TEJAPI_MediumWeek5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dependent-split",
   "metadata": {},
   "source": [
    "## Week 5 Medium - 定期定額投資回測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fluid-albert",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.transforms as transforms\n",
    "import numpy_financial as npf\n",
    "\n",
    "\n",
    "import datetime\n",
    "\n",
    "import tejapi\n",
    "tejapi.ApiConfig.api_key = \"your key\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dutch-portuguese",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 資料撈取 from TEJ API\n",
    "\n",
    "TW0050 = tejapi.get(\n",
    "        'TWN/EWPRCD', \n",
    "        coid = '0050',\n",
    "        mdate={'gte':'2016-01-01', 'lte':'2020-12-31'}, \n",
    "        opts={'columns': ['mdate','close_adj']}, \n",
    "        paginate=True\n",
    "        )\n",
    "\n",
    "TW0050['mdate'] = TW0050.loc[:,'mdate'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grave-illness",
   "metadata": {},
   "source": [
    "### 1. 將資料整理成月資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "completed-service",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_adj</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mdate</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>49.8465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>49.4280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>48.9257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>48.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>48.0887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25</th>\n",
       "      <td>116.4129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>117.4405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>117.3427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>119.0064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>119.6425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1224 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            close_adj\n",
       "mdate                \n",
       "2016-01-04    49.8465\n",
       "2016-01-05    49.4280\n",
       "2016-01-06    48.9257\n",
       "2016-01-07    48.0050\n",
       "2016-01-08    48.0887\n",
       "...               ...\n",
       "2020-12-25   116.4129\n",
       "2020-12-28   117.4405\n",
       "2020-12-29   117.3427\n",
       "2020-12-30   119.0064\n",
       "2020-12-31   119.6425\n",
       "\n",
       "[1224 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TW0050 = TW0050.set_index('mdate')\n",
    "TW0050"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "demonstrated-bulgarian",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_adj</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mdate</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>49.8465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>49.8465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>51.7717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>53.4877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>50.8510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>101.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>100.8520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>101.1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>111.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>119.6425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            close_adj\n",
       "mdate                \n",
       "2016-01-01    49.8465\n",
       "2016-02-01    49.8465\n",
       "2016-03-01    51.7717\n",
       "2016-04-01    53.4877\n",
       "2016-05-01    50.8510\n",
       "...               ...\n",
       "2020-09-01   101.1456\n",
       "2020-10-01   100.8520\n",
       "2020-11-01   101.1946\n",
       "2020-12-01   111.8621\n",
       "2020-12-31   119.6425\n",
       "\n",
       "[61 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TW0050_monthly = TW0050.resample('MS').first()\n",
    "TW0050_monthly = TW0050_monthly.append(TW0050.iloc[-1])\n",
    "TW0050_monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prompt-peter",
   "metadata": {},
   "source": [
    "### 2. 每月月初投入10000新台幣、期間不領回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "graduate-occupation",
   "metadata": {},
   "outputs": [],
   "source": [
    "TW0050_monthly['每月購買股數']= np.floor(10000 / TW0050_monthly['close_adj'])\n",
    "TW0050_monthly['每月購買股數'][-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "velvet-airfare",
   "metadata": {},
   "outputs": [],
   "source": [
    "TW0050_monthly['累積股數'] = TW0050_monthly['每月購買股數'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "secure-stand",
   "metadata": {},
   "outputs": [],
   "source": [
    "TW0050_monthly['累積價值(月)'] = round(TW0050_monthly['累積股數'] * TW0050_monthly['close_adj'],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "quiet-romance",
   "metadata": {},
   "outputs": [],
   "source": [
    "TW0050_monthly['原始價值(月)'] = [10000*i for i in range(len(TW0050_monthly.index))]\n",
    "TW0050_monthly['原始價值(月)'] = TW0050_monthly['原始價值(月)'].shift(-1)\n",
    "TW0050_monthly['原始價值(月)'][-1] = TW0050_monthly['原始價值(月)'][-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "wicked-interim",
   "metadata": {},
   "outputs": [],
   "source": [
    "TW0050_monthly['累積報酬(月)'] = round((TW0050_monthly['累積價值(月)']-TW0050_monthly['原始價值(月)'])/TW0050_monthly['原始價值(月)'], 6) +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "nuclear-journalist",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_adj</th>\n",
       "      <th>每月購買股數</th>\n",
       "      <th>累積股數</th>\n",
       "      <th>累積價值(月)</th>\n",
       "      <th>原始價值(月)</th>\n",
       "      <th>累積報酬(月)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>49.8465</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>9969.30</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.996930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>49.8465</td>\n",
       "      <td>200.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>19938.60</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.996930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>51.7717</td>\n",
       "      <td>193.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>30700.62</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.023354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>53.4877</td>\n",
       "      <td>186.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>41666.92</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>1.041673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>50.8510</td>\n",
       "      <td>196.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>49579.72</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.991594</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>2020-09-01</th>\n",
       "      <td>101.1456</td>\n",
       "      <td>98.0</td>\n",
       "      <td>8158.0</td>\n",
       "      <td>825145.80</td>\n",
       "      <td>570000.0</td>\n",
       "      <td>1.447624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>100.8520</td>\n",
       "      <td>99.0</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>832734.96</td>\n",
       "      <td>580000.0</td>\n",
       "      <td>1.435750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>101.1946</td>\n",
       "      <td>98.0</td>\n",
       "      <td>8355.0</td>\n",
       "      <td>845480.88</td>\n",
       "      <td>590000.0</td>\n",
       "      <td>1.433018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>111.8621</td>\n",
       "      <td>89.0</td>\n",
       "      <td>8444.0</td>\n",
       "      <td>944563.57</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>1.574273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>119.6425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8444.0</td>\n",
       "      <td>1010261.27</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>1.683769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            close_adj  每月購買股數    累積股數     累積價值(月)   原始價值(月)   累積報酬(月)\n",
       "mdate                                                                \n",
       "2016-01-01    49.8465   200.0   200.0     9969.30   10000.0  0.996930\n",
       "2016-02-01    49.8465   200.0   400.0    19938.60   20000.0  0.996930\n",
       "2016-03-01    51.7717   193.0   593.0    30700.62   30000.0  1.023354\n",
       "2016-04-01    53.4877   186.0   779.0    41666.92   40000.0  1.041673\n",
       "2016-05-01    50.8510   196.0   975.0    49579.72   50000.0  0.991594\n",
       "...               ...     ...     ...         ...       ...       ...\n",
       "2020-09-01   101.1456    98.0  8158.0   825145.80  570000.0  1.447624\n",
       "2020-10-01   100.8520    99.0  8257.0   832734.96  580000.0  1.435750\n",
       "2020-11-01   101.1946    98.0  8355.0   845480.88  590000.0  1.433018\n",
       "2020-12-01   111.8621    89.0  8444.0   944563.57  600000.0  1.574273\n",
       "2020-12-31   119.6425     0.0  8444.0  1010261.27  600000.0  1.683769\n",
       "\n",
       "[61 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TW0050_monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "derived-transport",
   "metadata": {},
   "source": [
    "### 3. 作圖（累積資產、月報酬）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "humanitarian-spyware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EhPrvU5GHNYvbkSIiIiLZReXsLK5gSj//vxUJCmEiIiLSTCRvKQAIiPlgoBAmIiIizURyZiGJHaLoERvldimNohAmIiIiAa+ypo6V24sD5lYkKISJiIhIM7BqRwmVNR6mBMitSFAIExERkWYgeUsB4SFBTOgV63YpjaYQJiIiIgHvs8xCxvUKjNYUhymEiYiISEDLKalge2E5UwNoPhgohImIiEiAS850VswJlNYUhymEiYiISED7bEsB3dpHktghMFpTHKYQJiIiIgGrqraOr7YVM7VfJ4wxbpdzShTCREREJGCtzd5HRXVdQPUHO0whTERERAJW8pYCwoKDmNgncFpTHKYQJiIiIgEreUshYxPb0yosxO1STplCmIiIiASkvP2HyCooC8hbkaAQJiIiIgHqsy2B2ZriMIUwERERCUjJWwro2jaSPp2i3S7ltCiEiYiISMCprvXw1bZizu7XMeBaUxymECYiIiIBJ2XnPsqqagP2ViQohImIiEgASs4sICTIMKlPB7dLOW0KYSIiIhJwPttSyOie7YgOD7zWFIcphImIiEhA2Xugks17S5nav5PbpZwRhTAREREJKO9u3AMQsP3BDgvca3giIiLSYtTUeViWtpfnvtjB+pz99I+LYUDnGLfLOiMKYSIiIuK39ldUM391Di+tyGbPgUoSO0Txx9mDmTMyIWBbUxymECYiIiJ+Z2tBGS98tYPFKXkcqqljUp9Y/nTpEKb170RQUGCHr8MUwkRERMQv7Cwu5/OsIj7IyGd5ZiFhIUFcOrwLt05OZEDn1m6X53UKYSIiIuKK/RXVfLWtmM+zivhiayE5JYcA6No2kp/O7Me147rTITrc5Sp9RyFMREREmkxOSQUL1uzii6wiUvMOYC1Eh4cwvlcst5/Vi8l9OpDYISrg53s1hkKYiIiI+Jy1ltfX5fG7JekcqqljeLe23Du9L2f17cCwbm0JDW55XbMUwkRERMSnDlTU8Os3N/Ju6h7G9mzP368aRrf2rdwuy3UKYSIiIuIzX20r4mcLN1BYWsUvzuvPXVN6E9xMnm48UwphIiIi4nXVtR7+/uEWnl6+ncTYKF6/ZyJDE9q6XZZf8ekNWGPM+caYLcaYrcaY+4/xehtjzNvGmA3GmHRjzC2+rEdERER8b2tBKZc9+SX/99l25o7pzjv3TlYAOwafXQkzxgQDTwAzgVxgjTFmibU2o8Fu3wcyrLUXG2M6AluMMfOstdW+qktERER8Z96qnfzx7QyiwkN4+oZRnDu4s9sl+S1f3o4cC2y11m4HMMYsAGYDDUOYBWKM8xxqNFAC1PqwJhEREfGRTXsO8ps30jirbwf+fuUwOrWOcLskv+bL25FdgZwG3+fWb2vocWAgsBvYCPzIWuvxYU0iIiLiI59uKQBQAGskX14JO9ajD/ao788D1gPTgd7Ah8aYz621B791IGPuAO4AiIuLIzk52evFHq2srKxJziONpzHxTxoX/6Mx8T8tZUyWrD5Et5ggMtat/NZtL3/l9rj4MoTlAt0afJ+Ac8WroVuAh6y1FthqjNkBDABWN9zJWvs08DTA6NGj7dSpU31V8xHJyck0xXmk8TQm/knj4n80Jv6nJYxJeVUt2z78gFsnJzJ16kC3y2kUt8fFl7cj1wB9jTGJxpgwYC6w5Kh9dgEzAIwxcUB/YLsPaxIREREfWLGtmJo6y5S+Hd0uJWD47EqYtbbWGPMD4H0gGHjeWptujLmr/vWngAeBF4wxG3FuX95nrS3yVU0iIiLiG8uzCokMDWZUz3ZulxIwfNqs1Vq7FFh61LanGvx+N3CuL2sQERER31ueWciE3rGEhwS7XUrAaHmrZYqIiIhX7SwuJ7u4grP7dnC7lICiECYiIiJnZHlmIQBn99N8sFOhECYiIiJn5LPMIhLaRZLYIcrtUgKKQpiIiIictupaDyu2FXF2v444C+BIYymEiYiIyGlbt2sf5dV1nK3WFKdMIUxERERO2/LMQoKDDBP7xLpdSsBRCBMREZHTtjyrkJHd29I6ItTtUgKOQpiIiIiclqKyKtLyDupW5GlSCBMREZHT8kWWs8jNlP4KYadDIUxEREROy/LMQtpHhTGkSxu3SwlICmEiIiJyyjwey/KsIib36UBQkFpTnA6FMBERETllm/YepKisSl3yz4BCmIiIiJyy5ZnOfDCtF3n6FMJERETklC3PLGRA5xg6tY5wu5SApRAmIiIip6S8qpa1O0uYoluRZ0QhTERERE7Jyu3F1NRZzQc7QwphIiIicko+yywkMjSY0T3buV1KQFMIExERkVOyPLOQ8b3aEx4S7HYpAU0hTERERBptV3EF2cUVuhXpBQphIiIi0mifZRUCaFK+FyiEiYiISKMtzywkoV0kiR2i3C4l4CmEiYiISKPU1HlYsa2Ys/t1xBgtVXSmFMJERESkUdbt3EdZVS1n99WtSG9QCBMREZFGWZ5VSHCQYWKfWLdLaRYUwkREROSkqms9vL1hD6N6tKN1RKjb5TQLCmEiIiJyUq+u2cWukgruntLb7VKaDYUwEREROaHyqlr+9fFWxvZsz9T+mg/mLQphIiIickLPf7GDorIq7ps1QE9FepFCmIiIiBxXSXk1Ty/fzsxBcYzqobUivUkhTERERI7ryU+3Ul5dyy/P6+92Kc2OQpiIiIgcU97+Q7y0YidzRibQNy7G7XKaHYUwEREROaZ/fpgJBn48s5/bpTRLCmEiIiLyHZn5pby+Lpcbx/ega9tIt8tplhTCRERE5Dv+9v4WosJC+P60Pm6X0mwphImIiPjQ+pz9PLjiEBty9rtdSqOl7Czhw4x87pzSi3ZRYW6X02wphImIiPhITZ2H+15LZdsBD9c/t4r1ARDErLU8/N4WOkSHc+vkRLfLadYUwkRERHzkP1/uYEt+KdcPDKNdqzBueHYVX+/a53ZZJ5S8pZDV2SX8aEYfWoWFuF1Os6YQJiIi4gO79x/i0Y+ymDGgEzO6h7DgjvG0jw7jxudWs85Pg5jHY3l42WZ6xLZi7tjubpfT7CmEiYiI+MAf387AYy2/v2Qwxhi6tI38VhBL2el/QeytDXls3lvKz87tT2iwIoKv6b+wiIiIl326uYBl6Xv54fS+dGvf6sj2+DZOEOsQHcZNz68mZWeJi1V+W3FZFX//IJPBXVpzUVK82+W0CAphIiIiXlRZU8fvlqTTu2MUt5/V6zuvO0FsAh1jwuuviLkfxLbsLWX2E19SWFrFby8aRFCQFuluCgphIiIiXvTEp1vZVVLBg5cOISzk2H/Ndm4Twfzbx9OpdQQ3PreatdnuBbGPN+Vz+ZNfUl3rYeGdExjXK9a1WloahTAREREv2VZYxv99tp1Lh3dhYu8OJ9y3c5sIFtwxnrjWEdz4/GpeX5fL17v2sau4grKqWqy1Pq3VWsszy7dz20trSewYxZIfTGZYt7Y+Pad8m549FRER8QJrLb99K43w0CB+feHARr0nrnUE8+8YzzXPrOSnCzd867WwkCBio5zWFrHRYUzp15HbjnF783RU13r4zRsbWZSSywVJnfn7lcOJDAv2yrGl8RTCREREvODt1D18ubWYP84eTKeYiEa/L651BEvvPYtNew6yr6Ka4rJqSsqdr+LyavaVV7OtsIy/vLeZS4Z1oVPrxh/7WIrLqrj75XWszi7h3hl9+fGMvpoD5hKFMBERkTN0sLKGB9/JYGhCG64b1+OU3x8RGsyI7u2O+/qOonKmPZLMopTcM1rLccveUr734hoKS6t47JoRXDKsy2kfS86c5oSJiIicoX98kElRWRV/unQIwT64qpTYIYoJvWJZsGYXHs/pzRX7IquIOf/+iupaD6/eOUEBzA8ohImIiJyBtLwDvLQim+vH9WBoQlufneeacd3JKTnEl9uKTvm9tXUe7lucSuc2Ebz1g0kM1wR8v6AQJiIicpqKyqr4xWuptI8K5+fn9ffpuc4bHEe7VqHMX73rlN/7Xtpe8vYf4r7zBxDfJtIH1cnpUAgTERE5DWl5B5j9+JdsLyzjr1ck0SYy1KfnCw8JZs7IBD5Iz6ewtKrR77PW8szn2+nVIYoZAzr5sEI5VQphIiIip+jtDbu54qmvsNby2l0TmT4grknOO3dsd2o9lsXrchv9ntU7SkjNPcCtkxP1FKSf8WkIM8acb4zZYozZaoy5/zj7TDXGrDfGpBtjPvNlPSIiImeizmN5eNlmfjj/a5K6tmHJDyeTlNCmyc7fp1M0YxPbs2B14yfoP/P5Dtq1CmXOyAQfVyenymchzBgTDDwBzAIGAdcYYwYdtU9b4EngEmvtYOBKX9UjIiJyJg5W1nDbi2v4d/I2rh3XnXm3jadDdHiT13Ht2O5kF1ewcnvxSffdVljGx5vzuWFCTzVj9UO+7BM2Fthqrd0OYIxZAMwGMhrscy3wurV2F4C1tsCH9YiIiJyWbYVl3P7SWnYVV/CnS4dw/fhT7wXmLecP6UybJaG8snoXE/uceGmk577YQWhwEDdOcK/eJldVCpvega0fQd2J587F13UDpjZJWcdifLU2lTHmCuB8a+1t9d/fAIyz1v6gwT6PAqHAYCAG+Je19qVjHOsO4A6AuLi4UQsWLPBJzQ2VlZURHR3t8/NI42lM/JPGxf9oTLxrQ2EtT22oIiQIfjA8gv7tT/2KkrfHZN6mKj7dVcs/prWiddix53kdrLb8LLmCCV1CuHVI01+xa0rGU0P7kq+Jy/+M2OLVBHuqqQprT01ozAnfl9tmDHv73eDT2qZNm5ZirR19rNd8eSXsWP9XHJ34QoBRwAwgElhhjFlprc381pusfRp4GmD06NF26tSp3q/2KMnJyTTFeaTxNCb+SePifzQm3vN++l4efT+FQfGtefrG0XRte3rtHbw9Jl0GlvLhP5eTH9GDS84+9nqS//ooixpPJr+9aiJ9Op04jAQkjwdyVsHGhZD+Jhwqgcj2MOpGSLqK8G5jCTcnfhBhr8s/K74MYblAtwbfJwC7j7FPkbW2HCg3xiwHhgGZiIiIuGhrQRk/W7iBoQltWXD7eL+aU9UvLoZRPdoxf/UubjsrEXNU2KisqeOlFdlMH9CpeQUwayE/HdIWw8bX4MAuCImEARfA0Kuh93QI9m2rEG/yZQhbA/Q1xiQCecBcnDlgDb0FPG6MCQHCgHHAP31Yk4iIyEmVVdVy53/XEh4SxFPXj/SrAHbYNWO78/NFG1i1o4TxvWK/9dobX+dRXF7NbWclulSdlxVtdYJX2mIo2gImGHpNhem/gQEXQnhgBk2fhTBrba0x5gfA+0Aw8Ly1Nt0Yc1f9609ZazcZY5YBqYAHeNZam+armkRERE7GWsvPF24gu7iC/35vrN92mL8wKZ4/vJ3OgtW7vhXCPB7Ls59vZ3CX1kw4KpwFlP27IO11J3jtTQUM9JgE4+6EQbMh6sQPJQQCX14Jw1q7FFh61Lanjvr+b8DffFmHiIhIYz312XaWpe/lgQsHMrG3//5FHxkWzOUjujJ/TQ6/K6+mXVQYAMmZBWwrLOfRq4d/5zal3ysvgvQ3IHUh5K52tnUdDef9BQZfCq2b16LjPg1hIiIigeTzrEL+9v5mLhoaz/cm+/+tvLlju/Piip28/nXekXqfWb6D+DYRXDg03uXqGqm6HDYvdSbYb/0YbB10GgwzfgeDL4P2/j8Op0shTEREBMjdV8G987+mT6doHp4zNCCuIg2Mb83wbm2Zv3oXt07qSfrug6zYXsyvLxhAaLAfr0xYVwvbP3WueG1+F2rKoXUCTPwhDL0K4ga7XWGTUAgTEZEWr7KmjrteTqG2zvJ/N4wmKjxw/nq8dmx3frk4lZSd+/jvyp1Eh4cwd2x3t8v6rsMtJdIWO7ccK4ogoi0MvRKSroLuEyDIj4OjDwTO/2UiIiI+YK3lgTfTSMs7yLM3jiaxQ5TbJZ2Si4bF88d3Mnj0oyxWbC/m5ok9aR3hJ20arIXdX38TvA7mOS0l+p3nXPHqcw6ENO9GsieiECYiIi3avFW7eC0ll3tn9OWcQXFul3PKWoWFMHt4F+at2kVwkOGWST3dLgnyM75pKbFvBwSFOoHrnD9A//MDtqWEtymEiYhIi5Wycx9/eDudqf078uMZfd0u57RdM7Y781bt4oKkeBLatXKniOJtkP6601aiIANMECROgbN+BgMvgsh27tTlxxTCRESkRSosreKeeSnEt4nk0auHExTk/xPxj2dI1zY8evVwJvRu4r5gB3Kd24xpi53bjgDdxsMFjzi9vKI7NW09AUYhTEREWpyaOg/ff2UdBw7V8PrdY2nbKsztks7YpSO6Ns2Jygog4y0neO1a4WzrMgLO/ZPTUqJNQtPU0QwohImISIvzl6WbWb2jhEevHs6gLq3dLsf/VR6Eze84LSV2fAbWAx0HwLQHYMjlENvb7QoDUqNDmDEmqn6hbRERkYD11vo8nv9yBzdP7Nl0V48CUW01bP3IaaK65T2orYS2PWDyT2DIFRA3yO0KA95JQ5gxZiLwLBANdDfGDAPutNbe4+viREREvGnz3oPcv3gjY3q249cXDHS7HP/j8UDOSueKV8abcGgfRLaHEdc7vby6jYUAaGIbKBpzJeyfwHnAEgBr7QZjzNk+rUpERMTLDhyq4a7/phAdEcIT144kLKRlNQY9LmudBbLTFkPaG3Bgl9PLa8CFTi+v3tMh2E/6jjUzjbodaa3NOWr5hjrflCMiIuJ9Ho/lp6+uJ3ffIRbcMZ5OrSPcLsl9hVu+6eVVvBWCQqDXVJj+gBPAwqPdrrDZa0wIy6m/JWmNMWHAvcAm35YlIiLiPY9/upWPNxfwh0sGM7pne7fLcc++bKePV9rrkL8RMNBzMkz4AQy8BKKauMVFC9eYEHYX8C+gK5ALfAB835dFiYiIeMunWwr450eZXDaiKzdO6OF2OU2vrNDp5bVxIeSucbYljIXzH3Z6ebWOd7e+FuykIcxaWwRc1wS1iIiIeNWu4gp+NP9rBnRuzZ8vS8K0lEnlVWWwZakzwX7bJ2DrIG4IzPgdDJkD7VpgGPVDjXk68j+APXq7tfZWn1QkIiJyhsqqalmzo4SHl20G4P+uH0VkWLDLVflYXQ1s+9S54rX5XaipgDbdYNK9zpONainhdxpzO/KdBr+PAC4DdvumHBERkVN3qLqOlJ37+GpbESu2F5Oae4A6jyUyNJh/Xz+S7rEurafoax6P07U+bbHTUqKiGCLawtCrnScbu42HID0F6q8acztyccPvjTHzgY98VpGIiEgjbMw9wMeb8/lqWzHrd+2nus5DcJBhWEIb7p7Sm4m9YxnZox0Roc3sCpi1kLfOCV7pb0DpbghtBf3Oh6Qroc85EBL4yzC1BKezbFFfoLu3CxERETmZypo63t6wm/+u3Elq7gGMgSFd2nDzpJ5M6B3LmJ7tiQ5vhivyWQv56d+0lNi/E4LDoM9MGPIg9J8FYVFuVymnqDFzwkpx5oSZ+l/3Avf5uC4REZEjckoqeHnlTl5dm8P+ihr6dIrmD5cMZvbwLs1i8e3jOrjHmeO1YQEUZIAJdnp5TbnP6eUV2dbtCuUMNOZ2ZExTFCIiItKQx2P5LKuQ/67YyadbCggyhnMHxXHDhB5M6BXbfJ90rC53JtZvmA/bk53FshPGwgWPwKBLIbqj2xWKlxw3hBljRp7ojdbadd4vR0REBFJ2lvDThRvYWVxBx5hwfji9L9eM7UZ8m0i3S/MNjwd2fulc8cp4E6rLoE13OOtnMOwaiO3tdoXiAye6Evb3E7xmgelerkVERISckgpufymFmIgQ/veaEZw3uHPzXOfRWtiz/ps1Gw/mQlgMDL7UCV7dJ+rJxmbuuCHMWjutKQsREREpr6rl9pfWUlvn4YVbxpLYoRlONi/Y9M0E+5LtEBQKfWbAOb935nmFNdN2GvIdjXqExBgzBBiE0ycMAGvtS74qSkREWh6Px/LzRRvIzC9tdgEssmIPLP+bs2ZjQQaYIEg8Gyb/BAZcBK1a8HqWLVhjno78HTAVJ4QtBWYBXwAKYSIi4jWPfZLFe2l7eeDCgZzdrxlMPj+Q6/TxSlvMuN1fO9u6T6ifYD8boju5W5+4rjFXwq4AhgFfW2tvMcbEAc/6tiwREQlUNXUetuwtZXCX1o1+gnFZ2h4e/SiLOSMT+N7kRB9X6ENlhc7E+rTFTid7gPjhbOt1M71n/wLaJLhanviXxoSwSmutxxhTa4xpDRQAvXxcl4iIBKDqWg/3zFvHR5vyGd2jHb++cCAju7c74Xs27TnIT17dwPBubfmfy4YEXuuJQ/tg0ztO8NrxmdNSouMAmPYADLkcYnuTk5xMbwUwOcqJWlQ8DswHVhtj2gLPAClAGbC6SaoTEZGAUVVbx/fnreOjTQVcM7Y7H2bkc/mTX3FBUmd+ed4Aeh5jjldJeTW3v7SW1pEhPH3DqMBZYqimEjKXwcZFkPUB1FVDu57OHK8hV2ixbGmUE10JywIeAbrgBK/5wEygtbU2tQlqExGRAFFVW8c9L6/j480F/OnSIVw/vgcPXDiQp5dv5+nl2/kwI5/rxvXg3hl9aR/ldLivqfNw98spFJRWsejOCXRqHXGSs7jMUwfZn0PqIti0BKoOQnQcjLnNCV5dR0KgXcUTV52oRcW/gH8ZY3oAc4H/4DwdOd8Yc8ham9VENYqIiB+rqq3j7pfX8cnmAv7nsiFcN64HAFHhIfxkZj+uG9edf36UyUsrslm8Lpd7pvbhlkk9+dO7GazaUcKjVw9nWLe27n6I47EW9qZC6kLndmPpHqeX18CLYeiV0PNsCG6Ga1VKk2jMskU7gYeBh40xI4Dngd8BAXLNWEREfKWypo67X07h0y2F/PmyJK4d1/07+3RqHcFfLh/KrZMSeei9zTy8bDPPfL6dkvJq7pzSi0tHdHWh8pMozPyml1dxltPLq+9MSPqzs1h2aDPt3C9NqjEtKkKB83Guhs0APgP+4OO6RETEz1XW1HHnf1P4LLOQv1yexDVjvxvAGuobF8NzN49hxbZi/vb+Zib0iuWX5w1oomobYV+208cr7XXI3wgY6DkZJtzjrNmoXl7iZSeamD8TuAa4EGci/gLgDmtteRPVJiIifqqypo47/pvC8sxCHro8ibknCWANTegdy+v3TPJhdaegdO+RXl7krnG2JYyB8x9yglfreFfLk+btRFfCfg28AvzcWlvSRPWIiIifq6yp4/aX1vLF1iIenpPE1WMaH8D8QuUB2PS2M89rx3LAQuckZ9mgwZc5TzmKNAGtHSkiIo1WWlnDnf9NYcX2Yh6+fChXjenmdkmNU1sFWR/CxoWwZRnUVTlh6+xfQNIV0LG/2xVKC6RHOkREpFEKSiu55T9r2Ly3lL9fOYzLR/p581GPx+lan/oqZLwFlfuhVQcYdRMkXQUJo9VSQlylECYiIie1o6icG59fRVFpNc/eNJpp/f103UNrYfe6bybYl+6G0CgYcCEMvQp6TYXgULerFAEUwkRE5CQ25OznlhecSevz7xjPcH/r6WUtFGR801JiXzYEh0GfmTDkQaelRNh3u/WLuE0hTEREjuuzzELufjmF9lFhvHTrWHp1jHa7pG8Ub/smeBVuBhMMvaY487wGXASRbd2uUOSEFMJEROSY3vg6l18sSqVvXAwv3jLGP5YV2p8D6a87wWvPBsBAj4lw4d9h4GyI7uh2hSKNphAmIiLf8fTybfx56WbG92rP0zeOpnWEi/Ooygog/U0neOWsdLZ1GQnn/dnp5dXGDzvuizSCQpiIiBzh8Vj+vHQTz36xgwuT4vnH1cMID3FhlbpD+5xeXhtfcxbNth7oNBim/z8Ycjm079X0NYl4mUKYiIgAUFJezU8Xrid5SyE3TejBby8eTHBQE7ZwqKmEzGWwcRFkfQB11U7YOuvnTvDqNLDpahFpAgphIiJCys4SfvDK1xSXVfPg7MFcP74Hpil6aHnqnCtdqYtg0xKoOgjRcTDmdqeJapcR6uUlzZZCmIhIC2at5ZnPt/PXZVvo0jaSxXdPJCmhja9PCnvWO7caN74GZXshLAYGXQJJV0Li2RDkwi1QkSamECYi0gQKSitZnlnExcPi3ZljdQwHKmr42aINfLQpn/MGx/HXK4bRJtKHE/ALNn/TUqJkGwSFQt+ZThPVfudDaKTvzi3ihxTCRER8qLC0iv/7bBsvr9pJZY2H5C0FPDZ3BEFNOdfqGNbn7Of789ZRUFrJby8axC2Tevrm9mPJ9m+61xekgwmCnmfBpB/BwIuhVXvvn1MkQCiEiYj4QFGZE77+u3In1bUeLh3RlbjWEfw7eRvxbSL4zYWDXKnLWssLX2Xz56Wb6BQTwaK7Jnq/A/7BPd/08spLcbZ1Gwez/gaDZkNMnHfPJxKgfBrCjDHnA/8CgoFnrbUPHWe/McBK4Gpr7Wu+rElExJeKy6p4evl2Xlqxk6raOmYP78oPp/ehV8dorLVUVNXyzOc7iG8Tya2TE5u0Nmud9hPPfL6DcwZ24pErh9G2VZh3Dl55ADKWwMaFsONzwEL8MJj5Rxh8GbTt7p3ziDQjPgthxphg4AlgJpALrDHGLLHWZhxjv4eB931Vi4iIrxWWVvHcFzt4aUU2h2rquGRYF+6d0ZfeDZb5Mcbw24sHs/dgJQ++m0HnNhFckBTfJPV5PJb/91Ya81bt4qYJPfjdxYPP/JZobZXTSiJ1IWS+D3VV0C4RpvzSmWDfoa93ihdppnx5JWwssNVaux3AGLMAmA1kHLXfD4HFwBgf1iIi4nV1HsvyrEJeXZ3DR5vyqbOWi4Z24Ucz+tCnU8wx3xMcZPjX3BFc9+wqfvzqejpEhzM20bfzomrrPPzytVRe/zqPu6f25pfn9T/9+V+eOtj5ldPLK+NN5wpYqw4w6mZngn3XUWopIdJIxlrrmwMbcwVwvrX2tvrvbwDGWWt/0GCfrsArwHTgOeCdY92ONMbcAdwBEBcXN2rBggU+qbmhsrIyoqP9aKFa0Zj4qZY4LoUVHj7Pq+WLvFpKKi0xYTCpSwhTEkKJjw5q1DHKqi1/WnWIg1WW34yPpGsj3lddZ9lT7qFbTBBBJwg6DcekxmN5akMVKfl1zOkbysW9T+P2o7XElGYSl/85HQu/ILx6H3VBERR1GEd+3BT2tRuGDdIU4xNpiT8ngaApxmXatGkp1trRx3rNlz81x/oT4ujE9yhwn7W27kT/KrPWPg08DTB69Gg7depUL5V4fMnJyTTFeaTxNCb+qaWMS1VtHR+k5/Pqmhy+2FqEMXB2347MHdONGQPjCAtpXPhqaNjoCi578iueTIPX7xlP3HEWyN69/xAvr9zJgjU5lJRX06dTNPdM7c3Fw7oQGvzd8x4ek0PVddz1cgop+RX89qJBpzYHzVrIT/umpcT+XRAcBn3PhSGXE9zvfOLCotAU+8ZpKT8ngcbtcfFlCMsFujX4PgHYfdQ+o4EF9QGsA3CBMabWWvumD+sSETklaXkHuOn51RSXV9O1bSQ/OacfV4xOoGvbM+tr1a19K164ZQxX/98Kbv7PGhbeOZ6Y+oWyrbWs2lHCi19l80FGPtZaZgyMY3KfDsxfvYufLtzAPz7M5K4pvbliVAIRod/uPVZWVcv3XljD6uwSHp6TxNVjGjkxvmQHpL7qBK+iTDDB0HsaTP0VDLgQInzcyFWkBfFlCFsD9DXGJAJ5wFzg2oY7WGuP/LPMGPMCzu3IN31Yk4jIKSkoreT2l9YSERrMf783lkm9O3i1x9eQrm148vpRfO+FNdz98jqeuG4k76bu4aUV2WzeW0rbVqHcdlYi14/rQbf2rQC4YXwPPtlcwOOfbuWBN9N47OMsbj+rF9eO605UeAjlNZbrnl1FWt4BHr16OLOHdz1xEdXlzpON6+c5SwhhoMckGH83DLwEojp47fOKyDd8FsKstbXGmB/gPPUYDDxvrU03xtxV//pTvjq3iIg3VNXWcffL69hfUcNrd09gcBffXAWa0q8jf7k8iV+8lsqoBz+k1mMZGN+ah+ckccmwrkSGffsqV1CQ4ZxBccwY2IkV24p5Inkr/7N0E08kb+WmCT15fXUl+RWV/Pu6kZw7uPOxT2ot5K6Br/8LaW9AdanzZOP0B2DYNdAmwSefVUS+4dOZlNbapcDSo7YdM3xZa2/2ZS0iIqfCWsv/ezONlJ37eOLakT4LYIddObob5VW1rM/Zz7XjejCmZ7uTPsFojGFinw5M7NOBdbv28eSn2/jXx1mEBcNzN4/lrL4dv/um/TmQ9hqsf8W53RjaCgZdCiOuhx4T9WSjSBPS4ywiIsfwwlfZLFybyw+n9+HCoU3Ty+vmSaffvHVk93Y8e9NosvJLWbNmzbcDWGm+004ibTHkrHK2dRsHl/yv00g1/NjtNETEtxTCRESO8uXWIv707iZmDorjJ+f0c7ucU9I3Loa86CCoKIFNbzvBK/tzsB7oNBim/z8Ycjm07+V2qSItnkKYiEgDO4vLuWfeOnp3jOKfVw93faHtU1JVCpuXkpT6NCxfD55aJ2yd9XMneHUa6HaFItKAQpiISL3Syhpue3EtxsCzN44hOjwA/oisOeQsHbTxNefX2kqiwjvA+HtgyBxn/UbN8xLxSwHwJ4yIiO95PJafvLqe7UXl/PfWsXSPbeV2ScdXWw3bP3VuNW5+F6rLIKojjLwRhsxh5bYKpk6b7naVInISCmEiIsA/Pszko00F/P7iQUzs44d9sTwe2PlF/ZqNS6ByP0S0dW4zDpkDPSZDcP0f6duTXSxURBpLIUxEWrSq2jpeS8nl8U+3cvXobtw0safbJX3DWti7ETYuhI2LoXQ3hEY5neuHzIHe0yHkNNaCFBG/oBAmIi3OgYoaPt1SwIcZ+XyWWUhZVS1jerbjj5cOPmlvriaxb6dzxWvjIijcDEEh0OccOPdB6H8BhPnxrVIRaTSFMBFpEXL3VfBhRj4fZuSzekcJtR5Lh+hwLh4Wz8xBcUzu0/G0FuH2mrJC2PQWpC6CnJXOtm7j4cK/w6DLICrWvdpExCcUwkSkWftyaxEPvbeZjXkHAOjTKZrbz+7FzEFxDE9o624LikP7YfM7zgT77Z+BrYOOA5xeXklXQLue7tUmIj6nECYizVJhaRX/824Gb67fTY/YVvxq1gBmDoqjV8dodwurLoct7znBa+tHUFfthK3JP3bmeXUapJYSIi2EQpiINCsej2XBmhweem8Th2rquHd6H+6Z1oeI0OCTv9lXaqudwLVxEWQug5oKiImHMbc7wavrSAUvkRZIIUxEmo3New/y69c3sm7Xfsb3as+fLk2iTyeXrnx5PM7crtSFzrqNh/ZBq1gYdo0TvLpPgCAX56CJiOsUwkQk4FVU1/Kvj7J49osdtIkM5e9XDuPykV3dedIxP6O+pcRrcCAHQls5LSWSroLe0yA4tOlrEhG/pBAmIn4p/2AlC1bn8NaGPOo8lujwkG++IkKICg8hJjyE8NBgFqfkkrf/EHPHdOO+8wfQLqqJe2ft2wnprzvBKz8NTLDTw2vGb52WEuEuz0MTEb+kECYifsNay1fbinl55U4+yMinzmOZ1CeW2KhwyqtqKa2qZe/BSsoKaymrrKWsqpaqWg/942JYdNcExvRs33TFlu6F9DedCfa5q51tCWNg1t9g8GUQ3bHpahGRgKQQJiI+U1pZQ8rOfXSKiSChfSStI459K25/RTWvpeTyyqpdbC8qp12rUG6bnMi147rTIzbqhOeoqfMQEmSa5tZjRQlkvOUEr+wvAAtxSTDjd87yQWopISKnQCFMRHxi74FKbnhuFVkFZUe2tY4IIaFdK7q2iyShXSRd20ayeW8pb2/YTVWth1E92vHPGX2YNSS+0U8zhgb7eHJ75UHYstQJXts+AU8txPaBKfc5watjf9+eX0SaLYUwEfG6XcUVXPfcSkrKqnnsmhGEBBly91WQu+8QefsOsau4gq+2FlFeXUdUWDBXjErgunE9GNSltdulO6orIOt9J3hlfgB1VdCmG0z4vvNkY+ehaikhImdMIUxEvCozv5Trn11FdZ2HV24fz7BubY+5n7WW/RU1RIQGExnmYg+vw2qrYdvHTvDavBRqyiE6Dkbf4gSvhDEKXiLiVQphIuI1G3L2c9N/VhMWHMTCOyfQLy7muPsaY5r+KcajeTyw8wunl9emJVB5ACLbwdArneDVYxIE+UFAFJFmSSFMRLxixbZibntxDe2jw5j3vfF0j23ldknHZi3s3Vjfy2sxlO6GsGgYcJGzXmOvqerlJSJNQiFMRM7Yx5vyuXveOnq0b8XLt40jrnWE2yV9176dzrJBGxdB4WYICoE+M+G8P0G/WRDmp6FRRJothTAROSMrd9fy7AcpDOrSmhduGUt7t28xNlRW4LSU2Pias4QQOMsFXfgPp5dXqybsKyYichSFMBE5LXUey7xVO/m/1CrGJrbn2ZtGE3OcPmBN6tA+2PS2M8F+x3KwHug40OleP+QKaNfD7QpFRACFMBE5BdZa1ufs5+0Ne3h3427yD1YxrGMwL946ttF9vXyiqhS2vOcEr60fg6cG2iXCWT+DwZdD3CD3ahMROQ6FMBE5IWstGXsO8vaGPbyTupvcfYcICw5iav+OXDysC5HFW9wJYLXVsPVD58nGzGVQWwmtu8K4O50nG7uMUEsJEfFrCmEiAkBlTR2llbWUVtbU/1rLmuwS3k7dzfbCcoKDDGf17cCPz+nHuYPjjixBlJyc2XRFejywa4XzZGP6m1C5H1rFwojrnVuN3cZBkI876IuIeIlCmEgLtLWgjD++k8Gu4vIjgau6zvOd/YyB8Ymx3Da5F+cP6ezepPv8dOeKV9piOJADoa1gwIWQdBX0nqaWEiISkBTCRFqY19fl8sCbaYSHBDG5b0diIkKIiQihdUTokd/HhDu/T+wYRacYl9pNlOyA9NedXl4F6WCCofd0Z4J9/wsgPNqdukREvEQhTKSFqKiu5bdvpfNaSi5jE9vz2NwRdG7jZ/28Du52bjOmLYa8tc62hDEw629OS4nojq6WJyLiTQphIi3A5r0H+f68dWwvKufeGX25d3ofQoL9ZO5UeZHTyyvtddj5JWCdBbLP+YMTvNRSQkSaKYUwkWbMWsuCNTn8fkk6rSNDmfe9cUzs08HtspwnGzPfg69fdlpK2Dro0A+m/gqGXA4d+rpdoYiIzymEiTRTpZU1/PqNNN7esJuz+nbgH1cNp2NMuLtF7d0IX8+D1FfhUAnEdIFJ9zpPNsYNVksJEWlRFMJEmhFrLTklh0jN28/f3t9C7r5D/PL8/tx1dm+CglwKOBUlzrJB61+GPRsgOMx5snH49c6TjUEuNnkVEXGRQphIgKqp85CVX0b67gNk7DlI+u6DbNp9kNKqWgC6tIng1TvGM7qnC+sjVldA1vvOBPvM96Gu2pnnNetvkHSF1mwUEUEhTCSg1Hksy9L28p8vd5Cae+BIb6/I0GAGxMcwe0QXBsW3YXCX1gyIjyE8pAmvMtVWwbZPnOC1eSnUlEN0HIy+FYZfB/FDm64WEZEAoBAmEgAqa+pYvC6XZ5ZvJ7u4gsQOUdwyqSeDurRmcJc2JHaIItiN2411tbQrWQ9vLXIWza48AJHtYOiVztJBPSbpdqOIyHEohIn4sQOHanh55U7+82U2RWVVDEtow7+vG8m5gzu7E7rAWTooZ6VzxSv9TYZVFEFYDAy8yAlevaaqg72ISCMohIn4ob0HKnn+yx28smoXZVW1nN2vI3dN6cWEXrEYN54gtBZ2f10fvN6Ag3kQEgn9ziPNDGDIpT+BUD9r/Coi4ucUwkT8SEFpJY99nMWra3Ko81guGtqFO6f0YnCXNi4VtMl5sjFtMezbAUGh0Occp5Fq/1kQHk1RcrICmIjIaVAIE/EDZVW1PL18O89+vp3qWg9Xj+nGnWf3pntsq6YvZn8ObFzkhK+CdDBBkDgFzvqZc8sxsl3T1yQi0gwphIm4qLrWw/zVu3js4yyKy6u5cGg8Pz+3P4kdopq2kIoSyHgTUhfBrq+cbd3GwQWPwKDZEN2paesREWkBFMJEXGCt5d2Ne/jb+1vYWVzB+F7teX7WQIZ1a9t0RdQcgi3vOVe9sj4ET42zdND0ByDpSmjXs+lqERFpgRTCRJrYV9uKeOi9zaTmHqB/XAz/uXkMU/t3bJoJ97XVsD25vpfXu1BdCtGdYdydTvCKH6alg0REmohCmEgTKauq5Y9vp7NwbS5d2kTwyJXDuGxEV9+3mvDUQfYXTvDatAQO7YOINjB4thO8ep6lXl4iIi5QCBNpAmuyS/jpwvXk7TvEPVN7c++MvkSE+jD4eDyQu8YJXhlvQlk+hEbBgAucXl69p0OIy4t5i4i0cAphIj5UXevhnx9l8tRn2+jWrhUL75zg27UcCzZB6kLnycYDuyA4HPqd6wSvvudBmAtPW4qIyDEphIn4SGZ+KT9esJ6MPQeZO6YbD1w0iOhwH/zIHciDtNecJxvzNzotJXpNg2m/hgEXQkRr759TRETOmEKYiJd5PJb/fJXNw8s2ExMewjM3jmbmoDjvnuTQfmd+V+pCZ74XFrqOgvMfhiGXq6WEiEgA8GkIM8acD/wLCAaetdY+dNTr1wH31X9bBtxtrd3gy5pEfGlXcQX3v57KV9uKOWdgJ/5y+VA6xnhp7lV1udNSIu112Poh1FVD+14w5T4YehXE9vbOeUREpEn4LIQZY4KBJ4CZQC6wxhizxFqb0WC3HcAUa+0+Y8ws4GlgnK9qEvGV7KJynvh0K69/nUd4SBAPXZ7E1WO6nXnbidoq2PqRM8F+y3tQU+G0lBj9PefJxq4j1VJCRCRA+fJK2Fhgq7V2O4AxZgEwGzgSwqy1XzXYfyWQ4MN6RLxuW2EZT3yylTfX5xEaHMSNE3pw59m96dzmDNZSrKuBHZ85V7w2vQNVByCyPQyb60yw7z5BLSVERJoBX4awrkBOg+9zOfFVru8B7/mwHhGvycov5X8/2crbqbuJCAnme5MTuf3sXnSKOc3w5fHArhXftJSoKIbw1jDwYmeOV+IUCA716mcQERF3GWutbw5szJXAedba2+q/vwEYa6394TH2nQY8CUy21hYf4/U7gDsA4uLiRi1YsMAnNTdUVlZGdHS0z88jjecPY5JT6mHJtmrW7q0jLBhmdA/l/J6htA4/jVuC1hJTmkWngs/pVPAl4dXF1AWFU9RhLIUdJ1PSfiSe4DDvfwgv84dxkW/TmPgfjYl/aopxmTZtWoq1dvSxXvPllbBcoFuD7xOA3UfvZIwZCjwLzDpWAAOw1j6NM1+M0aNH26lTp3q92KMlJyfTFOeRxnNrTKpq61iWtpdXVu1i1Y4SosND+P60Ptw6OZH2UacRkvLTnT5eaYth/04IDoO+58KQywnudz5xYVF4+VlKn9LPiv/RmPgfjYl/cntcfBnC1gB9jTGJQB4wF7i24Q7GmO7A68AN1tpMH9Yicsqyi8qZv3oXi1JyKSmvplv7SH55fn+uHdudtq1OMXzt3+UEr42LoCADTDD0muo82TjgQohs64uPICIifsxnIcxaW2uM+QHwPk6LiuettenGmLvqX38K+C0QCzxZ/xRZ7fEu2Yk0hepaDx9m5PPK6p18ubWY4CDDOQM7cd24Hkzu04GgU1nnsaLEmd+Vugh21T+D0m0cXPAIDL4Mojr45DOIiEhg8GmfMGvtUmDpUdueavD724DbfFmDSGNUVNfyny+z+c+X2RSVVdG1bSQ/m9mPq8Z0I671KUy2rznktJLYuAiyPgRPDXToD9MfcFpKtOvps88gIiKBRR3zpUWrrvUwf/Uu/veTrRSVVTGlX0dumtiDKf06EdzYq1611bDtk/peXkuhugxi4mHcnU4T1c5D1ctLRES+QyFMWqQ6j+WNr/N49KNMcvcdYmxie566fmTjF9euq4Xsz53gteltqNwPEW2dPl5D5kDPyerlJSIiJ6QQJi2KtZb30/fyyAeZbC0oY0jX1vzPZUmc3bfDybvbezyQs+qbXl7lhRAWDQMucoJXr6kQ4v8tJURExD8ohEmLYK3li61FPPL+FjbkHqBXxyievG4ks4Z0Pnn4ys+AjQth42I4sAtCIqDfeU7w6nsuhEY2zYcQEZFmRSFMmjWPx/LRpnyeTN7G+pz9dG0byV+vGMrlI7oSEhx0/DceyIO01yB1IeSnOS0lek+D6b9xWkqExzTdhxARkWZJIUyapdo6D2+n7ubfydvIzC+jW/tIHrx0CFeNTiA85DhztQ7tg4wlzpON2V8AFrqOgvMfdpYOiu7UpJ9BRESaN4UwaVYqa+pYlJLL08u3kVNyiP5xMTx69XAuGhp/7CtfVWVOS4m0xbD1I6elRPveMPV+p6VEbO+m/xAiItIiKIRJs1BcVsWra3P4z5fZFJZWMaJ7W3570WBmDOj03QarNZWw9cP6lhLLoPYQxHRxWkoMuRy6jFRLCRER8TmFMAlY1lrW7tzHyyt38t7GvVTXeTirbwcemzuC8b3af3vCfV0tbE925nltegeqS6FVBxhxnTPBvtt4CDrBHDEREREvUwiTgFNWVcsbX+cxb+VONu8tJSY8hGvHdee6cd3pG3fUhPmirbD+ZVg/H8r2QngbGDy7vpfX2RCsHwEREXGH/gaSgJFT6uE3b2zkza/zKK+uY3CX1jx0eRKXDO9Cq7AG/ytXlUL6m/D1y5Cz0nmyse9MGH6d01oiJNy1zyAiInKYQpj4tV3FFby7cQ/vpO4mffchwkNyuWhoF64f353h3dp+c8vR43EWyV7/ihPAasohti+c8wcYNhdiOrv6OURERI6mECZ+J2//Id5N3c27qXvYkHsAgOHd2nLNgDDuu2oKbVvVd6W3FnJTnAn26W9A6W4Ii4GkOTDiBkgYown2IiLitxTCxC/sOXCIpRv38k7qbr7etR+AoQlt+NWsAVyQFE+39q1ITk6mbWQo7E1zglfaYti/E4LDoM9MGPIg9J8FYVHufhgREZFGUAgT12wvLOP99HyWpe9lQ85+AAZ3ac0vz+/PRUld6B7b6pudi7bSI/tVSPslFG1x5nn1mgpT7nM62Ee2deMjiIiInDaFMGky1lrS8g7yfvpe3k/fS1ZBGeBc8frFef2ZNaQzvTpGf/OG/buc24xpi2HPBnpioMckp5/XoNkQ1cGlTyIiInLmFMLE5zbmHuD1r3P5ID2fvP2HCDIwLjGW68Z159zBnenStsEC2KX5kPGmE7xyVjnbuo6G8/7CioNxTDxvjiufQURExNsUwsQnKqpreXvDbuat2kVq7gHCQoI4q08HfnROX84ZGEf7qLBvdj68ZmPaYsj+HKwH4obAjN/C4MuhfSIA1cnJ7nwYERERH1AIE6/avPcgr6zaxRvr8iitqqVfXDS/v3gQl41MoE1k6Dc71lRC5jJnseysD6Cu2lmz8exfOMGr0wD3PoSIiEgTUAiTM1ZZU8fSjXuYt2oXKTv3ERYSxIVJ8Vw7rjuje7Rr0MurDrK/gNSFsGkJVB2E6DgYczsMvRLih6ulhIiItBgKYXLaDlTU8OKKbP7z5Q72VdSQ2CGK31wwkDmjEr653Wgt7P4aNr7m3G4s3eP08hp0CSRdCYlnQ1Cwux9ERETEBQphcsoKSit57osdvLxiJ+XVdcwY0InvTU5kQu/Yb656FWz6ppdXyXYICnWWDhr6F+h3PoRGnvgkIiIizZxCmDRaTkkFTy/fzqtrc6it83Dh0C7cM7U3A+NbOzsUb4P01yHtDShIBxPkXOma/BMYcBG0au/uBxAREfEjCmFyUlsLSnkyeRtvrd9NkIE5IxO4c0pvEjtEwcHd8NVLkPaac9sRoNt4mPU3p5dXTJy7xYuIiPgphTA5pvyDlby3cQ9LN+5lzc4SwkOCuGlCT24/O5H48GrIeB3eXQg7PgesM6l+5oMw+DJo283t8kVERPyeQpgcsfdAJUs37uG9tD2s3bkPa6FfXDQ/Oacf142KI3bPZ7DsL5D5PtRVQbtEmPJLZ4J9h75uly8iIhJQFMJauL0HKnl34x6WbtxDys59AAzoHMNPzunHBYM70udQKmz8X3jqTag8AK06wKibYehV0HWUWkqIiIicJoWwFiq7qJzHP93KG1/nUeexDOgcw89m9uOCpM70rtoEac/Cy29AWT6ERjmLZA+9ylk0Ozj0pMcXERGRE1MIa2G2F5bx+KdbeWv9bkKCDDdO6MEN47rTq24HpL0M816HA7sgONxpKTFkDvQ7D8Ki3C5dRESkWVEIayG2FpTx+CdZLNmwm7CQIG6Z2JO7h9QRu2MJvLoYirPABEPv6TDt1zDgAoho43bZIiIizZZCWDOXlV/KY59s5Z3U3USEBPOzsRHcFLOO6Kz/gbUbAQM9JsGEe2DgbIiKdbtkERGRFkEhrBmy1rJ25z6e+3wH72fspXvoAZ7pl8mU6uWEbkhxdkoYA+c/BIMuhdbxrtYrIiLSEimENSM1dR6WbtzDc1/sYGduHnMi1vJZxxS6HVyH2WmhcxKc83unl1e7nm6XKyIi0qIphDUDBypqmL9mFwu+3MKgshX8MnIVEyJTCLa1ENoXpt4Pgy+Hjv3cLlVERETqKYQFsOyicl74Yiu71n3A+Z7PWRq6hlZhFdjIzpiku5wmqvHD1MtLRETEDymEBRiPx7I8s4DlyR/QJXcpdwd/RVzQfurCowkefBkkXYlJPBuCgt0uVURERE5AISxAlFbW8HHyp5SlvMpZVcuZGlRAXWgItb3OgZFzCe53PoRGul2miIiINJJCmJ/bmZnK1k9fovvu97jU5FJHEMWdJ1A79reEDL6I4Mh2bpcoIiIip0EhzA+V7Mkm74uXicp8k141WfQAtkUNJXf4XSRMnEun6I5ulygiIiJnSCHMD9TWedi4bRcFqxbReefbJNWk0t5YMkxvPk/8MYPPvZne8YlulykiIiJepBDmkoKDlXyWkUvJhndI3LOUKXYdI0wNe4K7sLbHbbQZdx0DBg4nKEhPNoqIiDRHCmFNqLKmjg/Td5P+1bv03LOMWUGraGMqKA1ux+6ec+k48Qbie40lXi0lREREmj2FMB+z1rIhZx+rPl9GTNZbzLQruNgcoDq0FYd6n48dex0xvaYSE6yhEBERaUn0N7+PFBw8xOfLP6Y29TUmVX3OnaaIahPGwW7T8Yy7hrD+5xGmlhIiIiItlkKYF9V5LCtT1lL45X8Zuu8D5pg91BJMfqdJHBo3l8ghF9MhorXbZYqIiIgfUAjzgt35+aR98CIdt7/BJJuBB0NOm5EUjPopncZcQddW7d0uUURERPyMQthpqqmpZkPyG9R9/QrDyr/kXFPD7pBubBnwU3pNv4Ue7bu7XaKIiIj4MYWwU2A9HratT6Zk1QJ657/PaPaznxg2xV9Klym30mXABC2WLSIiIo2iEHYy1pKdvor8r+bRfc8y+tgCqmwo6VHjyB1xLYOnzGFEWITbVYqIiEiAUQg7jvKiXax6/ud0yV1KT08eCTaI9MhR7Or/IwZMvYaR7WLdLlFEREQCmE9DmDHmfOBfQDDwrLX2oaNeN/WvXwBUADdba9f5sqaT2Z62CvvGXVxYtx2PNWSEJ5HX51b6Tr2WYZ26uFmaiIiINCM+C2HGmGDgCWAmkAusMcYssdZmNNhtFtC3/msc8O/6X13TvnNP9gRF8HabWxg750cM6ao1G0VERMT7gnx47LHAVmvtdmttNbAAmH3UPrOBl6xjJdDWGBPvw5pOqm2HOAb+ZgUxQy8lTgFMREREfMSXIawrkNPg+9z6bae6j4iIiEiz48s5Ycfq1WBPYx+MMXcAdwDExcWRnJx8xsWdTFlZWZOcRxpPY+KfNC7+R2PifzQm/sntcfFlCMsFujX4PgHYfRr7YK19GngaYPTo0Xbq1KleLfRYkpOTaYrzSONpTPyTxsX/aEz8j8bEP7k9Lr68HbkG6GuMSTTGhAFzgSVH7bMEuNE4xgMHrLV7fFiTiIiIiF/w2ZUwa22tMeYHwPs4LSqet9amG2Puqn/9KWApTnuKrTgtKm7xVT0iIiIi/sSnfcKstUtxglbDbU81+L0Fvu/LGkRERET8kS9vR4qIiIjIcSiEiYiIiLhAIUxERETEBQphIiIiIi5QCBMRERFxgUKYiIiIiAsUwkRERERcYJxWXYHDGFMI7GyCU3UAiprgPNJ4GhP/pHHxPxoT/6Mx8U9NMS49rLUdj/VCwIWwpmKMWWutHe12HfINjYl/0rj4H42J/9GY+Ce3x0W3I0VERERcoBAmIiIi4gKFsON72u0C5Ds0Jv5J4+J/NCb+R2Pin1wdF80JExEREXGBroSJiIiIuKDFhDBjTDdjzKfGmE3GmHRjzI/qt7c3xnxojMmq/7Vd/fbY+v3LjDGPH3WsMGPM08aYTGPMZmPMHDc+U6Dz1pgYY2KMMesbfBUZYx516WMFPC//rFxjjNlojEk1xiwzxnRw4zMFOi+PydX145FujPmrG5+nOTiNMZlpjEmp/3lIMcZMb3CsUfXbtxpjHjPGGLc+V6Dz8rj8jzEmxxhT5rN6W8rtSGNMPBBvrV1njIkBUoBLgZuBEmvtQ8aY+4F21tr7jDFRwAhgCDDEWvuDBsf6AxBsrX3AGBMEtLfWqv/LKfLmmBx13BTgJ9ba5U3xOZobb42LMSYE2A0MstYW1f+FX2Gt/X2Tf6gA58UxiQW+BkZZawuNMS8CL1lrP276TxXYTmNMRgD51trdxpghwPvW2q71x1oN/AhYCSwFHrPWvtf0nyrweXlcxuP0Jc2y1kb7ot4WcyXMWrvHWruu/velwCagKzAbeLF+txdxBgtrbbm19gug8hiHuxX4S/1+HgWw0+PlMQHAGNMX6AR87rvKmzcvjoup/4qq/5d9a5xQJqfIi2PSC8i01hbWf/8RoCv5p+E0xuRra+3h///TgQhjTHh9aGhtrV1hnasiLx1+j5w6b41L/WsrrbV7fFlviwlhDRljeuL8K3EVEHf4P3L9r51O8t629b990BizzhizyBgT58NyW4QzGZOjXAO8alvKJV4fO5NxsdbWAHcDG6m/IgY858t6W4Iz/FnZCgwwxvSsv1J5KdDNd9W2DKcxJnOAr621VTgBIbfBa7n12+QMneG4NIkWF8KMMdHAYuDH1tqDp3GIECAB+NJaOxJYATzixRJbHC+MSUNzgflnXpWc6bgYY0JxQtgIoAuQCvzKq0W2MGc6JtbafThj8irO1eJsoNabNbY0pzomxpjBwMPAnYc3HWM3/SPyDHlhXJpEiwph9X8pLAbmWWtfr9+cX385+PC95IKTHKYYqADeqP9+ETDSB+W2CF4ak8PHGgaEWGtTfFJsC+KlcRkOYK3dVn9lciEw0TcVN3/e+lmx1r5trR1nrZ0AbAGyfFVzc3eqY2KMScD5u+NGa+22+s25OP+wPywB3bY/I14alybRYkJY/ZyU54BN1tp/NHhpCXBT/e9vAt460XHq/zJ5G5hav2kGkOHVYlsIb41JA9egq2BnzIvjkgcMMsYcXrh2Js78DDlF3vxZMcZ0qv+1HXAP8Kx3q20ZTnVM6qeyvAv8ylr75eGd62+NlRpjxtcf80Ya/2eeHMVb49JkrLUt4guYjHOJNxVYX/91ARALfIzzr8GPcZ50PPyebKAEKMP518qg+u09gOX1x/oY6O725wvEL2+OSf1r24EBbn+uQP/y8s/KXTjBKxXnHy+xbn++QPzy8pjMx/mHYwYw1+3PFqhfpzomwANAeYN91wOd6l8bDaQB24DHqe9coC/Xx+Wv9T87nvpff+/teltMiwoRERERf9JibkeKiIiI+BOFMBEREREXKISJiIiIuEAhTERERMQFCmEiIiIiLlAIExEREXGBQpiI+DVjTKwxZn39115jTF7978uMMU968TyPGmPONsb82RjzcIPtPYwx2xusG3u6x19Qv8C8iAiA+oSJSOAwxvweKLPWenW9VmNMe2CptXa8MSYS+Bq4zFq7yRjzJrDIWjvvDI4fjNNE8npr7e1eKVpEAp6uhIlIQDLGTDXGvFP/+98bY140xnxgjMk2xlxujPmrMWajMWZZ/VpyGGNGGWM+M8akGGPeP7yWHHAFsAzAWnsI+CnwpDFmFhBjrZ1njPmFMWaNMSbVGPOHBnW8WX+8dGPMHQ22lxlj/miMWQVMwFkw+xxjTEhT/PcREf+nECYizUVv4EJgNvAy8Km1Ngk4BFxYH8T+F7jCWjsKeB74n/r3TgKOLPxurV2Ks+TPS8A9xphzgb7AWJyFyUcZY86u3/3W+uONBu41xsTWb48C0qyzWPYX1loPsBUY5pNPLyIBR/8iE5Hm4j1rbY0xZiMQTP2VLWAj0BPoDwwBPnTW+CUY2FO/TzxQeNTxngAirbVbjDG3A+fi3KYEiMYJZctxgtdl9du71W8vBuqAxUcdswDoQoPAJyItl0KYiDQXVQDWWo8xpsZ+M+HVg/NnnQHSrbUTjvHeQ0DEUds89V/Uv/cv1tr/a7iDMWYqcA4wwVpbYYxJbnCcSmtt3VHHjKg/l4iIbkeKSIuxBehojJkAYIwJNcYMrn9tE9DnBO99H7jVGBNd/96uxphOQBtgX30AGwCMP0kN/YD0M/kQItJ8KISJSItgra3GmYD/sDFmA7AemFj/8rvA1BO89wPgFWBF/e3O14AYnFueIcaYVOBBYOXxjmGMiQMOWWv3HG8fEWlZ1KJCRAQwxnwBXGSt3e+j4/8EOGitfc4XxxeRwKMrYSIijp8B3X14/P3Aiz48vogEGF0JExEREXGBroSJiIiIuEAhTERERMQFCmEiIiIiLlAIExEREXGBQpiIiIiIC/4/mEE0vSFPDwYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.plot(TW0050_monthly['累積價值(月)'], lw=1.5, label = '0050')\n",
    "plt.plot(TW0050_monthly['原始價值(月)'], lw=1.5, label = 'Original Asset')\n",
    "plt.xlabel('Time(Year)')\n",
    "plt.ylabel('Value')\n",
    "plt.legend(loc = 0) \n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "refined-radiation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret = np.log(TW0050_monthly['累積報酬(月)']/TW0050_monthly['累積報酬(月)'].shift(1))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ret.plot(kind=\"bar\", figsize=(12,6), stacked=True, ax=ax)\n",
    "\n",
    "##建立一個空陣列，長度為資料總月份的長度\n",
    "ticklabels = ['']*len(TW0050_monthly.index)\n",
    "\n",
    "##每6個月展示出其月份跟日期\n",
    "ticklabels[::6] = [item.strftime('%b %d') for item in TW0050_monthly.index[::6]]\n",
    "\n",
    "##每12個月額外展示出年份\n",
    "ticklabels[::12] = [item.strftime('%b %d\\n%Y') for item in TW0050_monthly.index[::12]]\n",
    "\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "ax.axhline(ret.mean(), c='r')\n",
    "\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Return')\n",
    "plt.title('0050 Monthly return')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bound-circus",
   "metadata": {},
   "source": [
    "### ４. 績效／統計指標"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "monetary-functionality",
   "metadata": {},
   "outputs": [],
   "source": [
    "###年報酬\n",
    "cagr = (TW0050_monthly['累積報酬(月)'][-1]) ** (1/5) -1\n",
    "\n",
    "###標準差\n",
    "std = ret.std()*np.sqrt(12)\n",
    "\n",
    "###夏普比率\n",
    "sharpe_ratio = (cagr-0.01)/std\n",
    "\n",
    "###最大回撤\n",
    "roll_max = TW0050_monthly['累積價值(月)'].cummax()\n",
    "monthly_dd =TW0050_monthly['累積價值(月)']/roll_max - 1.0\n",
    "max_dd = monthly_dd.cummin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dense-falls",
   "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>0050</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>年化報酬率(%)</th>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年化標準差(%)</th>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>夏普比率</th>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>期間最大回撤(%)</th>\n",
       "      <td>-15.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0050\n",
       "年化報酬率(%)   10.98\n",
       "年化標準差(%)   15.54\n",
       "夏普比率        0.64\n",
       "期間最大回撤(%) -15.68"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(columns=['0050'], index=['年化報酬率(%)', '年化標準差(%)', '夏普比率', '期間最大回撤(%)'], \n",
    "             data = np.round(np.array([100*cagr, 100*std, sharpe_ratio, 100*max_dd[-1]]),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corresponding-guatemala",
   "metadata": {},
   "source": [
    "### 附錄：將上述個別的程式碼做成Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "hidden-lodging",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dollar_Cost_Averaging(data):    \n",
    "\n",
    "    data['每月購買股數']= np.floor(10000 / data['close_adj'])\n",
    "    data['累積股數'] = data['每月購買股數'].cumsum()\n",
    "    data['累積價值(月)'] = round(data['累積股數'] * data['close_adj'],2)\n",
    "    data['原始價值(月)'] = [10000*i for i in range(len(data.index))]\n",
    "    data['原始價值(月)'] = data['原始價值(月)'].shift(-1)\n",
    "    data['原始價值(月)'][-1] = data['原始價值(月)'][-2]\n",
    "    data['累積報酬(月)'] = round((data['累積價值(月)']-data['原始價值(月)'])/data['原始價值(月)'], 6) + 1\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "accomplished-sierra",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Performance(data, ticker):\n",
    "    \n",
    "    ret = np.log(data['累積報酬(月)']/data['累積報酬(月)'].shift(1))\n",
    "    \n",
    "    cagr = (data['累積報酬(月)'][-1]) ** (1/5) -1\n",
    "    std = ret.std()*np.sqrt(12)\n",
    "    sharpe_ratio = (cagr-0.01)/std\n",
    "\n",
    "    roll_max = data['累積價值(月)'].cummax()\n",
    "    monthly_dd = data['累積價值(月)']/roll_max - 1.0\n",
    "    max_dd = monthly_dd.cummin()\n",
    "    \n",
    "    df = pd.DataFrame(columns=[ticker], index=['年化報酬率(%)', '年化標準差(%)', '夏普比率', '期間最大回撤(%)'], \n",
    "            data = np.round(np.array([100*cagr, 100*std, sharpe_ratio, 100*max_dd[-1]]),2))\n",
    "    \n",
    "    return df"
   ]
  }
 ],
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