a_share/ipynb/floor_ceiling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 加载日线数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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></th>\n",
       "      <th>close</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "      <th>turnover</th>\n",
       "      <th>is_paused</th>\n",
       "      <th>high_limit</th>\n",
       "      <th>low_limit</th>\n",
       "      <th>avg_price</th>\n",
       "      <th>prev_close</th>\n",
       "      <th>quote_rate</th>\n",
       "      <th>is_st</th>\n",
       "      <th>dde_l</th>\n",
       "      <th>l_net_value</th>\n",
       "      <th>net_flow_rate</th>\n",
       "      <th>act_buy_xl</th>\n",
       "      <th>pas_buy_xl</th>\n",
       "      <th>act_sell_xl</th>\n",
       "      <th>pas_sell_xl</th>\n",
       "      <th>act_buy_l</th>\n",
       "      <th>pas_buy_l</th>\n",
       "      <th>act_sell_l</th>\n",
       "      <th>pas_sell_l</th>\n",
       "      <th>act_buy_m</th>\n",
       "      <th>pas_buy_m</th>\n",
       "      <th>act_sell_m</th>\n",
       "      <th>pas_sell_m</th>\n",
       "      <th>buy_l</th>\n",
       "      <th>sell_l</th>\n",
       "      <th>turnover_rate</th>\n",
       "      <th>turnover_rate_f</th>\n",
       "      <th>volume_ratio</th>\n",
       "      <th>pe</th>\n",
       "      <th>pe_ttm</th>\n",
       "      <th>pb</th>\n",
       "      <th>ps</th>\n",
       "      <th>ps_ttm</th>\n",
       "      <th>dv</th>\n",
       "      <th>dv_ttm</th>\n",
       "      <th>total_share</th>\n",
       "      <th>float_share</th>\n",
       "      <th>free_share</th>\n",
       "      <th>total_mv</th>\n",
       "      <th>circ_mv</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th>market</th>\n",
       "      <th>exchange</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>603893.SH</th>\n",
       "      <th>2025-01-20</th>\n",
       "      <td>143.75</td>\n",
       "      <td>142.29</td>\n",
       "      <td>140.59</td>\n",
       "      <td>148.59</td>\n",
       "      <td>25585700.0</td>\n",
       "      <td>3.696524e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153.1</td>\n",
       "      <td>125.22</td>\n",
       "      <td>144.23</td>\n",
       "      <td>139.16</td>\n",
       "      <td>3.2924</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-260488320.0</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-7.05</td>\n",
       "      <td>270501610.0</td>\n",
       "      <td>253398340.0</td>\n",
       "      <td>322314720.0</td>\n",
       "      <td>417253810.0</td>\n",
       "      <td>420955560.0</td>\n",
       "      <td>305130150.0</td>\n",
       "      <td>364707190.0</td>\n",
       "      <td>406198260.0</td>\n",
       "      <td>757511110.0</td>\n",
       "      <td>625888770.0</td>\n",
       "      <td>640701660.0</td>\n",
       "      <td>662548540.0</td>\n",
       "      <td>1.063138e+09</td>\n",
       "      <td>882799797.0</td>\n",
       "      <td>6.1108</td>\n",
       "      <td>15.0226</td>\n",
       "      <td>1.07</td>\n",
       "      <td>447.2092</td>\n",
       "      <td>147.3861</td>\n",
       "      <td>17.3706</td>\n",
       "      <td>28.2601</td>\n",
       "      <td>21.2431</td>\n",
       "      <td>0.1387</td>\n",
       "      <td>0.1387</td>\n",
       "      <td>41890.1601</td>\n",
       "      <td>41869.4101</td>\n",
       "      <td>17031.5269</td>\n",
       "      <td>6.032183e+06</td>\n",
       "      <td>6.029195e+06</td>\n",
       "      <td>瑞芯微</td>\n",
       "      <td>福建</td>\n",
       "      <td>半导体</td>\n",
       "      <td>主板</td>\n",
       "      <td>SSE</td>\n",
       "      <td>2020-02-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       close    open     low    high      volume  \\\n",
       "code      date                                                     \n",
       "603893.SH 2025-01-20  143.75  142.29  140.59  148.59  25585700.0   \n",
       "\n",
       "                          turnover  is_paused  high_limit  low_limit  \\\n",
       "code      date                                                         \n",
       "603893.SH 2025-01-20  3.696524e+09        0.0       153.1     125.22   \n",
       "\n",
       "                      avg_price  prev_close  quote_rate  is_st        dde_l  \\\n",
       "code      date                                                                \n",
       "603893.SH 2025-01-20     144.23      139.16      3.2924    0.0 -260488320.0   \n",
       "\n",
       "                      l_net_value  net_flow_rate   act_buy_xl   pas_buy_xl  \\\n",
       "code      date                                                               \n",
       "603893.SH 2025-01-20        -0.43          -7.05  270501610.0  253398340.0   \n",
       "\n",
       "                      act_sell_xl  pas_sell_xl    act_buy_l    pas_buy_l  \\\n",
       "code      date                                                             \n",
       "603893.SH 2025-01-20  322314720.0  417253810.0  420955560.0  305130150.0   \n",
       "\n",
       "                       act_sell_l   pas_sell_l    act_buy_m    pas_buy_m  \\\n",
       "code      date                                                             \n",
       "603893.SH 2025-01-20  364707190.0  406198260.0  757511110.0  625888770.0   \n",
       "\n",
       "                       act_sell_m   pas_sell_m         buy_l       sell_l  \\\n",
       "code      date                                                              \n",
       "603893.SH 2025-01-20  640701660.0  662548540.0  1.063138e+09  882799797.0   \n",
       "\n",
       "                      turnover_rate  turnover_rate_f  volume_ratio        pe  \\\n",
       "code      date                                                                 \n",
       "603893.SH 2025-01-20         6.1108          15.0226          1.07  447.2092   \n",
       "\n",
       "                        pe_ttm       pb       ps   ps_ttm      dv  dv_ttm  \\\n",
       "code      date                                                              \n",
       "603893.SH 2025-01-20  147.3861  17.3706  28.2601  21.2431  0.1387  0.1387   \n",
       "\n",
       "                      total_share  float_share  free_share      total_mv  \\\n",
       "code      date                                                             \n",
       "603893.SH 2025-01-20   41890.1601   41869.4101  17031.5269  6.032183e+06   \n",
       "\n",
       "                           circ_mv name area industry market exchange  \\\n",
       "code      date                                                          \n",
       "603893.SH 2025-01-20  6.029195e+06  瑞芯微   福建      半导体     主板      SSE   \n",
       "\n",
       "                       list_date  \n",
       "code      date                    \n",
       "603893.SH 2025-01-20  2020-02-07  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.max_columns = None\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "# 需要从 HuggingFace dataset 上手动下载的 csv 文件放在本地。本代码库的 dataset 目录已经加入了.gitignore，不会将数据集提交到代码库。\n",
    "data_path = '../../dataset/all-prices-with-values-250303.csv'\n",
    "prices_df = pd.read_csv(data_path, index_col=(0,1))\n",
    "prices_df.xs(('603893.SH', '2025-01-20'), level=(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 计算近400个交易日内连板中出现地天板的数量和分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 使用 Pandas 进行计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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></th>\n",
       "      <th>max_high_limit_days</th>\n",
       "      <th>end_date</th>\n",
       "      <th>first_floor_ceiling_day</th>\n",
       "      <th>max_count_floor_ceiling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</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 rowspan=\"2\" valign=\"top\">000536.SZ</th>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2024-09-13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-11-07</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000701.SZ</th>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2024-04-03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000759.SZ</th>\n",
       "      <th>24</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2025-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000801.SZ</th>\n",
       "      <th>24</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2024-11-25</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              max_high_limit_days    end_date  first_floor_ceiling_day  \\\n",
       "code                                                                     \n",
       "000536.SZ 26                  5.0  2024-09-13                      5.0   \n",
       "          30                 10.0  2024-11-07                      7.0   \n",
       "000701.SZ 6                   5.0  2024-04-03                      5.0   \n",
       "000759.SZ 24                  6.0  2025-01-02                      1.0   \n",
       "000801.SZ 24                  6.0  2024-11-25                      6.0   \n",
       "\n",
       "              max_count_floor_ceiling  \n",
       "code                                   \n",
       "000536.SZ 26                      1.0  \n",
       "          30                      2.0  \n",
       "000701.SZ 6                       1.0  \n",
       "000759.SZ 24                      1.0  \n",
       "000801.SZ 24                      1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 继续使用 is_high_limit 连续分段的思路，同时添加地天板的列\n",
    "def cacluate_floor_ceiling_in_continuous_high_limit(source_data_frame, last_n_days=400):\n",
    "    source_data_frame = source_data_frame.iloc[-last_n_days:]\n",
    "    is_high_limit = (source_data_frame['close'] == source_data_frame['high_limit']) * 1 * (\n",
    "        (source_data_frame['is_st'] < 1) * 1)\n",
    "    if (is_high_limit < 1).all():\n",
    "        return pd.DataFrame()\n",
    "\n",
    "    source_data_frame['is_high_limit'] = is_high_limit\n",
    "    # 判断是否是地天板\n",
    "    source_data_frame['is_floor_ceiling'] = (source_data_frame['close'] == source_data_frame['high_limit']) * 1 \\\n",
    "        * ((source_data_frame['is_st'] < 1) * 1) \\\n",
    "        * ((source_data_frame['low'] == source_data_frame['low_limit']) * 1)\n",
    "\n",
    "    is_segment_start = (is_high_limit.diff() != 0) * 1\n",
    "    source_data_frame['segment_start'] = is_segment_start\n",
    "    source_data_frame['segment_index'] = is_segment_start.cumsum()\n",
    "    serie_high_limit = source_data_frame.groupby(by='segment_index').apply(\n",
    "        lambda x: pd.DataFrame({\n",
    "            'date': x.index.get_level_values(\"date\"),\n",
    "            'high_limit_days': x['is_high_limit'].cumsum(),\n",
    "            'is_floor_ceiling': x['is_floor_ceiling'],\n",
    "        }),\n",
    "        include_groups=False\n",
    "    )\n",
    "    serie_high_limit = serie_high_limit.groupby(by='segment_index').agg(\n",
    "        max_high_limit_days=('high_limit_days', 'max'),\n",
    "        end_date=(\"high_limit_days\", lambda x: x.idxmax()[2]),\n",
    "        # 如果连板中有地天板，则取第一个地天板所在的连板位置\n",
    "        first_floor_ceiling_day=(\"is_floor_ceiling\", lambda x: serie_high_limit.loc[x[x == 1].index, 'high_limit_days'].iloc[0] if len(\n",
    "            x[x == 1]) > 0 else np.nan),\n",
    "        # 输出在连板中的地天板个数\n",
    "        max_count_floor_ceiling=('is_floor_ceiling', 'sum'),\n",
    "    )\n",
    "    return serie_high_limit\n",
    "\n",
    "fc_df = prices_df.groupby(level=0).apply(\n",
    "    lambda x: cacluate_floor_ceiling_in_continuous_high_limit(x, 400))\n",
    "fc_df = fc_df[(fc_df['max_high_limit_days'] > 2) &\n",
    "              (fc_df['max_count_floor_ceiling'] > 0)]\n",
    "fc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 使用 Matplotlib 绘制地天板在连板中的数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribute_data = fc_df.groupby(by=['max_high_limit_days', 'first_floor_ceiling_day']).size().reset_index(name='count')\n",
    "subplots_num = distribute_data['max_high_limit_days'].nunique()\n",
    "\n",
    "fig, axs = plt.subplots(subplots_num // 3, 3, figsize=(25, 15))\n",
    "x_ticks_end = distribute_data['max_high_limit_days'].max() + 1\n",
    "y_ticks_end = distribute_data['count'].max() + 1\n",
    "\n",
    "for i, (key, group) in enumerate(distribute_data.groupby('max_high_limit_days')):\n",
    "    m = i // 3\n",
    "    n = i % 3\n",
    "    for first_floor_ceiling_day, count in group.groupby('first_floor_ceiling_day')['count']:\n",
    "        axs[m, n].bar(first_floor_ceiling_day, count, label=f'{first_floor_ceiling_day} days')\n",
    "        axs[m, n].set_xticks(np.arange(x_ticks_end))\n",
    "        axs[m, n].set_yticks(np.arange(y_ticks_end))\n",
    "        axs[m, n].set_title(f'{int(key)}连板中出现地天板的位置和次数')\n",
    "        axs[m, n].set_xlabel('位置：连板的第 N 天')\n",
    "        axs[m, n].set_ylabel('数量')\n",
    "\n",
    "plt.subplots_adjust(hspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 计算近400个交易日内的破板的躺地板和翘地板数量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 使用 Pandas 计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>is_target_low_limit_matched</th>\n",
       "      <th>break_date</th>\n",
       "      <th>pre_high_limit_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-02-28</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">000006.SZ</th>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-08-14</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-11-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000007.SZ</th>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-07-04</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-06-25</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              is_target_low_limit_matched  break_date  pre_high_limit_days\n",
       "code                                                                      \n",
       "000004.SZ 18                          1.0  2024-02-28                  1.0\n",
       "000006.SZ 3                           1.0  2023-08-14                  1.0\n",
       "          15                          1.0  2024-11-01                  1.0\n",
       "000007.SZ 3                           1.0  2024-07-04                  2.0\n",
       "000010.SZ 23                          1.0  2024-06-25                  3.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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></th>\n",
       "      <th>is_target_low_limit_matched</th>\n",
       "      <th>break_date</th>\n",
       "      <th>pre_high_limit_days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000004.SZ</th>\n",
       "      <th>40</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-10-09</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000010.SZ</th>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-08-22</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000011.SZ</th>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-08-12</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">000017.SZ</th>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-02-06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-02-22</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              is_target_low_limit_matched  break_date  pre_high_limit_days\n",
       "code                                                                      \n",
       "000004.SZ 40                          1.0  2024-10-09                  2.0\n",
       "000010.SZ 7                           1.0  2023-08-22                  3.0\n",
       "000011.SZ 7                           1.0  2024-08-12                  2.0\n",
       "000017.SZ 7                           1.0  2024-02-06                  1.0\n",
       "          9                           1.0  2024-02-22                  2.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def break_continuous_high_limit(source_data_frame, break_condition_func, last_n_days=400):\n",
    "    source_data_frame = source_data_frame.iloc[-last_n_days:]\n",
    "    is_high_limit = (source_data_frame['close'] == source_data_frame['high_limit']) * 1 * (\n",
    "        (source_data_frame['is_st'] < 1) * 1)\n",
    "    source_data_frame['is_high_limit'] = is_high_limit\n",
    "    \n",
    "    is_target_low_limit_matched = break_condition_func(source_data_frame)\n",
    "    if (is_high_limit < 1).all() or (is_target_low_limit_matched < 1).all():\n",
    "        return pd.DataFrame()\n",
    "\n",
    "    source_data_frame['is_target_low_limit_matched'] = is_target_low_limit_matched\n",
    "    is_segment_start = (is_high_limit.diff() != 0) * 1\n",
    "    source_data_frame['segment_start'] = is_segment_start\n",
    "    source_data_frame['segment_index'] = is_segment_start.cumsum()\n",
    "\n",
    "    by_segments = source_data_frame.groupby(by='segment_index').apply(\n",
    "        lambda x: pd.DataFrame({\n",
    "            'high_limit_days': x['is_high_limit'].cumsum(),\n",
    "            # 在连板区间内下面的值应该为0，只有在 high_limit_days 为 0 的连续区间内才会为1\n",
    "            'is_target_low_limit_matched': x['is_target_low_limit_matched'],\n",
    "        }),\n",
    "        include_groups=False\n",
    "    )\n",
    "    # 给每行数据添加前一天的涨跌天数号，na 填充为0\n",
    "    by_segments['pre_high_limit_days'] = by_segments['high_limit_days'].shift(1).fillna(0)\n",
    "    by_segments = by_segments[(by_segments['is_target_low_limit_matched'] > 0)]\n",
    "    result = by_segments.groupby(by='segment_index').agg(\n",
    "        is_target_low_limit_matched=('is_target_low_limit_matched', 'max'),\n",
    "        break_date=(\"is_target_low_limit_matched\", lambda x: x.idxmax()[2]),\n",
    "        pre_high_limit_days=('is_target_low_limit_matched',\n",
    "                             lambda x: by_segments.loc[x.idxmax(), 'pre_high_limit_days']),\n",
    "    )\n",
    "    return result\n",
    "\n",
    "# 判断是：前一天涨停、第二天收盘为跌停的\n",
    "def is_low_limit_close_after_high_limit(source_data_frame):\n",
    "    return(source_data_frame['is_high_limit'].diff() < 0) * 1 * (\n",
    "        source_data_frame['close'] == source_data_frame['low_limit']) * 1 * ((source_data_frame['is_st'] < 1) * 1)\n",
    "    \n",
    "\n",
    "# 判断是：前一天涨停、第二天跌停又翘板成功的\n",
    "def is_low_limit_low_after_high_limit(source_data_frame):\n",
    "    return (source_data_frame['is_high_limit'].diff() < 0) * 1 * (\n",
    "        source_data_frame['close'] != source_data_frame['low_limit']) * 1 * (\n",
    "            source_data_frame['low'] == source_data_frame['low_limit']) * ((source_data_frame['is_st'] < 1) * 1)\n",
    "\n",
    "break_and_lie_on_floor_df = prices_df.groupby(level=0).apply(\n",
    "    lambda x: break_continuous_high_limit(x, is_low_limit_close_after_high_limit, 400))\n",
    "break_and_lie_on_floor_df = break_and_lie_on_floor_df[break_and_lie_on_floor_df['pre_high_limit_days'] > 0]\n",
    "display(break_and_lie_on_floor_df.head())\n",
    "\n",
    "break_and_situp_from_floor_df = prices_df.groupby(level=0).apply(\n",
    "    lambda x: break_continuous_high_limit(x, is_low_limit_low_after_high_limit, 400))\n",
    "break_and_situp_from_floor_df = break_and_situp_from_floor_df[break_and_situp_from_floor_df['pre_high_limit_days'] > 0]\n",
    "break_and_situp_from_floor_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 使用 Matplotlib 绘制地天板、躺地板和翘地板的数量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取所有连板涨停的天数值\n",
    "break_and_lie_on_floor_df['current_day'] = break_and_lie_on_floor_df['pre_high_limit_days'] + 1\n",
    "break_and_situp_from_floor_df['current_day'] = break_and_situp_from_floor_df['pre_high_limit_days'] + 1\n",
    "\n",
    "all_days = np.arange(1, int(max(\n",
    "    break_and_lie_on_floor_df['current_day'].max(\n",
    "    ), break_and_situp_from_floor_df['current_day'].max()\n",
    ")) + 1)\n",
    "\n",
    "# 地天板出现在每个位置（连板的第N天）的总数量分布\n",
    "grouped_fc_data = fc_df.groupby('first_floor_ceiling_day').size()\n",
    "# 将所有缺失的位置填充为0，以匹配 all_days 横坐标\n",
    "grouped_fc_data = pd.Series([grouped_fc_data.get(day, 0)\n",
    "                            for day in all_days], index=all_days)\n",
    "\n",
    "# 连板第N天破板、躺地板的总数量分布\n",
    "grouped_break_and_lie_on_floor_data = break_and_lie_on_floor_df.groupby('current_day').size()\n",
    "grouped_break_and_lie_on_floor_data = pd.Series(\n",
    "    [grouped_break_and_lie_on_floor_data.get(day, 0) for day in all_days], index=all_days)\n",
    "\n",
    "# 连板第N天破板、翘地板的总数量分布\n",
    "grouped_break_and_situp_from_floor_data = break_and_situp_from_floor_df.groupby('current_day').size()\n",
    "grouped_break_and_situp_from_floor_data = pd.Series(\n",
    "    [grouped_break_and_situp_from_floor_data.get(day, 0) for day in all_days], index=all_days)\n",
    "\n",
    "# 创建三个并排的柱状图\n",
    "plt.figure(figsize=(12, 6))\n",
    "x = np.arange(len(all_days))\n",
    "width = 0.3\n",
    "\n",
    "plt.bar(x - width, grouped_fc_data.values, width,\n",
    "        label='地天板', color='orange', alpha=0.8)\n",
    "plt.bar(x, grouped_break_and_lie_on_floor_data.values, width,\n",
    "        label='躺地板', color='purple', alpha=0.8)\n",
    "plt.bar(x + width, grouped_break_and_situp_from_floor_data.values, width,\n",
    "        label='翘地板', color='blue', alpha=0.8)\n",
    "\n",
    "# 在每个柱子上方显示具体数值\n",
    "for i, v in enumerate(grouped_fc_data.values):\n",
    "    if v > 0:\n",
    "        plt.text(i - width, v, str(int(v)), ha='center', va='bottom')\n",
    "for i, v in enumerate(grouped_break_and_lie_on_floor_data.values):\n",
    "    if v > 0:\n",
    "        plt.text(i, v, str(int(v)), ha='center', va='bottom')\n",
    "for i, v in enumerate(grouped_break_and_situp_from_floor_data.values):\n",
    "    if v > 0:\n",
    "        plt.text(i + width, v, str(int(v)), ha='center', va='bottom')\n",
    "\n",
    "plt.xticks(x, all_days)\n",
    "plt.title('最近400个交易日中，连板中出现地天板，以及连板后破板躺地板、翘地板的数据分布')\n",
    "plt.xlabel('连板的第N天（或者破板的位置处于连板的第几天）')\n",
    "plt.ylabel('股票数量')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 最近400个交易日内涉及涨停连板的地天板、躺地板、翘地板数量汇总"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近400个交易日一共出现地天板 62 次, 占触地比重的 4.14%\n",
      "开板后没有地天板，直接躺地板有 907 次, 占触地比重的 60.51%\n",
      "开板后没有地天板，从地板上不断挣扎的有 530 次, 占触地比重的 35.36%\n"
     ]
    }
   ],
   "source": [
    "fc_total = grouped_fc_data.sum()\n",
    "lie_on_floor_total = grouped_break_and_lie_on_floor_data.sum()\n",
    "situp_from_floor_total = grouped_break_and_situp_from_floor_data.sum()\n",
    "total = fc_total + lie_on_floor_total + situp_from_floor_total\n",
    "\n",
    "print(f\"最近400个交易日一共出现地天板 {fc_total} 次, 占触地比重的 {fc_total/total:.2%}\")\n",
    "print(f\"开板后没有地天板，直接躺地板有 {lie_on_floor_total} 次, 占触地比重的 {lie_on_floor_total/total:.2%}\")\n",
    "print(f\"开板后没有地天板，从地板上不断挣扎的有 {situp_from_floor_total} 次, 占触地比重的 {situp_from_floor_total/total:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 附：可以输出具体是哪些股票、什么时候发生的，从来用于定位分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max_high_limit_days</th>\n",
       "      <th>end_date</th>\n",
       "      <th>first_floor_ceiling_day</th>\n",
       "      <th>max_count_floor_ceiling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</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>000759.SZ</th>\n",
       "      <th>24</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2025-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002178.SZ</th>\n",
       "      <th>28</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2024-11-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002682.SZ</th>\n",
       "      <th>10</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002691.SZ</th>\n",
       "      <th>30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2025-02-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002693.SZ</th>\n",
       "      <th>30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2024-10-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600327.SH</th>\n",
       "      <th>14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2024-11-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600550.SH</th>\n",
       "      <th>14</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2024-09-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600696.SH</th>\n",
       "      <th>20</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2024-09-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603117.SH</th>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603516.SH</th>\n",
       "      <th>10</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2024-02-20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              max_high_limit_days    end_date  first_floor_ceiling_day  \\\n",
       "code                                                                     \n",
       "000759.SZ 24                  6.0  2025-01-02                      1.0   \n",
       "002178.SZ 28                  4.0  2024-11-07                      1.0   \n",
       "002682.SZ 10                  3.0  2023-12-27                      1.0   \n",
       "002691.SZ 30                  4.0  2025-02-07                      1.0   \n",
       "002693.SZ 30                  3.0  2024-10-28                      1.0   \n",
       "600327.SH 14                  4.0  2024-11-29                      1.0   \n",
       "600550.SH 14                  3.0  2024-09-13                      1.0   \n",
       "600696.SH 20                  3.0  2024-09-23                      1.0   \n",
       "603117.SH 4                   3.0  2024-11-05                      1.0   \n",
       "603516.SH 10                  3.0  2024-02-20                      1.0   \n",
       "\n",
       "              max_count_floor_ceiling  \n",
       "code                                   \n",
       "000759.SZ 24                      1.0  \n",
       "002178.SZ 28                      1.0  \n",
       "002682.SZ 10                      1.0  \n",
       "002691.SZ 30                      2.0  \n",
       "002693.SZ 30                      1.0  \n",
       "600327.SH 14                      1.0  \n",
       "600550.SH 14                      1.0  \n",
       "600696.SH 20                      1.0  \n",
       "603117.SH 4                       1.0  \n",
       "603516.SH 10                      1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 连板中，出现第一个涨停板就是地天板的\n",
    "fc_df[fc_df['first_floor_ceiling_day'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max_high_limit_days</th>\n",
       "      <th>end_date</th>\n",
       "      <th>first_floor_ceiling_day</th>\n",
       "      <th>max_count_floor_ceiling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</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>000536.SZ</th>\n",
       "      <th>30</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2024-11-07</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001300.SZ</th>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2023-11-22</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002423.SZ</th>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2024-10-10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002691.SZ</th>\n",
       "      <th>30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2025-02-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              max_high_limit_days    end_date  first_floor_ceiling_day  \\\n",
       "code                                                                     \n",
       "000536.SZ 30                 10.0  2024-11-07                      7.0   \n",
       "001300.SZ 4                   9.0  2023-11-22                      7.0   \n",
       "002423.SZ 6                   8.0  2024-10-10                      6.0   \n",
       "002691.SZ 30                  4.0  2025-02-07                      1.0   \n",
       "\n",
       "              max_count_floor_ceiling  \n",
       "code                                   \n",
       "000536.SZ 30                      2.0  \n",
       "001300.SZ 4                       2.0  \n",
       "002423.SZ 6                       2.0  \n",
       "002691.SZ 30                      2.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 某只股票在连板中，出现过两次地天板的\n",
    "fc_df[fc_df['max_count_floor_ceiling'] > 1]"
   ]
  }
 ],
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