ycytai/stuckholder.ipynb

## stuckholder.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.dates import YearLocator, DateFormatter\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2024-04-30</th>\n",
       "      <td>158.60</td>\n",
       "      <td>159.35</td>\n",
       "      <td>158.25</td>\n",
       "      <td>158.25</td>\n",
       "      <td>4792.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-02</th>\n",
       "      <td>157.35</td>\n",
       "      <td>157.35</td>\n",
       "      <td>156.00</td>\n",
       "      <td>156.15</td>\n",
       "      <td>6666.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-03</th>\n",
       "      <td>158.55</td>\n",
       "      <td>158.65</td>\n",
       "      <td>156.45</td>\n",
       "      <td>156.95</td>\n",
       "      <td>5935.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-06</th>\n",
       "      <td>159.00</td>\n",
       "      <td>160.15</td>\n",
       "      <td>159.00</td>\n",
       "      <td>159.20</td>\n",
       "      <td>9032.607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-07</th>\n",
       "      <td>160.25</td>\n",
       "      <td>160.55</td>\n",
       "      <td>159.50</td>\n",
       "      <td>160.10</td>\n",
       "      <td>6699.483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high     low   close    volume\n",
       "date                                                \n",
       "2024-04-30  158.60  159.35  158.25  158.25  4792.141\n",
       "2024-05-02  157.35  157.35  156.00  156.15  6666.498\n",
       "2024-05-03  158.55  158.65  156.45  156.95  5935.465\n",
       "2024-05-06  159.00  160.15  159.00  159.20  9032.607\n",
       "2024-05-07  160.25  160.55  159.50  160.10  6699.483"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dividend_df = pd.read_csv(\n",
    "    '0050_dividend.csv', \n",
    "    parse_dates=['ex_dividend_date', 'dividend_receive_date']\n",
    ")\n",
    "price_df = pd.read_csv(\n",
    "    '0050_price.csv', \n",
    "    index_col='date',\n",
    "    parse_dates=['date']\n",
    ")\n",
    "price_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>dividend_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dividend_receive_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dividend_amount\n",
       "dividend_receive_date                 \n",
       "2005                              1.85\n",
       "2006                              4.00\n",
       "2007                              2.50\n",
       "2008                              2.00\n",
       "2009                              1.00\n",
       "2010                              2.20\n",
       "2011                              1.95\n",
       "2012                              1.85\n",
       "2013                              1.35\n",
       "2014                              1.55\n",
       "2015                              2.00\n",
       "2016                              0.85\n",
       "2017                              2.40\n",
       "2018                              2.90\n",
       "2019                              3.00\n",
       "2020                              3.60\n",
       "2021                              3.40\n",
       "2022                              5.00\n",
       "2023                              4.50\n",
       "2024                              3.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dividend_by_year = dividend_df[['dividend_receive_date', 'dividend_amount']].groupby(\n",
    "    dividend_df['dividend_receive_date'].dt.year,\n",
    ").sum(numeric_only=True)\n",
    "dividend_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_period_dividend(start, end):\n",
    "    return sum(\n",
    "        dividend_df[\n",
    "            (dividend_df['ex_dividend_date'] > start)\n",
    "            & (dividend_df['ex_dividend_date'] < end)\n",
    "        ]['dividend_amount']\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,5))\n",
    "\n",
    "ax = fig.add_subplot()\n",
    "ax.set_title('Historical price', fontsize=16, fontweight='bold')\n",
    "ax.plot(\n",
    "    price_df.close, \n",
    "    linewidth=1, alpha=1, color='#1e609e'\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dividend_by_year.index = pd.to_datetime(dividend_by_year.index, format='%Y')\n",
    "\n",
    "fig = plt.figure(figsize = (12,5))\n",
    "ax = fig.add_subplot()\n",
    "ax.set_title('Dividend received of each year', fontsize=16, fontweight='bold')\n",
    "ax.xaxis.set_major_locator(YearLocator(1))\n",
    "ax.xaxis.set_major_formatter(DateFormatter(\"%Y\"))\n",
    "container = ax.bar(\n",
    "    dividend_by_year.index, \n",
    "    dividend_by_year['dividend_amount'], \n",
    "    edgecolor=\"black\", \n",
    "    width=200,\n",
    "    linewidth=0.7\n",
    ")\n",
    "ax.bar_label(container, fmt='%.1f', padding=3)\n",
    "ax.set_ylim(0, 6)\n",
    "plt.xticks(dividend_by_year.index)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = price_df['close'].values\n",
    "dts = price_df.index.values\n",
    "profit_date = []\n",
    "profit_price = []\n",
    "profit_period_dividend = []\n",
    "for i in range(len(prices)-1):\n",
    "    holding_price = prices[i]\n",
    "    today = dts[i]\n",
    "\n",
    "    is_profitable = False\n",
    "    ptr = i + 1\n",
    "    while not is_profitable:\n",
    "        coming_price = prices[ptr]\n",
    "        coming_date = dts[ptr]\n",
    "        period_dividend = get_period_dividend(today, coming_date)\n",
    "\n",
    "        ret = ((coming_price + period_dividend) / holding_price) - 1\n",
    "        is_profitable = ret > 0\n",
    "\n",
    "        if is_profitable:\n",
    "            profit_date.append(coming_date)\n",
    "            profit_price.append(coming_price)\n",
    "            profit_period_dividend.append(period_dividend)\n",
    "            break\n",
    "\n",
    "        ptr += 1\n",
    "        if ptr == len(prices):\n",
    "            profit_date.append(None)\n",
    "            profit_price.append(None)\n",
    "            profit_period_dividend.append(None)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit_date</th>\n",
       "      <th>profit_price</th>\n",
       "      <th>profit_dividend</th>\n",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2004-03-01</th>\n",
       "      <td>50.8</td>\n",
       "      <td>52.00</td>\n",
       "      <td>50.80</td>\n",
       "      <td>51.95</td>\n",
       "      <td>7495.0</td>\n",
       "      <td>2004-03-02</td>\n",
       "      <td>52.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-02</th>\n",
       "      <td>52.1</td>\n",
       "      <td>52.90</td>\n",
       "      <td>52.10</td>\n",
       "      <td>52.80</td>\n",
       "      <td>13953.0</td>\n",
       "      <td>2004-03-04</td>\n",
       "      <td>53.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-03</th>\n",
       "      <td>52.8</td>\n",
       "      <td>52.95</td>\n",
       "      <td>52.50</td>\n",
       "      <td>52.70</td>\n",
       "      <td>19671.0</td>\n",
       "      <td>2004-03-04</td>\n",
       "      <td>53.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-04</th>\n",
       "      <td>53.0</td>\n",
       "      <td>53.15</td>\n",
       "      <td>52.55</td>\n",
       "      <td>53.15</td>\n",
       "      <td>16061.0</td>\n",
       "      <td>2005-12-27</td>\n",
       "      <td>51.40</td>\n",
       "      <td>1.85</td>\n",
       "      <td>663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-05</th>\n",
       "      <td>53.5</td>\n",
       "      <td>53.70</td>\n",
       "      <td>52.50</td>\n",
       "      <td>52.50</td>\n",
       "      <td>22078.0</td>\n",
       "      <td>2005-12-23</td>\n",
       "      <td>51.00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>658.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open   high    low  close   volume profit_date  profit_price  \\\n",
       "date                                                                       \n",
       "2004-03-01  50.8  52.00  50.80  51.95   7495.0  2004-03-02         52.80   \n",
       "2004-03-02  52.1  52.90  52.10  52.80  13953.0  2004-03-04         53.15   \n",
       "2004-03-03  52.8  52.95  52.50  52.70  19671.0  2004-03-04         53.15   \n",
       "2004-03-04  53.0  53.15  52.55  53.15  16061.0  2005-12-27         51.40   \n",
       "2004-03-05  53.5  53.70  52.50  52.50  22078.0  2005-12-23         51.00   \n",
       "\n",
       "            profit_dividend  waiting_days  \n",
       "date                                       \n",
       "2004-03-01             0.00           1.0  \n",
       "2004-03-02             0.00           2.0  \n",
       "2004-03-03             0.00           1.0  \n",
       "2004-03-04             1.85         663.0  \n",
       "2004-03-05             1.85         658.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = price_df[:-1].copy()\n",
    "result['profit_date'] = profit_date\n",
    "result['profit_price'] = profit_price\n",
    "result['profit_dividend'] = profit_period_dividend\n",
    "result['waiting_days'] = (result['profit_date'] - result.index).dt.days\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit_date</th>\n",
       "      <th>profit_price</th>\n",
       "      <th>profit_dividend</th>\n",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2024-04-09</th>\n",
       "      <td>160.00</td>\n",
       "      <td>163.35</td>\n",
       "      <td>159.90</td>\n",
       "      <td>163.25</td>\n",
       "      <td>9043.462</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-10</th>\n",
       "      <td>162.75</td>\n",
       "      <td>163.30</td>\n",
       "      <td>162.45</td>\n",
       "      <td>163.00</td>\n",
       "      <td>5731.265</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-11</th>\n",
       "      <td>162.05</td>\n",
       "      <td>162.50</td>\n",
       "      <td>161.65</td>\n",
       "      <td>162.50</td>\n",
       "      <td>4677.727</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-12</th>\n",
       "      <td>162.60</td>\n",
       "      <td>162.80</td>\n",
       "      <td>161.95</td>\n",
       "      <td>162.10</td>\n",
       "      <td>6129.174</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high     low   close    volume profit_date  \\\n",
       "date                                                               \n",
       "2024-04-09  160.00  163.35  159.90  163.25  9043.462         NaT   \n",
       "2024-04-10  162.75  163.30  162.45  163.00  5731.265         NaT   \n",
       "2024-04-11  162.05  162.50  161.65  162.50  4677.727         NaT   \n",
       "2024-04-12  162.60  162.80  161.95  162.10  6129.174         NaT   \n",
       "\n",
       "            profit_price  profit_dividend  waiting_days  \n",
       "date                                                     \n",
       "2024-04-09           NaN              NaN           NaN  \n",
       "2024-04-10           NaN              NaN           NaN  \n",
       "2024-04-11           NaN              NaN           NaN  \n",
       "2024-04-12           NaN              NaN           NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result['profit_date'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4969.000000\n",
       "mean       21.680419\n",
       "std       103.748471\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         3.000000\n",
       "75%         7.000000\n",
       "max      2346.000000\n",
       "Name: waiting_days, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_result = result.dropna()\n",
    "cleaned_result['waiting_days'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit_date</th>\n",
       "      <th>profit_price</th>\n",
       "      <th>profit_dividend</th>\n",
       "      <th>waiting_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2007-11-06</th>\n",
       "      <td>67.00</td>\n",
       "      <td>67.55</td>\n",
       "      <td>66.80</td>\n",
       "      <td>67.50</td>\n",
       "      <td>5266.693</td>\n",
       "      <td>2011-01-27</td>\n",
       "      <td>62.45</td>\n",
       "      <td>5.20</td>\n",
       "      <td>1178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-11-01</th>\n",
       "      <td>70.60</td>\n",
       "      <td>70.70</td>\n",
       "      <td>69.05</td>\n",
       "      <td>69.45</td>\n",
       "      <td>4745.624</td>\n",
       "      <td>2014-03-06</td>\n",
       "      <td>59.15</td>\n",
       "      <td>10.35</td>\n",
       "      <td>2317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-31</th>\n",
       "      <td>70.40</td>\n",
       "      <td>70.55</td>\n",
       "      <td>69.85</td>\n",
       "      <td>70.20</td>\n",
       "      <td>4157.381</td>\n",
       "      <td>2014-03-28</td>\n",
       "      <td>59.90</td>\n",
       "      <td>10.35</td>\n",
       "      <td>2340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-30</th>\n",
       "      <td>70.60</td>\n",
       "      <td>70.80</td>\n",
       "      <td>70.15</td>\n",
       "      <td>70.45</td>\n",
       "      <td>2821.980</td>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>60.60</td>\n",
       "      <td>10.35</td>\n",
       "      <td>2345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-29</th>\n",
       "      <td>69.85</td>\n",
       "      <td>70.65</td>\n",
       "      <td>69.85</td>\n",
       "      <td>70.65</td>\n",
       "      <td>3090.209</td>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>60.60</td>\n",
       "      <td>10.35</td>\n",
       "      <td>2346.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close    volume profit_date  profit_price  \\\n",
       "date                                                                         \n",
       "2007-11-06  67.00  67.55  66.80  67.50  5266.693  2011-01-27         62.45   \n",
       "2007-11-01  70.60  70.70  69.05  69.45  4745.624  2014-03-06         59.15   \n",
       "2007-10-31  70.40  70.55  69.85  70.20  4157.381  2014-03-28         59.90   \n",
       "2007-10-30  70.60  70.80  70.15  70.45  2821.980  2014-04-01         60.60   \n",
       "2007-10-29  69.85  70.65  69.85  70.65  3090.209  2014-04-01         60.60   \n",
       "\n",
       "            profit_dividend  waiting_days  \n",
       "date                                       \n",
       "2007-11-06             5.20        1178.0  \n",
       "2007-11-01            10.35        2317.0  \n",
       "2007-10-31            10.35        2340.0  \n",
       "2007-10-30            10.35        2345.0  \n",
       "2007-10-29            10.35        2346.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_result.sort_values('waiting_days', ascending=True).tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3498"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cleaned_result[cleaned_result['waiting_days'] <= 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 2, 3, 4, 7, 16, 179]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts = np.array_split(cleaned_result['waiting_days'].sort_values().values, 10)\n",
    "[int(np.mean(part)) for part in parts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2004, 2007, 2008, 2011, 2015, 2018, 2022}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set((cleaned_result[cleaned_result['waiting_days'] >= 365]).index.year)\n",
    "\n",
    "# 3, 1, 3, 4, 3, 4"
   ]
  }
 ],
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