
       "        [ 1.000e+00, -2.780e-02],\n",
       "        [ 1.000e+00, -2.360e-02],\n",
       "        [ 1.000e+00,  3.810e-02],\n",
       "        [ 1.000e+00,  2.310e-02],\n",
       "        [ 1.000e+00,  2.450e-02],\n",
       "        [ 1.000e+00,  4.700e-03],\n",
       "        [ 1.000e+00,  9.900e-03],\n",
       "        [ 1.000e+00, -3.100e-02],\n",
       "        [ 1.000e+00, -3.500e-03],\n",
       "        [ 1.000e+00,  3.300e-02],\n",
       "        [ 1.000e+00, -5.900e-02],\n",
       "        [ 1.000e+00, -1.660e-02],\n",
       "        [ 1.000e+00, -3.680e-02],\n",
       "        [ 1.000e+00, -2.600e-03],\n",
       "        [ 1.000e+00, -8.300e-02],\n",
       "        [ 1.000e+00,  7.200e-03],\n",
       "        [ 1.000e+00, -6.010e-02],\n",
       "        [ 1.000e+00,  2.200e-02],\n",
       "        [ 1.000e+00, -9.070e-02],\n",
       "        [ 1.000e+00,  6.800e-03],\n",
       "        [ 1.000e+00, -4.570e-02],\n",
       "        [ 1.000e+00,  2.840e-02],\n",
       "        [ 1.000e+00,  6.390e-02],\n",
       "        [ 1.000e+00, -1.180e-02],\n",
       "        [ 1.000e+00, -6.310e-02],\n",
       "        [ 1.000e+00,  3.060e-02],\n",
       "        [ 1.000e+00, -4.000e-03],\n",
       "        [ 1.000e+00, -1.170e-02],\n",
       "        [ 1.000e+00,  5.000e-03],\n",
       "        [ 1.000e+00,  1.030e-02],\n",
       "        [ 1.000e+00, -1.830e-02],\n",
       "        [ 1.000e+00,  6.640e-02],\n",
       "        [ 1.000e+00, -1.240e-02],\n",
       "        [ 1.000e+00, -1.030e-02],\n",
       "        [ 1.000e+00, -5.820e-02],\n",
       "        [ 1.000e+00,  4.610e-02],\n",
       "        [ 1.000e+00,  2.100e-03],\n",
       "        [ 1.000e+00,  7.560e-02],\n",
       "        [ 1.000e+00,  1.297e-01],\n",
       "        [ 1.000e+00,  2.380e-02],\n",
       "        [ 1.000e+00, -5.840e-02],\n",
       "        [ 1.000e+00,  9.100e-03],\n",
       "        [ 1.000e+00,  3.740e-02],\n",
       "        [ 1.000e+00,  2.450e-02],\n",
       "        [ 1.000e+00, -2.500e-03],\n",
       "        [ 1.000e+00,  4.000e-04],\n",
       "        [ 1.000e+00, -2.670e-02],\n",
       "        [ 1.000e+00,  1.440e-02],\n",
       "        [ 1.000e+00,  4.680e-02],\n",
       "        [ 1.000e+00,  4.500e-03],\n",
       "        [ 1.000e+00, -4.210e-02],\n",
       "        [ 1.000e+00,  3.640e-02],\n",
       "        [ 1.000e+00, -3.000e-04],\n",
       "        [ 1.000e+00, -3.000e-03],\n",
       "        [ 1.000e+00,  1.650e-02],\n",
       "        [ 1.000e+00, -8.890e-02],\n",
       "        [ 1.000e+00,  2.610e-02],\n",
       "        [ 1.000e+00, -7.000e-02],\n",
       "        [ 1.000e+00, -8.110e-02],\n",
       "        [ 1.000e+00,  5.060e-02],\n",
       "        [ 1.000e+00,  8.730e-02],\n",
       "        [ 1.000e+00, -3.130e-02],\n",
       "        [ 1.000e+00, -2.180e-02],\n",
       "        [ 1.000e+00, -6.720e-02],\n",
       "        [ 1.000e+00, -4.080e-02],\n",
       "        [ 1.000e+00,  8.630e-02],\n",
       "        [ 1.000e+00, -1.890e-02],\n",
       "        [ 1.000e+00,  6.370e-02],\n",
       "        [ 1.000e+00,  1.500e-03],\n",
       "        [ 1.000e+00, -7.200e-03]])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.mat(np.concatenate([np.ones((len(mkt)-1, 1)), mkt[1:, None]], axis=1))\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.02225903, 0.00567464, 0.00505802, 0.00294597, 0.00370042,\n",
       "         0.00129079],\n",
       "        [0.00567464, 0.01465269, 0.00568911, 0.00331354, 0.00416212,\n",
       "         0.00145184],\n",
       "        [0.00505802, 0.00568911, 0.01794523, 0.00295348, 0.00370985,\n",
       "         0.00129408],\n",
       "        [0.00294597, 0.00331354, 0.00295348, 0.01116744, 0.00216075,\n",
       "         0.00075372],\n",
       "        [0.00370042, 0.00416212, 0.00370985, 0.00216075, 0.01081704,\n",
       "         0.00094674],\n",
       "        [0.00129079, 0.00145184, 0.00129408, 0.00075372, 0.00094674,\n",
       "         0.00446373]])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = np.mat(R)\n",
    "AB_hat = (X.T*X).I*(X.T*Y)\n",
    "ALPHA = AB_hat[0]#这里取的是AB_hat的第一行，总之就是常数项的系数\n",
    "BETA = AB_hat[1]#这里取的是AB_hat的第二行，总之就是市场超额收益率的系数\n",
    "RESD = Y - X*AB_hat\n",
    "covfactor = np.cov(mkt[1:])\n",
    "covresidual = np.diag(np.diag(np.cov(RESD, rowvar=False)))\n",
    "Cov_Factor = BETA.T*covfactor*BETA + covresidual\n",
    "Cov_Factor##方法3：因子模型估计法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.02225903, 0.00685678, 0.00695954, 0.00516692, 0.00501267,\n",
       "         0.00073603],\n",
       "        [0.00685678, 0.01465269, 0.00605668, 0.00389646, 0.00671669,\n",
       "         0.00172267],\n",
       "        [0.00695954, 0.00605668, 0.01794523, 0.00425031, 0.00442036,\n",
       "         0.00104571],\n",
       "        [0.00516692, 0.00389646, 0.00425031, 0.01116744, 0.00299279,\n",
       "         0.00047199],\n",
       "        [0.00501267, 0.00671669, 0.00442036, 0.00299279, 0.01081704,\n",
       "         0.00092919],\n",
       "        [0.00073603, 0.00172267, 0.00104571, 0.00047199, 0.00092919,\n",
       "         0.00446373]])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = 0.5\n",
    "Cov_Shrink = c*Cov_Sample + (1-c)*Cov_Factor\n",
    "Cov_Shrink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.13492256]\n",
      " [0.00901021]\n",
      " [0.01138853]\n",
      " [0.0169289 ]\n",
      " [0.3230777 ]\n",
      " [0.5046721 ]]\n",
      "\n",
      "[[ 0.15277552]\n",
      " [-0.14621159]\n",
      " [ 0.01121577]\n",
      " [-0.00120223]\n",
      " [ 0.41067723]\n",
      " [ 0.5727453 ]]\n"
     ]
    }
   ],
   "source": [
    "uhat = np.mean(R, axis=0)\n",
    "A = np.mat(np.concatenate([uhat[:, None], np.ones((len(uhat), 1))], axis=1)).T\n",
    "up = np.mean(uhat)\n",
    "b = np.mat(np.array([up, 1])[:, None])\n",
    "omega_Shrink = Cov_Shrink.I*A.T*(A*Cov_Shrink.I*A.T).I*b\n",
    "print(omega_Shrink)\n",
    "omega_Sample = Cov_Sample.I*A.T*(A*Cov_Sample.I*A.T).I*b\n",
    "print()\n",
    "print(omega_Sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>661</th>\n",
       "      <th>858</th>\n",
       "      <th>600196</th>\n",
       "      <th>600276</th>\n",
       "      <th>600519</th>\n",
       "      <th>600900</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.207138</td>\n",
       "      <td>-0.049509</td>\n",
       "      <td>0.030700</td>\n",
       "      <td>0.077593</td>\n",
       "      <td>0.280550</td>\n",
       "      <td>0.453528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.212465</td>\n",
       "      <td>-0.033178</td>\n",
       "      <td>0.017228</td>\n",
       "      <td>0.073487</td>\n",
       "      <td>0.278148</td>\n",
       "      <td>0.451850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.210014</td>\n",
       "      <td>-0.031199</td>\n",
       "      <td>0.008308</td>\n",
       "      <td>0.077506</td>\n",
       "      <td>0.285647</td>\n",
       "      <td>0.449724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.212620</td>\n",
       "      <td>-0.034185</td>\n",
       "      <td>0.004245</td>\n",
       "      <td>0.085995</td>\n",
       "      <td>0.284604</td>\n",
       "      <td>0.446722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.219615</td>\n",
       "      <td>-0.042209</td>\n",
       "      <td>0.018370</td>\n",
       "      <td>0.086381</td>\n",
       "      <td>0.285745</td>\n",
       "      <td>0.432098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.111745</td>\n",
       "      <td>-0.006676</td>\n",
       "      <td>0.008226</td>\n",
       "      <td>0.026951</td>\n",
       "      <td>0.316348</td>\n",
       "      <td>0.543406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.149310</td>\n",
       "      <td>-0.016662</td>\n",
       "      <td>0.023747</td>\n",
       "      <td>0.016585</td>\n",
       "      <td>0.312064</td>\n",
       "      <td>0.514956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.144827</td>\n",
       "      <td>-0.012052</td>\n",
       "      <td>0.025989</td>\n",
       "      <td>0.012523</td>\n",
       "      <td>0.315518</td>\n",
       "      <td>0.513197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.139491</td>\n",
       "      <td>0.002624</td>\n",
       "      <td>0.020286</td>\n",
       "      <td>0.011511</td>\n",
       "      <td>0.315709</td>\n",
       "      <td>0.510380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.147367</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.011975</td>\n",
       "      <td>0.012013</td>\n",
       "      <td>0.317157</td>\n",
       "      <td>0.499983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661       858       600196    600276    600519    600900\n",
       "0   0.207138 -0.049509  0.030700  0.077593  0.280550  0.453528\n",
       "1   0.212465 -0.033178  0.017228  0.073487  0.278148  0.451850\n",
       "2   0.210014 -0.031199  0.008308  0.077506  0.285647  0.449724\n",
       "3   0.212620 -0.034185  0.004245  0.085995  0.284604  0.446722\n",
       "4   0.219615 -0.042209  0.018370  0.086381  0.285745  0.432098\n",
       "..       ...       ...       ...       ...       ...       ...\n",
       "62  0.111745 -0.006676  0.008226  0.026951  0.316348  0.543406\n",
       "63  0.149310 -0.016662  0.023747  0.016585  0.312064  0.514956\n",
       "64  0.144827 -0.012052  0.025989  0.012523  0.315518  0.513197\n",
       "65  0.139491  0.002624  0.020286  0.011511  0.315709  0.510380\n",
       "66  0.147367  0.011505  0.011975  0.012013  0.317157  0.499983\n",
       "\n",
       "[67 rows x 6 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shrink = pd.DataFrame(columns=data.columns.values[2:8])\n",
    "df_sample=pd.DataFrame(columns=data.columns.values[2:8])\n",
    "df_factor=pd.DataFrame(columns=data.columns.values[2:8])\n",
    "for i in range(67):\n",
    "    p1 = data.iloc[:i+100, 2].values\n",
    "    p2 = data.iloc[:i+100, 3].values\n",
    "    p3 = data.iloc[:i+100, 4].values\n",
    "    p4 = data.iloc[:i+100, 5].values\n",
    "    p5 = data.iloc[:i+100, 6].values\n",
    "    p6 = data.iloc[:i+100, 7].values\n",
    "    rf = data.iloc[:i+100, 1].values\n",
    "    mkt = data.iloc[:i+100, 0].values\n",
    "    r1 = np.log(p1[1:]) - np.log(p1[:-1])\n",
    "    r2 = np.log(p2[1:]) - np.log(p2[:-1])\n",
    "    r3 = np.log(p3[1:]) - np.log(p3[:-1])\n",
    "    r4 = np.log(p4[1:]) - np.log(p4[:-1])\n",
    "    r5 = np.log(p5[1:]) - np.log(p5[:-1])\n",
    "    r6 = np.log(p6[1:]) - np.log(p6[:-1])\n",
    "    rexc1 = r1 - rf[1:i+101]\n",
    "    rexc2 = r2 - rf[1:i+101]\n",
    "    rexc3 = r3 - rf[1:i+101]\n",
    "    rexc4 = r4 - rf[1:i+101]\n",
    "    rexc5 = r5 - rf[1:i+101]\n",
    "    rexc6 = r6 - rf[1:i+101]\n",
    "    R = np.concatenate([rexc1[:, None],\n",
    "                        rexc2[:, None],\n",
    "                        rexc3[:, None],\n",
    "                        rexc4[:, None],\n",
    "                        rexc5[:, None],\n",
    "                        rexc6[:, None]],\n",
    "                       axis=1)\n",
    "    Cov_Sample = np.mat(np.cov(R, rowvar=False))\n",
    "    X = np.mat(np.concatenate([np.ones((len(mkt)-1, 1)), mkt[1:, None]], axis=1))\n",
    "    Y = np.mat(R)\n",
    "    AB_hat = (X.T*X).I*(X.T*Y)\n",
    "    ALPHA = AB_hat[0]#这里取的是AB_hat的第一行，总之就是常数项的系数\n",
    "    BETA = AB_hat[1]#这里取的是AB_hat的第二行，总之就是市场超额收益率的系数\n",
    "    RESD = Y - X*AB_hat\n",
    "    covfactor = np.cov(mkt[1:])\n",
    "    covresidual = np.diag(np.diag(np.cov(RESD, rowvar=False)))\n",
    "    Cov_Factor = BETA.T*covfactor*BETA + covresidual\n",
    "    c = 0.5\n",
    "    Cov_Shrink = c*Cov_Sample + (1-c)*Cov_Factor\n",
    "    uhat = np.mean(R, axis=0)\n",
    "    A = np.mat(np.concatenate([uhat[:, None], np.ones((len(uhat), 1))], axis=1)).T\n",
    "    up = np.mean(uhat)\n",
    "    b = np.mat(np.array([up, 1])[:, None])\n",
    "    omega_Shrink = Cov_Shrink.I*A.T*(A*Cov_Shrink.I*A.T).I*b\n",
    "    omega_Factor = Cov_Factor.I*A.T*(A*Cov_Factor.I*A.T).I*b\n",
    "    omega_Sample = Cov_Sample.I*A.T*(A*Cov_Sample.I*A.T).I*b\n",
    "    newrow_shirink=np.asarray(omega_Shrink)\n",
    "    newrow_shirink=newrow_shirink.reshape(-1)\n",
    "    newrow_sample=np.asarray(omega_Sample)\n",
    "    newrow_sample=newrow_sample.reshape(-1)\n",
    "    newrow_factor=np.asarray(omega_Factor)\n",
    "    newrow_factor=newrow_factor.reshape(-1)\n",
    "    df_shrink.loc[len(df_shrink)]=newrow_shirink\n",
    "    df_sample.loc[len(df_sample)]=newrow_sample\n",
    "    df_factor.loc[len(df_factor)]=newrow_factor\n",
    "df_shrink\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>661</th>\n",
       "      <th>858</th>\n",
       "      <th>600196</th>\n",
       "      <th>600276</th>\n",
       "      <th>600519</th>\n",
       "      <th>600900</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.193971</td>\n",
       "      <td>-0.028580</td>\n",
       "      <td>0.099456</td>\n",
       "      <td>0.117262</td>\n",
       "      <td>0.240696</td>\n",
       "      <td>0.377195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.199862</td>\n",
       "      <td>-0.011642</td>\n",
       "      <td>0.085507</td>\n",
       "      <td>0.110914</td>\n",
       "      <td>0.242019</td>\n",
       "      <td>0.373340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.198058</td>\n",
       "      <td>-0.008130</td>\n",
       "      <td>0.077671</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>0.249280</td>\n",
       "      <td>0.370079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.199953</td>\n",
       "      <td>-0.011557</td>\n",
       "      <td>0.075070</td>\n",
       "      <td>0.121811</td>\n",
       "      <td>0.247786</td>\n",
       "      <td>0.366938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.203031</td>\n",
       "      <td>-0.019992</td>\n",
       "      <td>0.089057</td>\n",
       "      <td>0.127604</td>\n",
       "      <td>0.246588</td>\n",
       "      <td>0.353713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.100343</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.011099</td>\n",
       "      <td>0.049432</td>\n",
       "      <td>0.290040</td>\n",
       "      <td>0.479148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.133765</td>\n",
       "      <td>0.065404</td>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.040722</td>\n",
       "      <td>0.288680</td>\n",
       "      <td>0.448389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.128558</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.024690</td>\n",
       "      <td>0.037375</td>\n",
       "      <td>0.290800</td>\n",
       "      <td>0.448811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.121615</td>\n",
       "      <td>0.081475</td>\n",
       "      <td>0.019124</td>\n",
       "      <td>0.037911</td>\n",
       "      <td>0.290353</td>\n",
       "      <td>0.449522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.126990</td>\n",
       "      <td>0.088118</td>\n",
       "      <td>0.012635</td>\n",
       "      <td>0.040514</td>\n",
       "      <td>0.291939</td>\n",
       "      <td>0.439803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661       858       600196    600276    600519    600900\n",
       "0   0.193971 -0.028580  0.099456  0.117262  0.240696  0.377195\n",
       "1   0.199862 -0.011642  0.085507  0.110914  0.242019  0.373340\n",
       "2   0.198058 -0.008130  0.077671  0.113043  0.249280  0.370079\n",
       "3   0.199953 -0.011557  0.075070  0.121811  0.247786  0.366938\n",
       "4   0.203031 -0.019992  0.089057  0.127604  0.246588  0.353713\n",
       "..       ...       ...       ...       ...       ...       ...\n",
       "62  0.100343  0.069938  0.011099  0.049432  0.290040  0.479148\n",
       "63  0.133765  0.065404  0.023039  0.040722  0.288680  0.448389\n",
       "64  0.128558  0.069767  0.024690  0.037375  0.290800  0.448811\n",
       "65  0.121615  0.081475  0.019124  0.037911  0.290353  0.449522\n",
       "66  0.126990  0.088118  0.012635  0.040514  0.291939  0.439803\n",
       "\n",
       "[67 rows x 6 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_factor\n",
    "# # result=pd.concat(df,data[data['YYYYmm']>=201706]['YYYYmm'],axis=1)\n",
    "# # df['YYYYmm']=data[data['YYYYmm']>=201706]['YYYYmm']\n",
    "# YYYYmm=data[data['YYYYmm']>=201706]['YYYYmm']\n",
    "# YYYYmm=YYYYmm.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>661</th>\n",
       "      <th>858</th>\n",
       "      <th>600196</th>\n",
       "      <th>600276</th>\n",
       "      <th>600519</th>\n",
       "      <th>600900</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.225241</td>\n",
       "      <td>-0.114374</td>\n",
       "      <td>-0.075847</td>\n",
       "      <td>0.054357</td>\n",
       "      <td>0.360095</td>\n",
       "      <td>0.550528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.229909</td>\n",
       "      <td>-0.101592</td>\n",
       "      <td>-0.086268</td>\n",
       "      <td>0.052639</td>\n",
       "      <td>0.355374</td>\n",
       "      <td>0.549938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.225939</td>\n",
       "      <td>-0.101482</td>\n",
       "      <td>-0.095773</td>\n",
       "      <td>0.059390</td>\n",
       "      <td>0.362352</td>\n",
       "      <td>0.549575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.228334</td>\n",
       "      <td>-0.103657</td>\n",
       "      <td>-0.100824</td>\n",
       "      <td>0.066762</td>\n",
       "      <td>0.361608</td>\n",
       "      <td>0.547777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.241254</td>\n",
       "      <td>-0.111597</td>\n",
       "      <td>-0.089881</td>\n",
       "      <td>0.059371</td>\n",
       "      <td>0.366096</td>\n",
       "      <td>0.534757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.118905</td>\n",
       "      <td>-0.149731</td>\n",
       "      <td>0.006724</td>\n",
       "      <td>0.018679</td>\n",
       "      <td>0.394937</td>\n",
       "      <td>0.610486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.158248</td>\n",
       "      <td>-0.169417</td>\n",
       "      <td>0.024283</td>\n",
       "      <td>0.005395</td>\n",
       "      <td>0.393531</td>\n",
       "      <td>0.587960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.156108</td>\n",
       "      <td>-0.166881</td>\n",
       "      <td>0.026401</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.398824</td>\n",
       "      <td>0.585217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.154504</td>\n",
       "      <td>-0.151300</td>\n",
       "      <td>0.020938</td>\n",
       "      <td>-0.004755</td>\n",
       "      <td>0.400848</td>\n",
       "      <td>0.579766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.166832</td>\n",
       "      <td>-0.141100</td>\n",
       "      <td>0.011081</td>\n",
       "      <td>-0.009423</td>\n",
       "      <td>0.402870</td>\n",
       "      <td>0.569739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661       858       600196    600276    600519    600900\n",
       "0   0.225241 -0.114374 -0.075847  0.054357  0.360095  0.550528\n",
       "1   0.229909 -0.101592 -0.086268  0.052639  0.355374  0.549938\n",
       "2   0.225939 -0.101482 -0.095773  0.059390  0.362352  0.549575\n",
       "3   0.228334 -0.103657 -0.100824  0.066762  0.361608  0.547777\n",
       "4   0.241254 -0.111597 -0.089881  0.059371  0.366096  0.534757\n",
       "..       ...       ...       ...       ...       ...       ...\n",
       "62  0.118905 -0.149731  0.006724  0.018679  0.394937  0.610486\n",
       "63  0.158248 -0.169417  0.024283  0.005395  0.393531  0.587960\n",
       "64  0.156108 -0.166881  0.026401  0.000331  0.398824  0.585217\n",
       "65  0.154504 -0.151300  0.020938 -0.004755  0.400848  0.579766\n",
       "66  0.166832 -0.141100  0.011081 -0.009423  0.402870  0.569739\n",
       "\n",
       "[67 rows x 6 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample\n",
    "# YYYYmm.drop(columns=['index'],inplace=True)\n",
    "# YYYYmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = df_shrink.index\n",
    "x=x.values.tolist()\n",
    "# print(type(x[1]))\n",
    "# for i in range(len(x)):\n",
    "#     x[i]=x[i][:3]+'\\n'+x[i][3:]\n",
    "plt.figure(figsize=(20,8))\n",
    "names = {}\n",
    "for j in range(6):\n",
    "    names[f'y{j + 1}'] = df_shrink[df_shrink.columns[j]].values.tolist()\n",
    "    plt.plot(x, names[f'y{j + 1}'],label = df_shrink.columns[j])\n",
    "    plt.title('shrink')\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = df_sample.index\n",
    "x=x.values.tolist()\n",
    "# print(type(x[1]))\n",
    "# for i in range(len(x)):\n",
    "#     x[i]=x[i][:3]+'\\n'+x[i][3:]\n",
    "plt.figure(figsize=(20,8))\n",
    "names = {}\n",
    "for j in range(6):\n",
    "    names[f'y{j + 1}'] = df_sample[df_sample.columns[j]].values.tolist()\n",
    "    plt.plot(x, names[f'y{j + 1}'],label = df_sample.columns[j])\n",
    "    plt.title('sample')\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = df_factor.index\n",
    "x=x.values.tolist()\n",
    "# print(type(x[1]))\n",
    "# for i in range(len(x)):\n",
    "#     x[i]=x[i][:3]+'\\n'+x[i][3:]\n",
    "plt.figure(figsize=(20,8))\n",
    "names = {}\n",
    "for j in range(6):\n",
    "    names[f'y{j + 1}'] = df_factor[df_factor.columns[j]].values.tolist()\n",
    "    plt.plot(x, names[f'y{j + 1}'],label = df_factor.columns[j])\n",
    "    plt.title('factor')\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.08414748, -0.12486972, -0.14080155, -0.45048249, -0.22841266,\n",
       "         0.14058195,  0.39778542,  0.35115312,  0.04546237,  0.20188208,\n",
       "        -0.0813046 ,  0.11522633, -0.05040059,  0.41058021, -0.14845266,\n",
       "         0.16183214,  0.02151477,  0.05184498,  0.23534427,  0.02309707,\n",
       "         0.091625  ,  0.13728226,  0.05740104, -0.24664304,  0.15415068,\n",
       "         0.21645456, -0.00480539,  0.09706027,  0.18264207, -0.12143134,\n",
       "        -0.14677662,  0.05916698, -0.13286478, -0.0844609 ,  0.01241701,\n",
       "         0.04565823,  0.01587335, -0.06065052, -0.14983007, -0.02328695,\n",
       "         0.10619786, -0.17978241, -0.03936724,  0.24792194,  0.07785861,\n",
       "         0.02692992,  0.05509656, -0.02566775,  0.22362344, -0.1568998 ,\n",
       "         0.13376523,  0.22268518,  0.14554306, -0.02697438,  0.05500368,\n",
       "         0.0595293 ,  0.01574998,  0.08531621,  0.06243794, -0.02850825,\n",
       "        -0.1260096 ,  0.13089295, -0.01958614,  0.11132247, -0.09399417,\n",
       "        -0.23783044,  0.09833971, -0.16828334,  0.00808531,  0.13706346,\n",
       "         0.08441937, -0.03471777, -0.07131651, -0.05922599,  0.06906976,\n",
       "         0.05037791,  0.26699881,  0.21546525, -0.46039615, -0.02822809,\n",
       "         0.16718875,  0.0483046 , -0.22646996, -0.05390526,  0.11313243,\n",
       "        -0.11115637,  0.0045957 ,  0.15240243, -0.00743288, -0.04135728,\n",
       "         0.02219285,  0.12456272, -0.01507136, -0.03556906, -0.01395476,\n",
       "        -0.01295757,  0.01023401,  0.03527939,  0.00568805,  0.11921695,\n",
       "        -0.05885928,  0.10642788,  0.09268824,  0.13311057,  0.00305955,\n",
       "         0.07239443, -0.06077149, -0.05865223,  0.12258811,  0.09413091,\n",
       "         0.06204098,  0.05920655, -0.1001528 ,  0.11185395,  0.02833002,\n",
       "        -0.2907811 ,  0.10093766, -0.11343072,  0.06026153,  0.53382272,\n",
       "        -0.04180963,  0.03820682,  0.06777848, -0.01791093,  0.05750139,\n",
       "         0.11462783,  0.14349465, -0.03625944,  0.01805918,  0.10804148,\n",
       "        -0.02851517,  0.12419038,  0.08376402,  0.11125037, -0.42525422,\n",
       "         0.13255458, -0.0609768 , -0.23508367,  0.01311565, -0.02151027,\n",
       "         0.20268748,  0.01263948, -0.07701911,  0.07285313,  0.09450062,\n",
       "        -0.19215268, -0.05921917, -0.23562421, -0.1805962 ,  0.07332969,\n",
       "        -0.00694168, -0.00499854, -0.44138251,  0.04207014, -0.08121065,\n",
       "        -0.06669754,  0.05489145,  0.34157477, -0.18585792, -0.09037988,\n",
       "        -0.03874646, -0.05087628,  0.0435114 , -0.01579529,  0.18910733]),\n",
       " array([-0.04192636, -0.40270649,  0.16179553, -0.44562827, -0.39944594,\n",
       "         0.23647491, -0.14878183,  0.05753316,  0.05908312,  0.06019287,\n",
       "         0.04001765,  0.0042156 ,  0.17033088,  0.1757108 , -0.11762361,\n",
       "         0.07017184,  0.23739726,  0.10725745, -0.08122787, -0.04019412,\n",
       "         0.00462552, -0.06906894, -0.05592855, -0.02566855,  0.1367248 ,\n",
       "         0.13263106,  0.07907221,  0.06129265,  0.06957718, -0.12216908,\n",
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       "         0.07890021,  0.0618754 ,  0.06124363, -0.10701204,  0.0106865 ,\n",
       "         0.02263637, -0.06295881, -0.07053452, -0.01812924,  0.01569022,\n",
       "         0.0661398 , -0.07195644,  0.0400234 , -0.00888895, -0.21980093,\n",
       "         0.04566011, -0.10170679, -0.04245732, -0.08984234, -0.01761165,\n",
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       "        -0.09859334,  0.02661984, -0.06728857, -0.06735218,  0.04213261,\n",
       "         0.14200878, -0.0477112 ,  0.06628759,  0.07828218, -0.03463095,\n",
       "        -0.01397872, -0.04314829,  0.02291243,  0.172141  ,  0.03203935,\n",
       "        -0.03763638,  0.19986248,  0.02720228, -0.04186304,  0.        ,\n",
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       "         0.06690059,  0.08338161,  0.05651221,  0.05098555,  0.15056388,\n",
       "         0.00179501, -0.00485394,  0.0317487 ,  0.15105331, -0.01542914,\n",
       "         0.19694981,  0.0548049 , -0.12748867, -0.11274727,  0.02411908,\n",
       "         0.14998111, -0.03846132, -0.05198606, -0.15322711,  0.09325196,\n",
       "        -0.344451  ,  0.0843946 , -0.02924581,  0.16986218,  0.45454479,\n",
       "         0.07461912, -0.01090331,  0.1526681 ,  0.02313017,  0.15890874,\n",
       "        -0.08630491,  0.017413  , -0.03489936,  0.04191587, -0.05287337,\n",
       "        -0.04507166, -0.04580954,  0.16429253,  0.08760044,  0.14380235,\n",
       "         0.23983379,  0.09844007, -0.08247622,  0.10043892,  0.03873264,\n",
       "         0.1389057 , -0.00243572, -0.03901465, -0.04387725,  0.06157683,\n",
       "         0.10046434, -0.05622923, -0.29969345, -0.09029897,  0.0841191 ,\n",
       "        -0.01321423,  0.02800921, -0.11546156, -0.04316031, -0.20321193,\n",
       "         0.04458634,  0.05560152,  0.1639211 , -0.12389288, -0.06489733,\n",
       "         0.01212785, -0.23715726,  0.16125221,  0.14142917,  0.1455514 ]),\n",
       " array([ 0.05148424, -0.11250677,  0.07555676, -0.27645301, -0.55796289,\n",
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       "        -0.05614473,  0.06116086,  0.03511597,  0.06609011,  0.02865526,\n",
       "         0.15172644,  0.05712845, -0.00306123,  0.02672203,  0.05939472,\n",
       "        -0.01511602,  0.06227513, -0.00268697, -0.2423858 , -0.27895833,\n",
       "         0.10732234, -0.06807257,  0.03636764,  0.01666705, -0.07506202,\n",
       "        -0.11306146,  0.05262242, -0.07872916, -0.01808056, -0.04987677,\n",
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       "        -0.05214946, -0.01076437, -0.00108284,  0.12605498, -0.01539973,\n",
       "         0.06022516,  0.        , -0.02403073, -0.04888371, -0.09796596,\n",
       "         0.12796337,  0.04291703,  0.05766564,  0.04220035, -0.03276227,\n",
       "         0.06127745, -0.17225194,  0.0867728 ,  0.01044396,  0.18448372,\n",
       "         0.21548289,  0.00924328,  0.1191239 , -0.08742912,  0.19202364,\n",
       "        -0.13939419,  0.0449572 ,  0.02001067, -0.07506092, -0.0156043 ,\n",
       "         0.03201981,  0.01873104,  0.04633256,  0.03766742,  0.05844774,\n",
       "         0.01243908,  0.19438972,  0.2135741 , -0.41194525, -0.02877896,\n",
       "         0.09605642, -0.02688108, -0.25570281, -0.01328188,  0.10463602,\n",
       "        -0.03160304, -0.00155481, -0.01357723,  0.12766511,  0.07658392,\n",
       "         0.00640891,  0.01227529,  0.0012613 , -0.02812286,  0.07410797,\n",
       "         0.03354033,  0.09081992,  0.04571433,  0.06994029, -0.022021  ,\n",
       "        -0.06120966,  0.00376648,  0.15571181,  0.15293823, -0.08318519,\n",
       "         0.19380295, -0.09196746, -0.01714541,  0.10888812, -0.1456457 ,\n",
       "         0.18530721, -0.11188665, -0.11212139, -0.2367492 ,  0.07740488,\n",
       "        -0.21682441,  0.09172466, -0.17930886, -0.00172043,  0.24839248,\n",
       "        -0.02309885, -0.16231681,  0.02238303,  0.03876454,  0.05005679,\n",
       "        -0.09000781,  0.00749067, -0.0122507 ,  0.05605332, -0.01591546,\n",
       "         0.02638676,  0.2008736 ,  0.02316241, -0.08952853,  0.09604901,\n",
       "         0.6088982 , -0.12678211, -0.10999241,  0.10193376, -0.04475272,\n",
       "         0.03755513, -0.17174608,  0.01245782, -0.14589496,  0.41472096,\n",
       "         0.14155028,  0.03858318,  0.12678471, -0.25736943, -0.19088896,\n",
       "        -0.04497337, -0.02142795, -0.14038535, -0.00306135,  0.22935066,\n",
       "        -0.21576413,  0.01889893,  0.00659918, -0.00431868, -0.08407512,\n",
       "        -0.3114171 ,  0.11099414,  0.11620073, -0.05143517, -0.03523321]),\n",
       " array([-0.05648251, -0.1955359 , -0.09317631, -0.10619112, -0.10218187,\n",
       "         0.08949428,  0.11111872, -0.04677972,  0.05582722,  0.02565872,\n",
       "        -0.16073531, -0.04703912,  0.07492359,  0.00343152,  0.17716986,\n",
       "         0.02968416,  0.13283477,  0.06491955, -0.09420603,  0.02847314,\n",
       "        -0.22614847,  0.08713889,  0.06558986, -0.15630566,  0.06682347,\n",
       "         0.04950813,  0.10583887, -0.031438  ,  0.21464732,  0.01624951,\n",
       "        -0.18683137,  0.05012888, -0.12468844, -0.39807859, -0.00227531,\n",
       "        -0.02504249,  0.08104321, -0.04403227, -0.06784516, -0.0069061 ,\n",
       "         0.0702395 , -0.14504071, -0.02075155, -0.00420249,  0.09454419,\n",
       "        -0.00209205,  0.04704344,  0.00928081, -0.00861503, -0.07567069,\n",
       "         0.0773332 ,  0.08131968,  0.00914641,  0.01983238, -0.09775291,\n",
       "         0.02782451, -0.17304753,  0.23820408, -0.04400343,  0.12769591,\n",
       "        -0.13742758,  0.06650819,  0.11423484, -0.01351909, -0.03697078,\n",
       "        -0.07951206, -0.02920804,  0.0234946 , -0.00332276,  0.02391743,\n",
       "         0.09086902,  0.04947076, -0.08796877,  0.04949746,  0.08266631,\n",
       "        -0.00863457,  0.3453875 ,  0.09593189, -0.34591911,  0.03952386,\n",
       "         0.06880522, -0.00730226, -0.11007665,  0.02180597,  0.04903368,\n",
       "        -0.01171348, -0.01597961, -0.13571052,  0.08526465, -0.00781434,\n",
       "         0.01624936,  0.0463549 ,  0.01548418, -0.02945211,  0.04994073,\n",
       "         0.05213969,  0.07528382, -0.01296795, -0.07429445,  0.01593977,\n",
       "         0.0119856 ,  0.05583375,  0.10160393,  0.11494236, -0.03081349,\n",
       "         0.05651052,  0.09282944, -0.04333539,  0.18271239, -0.04141597,\n",
       "        -0.09216468, -0.00487196, -0.09534978, -0.04029578, -0.04088498,\n",
       "        -0.03070276,  0.06094935, -0.21572272,  0.11282077,  0.10244348,\n",
       "         0.00503165, -0.05614796,  0.05994304,  0.01414195,  0.17885474,\n",
       "         0.00783927,  0.11904924, -0.05635613,  0.01868351,  0.00535583,\n",
       "        -0.04533552,  0.09022696,  0.01048488, -0.16695634,  0.15940099,\n",
       "         0.02228504,  0.00517835, -0.05469987, -0.01097067, -0.03260599,\n",
       "         0.25943477, -0.07158524, -0.01907044, -0.10024374, -0.09075979,\n",
       "         0.02582303, -0.23876317, -0.22306999, -0.18489935,  0.10551537,\n",
       "        -0.01990616,  0.02941683, -0.21377132, -0.02147435, -0.08483628,\n",
       "        -0.22232911,  0.02809549,  0.20153983, -0.04974775, -0.01801335,\n",
       "         0.01261485,  0.13566587, -0.0092466 , -0.03318324,  0.09122   ]),\n",
       " array([-0.05502223, -0.03810134, -0.02470194, -0.27874851, -0.40748765,\n",
       "         0.09904997,  0.06558169, -0.05192294,  0.13765322, -0.03165337,\n",
       "         0.01393419, -0.01367276,  0.25434971,  0.01348878,  0.09432801,\n",
       "        -0.044973  ,  0.0984143 , -0.02379887, -0.00756594, -0.01013784,\n",
       "        -0.04964164, -0.20734869,  0.02184748, -0.0348747 ,  0.08507929,\n",
       "         0.13825269,  0.05795011, -0.03362978,  0.2274127 , -0.10758184,\n",
       "        -0.03277351,  0.00386913,  0.00652667,  0.01358536,  0.11143059,\n",
       "         0.04241445, -0.02833348,  0.0383476 , -0.11944316,  0.06902435,\n",
       "         0.02824433, -0.02702085, -0.03737169,  0.18370836,  0.01038195,\n",
       "         0.03002759, -0.11458251,  0.11198216,  0.00624571, -0.13543921,\n",
       "        -0.03289476, -0.15560295, -0.02204102, -0.035716  ,  0.02992786,\n",
       "         0.12471339, -0.02429061, -0.13248485,  0.00166034, -0.21638248,\n",
       "        -0.00612435,  0.04578634, -0.09688098,  0.02932131,  0.12645269,\n",
       "         0.08836583, -0.06725966, -0.07618859,  0.12117369, -0.01281022,\n",
       "         0.02434881, -0.03419297, -0.00537567,  0.19619223, -0.0678559 ,\n",
       "         0.07536901,  0.2778062 ,  0.04496359, -0.30040976, -0.02624088,\n",
       "         0.11876924,  0.01794268, -0.08380415,  0.06865724,  0.14175674,\n",
       "         0.01427336,  0.05147839,  0.09875197,  0.07020252, -0.0094973 ,\n",
       "        -0.04039359,  0.06510271,  0.00354772,  0.04614814,  0.03027185,\n",
       "         0.02929774,  0.08560999,  0.06783957,  0.06882517,  0.06322682,\n",
       "         0.01966341,  0.02042503,  0.05253044,  0.17725698,  0.02076886,\n",
       "         0.10018231,  0.09178617, -0.05224788, -0.05962425, -0.03133637,\n",
       "         0.1255129 , -0.0265362 , -0.00688664, -0.09714408,  0.10203273,\n",
       "        -0.28512826,  0.02890946,  0.04331375,  0.15597223,  0.21380776,\n",
       "         0.13149182, -0.09018984,  0.10040443, -0.011653  ,  0.16056349,\n",
       "         0.00698083,  0.02576097, -0.04419063,  0.0467213 , -0.1166003 ,\n",
       "         0.00398143,  0.0498258 ,  0.13036482,  0.07670057,  0.06808119,\n",
       "         0.13730278,  0.06254853, -0.06833338,  0.00091058,  0.02594171,\n",
       "         0.15336937,  0.0574659 ,  0.00311397, -0.05508946, -0.00110564,\n",
       "         0.10007444, -0.07550313, -0.20291033, -0.07478947,  0.16091302,\n",
       "        -0.00214437,  0.1156682 , -0.08285153, -0.05254921, -0.04069633,\n",
       "         0.0616876 , -0.01340728,  0.12537474, -0.07436529,  0.01337386,\n",
       "        -0.02713192, -0.32716984,  0.16927384,  0.07700737,  0.06650532]),\n",
       " array([ 0.02794128, -0.21499658, -0.00427961,  0.04609755,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        , -0.10649882,  0.04527675,  0.13298698, -0.16319185,\n",
       "        -0.02269386,  0.0294028 , -0.00745716, -0.04438372,  0.01629838,\n",
       "        -0.03685247,  0.08641216, -0.10821358,  0.01940239, -0.41835812,\n",
       "        -0.037179  , -0.03337922,  0.05954894, -0.07136786,  0.        ,\n",
       "         0.03632046, -0.00895719,  0.02412815,  0.03210152, -0.10630601,\n",
       "        -0.02601105, -0.06740903, -0.06434072, -0.00953902,  0.04681042,\n",
       "        -0.00765115,  0.00306749, -0.00153257,  0.05664704, -0.0145988 ,\n",
       "        -0.03288787, -0.01685864, -0.00154679, -0.0062112 , -0.00938974,\n",
       "         0.07713573,  0.09438346, -0.03779127,  0.02176957, -0.04121462,\n",
       "         0.05855996, -0.08845542, -0.00289436, -0.02941389, -0.00749067,\n",
       "        -0.02898754,  0.01688451, -0.03879462, -0.0469722 , -0.01000008,\n",
       "        -0.04803668,  0.05803676,  0.0310229 ,  0.09208957,  0.08835456,\n",
       "         0.05864873,  0.04456183,  0.05991222,  0.19609926, -0.04701105,\n",
       "        -0.01484441,  0.27539352, -0.00151515,  0.11042726,  0.        ,\n",
       "        -0.09090712,  0.00814518, -0.11309272, -0.0604182 ,  0.07435857,\n",
       "         0.00406339,  0.0104882 ,  0.00240481,  0.05831423,  0.04070114,\n",
       "        -0.03617966,  0.01121507, -0.00821207, -0.05231962,  0.02880697,\n",
       "        -0.00461539,  0.02286685,  0.04566331,  0.06413347,  0.03776534,\n",
       "        -0.01111486, -0.01523713,  0.00599003,  0.05424183,  0.03398535,\n",
       "        -0.05430351,  0.01968943,  0.03522143, -0.0277026 ,  0.00560226,\n",
       "         0.06022289, -0.05836243,  0.04245635, -0.06248205,  0.03478612,\n",
       "        -0.04814934, -0.08416179,  0.10131051,  0.03404848,  0.02642796,\n",
       "         0.        ,  0.04859256,  0.01067126,  0.04479176, -0.0053562 ,\n",
       "        -0.02116768, -0.02050501,  0.01500445,  0.01369509, -0.06926494,\n",
       "        -0.00702168,  0.01515181,  0.00921135, -0.00344432,  0.08538076,\n",
       "        -0.03055719,  0.0493862 , -0.00884731, -0.00892629,  0.05888571,\n",
       "        -0.04839243,  0.02677812,  0.0035506 ,  0.08210484, -0.07102719,\n",
       "        -0.00552349,  0.03852328, -0.08279196,  0.01773651,  0.12886696,\n",
       "        -0.03468556,  0.06600803, -0.03496546,  0.05589038, -0.05224738,\n",
       "         0.03220314,  0.05564122, -0.03818877,  0.03234324,  0.00417886,\n",
       "        -0.05309466, -0.11547699,  0.04629563, -0.0104217 , -0.01005034])]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rlist=[]\n",
    "rlist.append(r1)\n",
    "rlist.append(r2)\n",
    "rlist.append(r3)\n",
    "rlist.append(r4)\n",
    "rlist.append(r5)\n",
    "rlist.append(r6)\n",
    "rlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>661</th>\n",
       "      <th>858</th>\n",
       "      <th>600196</th>\n",
       "      <th>600276</th>\n",
       "      <th>600519</th>\n",
       "      <th>600900</th>\n",
       "      <th>661ret</th>\n",
       "      <th>858ret</th>\n",
       "      <th>600196ret</th>\n",
       "      <th>600276ret</th>\n",
       "      <th>600519ret</th>\n",
       "      <th>600900ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.193971</td>\n",
       "      <td>-0.028580</td>\n",
       "      <td>0.099456</td>\n",
       "      <td>0.117262</td>\n",
       "      <td>0.240696</td>\n",
       "      <td>0.377195</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>-0.001457</td>\n",
       "      <td>0.006956</td>\n",
       "      <td>-0.008712</td>\n",
       "      <td>0.016566</td>\n",
       "      <td>0.024191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.199862</td>\n",
       "      <td>-0.011642</td>\n",
       "      <td>0.085507</td>\n",
       "      <td>0.110914</td>\n",
       "      <td>0.242019</td>\n",
       "      <td>0.373340</td>\n",
       "      <td>0.023827</td>\n",
       "      <td>-0.001753</td>\n",
       "      <td>-0.001883</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>0.015302</td>\n",
       "      <td>0.014099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.198058</td>\n",
       "      <td>-0.008130</td>\n",
       "      <td>0.077671</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>0.249280</td>\n",
       "      <td>0.370079</td>\n",
       "      <td>-0.011658</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.004754</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.004902</td>\n",
       "      <td>-0.004113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.199953</td>\n",
       "      <td>-0.011557</td>\n",
       "      <td>0.075070</td>\n",
       "      <td>0.121811</td>\n",
       "      <td>0.247786</td>\n",
       "      <td>0.366938</td>\n",
       "      <td>0.021281</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.006801</td>\n",
       "      <td>0.005061</td>\n",
       "      <td>-0.005591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.203031</td>\n",
       "      <td>-0.019992</td>\n",
       "      <td>0.089057</td>\n",
       "      <td>0.127604</td>\n",
       "      <td>0.246588</td>\n",
       "      <td>0.353713</td>\n",
       "      <td>0.018819</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>0.013867</td>\n",
       "      <td>0.012965</td>\n",
       "      <td>0.012953</td>\n",
       "      <td>0.002119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.100343</td>\n",
       "      <td>0.069938</td>\n",
       "      <td>0.011099</td>\n",
       "      <td>0.049432</td>\n",
       "      <td>0.290040</td>\n",
       "      <td>0.479148</td>\n",
       "      <td>-0.003888</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>-0.003456</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>-0.007869</td>\n",
       "      <td>-0.025440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.133765</td>\n",
       "      <td>0.065404</td>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.040722</td>\n",
       "      <td>0.288680</td>\n",
       "      <td>0.448389</td>\n",
       "      <td>-0.006805</td>\n",
       "      <td>-0.015511</td>\n",
       "      <td>0.002557</td>\n",
       "      <td>0.005525</td>\n",
       "      <td>-0.094448</td>\n",
       "      <td>-0.051779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.128558</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.024690</td>\n",
       "      <td>0.037375</td>\n",
       "      <td>0.290800</td>\n",
       "      <td>0.448811</td>\n",
       "      <td>0.005594</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.002869</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>0.049225</td>\n",
       "      <td>0.020778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.121615</td>\n",
       "      <td>0.081475</td>\n",
       "      <td>0.019124</td>\n",
       "      <td>0.037911</td>\n",
       "      <td>0.290353</td>\n",
       "      <td>0.449522</td>\n",
       "      <td>-0.001921</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>-0.000984</td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>0.022359</td>\n",
       "      <td>-0.004685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.126990</td>\n",
       "      <td>0.088118</td>\n",
       "      <td>0.012635</td>\n",
       "      <td>0.040514</td>\n",
       "      <td>0.291939</td>\n",
       "      <td>0.439803</td>\n",
       "      <td>0.024015</td>\n",
       "      <td>0.012826</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.003696</td>\n",
       "      <td>0.019416</td>\n",
       "      <td>-0.004420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         661       858    600196    600276    600519    600900    661ret  \\\n",
       "0   0.193971 -0.028580  0.099456  0.117262  0.240696  0.377195  0.001103   \n",
       "1   0.199862 -0.011642  0.085507  0.110914  0.242019  0.373340  0.023827   \n",
       "2   0.198058 -0.008130  0.077671  0.113043  0.249280  0.370079 -0.011658   \n",
       "3   0.199953 -0.011557  0.075070  0.121811  0.247786  0.366938  0.021281   \n",
       "4   0.203031 -0.019992  0.089057  0.127604  0.246588  0.353713  0.018819   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "62  0.100343  0.069938  0.011099  0.049432  0.290040  0.479148 -0.003888   \n",
       "63  0.133765  0.065404  0.023039  0.040722  0.288680  0.448389 -0.006805   \n",
       "64  0.128558  0.069767  0.024690  0.037375  0.290800  0.448811  0.005594   \n",
       "65  0.121615  0.081475  0.019124  0.037911  0.290353  0.449522 -0.001921   \n",
       "66  0.126990  0.088118  0.012635  0.040514  0.291939  0.439803  0.024015   \n",
       "\n",
       "      858ret  600196ret  600276ret  600519ret  600900ret  \n",
       "0  -0.001457   0.006956  -0.008712   0.016566   0.024191  \n",
       "1  -0.001753  -0.001883   0.001768   0.015302   0.014099  \n",
       "2  -0.000015  -0.004754   0.001355   0.004902  -0.004113  \n",
       "3   0.000056   0.000283   0.006801   0.005061  -0.005591  \n",
       "4  -0.000635   0.013867   0.012965   0.012953   0.002119  \n",
       "..       ...        ...        ...        ...        ...  \n",
       "62  0.000848  -0.003456   0.000624  -0.007869  -0.025440  \n",
       "63 -0.015511   0.002557   0.005525  -0.094448  -0.051779  \n",
       "64  0.011250   0.002869  -0.000346   0.049225   0.020778  \n",
       "65  0.011523  -0.000984  -0.001258   0.022359  -0.004685  \n",
       "66  0.012826  -0.000445   0.003696   0.019416  -0.004420  \n",
       "\n",
       "[67 rows x 12 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count=0\n",
    "for i in df_factor.columns.values:\n",
    "    df_factor[str(i)+'ret']=df_factor[i]*rlist[count][98:]\n",
    "    count+=1\n",
    "df_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>0.004902</td>\n",
       "      <td>-0.004113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021281</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.006801</td>\n",
       "      <td>0.005061</td>\n",
       "      <td>-0.005591</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.018819</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>0.013867</td>\n",
       "      <td>0.012965</td>\n",
       "      <td>0.012953</td>\n",
       "      <td>0.002119</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.003888</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>-0.003456</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>-0.007869</td>\n",
       "      <td>-0.025440</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.006805</td>\n",
       "      <td>-0.015511</td>\n",
       "      <td>0.002557</td>\n",
       "      <td>0.005525</td>\n",
       "      <td>-0.094448</td>\n",
       "      <td>-0.051779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.005594</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.002869</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>0.049225</td>\n",
       "      <td>0.020778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.001921</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>-0.000984</td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>0.022359</td>\n",
       "      <td>-0.004685</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.024015</td>\n",
       "      <td>0.012826</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.003696</td>\n",
       "      <td>0.019416</td>\n",
       "      <td>-0.004420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret\n",
       "0   0.001103 -0.001457   0.006956  -0.008712   0.016566   0.024191\n",
       "1   0.023827 -0.001753  -0.001883   0.001768   0.015302   0.014099\n",
       "2  -0.011658 -0.000015  -0.004754   0.001355   0.004902  -0.004113\n",
       "3   0.021281  0.000056   0.000283   0.006801   0.005061  -0.005591\n",
       "4   0.018819 -0.000635   0.013867   0.012965   0.012953   0.002119\n",
       "..       ...       ...        ...        ...        ...        ...\n",
       "62 -0.003888  0.000848  -0.003456   0.000624  -0.007869  -0.025440\n",
       "63 -0.006805 -0.015511   0.002557   0.005525  -0.094448  -0.051779\n",
       "64  0.005594  0.011250   0.002869  -0.000346   0.049225   0.020778\n",
       "65 -0.001921  0.011523  -0.000984  -0.001258   0.022359  -0.004685\n",
       "66  0.024015  0.012826  -0.000445   0.003696   0.019416  -0.004420\n",
       "\n",
       "[67 rows x 6 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_factornew=df_factor.iloc[:,6:12]\n",
    "df_factornew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.014283</td>\n",
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       "      <td>0.027891</td>\n",
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       "      <th>4</th>\n",
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       "      <th>62</th>\n",
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       "      <td>0.000624</td>\n",
       "      <td>-0.007869</td>\n",
       "      <td>-0.025440</td>\n",
       "      <td>-0.039182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.006805</td>\n",
       "      <td>-0.015511</td>\n",
       "      <td>0.002557</td>\n",
       "      <td>0.005525</td>\n",
       "      <td>-0.094448</td>\n",
       "      <td>-0.051779</td>\n",
       "      <td>-0.160461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.005594</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.002869</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>0.049225</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>0.089370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.001921</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>-0.000984</td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>0.022359</td>\n",
       "      <td>-0.004685</td>\n",
       "      <td>0.025035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.024015</td>\n",
       "      <td>0.012826</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.003696</td>\n",
       "      <td>0.019416</td>\n",
       "      <td>-0.004420</td>\n",
       "      <td>0.055086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret       ret\n",
       "0   0.001103 -0.001457   0.006956  -0.008712   0.016566   0.024191  0.038647\n",
       "1   0.023827 -0.001753  -0.001883   0.001768   0.015302   0.014099  0.051360\n",
       "2  -0.011658 -0.000015  -0.004754   0.001355   0.004902  -0.004113 -0.014283\n",
       "3   0.021281  0.000056   0.000283   0.006801   0.005061  -0.005591  0.027891\n",
       "4   0.018819 -0.000635   0.013867   0.012965   0.012953   0.002119  0.060088\n",
       "..       ...       ...        ...        ...        ...        ...       ...\n",
       "62 -0.003888  0.000848  -0.003456   0.000624  -0.007869  -0.025440 -0.039182\n",
       "63 -0.006805 -0.015511   0.002557   0.005525  -0.094448  -0.051779 -0.160461\n",
       "64  0.005594  0.011250   0.002869  -0.000346   0.049225   0.020778  0.089370\n",
       "65 -0.001921  0.011523  -0.000984  -0.001258   0.022359  -0.004685  0.025035\n",
       "66  0.024015  0.012826  -0.000445   0.003696   0.019416  -0.004420  0.055086\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_factornew['ret']=df_factornew.sum(axis=1)\n",
    "df_factornew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YKP6rra3V6i0QhGZEKzjMgwNQmoogCCITTMk3SR+v14tdu3Zh9erV4mMGgwFLlizBtm3bMtr34cOH0dTUBJvNhkWLFmHNmjWYOHGi7LYejwcej0f8eXh4GADg8/ng8/kyOo5o2P7U3m+hQusRSfR6DLm8AACHxQCfzwcjeHAcwPPAsMsNiyF+kK8X6ByJhNYjElqPWIp5TVJ5z5oGOL29vQgEAmhoaIh4vKGhAQcPHkx7vwsXLsTvf/97nHPOOejo6MBDDz2Eyy67DJ9++inKyspitl+zZg0eeuihmMc3btwIh8OR9nEkYtOmTZrst1Ch9YiErcexMwYABhw/tA/r+z8FAJg5I7w8h/UbN6PWlsODzDJ0jkRC6xEJrUcsxbgmLpdL8baaBjhacc0114j/P2vWLCxcuBCTJk3Cn/70J9xxxx0x269evRqrVq0Sfx4eHkZzczOuvvpqlJeXq3psPp8PmzZtwtKlS2E2m1XddyFC6xFJ9Hr816kPgKFhXLpgHhbPqAcAPPzxFvQ5vVh48WU4pzE2YNcbdI5EQusRCa1HLMW8JiwDowRNA5za2loYjUZ0dXVFPN7V1ZXQQJwqlZWVmDZtGo4cOSL7e6vVKuvnMZvNmp0cWu67EKH1iIStx6hH8NlUldrE9XFYjehzAl6eK6o1o3MkElqPSGg9YinGNUnl/WpqMrZYLJg3bx42b94sPhYMBrF582YsWrRItdcZHR3F0aNHMW7cONX2SRDZgDX6Y6MaAMBhpoGbBEEQmaJ5imrVqlW47bbbMH/+fCxYsACPP/44nE4nVqxYAQC49dZbMX78eKxZswaAYEzev3+/+P9tbW3Yu3cvSktLMXXqVADAP/3TP+ELX/gCJk2ahPb2djzwwAMwGo24+eabtX47BKEqw1HDNgEa10AQBKEGmgc4N954I3p6enD//fejs7MTc+bMwYYNG0Tj8alTp2AwhIWk9vZ2zJ07V/z50UcfxaOPPoorrrgCW7ZsAQCcOXMGN998M/r6+lBXV4dLL70UH3zwAerq6rR+OwShGm5fAF5/EEB4VAMQ7oVDzf4IgiDSJysm45UrV2LlypWyv2NBC6OlpQU8n7jB2XPPPafWoRFEzmBjGjgOKJX0v6GJ4gRBEJlDs6gIIkcw/02p1QSDgRMft9PATYIgiIyhAIcgcgRTcMptkVUBDjMN3CQIgsgUCnAIIkewMQ1sDhXDTh4cgiCIjKEAhyByRFwFh6qoCIIgMoYCHILIEcyDIy0RB8hkTBAEoQYU4BBEjhiRafIHkMmYIAhCDSjAIYgcMTzGUlTyCg4FOARBEOlDAQ5B5Ai5MQ2AJEXlI5MxQRBEulCAQxA5YkRmTAMA2M2k4BAEQWQKBTgEkSNYmXhsFRUN2yQIgsgUCnAIIkcwBSfWZEwKDkEQRKZQgEMQOSJZmTgFOARBEOlDAQ5B5Ih4Ck64Dw6ZjAmCINKFAhyCyBFhD06cUQ2+AHiez/pxEQRB6AEKcAgiBwSDPEa98RQcIeDhecDjD2b92AiCIPQABTgEkQNGPX4wcSZm2GaoTBwgHw5BEES6UIBDEDlgxCOoNxaTATZJQAMARgMHq0n4aNJEcYIgiPSgAIcgckB4TINZ9vc0cJMgCCIzKMAhiBwglohHpacYDhq4SRAEkREU4BBEDhhlJeJ2eQWHmv0RBEFkBgU4BJEDht3yk8QZNHCTIAgiMyjAIYgcwEzG8Tw4NHCTIAgiMyjAIYgcIDb5sydWcCjAIQiCSA8KcHTIqMePv1u3Db/ZcjTXh0LEYdQj3+SPQRPFCYIgMoMCHB2y/Vgfdpzox7M7Tub6UIg4JPPgkMmYIJLzzT/uwXX//h78Aer4TcQif3UlCprT/S4AwJDLl+MjIeIRb9AmgwZuEkRieJ7HXz5uR5AHzgyMoaW2JNeHROQZpODokNMDYwAElSAQpGGN+YjYByeOB4cUHIJIjDcQBLu8sZQvQUihAEeHMAUHAEbcpOLkI2IfHGscBcccavTnowCHSE7nkBv/9MJH+LRtKNeHkjXc3nBaigIcQg4KcHQIU3AAYGiMApx8RPTgxGn0R6MaiFR4aU8bXtx1Bv/17vFcH0rWGJME/+yGgSCkUICjM3iexxmJgkMBTn4S9uAkS1HRhZtITs+IBwDQO+rJ8ZFkj4gAhxQcQgYKcHTG0JhPbCLHfibyC56XenASKzjkwSGU0O/0hP7rzfGRZA+pujlCAQ4hAwU4OuN0/1jEzxTg5B++IOALCO7IpKMaKMAhFNAXCmyKKsChFBWRBApwdMbpAVfEzxTg5B/u0HWZ44ASS7wUFU0TJ5TDAps+pxc8XxyVk+6IFBVd54hYKMDRGdIKKoACnHxkLHRdLrOaYDBwstuEh21SgEMkhwU4Xn+waIJiqbpJCg4hBwU4OiNGwaFmf3nHWOhaHK/JHyAdtkkXbiIxPM+LKSqgeNJU0uCfPDiEHBTg6AzmwZlQZQdACk4+MhYQVJt4BmOATMaEcpzeALz+cE+YYgxwSMEh5MhKgLN27Vq0tLTAZrNh4cKF2LFjR9xt9+3bh+uvvx4tLS3gOA6PP/54xvssJpiCM7OpAgAFOPmIW1Rw4k9KoWGbhFL6RyMDmmIJcNxUJk4kQfMA5/nnn8eqVavwwAMPYPfu3Zg9ezaWLVuG7u5u2e1dLhemTJmCRx55BI2Njarss1gIBnmcCTX5O38CBTj5CvPglCdKUYUUHH+Qj7g7J4ho+pyeqJ+LI8CRBv9OCnAIGTQPcB577DHceeedWLFiBc4991ysW7cODocDv/vd72S3v/DCC/GLX/wCN910E6xWqyr7LBZ6Rj3w+oMwGjic01AGgAKcfIR5cOKViAPhFBVAKg6RmAFXZEAzUCwBDnlwiCRoOk3c6/Vi165dWL16tfiYwWDAkiVLsG3btqzt0+PxwOMJ3+UMDw8DAHw+H3w+dQMAtj+196uE493C+xpXbkW5TYhdh1zenBwLI5frkY/4fD7Rg1NiNSZcF7ORgy/AY8jlhiO+2FPw0DkSSarr0T0U2fuqZ2RMV2sZbz2ckjl7o26/rt5zMor5M5PKe9Y0wOnt7UUgEEBDQ0PE4w0NDTh48GDW9rlmzRo89NBDMY9v3LgRDocjreNIxqZNmzTZbyI+7OEAGGEPurB3x/sATOgbGcP69euzfizR5GI98hW3Xwg+u06fwPr1x+JuZ4IRPnDY8MabaLBn6+hyB50jkShdj/fbhM8946ODx7Def0Sjo8od0etx8LgBLAkx6HTnxXUu2xTjZ8blciXfKISmAU6+sHr1aqxatUr8eXh4GM3Nzbj66qtRXl6u6mv5fD5s2rQJS5cuhdmc3dvuY28dBY4cxeypzfji0qn46d6tGAtwWLb8Ghjj9FvRmlyuRz7i8/nw9L9vBgBccP50tF7SEnfbn+7birFhDxYsuhTnNal7nuYTdI5Ekup6fPLXz4BTJ1BiMcLpDaC0pgGtrXOzcKTZId56vPPSPqCzDQDgDeb2OpdtivkzwzIwStA0wKmtrYXRaERXV1fE411dXXENxFrs02q1yvp5zGazZieHlvuOR/uQkIabVFOCmvKwMjXmB6pKcvshyMV65CvMg1NVYk24JkKXYw+8Qa4o1o7OkUiUrsdg6IQ6q74UH58ZwoDLp8t1jF4PTyCyY7MnyKHCqr/3nYhi/Myk8n41NRlbLBbMmzcPmzdvFh8LBoPYvHkzFi1alDf71AusRLy52gGz0YCSkFGVjMb5hTvkwUnU6A+gieKEMlhZ+NT6UgDAQJE094w231OpOBGN5imqVatW4bbbbsP8+fOxYMECPP7443A6nVixYgUA4NZbb8X48eOxZs0aAIKJeP/+/eL/t7W1Ye/evSgtLcXUqVMV7bNYYU3+mqsFw0aF3QynN0ABTp4RrqJKHODQwE1CCaws/Ox6oXKyb9STaHPd4I4aY0LN/ohoNA9wbrzxRvT09OD+++9HZ2cn5syZgw0bNogm4VOnTsFgCAtJ7e3tmDs3nD9+9NFH8eijj+KKK67Ali1bFO2zGPEFgugIVVM0VwnpqXK7Ge1Dbgpw8gxxFlWCMnGABm4SyugP9cFhCs6w2w9fIAizUd+N6qPntNHATSKarJiMV65ciZUrV8r+jgUtjJaWFkXTcBPtsxjpGHQjyANWkwF1ZYLfqCI0CoACnPxCbPSXYFQDADjYPCoauEkkgHUynlxbAo4DeF7ojVNfZsvxkWlLtLI5QgoOEYW+Q/wigvlvJlTZwXGCx4MCnPwjEOThET04ie8vwikqunDnM0puyLTC7QvAGfqiryu1osphAVAc4xpYiopVTpEHh4iGAhydcLqfBTjh6ikKcPIP6UU4eYqKBm7mO/+76wzmPLwJ24/15eT1WRdjk4FDud2E6pLiCXBYiqq2VHjPNK5BG073uzDoKszziQIcnRCuoAp3hKMAJ/9gMrrVZIDVZEy4LZmM8583D3VjaMyHtw715OT1+0LpqaoSCziOQ3URKTgswGEpeUpRqc+gy4vFj23FTU9+kOtDSQsKcHSCWEElp+AUSdloITAcai+faA4Vg0zG+Q+b+9Q+OJZkS21ggUxNSLkpKgVHkpoDKEWlBZ3Dbnj9QRzsHIHHX3jXIQpwdIK0Bw6j0kEKTr7B7jKTpacAwG6mFFW+058nAQ4LbKqKJMAJBnl4/EEAYQWHysTVx+MLiv/fNVR47QcowNEJcgpOOaWo8o5wgJO8G6eYovLRhTtfGQypo7kKcPqiApyaIglwpCXiYoBDCo7qeAPhAKd9KDfneCZQgKMDxrwB9Iaae5EHJ79hAY6yFBUpOPkMz/PoD5kvO4fd8Eu+DLIF64FTbCkqaYBTUxLy4FCAozpSBaeDAhwiF5wJpafKrCYxqAEowMlHmAdHSYrKQQFOXuPyBuANpUmCPNA1kn0JP5yisob+WyQBTugzYTMbRKWaUlTq4w2Erz3tg+4cHkl6UICjA8QeONUOsQcOQAFOPjKcToqKApy8JDqIyEWailVRVZcWl4LDeuDYzUaUWoXPCaWo1IcUHCLnhP039ojHWYAz6vHnRD4nYhlNyWTMqqiye+H+5MwQ1r51BD46ZxIyENUbpG0g+18AooLjKK4AZywiwCEFRyukHpwOUnCIXMCa/EkrqIDIUQDD9OHPC4ZT8ODkSsH58Wv78Yu/HsLWHPV2KRSig4i2HCg4zANUHeXBGXB5c9phWWvEFJXFiNLQZ4kUHPWRKjjtQxTgEDlALBGPUnDMRgNKrcKHn9JU+cFIOh6cLM+iOhUKmNl/CXmiFZxcpKjEPjhRKSpfgNe16TZSwRE+S+yzRaiHJ0ApKiLHiCmqKAUHIB9OvpFKmXguqqj8gSC6R4Q7ta7hwrtjyyb9zsjPVLYDHH8gKJaps8DGZjaKgTEbwqlHpB6cMomCoxfVatDlxT/813b8efeZnB6HR3JzNejyFZwfkAIcHSDX5I9BvXDyi/7QF1KlXYmCI2zj9QcRCGbnwt076gV7qY4ClKSzCZvPM7m2BED2q0wGQucSx0EcsgmEg50+HftwRAXHElZwgjzg9unDN7ZxfxfeOdyLP7x/IqfH4Y3y4RVaLxwKcAqcIZdPVAUmRKWoAKDCTimqfKIv1K+oNtRePhHsThzIntFYKkN3UoCTEJYeOq+pHED2FRz2+pV2szhRGwj3xBnQc4DjFb54mWLFikdHPPq4zh3pHgWQ++u2JypgLDSjMQU4BQ5Tb2pLLeIdv5TwPCr9XuwKhWCQFxUc5plIhNVkEC/c2ZKGpUFNx3Bh3a1lG+bBOa+pAoDQaG44iz6QvlCTP6bYMIphXIPUg8NxnKji6KWS6nDXCIDcF4fEKDg56tidLhTgFDisyd+Eqtj0FEAenHxiwOUVU001JckDHI7j4MjyPCppWqpryKMbT4MWsABifJUdVaG5b9n8AggP2oxUA4shRSX14ABCk1NAP5VUh0MKzvCYL6efwWgFh1JURFZJZDAGKMDJJ3pDpk+HiYfZqOyjl+2J4p0SY7E3ENS1CpApAyGTcbXDgqZKIT2ciwAnWsFhPXGiq7z0BFM0mRFfLBXXgYLj8vpxJtRTyR/kI8ZSZBvWydhsFKRkSlERWSVeiTijMnSxowAn9zD/TVnyAiqRbA/cjDYWk9E4PqwHTVWJWQxwstnsj3UxrooOcELpzz4dV1GxL31bSMERS8V1oOAc7XZG/JzLazdTcNgNNCk4RFaJ1+SPQVVU+UOPGOAol5yzPY+qM+oCRkZjeXieF0281SUWjGcBThbvcMMpqsgAp6akCBScqBRViY48OIe7RyJ+Hh7L3XtiHpyWGqFSsNBueCjAKXBOD7AxDZSiyndYiioVBSfbvXBYioqlPTqoF44sIx4//CE/VZXDgqZKG4Asp6hc8ikqVjKuaw+OmKISvsLKdNTNmPlvGNk0rkfDFBwxwBkcKyhfHgU4BQzP86LJuLlaPkUVDnAK54N/sHMYl//8Lfzvrtw2uVKb3kxSVFkIcIJBHl1DwjHOba4EAHQV2B1bthgM+W/sZiNsZmNuPDijkV2MGeznfmf2p5tni2gFp1RHJuPDXVEBTg5vTkUFp1a4gXZ6Azmv7EoFCnAKmJ5RD9y+IAwcxAtsNIVYJv7WwR6c6nfhL5905PpQVKV3JPUUVXjgpvYBTr/LC28gCI4DZk2oBFB4knS2iFZP8spkHKqqGnDqV7WN9eAI17mRAvryjceRUIrKahK+nnOq4PiFda6wm1EZqhQspJENFOAUMKyCalyFPW5VTiGmqNiIAL1J7JkoONlo9Mf8NrWlVkysEb6wO6kXjizMf1NVIvwxJ1Sy9XLDn6Up7H1JqqhGPX7xC0pvxK2iKvBGf25fQJwBNzt0kzHkyqGC4xfOZavJgHEVwjleSJVUFOAUMOEeOPLqDRAOcJzeAHxZuvBmCpuFpLdOrKIHJ3kLHJFspqiYWjOuwoaGclvEY0QkTD1hfpfaUivMRg5BHuga0T41FAzyook4ug9Oud0kdjbWq4oTrw+O01PYAd2xHieCvHDdnlIn+F5ymRLyhAIci8mApoqQz4wUHCIbJKugAoByydTqXOZyU6FrWPiC0FsPlt40qqjsWZwoziqoGstt4t1a55C7oEyF2WIgKkVlMHDimmUjTTXs9olNI5mKxOA4TmI01qcPJ8aDY2MTxQs7RcUqqM6uLxVvTnPqwREVHCPGhYz0pOAQWUFs8henggoATEaDeHdTKGkqpuDoSWLneV7sS5KvJmNWQTWuwobGkILj8gZ00VtEbaIVHABZraRi6akyqwlWkzHm9zU6H9cgenAs0Sbj5Ne4YbcPX/39h/i/vW3aHWCasBlUZzeUiS0+cuvBCSs4YgBPCg6RDU4nqaBiFFIvHJ7nRQUHAAZzmH9Wk+Exv1iRkFqAw0zG2gcZLB3VUGGD3WIUTYXUCyeWaAUHCBuN27IQ4IgBVpyRH9V6D3BCwzajFRwlVVTvfNaLNw9243fvndDs+NKFVVCdXV8aDnBy2QdH4sFpIgWHyCbhACe+ggMUltF4aMwnfqgA/XRjZU3+Sq0mmFP41NmzOIuqU+LBASCqOOTDiYV5W9gMKgDhZn9Z6GbMPhfRBmOG3gOcuLOoFKSoekIKsSsPlUkxRdVQKtoLctrJOKSgSxUcqqIiNMcfCKI9FEknSlEBhRXgSNUbQD/dWJn/plbBFHEpWU1RhQKZxnLhQtYYCnQKrRdOMMije8SNfqcXQ2M+uLx+eP1BBIPqeYnCYxpiFZxspKjidTFmsABHb0Z9QFB5RQ9OTBVV8qCFmf1zOeNJDo8/gBN9wk3r2fX5laKymoxoElNUhePLMyXfhMhHjvSMIhDk4bAYUV9mTbhtIQU4zH/D0EupeLoBTrY6GfM8H1FFJf1voSk4//C77XjvSJ/s7wwccM3McVh7ywUZvYY4psEhF+Bov15yKTIpep4o7gvwosE6ZhaVAgWHfRazcdOQCid6XQgEeZRZTWgot6JtMH8CHIvJIF67vP4g+pxe1JYm/t7JB0jBKVB2nRwAAMydWAlDqCQ0HuFmf/kf4MQoODq5QLMmf/HuuOMhenA0vtscHvOLd7RMuWGl4oXUC8cfCOL9o/LBDQAEeeAvn3RknLoZkFFwxmdRwRFTVHECZj2nqKTKS3QnY48/GJHilkMMcPJMwWHpqakNpeA4DhV24T3lyoPD83yEB8dqMqIudDNdKD4cUnAKFBbgzJtYlXRbZhYtBAWna1ivCo7wPlK96wmnqNK7yLUPjqGx3JY0CGYVVFUOs3hXXIgKTu+oFzwPGA0cDv14OYI84A8Gxbv+a9e+h1P9LuxrH8JlZ9el9RpCDxrhsxRpMhbWa8Tjx7Dbh3JbCm7yFGFjGJKlqPQY4DD/jdHAwWwUzms2bBMAnB4/LKb4NxI9khQVz/PguMSfjWwhNRgDiEhRBYN80s+w2nglfdMsoa7KTRU29Ix40D40hvMnVGT1eNKBFJwCZXcowLlgUvIAp5CqqLpDX7TsmqMbBSd01xg9NygZmaSo3tjfhYsfeRO/3HQo6bbMONhYEa7Ia5T0wikUWIqzttQCk9EAi8kAh8WECrsZ1SUWnD9euCjvax9O+zVG3H4xRVIpMRk7LCbRdKy1ihPuYiwfMOs5wBG7GJuNYnBiNhpgC7n3k/lwmJrK8+EUTD4glojXlwGAGCDzPDCahSrKaKRKGBsbEe5mnPj87hgawwfH+sRgNFdQgFOA9I56RDPaXAUKTiF5cFiKanJoeq1eLtC5MBnvOiUEwfH8KFLCBuPwFyZTcDoLaKJ4d+j8qS+zyf7+vPHlAIBP24bSfg1mMC6V6UGTLaOxUpOxXj4/UqLnUDGUzKPieT6i+WE2qhOVIk1RAcL7Y8pJLpr9SQMcS2gUkNjsL8lNz18+7sBNT36Alc/u1u4AFUABTgHC1JtpDeFul4kopACH3YFPHyfcxejlAs1k8do4d9zxcGQwbPNMqFz5cNdI0goidsGSKjjMgzPo8uWdITMe3SMswJFf55lNmSs4/VFzqKSEe+FoGxQm64PDAp8Bl1fV6rF8IFxBFfn1VRaqpHImUDuc3gDcvvAXdzb6SynBFwjieK8TQDhFBYRVnFz4cKQGY6aUSSupErHjeD8AYH5LtYZHmJysBDhr165FS0sLbDYbFi5ciB07diTc/oUXXsD06dNhs9lw/vnnY/369RG/v/3228FxXMS/5cuXa/kW8gp2Zz5PQXoKKKwAhyk40xuFO229BDhMFk+3imrMF0j5i6ot1CfJ6Q0kbT4X3QMHEMZ8MAWpUFQcFiDXl8sHOOc1CefV8V4nRtKsThlkBmNH7N8yG0ZjQYVIrOBUho4tyBfG5z4V3N7IHjiMUgW9cHqj5oTlOoXCONnngi8gVMU2SW4yRKNxDiqpRIOxZJBzeFxD/PM7GOTx4QkhwFkwWecBzvPPP49Vq1bhgQcewO7duzF79mwsW7YM3d3dstu///77uPnmm3HHHXdgz549uPbaa3Httdfi008/jdhu+fLl6OjoEP/98Y9/1PqtZMThrhF894WPxPlRmSD6bxSkp4DCCXB4nhe/oGaMCwU4OuiDw/N82h4cFmAAgDvFsRVnJA3nDnWOJNy2Y5gpOOEAh+M48edC8eEwBacuToqqptQqBnEHOhKvSTzkxjQwmNFYy2Z/Tm9A/PKJVyZuMRlERUMPnyEp0XOoGGKpeAIPDvscMvIlRXWEpafqSyPMxLn0T4o9cCSdScPN/uJfD470jGLA5YPdbBQ9b7lC8wDnsccew5133okVK1bg3HPPxbp16+BwOPC73/1OdvsnnngCy5cvx3e/+13MmDEDP/7xj3HBBRfg3/7t3yK2s1qtaGxsFP9VVSn7ss8F/kAQ3/zjHryw6wz+Z/vJjPbl8Qfw0RnBP6C2gjPi9uHPu8/kbP7TgMsHX0BQKc5pEFJUA05vwTSViocwU0u4WKSs4Egu4qlcjN2+gPhlDwCHuhJ/mXfJKDjSnwulVDzswYmfCjwvlKZK14eTqAdNNjw4/aF0p9VkiAiAo9HrPKq4HhybAgUnKsDJl9Qrq6CaKklPAdIUVe4UHItEwWEBfOewWzTaR7P9mOD5mzepCmZjbl0wmpaJe71e7Nq1C6tXrxYfMxgMWLJkCbZt2yb7nG3btmHVqlURjy1btgwvv/xyxGNbtmxBfX09qqqqcNVVV+EnP/kJampqZPfp8Xjg8YRP7OFhIf/u8/ng86l74rD9Sff77I7TOBi6gz7T58roNT86PQivP4gqhxkTKiyK9lViFu4IXN4AXG5P3JPuiTc+w3++ewJHu0dwz+KpaR+jFLn1iEd7v/Ahr3KYUW0XjtEf5NE/MibeyRQinYNCbt1hMcLMCReFVM4Bm9kAty+IYZcbFVZlF4xToXw+40D7UMLXZFVUtQ5TxHb1oYCsrT+z8zYRqZwjyegOBWI1Ue9DyrmNpXjjQBc+aRtM6zV7Qypjhc0Y8/yGUuE8bRscS/v9JFuPriHhb1tdYoHfH//LvNJhBvqA7iEXfL6ytI4lH4hej9ExIWCzmQwRa1QSUhqGXJ64a9cZNWZgZCz+ttnkUKfwnTSlxhFxPKVWIYgbcEYep5qfmXg43cJ3ptkYXucqmxFGA4dAkEf7wKg4zkXKB6E+VPMmVmhyfKnsU9MAp7e3F4FAAA0NDRGPNzQ04ODBg7LP6ezslN2+s7NT/Hn58uX48pe/jMmTJ+Po0aP4wQ9+gGuuuQbbtm2D0Rh7R7NmzRo89NBDMY9v3LgRDkfiMQfpsmnTJgCAyw/8fI8RgBBk7D/ZgfXrz6S937faOQBGjLd68Prrryt6jhBoC3/qP7+2Ie6wxy37hON8acdRTPN8lvYxysHWIxEHBoX3ZocXmzf9FVaDEZ4gh5fWb0Jd4nmiec3RYQAwwc75xXVQsh4MIy/8XTZu3oJxCk/Xg6G1ZOw6Gv+88wSAYbdwfny8/W18JvkIjfYYABiw/eNDaB49oPiY0yGVNYnHqR5hrQ5/vBPe4/LbuPqFtfngUBvWrz+V8mt8fFRYk54zx7F+/bGI3w15AcCEzqExvPqX9TBm0Lok3nrsGxCO3+gfi/EnSvGPCsf59vbd8J8obBUUCK/Hzk7h/Q/2dUe8/75O4f3u/fQg1g/vl93HB6eFbRjvbd8J55Hcr82eo8J5O3TqINavD3/OBruF4939yQE0DO6LeZ4an5l4fDYkrLN3zBmxzmUmIwa9HP78+ptoiYqbeR5455DwXoKdh7B+ffIWFanicim3eRRko7+bbrpJ/P/zzz8fs2bNwllnnYUtW7Zg8eLFMduvXr06QhUaHh5Gc3Mzrr76apSXl6t6bD6fD5s2bcLSpUthNpvx09cPwek/iRKLUcidG+1obb087f2v/+NeAN1YPv8ctF4+WfHz7t/7Jkbcflx48RWYUlcS83ue5/HA3i0AfGh3cbjg0qtko/NUiV6PRLh2twEH9mHq+Fq0ts7DLw6+gzMDY5h14cWYO7Ey42PJFRv2dQH7PsLE+kosXXqB4vVg/PzA23AOujH/okswW2FzreEPzwAH9uPs+hIc7nai12PA0mVLZdW7Yz1OYMd7KLEa8eUvXB3xu4Edp7Gp7QBs1Y1obZ2j6LVTJZVzJBHBII/vbH8DAI9rl18Vk25jzB1y4z8PvY1utwFXLV0Sk+pIxqvP7AG6e7Bw7ky0Xtgccww/3vsGfAFg3iWfE1NWqZBsPcZ2twEH92HyOOFzEo+3PZ/i04F2jJ9yDlqvmJLyceQL0evR/u4J4PhnmNw8Hq2t54vbHdh0GO90HUfjxBa0tk6X3df2V/cDZ8KB/oyZs9E6t0nrt5CQQJDHdz/cDCCIG1uvwCTJ8OQDmw7jva7jaGiOfE9qfWYS4fisB9i/BzVVFWhtvUh8/A9tO7D71CAmn3cBrpnZGPGck30uDH/wLsxGDnf97bKUP1tKYBkYJWga4NTW1sJoNKKrqyvi8a6uLjQ2Nso+p7GxMaXtAWDKlCmora3FkSNHZAMcq9UKqzU2J282mzU7OcxmM04NevDfHwh3iPd/4Vx8/38/QdewBwajCcY0ulLyPI/dpwXfwIIptSkde4XdjBG3H04/L/u87mE3BiV53veODuCmBRNTPsZ4KFnrvtCE5oZyO8xmM2pKLDgzMIZhT1Czv1M2GAyVeNaV2cT3kcq5x8Y1eINQ/JzOkP/moim1aB90w+kN4MyQF9MaYlMVvS7h+MZV2GP2P6FKCIa7Rjya/w0y/Tz2jXrgD/kCGitLYDbJp/Oaa0yoKbGgz+nFsT43ZjdXpvQ64b9n7HoBwjqe6neh2+nHpLr030+89RhyC76RWsn5JEdtyGg95A4U9OeHwdaD2WYctsj1KbML6VSXN/71ot8ZmdJL5TOlFW29Tnj9QVhNBkyuK4/4bqgMtZUY8cj/DbX8DgvwwufHZjZGvEZTpR27Tw2ie9QX89q7TwvBx9zmKpQ5Mr9BliOV96upA8hisWDevHnYvHmz+FgwGMTmzZuxaNEi2ecsWrQoYntAkOHibQ8AZ86cQV9fH8aNG6fOgavET17bD3+Qx1XT63H9BRNg4ARPSV+U0U0pZwbG0DPigcnAYVaKbbKTzaOKNqFuOdST1jFmAisRZ/1X9NKsTOyBk2QoajzSafbHKqiaq+2Y1igENfEqqaKHbEppTGFcQ8+IJ6fzzpipurrEIjZIk4PjOJwbKhf/tD11ozGrSqqUqaICwkZMrYzG/c74JmcpejcZR1dRlSmYKM5MxqzrcT6YjA+HOhifVVcac+Ob2z44wtpEf5YSDZX94Ljgv8l1eThDc4vzqlWr8Nvf/hZ/+MMfcODAAXz961+H0+nEihUrAAC33nprhAn529/+NjZs2IBf/vKXOHjwIB588EHs3LkTK1euBACMjo7iu9/9Lj744AOcOHECmzdvxpe+9CVMnToVy5Yt0/rtKObtw71461APTAYO//z5GTAZDeIXd7ImSfHYHep/c974ipSlv2SVVOzLb0KVcPK+e6QXvkB225izEvGGUA8T1sSs0Mtcw12M0wtw0hnXwAKc8ZUOsSItXoDDjJdyKUkW4PSOehIOMRwa82HZ42/jb9e9n7Oqt2RN/qTMHM8qqVJv+DcoM4dKSrjZnzYBTp/CAIeVsetlnhvDnaRMXEmAM6FKSAPlQ5k462B8dkNpzO/K86EPTlSAw26E5AJ41uCvaAKcG2+8EY8++ijuv/9+zJkzB3v37sWGDRtEI/GpU6fQ0dEhbn/xxRfj2WefxZNPPonZs2fjxRdfxMsvv4yZM2cCAIxGIz7++GN88YtfxLRp03DHHXdg3rx5eOedd2TTULkgEAT+JWSuuu3iFpxVJ5y44Z4i6V34UhmwGU2yAOezkILz5bnjUVNiwajHj50nBtI6Tin//cEp7OhWlo5jCk49U3Ac2bkDPdI9okp/oniw5mJ1KZaIM1iKKjUFR3g/E6rsOIcpOHFKxVkTPzkFp9phgcVoAM+HA1A5dhzvR7/Ti8PdoxhOUKarJWyOWZ2SACdUKr4/RQUnEOTDjf5kOhkD2jf7U6zghM43vcxzY4izqCzyAU6iUQ1s6O3EkM8lHyaKH4kasimFXbdzUSYu7WQsJdwLJ/L8bhscw5mBMRgNnKIZidkgKybjlStXigpMNFu2bIl57IYbbsANN9wgu73dbsdf//pXNQ9Pdd7t4nCs14nqEgu+tfhs8fGmCjv2YFBW2lOCGOCkcfIkVXBCH7Lp48px+bQ6vLSnDVs+68ais+RL75XQPeLGw385CAMM+L43uQ+AfUGJKapS7QMcp8ePL/7beyixmrDjB4s1mSysnoKjLHDw+ANisDihyg5n6I72s3gBjsyYBobBwKGhworT/WPoGnaLd77RsM6lgHDhUzJCRG3CCk7y3D/raHygcwS+QFBxv47hMR9Y+w+5Rn+ARMHRqNlfqgqOXlNU8frgOOMoOG5fQFR3mkNK9VgejGpgKaqp9bH+uHzogxM7b00+E/FhSL2ZOb5CDDZzDc2iUpkBlxcbTgvLumrptIgLfWMGwwudHj8OdAhy+gWTKlN+foUjfoATDPI4HPrym9ZQhivPqQMAbM3Qh8PSJEFwON7nTLhtMMjHpBiyoeC0D47B5Q2gZ8QDr0Ypud5MPTihC7lL4d1mRyiAtpuNqC6xiB6cU/0u2SApkQcHCKeuEvlwmDSdbDst6WHnT5wxDVImVjtQZjXB6w+KU5yVwNKlZTZT3KBofAKPghr0h4ZFxhvTwKgJGVT1GuDEeHBCwzbjpajY+WExGkSVONcKTjDIh6eIy6aoQgFODlTReAoOC+Cj09bbQ9eAhXmSngIowFGdX715FK4Ah3MaSnFTVAlpotxlMj46PYggL1w8x8ncaScjkYJzZkD4kreYDGipceDys+tg4ICDnSMZyezS5x7tSRzg9Lu8YgUMSzFkw2TMgg8AcHu1CXD6MlRwUjUZs8ByQpUdHMehttSK2lILeD7cMVUKU3Aa4rQFYMpOvHENLq8/oitwh8aDJuMhzqFSEEgaDGGjcSqDNwcUqCdadzNmnYyTKThMAR3zBfLCTKsW7jjDNpN1MmbKV22pRfxM5dqD0zY4hjFfAGYjF1EeziiXGKf9WfZExvPg1IRM/DwPdElu1rczg3GOB2xKoQBHRT7rGsEfPxR6LPxz6zkwGeVzl+nM9WHpqXRzm4kCHObNmFpXCpPRgKoSi1g6u/Wz9FUc6RfdsSQBDmuxX1tqEe+MsxPghCvatLibG/MG4AxdRFMd08CwW1KbKM78N+OrwoHwtDhGY7cvIF744yk445JUUu09NSgGp0D6HrNMCY9pUFaems7IhkRzqBhMwh/x+FU3h7p94fOpJslk+hKLUWyz3+dMr3IzHxlLNmzT65cdTCsOvC2zis/N9bBNpt5MqS2N+b4AENHBPZF5WgviVVFxHBdzs94z4sGxHic4DriQAhx98uKuMwgEeZxfFcSiKbHelVRKbqNhE8TnZxrgyJTxMm8GM6MCwOfOqQcAbDkkPxRVCdIqkmO9iQOcLvHuO/zlxAIcLU2SWgc4bP9WkyHtvHSqd5tSBYcRz2jMggKrySC09peBpajipVZ3SPw3QO5SVN0ppKgAYOZ4puAoD3AGxEni8T1GDotJ/L3aKg57fZOBEyts4sFxnOQzlPtxBGoRz4PDysR5Xj6dKw68LbGkVZmoBayCaqpMegoQxiSwz3+2S8XjeXCA2Jse5sE7p6FMtEPkAxTgqMjqa6bjib+bhWtb5KVEdmfXlWBQmRzBIC9OEE/HYAwkUXA6w/4bBvPhvHu4N2F5cCKkLvvkCk5kiTgQDnBGPH7NBoD2SVJUWsj4PZL0VLoG5nCKStkFLlxBFZa8Wal4tNFYWkEV7/iSTRRnFzfWbToXAY50Er2SFBUQLhXf3z4se8cvR38oUKhKkh7SKk3FzteqEoui84l9hnSl4MTx4FhNBphCfWTk0lRSs79YmZhjBedwggoqBjMaZ3uieDwPDiAUzABAe+gazzx4F8nc2OcSCnBUhOM4tJ7fiNo4CnldqVVs9hc91TYRR3qE0lu72YjpjbFOeyUkCnDCCk74QzazqQK1pRY4vQHsPNkf8xwlSE2Wx/tcCYO6Lpn0QrnNLDa+GtSogZzmCo5EFk+XVO82mXImVXCY0fhgVIqKBaGNcdJT0t/JBTi+QBC7Tw4CAL44uylin9lkxOOH2ydckJWmqKbUlsBqMsDpDeBEEhM8g5WIVydIUQHSXjjqBnssRZbMYMwQFZwC7yUlxR2nTJzjuLAPxxN7vZCa/VlwlGtvEqugOlumgoqRq1448Tw4ADAudLPObAjb86z/DYMCnCwibfaXyl0u89/Maa6UzdMqIV6A4wsEcbRH+JBJFRyDgcPl0wQVJ92uxtK7V48/mLBsNrrJHzsGJvVr5cOJMBlrkqIS9p9uDxxAouAoPL5wiiqs4LC/bc+IJ2ItO8UKqvjGdSZHdw27Y5SOfe3DGPMFUGE3i+dLx5A7683+WKqtzGqK+eKLh8lowIxxrKOxMqOx6MFJEmBo1QtHiQdIiqjgjOonwImn4ACJe+FI1dR8SFHxfOIKKkauSsWZai4b4Eh64Qy5fDgYmoaeT/4bgAKcrCP6cFK48GXS/4ZRaQ9XVEhTTsd7nfAFeJRaTeJFmXFlBj4cqXm1wix82bFASo7oJn8MrXt5RCg4Glzswnn/DBQcs3KTsdcfFNNOUgWn1GoSf5amqTrEHjjxVY8I5TEq1cF6X1zYUiXK1i5vIOtlrSxArlPov2Gk6sNhSkiyCiatxjWIPXAUBsx6GXciJZ4HB0jczVhUU0stYQUnhymqMwNjGPX4YTRwaKmJHYDMCJeKZ1nBCcRXcMLntxsfnugHzwNT6koUNdnMJhTgZJkmMfJVruBk6r8BBAMeS9lLVZyw/6Y0Jqd/+dm1MHDAZ12jKbedZ+/PYTFiclnyACe6yR9D6wu01Jugpcm4tixzBUdJgNMxNAaeF2btRKcx5EY2iE3+EkyONxkN4oUrOk3FDMYXtlTDbjGKRuV0KgUzoSeFMQ1SWCXVPoUjG5QqKOMrBfVM7WZ/SnvgMPSWogoGeTEVKafUiQGOTIDNgsO6Umta893U5rWPhQ7+8yZWJZydFu5mnOUqKl98D45UwWHXgIWT88t/A1CAk3XClVTKLnz9Tq9YgcRMnOlgMHAoC334pQGOXAUVo9JhwdzQWIhUVRx259pYbkNDSEhQpOBEfUFpHeD0jkhMxloGOGn2wAFSMxmHZ1DZYwJWuUqqjuHkCo7w+9jAPBjksZMFOKHc+7go82G2SLVEnMFGNnzaPqQorTaQZA4VQysFR+mYBobeUlQeifosm6IKeXBG5BSc0bAfzi5J++ZidhrP8/jzbqGlyJcvGJ9wW9YLJ9sm47CCE7vO7EZ9wOUTG8LmU4M/BgU4WSZZT5FomHoztb407vRipYS7GYcvdnIVVFKuTNOHwy7sTZU21NuFC0i8jrGBIC/mx7Op4Dg9/oigRhMPzghrLpYdk3GbjP+GwQKczyQKTleSLsaMcaxUXHLeHusdxYDLB5vZIAYK45JUXGlFqhVUjGmNpTAZOAy6fIpUyrCCk7gUlqV7O4fdqjZoY4FKqgqOXlJU0s9rohRV9LgGXyAoFipIy8QDQV6zDuaJ2Nc+jMPdo7CYDLjm/HEJt81ViiqRglNuN4k3XuyGKd8MxgAFOFkn1WZ/rP9NOgM2o5EzGosKTpwA53PTBR/O+0d6UyrVZhVUTRU2NNhZikq+UqXf6UUgyIPjYpvhaXmBjr6r1dKDk5mCo3zYpnTIZjRis7+uEfA8D38gKAYGyRWc2F44O44L5+bc5rDEno7HTA1S7YHDsJqMODu0Lsk6GvsDQfGzk8xkXFtqhdnIIcgDXSPqlWiHFRxl71P8/OgkRcUCHIvJIFZYSimL082YfdaNBg5VDkuE+qNVB/NE/Hl3GwBg6YyGpHPbcmYyTuDBkTb7A4TrTVNl7DUn11CAk2XE8jqlAY4K/htGdIAz5g3gZGiK9rQ45efnjitHbalVKBdPYbq4mKKqsKE+dN73O72ygQpr911bao2pEtPyAt0TVaqvRYqKvUZdBh4c8a7U60+qMslVUDHOqhPUihG3Hx1DbvSMehDkhaZxtUm+MOVKxT+MSk8BQkALZL8XTropKgCYyUY2JOloPCj5gqlM8qVkMHDizYyaPpxUU1Q1elNw4nQxZsQzGbMbjeoSCwwGDmajAWajECC5fNn1tvgDQbzykRDgJEtPARIPTpaN+x5JMCmHNKDJR/UGoAAn60hLbpM1+wsGebGNfCb+G0Z0N+PD3SPgeeEiGE9hMBg4XCGmqZT7cJgHo6nCBqsx/MV3TMaHI1cizhADHA08BH0aBzhuX0AsV81EwakttaC+zIogL8wkS4TowZFRcCwmAybXCtUahzpHxCCkodwGg8zdsJRxMt4x1txLOntGnFuVxkDZTEg3RQWEG/4lKxVnPXAq7GZF7RrY+ZxKz6tksEC/RmEVFVOahsZ8WZ9lpAXuBCXiAFAaGrgZ7cHpkVFSbTnqhfPO4V70jnpRU2IRWyskQuyDk0ceHCAyrX1RHhqMAQpwsk59mQ1GA6eo2Z90CCb7YsqECju72Akf/mT+G8bnpqfuw5F6cAChhBCQNxrLNfljaFkF0hsVNLlVvtCxqg2zkUsqQyeC4zjMbxEUvJ0nE6toiVJUQKTRuFOh/waQjGsIPad9cAxtg2MwGriI4Lspg4GymZBuigpQXirOuhgrVU8SNddMB7/ER6L0GCrtZnCcML5gMMtfkFog9sCJ0+so3sBNaYk4I1cDN/+8R1BvvjC7Ke5Eeik562ScwIMDRPbOIgWHACDkgNldZjIZP3oIZqZEX3ATVVBJuWyqMF38cPeo+AWaCJ7nxffGvjynhAI0OaMxSy/IKTisHLdPEw9OZIDJyk/Vgl1Ua0rSH9PAmD9JuIDsPBG/q7QvIN8DR4o4skGq4CgJcCQeHJ7nxfTUzKZylEhmbEnnrSmpTvEHgnj3SB/cGXzHSJWyujRSVNMby8FxQqDNlCA5lBqMGeUqBzgDLuUpMobJaBA/93pIUzG1Rc5gDECsFI1OUUlLxBm5GNcw4vZh475OAMrSU0B+9sEBwjev9WVWTKqJTYnnAxTg5IBxCo2Yh0LdIZMFIEqJDnAOheagJNt/hcMslotvP5Z8bMPQmE+8K2J3/mEFJ9ZoLDdokyEduKl2OSdT0FLtFJzq/jPpgcNgHUJ3nhyIOzepc8iNIC9ckOripMSkIxvY1O9xCXrgMFh1m9snGG13HA/3v5EyLsVmfy/tacOKP+zCX06lfyliPXCsJoNYUpsKJVaTGIAnMhorbfLHUFvBYQFKpUNZioyhp0qqcBdj+fefVMEpy22K6vVPOuHxB3FWXQnOD6VGkxE2GeeXB+fKc+oxpa4Ed1w6OeMbOK2gACcHjFPY7I8FIMlSSEqJUXAUpqgAwWwMhGenJIKV29aUWMSLyFm18VNU8Zr8AeGLsz/Iq26y6w1d8JnaoVmAk4H/hjFjXBkcFiNG3H581j0iu83pkLo2viq2Bw6DzTI70hNu3pisggoQvgzY36JjyC1rMAaQcrO/j84MAgDanOlfIEX/TXn6Spl08GY8wgFGagGOWnPU2PtUGmAx9GQ0didLUVnl++BIJ4kzcpGi+vMe1vtmguJzlZ1H0V3otSaZgtNQbsOb37kS/3jFWVk7plShACcHKG32xwKQdAdsRhMOcLwYcvnEdMa0BHNQGGxWSrxeNlLYADapy/6skIJzut8VUwnUlSBFZTMbxQvRgMoXaHZX1xyqOFK7D4443E+FAMdkNIhel3jVbNImf/FornLAZjbA6w+KKkyiOVRSmBp3sHMYn4WCb7nZM43lys5vIHw+9Wfgw2UpzniqlRLOC1VSfZqgkmogxQqmSru65b3Pbj8FINx9WSlajzvJJsmqqErEFFXkmst9Ftk+tOh/JceZARc+CCng185Vlp4CwqoUkL00Fc/zCaeJFwqFe+QFjJJmf16/ZAimBikq5u8ZX2lHmS15Pn9qHQtw5NUDKWIFVWVYGagttaDMZkKQB072Rfp4WJl4vBJfrUrF2V2dqOCofCfXM6KeggMk9+EkKhFnGAycqNixi74SBUe63WsfCS3mp9aXyn7Zs8BWSan4kW4hZTnoFTxE6SAajNPw3zBmTagEAHx4In4KkJ1/SgddhhtrZv6l9PGZQbz+aSc4DvjmVVNTei77e5zqT+6fy3cSzaEC4vfBkXYxZmR74Ob/7W0HAFw0pTrhTUg0RkkX+mxVUvmDPJgjwGpUNrw2H6EAJwcoSVEd73XCH+RRZjWJlSmZIhfgKFFvAGBqaLtTMgpMNCz1IVUGOI7DWXWxKlBAUk0mp+AA2pWKM+Nhc7UQEGiXosrcgwOE1ZIP4yo4iSuoGNFNHZVUUQHhAOftwz0RxxNvu2QBzpDLJ64RDy7t3jnSFFW6XDCxCiUWI3pHPXF9OOEKJmUGXzU9OL/46yEAwHVzxqecsp4xTtg+UfqtUEg0SRxI3gdH+lnM5sDNiNEMcyek/PzyLPfCkabCrHH8ToVA4R55AcOa/SXyKIgBSGOZagauSskdpei/UagO1ZVaUWE3I8gLwVciWIoq+i6FBThSH05fqNmcgQNq4igdWig40tbtWntw1JqwO2diJYwGDm2DY7Jl2OExDUkCHMnf3MApPz5mRvYFhFu7C1vkm0+y7ZKZ6I/0RKqBZ9JsiNcdZ45ZKlhMBlwytRZA/H5PSgdtMtQKcLYd7cM7h3thMnC4Z8m0lJ9/7riQv6hjOCdzl9SEtXJIVibuC/Bi5/VAkBf/dpFVVMpnvGXKJ21DONrjhNVkwDXnN6b8/HKV053JkM78sqhQwZsrCvfIC5hxkpLbeM3+WAWVWgZjIPwhcfuC+CTkNVDq7+E4DlPrhQAlmdE43AMn8ouWPV8a4DD/TV2ZVbb1OgBUa+AhYPsyGjixOZ3afXDU9OAAwt0puxuX64ejJEUFRJ5TwkgBZZeB6FRWPAVnnGQOUyKi/VxpBzgqpKgAoSoEAN6KE+CkW0U1mEFgzvM8Ht0oqDc3L5iIiWmU457dUAqjgUO/0yt+3gqVZApOiSXsV2FpqgGXF0Ee4LjIv50tiwoOG81w9XmNiiwB0bDqwGx5cJiCYzZySZuA5jMU4OQA1uwvkKDZ36HOUAm3whSSEsqsJjAxiJkpUwmgzg4FKEe6EvtwxB44lZFfOGfJNPvrSlBBxZCWiqsF88dUl1hQonWZuEoBDhD24eyK8uH4FfTAYUiDWqXpKSAywBlXYYv7OkoHysYEOGk2B2QBTl0GKSoAuPIcoaHl3tODsueaqOAoDnCE7UY8/ri+nmS8dagbu04OwGY2pOy9YdjMRtFDt78jcTPDfCeZB8do4MTPM0tTsc9hlcMSUV6frSoqXyCIVz8S/DdfTsFcLEXtnkrJYOpXIas3AAU4OUHa7C9ex9dwE75y1V7XYODEngr+IA8DF04bKYEpMEdkSr0Z0i/amBQVU3C6neIFP3z3Hf/LiX2hqNnsj+2rttSqyZ2cNAWmlgcHiO/D6RgS1ECLMX4PHEZdmVVMVyo1GAORwdCFLdVxU6fSPk+JUiIswJlYLZwn6So4PRmMaZDSVGnH9MYyBPmwz4jhCwTFZoLVKaaoeB7ic1MhGOTxi79+BgC47eIW1CvoVxSPc0NVYqn4cNoHx2TbOuSSsdBgzHgpKiCcpmJr3jvCPuuRf7dwikrbAOftz3rQ5/SittSCy86uTWsf2e6FwxQca5xAslCgACdHjJMZXshwevxixYNSE7BSpCMDWmpL4t4JySGmqLriX/S6RzwIBHmYjVzMF+3EagdMBg5jvgA6QkGQWEGVZQVH2rqdXSzdvmDad9rRRE8vVgs2suFg53CEXC2dQZVMUuY4TjQaKy0RB8JzpoDY/jdS2D6d3kBMPxIpLFC+InTRbxtM3WTsDwTFYDXTFBUAXBFScbZGjSVh6SmOC99NJ8NiMoiplHTuvF/f14UDHcMos5rwtcsz6zXCyuCTTUxnBIM8bli3Da1PvINTfflTfZVsFhUQazQO98CJvB5lK0XF0lNfnD0+7Y704jyqLKWoxBJxUnCIdGBfAu0yAQ7zuNSWWuMab9NFGuBEV9Mk4+zQ9sd7nXFLelnvE7kBjmajQWzpfTT0HsVBmwm+nKo1UHCk6SPpxdKjUiOt6OnFatFQbkNztR1BHthzalB8nFWuJUtPMZgSlEqPpVKrSVRJFk2JP1xP2uyvI07Q4vYFxKDsimlCgJOOgtM76gXPC4FkTYoN8OT4XMiHs/WznohgdyA0h6rSbo7rFZMjXaNxgAce33wEAHDn5VMUp8XiwRp17u9QFuAc7hYaQXr8QTyz/WRGr60myTw4AFAaUjuYB0euRBwIj2rQMkUVDPLYfLALAHBdmukpQDJRPMsm40KuoAIowMkZYQUn9qKudoM/KdIAJ1UDc1OFDSUWI/xBPqaXDaNNpsmflGijcaImfwwtBm6GU1SWCBVLrbs5uenFanGhjA9HaYk445uLp+Lluy/BDfObU3rtdf8wD7+9db74d4xHsmZ/R3tGwfNCZR9rWd894km56RoLkGtL1Qkk502qQpnVhD6nVzTiA6n7bxii0XgstXN3RzeHE30u1JRY8NVLJ6f0XDlmhAKck30ujChQAXaeDJ9bz+88nbVmeMkQZ1ElSFFFz6PqidOuwW4Rvv60fG+9ox64fUEYuHC5fjqIKaoslYmTB4fICOZ9kFNwwj1qNAhwJIMCU51xxXGc6KOJ1/CPeYriNbKKLhUPp6iSBzhq9sERB2GWCtVbrFunWgGO3PRitZgv48NR0sVYitVkxJzmypTUCEDoF7P03Iak2yVKwQJh/83UulJUOcywGgS1pC1Fo3F3gkn06WA2GnBpKGUmraZilVBK/TeMdJr9eXwBbDgjnI/f+NxUMeWSCVUlFrGf1sHO5M06d0nOrUGXTzTJKsHtC2hWjq5IwYka1xD24EReY+xm7RUcZpxvLLdlNDA52yZjLyk4RCYwhUPuC+BQJzMYq+u/AaJSVGkoRMl8OB1ikz/5L5zoZn9KSnzZl8qIx6/aLJZeZ+RFz67y4D1WIp7J+IB4sP4ze04PiKnCsIKTH1N9Wam4XAAPhFOUU+tLwXEcqkN//lTTVEpM6qnyObFcPOzDEbsYp6ngpPLF9OyHZzDo5dBYbsUtCyem9HqJSMVozNoQsFTk/3ygLE219/Qg5v14E3748qdpHmViFHlworoZ9znlR3nYUzQZv3+0F7c/tQOnU+gILd7wKVRW4yGWiWc5RUUKDpEWjQnucA9pUEHFYBdci8mASdVp9NSoDw9rlCNZikqspOpxwh8ISroYxw9wKuxmMKFBrTRVWMERvrDUnksTL++vBmfVlaLCbobbFxRNo2cUNvnLFqzZn1wKFgifPyxgrrEKd/ypfHkA6nQxjoYZjT8+M4i+0N9RnEOVqoKTYoDj8Qew7u1jAIBvfu6slIoAksF8OPvaE5eKd4+4carfBY4D1nz5fFiMBnx0ZggfnR5M+Dye5/HgK/vg9Abw5kH5XkKZIio4lvhfXaVR86jCn8U4VVQKP/PPbj+FLYd68PKeNsXHy5pvxrseKiXcyTjLCo6JqqiINGiqCDdDkzb763d6xR4tZyfxOaQDu+BOrStNSzJNpuAkT1EJvXB6Rjw41utUZBA1SCqR1Gr2F31XZ0/xYtc76sE/vfBR3LlQao9pkGIwcJg/SVBxdp7ohz8QFHvO5JuCE68XDlPwWMBbHYpP2ER0pYg9cFRKUQFCsH3uuHLwknLx/pDJWGsF51SfC/1OH6xGHtfNbUrptZJxblO4o3EidofUm3MaytBSW4K/mTUOAPD0tsQqzqsfd2BvKAjqGHLDpUGHYNGDo6SKKqZMPDpFlZqCw/6GqaiMya6HSgmbjLPlwSn8QZsABTg5g3XujW72x9JTzdV2cTKumrC01EUJqmAScbbEJCzXhZmZSqOb/DHKbGbRULztaB8AIb2QzCCqZql4MMiLZdxMwbGleLH7y8cdeHHXGdz59E5RRZDSp3IX42iYD2fniQF0SUrz1UzVZEKiZn/+QFAc98Ea0FWHFJyUU1QqjGmQgzX92xJKUw2IgzZT60IrBjguZQEOM79XmKG4w7RSWKn4Z52jCQebsmn180JB9D8smgQAePXj9rg3GG5fAD97/WDEY8d6Eo90SQdlVVRhDw7P8+LNTHRFanjYprKggfXVSSUIb4vT1T1VpKMasjFuI6zgFHaIUNhHX8DEa/YnNvhrUD89BQj+gq3fvRKrW6en9fzmagcsJgM8/qAovzLGvAEMhC7kiT7QzIcjDXCSoWazv2G3D/5QcMZ6Y9jNqZmMWS58wOXDff/7ScxFR+y9oVGAw3w4O0/2i2md8ZXJe+Bki0Qp2FP9LvgCPOxmo3hnW8M8OCmmqNRq8hfN56aHy8Wls4xSVXAqUzQZs9dROM8zJSZU2VFmNcEbCCZs4Mf8N6zn0pzmSswcXw6vP4g/7Twt+5yn3juBtsExNJbbMHO8cO06lmRmXTqIHpxEjf5CN4ZOjx9DYz5xdlq0SpxqiopVn6UShLOUvVoeHG8gqFori0SIVVQU4BDpIldpEvbfqJ+eYkyqKUn77tBo4DClVkgzHY6qpGoPqTelVpNY1iiHGOAcCwU4Cjq0Mu+DGh4cFnyU20ziBzjc7E/Zxc4pUXrePNiNP+6IvPBrmaICgJnjK2AxGtA76sV7R3oBZH4RVRN2bo96/DG+AZaemlJXIgZkNekqOMxknEGXXznmNlei3GbCoMuHj84MhudQaezBYQFOqUn9u3SO4zAjidHY7QuIHp15E6vF5916UQsA4JntJ2OU295RD9a+JfTs+d7yc0SvzzGVuyD7AkExWEmk4JTZwmXi7HNYZjPFpLXYPnwBPqGixWAKTvvgWNwZgtG0DYRvPjKhxGISfYjZMBqTB4fIGLlmfyxFpUWJuFqwhn/RQzfDQzYTf9kwHw676CfqgcOoDgUKfSqUivewnLzkrj/VfDyTtVnp7U/+sh8n+4Q71njTi9XEZjZi1gTBU/HyXsH0OKEyP/w3gNBEjX25R6s40QZjIOzB6XN64UzQ/VhKMMiLfjW1FRyT0YDLpoXSVAe701ZwUi3vFQMcDRQcQGo0lg9wPj4zBF+AR12ZFc3V4S/lL8xuQoXdjNP9Y9j6WaSB+PE3PsOox4/zx1fg2jnjMSV0A6N2isrtCwchSj047LMu9zmUqkBKVBwW4PiDfNz+TpHb+8S+NZmmqAwGLqtGY/LgpMDatWvR0tICm82GhQsXYseOHQm3f+GFFzB9+nTYbDacf/75WL9+fcTveZ7H/fffj3HjxsFut2PJkiU4fPiwlm9BE6Kb/fE8L2nyp02KSg2mRpV6M+JNEY95fn1k8JaoizFDTQWH5eSl/phU27azJmJfWTQJF02phssbwKo/fSQGN3LTi9WG+XBO9+dXBRWDnd/R89akPXAYdhNQEWpHr7QXzoDLK6YatfA6sXLxLZ/1iHPFUv17pqvgaJGiApKXirMGf/MnVUXMGrNbjPi7+RMARJqNP+sawbPbTwEA/vnzM2CQKLzHetVVcJi6ynGJvSHSPjhyn3WGxWgQVRF3khsbXyAYcW1QojS2h9JTFXazKr2MmCo+lAWjMXlwFPL8889j1apVeOCBB7B7927Mnj0by5YtQ3e3fBnh+++/j5tvvhl33HEH9uzZg2uvvRbXXnstPv003Ffh5z//OX71q19h3bp12L59O0pKSrBs2TK43anPsskl0c3+OobcGPH4YTJwmBy6SOQjZ4fmY8UqOKEp4knmG51VH/neEpWIM9Qc1yDXhM+eYoDj8gjbldnMePSG2SizmrDr5ADWbT0ad3qx2jAfDmNCdX4GONEKjrQHjhQm4ystFe+WTITX4k7zimmsXHxIDGjTTlGlaDLWIkUFhI3G+zuGZc2qu6IMxlK+ctEkcJzgS2Jq5U/XH0CQB5ad1yAWLkgVHDUNsVKDcbxBr0BkHxzxs14W+3fjOE7xuIboYalKzlG1KqgY4jyqLKSomAen0AMc9ct0onjsscdw5513YsWKFQCAdevW4S9/+Qt+97vf4b777ovZ/oknnsDy5cvx3e9+FwDw4x//GJs2bcK//du/Yd26deB5Ho8//jh++MMf4ktf+hIA4Omnn0ZDQwNefvll3HTTTTH79Hg88HjClUrDw8Ldi8/ng8+n7snC9qdkv/WlbF7PGHw+H/a1CReXybUOcHwAvjxpjx5NS6gr25HuEXi9XvFic2ZAuOg1llli1kG6HjV2IxwWo3hRqXYYk65XuU0IQPpHPRn/zbpD3ZOrHWZxX1aT8B6cbmXnBOuxYTMCDaVm/PDz5+D7f96Hx9/4DByEu5+aEnPMvlI5P5IxqylSCZOuez7A0kZtA07xuHieF1NUk6ptEZ/B8RU27O8YwcneUfh88Yd5MjpC51tdqTbvu9JmwMymcnwaUjuMBg42I5/Sa5WYhfNqxOOH2+NN2jm6L2SaLjWrc45E01Jlg9nIYWjMh1O9IxFqazDIY1fIYDxnQnnM6zeVW3D51FpsPdyLp98/jkum1mDLoR6YjRz+aelUcftxZcK8Lpc3gNN9o3GbfiqF7Xd0TLiG28yGhGsTulRg1ONH17AQZEg/6xHbmg0Y9QAjYx74fPGD1/7RSMXmVN9o0r/PyT7hPB9XYVXlb8lGUPSPulW9jsjhDqXgTQbtXiNdUjkeTQMcr9eLXbt2YfXq1eJjBoMBS5YswbZt22Sfs23bNqxatSrisWXLluHll18GABw/fhydnZ1YsmSJ+PuKigosXLgQ27Ztkw1w1qxZg4ceeijm8Y0bN8Lh0Ma3sGnTpqTbnBgBABOOdQ5g/fr12NzGATCiLDASk5bLJ/xBwAAjnJ4A/vjy66gMqb+fHDUAMKDn5CGsXx9ZMhq9HjVmI1xe4WL/2Ucfwnkk8WseHRTW5kRnX8Zrsyd0nH1tJ7B+/XEAQPtp4bH9h45gveezpPto6zIC4HDgk49gad8LKw/Mqjbg434DHt34GQAOcMf/Oyo5P5TQaDeic0xYx0O7t6F7nyq7VYWRLuFvtuPTI1jvFtZ00AM4PSYYwOPAjrdxWHKD6BvqAmDA27v3o6Y/eSfc7d3C/jn3sGaflybOgE9DQrfdEMSGDa+n9HzBuypcZv/86utJU08nOoXzqtSs3jkSTb3ViDYXh/9+bQvOrw4rLJ0uYHDMBDPH4+Te99D2cexzpxk5bIURf/zgBP6y+wQADpfUB7B/+1bsl2xXYzGi283hufVv4ZwKdVScre9uA2AC/N6Ef+9RHwCY4PIGsHv/UQAG9LeHP+tSeJ+w3pu3voNjCWyPp0eFfTI++PQIproTXyfePSlcU3yDXaqcn85BYX/bdu2FqU1YU63OkSPHhdc6cfSwouthNnG5lFdaahrg9Pb2IhAIoKEhcnZNQ0MDDh48KPuczs5O2e07OzvF37PH4m0TzerVqyOCpuHhYTQ3N+Pqq69Gebm6Xhefz4dNmzZh6dKlMJsTX806h93410/fxojfgGXLr8Zbf/4UONWBK+ZMQ+uVU1Q9LrVZe/Q9HOt1ovn8BbhsqjC75/HP3gXgwvLLF2LhZOEOPN56bHZ+gtMfdwAAvty6JOkk6Entw/jNgQ8QMNrQ2npFRsf+6jN7gO4eLLpgJlovFIZNHnnzCN5sP4ZxzRPR2npu0n38+7H3gZFRXLZoAS6dKkjzF13pxd/82/vimIZzJjWhtXVWxPNSOT+U8L5vP57feQYmA4ebvnRNyrOltGRsdxvWn94Hc0UdWlvnAQDePdIH7N6FltoSfPFvLgUQXpNLZp+DLR2HYalqRGvrnKT7P7X1GHD0CGZMmYDW1pmavIdxpwax8beCZ7ChqhStrZekvI8f7dkMpzeABZdeiUk1iW+o/uXTrQA8KDXxqp0j0Wxxf4qX9rTD0TQNrVedJT7+wq4zwEf7MWdSNb74NxfKPndZkMfr//oOzgy64RoTfFO/uP0ysRye8X/9e/DmoR7UTzkPrRmOm2Dnx6wL5gOf7kVVeeK/g8cfxD/vfAMAEHBUAxjERXPDn3Upa4++j77uUcyZvxCXnBW/N9gHx/qBT3aKP/OOarS2Lkh43Jv+9DHQ3olFs6ej9dKWxG9SAe969+Hj/jZMPOscLL24WdXrSDRvvfgJ0N2B88+bgdZLWlTffyawDIwSNE9R5QNWqxVWa6zJzGw2a3JyKN13U5VJbPY36A7is25Bcp/RVKHZcanFtIYyHOt14nifG1fNMIPnedFLNLGmLOb4o9djWmMZ8HGH0Jyu3JG0f0t9hfDFMODywmQyJczBJ6Mv5IeoL3eIx1RiEwIsjx+K1t4VSh9WlFjF7Rsrzfj5387CV3+/M7R/e9x9qXXuLZxSg+d3nkFTpR02q3aG5nRorhG8GJ3DHvG9nggZoqfWx54jE2uF7dsG3YrWps8lyOiNFfHXOVPmTa5FpcOMQZcPNZK/dSpU2M1wegNw+viEz+d5XjTRl5q1uz7NHF+Jl/a042DXaMT+95wWvjgubKmOf94C+MqiFjwSaur37cXTUFcRG7RNbSjDm4d6cKJf2d9SCT5e+Mw7LKaE+zSbBQOxNxDEydD51lDhkH2OI5T28QW5hPt0+QXFxGzk4AvwaFdwjnaGmlBOrClVZQ0qQ/4vpzco7k+rc4QVrNkt2n1Hpksqx6Opg6i2thZGoxFdXV0Rj3d1daGxsVH2OY2NjQm3Z/9NZZ/5itHAoSHkUzgz4BK9CflcQcWYWh9ZSdXv9MLjD4LjgIaK5BUtrBdOXWnyLsZA2GTsC/DilOB0YSbgurJYk7HSPjjMZFxiibxHuGp6A24NdX6d3VyR0XEqYfnMRnz+/HH41uKzNX+tVJFr9nckjsEYAJpTNhlr0+RPitHA4fKzBbNxVZqlTay8dzCJOXTE4xf7vJRoeOvJSsWjRzbsimrwF48b5zejvsyKmePL8ZWLJsluE66kUq9UnLVwSNQDh8GMxj0j8auopPtK1s2YmYzZLL6OYXfSwb9tCttmKCVcRZW9MvFCNxlrevQWiwXz5s3D5s2bxceCwSA2b96MRYsWyT5n0aJFEdsDQp6RbT958mQ0NjZGbDM8PIzt27fH3Wc+w74EPjjWB68/CLvZmHflvnKwSqojoWZ/rCV/balVUXOoi6fW4rymctx4oTL52mY2ip1H+zPshSOOaSiR6YOjuNGfcMFzyHRUfeiL5+Gd730OX5yt7iwhORwWE9becgH+dt4EzV8rVaTN/lgX2EQBDvsiGHb7FV3Ew2Ma1G3yF80tCyfCZjbgimn1aT1faTdjdl47LEYkaNSbMaxU/MzAmHhMfaMeMRi5YGLiAKeqxIJ3vv85vPSNS+JWr4UrqdQrFWd9cGwKFie6LDtePyqHwgaf7PydXFsCm9kAnkfCXji+QBBdw+p0MWZktw8OdTJWxKpVq/Db3/4Wf/jDH3DgwAF8/etfh9PpFKuqbr311ggT8re//W1s2LABv/zlL3Hw4EE8+OCD2LlzJ1auXAlAKO2755578JOf/ASvvPIKPvnkE9x6661oamrCtddeq/XbUR02lJDNvJnWUJo37fYTIQ7d7B4Fz/Mpz1ypsJvxl29dhm8vUa48iAM3M+iF4/L6xeotaaM/dtFU0ujPHwiKF1u5eWEcx6G52pFRGk0PSJv9sQD4qEyTP0aJ1SR6sc4omPcT7mKs7fythVNqcODh5fj7NL0kSnvh9IkTy7VNCVTYzeJN1IGQisPUm6n1pWIqJBFWkzFhN/QpoWaebYNjilXRZITLxJN/bUUHOHJl4oB0HpWyMvFyu0kcaMv6T8nROeRGkBdSZbUl6pyf2Ry46aVGf8q48cYb8eijj+L+++/HnDlzsHfvXmzYsEE0CZ86dQodHR3i9hdffDGeffZZPPnkk5g9ezZefPFFvPzyy5g5M2wi/N73vodvfvObuOuuu3DhhRdidHQUGzZsgM2m7Z2cFowL9YDZfSo0wbcxfzsYSzmrrhQcBwy6fOhzetEh9nzQ7m/A0lSZKDhMvbGZDSiR3AmmouC4JNuUWAu7lbnWSIduDrq8ogH7rLrYAAcINytM1kiN5/mspKgYmQSrFZJBiYlgTf60bA7JiO5ovCt0/Zkv0/8mHWpKLCi3mcDzwIk+ddJUbgWDNhksRQUIKo3DIp/zU/q5Z3+7Mls4OEw0dFPa1V2tG1axD04WOxkX+qiGrJiMV65cKSow0WzZsiXmsRtuuAE33HBD3P1xHIeHH34YDz/8sFqHmDOYgsNGm+TziAYpNrMRzVUOnOp34XDXqGgwTtbkLxPEACcDBaeHDcEssUZ8adlCd4VK7jaZ/8Zk4GDRsJGfHhhXYcPBzhF0DI6JAWVThU1W+QKACdUOfHRmKKkPZ8TjF1U0rVNUmaJUwekPdd1NdRxEOpzbVI6N+7vEjsaJGvylA8dxmFJXir2nB3Gsx6mKr3As9PdONGiTUSY5vxJ1uXYoVG6ZglNmNaE5pOAkUhnVmiIuJZseHFJwCFWIboJVKAoOAJxdH/bhaPGBjoYFOAMZdDNmCk5t1F1/KgqO1H9T7GmoZDSGAt6OIbfovzlLJj3FUKrgMP9NmdWk6Asvl7AAZzBJYN6XAwVnf8cwPP4APm4TBmyy8R9qwNJUR7vV8eGwm49Ec6gYUgWnJsHAW6Wp6REPU3BM4oyuRCkqtbsYAxIPDnUyVkxhH70OKOQAR1pJldUUVQYBjjjlO+pLxJbCsE02DDKeCkGEkY5rSGQwZii5OwbC1TF1Gvtv1KAi5GlRajLW2oMDhI3GR7pHsPvkILz+IGpKLGhJ0qcnFVgaUq1KqrFUUlRKFRxzaFRDUpNxSMGxmUUPTq4UnGG3P+4IjMNdI/jJa/szTmN5A/pQcOgKnWOkKZ0qh1mz6dNaIDUaK51DlQmqBDhxykaZCqBIwWEl4hTgJEUcuDk0BuOIoHYlCnAUKzhZ9N9kivIUlUTBGdH2mMZX2lFhN2NozIfnPhSGZV4QNWAzU8RScZUqqdL14CQKcOwW4Qs82Y3NsBjgmMRr3OkE52jboLoVVED4PAoE+bim6Idf2493Dvei1GbCPUumpf1aHh+ViRMqUFdmFbvPTmsoK6iUx9khv9DBzhF0hb5wspGiyiTAYWmAaNk6lT44rGdGSZ6nRvIB9mUQoeDEMRgDQHM1q1BxJRzUKCo4ee6/AaQBTuLqF+Yty0aKiuM4MU21/hOhyEMtgzGDpSLVGro55k3Pg1OXIEVlD5mPk3twhOC0XFKB1jPiiXu9aAupO2qmqGxmA8xG4fth2B17Lo15A9h+XJgG//7Rvoxeiyk4FOAQGSFt9je9gNJTAHBWKMfe7/SCD5VEJhu5kAlqlIkzk3GMghMKcHwBHr5A4gZeztDFMF5lBhFmXChleXrAJcr2iRQc9oXg9AYwkGACt1giXkAKjtIqqqospKiAcJqKNRdM1uAvVSbVOGDgBEM4+9xlwlgqHhxpiirBOSI2+lOcojKh0mEW9y+nNPI8LyraagY4HMeF01Qy59L2432iOXjPqQFF6fZ4hBWcwr6JowAnD2CVVNMKLMAps5kjPETjVCyJlIOpLhkpOKyKKlrBkdwVJlNxwh6cwv7wZ4PGUBsEty8Inhe+vGsSpAxsZqMYtCTyOLCJ8IUU4CQ1GY9mT8EBwkZjQPBazByvbudtq8ko+lWO9WTuw0ktRRUOEpVUUbmVKjg2MziOS1gqPuDyicFYY4aT1KMJN/uLVXDe/qxX/H9fIDwZPh304sEp7KPXCd+48iy0nt+Ivzlf+863aiO9G2/S0H8DSBScjEzGwnOjvU5SKTaZD4dMxsopsZpQLvFDJFJvGOE0VXyPA2scqHWTPzVgAY7TG0ioDmazDw4QVnAAYNb4Ck3u1lkllRoBjmgyVtTJOLxNYg8OU3Dipw+9/nBjz7LQuRw2Gseeo6yCqq7MqkhtSgX2WZIzEb99WGgWy1T094/2xmyjBH8giECobwmlqIiMWTyjAf9+yzxUZEmaVhPpF9Y4DSuogPCFf8TtTzoHJh5MwYmWrTmOC/twvIn37aIUVUpIfVlKApyw0VhewekadmNn6O70vCbt531lijTAi5emGvMGxC/wagWdhNVgan2p2MdJrf430UypVW9kAwsylFVRha+licrE7QqqJ0ckwQRLTbFS8TMy/ZpY0KOFH5EpOCNRCk774BiOdI/CwAFfv1KYEJ+uD8crCcJJwSGKGjZ8DlA33yxHhd0MlgFLJvfL4QsERV+HnFdIaSWVk0zGKSGV6eN1MJbCSsXjdYr9445TCAR5XNhSVRCNMU1Gg2h6jVdJ1Rdq8mcxGiLUBy0xGw2YNUEIEBedVaPJa4gKjgql4il5cBRWUSlp9MeCCYfFCFMoIFSi4EzQMMCJTlG9/Zmg3sxprsTymcLQ6U/ahiKCM6VIbx4LvZFpYR89kXPY0E1A2woqQDBkszk5fWmkqViDQAMXTndJUVpJxVJUDkpRKULaOiA1BSf2y8MXCOLZ7UJZ8z8salHnALNAeZJScWl6KpuVlI/eMBtr//4CXDGtTpP9h1NUaig4ylNUTDWzmAwRClo0Shp8Sg3GjOYEHhy1p4hLidfNmKWnLju7DhOqHJhY7UAgyOPDE/0pvwYb02A0cGJAV6gU9tETOUda8hvdtFALMulmzCo5qkussmZoNq4hmYLDRjVk60670JGeF6l5cGK/PDbt70L3iAe1pRYsP69RvYPUGNFoHFfBya7/htFSW4LPzxqnWVDFFLtT/S6xO266pNLob3JtCW5e0IzvLJ2W8L0pGbbJVJAyiXE5PHAz9hzVoosxg82jkqao/IEg3j0s+G0uDwWqF4cUufePpJ6m8vr1USIOUIBDZEhViUW841aSfsiU6gwUHGYwro2Tk081RUUeHGWwFJXdbFRkRJcqONH9U57edgIAcNOFEwvKH5CsVHwgTn+mQqe+zIoSixFBHjjVl3xCfCJS8eBwHIc1X56Ff7zirITbsX15/GFjbTTDcgpOyIMz4PJh1BOZLtJybI14HklSTx+dGcKw249ymwmzo1KO246lHuCwQLSQPl/xKPx3QOSc391+IZ7+6gLxzltL2B0u64OSCn1xeuAwwibjJAqOl3UyJgVHCcwnc/74CkVtBMZV2GHghC8daf+Uw10j+OBYPwwc8PcLJ2p2vFpQ6VCWopJLnRYybOgmABzNoJKK5yUeHIt6X1vSm5R4qWlpiTijzGYW/6bRZnhRwVGxizEj3AcnHFQx/82lZ9eKKaVFU4QAZ3/HcMpqt4cUHIIIM62hTJRGtaYuVP300/UH8K0/7sEnZ4YUP1ecQxXnLtmmcOAmu2MjBUcZsydU4PcrLsS/3jRH0fYWk0HsnyMtFf+fD04CAJbMaNDc76U2YjfjOM0Lc5WiygZniUbj9H04fl4IcgBlCo5SpF/i8dJUch4cQKI0Ss5Rty8gKsXapKhYFVX4PGL+m8vPDl+D68ttmFpfCp4XGgCmgkcnk8QBCnCIAuOOSyfj4rNqEAjyeOWjdnzh397Fjf+xDW/s70IwjsTMYI3U4jWaUzpRPOzBoQBHCRzH4cpz6lO64E+ojhxoOOrx4393twEAbi0gczEj2TwqNmhTy07guYIpOJn0wpHGHmr2ljEYuKTFBdJBm1Lkqv2YelNiMYp/czVhhmk29mNozIePTg8CQMxNJvPhbEuxXDzswSl8hZoCHKKgaKktwbN3XoS/fOtSXDd3PEwGDtuP9+P/Pb0TS/51K/6083TcuTfxxjQw7ApKRgGpB6fwLwD5SnQl1ct72jDq8WNKbYl44S4kypWajHXmwQHUqaQK2W9gNnIwq1zZ40hiNA6nqOQVHKnKKPXfaGHcjlZw3j/ahyAvmPejVU2Wpkq1H46o4BR4BRVAAQ5RoJzXVIF/vXEO3vn+5/CPV0xBmc2EYz1OfO/Fj8U7/Wj6kpmMFZaJhz04pOBoRXNVWMHheV5MT91y0SRNx4FoRVIFJ9QHR5cKDmv2l0EvHNZ7U+3OwNJ9xlNuh8UqqsjPe3OUygho678BpCZj4SbrnVCVlDQ9xbgoFOAc7h5Fd2gYshJEBcdc+OFB4b8DoqgZV2HH6mtmYNvqxbht0SQAwBObP5Ntid+bRMFJ3YNDCo5WSO+OPzwxgIOdI7CZDfjbeRNyfGTpodRkXF2S/6MnUmVyraDgDLp8aY9ZYQGOmv4bRljBkR/XEC9FFZ5HJVFwNOxiDIRNxiMeP4I88I5YHl4bs21ViUWcN/bBMeX9cMQqKlJwCCI/KLWa8P1rpqO21ILT/WN4SUbFSRbghFNU8Uc1+AJB8Q6HPDjaIb07/u+QenPtnPGa+BqyQbIycT2bjO0Wo+i/SjdNxVJUSpr8pQrbZ3IPTpSCUxWr4LRpMEVcCjsGngdOjgKdwx5YTAYsnCyfthXLxVOYSxVWcAr/Bo4CHEI3OCwmfC3U9+LXbx2OUHF4npeYjBOnqBIpONI8PVVRaYfUg7Ph0w4AwD+EFLpCJFGKyhcIil+iekxRAZkP3fQGhLSkFgoO22cyD06sguMI/d4vVse1DQrBjlYBjs1sFCu/dvcK/104uTpu4JeO0Zg8OASRp9yycBJqS6043T+G/911Rnx8aMwHf6jKKlmAk8iDw2Rss5HTRRllvjKuwg6TgYM/yMMX4HHBxMqCGKwZD7GTsUyZuHSESKEqVMmYEkpTHU1TwdHSg5OsmzELPqNNxnaLUfTzsUqqdqbgaOTBAcLnyN4+IeiT898wFkyuhtHA4USfSzRAJ4M8OASRp9gtRnGa7q/fPCJ+WFlvijKbKW75o01BFZWTeuBkBaOBi/AxFLJ6A4S/lMZ8gYhhhkA4PVXlsBSkgVoJmTb782XBgxPvxmY4jgcHkA7ddCEY5NExpK0HB5AM3PSFApwEPcjKbGacP164MVCq4jAPjpUUHILIP25ZOBF1ZVa0DY7hxZCKw/w3dQkmCytJUTmpB07WYGmq6hILWs8fl+OjyYwymxmsajg6TdWvY/8NY0qGzf68GnpwbIpTVLGfeakZvmfUA1+Ah9HAoaFMO7O4VElqKLdiWkPiETmLUkxTeanRH0HkLzazEV8PeXHWviWoOMn8N4DCAId64GSN6Y1CBcjNC5oLvumY0cChzMqatEUGOHo2GDOYgnOqzyVb4ZgMFntoqeDIKbdef1D0pJTLKDhSMzxLATWW2zSdwl0uSWNeOrUmab+diyVG43g9wqTQqAaCyHP+fuFE1IdUnD/tPJ20ggoA7KEZNwk9OCEFx0EKjuZ8a/FUPHHTHNyzZFquD0UVKuKUiveHzk29DdqUMq7cBrvZCH+Ql53AnQyflh6cBDc20pEIpYkUnIExsURcK4MxQxpoXTY1tjw8mvmTqmE2cmgfcuOkgoGnpOAQRJ5jMxvxjSvDKk57KDeeKMAR++Ak8uCEFJwSUnA0p9JhwZfmjFe9c22uCFdSRfaCKYYUlcHAif1w0qmk8gZDVVQqDtpk2EN+OrnPPfPflFiMMMr4o6Sl4u1iF2Ob6scohZ1HHHhcfFZ10u3tFiPmNlcBUDZd3EOjGggi/7lpwUQ0ltvQMeTGcztOA1AhReWhLsZEesQrFe/TcZM/KZn4cLLT6C++giNnMAbCKarT/WNiikrLCioAKLcL156JpcqnzzMfjpKxDTRskyAKAJvZiG98TlBx2JdK4hSV8jJxUnCIVKm0C19G0RPFB1z6HbQphZWKH+9NI0WloQcnUXuIeE3+GE2VNnCccFP08Zmh0GPaBjgLJ9fAZjbgonrlXiZpP5xkPhyxiooCHILIb/5ufjMay8OScbw5VIBEwUlYJk4eHCI9ykUFJ3IkADPAV+k8wKkNVRYNulIf1yD2wdGwk7HcqIZEFVSAkMZpKBOuL5+0CQGO1h6cy6fVYe8PF+PihuSGYcaciZWwmgzoHfXgSHdiBY08OARRINjMRtwdUnGAJAqOJEUV7y6HPDhEusRLUTEPjt4VHGaOHXbLj6tIhJYpqkSdjJkHpzxBA0ZmNA6EGolqHeAAkPUDJcJqMmJaQxkA4FQSkzd5cAiigPi7C5sxubYEdrNRLFeVg90dBnnAG6eUlTX6Iw8OkSpiN+MiNBkDySeqJyJXjf7iDdqUwnw4DK1TVOnCvDvJAkw9KTh0lSZ0j9VkxMvfuAQunz/hl4j04un2BmXvYNhdXgl1MiZSRG7gZjDIF40HR/yCHZOf2p0ILRv9JVJwkqWoAKBZYiqudJjz9uZHnETuTrz+evLg5OdfgiBUpsJhRgUSz/kxGw3i/KMxX0B2e3FUg7Xw5Vsiu1TK9MEZHPMhlNnQvQcnoxRVaNimlrOo5PvgJDYZA+FxDUB20lPpwt5DsgBHTwpO4b8DglCRZKXiYQ8O3RsQqSGXoul3Ck3+ym0m3fT7iUe5RMFS0lFXipYpKnuCTsZMbZPrYsyYUB0OavI1PQVIAswkKULqZEwQOiXZwE3qg0Oki1yAEx4hou8eOED4CzbIA84ElYpyaJmicphDjf7SVHCaC0bBYQoaKTiq0N/fj1tuuQXl5eWorKzEHXfcgdHRxCVqbrcbd999N2pqalBaWorrr78eXV1dEdtwHBfz77nnntPyrRBFQjIFh/rgEOkimowlfXCY/0bvBmMAsJkNMBuFVFMyFSEaTauoLPGrJ0c8yT044ypsYlVTfgc4ykzGVEWlkFtuuQX79u3Dpk2b8Nprr+Htt9/GXXfdlfA59957L1599VW88MIL2Lp1K9rb2/HlL385ZrunnnoKHR0d4r9rr71Wo3dBFBOJmn4B1AeHSB+WovH4g+L5xboYK+1IW8hwHJe2D4d9HLX04PB8+MudISo41vgpKpPRIPba0rqLcSaw80+pB0cPKSrNrtIHDhzAhg0b8OGHH2L+/PkAgF//+tdobW3Fo48+iqamppjnDA0N4b/+67/w7LPP4qqrrgIgBDIzZszABx98gIsuukjctrKyEo2NjVodPlGkJE1RkYJDpEmZ1QQDJ6Rohsd8sJmN6B8tjgoqRrndjD6nN+VKqmxUUQFCJZU0iBpR0AcHAG66sBmvfNSOi6bUqH58ahE2GSdTcKiKKinbtm1DZWWlGNwAwJIlS2AwGLB9+3Zcd911Mc/ZtWsXfD4flixZIj42ffp0TJw4Edu2bYsIcO6++278v//3/zBlyhR87Wtfw4oVK+KOjfd4PPB4POLPw8PDAACfzwefL3VHfyLY/tTeb6FSaOthMwnn0KjbK3vMrIrKakzvPRXaemSDYlqTcpsZg2M+9I6MocpuRM+IGwBQaTfFrIMe16PMJgQP/SNjit+f2+OFnxc+lyYENVkXi8kArz+IYZcbZZbw9whLpdlNif8eX7u8BV+7vAVAdv5u6ZwjJWbhfQ25En/vMQWH02itMyWVY9IswOns7ER9fX3ki5lMqK6uRmdnZ9znWCwWVFZWRjze0NAQ8ZyHH34YV111FRwOBzZu3IhvfOMbGB0dxbe+9S3Z/a5ZswYPPfRQzOMbN26Ew+GQeUbmbNq0SZP9FiqFsh4jAwYABmzftQfc6ch8vD8I+ALCR+a9rW/CkcGnp1DWI5sUw5qYgkYAHP765ts4XA58elg437pOHcH69YcjttXjenhHhPf7zo5d8BxXVkklZIWFD9vbb74BLcRTE4zwgsNf33gLjZKvhCGX8Pfaue0dHMtDH3gq58gZJwCY0Ds0ivXr18fdbswnvOf3tm7Bvjx8zy6X8llmKV+i77vvPvzsZz9LuM2BAwdS3W1K/OhHPxL/f+7cuXA6nfjFL34RN8BZvXo1Vq1aJf48PDyM5uZmXH311SgvL1f12Hw+HzZt2oSlS5fCbE4saxYDhbYerw9/hP2DXZg24zy0LpwY8buhMR+w/S0AwBdbl6dVZVBo65ENimlN/vPUB+htG8aMOfOxeHo9nu/eCfT145L5s9E6R0jb63k9/jryEQ4OdWHytHPRumiSoud0DjqBHe8BAL74+WtgSHFMgRLW7NsK17AHFy66BOePrwAg+HH8294QXnf50qRpqmySzjlyesCFX3z8LrwworV1mew2wSCPb28TgqblVy/JS/M7y8AoIeUA5zvf+Q5uv/32hNtMmTIFjY2N6O7ujnjc7/ejv78/rnemsbERXq8Xg4ODESpOV1dXQr/NwoUL8eMf/xgejwdWa2zIabVaZR83m82aXUC03HchUijrwczD3gBijtfjFNJTFqMBJfbMbm0KZT2ySTGsSWXITOz08jCbzRhwCedUXbk95r3rcT0qHMLnhr1/Jfh54UbCZjbAatXmC1f43HvgC3LicQ1JbA2VpfaU5z9lg1TOkepSwQDt9gUBg1G275K0uKLUboXZnH/FFKl8JlI++rq6OtTV1SXdbtGiRRgcHMSuXbswb948AMCbb76JYDCIhQsXyj5n3rx5MJvN2Lx5M66//noAwKFDh3Dq1CksWrQo7mvt3bsXVVVVskEMQaRCojJxlziHigzGRHpE98Jhjf5qSorj2qV0HpIU9lnUokScIY5rkHzumf+m1GrKy+AmVUollZ8jbvmxNR5fuIpMD31wNAvPZsyYgeXLl+POO+/EunXr4PP5sHLlStx0001iBVVbWxsWL16Mp59+GgsWLEBFRQXuuOMOrFq1CtXV1SgvL8c3v/lNLFq0SDQYv/rqq+jq6sJFF10Em82GTZs24ac//Sn+6Z/+Sau3QhQRtgQBDmtO5qAuxkSaSAMcnucx4BS+RKtL8y8VoAVKu+lKYaqCFiXiDHHgpqR6UkmTv0LCZDSgxGKE0xvA8JhPPsAJCO/fwAEmHQR1mv7lnnnmGaxcuRKLFy+GwWDA9ddfj1/96lfi730+Hw4dOhRhGvrXf/1XcVuPx4Nly5bh3//938Xfm81mrF27Fvfeey94nsfUqVPx2GOP4c4779TyrRBFgtgHR6ZMnBQcIlOkAc6oxy9Ora8ugj44gGRcQ1oKjnaKgk1m4KbeAhxA6Gbs9Abi9sJhCo7FZIhblVxIaPqXq66uxrPPPhv39y0tLTGdI202G9auXYu1a9fKPmf58uVYvny5qsdJEIxEg/dG2aBNUnCINJEGOP2hJn92s1GT/i75SDnrpptCHxx36Es3GwqO9HMfniSuHx9Uud2EzuH4ASYLuPXQxRigWVQEEUE4RRWM+R27uyulLsZEmkgDHNbFOB8rVbSiIh0Fx6u9B4fdtIzJKDjlOlNwgPjN/qQKjh7Qx7sgCJUQTcYyKSrWxdhRJHfbhPpEKDjioM3iCXDKZQaOJiMbHhy5FNWwDhWc8DwqeQUtrODoIzTQx7sgCJWwW4SPhNwsKhdNEicypMIRm6IqJgUnHZMxU1O19ODIp6j058FJtv6e0PsnBYcgdEiiMvGwB4cUHCI9ij1FxcrERzx+BIPKOhmPZUHBCSu3YWUjHODoT8GJZzJmCo5FpkdOIaKPd0EQKmFLME3cFbr4kQeHSBcxwHH5JD1wiijACQULPA+MepUZjdlnUUsjtlxxQdhkrJ/Pe7IqNubBsWoYTGYTCnAIQkIiBYf64BCZwgIcbyCI9kFh0GZ1kTT5A4QbCJb+UJqmyqaCI+fB0ZfJWJmCYyUFhyD0h12m4ReD+uAQmSLtinu81wkAqC7RTwpECWEfiFIFJ3seHLesB0c/f5+kHhy/8P6tGq51NtHHuyAIlUjswSEFh8gMjuNEFScc4BSPggOkPq4hKwqOpVga/SVRcPzkwSEI3ZJoVAPz4JCCQ2QCC3DYOVZMJmMg9Uoqdxb64Mjd2DAPTj5NEc8UtvYjnngKDvPg6CM00Me7IAiVEFNUvmBMlQfz4JSQgkNkQPQXZjGZjIHYgaPJyMaohkSN/vSk4IjqWZz0ICk4BKFjpHeJ7G6GwTw4DlJwiAyoiApwimXQJiNcyZOaB0fbFJXwVSifotKPgpO0k7GfRjUQhG6RXkSj01ROZjImBYfIAGmAYzZyKCuytgPheVSpKjhapqhMEa/l9gXEiiJdKTi2cHAZPQcSCAc41OiPIHSI0cCJH+6YAMdLnYyJzKmUBDjVJRZdTG1OhVQnirPKJi19IWIfnNBnnKk3HAeU6uiGhgVrgSAv6zMUq6gowCEIfRJvHhWZjAk1qIgIcIqrggpIvUx8wCUEQtGpPTWRjmrgeV5M4ZRaTDAY9BOAOixGsU2BXCWVlxQcgtA3dpluxl5/EL6AIOlSmTiRCZEBjn78HUpJtUycjbSo1dCrxFLTgSAPbyAo+oP0lJ4ChDYFZQlShOTBIQidI9e2nflvAKCEZlERGUAKjvIycZfXLxp/azRcK+l8Obc3KBnToL8ANNFEcVJwCELn2GRSVM5QespqMsCkkxJKIjdIy8SLrUQcSK2Kqm9UUG9MHI9SDVPDZqMBplDqxuXz67JEnBE2GidScPRxjdPHuyAIFWH9NqQKjosMxoRKVDoiTcbFBlOwlCg4vaPCQNIyMzQ3Y0uNxnps8sdI1M3YGzIZk4JDEDrFLjOXhqWoHJSeIjKkwl7cAU4qZeK9IQWnNAtxhkMyrkHPCk6iXjik4BCEzpGronKG5lCVkoJDZEgFpagAACMePwLB2F4sUsIKTuLt1EA6rkGvJmMgcRUbeXAIQufIzaNiHhxScIhMKXYFRxo0jCbx4fRJUlRaY5eMaygGk3FiBUcf1zkKcAgiCrnBe+EeOPq7oyOyi8NihNko+ElqimxMAyB8edpCPrdkpeIsRZWVAMccHteg5xRVokaLXp2lqPT31yOIDBE9ODIpKlJwiEzhOA53XjYFJ/tcmFJbmuvDyQnlNjPcPg+GxnxoTrBdD1NwLNqnqFh/K7dP3wpOeQKTsd46GVOAQxBRyCk44hwqUnAIFfje8um5PoScUm43o3vEk1TByWaKiqWmXd6A6E8p16OCk6APEXlwCELnyHtwQmXi1MWYIDImXEmV2IOTiyqqMV8AIx6m4Ojv856oTJw8OAShc8L9MILiYy5WJk5zqAgiYyoUDtzMSRWVN9zor1yHKapwmThVURFE0SE3i4oUHIJQj3IFzf58gSAGQ4M2y7NSRSVRcESTsf4CnESzwKgPDkHoHPLgEIS2KJlH1R8asmngAEcWPnb2iEZ/ek5RkYJDEEWLzRLb6E8sE6cqKoLImLCKEN+D0zMipKeqSywwaDulAQDgCN3YDLp88AWElJgeAxzmfxqNarTI88IkdYACHILQLfIKTqhMnBQcgsgYJQpOX0jBqc1SM0Sm4HQNuwEAHKfPlLQ07SZttMjSUwClqAhCt8h5cEjBIQj1SNRsjtHLFJwsNUNkAU536HVLrSYYsiEdZRmLySAGMNL1Z+oN20YP6ONdEISK2C2x08RHyYNDEKqRaB4Sg1VQ1ZZYs3JMjigFR48VVAy5ANPjkwQ4Rn2EBvp4FwShIjaZYZsuqqIiCNVIVMnDEFNU2VJwQp97PY9pYMj1wpH6bzhOH8oVBTgEEUWiKirqg0MQmVOhoEy8V2Iyzgb2qJsXPQc4ch4oj09fYxoACnAIIgZxFlXoA8/zPCk4BKEi4hdsgiqq3hwpOAw9p6gSKTgU4BCEjmEXOl+Ahy8QhMcfhD9UTllCCg5BZAzzgIx6/PBLzK1SmIKTrQAnepBuMSg4IzIeHL2MaQA0DHD6+/txyy23oLy8HJWVlbjjjjswOjqa8DlPPvkkrrzySpSXl4PjOAwODqqyX4JIBZvkTs7tC4jqDRCeOEwQRPpIgwe5hnNA2GRckyWTsc0cHeDoV8GR60Oktx44gIYBzi233IJ9+/Zh06ZNeO211/D222/jrrvuSvgcl8uF5cuX4wc/+IGq+yWIVLCaDGAeuzFfQPTf2MwGGHVYNkoQ2cZsNIiKiZzROBjkxU7GNaTgqE5ZQgVHPwGOJn/BAwcOYMOGDfjwww8xf/58AMCvf/1rtLa24tFHH0VTU5Ps8+655x4AwJYtW1TdL0GkAsdxsJuNcHkDcHuDotmY/DcEoR7lNjNc3oBsqfjQmE9MC9dkyWQcG+DoWMGRmebuDQjXOT0pOJpcsbdt24bKykoxCAGAJUuWwGAwYPv27bjuuuuyul+PxwOPxyP+PDw8DADw+Xzw+RJPs00Vtj+191uoFOp62MwGYSbNmDvcxdhizPh9FOp6aAmtSSTFsh5lNiM6h4H+0TH4fI6I33UMOgEIX8QcL3z+tF4PEyK9QA4zl7d/g0zPEYdZCGKGxrziPpwhNcdizN/3DaT2njUJcDo7O1FfXx/5QiYTqqur0dnZmfX9rlmzBg899FDM4xs3boTD4ZB5RuZs2rRJk/0WKgW3Hn4jAA6bt7yDsQAHwIiAx4X169ersvuCW48sQGsSid7Xwz8mfMa2vL8Dg4f4iN8dHhI+czb4xHXQej14HuBgBA8hDX304KdY3/uJpq+ZKemuybEeYX2Pn+nA+vVtAICdoceGB/tVu85pgcvlUrxtSgHOfffdh5/97GcJtzlw4EAqu8wKq1evxqpVq8Sfh4eH0dzcjKuvvhrl5eWqvpbPJ3wgly5dCrNZvxKnUgp1PX515D309zhxwYKLMDTmAw58hMbaKrS2Lshov4W6HlpCaxJJsazHS327cXykF2efez5a502I+N1fPukE9n+MSY3VWLp0TtbW4593b4YzVFRw2UXzceW0Ok1fL10yPUesB7vxP0f2wlpaidbWiwAAzl1twJF9aGqoR2vrBWofsmqwDIwSUgpwvvOd7+D2229PuM2UKVPQ2NiI7u7uiMf9fj/6+/vR2NiYyktGkO5+rVYrrNZYJ77ZbNbsA6PlvguRQlsP1gvHF+QQylChxGpS7T0U2npkA1qTSPS+HlWh6iinl495nwMhb0hdmVX8XTbWw24xiQFOdakt79c/3TWpKrEBAEY9AfH5gZByZTOrd53TglSOLaUAp66uDnV1ySPaRYsWYXBwELt27cK8efMAAG+++SaCwSAWLlyYyktmZb8EEY20mzGroiqlOVQEoRqi0VWmiqpvlDX5y06JOIPNoQN0bjKWnUUV6mRs1o/JWJN3MmPGDCxfvhx33nknduzYgffeew8rV67ETTfdJFY6tbW1Yfr06dixY4f4vM7OTuzduxdHjhwBAHzyySfYu3cv+vv7Fe+XINRAOo+K3dFRDxyCUA/2JTskM64h2z1wGA5z+DOu7zLxBH1wdDJoE9CwD84zzzyD6dOnY/HixWhtbcWll16KJ598Uvy9z+fDoUOHIgxD69atw9y5c3HnnXcCAC6//HLMnTsXr7zyiuL9EoQaSBUclzhJXD8dPgki18jNQ2KIk8TLslMizrBJSsWLQcHx+oPiSBqxD46OFBzNQtTq6mo8++yzcX/f0tICno90zj/44IN48MEHM9ovQaiBdB4VKTgEoT5y3XQZvTlKUTlCNzYGDiix6PeGptRiAscJlWMjbj9sZqNEwdHP+9ZPqEYQKsIUHHeEB0c/H3yCyDWKFJwsdTFmsGZ/pVYTOE6/XcsNBg6lFjZwU1h/PSo4+nknBKEiNqnJmBQcglAdOaMrAPA8LwlwsqvgsBSVntNTjPD6CzdwYidj8uAQhL5hKaoxb5A8OAShAWEFJzJF5fIG4A6pCblKUenZYMxg75EUHIIoMiLKxL3CBZgUHIJQj4o4Cg5Tb2xmQ8x8KK1hNzblxaDgRAWYVEVFEEVCpAdHkG6pDw5BqAczGbu8AfgC4TlQ0vRUtn0wYoBj1/9nPb6Cox+lmgIcgpDBZpH2wWEKjn4++ASRa6Q3DFKjca4qqIDwjU0xeHDCAU6kgmMlBYcg9E1kHxxBwSkhBYcgVMNkNIhBjrRUPFcVVABwxbQ6NFfbsey8hqy/draJNnl7/PrrZExXbIKQQd6DQwoOQahJuc2EUY8/QsHJ1ZgGAJg7sQrvfO+qrL9uLohRcPzkwSGIooDNpBnz0iwqgtAKuVLxXJWIFxvRfYg8LMAx6Scs0M87IQgVYX1wBse8CIYabjsowCEIVZErFRfnUOUgRVVMMJ/RcJSCYzXpR6mmAIcgZGApKiaXSx8jCEIdwuMa8sNkXEywtRerqEjBIYjigJWLukJdjO1mI4wG/bZuJ4hcIKaoxihFlW3iKzj6CQv0804IQkWi1RqqoCII9RFTVFIFZyR3VVTFREwfnFAVFSk4BKFzYgMcSk8RhNqEFZywisAUBVJwtCWeyZgUHILQObaoknAa00AQ6lMeUhGGQl+yfU5BvTEZOHGUA6ENbO1HPX4Eg3y4TJwCHILQNzEKDvXAIQjViS4T7x0RDMbVJRYYyPOmKWztgzzg9PolCo5+rnUU4BCEDGajASbJBZY8OAShPtFpkl4nGYyzhdVkgNkoXOP6neFqUVJwCKIIkKo45MEhCPUJl4kLvhvRYFxGAY7WcBwnVlKxyjWAPDgEURRIfTjkwSEI9YlRcFgPnBKqoMoGzIfTMyJRcGhUA0HonwgFhzw4BKE6FVEenL5RUnCySbSCYzEadOV9ogCHIOIQmaIiBYcg1IYZXd2+IDz+QE4niRcjLEUoBjg6Sk8BFOAQRFykKSoKcAhCfcqsJnAhwWDE7RdTVDUlpOBkgzKrEGCykTR68t8AFOAQRFzs5vDHw0EpKoJQHYOBQ6k13Aunl1JUWYV1MyYFhyCKjEgPDik4BKEFUqNxWMGhFFU2YClCFuCQgkMQRYKdUlQEoTnsS3ZwzIf+UB+cOlJwskJYwRECS1JwCKJIsEkUHAf1wSEITWClyqf6XAjywmPVpOBkBaaesf5DeupiDFCAQxBxoRQVQWgPU3CO9YwCACodZph11IslnxEninuERouk4BBEkSANcMhkTBDawFSEY71OADSmIZuURw00JQ8OQRQJUg9OKXlwCEITKkQFhwU4lJ7KFkzBYZCCQxBFAnlwCEJ7WLO59qExAEANKThZg6lnDFJwCKJIIA8OQWgP+5LlQwbjOgpwskZ0gGMhkzFBFAfSFJU02CEIQj2ifSCUosoe0SkqUnAIokhgQU2JxairAXQEkU+UR33JUooqe5AHhyCKFObBcZDBmCA0I1bBoQAnW5iMhogKUYvOyvP19W4IQkVYiqqESsQJQjOifSCUosouUhXHatZXSKCvd0MQKjKjsQzlNhMumlKT60MhCN3CqqgYpOBkF2mAaSUFRxn9/f245ZZbUF5ejsrKStxxxx0YHR1N+Jwnn3wSV155JcrLy8FxHAYHB2O2aWlpAcdxEf8eeeQRjd4FUczUl9uw84dL8cj1s3J9KAShWyooRZVTIhUcfanVmgU4t9xyC/bt24dNmzbhtddew9tvv4277ror4XNcLheWL1+OH/zgBwm3e/jhh9HR0SH+++Y3v6nmoROEiN5MdwSRb5RYTGAe/hKLMaJ6kdAeqQdKbx4cTdyTBw4cwIYNG/Dhhx9i/vz5AIBf//rXaG1txaOPPoqmpibZ591zzz0AgC1btiTcf1lZGRobG9U8ZIIgCCIHGAwcymxmDI35qIIqB5RJU1Q68+BoEuBs27YNlZWVYnADAEuWLIHBYMD27dtx3XXXZbT/Rx55BD/+8Y8xceJE/P3f/z3uvfdemEzx34rH44HH4xF/Hh4eBgD4fD74fL6MjiUatj+191uo0HpEQusRC61JJMW4HmU2kxDglJhj3ncxrkcy1FyTEks4qDGCz/t1TuX4NAlwOjs7UV9fH/lCJhOqq6vR2dmZ0b6/9a1v4YILLkB1dTXef/99rF69Gh0dHXjsscfiPmfNmjV46KGHYh7fuHEjHA5HRscTj02bNmmy30KF1iMSWo9YaE0iKar18BoBcPCPDmD9+vWymxTVeihEjTXpaTOAuVUO7PsE67s/znifWuJyuRRvm1KAc9999+FnP/tZwm0OHDiQyi5TZtWqVeL/z5o1CxaLBf/4j/+INWvWwGqVlzdXr14d8bzh4WE0Nzfj6quvRnl5uarH5/P5sGnTJixduhRmszn5E3QOrUcktB6x0JpEUozr8cfOD3Hm+ADOPWsiWlvPjfhdMa5HMtRck1Nbj2Fz+xEAwIJ5c3HNzPy2f7AMjBJSCnC+853v4Pbbb0+4zZQpU9DY2Iju7u6Ix/1+P/r7+1X3zixcuBB+vx8nTpzAOeecI7uN1WqVDX7MZrNmHxgt912I0HpEQusRC61JJMW0HhUOofdNQ7kt7nsupvVQihprUllqE//fYbXk/RqncnwpBTh1dXWoq6tLut2iRYswODiIXbt2Yd68eQCAN998E8FgEAsXLkzlJZOyd+9eGAyGmJQYQRAEURjMbq7EX/d1Ye7EqlwfStEhHZWht6pRTTw4M2bMwPLly3HnnXdi3bp18Pl8WLlyJW666SaxgqqtrQ2LFy/G008/jQULFgAQvDudnZ04ckSQyz755BOUlZVh4sSJqK6uxrZt27B9+3Z87nOfQ1lZGbZt24Z7770XX/nKV1BVRR8MgiCIQuTrV5yFmy+ciKoS6mKcbSIa/ekswNHs3TzzzDOYPn06Fi9ejNbWVlx66aV48sknxd/7fD4cOnQowjC0bt06zJ07F3feeScA4PLLL8fcuXPxyiuvABBSTc899xyuuOIKnHfeefiXf/kX3HvvvRH7JQiCIAoLjuMouMkRZaTgpE51dTWeffbZuL9vaWkBz/MRjz344IN48MEH4z7nggsuwAcffKDWIRIEQRBEURPRB8ekryaL+grXCIIgCIJQjHQWmN4UHH29G4IgCIIgFFNGHhyCIAiCIPRGicWIMqsJRgMXYTjWA5p5cAiCIAiCyG84jsN/3DoPI24/KhwU4BAEQRAEoRMuPqs214egCZSiIgiCIAhCd1CAQxAEQRCE7qAAhyAIgiAI3UEBDkEQBEEQuoMCHIIgCIIgdAcFOARBEARB6A4KcAiCIAiC0B0U4BAEQRAEoTsowCEIgiAIQndQgEMQBEEQhO6gAIcgCIIgCN1BAQ5BEARBELqDAhyCIAiCIHRHUU4T53keADA8PKz6vn0+H1wuF4aHh2E262v0fDrQekRC6xELrUkktB6R0HrEUsxrwr632fd4IooywBkZGQEANDc35/hICIIgCIJIlZGREVRUVCTchuOVhEE6IxgMor29HWVlZeA4TtV9Dw8Po7m5GadPn0Z5ebmq+y5EaD0iofWIhdYkElqPSGg9YinmNeF5HiMjI2hqaoLBkNhlU5QKjsFgwIQJEzR9jfLy8qI78RJB6xEJrUcstCaR0HpEQusRS7GuSTLlhkEmY4IgCIIgdAcFOARBEARB6A4KcFTGarXigQcegNVqzfWh5AW0HpHQesRCaxIJrUcktB6x0JoooyhNxgRBEARB6BtScAiCIAiC0B0U4BAEQRAEoTsowCEIgiAIQndQgEMQBEEQhO6gAIcgCIIgCN1BAY6KrF27Fi0tLbDZbFi4cCF27NiR60PKGm+//Ta+8IUvoKmpCRzH4eWXX474Pc/zuP/++zFu3DjY7XYsWbIEhw8fzs3BZoE1a9bgwgsvRFlZGerr63Httdfi0KFDEdu43W7cfffdqKmpQWlpKa6//np0dXXl6Ii15Te/+Q1mzZoldl5dtGgRXn/9dfH3xbQWcjzyyCPgOA733HOP+FixrcmDDz4IjuMi/k2fPl38fbGtBwC0tbXhK1/5CmpqamC323H++edj586d4u+L7bqaKhTgqMTzzz+PVatW4YEHHsDu3bsxe/ZsLFu2DN3d3bk+tKzgdDoxe/ZsrF27Vvb3P//5z/GrX/0K69atw/bt21FSUoJly5bB7XZn+Uizw9atW3H33Xfjgw8+wKZNm+Dz+XD11VfD6XSK29x777149dVX8cILL2Dr1q1ob2/Hl7/85RwetXZMmDABjzzyCHbt2oWdO3fiqquuwpe+9CXs27cPQHGtRTQffvgh/uM//gOzZs2KeLwY1+S8885DR0eH+O/dd98Vf1ds6zEwMIBLLrkEZrMZr7/+Ovbv349f/vKXqKqqErcptutqyvCEKixYsIC/++67xZ8DgQDf1NTEr1mzJodHlRsA8C+99JL4czAY5BsbG/lf/OIX4mODg4O81Wrl//jHP+bgCLNPd3c3D4DfunUrz/PC+zebzfwLL7wgbnPgwAEeAL9t27ZcHWZWqaqq4v/zP/+zqNdiZGSEP/vss/lNmzbxV1xxBf/tb3+b5/niPD8eeOABfvbs2bK/K8b1+P73v89feumlcX9P19XkkIKjAl6vF7t27cKSJUvExwwGA5YsWYJt27bl8Mjyg+PHj6OzszNifSoqKrBw4cKiWZ+hoSEAQHV1NQBg165d8Pl8EWsyffp0TJw4UfdrEggE8Nxzz8HpdGLRokVFvRZ33303Pv/5z0e8d6B4z4/Dhw+jqakJU6ZMwS233IJTp04BKM71eOWVVzB//nzccMMNqK+vx9y5c/Hb3/5W/D1dV5NDAY4K9Pb2IhAIoKGhIeLxhoYGdHZ25uio8ge2BsW6PsFgEPfccw8uueQSzJw5E4CwJhaLBZWVlRHb6nlNPvnkE5SWlsJqteJrX/saXnrpJZx77rlFuRYA8Nxzz2H37t1Ys2ZNzO+KcU0WLlyI3//+99iwYQN+85vf4Pjx47jsssswMjJSlOtx7Ngx/OY3v8HZZ5+Nv/71r/j617+Ob33rW/jDH/4AgK6rSjDl+gAIQu/cfffd+PTTTyP8BMXIOeecg71792JoaAgvvvgibrvtNmzdujXXh5UTTp8+jW9/+9vYtGkTbDZbrg8nL7jmmmvE/581axYWLlyISZMm4U9/+hPsdnsOjyw3BINBzJ8/Hz/96U8BAHPnzsWnn36KdevW4bbbbsvx0RUGpOCoQG1tLYxGY4yjv6urC42NjTk6qvyBrUExrs/KlSvx2muv4a233sKECRPExxsbG+H1ejE4OBixvZ7XxGKxYOrUqZg3bx7WrFmD2bNn44knnijKtdi1axe6u7txwQUXwGQywWQyYevWrfjVr34Fk8mEhoaGoluTaCorKzFt2jQcOXKkKM+RcePG4dxzz414bMaMGWLarpivq0qhAEcFLBYL5s2bh82bN4uPBYNBbN68GYsWLcrhkeUHkydPRmNjY8T6DA8PY/v27bpdH57nsXLlSrz00kt48803MXny5Ijfz5s3D2azOWJNDh06hFOnTul2TaIJBoPweDxFuRaLFy/GJ598gr1794r/5s+fj1tuuUX8/2Jbk2hGR0dx9OhRjBs3rijPkUsuuSSmtcRnn32GSZMmASjO62rK5NrlrBeee+453mq18r///e/5/fv383fddRdfWVnJd3Z25vrQssLIyAi/Z88efs+ePTwA/rHHHuP37NnDnzx5kud5nn/kkUf4yspK/v/+7//4jz/+mP/Sl77ET548mR8bG8vxkWvD17/+db6iooLfsmUL39HRIf5zuVziNl/72tf4iRMn8m+++Sa/c+dOftGiRfyiRYtyeNTacd999/Fbt27ljx8/zn/88cf8fffdx3Mcx2/cuJHn+eJai3hIq6h4vvjW5Dvf+Q6/ZcsW/vjx4/x7773HL1myhK+treW7u7t5ni++9dixYwdvMpn4f/mXf+EPHz7MP/PMM7zD4eD/53/+R9ym2K6rqUIBjor8+te/5idOnMhbLBZ+wYIF/AcffJDrQ8oab731Fg8g5t9tt93G87xQ0vijH/2Ib2ho4K1WK7948WL+0KFDuT1oDZFbCwD8U089JW4zNjbGf+Mb3+Crqqp4h8PBX3fddXxHR0fuDlpDvvrVr/KTJk3iLRYLX1dXxy9evFgMbni+uNYiHtEBTrGtyY033siPGzeOt1gs/Pjx4/kbb7yRP3LkiPj7YlsPnuf5V199lZ85cyZvtVr56dOn808++WTE74vtupoqHM/zfG60I4IgCIIgCG0gDw5BEARBELqDAhyCIAiCIHQHBTgEQRAEQegOCnAIgiAIgtAdFOAQBEEQBKE7KMAhCIIgCEJ3UIBDEARBEITuoACHIAiCIAjdQQEOQRAEQRC6gwIcgiAIgiB0BwU4BEEQBEHojv8PiZ/1lI6TL/sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_factornew.index\n",
    "x=x.values.tolist()\n",
    "plt.title('Factor')\n",
    "y=df_factornew['ret'].values.tolist()\n",
    "plt.plot(x, y)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06183731613101824\n",
      "0.012946400763585286\n",
      "0.2093622681837454\n"
     ]
    }
   ],
   "source": [
    "std=df_factornew['ret'].values.std()\n",
    "mean=df_factornew['ret'].values.mean()\n",
    "sharpe=mean/std\n",
    "print(std)\n",
    "print(mean)\n",
    "print(sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>661ret</th>\n",
       "      <th>858ret</th>\n",
       "      <th>600196ret</th>\n",
       "      <th>600276ret</th>\n",
       "      <th>600519ret</th>\n",
       "      <th>600900ret</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001281</td>\n",
       "      <td>-0.005831</td>\n",
       "      <td>-0.005305</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.024784</td>\n",
       "      <td>0.035307</td>\n",
       "      <td>0.046197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027409</td>\n",
       "      <td>-0.015296</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.022469</td>\n",
       "      <td>0.020769</td>\n",
       "      <td>0.058089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.013299</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.007125</td>\n",
       "      <td>-0.006108</td>\n",
       "      <td>-0.005890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024301</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>-0.000380</td>\n",
       "      <td>0.003728</td>\n",
       "      <td>0.007386</td>\n",
       "      <td>-0.008347</td>\n",
       "      <td>0.027191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.022361</td>\n",
       "      <td>-0.003543</td>\n",
       "      <td>-0.013996</td>\n",
       "      <td>0.006032</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.033290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.004607</td>\n",
       "      <td>-0.001816</td>\n",
       "      <td>-0.002094</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>-0.010715</td>\n",
       "      <td>-0.032414</td>\n",
       "      <td>-0.051410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.008051</td>\n",
       "      <td>0.040179</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>-0.128751</td>\n",
       "      <td>-0.067896</td>\n",
       "      <td>-0.161093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.006792</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.067510</td>\n",
       "      <td>0.027093</td>\n",
       "      <td>0.077551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.002440</td>\n",
       "      <td>-0.021398</td>\n",
       "      <td>-0.001077</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.030868</td>\n",
       "      <td>-0.006042</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.031549</td>\n",
       "      <td>-0.020537</td>\n",
       "      <td>-0.000390</td>\n",
       "      <td>-0.000860</td>\n",
       "      <td>0.026793</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>0.030829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret       ret\n",
       "0   0.001281 -0.005831  -0.005305  -0.004038   0.024784   0.035307  0.046197\n",
       "1   0.027409 -0.015296   0.001900   0.000839   0.022469   0.020769  0.058089\n",
       "2  -0.013299 -0.000182   0.005862   0.000712   0.007125  -0.006108 -0.005890\n",
       "3   0.024301  0.000503  -0.000380   0.003728   0.007386  -0.008347  0.027191\n",
       "4   0.022361 -0.003543  -0.013996   0.006032   0.019231   0.003203  0.033290\n",
       "..       ...       ...        ...        ...        ...        ...       ...\n",
       "62 -0.004607 -0.001816  -0.002094   0.000236  -0.010715  -0.032414 -0.051410\n",
       "63 -0.008051  0.040179   0.002695   0.000732  -0.128751  -0.067896 -0.161093\n",
       "64  0.006792 -0.026910   0.003068  -0.000003   0.067510   0.027093  0.077551\n",
       "65 -0.002440 -0.021398  -0.001077   0.000158   0.030868  -0.006042  0.000068\n",
       "66  0.031549 -0.020537  -0.000390  -0.000860   0.026793  -0.005726  0.030829\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count=0\n",
    "for i in df_sample.columns.values:\n",
    "    df_sample[str(i)+'ret']=df_sample[i]*rlist[count][98:]\n",
    "    count+=1\n",
    "df_samplenew=df_sample.iloc[:,6:12]\n",
    "df_samplenew['ret']=df_samplenew.sum(axis=1)\n",
    "x = df_samplenew.index\n",
    "x=x.values.tolist()\n",
    "df_samplenew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.053719231838837686\n",
      "0.01267723433126496\n",
      "0.2359906107611098\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Sample')\n",
    "y=df_samplenew['ret'].values.tolist()\n",
    "plt.plot(x, y)\n",
    "plt.grid()\n",
    "std=df_samplenew['ret'].values.std()\n",
    "mean=df_samplenew['ret'].values.mean()\n",
    "sharpe=mean/std\n",
    "print(std)\n",
    "print(mean)\n",
    "print(sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.057262407313957674\n",
      "0.013028020151609924\n",
      "0.2275143634842322\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=0\n",
    "for i in df_shrink.columns.values:\n",
    "    df_shrink[str(i)+'ret']=df_shrink[i]*rlist[count][98:]\n",
    "    count+=1\n",
    "df_shrinknew=df_shrink.iloc[:,6:12]\n",
    "df_shrinknew['ret']=df_shrinknew.sum(axis=1)\n",
    "x = df_shrinknew.index\n",
    "x=x.values.tolist()\n",
    "plt.title('Shrink')\n",
    "y=df_shrinknew['ret'].values.tolist()\n",
    "plt.plot(x, y)\n",
    "plt.grid()\n",
    "std=df_shrinknew['ret'].values.std()\n",
    "mean=df_shrinknew['ret'].values.mean()\n",
    "sharpe=mean/std\n",
    "print(std)\n",
    "print(mean)\n",
    "print(sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_factornew.to_excel('factor.xlsx')\n",
    "df_samplenew.to_excel('sample.xlsx')\n",
    "df_shrink.to_excel('shrink.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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>661ret</th>\n",
       "      <th>858ret</th>\n",
       "      <th>600196ret</th>\n",
       "      <th>600276ret</th>\n",
       "      <th>600519ret</th>\n",
       "      <th>600900ret</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001281</td>\n",
       "      <td>-0.005831</td>\n",
       "      <td>-0.005305</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.024784</td>\n",
       "      <td>0.035307</td>\n",
       "      <td>0.046197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027409</td>\n",
       "      <td>-0.015296</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.022469</td>\n",
       "      <td>0.020769</td>\n",
       "      <td>0.058089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.013299</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.007125</td>\n",
       "      <td>-0.006108</td>\n",
       "      <td>-0.005890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024301</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>-0.000380</td>\n",
       "      <td>0.003728</td>\n",
       "      <td>0.007386</td>\n",
       "      <td>-0.008347</td>\n",
       "      <td>0.027191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.022361</td>\n",
       "      <td>-0.003543</td>\n",
       "      <td>-0.013996</td>\n",
       "      <td>0.006032</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.033290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.004607</td>\n",
       "      <td>-0.001816</td>\n",
       "      <td>-0.002094</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>-0.010715</td>\n",
       "      <td>-0.032414</td>\n",
       "      <td>-0.051410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.008051</td>\n",
       "      <td>0.040179</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>-0.128751</td>\n",
       "      <td>-0.067896</td>\n",
       "      <td>-0.161093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.006792</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.067510</td>\n",
       "      <td>0.027093</td>\n",
       "      <td>0.077551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.002440</td>\n",
       "      <td>-0.021398</td>\n",
       "      <td>-0.001077</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.030868</td>\n",
       "      <td>-0.006042</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.031549</td>\n",
       "      <td>-0.020537</td>\n",
       "      <td>-0.000390</td>\n",
       "      <td>-0.000860</td>\n",
       "      <td>0.026793</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>0.030829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret       ret\n",
       "0   0.001281 -0.005831  -0.005305  -0.004038   0.024784   0.035307  0.046197\n",
       "1   0.027409 -0.015296   0.001900   0.000839   0.022469   0.020769  0.058089\n",
       "2  -0.013299 -0.000182   0.005862   0.000712   0.007125  -0.006108 -0.005890\n",
       "3   0.024301  0.000503  -0.000380   0.003728   0.007386  -0.008347  0.027191\n",
       "4   0.022361 -0.003543  -0.013996   0.006032   0.019231   0.003203  0.033290\n",
       "..       ...       ...        ...        ...        ...        ...       ...\n",
       "62 -0.004607 -0.001816  -0.002094   0.000236  -0.010715  -0.032414 -0.051410\n",
       "63 -0.008051  0.040179   0.002695   0.000732  -0.128751  -0.067896 -0.161093\n",
       "64  0.006792 -0.026910   0.003068  -0.000003   0.067510   0.027093  0.077551\n",
       "65 -0.002440 -0.021398  -0.001077   0.000158   0.030868  -0.006042  0.000068\n",
       "66  0.031549 -0.020537  -0.000390  -0.000860   0.026793  -0.005726  0.030829\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_samplenew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>0.006956</td>\n",
       "      <td>-0.008712</td>\n",
       "      <td>0.016566</td>\n",
       "      <td>0.024191</td>\n",
       "      <td>0.038647</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023827</td>\n",
       "      <td>-0.001753</td>\n",
       "      <td>-0.001883</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>0.015302</td>\n",
       "      <td>0.014099</td>\n",
       "      <td>0.051360</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.011658</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.004754</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.004902</td>\n",
       "      <td>-0.004113</td>\n",
       "      <td>-0.014283</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021281</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.006801</td>\n",
       "      <td>0.005061</td>\n",
       "      <td>-0.005591</td>\n",
       "      <td>0.027891</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.018819</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>0.013867</td>\n",
       "      <td>0.012965</td>\n",
       "      <td>0.012953</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.060088</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.003888</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>-0.003456</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>-0.007869</td>\n",
       "      <td>-0.025440</td>\n",
       "      <td>-0.039182</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.006805</td>\n",
       "      <td>-0.015511</td>\n",
       "      <td>0.002557</td>\n",
       "      <td>0.005525</td>\n",
       "      <td>-0.094448</td>\n",
       "      <td>-0.051779</td>\n",
       "      <td>-0.160461</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.005594</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>0.002869</td>\n",
       "      <td>-0.000346</td>\n",
       "      <td>0.049225</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>0.089370</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.001921</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>-0.000984</td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>0.022359</td>\n",
       "      <td>-0.004685</td>\n",
       "      <td>0.025035</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.024015</td>\n",
       "      <td>0.012826</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.003696</td>\n",
       "      <td>0.019416</td>\n",
       "      <td>-0.004420</td>\n",
       "      <td>0.055086</td>\n",
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       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
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       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret       ret\n",
       "0   0.001103 -0.001457   0.006956  -0.008712   0.016566   0.024191  0.038647\n",
       "1   0.023827 -0.001753  -0.001883   0.001768   0.015302   0.014099  0.051360\n",
       "2  -0.011658 -0.000015  -0.004754   0.001355   0.004902  -0.004113 -0.014283\n",
       "3   0.021281  0.000056   0.000283   0.006801   0.005061  -0.005591  0.027891\n",
       "4   0.018819 -0.000635   0.013867   0.012965   0.012953   0.002119  0.060088\n",
       "..       ...       ...        ...        ...        ...        ...       ...\n",
       "62 -0.003888  0.000848  -0.003456   0.000624  -0.007869  -0.025440 -0.039182\n",
       "63 -0.006805 -0.015511   0.002557   0.005525  -0.094448  -0.051779 -0.160461\n",
       "64  0.005594  0.011250   0.002869  -0.000346   0.049225   0.020778  0.089370\n",
       "65 -0.001921  0.011523  -0.000984  -0.001258   0.022359  -0.004685  0.025035\n",
       "66  0.024015  0.012826  -0.000445   0.003696   0.019416  -0.004420  0.055086\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_factornew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.002147</td>\n",
       "      <td>-0.005765</td>\n",
       "      <td>0.019309</td>\n",
       "      <td>0.029086</td>\n",
       "      <td>0.043432</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.025329</td>\n",
       "      <td>-0.004995</td>\n",
       "      <td>-0.000379</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.017586</td>\n",
       "      <td>0.017064</td>\n",
       "      <td>0.055777</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.012361</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>-0.000509</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.005617</td>\n",
       "      <td>-0.004999</td>\n",
       "      <td>-0.011379</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.022629</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.004801</td>\n",
       "      <td>0.005813</td>\n",
       "      <td>-0.006807</td>\n",
       "      <td>0.026618</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.020356</td>\n",
       "      <td>-0.001340</td>\n",
       "      <td>0.002860</td>\n",
       "      <td>0.008777</td>\n",
       "      <td>0.015010</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.048251</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>-0.004330</td>\n",
       "      <td>-0.000081</td>\n",
       "      <td>-0.002562</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>-0.008583</td>\n",
       "      <td>-0.028852</td>\n",
       "      <td>-0.044067</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.007596</td>\n",
       "      <td>0.003951</td>\n",
       "      <td>0.002636</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>-0.102098</td>\n",
       "      <td>-0.059466</td>\n",
       "      <td>-0.160323</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.006302</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>-0.000116</td>\n",
       "      <td>0.053409</td>\n",
       "      <td>0.023759</td>\n",
       "      <td>0.084430</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.002203</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>-0.001043</td>\n",
       "      <td>-0.000382</td>\n",
       "      <td>0.024312</td>\n",
       "      <td>-0.005319</td>\n",
       "      <td>0.015735</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.027868</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>-0.000422</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>0.021093</td>\n",
       "      <td>-0.005025</td>\n",
       "      <td>0.046284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret       ret\n",
       "0   0.001178 -0.002524   0.002147  -0.005765   0.019309   0.029086  0.043432\n",
       "1   0.025329 -0.004995  -0.000379   0.001171   0.017586   0.017064  0.055777\n",
       "2  -0.012361 -0.000056  -0.000509   0.000929   0.005617  -0.004999 -0.011379\n",
       "3   0.022629  0.000166   0.000016   0.004801   0.005813  -0.006807  0.026618\n",
       "4   0.020356 -0.001340   0.002860   0.008777   0.015010   0.002588  0.048251\n",
       "..       ...       ...        ...        ...        ...        ...       ...\n",
       "62 -0.004330 -0.000081  -0.002562   0.000340  -0.008583  -0.028852 -0.044067\n",
       "63 -0.007596  0.003951   0.002636   0.002250  -0.102098  -0.059466 -0.160323\n",
       "64  0.006302 -0.001943   0.003020  -0.000116   0.053409   0.023759  0.084430\n",
       "65 -0.002203  0.000371  -0.001043  -0.000382   0.024312  -0.005319  0.015735\n",
       "66  0.027868  0.001675  -0.000422   0.001096   0.021093  -0.005025  0.046284\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shrinknew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_samplenew['ret']=df_samplenew.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>0.027409</td>\n",
       "      <td>-0.015296</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.022469</td>\n",
       "      <td>0.020769</td>\n",
       "      <td>0.116179</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.000182</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.007125</td>\n",
       "      <td>-0.006108</td>\n",
       "      <td>-0.011780</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.024301</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>-0.000380</td>\n",
       "      <td>0.003728</td>\n",
       "      <td>0.007386</td>\n",
       "      <td>-0.008347</td>\n",
       "      <td>0.054383</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-0.003543</td>\n",
       "      <td>-0.013996</td>\n",
       "      <td>0.006032</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.066579</td>\n",
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       "      <th>62</th>\n",
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       "      <td>-0.001816</td>\n",
       "      <td>-0.002094</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>-0.010715</td>\n",
       "      <td>-0.032414</td>\n",
       "      <td>-0.102820</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>-0.008051</td>\n",
       "      <td>0.040179</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>-0.128751</td>\n",
       "      <td>-0.067896</td>\n",
       "      <td>-0.322185</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.006792</td>\n",
       "      <td>-0.026910</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.067510</td>\n",
       "      <td>0.027093</td>\n",
       "      <td>0.155102</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>-0.002440</td>\n",
       "      <td>-0.021398</td>\n",
       "      <td>-0.001077</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.030868</td>\n",
       "      <td>-0.006042</td>\n",
       "      <td>0.000136</td>\n",
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       "    <tr>\n",
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       "      <td>-0.020537</td>\n",
       "      <td>-0.000390</td>\n",
       "      <td>-0.000860</td>\n",
       "      <td>0.026793</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>0.061658</td>\n",
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       "<p>67 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "      661ret    858ret  600196ret  600276ret  600519ret  600900ret       ret\n",
       "0   0.001281 -0.005831  -0.005305  -0.004038   0.024784   0.035307  0.092395\n",
       "1   0.027409 -0.015296   0.001900   0.000839   0.022469   0.020769  0.116179\n",
       "2  -0.013299 -0.000182   0.005862   0.000712   0.007125  -0.006108 -0.011780\n",
       "3   0.024301  0.000503  -0.000380   0.003728   0.007386  -0.008347  0.054383\n",
       "4   0.022361 -0.003543  -0.013996   0.006032   0.019231   0.003203  0.066579\n",
       "..       ...       ...        ...        ...        ...        ...       ...\n",
       "62 -0.004607 -0.001816  -0.002094   0.000236  -0.010715  -0.032414 -0.102820\n",
       "63 -0.008051  0.040179   0.002695   0.000732  -0.128751  -0.067896 -0.322185\n",
       "64  0.006792 -0.026910   0.003068  -0.000003   0.067510   0.027093  0.155102\n",
       "65 -0.002440 -0.021398  -0.001077   0.000158   0.030868  -0.006042  0.000136\n",
       "66  0.031549 -0.020537  -0.000390  -0.000860   0.026793  -0.005726  0.061658\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_samplenew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10743846367767537\n",
      "0.02535446866252992\n",
      "0.2359906107611098\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Sample')\n",
    "y=df_samplenew['ret'].values.tolist()\n",
    "plt.plot(x, y)\n",
    "plt.grid()\n",
    "std=df_samplenew['ret'].values.std()\n",
    "mean=df_samplenew['ret'].values.mean()\n",
    "sharpe=mean/std\n",
    "print(std)\n",
    "print(mean)\n",
    "print(sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "base"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}